first commit

dynamicDeploy/.idea/dynamicDeploy.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

dynamicDeploy/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

dynamicDeploy/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

dynamicDeploy/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

dynamicDeploy/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/dynamicDeploy.iml" filepath="$PROJECT_DIR$/.idea/dynamicDeploy.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

dynamicDeploy/.idea/workspace.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="d8fba307-6469-4459-843c-5fa614b7698e" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2YvsyaE8wVdE3AeXnQCHS55ts8U" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
+    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="d8fba307-6469-4459-843c-5fa614b7698e" name="Changes" comment="" />
+      <created>1701417643165</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1701417643165</updated>
+    </task>
+    <servers />
+  </component>
+</project>
\ No newline at end of file

dynamicDeploy/DE.py

+import time
+
+import numpy as np
+import random
+
+import Users
+import constants as C
+import ObjectiveFuction as OF
+
+import matplotlib.pyplot as plt
+
+import tool
+
+
+class Population:
+    def __init__(self, pre_uavs,users,r_c,r_p,dim, factor, rounds, size, object_func,uav_num, CR=0.75):
+        self.min_range = C.min_range
+        self.max_range = C.max_range
+        self.min_height=C.min_height
+        self.max_height=C.max_height
+        self.users=users
+        self.pre_uavs=pre_uavs
+        self.r_c=r_c
+        self.r_p=r_p
+        self.dimension = dim
+        self.factor = factor
+        self.rounds = rounds
+        self.size = size
+        self.cur_round = 1
+        self.best_pos=tool.getUavInitialPos(uav_num, users)
+        self.best=0
+        self.best_index=0
+        self.CR = CR
+        self.uav_num=uav_num
+        self.get_object_function_value = object_func
+        # 初始化种群
+        self.individuality = self.init_population()
+        self.init_alloc()
+        # self.object_function_values= [self.get_object_function_value(self.pre_uavs,v,self.users,self.r_c,self.r_p) for v in self.individuality]
+        self.object_function_values = [self.get_object_function_value(self.pre_uavs,v,self.users,self.r_p,self.p_all,self.b_all,self.c_association,self.p_association)
+                                       for v in self.individuality]
+        self.mutant = None
+
+
+    def init_population(self):
+        X = []
+        # users=Users.generate(100)#用户位置
+        for i in range(self.size):
+            uavs = []
+            if i==0:
+                uavs = tool.getUavInitialPos(self.uav_num, users)
+            else:
+                for index in range(self.uav_num):
+                    x = np.random.uniform(self.min_range, self.max_range)
+                    y = np.random.uniform(self.min_range, self.max_range)
+                    z = np.random.uniform(self.min_height, self.max_height)
+                    uavs.append([x, y, z])
+
+            X.append(uavs)
+        X = np.array(X)
+        return X
+
+    def init_alloc(self):
+        c_association, p_association, p_all, b_all = OF.objective_resource(self.pre_uavs, self.users, self.r_c)
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    def init_r_c(self):
+        r_c={}
+        for i in range(len(users)):
+            r_c[i] = random.randint(1,4)
+        return r_c
+
+    def mutate(self):
+        self.mutant = []
+        for i in range(self.size):
+            r0, r1, r2 = 0, 0, 0
+            while r0 == r1 or r1 == r2 or r0 == r2 or r0 == i:
+                r0 = random.randint(0, self.size - 1)
+                r1 = random.randint(0, self.size - 1)
+                r2 = random.randint(0, self.size - 1)
+            tmp = self.best_pos + (self.individuality[r1] - self.individuality[r2]) * self.factor
+            for i in range(len(tmp)):
+                for j in range(0,2):
+                    if tmp[i][j] > self.max_range:
+                        tmp[i][j]= self.max_range
+                    elif tmp[i][j] < self.min_range:
+                        tmp[i][j]= self.min_range
+                if tmp[i][2] > self.max_height:
+                    tmp[i][2] = self.max_height
+                elif tmp[i][2] < self.min_height:
+                    tmp[i][2] = self.min_height
+            # for t in range(self.dimension):
+            #     if tmp[t] > self.max_range or tmp[t] < self.min_range:
+            #         tmp[t] = random.uniform(self.min_range, self.max_range)
+            self.mutant.append(tmp)
+
+    def crossover_and_select(self):
+        for i in range(self.size):
+            Jrand = random.randint(0, self.dimension)
+            for j in range(self.dimension):
+                if random.random() > self.CR and j != Jrand:
+                    self.mutant[i][j] = self.individuality[i][j]
+                # tmp = self.get_object_function_value(self.pre_uavs,self.mutant[i],self.users,self.r_c,self.r_p)
+                tmp = self.get_object_function_value(self.pre_uavs,self.mutant[i], self.users, self.r_p, self.p_all, self.b_all, self.c_association, self.p_association)
+                if tmp > self.object_function_values[i]:
+                    self.individuality[i] = self.mutant[i]
+                    self.object_function_values[i] = tmp
+
+    def print_best(self):
+        self.best= max(self.object_function_values)
+        self.best_index = self.object_function_values.index(self.best)
+        self.best_pos=self.individuality[self.best_index]
+        print("轮数：" + str(self.cur_round))
+        print("最佳个体：" + str(self.best_pos))
+        print("目标函数值：" + str(self.best))
+        return self.best[0]
+
+
+    def evolution(self):
+        start_time = time.time()
+        best=[]
+        while self.cur_round <= self.rounds:
+            self.mutate()
+            self.crossover_and_select()
+            m=self.print_best()
+            best.append(m)
+            self.cur_round = self.cur_round + 1
+        x=[i for i in range(self.rounds)]
+        print(best)
+        end_time = time.time()
+        run_time = end_time - start_time
+        print(run_time)
+        plt.plot(best, label='折线图')
+        plt.show()
+
+
+# 测试部分
+if __name__ == "__main__":
+
+    users = Users.getUsers("./data.csv")
+    r_c={}
+    r_p={}
+    r=1
+    for i in range(len(users)):
+        r_c[i] = 4
+    for i in range(len(users)):
+        r_p[i] = r
+        r += 1
+    pre_uavs = tool.getUavInitialPos(4, users)
+    p = Population(pre_uavs,users,r_c,r_p,dim=4, factor=0.8, rounds=100, size=30, object_func=OF.objective,uav_num=4,)
+    p.evolution()
+
+

dynamicDeploy/GDOP.py

+import numpy as np
+from numpy.linalg import LinAlgError
+from numpy import seterr
+seterr(all='raise')
+
+
+def get_toa_gdop(bs_list, xyz):
+    """
+    compute the gdop of the toa
+    :param bs_list: the base station coordinate
+    :param xyz: the location coordinate of the tag
+    :return: the gdop value
+    """
+    bs_n = len(bs_list)
+    if bs_n < 3:
+        return None
+    h1 = np.mat(np.zeros((bs_n, 3)))
+
+    for i in range(0, len(bs_list)):
+        ri = np.sqrt((bs_list[i][0] - xyz[0]) ** 2 +
+                     (bs_list[i][1] - xyz[1]) ** 2)
+        if ri == 0:
+            h1[i, 0] = 0
+            h1[i, 1] = 0
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+        else:
+            h1[i, 0] = (bs_list[i][0] - xyz[0]) / ri
+            h1[i, 1] = (bs_list[i][1] - xyz[1]) / ri
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+    m_tmp = h1.T * h1
+    gdop_value = None
+
+    try:
+        g = m_tmp.I
+        gdop_value = np.sqrt(g[0, 0] + g[1, 1])
+        # print(gdop_value)
+    finally:
+        return gdop_value
+
+
+# 计算GDOP 不考虑时钟误差版本即DOP
+def calculate_gdop(uavs, estimate_value):
+    """
+    # :param point: 待定位点真实坐标
+    :param uavs: 无人机坐标集
+    :param estimate_value: 解算出的坐标
+    :return: gdop
+    """
+    # estimate_value.append(0)
+    estimate_value=[estimate_value[0],estimate_value[1],0]
+    var1 = np.array(uavs) - estimate_value
+    # 计算距离列向量
+    d_estimate = (np.sum(var1**2, axis=1)**0.5).reshape(-1, 1)
+    # 计算visible MATRIX
+    H = var1 / d_estimate
+    GDOP = float('inf')
+    try:
+        GDOP = np.trace(np.linalg.inv(np.dot(np.transpose(H), H))) ** 0.5
+    except:
+        GDOP=float('inf')
+    finally:
+        return GDOP
+
+
+
+if __name__ == '__main__':
+    bs_list=[[1,1,1],[2,3,4],[6,6,5]]
+    bs_list2 = [[1, 3, 3], [3, 1, 3], [5, 4, 3]]
+    xyz=[3,3.167,0]
+    print(calculate_gdop(bs_list,xyz))
+    print(get_toa_gdop(bs_list2,xyz))
\ No newline at end of file

dynamicDeploy/HHO.py

+import random
+import numpy
+import math
+from solution import solution
+import time
+
+
+def HHO(objf, lb, ub, dim, SearchAgents_no, Max_iter):
+    # dim=30
+    # SearchAgents_no=50
+    # lb=-100
+    # ub=100
+    # Max_iter=500
+
+    # initialize the location and Energy of the rabbit
+    Rabbit_Location = numpy.zeros(dim)
+    Rabbit_Energy = float("inf")  # change this to -inf for maximization problems
+
+    # Initialize the locations of Harris' hawks
+    X = numpy.random.uniform(0, 1, (SearchAgents_no, dim)) * (ub - lb) + lb
+
+    # Initialize convergence
+    convergence_curve = numpy.zeros(Max_iter)
+
+    ############################
+    s = solution()
+
+    print("HHO is now tackling  \"" + objf.__name__ + "\"")
+
+    timerStart = time.time()
+    s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
+    ############################
+
+    t = 0  # Loop counter
+
+    # Main loop
+    while t < Max_iter:
+        for i in range(0, SearchAgents_no):
+
+            # Check boundries
+
+            X[i, :] = numpy.clip(X[i, :], lb, ub)
+
+            # fitness of locations
+            fitness = objf(X[i, :])
+
+            # Update the location of Rabbit
+            if fitness < Rabbit_Energy:  # Change this to > for maximization problem
+                Rabbit_Energy = fitness
+                Rabbit_Location = X[i, :].copy()
+
+        E1 = 2 * (1 - (t / Max_iter))  # factor to show the decreaing energy of rabbit
+
+        # Update the location of Harris' hawks
+        for i in range(0, SearchAgents_no):
+
+            E0 = 2 * random.random() - 1;  # -1<E0<1
+            Escaping_Energy = E1 * (E0)  # escaping energy of rabbit Eq. (3) in the paper
+
+            # -------- Exploration phase Eq. (1) in paper -------------------
+
+            if abs(Escaping_Energy) >= 1:
+                # Harris' hawks perch randomly based on 2 strategy:
+                q = random.random()
+                rand_Hawk_index = math.floor(SearchAgents_no * random.random())
+                X_rand = X[rand_Hawk_index, :]
+                if q < 0.5:
+                    # perch based on other family members
+                    X[i, :] = X_rand - random.random() * abs(X_rand - 2 * random.random() * X[i, :])
+
+                elif q >= 0.5:
+                    # perch on a random tall tree (random site inside group's home range)
+                    X[i, :] = (Rabbit_Location - X.mean(0)) - random.random() * ((ub - lb) * random.random() + lb)
+
+            # -------- Exploitation phase -------------------
+            elif abs(Escaping_Energy) < 1:
+                # Attacking the rabbit using 4 strategies regarding the behavior of the rabbit
+
+                # phase 1: ----- surprise pounce (seven kills) ----------
+                # surprise pounce (seven kills): multiple, short rapid dives by different hawks
+
+                r = random.random()  # probablity of each event
+
+                if r >= 0.5 and abs(Escaping_Energy) < 0.5:  # Hard besiege Eq. (6) in paper
+                    X[i, :] = (Rabbit_Location) - Escaping_Energy * abs(Rabbit_Location - X[i, :])
+
+                if r >= 0.5 and abs(Escaping_Energy) >= 0.5:  # Soft besiege Eq. (4) in paper
+                    Jump_strength = 2 * (1 - random.random());  # random jump strength of the rabbit
+                    X[i, :] = (Rabbit_Location - X[i, :]) - Escaping_Energy * abs(
+                        Jump_strength * Rabbit_Location - X[i, :])
+
+                # phase 2: --------performing team rapid dives (leapfrog movements)----------
+
+                if r < 0.5 and abs(Escaping_Energy) >= 0.5:  # Soft besiege Eq. (10) in paper
+                    # rabbit try to escape by many zigzag deceptive motions
+                    Jump_strength = 2 * (1 - random.random())
+                    X1 = Rabbit_Location - Escaping_Energy * abs(Jump_strength * Rabbit_Location - X[i, :]);
+
+                    if objf(X1) < fitness:  # improved move?
+                        X[i, :] = X1.copy()
+                    else:  # hawks perform levy-based short rapid dives around the rabbit
+                        X2 = Rabbit_Location - Escaping_Energy * abs(
+                            Jump_strength * Rabbit_Location - X[i, :]) + numpy.multiply(numpy.random.randn(dim),
+                                                                                        Levy(dim))
+                        if objf(X2) < fitness:
+                            X[i, :] = X2.copy()
+                if r < 0.5 and abs(Escaping_Energy) < 0.5:  # Hard besiege Eq. (11) in paper
+                    Jump_strength = 2 * (1 - random.random())
+                    X1 = Rabbit_Location - Escaping_Energy * abs(Jump_strength * Rabbit_Location - X.mean(0))
+
+                    if objf(X1) < fitness:  # improved move?
+                        X[i, :] = X1.copy()
+                    else:  # Perform levy-based short rapid dives around the rabbit
+                        X2 = Rabbit_Location - Escaping_Energy * abs(
+                            Jump_strength * Rabbit_Location - X.mean(0)) + numpy.multiply(numpy.random.randn(dim),
+                                                                                          Levy(dim))
+                        if objf(X2) < fitness:
+                            X[i, :] = X2.copy()
+
+        convergence_curve[t] = Rabbit_Energy
+        if (t % 1 == 0):
+            print(['At iteration ' + str(t) + ' the best fitness is ' + str(Rabbit_Energy)])
+        t = t + 1
+
+    timerEnd = time.time()
+    s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
+    s.executionTime = timerEnd - timerStart
+    s.convergence = convergence_curve
+    s.optimizer = "HHO"
+    s.objfname = objf.__name__
+    s.best = Rabbit_Energy
+    s.bestIndividual = Rabbit_Location
+
+    return s
+
+
+def Levy(dim):
+    beta = 1.5
+    sigma = (math.gamma(1 + beta) * math.sin(math.pi * beta / 2) / (
+                math.gamma((1 + beta) / 2) * beta * 2 ** ((beta - 1) / 2))) ** (1 / beta)
+    u = 0.01 * numpy.random.randn(dim) * sigma
+    v = numpy.random.randn(dim)
+    zz = numpy.power(numpy.absolute(v), (1 / beta))
+    step = numpy.divide(u, zz)
+    return step
\ No newline at end of file

dynamicDeploy/ObjectiveFuction.py

+import Users
+import tool
+import tool as T
+import constants as C
+import math
+import GDOP
+import resourceAlloc as RA
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association,r_p):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    g=[]
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        g.append(gdop)
+        sum += gdop
+        # if gdop>C.G_max/(r_p[user_index]):
+        #     sum+=gdop*10**6
+        # else:
+        #     sum+=gdop
+    return g
+
+def real_gdop(users,uavs_pos,c_association,p_association,r_p):
+    sum = 0
+    for user_index in p_association:
+        uavs = []
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop = GDOP.calculate_gdop(uavs, users[user_index])
+        sum += gdop
+    return sum
+
+def energy(pre_uavs,uavs,time_slot=C.time_slot,speed=C.speed):
+    sum_e=0
+    for i in range(len(pre_uavs)):
+        dis=T.calculate_3d_distance_uavs(pre_uavs[i],uavs[i])
+        fly_energy=T.cal_fly_energy(dis,speed)
+        if fly_energy!=0:
+            # hover_energy = (C.P0 + C.P1) * (time_slot - dis / speed)
+            hover_energy=(C.P0+C.P1)*(time_slot-dis/speed)
+        #     10.5*(uavs[i][2]-30)
+        else:
+            hover_energy = (C.P0 + C.P1) * (time_slot - dis / speed)
+            if hover_energy<0:
+                hover_energy=0
+        sum_e+=fly_energy+hover_energy
+
+    # print(sum_e/len(pre_uavs))
+    return sum_e
+def function_rate(uavs,users,uav_to_user,p_all,b_all,r_c):
+    rate=[]
+    u=1.21
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            p_alloc=p_all[i]
+            b_alloc=b_all[i]
+            cur_rate = tool.cal_rate(b_alloc[j], p_alloc[j], uavs[i], users[j])
+            # if cur_rate<C.R_min*r_c[j]*u:
+            #     cur_rate=-100
+            rate.append(cur_rate)
+    return rate
+def real_rate(uavs,users,uav_to_user,p_all,b_all,r_c):
+    rate=[]
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            p_alloc=p_all[i]
+            b_alloc=b_all[i]
+            cur_rate = tool.cal_rate(b_alloc[j], p_alloc[j], uavs[i], users[j])
+            rate.append(cur_rate)
+    return rate
+def objective_resource_u(uavs_pos,users,r_c):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc_u(users, uavs_pos, r_c, p_association, uav_to_user)
+    return c_association,p_association,p_all,b_all
+def objective_resource(uavs_pos,users,r_c):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc(users, uavs_pos, r_c, p_association, uav_to_user)
+    return c_association,p_association,p_all,b_all
+def objective_resource_avg(uavs_pos,users,r_c):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc_avg(users,uavs_pos,r_c,p_association,uav_to_user)
+    return c_association,p_association,p_all,b_all
+
+def objective_resource_proportion(uavs_pos,users,r_c):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc_proportion(users,uavs_pos,r_c,p_association,uav_to_user)
+    return c_association,p_association,p_all,b_all
+
+def objective(pre_uavs,uavs_pos,users,r_c,r_p,p_all,b_all,c_association,p_association):
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    rate=function_rate(uavs_pos,users,uav_to_user,p_all,b_all,r_c)
+    # r=sum(rate)*10**6
+    r=sum(rate)
+    u=1.21
+    gdop = function_gdop(users, uavs_pos, c_association, p_association, r_p)
+    # e = energy(pre_uavs, uavs_pos)
+    objective=r-sum(gdop)
+    for i in range(len(rate)):
+        if rate[i]<C.R_min*r_c[i]:
+            objective-=10
+        if gdop[i]>C.G_max/r_p[i]:
+            objective-=10
+    # print("能耗：",e)
+    return objective, r / len(rate), sum(gdop) / len(users)
+    # return (r - gdop) - e, r / len(rate), gdop / len(users)
+
+def real_objective(pre_uavs,uavs_pos,users,r_c,r_p,p_all,b_all,c_association,p_association,time_slot):
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    rate = real_rate(uavs_pos, users, uav_to_user, p_all, b_all,r_c)
+    r = sum(rate)
+    # r = sum(rate)
+    gdop = real_gdop(users, uavs_pos, c_association, p_association, r_p)
+    # e = energy(pre_uavs, uavs_pos,time_slot)
+    # print("能耗：", e)
+    objective = r-gdop
+    return objective, r / len(rate), gdop / len(users)
+    # return (r - gdop) / e,e, r / len(rate), gdop / len(users)
+
+def gdop_satisfaction(users,uavs_pos,c_association,p_association,r_p):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    g=[]
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        g.append(gdop)
+        if gdop>C.G_max/(r_p[user_index]):
+            continue
+        else:
+            sum+=1
+    return sum/len(users)
+def rate_satisfaction(uavs,users,uav_to_user,p_all,b_all,r_c):
+    num=0
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            p_alloc=p_all[i]
+            b_alloc=b_all[i]
+            cur_rate = tool.cal_rate(b_alloc[j], p_alloc[j], uavs[i], users[j])
+            if round(cur_rate,4)>=C.R_min*r_c[j]:
+                num+=1
+    return num/len(users)
+
+def cal_satisfaction(uavs,users,p_all,b_all,r_c,r_p, c_association,p_association):
+    g=gdop_satisfaction(users,uavs,c_association,p_association,r_p)
+    uav_to_user = T.get_uav_to_user(c_association, uavs)
+    r=rate_satisfaction(uavs,users,uav_to_user,p_all,b_all,r_c)
+    return r,g
+
+def resource_objective(e,uavs_pos,users,r_p,p_all,b_all,c_association,p_association):
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    rate = function_rate(uavs_pos, users, uav_to_user, p_all, b_all)
+    r = sum(rate) * 10 ** 6
+    gdop = real_gdop(users, uavs_pos, c_association, p_association, r_p)
+    return (r - gdop) / e, r / len(rate), gdop / len(users)
+
+def objective_func(pre_uavs,uavs_pos,users,r_c,r_p,*args):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc_avg(users, uavs_pos, r_c, p_association, uav_to_user)
+    r=sum(rate)
+    gdop = function_gdop(users, uavs_pos, c_association, p_association,r_p)
+    # e=energy(pre_uavs,uavs_pos)
+    # print(e)
+    # print(p_all,b_all)
+    # print("rate:",rate,r)
+    # print("gdop:", gdop)
+    return r - sum(gdop),r/len(rate),sum(gdop)/len(users)
+
+def objective_func_optimal(pre_uavs,uavs_pos,users,r_c,r_p,*args):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    p_all,b_all,rate= RA.resource_alloc_u(users, uavs_pos, r_c, p_association, uav_to_user)
+    r=sum(rate)
+    gdop = function_gdop(users, uavs_pos, c_association, p_association, r_p)
+    # e=energy(pre_uavs,uavs_pos)
+    # print(p_all, b_all)
+    # print("rate:", rate,r)
+    # print("gdop:", gdop)
+    return r - sum(gdop),r/len(rate),sum(gdop)/len(users)
+
+def get_snr_gdop(uavs_pos,users,r_c,r_p):
+    c_association = T.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = T.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    uav_to_user = T.get_uav_to_user(c_association, uavs_pos)
+    gdop=[]
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        g=GDOP.calculate_gdop(uavs,users[user_index])
+        gdop.append(g)
+    p_all, b_all, rate = RA.resource_alloc_avg(users, uavs_pos, r_c, p_association, uav_to_user)
+    return rate,gdop
+def fixed_w(wmin,wmax,iter,maxgen,index,fitness):
+    # return (wmax +wmin)/2
+    return 0.5
+
+def linear_w(wmin,wmax,iter,maxgen,index,fitness):
+    return wmax - iter * (wmax - wmin) / maxgen
+
+def adapted_w(wmin,wmax,iter,maxgen,index,fitness):
+    avg=sum(fitness)/len(fitness)
+    if fitness[index]>avg:
+        return wmax
+    else:
+        w=wmin-(wmax-wmin)*(fitness[index]-min(fitness))/(avg-min(fitness))
+        return w
+def adapted_w2(wmin,wmax,iter,maxgen,index,fitness):
+    avg=sum(fitness)/len(fitness)
+    if fitness[index]<=avg:
+        return wmax
+    else:
+        w=wmin+(wmax-wmin)*(max(fitness)-fitness[index])/(max(fitness)-avg)
+        # w=wmax-(wmax-wmin)*(fitness[index]-min(fitness))/(max(fitness)-min(fitness))#效果不好
+        return w
+def adapted_w3(wmin,wmax,iter,maxgen,fitness):
+    avg = sum(fitness) / len(fitness)
+
+    A=iter*(math.log(wmax)-math.log(wmin))/maxgen-math.log(wmax)
+    w=math.exp(-A)
+    return w
+
+def dynamic_c1(iter,maxgen):
+    return 2*(math.sin(math.pi/2*(1-iter/maxgen)))**2
+
+def dynamic_c2(iter,maxgen):
+    return 2*(math.sin(math.pi*iter/2/maxgen))**2
+
+if __name__ == "__main__":
+    users = Users.getUsers("./data3.csv")
+    r_c = {}
+    r_p = {}
+    for i in range(len(users)):
+        r_c[i] = 2
+    r = 1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r += 1
+    # uavs = [[868.34535881, 315.18340086, 50.],
+    #         [292.96443022, 729.7612657, 77.75450997],
+    #         [732.25467955, 927.62861821, 50.],
+    #         [303.40107629, 240.31208079, 150.]]
+    # uavs=[[973.06680904,364.12885341,52.61221492],
+    #  [655.4558372,85.17834171,84.32290174],
+    # [920.58870216,343.97478717,69.12057386,]]
+    uavs = [[335, 772, 67], [382, 739, 75], [1758, 1683, 137], [885, 762, 133], [403, 1196, 94], [1049, 358, 88]]
+    uavs=[[ 937.13598572,1106.32026504 ,50.        ],
+ [1638.73822307,576.42231656,50.        ],
+ [ 863.950367,1616.3127536,50.        ],
+ [ 264.57183106,364.69333235,138.69332619],
+ [ 159.74510169,827.61881448,56.27541847],
+ [1035.04791004,808.44053304 , 140.02500318]]
+    uavs=[[1672.00438436 ,1012.9975498 ,   50.        ],
+ [ 434.16986638 ,1432.14203541 ,  50.        ],
+ [ 473.55681971 , 325.27416208 ,  71.45922555],
+ [1115.79267858, 1464.40107477 ,  50.        ],
+ [1833.57784143 , 263.09076035  , 50.        ],
+ [1675.2644499 , 1841.36678341  , 50.        ]]
+    c_association = T.get_c_association(users, uavs, math.ceil(len(users) / len(uavs)))
+    p_association = T.get_p_association(users, uavs, c_association, len(users) * 2 / len(uavs))
+    uav_to_user = T.get_uav_to_user(c_association, uavs)
+    p_all, b_all, rate = RA.resource_alloc_avg(users, uavs, r_c, p_association, uav_to_user)
+    print(objective(uavs,uavs,users,r_c,r_p,p_all,b_all,c_association,p_association))
+    # print(objective_func_optimal(uavs,uavs,users,r_c,r_p))

dynamicDeploy/PSO_comapre.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+import random
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+from static_pso import SPSO
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+def drawResultAndIter(resultAndIter):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(resultAndIter)
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('fitness')
+    plt.show()
+def draw_compare(resultArr):
+    labels=['Adapted w','Fixed w','Linear w']
+    markers=['o','s','^']
+    line=['-','--',':','-.']
+    for i in range(len(resultArr)):
+        plt.plot(resultArr[i], label=labels[i],linestyle=line[i])
+    # 设置图例
+    plt.legend()
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('x')
+    plt.ylabel('y')
+
+    # 显示图形
+    plt.show()
+
+
+def draw_c1_c2():
+    c1=[]
+    c2=[]
+    for i in range(100):
+        c1.append(OF.dynamic_c1(i,100))
+        c2.append(OF.dynamic_c2(i,100))
+    c1=np.array(c1)
+    c2=np.array(c2)
+    plt.plot(c1, label="c1")
+    plt.plot(c2, label="c2")
+    plt.show()
+def run_fixed_compare(users, k):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = 4
+    r=1
+    for i in range(len(users)):
+        r_p[i] = r
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,)
+    X, V = pso.initParticle(users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, OF.fixed_w,OF.objective)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def draw_w(maxIter):
+    w=[]
+    for i in range(maxIter):
+        w.append(OF.adapted_w3(0.4,0.9,i,maxIter,1,))
+    plt.plot(w)
+    plt.show()
+
+
+def run_linear_compare(users, k,num,t):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = 2
+    r=1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,num,t)
+    X, V = pso.initParticle(users)
+    # print(X)
+    c_association, p_association, p_all, b_all = OF.objective_resource_u(pre_uavs, users, r_c)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w,OF.objective)
+    pso.re_init(pre_uavs,users,r_c,r_p,c_association,p_association,p_all,b_all)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run(users, k,num):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    r = 1
+    for i in range(len(users)):
+        r_c[i] = 2
+    r = 1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=SPSO(pre_uavs,users,r_c,r_p,k,num)
+    pso.init_alloc()
+    X, V = pso.initParticle(users)
+    # print(X)
+    # global_pos, global_best, result = pso.pso(X, V, adapted_w3,OF.objective_func,dynamic_c1,dynamic_c2)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run_(users, k,num):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    r = 1
+    for i in range(len(users)):
+        r_c[i] = 2
+    r = 1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,num)
+    pso.init_alloc()
+    X, V = pso.initParticle(users)
+    # print(X)
+    # global_pos, global_best, result = pso.pso(X, V, adapted_w3,OF.objective_func,dynamic_c1,dynamic_c2)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective_func, )
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run_all(users,k):
+    resultArr=[]
+    global_pos1, global_best1, result1 = run(users, k)
+    global_pos2, global_best2, result2 = run_fixed_compare(users, k)
+    global_pos3, global_best3, result3 = run_linear_compare(users, k)
+    resultArr.append(result1)
+    resultArr.append(result2)
+    resultArr.append(result3)
+    draw_compare(resultArr)
+
+
+
+if __name__ == '__main__':
+
+    users = Users.getUsers("./data3.csv")
+    # global_pos, global_best,result=run(users,3)
+    # print(fitness(users,global_pos))
+    # run_all(users,4)
+    # draw_c1_c2()
+
+    # draw_data()
+    # draw_w(150)
+    p_num=70
+    # # global_pos, global_best, result = run_fixed_compare(users, 4)
+    start_time = time.time()
+    # global_pos, global_best, result = run_(users, 5,p_num)
+    end_time1 = time.time()
+    global_pos1, global_best1, result1 = run_linear_compare(users, 5,p_num,200)
+    end_time2 = time.time()
+    run_time=end_time1-start_time
+    run_time1 = end_time2 - end_time1
+    # run_time2=end_time2 - start_time-run_time1
+    print("程序运行时间为：", run_time1)
+    # print(global_best[0])
+    # print(list(result))
+    print(list(result1))
+    # # print("最优解",global_best[0],global_best1[0])
+    # drawResultAndIter(result1)
+    # plt.show()
+    # arr=[]
+    # arr_t=[]
+    # for k in range(30,110,20):
+    # # k=70
+    #     t=250
+    # # for t in range(100, 350, 50):
+    #     num = []
+    #     sum_time = []
+    #     for i in range(1):
+    #         start_time = time.time()
+    #         global_pos, global_best, result = run_linear_compare(users, 5,k,t)
+    #         num.append(global_best[0])
+    #         print(global_best)
+    #         end_time = time.time()
+    #         run_time = end_time - start_time
+    #         sum_time.append(run_time)
+    #         print("程序运行时间为：", run_time)
+    #         # drawResultAndIter(result)
+    #         # plt.show()
+    #     print(sum(num)/len(num))
+    #     print(sum(sum_time)/len(sum_time))
+    #     arr.append(sum(num)/len(num))
+    #     arr_t.append(sum(sum_time)/len(sum_time))
+    # print(arr)
+    # print(arr_t)
+    # num=[-9657.993479469107, -11023.13972392451, -10511.070624574759, -10724.3448906409, -10623.367312657523,
+    #  -9802.662654304893, -7603.911448332553, -9695.134404543633, -9575.730714137575, -10454.63434179702]
+    # print(sum(num)/10)
+
+
+

dynamicDeploy/Users.py

+import math
+import random
+
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+import pandas as pd
+
+
+# 随机生成U个用户的位置
+def generate_in_file(num,filename):
+    data = np.random.randint(1, 2000, (num, 2))
+    f = open(filename, 'w', encoding='utf-8', newline="")
+    #  2.基于文件对象构建csv写入对象
+    csv_write = csv.writer(f)
+    #  4.写入csv文件
+    for d in data:
+        csv_write.writerow(d)
+    return data
+
+def generate(num):
+    data = np.random.randint(1, 1000, (num, 2))
+    return data
+
+def generateNormalDistribution(num):
+    mu, sigma = 500, 170  # 有关于正态分布的值μ，σ
+    x = sigma * np.random.randn(1, num) + mu
+    x = ([i for item in x for i in item])  # 在这个循环中我们能把二维的数组转换成一维的数组，y2同理
+    y = sigma * np.random.randn(1, num) + mu
+    y = ([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x[i], y[i]])
+    f = open('../data_normal.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+# 用户按集群分布
+def generateGroupDistribution(num):
+    centers = [[500, 500], [1600, 400], [700, 1200], [1400, 1500]]
+    sigma = 100  # 有关于正态分布的值σ
+    group_num = 4
+    x_ = []
+    y_ = []
+    for i in range(group_num):
+        number = int(num / group_num)
+        print(centers[i])
+        x = sigma * np.random.randn(1, number) + centers[i][0]
+        y = sigma * np.random.randn(1, number) + centers[i][1]
+        x_.extend([i for item in x for i in item])
+        y_.extend([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x_[i], y_[i]])
+    f = open('../data_group.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+def generateNormal(mean, sigma):
+    x = sigma * np.random.randn(1) + mean
+    return x
+
+
+# 从csv文件读用户位置
+def getUsers(filename):
+    data = pd.read_csv(filename, header=None)
+    list = data.values.tolist()
+    array = np.array(list)
+    return array
+
+
+def getDistanceFromCenter(user, x, y):
+    return math.sqrt((user[0] - x) ** 2 + (user[1] - y) ** 2)
+
+
+def draw(data):
+    plt.figure(num=1, figsize=(10, 10))
+    x = data[:, 0]
+    y = data[:, 1]
+    plt.scatter(x, y)
+    plt.xlim(0, 2001)
+    plt.ylim(0, 2001)
+    ax = plt.gca()
+    ax.spines['left'].set_position(('data', 0))
+    ax.spines['bottom'].set_position(('data', 0))
+    # x1 = np.linspace(300, 700, 500)
+    # center1 = 500 + np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # center2 = 500 - np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # plt.plot(x1, center1,c='b')
+    # plt.plot(x1, center2,c='b')
+    # x2 = np.linspace(200, 800, 500)
+    # center21 = 500 + np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # center22 = 500 - np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # plt.plot(x2, center21, c='r')
+    # plt.plot(x2, center22, c='r')
+    # x3 = np.linspace(100, 900, 500)
+    # center31 = 500 + np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # center32 = 500 - np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # plt.plot(x3, center31, c='g')
+    # plt.plot(x3, center32, c='g')
+    # model = KMeans(n_clusters=5, random_state=0).fit(data)
+    # center = model.cluster_centers_
+    # for c in center:
+    #     plt.scatter(c[0], c[1],s=200,marker='p')
+    return plt
+
+
+if __name__ == '__main__':
+    # data=generate_in_file(30,"./data3.csv")
+    list=getUsers("./data3.csv")
+    # print(list)
+
+    # data=generateGroupDistribution(100)
+    # list = getUsers("../data_group.csv")
+    draw(list).show()
+

dynamicDeploy/constants.py

+f_c = 2 * 10 ** 9
+noise = -140 #森林噪声（dbm）
+N0=-117 #视距链路噪声（dbm）
+pmax=0.1
+B_max=2.5
+snr_thre=1
+R_min=0.3  #0.1  1.2才能传视频
+G_max=20
+min_range=0
+max_range=2000
+min_height=50
+max_height=150
+speed=40
+time_slot=10 #基本间隔
+run_time=5
+P0=580.65
+P1=790
+# P0=199.4
+# P1=88.65
+U=200
+vr=7.2
+A=0.79
+v=1#用户速度
+r_change_slot=60#60s
+
+
+import numpy as np
+
+# aircraft weight in Newton
+W = 100
+# air density
+rho = 1.225
+# blade radius in meter
+R = 0.5
+# blade angular velocity in rad/s
+omega = 400
+# Fuselage equivalent flat plate area in m^2
+S_FP = 0.0151
+# number of blades
+b = 4
+# blade or aerofoil chord length
+c = 0.0157
+# incremental correction factor to induced power
+k = 0.1
+# profile drag coefficient
+delta = 0.012
+
+# rotor disc area
+A = np.pi * R**2
+# tip speed of the rotor blade
+U_tip = omega * R
+# rotor solidity
+sigma = b * c / (np.pi * R)
+# fuselage drag ratio
+d_0 = S_FP / sigma / A
+# mean rotor induced velocity in hover
+v_0 = np.sqrt(W / (2 * rho * A))
+
+# hovering blade profile power
+# P_0 = (delta / 8) * rho * sigma * A * omega**3 * R**2
+P_0=580.65
+# hovering induced power
+P_i = (1 + k) * (W**(1.5)) / np.sqrt(2 * rho * A)
+
+print(P_0,P_i)
+# flight speed
+# V_kmh = np.linspace(1, 100, 20)
+# V = V_kmh / 3.6
+# V=V_kmh
+# propulsion power
+# P_origin = P_0 * (1 + (3 * V**2) / (U_tip**2)) + P_i * (np.sqrt(1+(V**4/(4*(v_0**4))))-V**2/(2*(v_0**2)))**0.5 + (0.5 * d_0 * rho * sigma * A * V**3)
+# print(P_origin)
\ No newline at end of file

dynamicDeploy/data.csv

+902,280
+652,886
+635,96
+946,288
+668,947
+835,969
+197,709
+295,717
+285,112
+988,589
+314,375
+763,913
+840,328
+383,610
+297,882

dynamicDeploy/data1.csv

+897,356
+978,371
+660,94

dynamicDeploy/data2.csv

+122,240
+712,790
+517,522
+664,35
+522,76
+37,154
+150,541
+51,54
+680,52
+146,251
+670,601
+89,395
+592,302
+179,181
+105,429
+399,625
+184,24
+116,72
+918,650
+564,280

dynamicDeploy/data3.csv

+1507,121
+1326,1837
+1283,544
+197,1742
+681,1491
+1530,1252
+703,1380
+335,1732
+107,1760
+832,658
+1031,1152
+34,1436
+172,1881
+871,162
+1042,801
+1107,1613
+764,1894
+1745,1696
+1009,1818
+1925,1669
+1768,1455
+1450,864
+310,1879
+934,691
+696,176
+1335,1028
+1590,157
+1408,1289
+1624,1750
+337,826

dynamicDeploy/data4.csv

+379,347
+553,1551
+662,1800
+1467,988
+1102,1009
+565,1128
+801,1014
+862,906
+953,1215
+1079,708
+1800,1236
+1270,1679
+1177,1737
+761,1091
+1184,541
+1643,310
+1400,792
+1966,637
+498,1651
+380,115
+1365,1508
+1060,264
+1933,316
+1200,619
+1675,1259
+228,1465
+574,715
+599,379
+5,1624
+775,1520
+1053,461
+1572,1038
+1571,151
+1714,103
+1311,1902
+997,1879
+1151,86
+1041,414
+1583,29
+1257,1490
+412,1404
+1404,976
+994,139
+1735,1601
+1258,916
+1695,328
+1520,922
+943,349
+648,1322
+1340,443

dynamicDeploy/draw.py

+import numpy as np
+from matplotlib import pyplot as plt
+
+plt.style.use('seaborn-whitegrid')
+import matplotlib as mpl
+plt.rcParams['font.family'] = 'Times New Roman'
+mpl.rcParams['axes.labelsize'] = 14
+mpl.rcParams['legend.fontsize'] = 12
+
+from matplotlib import rcParams
+from matplotlib.font_manager import FontProperties
+
+font_path = 'D://font//SimHei.ttf'  # 替换为实际的字体文件路径
+font_prop = FontProperties(fname=font_path)
+
+# 全局设置字体
+rcParams['font.family'] = font_prop.get_name()
+rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+# plt.rcParams['font.family'] = 'Times New Roman'
+rcParams['font.serif'] = ['Times New Roman']  # 设置英文和数字字体为Times New Roman
+# plt.rcParams['font.sans-serif'] = ['SimHei']
+
+def drawResultAndIter(resultAndIter):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(resultAndIter)
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('fitness')
+    plt.show()
+def draw_compare(resultArr):
+    labels=['Proposed IPSO','SPSO','PSO',"DE"]
+    markers=[".",'^','s','o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(resultArr)):
+        index=round(i/2)
+        plt.plot(resultArr[i], label=labels[i],  linestyle=line[i],)
+    # 设置图例
+    plt.legend()
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('Number of iterations')
+    plt.ylabel('Fitness value')
+
+    # 显示图形
+    plt.show()
+
+def draw_particle_num_iteration():
+    result1=[-468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.1551972601262, -468.1551972601262, -468.1551972601262, -468.1551972601262, -468.1551972601262, -468.1551972601262, -468.1551972601262, -468.0436266805678, -468.0436266805678, -468.0436266805678, -468.0436266805678, -461.65308302559686, -452.8888321326548, -401.59790153071805, -385.28811413225645, -382.4242776330168, -382.0262087928394, -378.0637949107663, -378.0637949107663, -372.8723066103669, -370.02164942729524, -367.647900225791, -367.647900225791, -367.647900225791, -365.07942447331345, -363.73763455243994, -361.3785664974921, -361.1325332089312, -358.2999971860925, -356.60219219164526, -355.2456615220885, -351.74190252287457, -351.51801238698283, -349.3988922312556, -348.0076691052095, -347.56430609242295, -344.8834863035785, -344.8834863035785, -344.47610281940064, -344.47610281940064, -343.40803405311647, -342.8429136352076, -342.1260442097806, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.7655717202232, -341.6013796163709, -341.46867293943427, -340.71606307177956, -340.71606307177956, -340.5245644836425, -340.5245644836425, -340.494723260078, -340.1992378680266, -340.1965721861093, -340.1686083217223, -340.02149387155004, -339.86521061017726, -339.3855932208739, -338.8985315339182, -338.8621918114104, -338.51612765899216, -338.46147524082403, -337.93380585347035, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823, -337.7706484447823]
+    result2=[-468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -462.38918872737554, -446.7201152518535, -374.9089810819999, -353.21345810702525, -353.21345810702525, -353.21345810702525, -353.21345810702525, -351.0239332341789, -350.5252500168065, -345.2519014763718, -345.2519014763718, -345.2519014763718, -341.25016754173606, -341.25016754173606, -340.78028482916557, -340.23735852096337, -339.54165088137324, -337.9304472931558, -337.9304472931558, -337.34690873042325, -337.34690873042325, -337.34690873042325, -337.23051081201385, -335.2943266906536, -335.2943266906536, -335.2943266906536, -335.2943266906536, -335.2943266906536, -335.2040143425279, -335.1504084053742, -334.4255463121278, -334.3708934855804, -334.3708934855804, -334.3708934855804, -334.3708934855804, -334.0968365456712, -334.0084860170438, -333.98218232414786, -333.5739670100702, -333.5739670100702, -333.5739670100702, -333.35779414221076, -333.10896959934286, -333.0870744371223, -333.0870744371223, -332.98800195750005, -332.86794150136444, -332.86794150136444, -332.86794150136444, -332.86405599181023, -332.53382174113653, -332.53382174113653, -332.5266649944851, -332.5266649944851, -332.397712869248, -332.2493305238271, -332.2493305238271, -332.17939507765624, -332.1073320511023, -331.9062571633988, -331.9062571633988, -331.5917943579286, -331.52541977712, -331.3998050150476, -331.38488210674757, -331.38488210674757, -331.2635257484103, -331.1970373158113, -331.1164338049966, -330.9608422914324, -330.928103777802, -330.928103777802, -330.8474350894784, -330.81907231644345, -330.7616393730558, -330.7272217317713, -330.59536395090504, -330.51086666129413, -330.4113619966248, -330.17181367583834, -329.98307637249064, -329.8407523772944, -329.8407523772944, -329.8407523772944, -329.8407523772944, -329.69627956288065, -329.6035907577809]
+    result3=[-468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -462.5417019072906, -452.9328610289716, -452.9328610289716, -452.9328610289716, -435.9161914810394, -431.8644714313303, -411.6643212172572, -360.06176418966726, -350.2930046373964, -346.25410814760437, -341.04387526991843, -332.59345245459883, -332.59345245459883, -332.59345245459883, -332.59345245459883, -332.59345245459883, -332.59345245459883, -332.59345245459883, -332.5339614853902, -332.05557122844266, -331.5483077511296, -331.49830864131627, -331.3010012148939, -331.2214978786753, -331.2214978786753, -331.1470643249009, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.43602237317333, -330.41126067997277, -330.1147720465841, -330.05009848709244, -330.05009848709244, -329.9742546449886, -329.9680955263456, -329.9680955263456, -329.9058817192863, -329.80335915642974, -329.72398979541265, -329.72398979541265, -329.72398979541265, -329.7001947611068, -329.657907434906, -329.5536923074401, -329.4506520638687, -329.43481400161227, -329.4299981161304, -329.39302331005354, -329.39302331005354, -329.39302331005354, -329.39302331005354, -329.39302331005354, -329.39302331005354, -329.3359167066494, -329.2949620043947, -329.25115220289047, -329.1813159212933, -329.017580099964, -328.9263641518387, -328.9263641518387, -328.9263641518387, -328.85862049894195, -328.85862049894195, -328.83837983933523, -328.83837983933523, -328.83837983933523, -328.68054432382024, -328.64547133959, -328.6005881503028, -328.5942056807135, -328.5942056807135, -328.5510393597613, -328.5049481966571, -328.4554701799052, -328.4263495199075, -328.4165781933845, -328.4165781933845, -328.4049995593526, -328.4049995593526, -328.38554075679485, -328.3817441098075, -328.3697870134776]
+    result4=[-468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -468.7054208305359, -439.95863043746203, -439.95863043746203, -380.8325586854756, -374.8986980867377, -370.80995620842435, -364.57132554652804, -357.10081704846436, -353.4342794179789, -351.7507205895996, -351.7507205895996, -349.1755921284063, -344.2505661090243, -343.8227644429094, -341.8747478205176, -341.51754540258145, -336.857509158093, -333.05472673444325, -333.05472673444325, -333.05472673444325, -332.6418514566392, -332.6418514566392, -331.70813177769526, -331.69080097411694, -331.31839707056224, -330.9749805391616, -330.9749805391616, -330.9749805391616, -329.49982161081306, -329.0109791791206, -329.0109791791206, -328.85906327718465, -328.85906327718465, -328.85906327718465, -328.17789679927876, -328.17789679927876, -328.17789679927876, -328.17789679927876, -328.17789679927876, -328.08037619933725, -328.08037619933725, -328.08037619933725, -328.08037619933725, -328.00487733187686, -327.9248872779793, -327.9248872779793, -327.86396333668694, -327.7469531220371, -327.71502338967986, -327.584844761086, -327.56303389177185, -327.4711956622603, -327.32673454376675, -327.32673454376675, -327.32673454376675, -327.32673454376675, -327.31980416224707, -327.31980416224707, -327.2931497487489, -327.1826910500803, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435, -327.0992806917435]
+    result=[]
+    result.append(result1)
+    result.append(result2)
+    result.append(result3)
+    result.append(result4)
+    labels = ['N=20', 'N=30', 'N=40', "N=50"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i], linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('Number of iterations')
+    plt.ylabel('Fitness value')
+
+    # 显示图形
+    plt.show()
+def draw_particle_num():
+    y=[-337.7706484447823,-329.6035907577809,-328.3697870134776,-327.0992806917435,-327.03770836,-326.66154288,-326.94715888,-326.51317971]
+    y=[-84.44711868595805, -76.46606150182288, -74.79680270185938,-68.5327703374397, -49.78739347833529, -48.85075425561384, -48.502744564027395, -48.5514696272856, ]
+    y=[y[i]-200 for i in range(len(y))]
+    x=[i for i in range(30,110,10)]
+    plt.plot(x,y,marker="o")
+    plt.xlabel('粒子数目', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('适应度', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('Number of particles')
+    # plt.ylabel('Fitness value')
+    # 显示图形
+    plt.show()
+def draw_iteration_num():
+    y = [-107.42648703395444, -67.73900895103472, -48.40847296622629, -48.2503277828317, -48.233]
+    y = [y[i] - 200 for i in range(len(y))]
+    x=[i for i in range(100,350,50)]
+    plt.plot(x,y,marker="o")
+    plt.xlabel('最大迭代次数', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('适应度', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('Number of iterations')
+    # plt.ylabel('Fitness value')
+    # 显示图形
+    plt.show()
+def draw_iteration_time():
+    y = [9.583384275436401, 14.269200563430786, 19.240224361419678, 23.800528287887573, 28.650663375854492]
+
+    x=[i for i in range(100,350,50)]
+    plt.plot(x,y,marker="o")
+    plt.xlabel('Number of iterations')
+    plt.ylabel('Running Time')
+    # 显示图形
+    plt.show()
+def draw_particle_time():
+    y=[1.8,2.48,3.25,4.45,4.77,6.14,6.35,7.14]
+    y = [10.263979434967041, 13.735095024108887, 17.127366304397583, 20.233509302139282, 23.74123501777649,
+         27.01459264755249, 30.504220008850098, 33.78894329071045]
+
+    x=[i for i in range(20,100,10)]
+    plt.plot(x,y,marker="o")
+    plt.xlabel('Number of particles')
+    plt.ylabel('Running Time')
+    # 显示图形
+    plt.show()
+
+def draw_time():
+    # 示例数据
+    categories = ['N=20','N=30','N=40',"N=50"]
+    values = [1.80, 2.48, 3.25,4.45]
+
+    # 画柱状图
+    plt.bar(categories, values, width=0.3)
+
+    # 添加标题和标签
+    plt.xlabel('Number of Particles')
+    plt.ylabel('Running Time')
+
+    # 显示图形
+    plt.show()
+
+
+
+def draw_avg_and_optimal():
+    result1=[12654.373658372977, 12698.124852392339, 12698.124852392339, 12698.124852392339, 12698.124852392339,
+     12784.627585245535, 12855.814525865842, 12913.558426647263, 12915.934247153287, 12977.984795175113,
+     13015.281920353611, 13093.009701299674, 13184.014386350036, 13184.014386350036, 13195.571585956532,
+     13199.161357718096, 13201.053736599482, 13201.42906070698, 13202.349502554316, 13204.101140958395,
+     13204.767219692543, 13205.579178477517, 13205.635955778602, 13205.849472375572, 13206.437483463262,
+     13206.437483463262, 13206.437483463262, 13207.042267408628, 13207.357492661287, 13207.462522470576,
+     13208.206733697672, 13208.502035934924, 13208.61477213886, 13208.877485925652, 13208.877485925652,
+     13208.877485925652, 13208.877485925652, 13208.878221427602, 13208.878221427602, 13208.895267093354,
+     13208.898922662087, 13208.898922662087, 13208.905277714153, 13208.905277714153, 13208.905277714153,
+     13208.909477417874, 13208.918432585331, 13208.926698051593, 13208.930765858488, 13208.930765858488,
+     13208.939498443095, 13208.947104395516, 13208.949829485067, 13208.951127530083, 13208.951127530083,
+     13208.951127530083, 13208.951127530083, 13208.95135826988, 13208.954723917783, 13208.960041085564,
+     13208.96022229033, 13208.960291734877, 13208.960563779741, 13208.960563779741, 13208.960624465715,
+     13208.960815668914, 13208.960921481228, 13208.960921481228, 13208.960921481228, 13208.960921481228,
+     13208.960921481228, 13208.960921481228, 13208.960921481228, 13208.960933079697, 13208.961060563277,
+     13208.961195104495, 13208.961509339404, 13208.961535809209, 13208.961535809209, 13208.961535809209,
+     13208.961639650932, 13208.961639650932, 13208.961639650932, 13208.961657578848, 13208.961657578848,
+     13208.961657578848, 13208.961657578848, 13208.961657578848, 13208.961657578848, 13208.961657578848,
+     13208.961657578848, 13208.961657578848, 13208.961657578848, 13208.961657578848, 13208.961657578848,
+     13208.961657578848, 13208.961657578848, 13208.96174204845, 13208.96174204845, 13208.96174204845, 13208.96174204845,
+     13208.96174204845, 13208.96174204845, 13208.96174204845, 13208.96179504098, 13208.96179504098, 13208.96179504098,
+     13208.961806614338, 13208.961808357684, 13208.961808357684, 13208.961842407056, 13208.961842407056,
+     13208.961842407056, 13208.961842407056, 13208.961842407056, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826, 13208.961845475826,
+     13208.961845475826, 13208.961845475826, 13208.961845475826]
+    result2=[12546.34565841827, 12588.540773407485, 12588.540773407485, 12598.352279425277, 12673.134649139105, 12716.852844543462, 12747.322963571929, 12851.947514990834, 12915.249566920551, 12933.688899372763, 12997.187934813499, 13032.85057942456, 13074.49677442072, 13126.761638032529, 13159.642738831892, 13164.285318389148, 13170.441412834045, 13182.695884519884, 13185.282166215922, 13185.282166215922, 13188.825480810741, 13191.035806816944, 13191.035806816944, 13191.035806816944, 13191.035806816944, 13194.336900189935, 13195.770247958522, 13200.766016617516, 13201.909027658054, 13205.213054841497, 13206.545407301941, 13206.545407301941, 13206.545407301941, 13206.545407301941, 13207.13137632963, 13207.13137632963, 13207.313354291893, 13207.313354291893, 13207.313354291893, 13207.313354291893, 13207.313354291893, 13207.317949948818, 13207.317949948818, 13207.317949948818, 13207.56742478775, 13207.56742478775, 13207.806281086981, 13207.823541195374, 13207.823541195374, 13207.863563589388, 13207.902584614356, 13207.974328856655, 13207.988281766904, 13208.056089638689, 13208.124528444583, 13208.156529315398, 13208.439971127023, 13208.532743539787, 13208.659632933042, 13208.805319679213, 13208.862881246772, 13208.867993532038, 13208.889814004358, 13208.889814004358, 13208.899761094804, 13208.925243458436, 13208.928788816103, 13208.939269422726, 13208.939269422726, 13208.939269422726, 13208.941466589933, 13208.942273739422, 13208.942273739422, 13208.943635078482, 13208.949068640786, 13208.949273623974, 13208.9509418807, 13208.953008516755, 13208.954205057998, 13208.954205057998, 13208.955928547657, 13208.955960618347, 13208.958697914964, 13208.959093494648, 13208.960002711334, 13208.961032886162, 13208.961708363679, 13208.962160791067, 13208.962321279187, 13208.962405462013, 13208.962419118237, 13208.962503751816, 13208.962526006457, 13208.962541901454, 13208.962555312111, 13208.962563715902, 13208.962569210189, 13208.962577439785, 13208.962578804047, 13208.962579819412, 13208.962581145764, 13208.962581145764, 13208.962582528713, 13208.962583365581, 13208.96258365127, 13208.962584184621, 13208.962584184621, 13208.962584280973, 13208.962584609106, 13208.962584632583, 13208.96258472418, 13208.962584755454, 13208.962584763005, 13208.962584769764, 13208.962584774892, 13208.962584776276, 13208.962584778363, 13208.96258477863, 13208.962584785186, 13208.962584797586, 13208.962584798543, 13208.962584807961, 13208.962584810099, 13208.96258481368, 13208.962584814326, 13208.962584815084, 13208.962584816052, 13208.962584816098, 13208.96258481672, 13208.96258481732, 13208.962584818146, 13208.962584818579, 13208.962584818913, 13208.962584819881, 13208.962584820518, 13208.96258482068, 13208.962584820847, 13208.962584821056, 13208.962584821393, 13208.962584821558, 13208.962584822078, 13208.962584822288, 13208.962584822346, 13208.96258482235, 13208.962584822395, 13208.96258482241, 13208.962584822426, 13208.962584822435, 13208.962584822442, 13208.962584822446]
+    result=[]
+    result.append(result2)
+    result.append(result1)
+    labels = ['Proposed IPSO', 'IPSO-Convex',]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i],marker=".",  linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('Number of iterations')
+    plt.ylabel('Fitness value')
+
+    # 显示图形
+    plt.show()
+
+def draw_different_scheme():
+    result=[]
+    r1 = [891.2325611996497, 1600.5425924965132, 1775.4870815806812, 1661.4116199224782, 1652.7495185102612,
+          1710.8784073287254, 1632.2869853821032, 1405.6533507661727, 1650.9536213892259, 1658.1079792799283,
+          1624.144480374085, 1545.76445338581, 1639.1266120258867, 1127.2930875942645, 1317.1395738824897,
+          1345.105710390593, 1378.5232814712679, 1327.0724268995493, 1330.9249552627593]
+    r2 = [891.2325611996497, 883.715113526627, 876.3959553809046, 830.7304507711245, 845.0421291362111,
+          827.2501812000526, 814.8217899127849, 812.3184790554467, 842.9970828342944, 814.2846787891641,
+          802.4874927983169, 782.0437417323735, 806.7063928223102, 786.5781581157238, 781.5375637858187,
+          775.6115826850452, 779.9561019378333, 758.7242396016157, 774.6145659301196]
+    r3 = [891.2325611996497, 1561.6473796943978, 1696.133036433594, 1576.2258342093562, 1612.7704168093424,
+          1666.1107731684942, 1590.5964927045738, 1355.3867322689455, 1616.717308910885, 1648.8909657658814,
+          1602.6547257313946, 1533.6937236070994, 1612.2631107899372, 865.4559944925546, 989.8355164496194,
+          947.8109554579601, 956.4500273954962, 943.496583620515, 951.8076756728141]
+    r4 = [891.2325611996497, 1562.4932623722132, 1439.563234892769, 1333.67939697328, 1375.433960883068,
+          1396.9313906196946, 1362.5403862412616, 1355.5740012398287, 1369.3279235802177, 1390.5501310784725,
+          1273.6471170780565, 1258.8546891420726, 1287.0312789124978, 1062.1383584131474, 1101.7900422155617,
+          1069.7977380322025, 1104.3686356795185, 1102.2059669514304, 1055.6203262924575]
+    r1=[r1[i]+30 for i in range(len(r1))]
+    r1 = [1360.7607912987135, 1504.358556431442, 1510.358556431442, 1530.762198775177, 1560.4967054945619,
+          1554.284066114814, 1545.178232947664, 1513.5062187721946, 1533.2359261572783, 1542.3607986592804,
+          1530.9772604314603, 1520.9763513132932, 1500.3421314596987, 1486.8508382122334, 1477.5046561493104,
+          1495.2388029555345, 1488.0705547859009, 1498.5823144700644, 1505.1972321392159]
+    r2 = [1360.7607912987135, 1342.6868397918622, 1351.076942156944, 1348.6967607676631, 1305.7697309059433,
+          1334.0079380535115, 1340.631676810195, 1264.0794070260333, 1250.2755715166297, 1259.0359655749717,
+          1235.7365818847247, 1210.8540231993932, 1187.0700864417918, 1250.973520760966, 1272.2990981114065,
+          1275.9274300716181, 1283.9165890909994, 1339.8944398117947, 1330.1369541735082]
+    r3 = [1360.7607912987135, 1438.4860339730903, 1433.5506808753112, 1442.0532937881321, 1433.5791765209397,
+          1451.397747456012, 1447.1476698317256, 1500.2645132840282, 1498.8871320400963, 1487.5912202083084,
+          1469.1515288455548, 1450.0951590515783, 1438.6063295488057, 1393.9031709762176, 1389.6028980434648,
+          1372.8642855567707, 1391.0126906087135, 1448.7421722224726, 1481.6461095049337]
+    r4 = [1360.7607912987135, 1451.6434679001545, 1337.2997902927973, 1436.0056930217063, 1393.9351761181565,
+          1378.9842033338696, 1394.1325433707088, 1393.486592169153, 1410.2590539230744, 1390.8296176899803,
+          1352.7301334989197, 1311.1561049824397, 1316.9231732129626, 1275.8644932462012, 1360.7775553809722,
+          1309.3802794556834, 1427.9778272286399, 1457.7546681216156, 1444.1114416435776]
+    # r1 = [1286.6258869799801, 1367.8335970782923, 1370.9156946669054, 1190.2697139694317, 1234.1949959094025,
+    #       1193.3778898014343, 1160.8864974917155, 1336.8872931650062, 1311.4039803391463, 1268.1415121519738,
+    #       1317.5417253995151, 1280.2718476783346, 1226.494604838992, 995.2957915789626, 1089.6418913213079,
+    #       1023.4517741824707, 1017.705364175426, 1027.116479504571, 1017.39607025769]
+    # r2 = [1286.6258869799801, 1283.76285107334, 1318.2135565850044, 1262.4073164271836, 1260.8977079809658,
+    #       1207.564228057728, 1206.0637537689183, 1182.7444361110997, 1125.2960116466227, 1123.038549466862,
+    #       1108.173947038534, 1068.855543273497, 1060.274183565858, 1109.8721890034105, 1037.0959548032442,
+    #       1063.7999281817351, 1069.9828898077806, 1093.0571131724482, 1139.914227185953]
+    # r3 = [1286.6258869799801, 1254.6578857735883, 1303.7174190032174, 1245.8526855673767, 1242.9613969587185,
+    #       1187.6155855632373, 1163.9606150553589, 1395.8359771055698, 1386.4663039624209, 1351.5367014610288,
+    #       1308.4208894201272, 1277.68240382242, 1201.5247356257541, 1068.518227152022, 1107.4848271036894,
+    #       1063.9756350196692, 1088.0386581017867, 1111.9907911161165, 1124.5795026010292]
+    # r4 = [1286.6258869799801, 1249.2181896049422, 1163.7903208533064, 1188.869978737665, 1383.4490363213624,
+    #       1349.3116465797877, 1212.5737760660265, 1274.364322759652, 1282.4006158902628, 1297.3844780763218,
+    #       1278.0833328086255, 1270.383809592912, 1298.801571131647, 1035.767446588172, 1100.0204682760864,
+    #       946.6780106193855, 1119.8578241105774, 1047.6688352210092, 1214.6636303350917]
+    r1 = [1309.9150756787853, 1416.892875704836, 1491.7204139848134, 1472.4811364054835, 1328.9953030640863,
+          1375.5525391191088, 1381.469568013049, 1438.8066828217484, 1440.5294945390162, 1398.781282349261,
+          1389.7204426094881, 1391.582227742863, 1452.4138776684595, 1323.5424116360987, 1410.0806595425895,
+          1438.0834754390123, 1346.6737372833456, 1352.629728558739, 1278.6901707524291]
+    r2 = [1309.9150756787853, 1274.1821785177617, 1283.8936284069239, 1276.8534355639592, 1261.270157553835,
+          1258.743220374898, 1276.4354038795307, 1294.0537868748556, 1242.1760975771713, 1203.940030077654,
+          1207.1088097362338, 1168.499524931428, 1200.992324882969, 1156.5264699810139, 1195.349363943187,
+          1183.2185323406943, 1163.0761396238697, 1158.5092457877918, 1148.1147267507092]
+    r3 = [1309.9150756787853, 1208.8190179435717, 1218.611774388484, 1214.5444264177877, 1178.3854772115626,
+          1194.005756279548, 1226.7678720865588, 1341.8818162828602, 1351.3791707199896, 1307.622278667928,
+          1206.0719649783168, 1195.973179687433, 1237.0042988196824, 1283.6549540415801, 1301.1697597475359,
+          1328.5812965025991, 1230.6529116061113, 1267.6075201936771, 1259.667925239849]
+    r4 = [1309.9150756787853, 1267.5178849859583, 1197.625520275931, 1220.7621322596258, 1218.8308339081993,
+          1307.700811299275, 1235.3311162243288, 1333.4524734380068, 1380.728512990628, 1312.3471665270324,
+          1302.7946145288183, 1262.1746862054267, 1163.9827334841368, 1218.9301065014429, 1119.9205697647878,
+          1109.450331395668, 1148.4201501743064, 1104.6236345008238, 1151.4041048886943]
+
+
+    result.append(r1)
+    result.append(r2)
+    result.append(r3)
+    result.append(r4)
+    labels = ['Proposed scheme', 'FDDR',"FRDD","SDDDR","FDFR"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i],marker=".", linestyle=line[i%4], )
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(0, 19)
+
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(0, 19, 1))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('time slots')
+    plt.ylabel('System Energy Efficiency')
+    # 显示图形
+    plt.show()
+
+def draw_different_scheme_satisfaction():
+    r1 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    r2 = [1.0, 1.0, 1.0, 1.0, 1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.8666666666666667, 0.8666666666666667,0.8666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333,0.9333333333333333, 0.9333333333333333]
+    r3 = [1.0, 0.8, 0.8666666666666667, 0.7333333333333333, 0.6, 0.6, 0.6, 0.6666666666666666, 0.6666666666666666,
+          0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666,
+          0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333]
+    r3=[0.9333333333333333, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8666666666666667, 0.8666666666666667, 0.6666666666666666, 0.7333333333333333, 0.6666666666666666, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.6, 0.6, 0.6, 0.7333333333333333, 0.7333333333333333, 0.6666666666666666]
+    r4 =[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+
+    result=[]
+    result.append(r1)
+    result.append(r2)
+    result.append(r3)
+    result.append(r4)
+    labels = ['Proposed scheme', 'FDDR', "FRDD", "SDDDR"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i], marker=".", linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(0, 19)
+    plt.ylim(0, 1.1)
+
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(0, 19, 1))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('time slots')
+    plt.ylabel('Communication Satisfaction')
+    # 显示图形
+    plt.show()
+def draw_different_scheme_satisfaction_g():
+    g1 = [0.9333333333333333, 1.0, 1.0, 1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 1.0, 1.0, 1.0,
+          1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    g2 = [0.9333333333333333,0.9333333333333333, 0.8666666666666667,  0.8666666666666667,  0.7333333333333333, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8, 0.8, 0.8,0.8,0.8, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333, 0.7333333333333333]
+    g3 = [0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333,
+          0.9333333333333333, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    g4 = [0.9333333333333333, 0.9333333333333333, 1.0, 1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333,
+          0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333,
+          0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+
+    result = []
+    result.append(g1)
+    result.append(g2)
+    result.append(g3)
+    result.append(g4)
+    labels = ['Proposed scheme', 'FDDR', "FRDD", "SDDDR"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i], marker=".", linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(0, 19)
+    plt.ylim(0, 1.1)
+
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(0, 19, 1))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('time slots')
+    plt.ylabel('Localization Satisfaction')
+    # 显示图形
+    plt.show()
+
+def draw_slot_compare():
+    result=[[1299.3570374805256, 1018.2689354125052, 1018.7746187555389, 1018.7090809119278, 1018.7921029944429, 1018.7747407295528, 1014.0268093958708, 955.1485599884267, 1010.122365171568, 1020.8539679402211, 1000.3460924396524, 983.0185921930898, 1001.9949859271056, 1018.9331241289651, 970.4297703762156, 993.0521832206775, 1016.5811537560148, 1018.9873083907402, 1019.2482751292833], [1299.3570374805256, 955.5638632401538, 929.1895124643144, 1018.0494054439747, 988.687790660067, 1014.9786686490513, 985.0628829209201, 955.245799658351, 964.8150161949783, 1016.8795883435956, 1029.1624089665993, 958.8560836681085, 940.1959644088128, 1017.7181699493798, 996.7439352037351, 1019.0622835757795, 953.0264231536681, 990.1194871093758, 936.475808278264], [1299.3570374805256, 1017.7055602458133, 994.5004504254122, 988.7818917521337, 963.7427293398699, 944.8095582419897, 957.067108495399, 979.2490627807105, 962.1780450751002, 972.6343983805053, 1033.670255895614, 1025.8087371668992, 1012.0890135564214, 1018.8621442779979, 997.7653759888321, 983.1521779007888, 1019.0413348846395, 952.1558224883776, 954.0513973945293], [1299.3570374805256, 1018.268935175176, 995.8087474344729, 990.7037441026249, 988.9720094787026, 1018.2082868895291, 991.2212647557297, 1007.3294233982771, 1023.4907490021022, 962.2016915179671, 978.082412079636, 971.9443413219389, 950.544126069441, 991.5541051435569, 970.2309596521279, 956.4666170406782, 948.7161327407202, 1016.3077509070416, 951.177660597132], [1299.3570374805256, 1018.2678493263568, 995.8793818124047, 990.7353724358787, 988.9579331778344, 977.4563955524744, 948.138125125953, 940.1866480861797, 939.896987716489, 966.9714716770775, 977.9872631465986, 981.3763322671907, 953.2065258930505, 954.7034235537245, 941.9163884545118, 911.8688837942306, 1014.9706335047204, 962.8668098930724, 962.8936809921414], [1299.3570374805256, 1018.2641577541285, 995.6107868624795, 990.5609842586114, 988.8080035551752, 977.5289875009772, 987.062056661658, 1001.4288625814581, 1002.0194648524549, 1013.0979856081176, 1021.1332654190495, 1008.985812205421, 998.5544941276504, 1019.0165440463907, 996.7761558648857, 981.76581105948, 952.9844069104482, 950.5813821472358, 952.5575113991316]]
+    result=[[1266.2371306931811, 1208.1970110848779, 1388.5413771030999, 1449.3211492594298, 1278.7296859116695, 1394.8020997423594, 1277.0419429965377, 1390.938789270484, 1242.2784932270065, 1299.1403812739584, 1343.3438613772796, 1271.3047123664858, 1360.5244631435555, 1373.0087316153933, 1337.9025145389612, 1246.4898869779095, 1271.268615295663, 1274.8389048686095, 1294.7043948698858], [1266.2371306931811, 1406.4053908025305, 1386.3948299231622, 1343.9738760031157, 1355.1049050221227, 1289.1121931980883, 1325.0411775700877, 1293.3711003720966, 1260.7116417295094, 1280.2046143335149, 1291.5332380239224, 1267.668665036152, 1258.7809434756568, 1298.1885204435066, 1281.2588067273894, 1226.783457692276, 1265.6585362930812, 1228.5649383150392, 1235.2366789947205], [1266.2371306931811, 1459.2377906025747, 1399.1481423528255, 1407.0479891139805, 1393.2985875457243, 1365.2759676236094, 1405.3843774554634, 1393.0831668132623, 1347.0016373806957, 1383.2507653287616, 1383.6773941394276, 1333.0012478223978, 1355.7453965574236, 1375.1262423053781, 1340.4553271028797, 1312.293596829912, 1337.4301415139469, 1299.9196215691334, 1291.3508785126078], [1266.2371306931811, 1299.2937054793135, 1289.2294400967069, 1270.3518378836493, 1283.6826975391223, 1251.9584811851028, 1300.9102664744667, 1300.7623149451686, 1270.200639438493, 1379.7242476104914, 1385.1729624220902, 1333.1817837168815, 1358.3209681033616, 1374.6915149924023, 1341.6605427044183, 1312.6018581922144, 1338.0302678067583, 1302.694799756844, 1290.8108804244478], [1266.2371306931811, 1490.564058426475, 1440.0570065948891, 1442.3089169422494, 1420.9777075809266, 1384.2782257175857, 1429.165058076329, 1428.6988122438504, 1368.8089906533137, 1379.8167511341449, 1398.755712106024, 1239.5144955291623, 1248.7796378308121, 1297.4654883707099, 1270.390574926196, 1238.3658074226698, 1339.9910539027328, 1304.1085082220372, 1291.9117736602407], [1266.2371306931811, 1423.3556637618335, 1389.1964781162253, 1359.9260585580066, 1354.6879542790905, 1321.5951768363047, 1296.2235155964663, 1344.3296638979364, 1300.688020163772, 1337.2339591770772, 1360.9276520775384, 1304.7456839648794, 1308.977890771906, 1267.0172069163502, 1259.0806657358025, 1209.1935171666776, 1244.5821370627616, 1212.219266263685, 1219.981564246517]]
+    result=[[1243.1292899707744, 1437.952652644042, 1404.129429807801, 1466.6561212317413, 1374.0159505575298, 1452.6428603103568, 1397.459450600962, 1399.8844505505783, 1470.0980237579495, 1295.0367256348368, 1361.3922691520825, 1363.4274339999395, 1361.4072763816678, 1076.9639237886965, 1104.2826074042205, 1086.6656395129896, 1040.3429157087257, 1069.766593398577, 1115.154068275955], [1243.1292899707744, 1425.4972310329304, 1389.3428368130587, 1411.0213767676132, 1385.8925766143877, 1192.706575462344, 1158.9517341510357, 1400.79861127426, 1401.193377096315, 1391.5525629638498, 1447.00363081752, 1443.8717565982536, 1428.0587282636402, 1085.7538448145217, 1082.9395433095265, 993.901212550637, 1042.7821224859217, 992.8513955694106, 1066.9082028475204], [1243.1292899707744, 1458.1370913018645, 1421.6391089791973, 1456.687034922671, 1314.8651953531012, 1322.4775385147184, 1302.1360890767032, 1406.9508800023473, 1407.967987471922, 1286.7066398777674, 1364.2055014265895, 1361.7251381027797, 1362.7759769067688, 1117.0070813655539, 1122.2662627127763, 1089.4704676134247, 1010.832594719583, 1044.4968845144244, 1130.358264542365], [1243.1292899707744, 1149.5705493403543, 1070.2521109356871, 1133.2053302483753, 1130.3165451572233, 1477.8045351260541, 1429.5155185968051, 1497.408350218828, 1514.7090480819907, 1431.0679416087846, 1321.7873006028647, 1336.9244019674877, 1332.8829642642934, 1062.8587900245789, 1053.5499170000219, 1027.973128324803, 1003.7237651426925, 1107.7093416835237, 1187.2214178430713], [1243.1292899707744, 1485.417942999133, 1439.7969186842467, 1455.9373480653976, 1444.110573249161, 1463.7136146469588, 1369.9301894806395, 1475.4018948953544, 1413.0530855233224, 1494.0054316526118, 1493.4860186797528, 1441.1293670079756, 1427.2035714898354, 1062.1427558839055, 1051.6214337643592, 1035.7292492810861, 1086.1124570548595, 1108.8914027099684, 1173.4244776069836], [1243.1292899707744, 1400.421601413154, 1316.0402497513794, 1389.468652936794, 1399.0792327125187, 1445.6104105864706, 1406.2390192122252, 1399.3889973661499, 1394.72514638148, 1270.4573990410977, 1322.6007938441278, 1316.6062169188547, 1312.4449772111304, 1137.105620137621, 1138.4493265558283, 1117.2769884563331, 1070.6899185825348, 1081.474959365313, 1089.2848425994243]]
+    result = [[1320.7266287079533, 1526.4679928927821, 1541.6466829006065, 1316.0512288118382, 1308.769691561029,
+               1448.5634616409686, 1358.3855395050696, 1340.851841032951, 1346.4610961812164, 1357.348392420678,
+               1287.9535324790108, 1316.4361704376345, 1374.0267354533535, 1399.3452993572366, 1427.5679524554935,
+               1418.8802363654247, 1366.777914314444, 1470.249489578498],
+              [1320.7266287079533, 1251.7706528540307, 1502.0475449248688, 1507.7822692137697, 1458.0782549932192,
+               1423.08421827599, 1354.8544170578027, 1337.1748022547933, 1278.1200674229299, 1275.0763785597226,
+               1355.6094076565782, 1318.4925528740378, 1376.7518875091102, 1398.3744645478428, 1427.8855725671706,
+               1418.5874180684134, 1361.5858433423568, 1364.0741129095477],
+              [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1505.714989960461, 1491.067848447097,
+               1444.9242334098553, 1355.6635066788222, 1337.060057536818, 1344.3605738406459, 1271.2111458249062,
+               1283.1608183869025, 1233.0713282236602, 1373.9231715928186, 1402.5491074934696, 1429.7021155200312,
+               1303.5954975318039, 1351.4742115968943, 1362.7604119346724],
+              [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1488.6361439452937,
+               1451.2911242206337, 1357.446746730729, 1341.2121206023373, 1348.6441238678733, 1357.3056760094305,
+               1357.7593532946837, 1319.091537706909, 1296.5049504805338, 1328.0478230069996, 1368.4574346309,
+               1349.0001156394105, 1414.6144318448096, 1416.61709293385],
+              [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1136.7619636179825,
+               1449.2129802147974, 1361.2099203305797, 1340.3129962365085, 1345.3708811074277, 1349.8759241335788,
+               1356.4013588466967, 1317.3846335900835, 1373.5168144885602, 1399.1126435652468, 1428.5314346422501,
+               1418.1413383087117, 1454.380799043778, 1471.5180575288075],
+              [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1136.7619636179825,
+               1132.6269691995567, 1274.9534022546165, 1265.9606268721025, 1286.434436186174, 1281.9603479206487,
+               1294.8951266671795, 1235.7768801649959, 1313.5938515129144, 1356.1709709499287, 1383.9221337321194,
+               1376.136619082958, 1424.2168584190804, 1429.717894388855]]
+
+    result=[[1320.7266287079533, 1526.4679928927821, 1541.6466829006065, 1316.0512288118382, 1308.769691561029, 1448.5634616409686, 1358.3855395050696, 1340.851841032951, 1346.4610961812164, 1357.348392420678, 1287.9535324790108, 1316.4361704376345, 1374.0267354533535, 1399.3452993572366, 1427.5679524554935, 1418.8802363654247, 1366.777914314444, 1470.249489578498],
+            [1320.7266287079533, 1251.7706528540307, 1502.0475449248688, 1507.7822692137697, 1458.0782549932192, 1423.08421827599, 1354.8544170578027, 1337.1748022547933, 1278.1200674229299, 1275.0763785597226, 1355.6094076565782, 1318.4925528740378, 1376.7518875091102, 1398.3744645478428, 1427.8855725671706, 1418.5874180684134, 1361.5858433423568, 1364.0741129095477],
+            [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1505.714989960461, 1491.067848447097, 1444.9242334098553, 1355.6635066788222, 1337.060057536818, 1344.3605738406459, 1271.2111458249062, 1283.1608183869025, 1233.0713282236602, 1373.9231715928186, 1402.5491074934696, 1429.7021155200312, 1303.5954975318039, 1351.4742115968943, 1362.7604119346724],
+            [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1488.6361439452937, 1451.2911242206337, 1357.446746730729, 1341.2121206023373, 1348.6441238678733, 1357.3056760094305, 1357.7593532946837, 1319.091537706909, 1296.5049504805338, 1328.0478230069996, 1368.4574346309, 1349.0001156394105, 1414.6144318448096, 1416.61709293385],
+            [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1136.7619636179825, 1449.2129802147974, 1361.2099203305797, 1340.3129962365085, 1345.3708811074277, 1349.8759241335788, 1356.4013588466967, 1317.3846335900835, 1373.5168144885602, 1399.1126435652468, 1428.5314346422501, 1418.1413383087117, 1454.380799043778, 1471.5180575288075],
+            [1320.7266287079533, 1251.7706528540307, 1253.2896297524624, 1212.8283613961432, 1136.7619636179825, 1132.6269691995567, 1274.9534022546165, 1265.9606268721025, 1286.434436186174, 1281.9603479206487, 1294.8951266671795, 1235.7768801649959, 1313.5938515129144, 1356.1709709499287, 1383.9221337321194, 1376.136619082958, 1424.2168584190804, 1429.717894388855]]
+    result=[[1328.1765246507357, 1503.4819127657738, 1413.0043866027665, 1463.0694754945252, 1411.3332686120364, 1430.8494924488095, 1348.4585284268035, 1425.5665531789396, 1434.6103288936818, 1382.0470220409627, 1400.9531567236882, 1430.8877968272081, 1388.6185194154302, 1349.9589680879892, 1331.6482975811632, 1349.4196364208437, 1333.4082035862293, 1289.1716043437505], [1328.1765246507357, 1353.1879576378208, 1405.8565440529696, 1432.7524257593964, 1401.1414708835648, 1413.2404297641222, 1383.0244353104806, 1424.8984233930191, 1431.198682113639, 1377.8356032938168, 1396.959114367267, 1431.17968510067, 1386.0419445035495, 1356.377703925818, 1332.1232554431833, 1352.6415842615386, 1334.979935409752, 1287.8872570069313], [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1461.5691341367276, 1411.0399445665012, 1428.6282956069683, 1342.4394201899177, 1410.6593127134165, 1421.27785518321, 1379.0053611335575, 1400.2550306243324, 1433.9203792901133, 1356.75918484904, 1345.2969702372804, 1335.91205104966, 1353.492373837457, 1338.6041420862807, 1291.1214993205556], [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1397.2181548623369, 1416.1632823180864, 1381.329183610079, 1423.0177897257574, 1381.024457636659, 1314.0230128358758, 1329.5899423905234, 1388.9476610421257, 1338.3554621772444, 1327.6922803222226, 1313.4015072363025, 1355.1396654592534, 1334.929881133014, 1288.7404759786446], [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977, 1239.6349292315638, 1256.4686678242904, 1277.2435737396827, 1267.0466827108758, 1178.9368661962508, 1368.468264013462, 1423.5411331544783, 1242.4019341077947, 1226.9254448575948, 1186.9326588398535, 1344.001558702772, 1337.9391635327902, 1297.4028580414574], [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977, 1311.5753634371717, 1388.2120232884542, 1431.8449807730888, 1431.3580503007593, 1378.1452369441306, 1396.4141506156877, 1433.377670872926, 1384.1169572273377, 1359.900905300984, 1330.6370094444353, 1353.823654014219, 1336.9776868893764, 1288.787931542987]]
+
+    labels = ['1', '2', "3", "4","5","6"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.',"-",":"]
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i], marker=".", linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(0, 19)
+
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(0, 19, 1))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('time slots')
+    plt.ylabel('System Energy Efficiency')
+
+    # 显示图形
+    plt.show()
+def draw_slot_satisfaction_compare():
+    r1 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9333333333333333, 0.9333333333333333,
+          0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333]
+    r2 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9333333333333333, 1.0, 1.0, 0.9333333333333333,
+          1.0, 1.0]
+    r3 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0]
+
+    g1 = [1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 1.0,
+          0.9333333333333333, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    g2 = [1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 1.0,
+          1.0, 1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    g3 = [1.0, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 1.0,
+          1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+    result = []
+    result.append(r1)
+    result.append(r2)
+    result.append(r3)
+    # result.append(r4)
+    # result.append(r5)
+    # result.append(r6)
+    labels = ['10 10', '10 20', "10 30", "10 40", "10 50", "10 60"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.', "-", ":"]
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(result[i], label=labels[i], marker=".", linestyle=line[i], )
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(0, 19)
+    plt.ylim(0, 1.1)
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(0, 19, 1))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('time slots')
+    plt.ylabel('Communication Satisfaction')
+
+    # 显示图形
+    plt.show()
+
+def draw_compare_pso_and_convex():
+    r1=[2156.572640825806, 1793.049723531689, 1536.9919020592397, 1731.792484946798, 1636.6545118475497,
+     1158.0510410573797, 1103.6215549974738, 782.5004345151718]
+    r2=[2102.3361983188765, 1643.2563392728337, 1363.8964216339489, 1375.9362936740881, 810.3157040900248,
+     744.929779050958, 823.9891822976343, 461.98393739269625]
+    r3=[2095.218778754906, 1632.1290265003292, 1508.3203473638766, 1414.1323142076471, 1114.4302718364665,
+     969.7470169582967, 920.7834622299312, 752.0135667914984]
+    r4=[2081.364506789164, 1441.4649220322065, 1352.8460409408997, 1251.814958273326, 827.5835603105922, 718.3196179478409,
+     702.6041582888906, 590.024193244058]
+    r1 = [1979.5154537856467, 1949.1967926150814, 1583.8373789254997, 1408.9597726122278, 1284.75424046391,
+          1062.356849679389, 773.64091282078, 669.5307009461131]
+    r2 = [1954.7080805746896, 1453.8382207915447, 1268.2785375163758, 987.7561390469604, 881.274376744466,
+          783.0926396247362, 555.7810370667859, 400.0913596289859]
+    r3 = [1979.5154537856467, 1572.289453569241, 1482.206203919101,1113.0080767351026, 1064.4678552375331,
+          910.7380100121287, 710.6949860041652, 473.73148513092895]
+    r4 = [1954.7080805746896, 1391.2020029825142, 1189.340170050845, 905.7558722161126, 805.0153107446824,
+          720.2723455091826, 520.5573981391487, 373.8077702943482]
+
+    r=[(r1[i]-r4[i])/r4[i] for i in range(len(r1))]
+    print(sum(r)/len(r))
+    result=[]
+    result.append(r1)
+    result.append(r2)
+    result.append(r3)
+    result.append(r4)
+    x=[i for i in range(5, 20, 2)]
+    labels = ['Proposed Scheme', 'PSO-Avg', "KMeans-Convex", "KMeans-Avg"]
+    markers = [".", '^', 's', 'o']
+    line = ['-', '--', ':', '-.']
+    for i in range(len(result)):
+        index = round(i / 2)
+        plt.plot(x,result[i], label=labels[i], marker="o", linestyle=line[i], zorder=2)
+    # 设置图例
+    plt.legend()
+    # 设置 x 轴范围
+    plt.xlim(4, 20)
+
+    # 设置 x 轴刻度间距
+    plt.xticks(np.arange(5, 20, 2))
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('Users Num')
+    plt.ylabel('System Energy Efficiency')
+
+    # 显示图形
+    plt.show()
+
+def draw_c_satisfaction():
+    # 示例数据
+    name_list = ['uavs=4,users=16', 'uavs=5,users=30', 'uavs=6,users=50',]
+    values1 = [1.0, 0.96, 0.85]
+    values2 = [0.8823529411764706, 0.86, 0.8]
+    values3 = [0.9411764705882353, 0.94, 0.83]
+    values4 = [0.8823529411764706, 0.8, 0.7]
+    improve=[(values1[i]-values4[i])/values4[i] for i in range(len(values1))]
+    print(sum(improve)/len(improve))
+    x = list(range(len(values1)))
+    print(x)
+    total_width, n = 0.8, 4
+    width = total_width / n
+    # 计算每个柱的位置
+    bar_positions = np.arange(len(name_list))
+
+    plt.bar(bar_positions - 2*width, values1, width=width, label='Proposed Scheme', color='white',edgecolor='black',tick_label=name_list,)
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions - width, values2, width=width, label='PSO-Avg', tick_label=name_list, color='white',edgecolor='black',hatch='/' )
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions, values3, width=width, label='KMeans-Convex', tick_label=name_list, color='white',edgecolor='black',hatch='\\')
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions+width, values4, width=width, label='KMeans-Avg', tick_label=name_list, color='white',edgecolor='black',hatch='//' )
+    # for i in range(len(bar_positions)):
+    #     bar_positions[i] = bar_positions[i] - 0.1
+    plt.xticks(bar_positions, name_list)
+
+    plt.ylabel('Communication Satisfaction of Users')
+    plt.legend()
+    plt.show()
+def draw_p_satisfaction():
+    name_list = ['uavs=4,users=16', 'uavs=5,users=30', 'uavs=6,users=50', ]
+    values1,values2,values3,values4 =[1.0,1,1], [1.0,1,1], [0.8235294117647058,0.9,0.88], [0.8235294117647058,0.9,0.88]
+
+    x = list(range(len(values1)))
+    print(x)
+    total_width, n = 0.8, 4
+    width = total_width / n
+    # 计算每个柱的位置
+    bar_positions = np.arange(len(name_list))
+    light_color = 'white'
+
+    plt.bar(bar_positions - 2 * width, values1, width=width, label='Proposed Scheme', color=light_color,edgecolor='black', tick_label=name_list, )
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions - width, values2, width=width, label='PSO-Avg', tick_label=name_list,  color=light_color,edgecolor='black', hatch='/')
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions, values3, width=width, label='KMeans-Convex', tick_label=name_list,  color=light_color,edgecolor='black', hatch='\\')
+    for i in range(len(x)):
+        x[i] = x[i] + width
+    plt.bar(bar_positions + width, values4, width=width, label='KMeans-Avg', tick_label=name_list, color=light_color,edgecolor='black',
+            hatch='//')
+    # for i in range(len(bar_positions)):
+    #     bar_positions[i] = bar_positions[i] - 0.1
+    plt.xticks(bar_positions, name_list)
+
+    plt.ylabel('Localization Satisfaction of Users')
+    plt.legend()
+    plt.show()
+
+def draw_dynamic_utility():
+    r=[-182.50035696943849, -177.79594156951626, -145.0853662930458, -142.68333401292557, -141.75346320018448,
+     -139.89634105148497, -139.25474456595703, -138.38238133386784, -130.0599933647533, -132.7028964739182,
+     -133.02973650029, -137.43839825328695, -140.7194869302244, -139.8250859102916, -138.70066639324273,
+     -144.02751920866464, -147.57584841736275]
+    r3=[-185.11489614688995, -183.32832233236752, -184.8632440312421, -185.90826130427084, -1046.4029821344411,
+     -705.1510281549287, -784.703021596474, -188.32844376690645, -1432.3437733694404, -1340.519117170665,
+     -1108.9134865693245, -1329.724190290336, -543.0716332673447, -4079.8195480534982, -801.905349792583,
+     -880.6140750428915, -185.65576844983727]
+    r2=[-182.5881732692689, -182.9274309066099, -151.37672315057665, -149.39333947365594, -149.79231667807625, -147.4212656957045, -148.77068968962027, -153.205403970754, -141.47000821147626, -142.7027807716164, -142.0437598710444, -143.8288448799893, -143.2228327270912, -144.59048160309982, -147.25800551225242, -146.7899663698531, -144.9709480279667]
+
+    r1 = [-184.06100597871276, -185.85049500451746, -146.8288332014583, -147.8690106029141, -150.89610670415044,
+          -161.58225234814634, -161.25450103761872, -160.210679762176, -174.61142139727096, -177.76804395633008,
+          -181.569335688453, -159.927794994072, -160.94409679626565, -166.21748449604434, -160.2738429197924,
+          -159.7484534802284, -158.85902270747295]
+    r2 = [-183.75262514436605, -185.87714908455004, -146.57818980968494, -147.60619957376412, -150.63793929032238,
+          -160.1327365871477, -160.41656576457314, -159.14883710280913, -177.61971965060854, -173.46045900149656,
+          -177.10133069993495, -159.18269253893232, -160.08874781747255, -163.4671368445193, -158.9553959759295,
+          -158.31327992554847, -157.30095288797912]
+    r3 = [-191.9539271586222, -495.0759766925493, -193.5965909690982, -192.5817448793148, -1195.945536180579,
+          -540.6940059847401, -629.8130961244544, -552.3027556855934, -189.9716865954716, -701.1440778210009,
+          -2998.227257864013, -2423.421441879022, -200.80692664994223, -1791.7088515380249, -879.184983460981,
+          -201.1295073193511, -1087.8150595700147]
+    r4_1 = [-183.75262514436605, -185.87714908455004, -186.438834653355, -184.1884585777314, -186.33933807260107,
+          -188.43716599179044, -188.19672726431384, -186.9874196689149, -189.13295993082926, -189.30032644825883,
+          -192.89436400905976, -195.0003337026544, -196.4162768042548, -196.62278977364838, -197.82920310513353,
+          -197.86530599940798, -198.22041251155667]
+    r4=[-191.9539271586222, -193.26796056081787, -193.08808786801166, -192.21772213830948, -194.02224268400286, -193.699253301448, -193.61243939244417, -191.9209212060532, -194.12611798707232, -194.22893921637396, -198.00070893060212, -199.68020061189088, -201.41765011390575, -202.61071672355132, -203.10880418272217, -202.69870082683104, -202.27407764837193]
+
+
+    # d=[(r1[i]-r3[i])/abs(r3[i]) for i in range(len(r1))]
+    d = [(r1[i] - r4[i]) / abs(r4[i]) for i in range(len(r1))]
+    d1 = [(r1[i] - r4_1[i]) / abs(r4_1[i]) for i in range(len(r1))]
+    print(sum(d)/len(d))
+    print(sum(d1) / len(d))
+    x=[i for i in range(1, 18, 1)]
+    plt.plot(x, r1, label="Proposed algorithm", marker="o", )
+    # plt.plot(x, r4, label="Non dual time scales", marker="*", )
+    plt.plot(x, r4, label="Fixed position and resource", marker="*", )
+    # plt.plot(x, r4_1, label="Fixed position-Convex", marker=".", )
+    plt.plot(x, r4_1, label="Fixed position dynamic resource", marker="^", )
+    # plt.plot(x, r2, label="Pso-Convex", marker="^", )
+    # plt.plot(x, r3, label="Kmeans-Avg", marker=".", )
+    plt.xlabel('时隙', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('通导效用', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('time slots')
+    # plt.ylabel("Communication Localization utility")
+
+    plt.legend()
+    # plt.ylim(-3000, 100)
+    plt.show()
+
+def draw_dynamic_c_satisfaction():
+    c1 = [0.8333333333333334, 0.8333333333333334, 0.7, 0.7, 0.7, 0.8, 0.8, 0.8, 0.7333333333333333, 0.8, 0.8, 0.7, 0.7,
+          0.7666666666666667, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666]
+    g1 = [0.6666666666666666, 0.6666666666666666, 0.8666666666666667, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8,
+          0.8, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.7333333333333333, 0.7333333333333333,
+          0.7333333333333333, 0.7666666666666667, 0.7666666666666667, 0.7666666666666667]
+    c2 = [0.4666666666666667, 0.5333333333333333, 0.6666666666666666, 0.5666666666666667, 0.6333333333333333,
+          0.6333333333333333, 0.5666666666666667, 0.6, 0.5, 0.5333333333333333, 0.4, 0.5333333333333333,
+          0.4666666666666667, 0.5666666666666667, 0.5333333333333333, 0.43333333333333335, 0.4]
+    g2 = [0.6666666666666666, 0.6666666666666666, 0.8666666666666667, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8,
+          0.8, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.7333333333333333, 0.7333333333333333,
+          0.7333333333333333, 0.7666666666666667, 0.7666666666666667, 0.7666666666666667]
+    c3 = [0.7666666666666667, 0.36666666666666664, 0.7666666666666667, 0.7333333333333333, 0.36666666666666664,
+          0.26666666666666666, 0.16666666666666666, 0.36666666666666664, 0.7666666666666667, 0.13333333333333333,
+          0.4666666666666667, 0.4, 0.6, 0.23333333333333334, 0.03333333333333333, 0.6333333333333333,
+          0.16666666666666666]
+    g3 = [0.6666666666666666, 0.4666666666666667, 0.6333333333333333, 0.6333333333333333, 0.23333333333333334,
+          0.43333333333333335, 0.4666666666666667, 0.5333333333333333, 0.7, 0.3, 0.3, 0.3333333333333333, 0.6, 0.4,
+          0.16666666666666666, 0.6, 0.43333333333333335]
+    c4_1=[0.4666666666666667, 0.5333333333333333, 0.6333333333333333, 0.6, 0.6, 0.5666666666666667, 0.4666666666666667, 0.5,
+     0.4, 0.4666666666666667, 0.3333333333333333, 0.43333333333333335, 0.43333333333333335, 0.4666666666666667,
+     0.4666666666666667, 0.4666666666666667, 0.36666666666666664]
+    c4=[0.7666666666666667, 0.8, 0.8, 0.8, 0.8, 0.6333333333333333, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6666666666666666, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333]
+    g4_1 = [0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.7, 0.7,
+          0.7, 0.7, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6]
+    g4 = [0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.7, 0.7,
+          0.7, 0.7, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6]
+
+    x = [i for i in range(1, 18, 1)]
+    plt.plot(x, c1,label="Proposed algorithm", marker="o", )
+    plt.plot(x, c4, label="Non dual time scales", marker="*", )
+    # plt.plot(x, c4, label="Fixed position and resource", marker="*", )
+    # plt.plot(x, c4_1, label="Fixed position-Convex", marker=".", )
+    plt.plot(x, c2,label="PSO-Convex", marker="^", )
+    plt.plot(x, c3,label="Kmeans-Avg", marker=".", )
+    # d1= [(c1[i] - c3[i]) / abs(c3[i]) for i in range(len(c1))]
+    # d2 = [(g1[i] - g3[i]) / abs(g3[i]) for i in range(len(g1))]
+    d11=(sum(c1)-sum(c3))/sum(c3)
+    d22=(sum(g1)-sum(g3))/sum(g3)
+    d1 = (sum(c1) - sum(c4)) / sum(c4)
+    d2 = (sum(g1) - sum(g4)) / sum(g4)
+    d1_1 = (sum(c1) - sum(c4_1)) / sum(c4_1)
+    d2_2 = (sum(g1) - sum(g4_1)) / sum(g4_1)
+    # d1 = [(c1[i] - c4[i]) / abs(c4[i]) for i in range(len(c1))]
+    # d2 = [(g1[i] - g4[i]) / abs(g4[i]) for i in range(len(g1))]
+    print(d1,d2)
+    print(d1_1,d2_2)
+    print(d11,d22)
+
+    plt.legend()
+    plt.xlabel('时隙', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('通信需求满足率', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('time slots')
+    # plt.ylabel('Communication Satisfaction of Users')
+
+    plt.show()
+
+def draw_dynamic_g_satisfaction():
+    c1 = [0.8333333333333334, 0.8333333333333334, 0.7, 0.7, 0.7, 0.8, 0.8, 0.8, 0.7333333333333333, 0.8, 0.8, 0.7, 0.7,
+          0.7666666666666667, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666]
+    g1 = [0.6666666666666666, 0.6666666666666666, 0.8666666666666667, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8,
+          0.8, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.7333333333333333, 0.7333333333333333,
+          0.7333333333333333, 0.7666666666666667, 0.7666666666666667, 0.7666666666666667]
+    c2 = [0.4666666666666667, 0.5333333333333333, 0.6666666666666666, 0.5666666666666667, 0.6333333333333333,
+          0.6333333333333333, 0.5666666666666667, 0.6, 0.5, 0.5333333333333333, 0.4, 0.5333333333333333,
+          0.4666666666666667, 0.5666666666666667, 0.5333333333333333, 0.43333333333333335, 0.4]
+    g2 = [0.6666666666666666, 0.6666666666666666, 0.8666666666666667, 0.8666666666666667, 0.8666666666666667, 0.8, 0.8,
+          0.8, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.7333333333333333, 0.7333333333333333,
+          0.7333333333333333, 0.7666666666666667, 0.7666666666666667, 0.7666666666666667]
+    c3 = [0.7666666666666667, 0.36666666666666664, 0.7666666666666667, 0.7333333333333333, 0.36666666666666664,
+          0.26666666666666666, 0.16666666666666666, 0.36666666666666664, 0.7666666666666667, 0.13333333333333333,
+          0.4666666666666667, 0.4, 0.6, 0.23333333333333334, 0.03333333333333333, 0.6333333333333333,
+          0.16666666666666666]
+    g3 = [0.6666666666666666, 0.4666666666666667, 0.6333333333333333, 0.6333333333333333, 0.23333333333333334,
+          0.43333333333333335, 0.4666666666666667, 0.5333333333333333, 0.7, 0.3, 0.3, 0.3333333333333333, 0.6, 0.4,
+          0.16666666666666666, 0.6, 0.43333333333333335]
+    g4_1 = [0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.7, 0.7,
+          0.7,0.7, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6]
+    g4=[0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6]
+
+    x = [i for i in range(1, 18, 1)]
+    plt.plot(x, g1,label="Proposed algorithm", marker="o", )
+    plt.plot(x, g4, label="Non dual time scales", marker="*", )
+    # plt.plot(x, g4, label="Fixed position and resource", marker="*", )
+    # plt.plot(x, g4_1, label="Fixed position-Convex", marker=".", )
+    plt.plot(x, g2,label="PSO-Convex", marker="^", )
+    plt.plot(x, g3,label="Kmeans-Avg", marker=".", )
+    plt.xlabel('时隙', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('定位需求满足率', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('time slots')
+    # plt.ylabel('Localization Satisfaction of Users')
+    plt.legend()
+    plt.show()
+
+def draw_two_pso():
+    r1=[-1196.9323426279452, -1023.2879873142075, -938.1347737903101, -839.9352308341875, -713.3749158686057,
+     -695.6398497337009, -692.6064311156695, -655.9969403981936, -652.2223431837863, -640.3367322566492,
+     -640.3367322566492, -629.4268704014644, -629.4268704014644, -619.2731245473466, -618.8899488986626,
+     -618.8899488986626, -618.8899488986626, -618.8899488986626, -613.6306159984326, -613.6306159984326,
+     -613.6306159984326, -613.6306159984326, -613.6306159984326, -613.6306159984326, -613.5003594617765,
+     -613.4642026310231, -612.7132692348612, -612.7132692348612, -612.7132692348612, -612.7132692348612,
+     -612.7132692348612, -612.7132692348612, -612.7132692348612, -612.7132692348612, -612.0582857242623,
+     -611.7966818160626, -611.4553346200508, -611.4553346200508, -610.4046128928244, -610.4046128928244,
+     -610.4046128928244, -610.3414237226195, -610.0636132324591, -609.9302397688457, -609.9302397688457,
+     -609.9302397688457, -609.8247476936176, -609.3586162393262, -608.7753594243752, -608.7753594243752,
+     -608.4847452080389, -608.4847452080389, -608.4847452080389, -608.4328989734311, -608.3579667611907,
+     -608.3579667611907, -608.3579667611907, -608.3579667611907, -608.2983547415181, -608.2545299241742,
+     -607.9579163498192, -607.9579163498192, -607.9579163498192, -607.9579163498192, -607.9579163498192,
+     -607.9579163498192, -607.9579163498192, -607.9251930979092, -607.8490809433433, -607.7401985001247,
+     -607.7401985001247, -607.7401985001247, -607.7401985001247, -607.729634835325, -607.729634835325,
+     -607.729634835325, -607.7292704587401, -607.6403860641153, -607.6403860641153, -607.6403860641153,
+     -607.6335094134449, -607.6025167411549, -607.5755885295946, -607.5722972661849, -607.5645509212286,
+     -607.541015826343, -607.4579266297451, -607.4012341854012, -607.4012341854012, -607.4012341854012,
+     -607.3827092828479, -607.3230904811196, -607.3081820002802, -607.2542297698676, -607.1699432866275,
+     -607.1357793051466, -607.1344263335025, -607.1083267606782, -607.1083267606782, -607.0837256958735,
+     -607.0365337511737, -606.9998609278682, -606.9012318909207, -606.8523765243652, -606.8523765243652,
+     -606.8523765243652, -606.842359971376, -606.7956110585856, -606.7956110585856, -606.7956110585856,
+     -606.7408729474714, -606.6802105356159, -606.6740828338604, -606.6740828338604, -606.6505920842787,
+     -606.6026225102125, -606.5935995966431, -606.5448191595901, -606.5448191595901, -606.5063607756622,
+     -606.5063607756622, -606.5063607756622, -606.4851726862596, -606.4851726862596, -606.4851726862596,
+     -606.4772055606807, -606.4772055606807, -606.4772055606807, -606.4771594380561, -606.4771594380561,
+     -606.470177358181, -606.470177358181, -606.470177358181, -606.470177358181, -606.456010919539, -606.4546567569781,
+     -606.4507733875905, -606.4025904682732, -606.3751148588892, -606.3730367007836, -606.3473268430255,
+     -606.3349603008672, -606.3349603008672, -606.3349603008672, -606.3337851788831, -606.319478725464,
+     -606.319478725464, -606.3032184946985, -606.3032184946985, -606.296861611934, -606.296861611934,
+     -606.2802118434549, -606.2453062088408, -606.2254450888618, -606.2254450888618, -606.2254450888618,
+     -606.2241058242523, -606.1716396591552, -606.1405887180167, -606.1089602732022, -606.0555927777,
+     -606.0533244250175, -605.9970026224007, -605.9553210622021, -605.9058515535407, -605.7808651617311,
+     -605.6894618878111, -605.6095113966787, -605.5357964277108, -605.2882820245106, -605.102346569551,
+     -604.9078201405405, -604.7106563329935, -604.433266025304, -604.3960675521805, -604.2827959000942,
+     -604.2579148274684, -604.2579148274684, -604.2579148274684, -604.2442110865913, -604.2413731581769,
+     -604.234528779501, -604.234528779501, -604.2265793947867, -604.2265793947867, -604.22352810453, -604.2139074961442,
+     -604.2108490203143, -604.1891481621628, -604.1891481621628, -604.1891481621628, -604.1891481621628,
+     -604.1891481621628, -604.1891481621628, -604.1891481621628, -604.1891481621628, -604.1891481621628,
+     -604.1891481621628, -604.1891481621628, -604.1891481621628]
+    r2=[-380.7411141759215, -380.7411141759215, -380.7411141759215, -363.72305859666784, -363.72305859666784,
+     -349.810356641212, -328.5825684550027, -328.5825684550027, -328.5825684550027, -328.5825684550027,
+     -325.5954018295555, -318.08045403389883, -318.08045403389883, -318.08045403389883, -315.8258698970216,
+     -311.07719296107484, -311.03875270173796, -311.03875270173796, -294.3404638065752, -294.3404638065752,
+     -290.5217035878852, -275.5763852875366, -265.23241780168416, -265.23241780168416, -265.23241780168416,
+     -265.23241780168416, -262.28605152664534, -262.28605152664534, -258.8284357693334, -256.22640857164606,
+     -256.22640857164606, -256.22640857164606, -256.22640857164606, -256.22640857164606, -255.53912739962215,
+     -255.53912739962215, -255.53912739962215, -255.33560728054294, -255.33560728054294, -255.28767766570354,
+     -255.28767766570354, -253.76995359761094, -252.83659157878247, -251.977103784552, -251.977103784552,
+     -251.977103784552, -251.977103784552, -251.977103784552, -251.977103784552, -251.53735983379426,
+     -248.71930609772627, -248.71930609772627, -247.83619225744548, -247.83283225965897, -247.78590955094347,
+     -247.78590955094347, -247.59373583633038, -247.5732508903926, -247.5732508903926, -247.5693554712022,
+     -247.5693554712022, -247.3226140469394, -247.3226140469394, -247.3226140469394, -247.14441530955818,
+     -247.14441530955818, -247.14441530955818, -246.6572067590505, -246.468983665093, -246.44367458023024,
+     -246.29257042607526, -246.29257042607526, -246.29257042607526, -246.29257042607526, -246.28983312227217,
+     -246.2107367702723, -246.0802533898576, -246.0802533898576, -246.0802533898576, -246.0802533898576,
+     -246.0802533898576, -246.0802533898576, -246.07747834189666, -246.049077629492, -246.04682457275493,
+     -246.04682457275493, -245.9802714581705, -245.9466906329154, -245.9466906329154, -245.87516567942754,
+     -245.8335567374412, -245.81565126884948, -245.81565126884948, -245.7225111268908, -245.62841620693212,
+     -245.62670140858825, -245.62054618646803, -245.62054618646803, -245.62054618646803, -245.61057598622915,
+     -245.61057598622915, -245.55937807397459, -245.5242572250821, -245.5242572250821, -245.5242572250821,
+     -245.5242572250821, -245.51646263534585, -245.51646263534585, -245.51646263534585, -245.51646263534585,
+     -245.51646263534585, -245.51646263534585, -245.5151690804198, -245.48847455697387, -245.48708931335642,
+     -245.4730102826531, -245.45908855084855, -245.45156674846427, -245.45156674846427, -245.45156674846427,
+     -245.45156674846427, -245.45156674846427, -245.45156674846427, -245.44924975582472, -245.44924975582472,
+     -245.44924975582472, -245.44924975582472, -245.44924975582472, -245.44924975582472, -245.4467777809799,
+     -245.4467777809799, -245.4467777809799, -245.44655918815045, -245.44655918815045, -245.44655918815045,
+     -245.44655918815045, -245.44482415235223, -245.44482415235223, -245.44482415235223, -245.44482415235223,
+     -245.44482415235223, -245.44482415235223, -245.44482415235223, -245.44482415235223, -245.44482415235223,
+     -245.44482415235223, -245.44482415235223, -245.44482415235223, -245.44482415235223, -245.44482415235223,
+     -245.44482415235223, -245.44427621306633, -245.44427621306633, -245.44427621306633, -245.44427621306633,
+     -245.44427621306633, -245.44427621306633, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143, -245.4441307955143,
+     -245.4441307955143, -245.44412490547342, -245.44412490547342, -245.44412490547342, -245.44412490547342,
+     -245.44412490547342, -245.44412490547342, -245.44412490547342, -245.44412490547342, -245.44412490547342,
+     -245.44412490547342, -245.44412490547342, -245.44412490547342, -245.44412490547342, -245.44412490547342]
+    plt.plot(r2, label="improved pso", )
+    plt.plot(r1, label="static pso",linestyle='--', )
+    d1 = [(r2[i] - r1[i]) / abs(r1[i]) for i in range(len(r1))]
+    print(sum(d1)/len(d1))
+    plt.xlabel('迭代次数', fontsize=14, fontproperties=font_prop)
+    plt.ylabel('适应度', fontsize=14, fontproperties=font_prop)
+    # plt.xlabel('iteration')
+    # plt.ylabel('fitness value')
+    plt.legend()
+    plt.show()
+
+def draw_scheme_compare():
+    r1=[-184.06100597871276, -185.85049500451746, -146.8288332014583, -147.8690106029141, -150.89610670415044,
+          -161.58225234814634, -161.25450103761872, -160.210679762176, -174.61142139727096, -177.76804395633008,
+          -181.569335688453, -159.927794994072, -160.94409679626565, -166.21748449604434, -160.2738429197924,
+          -159.7484534802284, -158.85902270747295]
+    r2 = [-184.47707786404925, -186.79859967274973, -187.18900537301826, -185.00993033401568, -186.98011160427623,
+          -186.7475686243944, -188.5738993568263, -186.66325649759037, -189.93822318891313, -190.1625116360844,
+          -193.71770035886448, -195.85488809139676, -195.6319247325472, -197.10555864062565, -198.25810453714203,
+          -198.28055212033237, -198.22276758982875, -198.55710227964607]
+    r3 = [-184.47707786404925, -186.09526372564545, -186.06078249070885, -147.85913907661956, -150.55241416659936,
+          -150.31251306017782, -153.33386123238563, -151.49498797995372, -153.77165045599617, -169.11425658693145,
+          -169.97320242448745, -170.8741998698821, -164.6200858129465, -164.28081292472123, -165.24268234023336,
+          -162.52560152043168, -161.74900753634054, -161.92402781925833]
+    plt.plot(r1, label="proposed algorithm", marker="o", )
+    plt.plot(r2, label="FDDR", marker="o", )
+    plt.plot(r3, label="FRDD", marker="o", )
+    plt.legend()
+    # plt.ylim(-3000, 100)
+    plt.show()
+
+# draw_scheme_compare()
+draw_two_pso()
+# draw_data()
+# draw_time()
+# draw_avg_and_optimal()
+# draw_different_scheme()
+draw_particle_num()
+draw_iteration_num()
+# draw_particle_time()
+# draw_iteration_num()
+# draw_iteration_time()
+draw_dynamic_utility()
+draw_dynamic_c_satisfaction()
+draw_dynamic_g_satisfaction()
+# draw_different_scheme()
+# draw_compare_pso_and_convex()
+# draw_c_satisfaction()
+# draw_p_satisfaction()
+# draw_slot_compare()
+# draw_slot_satisfaction_compare()
+# draw_different_scheme_satisfaction()
\ No newline at end of file

dynamicDeploy/dynamicProcess.py

+import math
+import random
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+import pandas as pd
+import constants as C
+import Users
+
+
+def gauss_markov_model(positions, step_size, max_range, ):
+    # 使用高斯-马尔科夫模型生成随机位置变化，考虑更长时间范围的随机性
+    delta = np.random.normal(0, step_size, size=positions.shape)
+    new_positions = positions + delta
+
+    # 限制位置在给定范围内
+    new_positions = np.clip(new_positions, 0, max_range)
+    # print(new_positions)
+
+    return new_positions
+
+def generate_users_pos(users,slot,num):
+    U=[]
+    U.append(users)
+    for i in range(num):
+        users=gauss_markov_model(users, slot*C.v, C.max_range, )
+        U.append(users)
+    print(U)
+    return U
+def generate_users_pos_in_file(users,slot,num):
+    U=[]
+    U.append(users)
+    for i in range(num):
+        # users=gauss_markov_model(users, slot*C.v, C.max_range, )
+        users = random_pos(users, slot * C.v, C.max_range)
+        U.append(users)
+    print(U)
+    U=np.array(U)
+    np.save('dynamic_users2.npy', U)
+    # 从文件加载NumPy数组
+    return U
+def get_users_pos_from_file():
+    U = np.load('dynamic_users2.npy')
+    # 打印加载的数组
+    # print("Loaded Data:")
+    # print(U)
+    # u1=U[2][1]
+    # u2=U[3][1]
+    # print(math.sqrt((u1[0]-u2[0])**2+(u1[1]-u2[1])*2))
+
+    return U
+
+def random_pos(positions, step_size, max_range,):
+    angles = np.random.uniform(0,2*np.pi, len(positions))
+    # angels=np.full((1, len(positions)), np.pi/4)
+    # Generate random distances within the outer circle
+    distances = step_size
+
+    # Convert polar coordinates to Cartesian coordinates
+    x = positions[:, 0] + distances * np.cos(angles)
+    y = positions[:, 1] + distances * np.sin(angles)
+    new_positions=[]
+    for i in range(len(x)):
+        new_positions.append(np.array([x[i],y[i]]))
+    new_positions=np.array(new_positions)
+    new_positions = np.clip(new_positions, 0, max_range)
+    print(new_positions)
+    return new_positions
+
+def random_r(users):
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = random.randint(1,4)
+        r_p[i]=random.randint(1,4)
+    return r_c,r_p
+
+def generate_random_r_in_file(users,num,filename):
+    #80% 1,2 20% 3,4
+    a=random.random()
+    dic={}
+    length=len(users)
+    for i in range(length):
+        dic[str(i)]=[]
+    df=pd.DataFrame(dic)
+    for i in range(num):
+        r=[]
+        for j in range(length):
+            a=random.random()
+            if a>0.8:
+                b=random.random()
+                if b>=0.8:
+                    r.append(3)
+                else:
+                    r.append(4)
+            else:
+                b = random.random()
+                if b >= 0.7:
+                    r.append(1)
+                else:
+                    r.append(2)
+        # r=[random.randint(1,4) for i in range(length)]
+        df.loc[len(df)] = r  # 通过 loc 添加一行
+    # print(df)
+    # 将 DataFrame 存入 CSV 文件
+    df.to_csv(filename, index=False)
+
+def read_csv_to_df(file_path):
+    df = pd.read_csv(file_path)
+    # 选择 DataFrame 中的一行（例如，第一行）
+
+    # 打印结果
+    # print(row_dict)
+    return df
+
+def get_r_c_and_r_p(df_c,df_p,index):
+    selected_c = df_c.iloc[index]
+    selected_p = df_p.iloc[index]
+    # 将选定的行转换为字典
+    r_c = {int(key): value for key, value in selected_c.to_dict().items()}
+    r_p = {int(key): value for key, value in selected_p.to_dict().items()}
+    return r_c,r_p
+
+
+users=Users.getUsers("./data3.csv")
+# generate_users_pos(users,10,5)
+# generate_random_r_in_file(users,50,'r_p.csv')
+# generate_users_pos_in_file(users,10,30)
+U=get_users_pos_from_file()
+# print(len(U))
+# df_c=read_csv_to_df('r_c.csv')
+# df_p=read_csv_to_df('r_p.csv')
+# r_c,r_p=get_r_c_and_r_p(df_c,df_p,0)
+# print(r_c,r_p)
+# U=get_users_pos_from_file()
+def update(frame, user_positions, sc, step_size, max_range,):
+    user_positions = gauss_markov_model(user_positions, step_size, max_range, )
+    # user_positions=random_pos(user_positions, step_size, max_range,)
+
+    sc.set_offsets(user_positions)
+    return sc,
+
+
+def create_dynamic_plot(num_users, max_range, step_size,):
+    # 初始化用户位置
+    user_positions = np.random.uniform(0, max_range, size=(num_users, 2))
+
+    # 创建散点图
+    fig, ax = plt.subplots()
+    sc = ax.scatter(user_positions[:, 0], user_positions[:, 1])
+
+    # 设置图形的标题和坐标轴标签
+    ax.set_title('Dynamic Point Plot with Gauss-Markov Model')
+    ax.set_xlabel('X-axis')
+    ax.set_ylabel('Y-axis')
+
+    # 使用FuncAnimation实现动态效果
+    animation = FuncAnimation(fig, update, frames=300, fargs=(user_positions, sc, step_size, max_range,), interval=100)
+
+    # 返回FuncAnimation对象
+    return animation
+def draw_dynamic_users():
+    # 设置参数
+    num_users = 15
+    max_range = 1000
+    step_size = 10
+    persistence_factor = 1
+
+    # 创建动态点位图
+    dynamic_plot = create_dynamic_plot(num_users, max_range, step_size,)
+
+    # 显示动画
+    plt.show()
+
+# draw_dynamic_users()
\ No newline at end of file

dynamicDeploy/improvedPSO.py

+import math
+import Users
+import numpy as np
+import random
+from pprint import pprint
+import constants as C
+import tool
+import ObjectiveFuction as OF
+
+# 定义混沌映射（Logistic映射）
+def logistic_map(x, r):
+    return r * x * (1 - x)
+class CPSO:
+    def __init__(self,pre_uavs,users,r_c,r_p,k,N=50):
+        #之后解析JSON，从JSON中读参数
+        self.N=N
+        self.k=k
+        self.pre_uavs=pre_uavs
+        self.users=users
+        self.d = 3  # 可行解维数
+        self.limit = [C.min_range,C.max_range]  # 边界位置限制
+        self.vlimit = [-50, 50]  # 边界速度限制
+        self.r_c = r_c
+        self.r_p=r_p
+
+        # self.height = 100
+        self.min_height = C.min_height
+        self.max_height = C.max_height
+        self.wmax = 0.9  # 惯性权重
+        self.wmin = 0.4
+        self.c1 = 1.5  # 自我学习因子
+        self.c2 = 1.5  # 群体学习因子
+        # self.c1 = 0.5  # 自我学习因子
+        # self.c2 = 0.5  # 群体学习因子
+        self.maxgen = 200
+        # self.users = Users.getUsers("./data.csv")
+        self.users = users
+        self.r=3.8
+        # self.c_association = c_association
+        # self.p_association = p_association
+        # self.p_all = p_all
+        # self.b_all = b_all
+
+        # self.init_alloc()
+
+
+    # #todo：防止无人机位置碰撞
+    # def punish_function(self,uavs):
+    #     # user_arr = [1, 2, 5]
+    #     punish=0
+    #     for i in range(len(uavs)):
+    #         for j in range(i+1,len(uavs)):
+    #             dis=self.cal_tool.getDistance(uavs[i],uavs[j])
+    #             if dis<200:
+    #                 punish+=100
+    #
+    #     return punish
+    def init_alloc(self,):
+        c_association, p_association, p_all, b_all=OF.objective_resource(self.pre_uavs,self.users,self.r_c)
+        self.c_association=c_association
+        self.p_association = p_association
+        self.p_all=p_all
+        self.b_all=b_all
+
+
+    def re_init(self,pre_uavs,users,r_c,r_p,c_association, p_association, p_all, b_all,):
+        self.pre_uavs=pre_uavs
+        self.user=users
+        self.r_c=r_c
+        self.r_p=r_p
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    def punish_f(self,uavs):
+        punish=0
+        max_dis=(C.time_slot-C.run_time)*C.speed
+        for i in range(len(uavs)):
+            dis=tool.calculate_3d_distance_uavs(uavs[i],self.pre_uavs[i])
+            if dis>max_dis:
+                punish=float('-inf')
+        return punish
+
+    # def init_r_c(self):
+    #     r_c={}
+    #     for i in range(len(self.users)):
+    #         r_c[i] = random.randint(1,4)
+    #     return r_c
+    # 初始化粒子群，k是无人机数量
+    def initParticle(self, users):
+        X = []
+        # users=Users.generate(100)#用户位置
+        V = np.random.uniform(self.vlimit[0], self.vlimit[1], (self.N, self.k, 3))
+        for i in range(self.N):
+            uavs=[]
+            uavs_k=tool.getUavInitialPos(self.k,self.users,)
+            # tool.getUavInitialPos(self.k, users)
+            # uavs_k=self.pre_uavs
+            if i==0:
+                uavs=tool.getUavInitialPos_fcm(self.k,self.users)
+            elif i==1:
+                uavs = tool.getUavInitialPos(self.k, self.users)
+            else:
+                for index in range(self.k):
+                    # x,y=generate_random_particles(uavs_k[index],30,1)
+                    x, y = generate_random_particles(uavs_k[index],C.v*5, 1)
+                    # x=np.random.uniform(self.limit[0],self.limit[1])
+                    # y=np.random.uniform(self.limit[0],self.limit[1])
+                    # z=np.random.uniform(self.min_height,self.max_height)
+                    z=self.min_height
+                    uavs.append([x[0],y[0],z])
+
+            X.append(uavs)
+        X = np.array(X)
+        return X, V
+
+    # 初始化个体和全局最佳历史位置和最佳适应度
+    def initBest(self, X,):
+        p_pos = X.copy()  # 每个个体的历史最佳位置
+        global_pos = np.zeros((self.k, 3))  # 种群的历史最佳位置5*3
+        p_best = np.full((self.N, 1), float('-inf'))  # 每个个体的历史最佳适应度
+        global_best = float('-inf')  # 种群历史最佳适应度
+        return p_pos, global_pos, p_best, global_best
+
+
+    def pso(self, X, V, update_w,get_fitness,):
+        # X, V, users = initParticlePos()
+        p_pos, global_pos, p_best, global_best = self.initBest(X)
+        result = np.zeros(self.maxgen)
+
+        for iter in range(self.maxgen):
+            # fitness = self.getfitness(X, users, k,P)  # 计算所有粒子适应度
+            fitness = np.zeros(self.N)
+            for i in range(len(X)):
+                # f,_,_=get_fitness(self.pre_uavs,X[i],self.users,self.r_c,self.r_p)
+                punish=self.punish_f(X[i])
+                if punish==float('-inf'):
+                    fitness[i]=punish
+                else:
+                    f, _, _ = get_fitness(self.pre_uavs,X[i],self.users,self.r_c,self.r_p,self.p_all,self.b_all,self.c_association,self.p_association)
+                    fitness[i]= f+self.punish_f(X[i])
+            for i in range(self.N):
+                if fitness[i] > p_best[i]:
+                    p_best[i] = fitness[i]  # 更新个体历史最佳适应度
+                    p_pos[i] = X[i].copy()  # 更新个体历史最佳位置(取值)
+            if max(p_best) > global_best:
+                global_best = max(p_best)  # 更新群体历史最佳适应度
+                max_index = p_best.argmax()
+                global_pos = X[max_index].copy()  # 更新群体历史最佳位置
+
+
+            # 权重更新
+            w = self.wmax - iter * (self.wmax - self.wmin) / self.maxgen
+            # w = update_w(self.wmin, self.wmax, iter, self.maxgen, fitness)
+            for i in range(self.N):
+                # V[i] = self.r * logistic_map(V[i], self.r)
+                # w=update_w(self.wmin,self.wmax,iter,self.maxgen,i,fitness)
+                # c1=update_c1(iter,self.maxgen)
+                # c2=update_c2(iter,self.maxgen)
+                V[i] = V[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                        global_pos - X[i])
+                # print(V[i])
+                for j in range(len(V[i])):
+                    for p in range(len(V[i][j])):
+                        if V[i][j][p] > self.vlimit[1]:
+                            V[i][j][p] = self.vlimit[1]
+                            # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        elif V[i][j][p] < self.vlimit[0]:
+                            V[i][j][p] = self.vlimit[0]
+                # print(V[i])
+
+            # 位置更新
+            X = X + V
+            # 边界位置处理
+            for i in range(self.N):
+                for j in range(len(X[i])):
+                    for p in range(2):
+                        if X[i][j][p] > self.limit[1]:
+                            X[i][j][p] = self.limit[1]
+                        elif X[i][j][p] < self.limit[0]:
+                            X[i][j][p] = self.limit[0]
+                    if X[i][j][2] > self.max_height:
+                        X[i][j][2] = self.max_height
+                    elif X[i][j][2] < self.min_height:
+                        X[i][j][2] = self.min_height
+
+            result[iter] = global_best
+            print(iter,global_pos,global_best)
+
+        # print(global_best)
+        # print(global_pos)
+
+        return global_pos,global_best,result
+
+
+# import matplotlib.pyplot as plt
+#
+# def logistic_chaotic_model(x0, r, num_steps):
+#     """
+#     实现Logistic混沌模型
+#
+#     参数:
+#     - x0: 初始条件
+#     - r: 控制参数
+#     - num_steps: 模拟的时间步数
+#
+#     返回:
+#     - states: 模拟的状态序列
+#     """
+#     states = [x0]
+#     for _ in range(num_steps - 1):
+#         x_next = r * states[-1] * (1 - states[-1])
+#         states.append(x_next)
+#     return states
+#
+# def generate_logistic_chaotic_points(num_points):
+#     """
+#     生成 Logistic 混沌模型的点集
+#
+#     参数:
+#     - num_points: 要生成的点的数量
+#
+#     返回:
+#     - points: 生成的点集 (x, y)
+#     """
+#     x0 = np.random.random()  # 随机选择初始条件
+#     r = 4  # 控制参数，可以根据需要进行调整
+#     num_steps = 1000  # 模拟的时间步数
+#
+#     x_states = logistic_chaotic_model(x0, r, num_steps)
+#     y_states = logistic_chaotic_model(x0 + 0.1, r, num_steps)  # 使用略微不同的初始条件
+#
+#     # 线性映射到[0, 1000]范围
+#     x_min, x_max = min(x_states), max(x_states)
+#     x_states_mapped = [((x - x_min) / (x_max - x_min)) * 1000 for x in x_states]
+#
+#     y_min, y_max = min(y_states), max(y_states)
+#     y_states_mapped = [((y - y_min) / (y_max - y_min)) * 1000 for y in y_states]
+#
+#     # 从映射后的状态序列中选择一些点
+#     indices = np.linspace(0, num_steps - 1, num_points, dtype=int)
+#     points = np.array([(x_states_mapped[i], y_states_mapped[i]) for i in indices])
+#
+#     return points
+#
+# def random_points(num):
+#     points=[]
+#     for i in range(num):
+#         x=random.randint(0,1000)
+#         y=random.randint(0,1000)
+#         points.append(np.array([x,y]))
+#     return np.array(points)
+#
+# # 生成 100 个点
+# num_points = 100
+# generated_points = generate_logistic_chaotic_points(num_points)
+# points2=random_points(num_points)
+# # 绘制生成的点
+# plt.figure(figsize=(12, 5))
+#
+# plt.subplot(1, 2, 1)
+# plt.scatter(generated_points[:, 0], generated_points[:, 1], color='b', marker='o')
+# plt.title('Logistic model')
+# plt.xlabel('x')
+# plt.ylabel('y')
+# # plt.xlim(0, 1000)
+# # plt.ylim(0, 1000)
+# plt.subplot(1, 2, 2)
+# plt.scatter(points2[:, 0], points2[:, 1], color='r', marker='o')
+# plt.title('随机生成的100个点')
+# plt.xlabel('x')
+# plt.ylabel('y')
+# plt.xlim(0, 1000)
+# plt.ylim(0, 1000)
+# plt.show()
+
+import matplotlib.pyplot as plt
+
+
+def generate_random_particles(center, outer_radius, num_particles):
+    # Generate random angles
+    angles = np.random.uniform(0, 2 * np.pi, num_particles)
+
+    # Generate random distances within the outer circle
+    distances = np.sqrt(np.random.uniform(0, outer_radius ** 2, num_particles))
+
+    # Convert polar coordinates to Cartesian coordinates
+    x = center[0] + distances * np.cos(angles)
+    y = center[1] + distances * np.sin(angles)
+
+    return x, y
+
+
+def plot_particles(x, y, center, outer_radius):
+    circle = plt.Circle(center, outer_radius, color='r', alpha=0.2, label='Outer Circle')
+
+    plt.scatter(x, y, label='Random Particles', color='blue')
+    plt.scatter(center[0], center[1], label='Center', color='red', marker='x')
+    plt.gca().add_patch(circle)
+
+    plt.title('Random Particles within Outer Circle')
+    plt.xlabel('X-axis')
+    plt.ylabel('Y-axis')
+    plt.legend()
+    plt.show()
+
+
+# Example usage
+# center_point = (0, 0)
+# outer_circle_radius = 5
+# num_particles = 100
+#
+# random_x, random_y = generate_random_particles(center_point, outer_circle_radius, num_particles)
+# plot_particles(random_x, random_y, center_point, outer_circle_radius)
+
+
+
+if __name__ == '__main__':
+    uavs=[[868.34535881,315.18340086,50.        ],
+          [292.96443022,729.7612657,77.75450997],
+ [732.25467955,927.62861821,50.        ],
+ [303.40107629,240.31208079,150.        ]]
+    # 示例
+
+
+
+
+
+

dynamicDeploy/km.py

+import numpy as np
+import random
+import time
+
+
+zero_threshold = 0.00000001
+
+
+class KMNode(object):
+    def __init__(self, id, exception=0, match=None, visit=False):
+        self.id = id
+        self.exception = exception
+        self.match = match
+        self.visit = visit
+
+
+class KuhnMunkres(object):
+    def __init__(self):
+        self.matrix = None
+        self.x_nodes = []
+        self.y_nodes = []
+        self.minz = float('inf')
+        self.x_length = 0
+        self.y_length = 0
+        self.index_x = 0
+        self.index_y = 1
+
+    def __del__(self):
+        pass
+
+    def set_matrix(self, x_y_values):
+        xs = set()
+        ys = set()
+        for x, y, value in x_y_values:
+            xs.add(x)
+            ys.add(y)
+
+        #选取较小的作为x
+        if len(xs) < len(ys):
+            self.index_x = 0
+            self.index_y = 1
+        else:
+            self.index_x = 1
+            self.index_y = 0
+            xs, ys = ys, xs
+
+        x_dic = {x: i for i, x in enumerate(xs)}
+        y_dic = {y: j for j, y in enumerate(ys)}
+        self.x_nodes = [KMNode(x) for x in xs]
+        self.y_nodes = [KMNode(y) for y in ys]
+        self.x_length = len(xs)
+        self.y_length = len(ys)
+
+        self.matrix = np.zeros((self.x_length, self.y_length))
+        for row in x_y_values:
+            x = row[self.index_x]
+            y = row[self.index_y]
+            value = row[2]
+            x_index = x_dic[x]
+            y_index = y_dic[y]
+            self.matrix[x_index, y_index] = value
+
+        for i in range(self.x_length):
+            self.x_nodes[i].exception = max(self.matrix[i, :])
+
+
+    def km(self):
+        for i in range(self.x_length):
+            while True:
+                self.minz = float('inf')
+                self.set_false(self.x_nodes)
+                self.set_false(self.y_nodes)
+
+                if self.dfs(i):
+                    break
+
+                self.change_exception(self.x_nodes, -self.minz)
+                self.change_exception(self.y_nodes, self.minz)
+
+    def dfs(self, i):
+        x_node = self.x_nodes[i]
+        x_node.visit = True
+        for j in range(self.y_length):
+            y_node = self.y_nodes[j]
+            if not y_node.visit:
+                t = x_node.exception + y_node.exception - self.matrix[i][j]
+                if abs(t) < zero_threshold:
+                    y_node.visit = True
+                    if y_node.match is None or self.dfs(y_node.match):
+                        x_node.match = j
+                        y_node.match = i
+                        return True
+                else:
+                    if t >= zero_threshold:
+                        self.minz = min(self.minz, t)
+        return False
+
+    def set_false(self, nodes):
+        for node in nodes:
+            node.visit = False
+
+    def change_exception(self, nodes, change):
+        for node in nodes:
+            if node.visit:
+                node.exception += change
+
+    def get_connect_result(self):
+        ret = []
+        for i in range(self.x_length):
+            x_node = self.x_nodes[i]
+            j = x_node.match
+            y_node = self.y_nodes[j]
+            x_id = x_node.id
+            y_id = y_node.id
+            value = self.matrix[i][j]
+
+            if self.index_x == 1 and self.index_y == 0:
+                x_id, y_id = y_id, x_id
+            ret.append((x_id, y_id, value))
+
+        return ret
+
+    def get_max_value_result(self):
+        ret = 0
+        for i in range(self.x_length):
+            j = self.x_nodes[i].match
+            ret += self.matrix[i][j]
+
+        return ret
+
+
+def run_kuhn_munkres(x_y_values):
+    process = KuhnMunkres()
+    process.set_matrix(x_y_values)
+    process.km()
+    return process.get_connect_result()
+
+
+# def test():
+#     values = []
+#     random.seed(0)
+#     for i in range(500):
+#         for j in range(1000):
+#             value = random.random()
+#             values.append((i, j, value))
+#
+#     return run_kuhn_munkres(values)
+
+if __name__ == '__main__':
+    s_time = time.time()
+    # ret = test()
+    # print("time usage: %s " % str(time.time() - s_time))
+    values = [
+        (1, (1,1), 3),
+        (1, (3,3), 4),
+        (2, (1,1), 2),
+        (2, (2,2), 1),
+        (2, (3,3), 3),
+        (3, (2,2), 4),
+        (3, (3,3), 5)
+    ]
+
+    print(run_kuhn_munkres(values))
\ No newline at end of file

dynamicDeploy/pso.py

+import math
+import Users
+import numpy as np
+import random
+from pprint import pprint
+import constants as C
+import tool
+import ObjectiveFuction as OF
+
+class PSO:
+    def __init__(self,pre_uavs,users,r_c,r_p,k,N=70,maxgen=200,):
+        #之后解析JSON，从JSON中读参数
+        self.N=N
+        self.k=k
+        self.pre_uavs=pre_uavs
+        self.users=users
+        self.d = 3  # 可行解维数
+        self.limit = [C.min_range,C.max_range]  # 边界位置限制
+        self.vlimit = [-500, 500]  # 边界速度限制
+        self.vlimit_h=[-10,10]
+        self.r_c = r_c
+        self.r_p=r_p
+        # self.height = 100
+        self.min_height = C.min_height
+        self.max_height = C.max_height
+        self.wmax = 0.9  # 惯性权重
+        self.wmin = 0.4
+        self.c1 = 1.496  # 自我学习因子
+        self.c2 = 1.496  # 群体学习因子
+        # self.c1 = 0.5  # 自我学习因子
+        # self.c2 = 0.5  # 群体学习因子
+        self.maxgen = maxgen
+        # self.users = Users.getUsers("./data.csv")
+        self.users = users
+        self.init_alloc()
+
+
+    # #todo：防止无人机位置碰撞
+    # def punish_function(self,uavs):
+    #     # user_arr = [1, 2, 5]
+    #     punish=0
+    #     for i in range(len(uavs)):
+    #         for j in range(i+1,len(uavs)):
+    #             dis=self.cal_tool.getDistance(uavs[i],uavs[j])
+    #             if dis<200:
+    #                 punish+=100
+    #
+    #     return punish
+    def punish_f(self,uavs):
+        punish=0
+        max_dis=(C.time_slot-C.run_time)*C.speed
+        for i in range(len(uavs)):
+            dis=tool.calculate_3d_distance_uavs(uavs[i],self.pre_uavs[i])
+            if dis>max_dis:
+                punish=float('-inf')
+        return punish
+
+    def re_init(self, pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all, ):
+        self.pre_uavs = pre_uavs
+        self.user = users
+        self.r_c = r_c
+        self.r_p = r_p
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    # def init_r_c(self):
+    #     r_c={}
+    #     for i in range(len(self.users)):
+    #         r_c[i] = random.randint(1,4)
+    #     return r_c
+    # 初始化粒子群，k是无人机数量
+    def initParticle(self, users):
+        X = []
+        # users=Users.generate(100)#用户位置
+        V = np.random.uniform(self.vlimit[0], self.vlimit[1], (self.N, self.k, 3))
+        V2 = np.random.uniform(self.vlimit_h[0], self.vlimit_h[1], (self.N, self.k, 3))
+        for i in range(self.N):
+            uavs=[]
+            if i==0:
+                uavs = tool.getUavInitialPos(self.k, users)
+            else:
+                for index in range(self.k):
+                    x=np.random.randint(self.limit[0],self.limit[1])
+                    y=np.random.randint(self.limit[0],self.limit[1])
+                    # z=np.random.randint(self.min_height,self.max_height)
+                    z=C.min_height
+                    uavs.append([x,y,z])
+
+
+            X.append(uavs)
+        X = np.array(X)
+        return X, V,
+
+    def init_alloc(self,):
+        c_association, p_association, p_all, b_all = OF.objective_resource_avg(self.pre_uavs, self.users, self.r_c)
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+
+    # 初始化个体和全局最佳历史位置和最佳适应度
+    def initBest(self, X,):
+        p_pos = X.copy()  # 每个个体的历史最佳位置
+        global_pos = np.zeros((self.k, 3))  # 种群的历史最佳位置5*3
+        p_best = np.full((self.N, 1), float('-inf'))  # 每个个体的历史最佳适应度
+        global_best = float('-inf')  # 种群历史最佳适应度
+        return p_pos, global_pos, p_best, global_best
+
+
+    def pso(self, X, V, update_w,get_fitness,):
+        # X, V, users = initParticlePos()
+        p_pos, global_pos, p_best, global_best = self.initBest(X)
+        result = np.zeros(self.maxgen)
+
+        fitness = np.zeros(self.N)
+        for iter in range(self.maxgen):
+            # fitness = self.getfitness(X, users, k,P)  # 计算所有粒子适应度
+
+            for i in range(len(X)):
+                # punish = self.punish_f(X[i])
+                # if punish == float('-inf'):
+                #     fitness[i] = punish
+                # else:
+                f, _, _ = get_fitness(self.pre_uavs, X[i], self.users, self.r_c, self.r_p, self.p_all, self.b_all,
+                                      self.c_association, self.p_association)
+                fitness[i] = f
+                # f,_,_=get_fitness(self.pre_uavs,X[i],self.users,self.r_c,self.r_p)
+                # f,_,_=get_fitness(self.pre_uavs, X[i], self.users, self.r_c,self.r_p, self.p_all, self.b_all, self.c_association,
+                #             self.p_association)
+                # fitness[i]= f+self.punish_f(X[i])
+            for i in range(self.N):
+                if fitness[i] > p_best[i]:
+                    p_best[i] = fitness[i]  # 更新个体历史最佳适应度
+                    p_pos[i] = X[i].copy()  # 更新个体历史最佳位置(取值)
+            if max(p_best) > global_best:
+                global_best = max(p_best)  # 更新群体历史最佳适应度
+                max_index = p_best.argmax()
+                global_pos = X[max_index].copy()  # 更新群体历史最佳位置
+
+
+            # 权重更新
+            # w = self.wmax - iter * (self.wmax - self.wmin) / self.maxgen
+            # w = (self.wmax +self.wmin)/2
+            w=update_w(self.wmin,self.wmax,iter,self.maxgen,iter,fitness)
+            for i in range(self.N):
+                V[i] = V[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                        global_pos - X[i])
+                # V2[i] = V2[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                #         global_pos - X[i])
+                # print(V[i])
+                for j in range(len(V[i])):
+                    for p in range(len(V[i][j])):
+                        if V[i][j][p] > self.vlimit[1]:
+                            V[i][j][p] = self.vlimit[1]
+                            # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        elif V[i][j][p] < self.vlimit[0]:
+                            V[i][j][p] = self.vlimit[0]
+                        # if V2[i][j][p] > self.vlimit_h[1]:
+                        #     V2[i][j][p] = self.vlimit_h[1]
+                        #     # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        # elif V2[i][j][p] < self.vlimit_h[0]:
+                        #     V2[i][j][p] = self.vlimit_h[0]
+                # print(V[i])
+
+            # 位置更新
+            X = X + V
+            # 边界位置处理
+            for i in range(self.N):
+                for j in range(len(X[i])):
+                    for p in range(2):
+                        if X[i][j][p] > self.limit[1]:
+                            X[i][j][p] = self.limit[1]
+                        elif X[i][j][p] < self.limit[0]:
+                            X[i][j][p] = self.limit[0]
+                    X[i][j][2]=self.min_height
+                    # if X[i][j][2] > self.max_height:
+                    #     X[i][j][2] = self.max_height
+                    # elif X[i][j][2] < self.min_height:
+                    #     X[i][j][2] = self.min_height
+
+            result[iter] = global_best
+            print(iter,global_pos,global_best)
+
+        # print(global_best)
+        # print(global_pos)
+
+        return global_pos,global_best,result
+
+
+
+if __name__ == '__main__':
+    uavs=[[868.34535881,315.18340086,50.        ],
+          [292.96443022,729.7612657,77.75450997],
+ [732.25467955,927.62861821,50.        ],
+ [303.40107629,240.31208079,150.        ]]
+    # 示例
+    # print(0.012 / 8 * 1.225 * 0.05 * 0.79 * 400 ** 3 * 0.5 ** 3)
+    # print((1+0.1)*100**1.5/math.sqrt(2*1.225*0.79))
+    best=[[335,772,67], [382,739,75], [1758,1683,137], [885,762,133], [403,1196,94], [1049,358,88]]
+
+
+
+
+
+
+

dynamicDeploy/r_c.csv

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dynamicDeploy/r_p.csv

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dynamicDeploy/report.log

+2023-12-08 19:08:42,025 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:08:42,055 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.89331204358824, best pos: [-0.20657694 -1.67142459]
+2023-12-08 19:12:57,642 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:12:57,668 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 31.816129723241335, best pos: [1.84784183 1.87663107]
+2023-12-08 19:13:47,923 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:13:47,950 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 56.773199869733325, best pos: [3.23664274 0.52956792]

dynamicDeploy/resourceAlloc.py

+import math
+import random
+import warnings
+
+import tool
+import Users
+import cvxpy as cp
+import numpy as np
+import constants as C
+
+
+def cal_b_u(users,uavs,uav_index,uav_to_user,r_c,p_alloc,B_max,R_min,u=1.05):
+    users_index = uav_to_user[uav_index]
+    n=len(users_index) # 变量数量
+    # 定义变量
+    x = cp.Variable(n, pos=True)  # 非负变量
+    c=[]# 目标函数系数
+    for i in users_index:
+        p=p_alloc[i]
+        snr = tool.cal_snr_f(uavs[uav_index], users[i], p)
+        factor=math.log2(1 + snr)
+        c.append(factor)
+    c = np.array(c)
+    # print(c)
+    # 定义目标函数
+    objective = cp.Maximize(c @ x)
+    constant_limit = B_max  # 约束条件中的常数上限
+    # 定义约束条件
+    constraints = [cp.sum(x) <= constant_limit]
+    min_cons=[]
+    # u=1.05# 1.05
+    for i in range(len(users_index)):
+        index=users_index[i]
+        p = p_alloc[index]
+        snr = tool.cal_snr_f(uavs[uav_index], users[index], p)
+        lower_limit = R_min*(r_c[index])*u/math.log2(1 + snr)
+        lower_limit=round(lower_limit, 4)
+        # print(lower_limit)
+        min_cons.append(lower_limit)
+        constraints.append(x[i] >= lower_limit)
+    # 构建问题并求解
+    problem = cp.Problem(objective, constraints)
+    problem.solve()
+
+    # 输出求解结果
+    # print("带宽分配后速率最大值：", problem.value)
+    # print("带宽最优解：", x.value)
+    # rate=[]
+    # for i in range(len(users_index)):
+    #     rate.append(c[i]*x.value[i])
+    # print("速率：",rate)
+    b_alloc = {}
+    if problem.status == cp.OPTIMAL:
+        b_alloc_list=x.value
+        for i in range(len(users_index)):
+            b_alloc[users_index[i]]=b_alloc_list[i]
+        return b_alloc, problem.value
+    else:
+        # todo:没有最优解的情况
+        sum_weight = 0
+        sum_rate=0
+        for i in users_index:
+            sum_weight += r_c[i]
+        for i in users_index:
+            # b_alloc[i] = C.B_max * r_c[i] / sum_weight
+            b_alloc[i]=C.B_max/len(users_index)
+            cur_rate = tool.cal_rate(b_alloc[i], p_alloc[i], uavs[uav_index], users[i])
+            sum_rate += cur_rate
+        return b_alloc,sum_rate
+        # lower_cons={}
+        # for i in range(len(users_index)):
+        #     lower_cons[min_cons[i]]=users_index[i]
+        # sorted_list=sorted(min_cons)
+        # sum_b=0
+        # cur_index=len(sorted_list)
+        # for i in range(len(sorted_list)):
+        #     if sorted_list[i]+sum_b<=B_max:
+        #         sum_b+=sorted_list[i]
+        #         index=lower_cons[sorted_list[i]]
+        #         b_alloc[index]=sorted_list[i]
+        #     else:
+        #         cur_index=i
+        #         break
+        # num_b=0
+        # rest_b=B_max-sum_b
+        # for i in range(cur_index,len(sorted_list)):
+        #     num_b+=sorted_list[i]
+        # for i in range(cur_index, len(sorted_list)):
+        #     index=lower_cons[sorted_list[i]]
+        #     b_alloc[index]=rest_b*sorted_list[i]/num_b
+
+def cal_b_maxmin(users,uavs,uav_index,uav_to_user,r_c,p_alloc,B_max,R_min):
+    users_index = uav_to_user[uav_index]
+    n=len(users_index) # 变量数量
+    # 定义变量
+    x = cp.Variable(n, nonneg=True)  # 非负变量
+    min_rate=cp.Variable()
+    c=[]# 目标函数系数
+    constant_limit = B_max  # 约束条件中的常数上限
+    # 定义约束条件
+    constraints = [cp.sum(x) <= constant_limit]
+    for i in range(len(users_index)):
+        index=users_index[i]
+        p=p_alloc[index]
+        snr = tool.cal_snr_f(uavs[uav_index], users[index], p)
+        factor=math.log2(1 + snr)
+        constraints.append(min_rate<=cp.multiply(x[i],factor))
+        c.append(factor)
+    c = np.array(c)
+    print(c)
+    # 定义目标函数
+    objective = cp.Maximize(min_rate)
+
+    min_cons=[]
+    for i in range(len(users_index)):
+        index=users_index[i]
+        p = p_alloc[index]
+        snr = tool.cal_snr_f(uavs[uav_index], users[index], p)
+        lower_limit = R_min*r_c[index]/math.log2(1 + snr)
+        # lower_limit=round(lower_limit, 2)
+        print(lower_limit)
+        min_cons.append(lower_limit)
+        constraints.append(x[i] >= lower_limit)
+    # 构建问题并求解
+    problem = cp.Problem(objective, constraints)
+    problem.solve()
+
+    # 输出求解结果
+    # print("带宽分配后最大最小速率为：", problem.value)
+    # print("带宽最优解：", x.value)
+    rate=[]
+    for i in range(len(users_index)):
+        rate.append(c[i]*x.value[i])
+    # print("速率：", rate)
+    b_alloc = {}
+    if problem.status != cp.INFEASIBLE:
+        b_alloc_list=x.value
+        for i in range(len(users_index)):
+            b_alloc[users_index[i]]=b_alloc_list[i]
+    else:
+        # todo:没有最优解的情况,将所需带宽从小到大排序，依次分配，直到剩下的带宽不够分配，然后将剩余带宽按所需比例分配其他人
+        lower_cons={}
+        for i in range(len(users_index)):
+            lower_cons[min_cons[i]]=users_index[i]
+        sorted_list=sorted(min_cons)
+        sum_b=0
+        cur_index=len(sorted_list)
+        for i in range(len(sorted_list)):
+            if sorted_list[i]+sum_b<=B_max:
+                sum_b+=sorted_list[i]
+                index=lower_cons[sorted_list[i]]
+                b_alloc[index]=sorted_list[i]
+            else:
+                cur_index=i
+                break
+        num_b=0
+        rest_b=B_max-sum_b
+        for i in range(cur_index,len(sorted_list)):
+            num_b+=sorted_list[i]
+        for i in range(cur_index, len(sorted_list)):
+            index=lower_cons[sorted_list[i]]
+            b_alloc[index]=rest_b*sorted_list[i]/num_b
+
+    return b_alloc,problem.value
+def cal_p_by_CVX_u(users,uavs,uav_index,uav_to_user,r_c,b_alloc,P_rest,R_min,snr_thre,u=1.05):
+    users_index = uav_to_user[uav_index]
+    # 定义变量和常数
+    n = len(users_index)  # 变量数量
+    a = []  # 目标函数系数
+    for i in users_index:
+        a.append(b_alloc[i])
+    a=np.array(a)
+    constant_limit = P_rest  # 约束条件中的常数上限
+    c=[]
+    for i in users_index:
+        loss = tool.forest_path_loss(uavs[uav_index], users[i])
+        factor=(10 ** (-loss / 10))/((10 ** (C.noise / 10))/1000)
+        c.append(factor)
+    c=np.array(c)
+    # 定义变量
+    x = cp.Variable(n, pos=True)  # 正数变量
+    # 定义目标函数
+    log_terms = [cp.log(1+c[i]*x[i])/np.log(2) for i in range(n)]  # log 函数每项
+    objective = cp.Maximize(a @ log_terms)
+    # u=1.05
+    # 定义约束条件
+    constraints = [cp.sum(x) <= constant_limit]
+    for i in range(len(users_index)):
+        index=users_index[i]
+        loss = tool.forest_path_loss(uavs[uav_index],users[index])
+        lower_limit=10**(snr_thre/10)*((10 ** (C.noise / 10))/1000) / (10 ** (-loss / 10))
+        constraints.append(x[i] >= lower_limit)
+        number = R_min * (r_c[index])*u/ b_alloc[index]
+        noise_power = (10 ** (C.noise / 10)) / 1000
+        lower_limit2=(math.pow(2,number)-1)*noise_power/10 ** (-loss / 10)
+        constraints.append(x[i] >= lower_limit2)
+
+    # 构建问题并求解
+    problem = cp.Problem(objective, constraints)
+    problem.solve()
+    p_alloc={}
+    if problem.status == cp.OPTIMAL:
+        # 输出求解结果
+        # print("功率分配后最大值：", problem.value)
+        # print("功率最优解：", x.value)
+        p_alloc_list = x.value
+        for i in range(len(users_index)):
+            p_alloc[users_index[i]] = p_alloc_list[i]
+        return p_alloc,problem.value
+    else:
+        sum_weight = 0
+        sum_rate=0
+
+        for i in users_index:
+            sum_weight += r_c[i]
+        for i in users_index:
+            # p_alloc[i] = P_rest * r_c[i] / sum_weight
+            p_alloc[i]=P_rest/len(users_index)
+            cur_rate = tool.cal_rate(b_alloc[i], p_alloc[i], uavs[uav_index], users[i])
+            sum_rate+=cur_rate
+
+        return p_alloc,sum_rate
+
+        # p_alloc=cal_p(users,uavs,uav_index,uav_to_user,r_c,b_alloc,P_rest,R_min,snr_thre)
+    # print(p_alloc)
+
+
+def cal_p(users,uavs,uav_index,uav_to_user,r_c,b_alloc,P_rest,R_min,snr_thre):
+    users_index = uav_to_user[uav_index]
+    # 人数
+    n = len(users_index)
+    p_alloc={}
+    num=0
+    for i in users_index:
+        loss = tool.forest_path_loss(uavs[uav_index], users[i])
+        noise_power = (10 ** (C.noise / 10)) / 1000
+        num+=noise_power/10 ** (-loss / 10)
+    sum_p=0
+    cur_index=0
+    for i in users_index:
+        # lamda=n*b_alloc[i]/((P_rest+num)*math.log(2))
+        loss = tool.forest_path_loss(uavs[uav_index], users[i])
+        noise_power = (10 ** (C.noise / 10)) / 1000
+        p1=(P_rest+num)/n-noise_power/10 ** (-loss / 10)
+        p2=tool.get_p_by_snr_f(snr_thre,uavs[uav_index],users[i],C.noise)
+        number=R_min*r_c[i]/b_alloc[i]
+        p3=(math.pow(2,number)-1)*noise_power/10 ** (-loss / 10)
+        max_p=max(max(p1,p2),p3)
+        p_alloc[i]=max_p
+        sum_p+=max_p
+        if p1>p3:
+            cur_index=i
+        print(p1,p2,p3)
+    print(sum_p)
+    if sum_p-P_rest>0:
+        p_alloc[cur_index]-=sum_p-P_rest
+    print(p_alloc)
+    return p_alloc
+
+def resource_alloc_u(users,uavs,r_c,p_connection,uav_to_user):
+    with warnings.catch_warnings():
+        # 忽略特定类型的警告
+        warnings.filterwarnings("ignore", message="Solution may be inaccurate.", category=UserWarning)
+
+        best=float('-inf')
+        p_rest=tool.get_rest_p(users,uavs,p_connection)
+        # p_alloc={}
+        # b_alloc={}
+        #平分功率，带宽
+        p_all = []
+        b_all = []
+        best_all = []
+        rate=[]
+        for i in range(len(uavs)):
+            p_alloc={}
+            b_alloc={}
+            sub=1000
+            for j in uav_to_user[i]:
+                b_alloc[j] = C.B_max / len(uav_to_user[i])
+                p_alloc[j] = p_rest[i] / 4
+            while sub>1:
+                # b_alloc, cur_objective = cal_b(users, uavs, i, uav_to_user, r_c, p_alloc, C.B_max, C.R_min)
+                p_alloc,best=cal_p_by_CVX_u(users, uavs, i, uav_to_user, r_c, b_alloc, p_rest[i], C.R_min, C.snr_thre,1.05)
+                b_alloc, cur_objective = cal_b_u(users, uavs, i, uav_to_user, r_c, p_alloc, C.B_max, C.R_min,1.05)
+                sub=abs(best-cur_objective)
+                # best=max(best,cur_objective)
+            for j in uav_to_user[i]:
+                cur_rate = tool.cal_rate(b_alloc[j], p_alloc[j], uavs[i], users[j])
+                rate.append(cur_rate)
+            p_all.append(p_alloc)
+            b_all.append(b_alloc)
+            best_all.append(best)
+        # print("功率：",p_all)
+        # print("带宽：",b_all)
+        # print("所有速率：",best_all)
+        return p_all,b_all,rate
+
+def resource_alloc(users,uavs,r_c,p_connection,uav_to_user):
+    with warnings.catch_warnings():
+        # 忽略特定类型的警告
+        warnings.filterwarnings("ignore", message="Solution may be inaccurate.", category=UserWarning)
+
+        best=float('-inf')
+        p_rest=tool.get_rest_p(users,uavs,p_connection)
+        # p_alloc={}
+        # b_alloc={}
+        #平分功率，带宽
+        p_all = []
+        b_all = []
+        best_all = []
+        rate=[]
+        for i in range(len(uavs)):
+            p_alloc={}
+            b_alloc={}
+            sub=1000
+            for j in uav_to_user[i]:
+                b_alloc[j] = C.B_max / len(uav_to_user[i])
+                p_alloc[j] = p_rest[i] / 4
+            while sub>1:
+                # b_alloc, cur_objective = cal_b(users, uavs, i, uav_to_user, r_c, p_alloc, C.B_max, C.R_min)
+                p_alloc,best=cal_p_by_CVX_u(users, uavs, i, uav_to_user, r_c, b_alloc, p_rest[i], C.R_min, C.snr_thre,1)
+                b_alloc, cur_objective = cal_b_u(users, uavs, i, uav_to_user, r_c, p_alloc, C.B_max, C.R_min,1)
+                sub=abs(best-cur_objective)
+                # best=max(best,cur_objective)
+            for j in uav_to_user[i]:
+                cur_rate = tool.cal_rate(b_alloc[j], p_alloc[j], uavs[i], users[j])
+                rate.append(cur_rate)
+            p_all.append(p_alloc)
+            b_all.append(b_alloc)
+            best_all.append(best)
+        # print("功率：",p_all)
+        # print("带宽：",b_all)
+        # print("所有速率：",best_all)
+        return p_all,b_all,rate
+
+def resource_alloc_proportion(users,uavs,r_c,p_connection,uav_to_user):
+    best = float('-inf')
+    p_rest = tool.get_rest_p(users, uavs, p_connection)
+
+    p_all=[]
+    b_all=[]
+    sum_weight=[0 for i in range(len(uavs))]
+    rate=[]
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            sum_weight[i] += r_c[j]
+    for i in range(len(uavs)):
+        p_alloc = {}
+        b_alloc = {}
+        for j in uav_to_user[i]:
+            p_alloc[j] = p_rest[i]*r_c[j] /sum_weight[i]
+            b_alloc[j]=C.B_max*r_c[j] /sum_weight[i]
+            cur_rate=tool.cal_rate(b_alloc[j],p_alloc[j],uavs[i],users[j])
+            rate.append(cur_rate)
+        p_all.append(p_alloc)
+        b_all.append(b_alloc)
+    # print(p_alloc,b_alloc)
+
+    return p_all,b_all,rate
+
+def resource_alloc_avg(users,uavs,r_c,p_connection,uav_to_user):
+    best = float('-inf')
+    p_rest = tool.get_rest_p(users, uavs, p_connection)
+
+    p_all=[]
+    b_all=[]
+    sum_weight=[0 for i in range(len(uavs))]
+    rate=[]
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            sum_weight[i] += r_c[j]
+    for i in range(len(uavs)):
+        p_alloc = {}
+        b_alloc = {}
+        for j in uav_to_user[i]:
+            p_alloc[j] = p_rest[i]/len(uav_to_user[i])
+            b_alloc[j]=C.B_max/len(uav_to_user[i])
+            cur_rate=tool.cal_rate(b_alloc[j],p_alloc[j],uavs[i],users[j])
+            rate.append(cur_rate)
+        p_all.append(p_alloc)
+        b_all.append(b_alloc)
+    # print(p_alloc,b_alloc)
+
+    return p_all,b_all,rate
+#非凸求解不了
+def resource_CVX(users,uavs,uav_index,uav_to_user,r_c,B_max,P_rest,R_min,snr_thre):
+    users_index = uav_to_user[uav_index]
+    # 人数
+    n = len(users_index)
+    b = cp.Variable(n,pos=True)  # 正数变量)
+    p = cp.Variable(n,pos=True)
+    c = []
+    log_terms = 0  # 将 log_terms 初始化为 0
+    for i in range(len(users_index)):
+        index=users_index[i]
+        loss = tool.forest_path_loss(uavs[uav_index], users[index])
+        factor = (10 ** (-loss / 10)) / ((10 ** (C.noise / 10)) / 1000)
+        c.append(factor)
+        log_terms += b[i]*cp.log(1 + factor * p[i]) / np.log(2)
+    c = np.array(c)
+    # log_terms = [cp.log(1 + c[i] * p[i]) / np.log(2) for i in range(n)]  # log 函数每项
+    # objective = cp.Maximize(cp.sum(cp.multiply(b,log_terms)))
+    # log_terms = [cp.log(1 + c[i] * p[i]) / np.log(2) for i in range(n)]  # log 函数每项
+    objective = cp.Maximize(log_terms)
+    constraints = [cp.sum(p) <= P_rest,cp.sum(b) <= B_max]
+    for i in range(len(users_index)):
+        index = users_index[i]
+        loss = tool.forest_path_loss(uavs[uav_index], users[index])
+        lower_limit = 10 ** (snr_thre / 10) * ((10 ** (C.noise / 10)) / 1000) / (10 ** (-loss / 10))
+        constraints.append(p[i] >= lower_limit)
+        # number = R_min * r_c[index] / b[i]
+        noise_power = (10 ** (C.noise / 10)) / 1000
+        # lower_limit2 = (math.pow(2, R_min * r_c[index] / b[i]) - 1) * noise_power / 10 ** (-loss / 10)
+        factor = (10 ** (-loss / 10)) / ((10 ** (C.noise / 10)) / 1000)
+        constraints.append( b[i]*cp.log(1 + factor * p[i]) / np.log(2)>=R_min * r_c[index] )
+        # constraints.append(p[i] >= (math.pow(2, R_min * r_c[index] / b[i]) - 1) * noise_power / 10 ** (-loss / 10))
+        # snr = tool.cal_snr_f(uavs[uav_index], users[index], p[i])
+        # lower_limit = R_min * r_c[index] / math.log2(1 + p[i]*(10 ** (-loss / 10))/noise_power)
+        # lower_limit=round(lower_limit, 2)
+        # constraints.append(b[i] >= R_min * r_c[index] / math.log2(1 + p[i]*(10 ** (-loss / 10))/noise_power))
+    problem = cp.Problem(objective, constraints)
+    problem.solve(verbose=True)
+    return
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data4.csv")
+    uavs = tool.getUavInitialPos(7, users)
+    c_connection = tool.get_c_association(users, uavs, math.ceil(len(users) / len(uavs)))
+    p_connection = tool.get_p_association(users, uavs, c_connection, math.ceil(len(users) * 2 / len(uavs)))
+    uav_to_user=tool.get_uav_to_user(c_connection, uavs)
+    print(uav_to_user)
+    p_alloc ={}
+    r_c = {}
+    r=1
+    for i in range(len(uavs)):
+        for j in uav_to_user[i]:
+            p_alloc[j]=0.09/4
+            r_c[j]=2
+            r=r+1
+    print(r_c)
+    p_rest = tool.get_rest_p(users, uavs, p_connection)
+    # cal_b_maxmin(users, uavs, 0, uav_to_user, r_c, p_alloc, C.B_max, C.R_min)
+    # cal_b(users, uavs, 0, uav_to_user, r_c, p_alloc, C.B_max, C.R_min)
+    # resource_alloc_avg(users, uavs, r_c, p_connection, uav_to_user)
+    resource_alloc(users, uavs, r_c, p_connection, uav_to_user)
+    # resource_CVX(users,uavs,0,uav_to_user,r_c,C.B_max,p_rest[0],C.R_min,C.snr_thre)
+    # B_max=10/4 #10M
+    # R_min=0.2
+    # p_rest = tool.get_rest_p(users, uavs, p_connection)
+    # print(p_rest)
+    # b_alloc=cal_b(users,uavs,0,uav_to_user,r_c,p_alloc,B_max,R_min)
+    # print(b_alloc)
+    # p_alloc=cal_p_by_CVX(users,uavs,0,uav_to_user,r_c,b_alloc,p_rest[0],R_min,0)
+    # b_alloc = cal_b(users, uavs, 0, uav_to_user, r_c, p_alloc, B_max, R_min)
+    # p_alloc = cal_p_by_CVX(users, uavs, 0, uav_to_user, r_c, b_alloc, p_rest[0], R_min, 0)
+
+

dynamicDeploy/run.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+import constants as C
+import dynamicProcess as dynamic
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+#slot1为长时间尺度，slot2短时间尺度
+def whole_process(users,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    e=0
+    for i in range(0,10*slot1,slot2):
+        users_loc = dynamic.gauss_markov_model(users, C.v * slot2, C.max_range, )
+        users = users_loc
+        if i%C.r_change_slot==0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot1 != 0:
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            f,e,rate,gdop=OF.real_objective(pre_uavs, uavs, users, r_p, p_all, b_all, c_association, p_association,slot2)
+            print(f,rate,gdop)
+            print("悬停时隙能耗：", e)
+            fitness.append(f)
+        else:#资源重分配，无人机重部署
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            pso.re_init(pre_uavs,users,r_c,r_p,c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f,e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_p, p_all, b_all, c_association, p_association,slot2)
+            print("飞行时隙能耗：",e)
+            pre_uavs=global_pos
+            uavs=global_pos
+            fitness.append(f)
+            # fitness.append(global_best[0])
+
+    print(fitness)
+def whole_process_new(U,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    users=U[0]
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c,r_p, p_all, b_all, c_association, p_association, slot2)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    fitness.append(f)
+
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    e=0
+    j=1
+    r_s=[]
+    g_s=[]
+    r_s.append(r)
+    g_s.append(g)
+    for i in range(10,3*C.r_change_slot,slot2):
+        users= U[j]
+        j+=1
+        if i%C.r_change_slot==0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot1 != 0:
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            # c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+            f,e,rate,gdop=OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association, p_association,slot2)
+            r,g=OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            r_s.append(r)
+            g_s.append(g)
+            print(f,rate,gdop)
+            print("满足率",r,g)
+            print("悬停时隙能耗：", e)
+            fitness.append(f)
+        else:#资源重分配，无人机重部署
+            # c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+            pso.re_init(pre_uavs,users,r_c,r_p,c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+
+            print("pso:",global_best)
+            c_association, p_association, p_all, b_all = OF.objective_resource(global_pos, users, r_c)
+            r, g = OF.cal_satisfaction(global_pos, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            print("满足率", r, g)
+            r_s.append(r)
+            g_s.append(g)
+            f,e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_c,r_p, p_all, b_all, c_association, p_association,slot2)
+            if f>global_best[0]:
+                fitness.append(f)
+            else:
+                fitness.append(global_best[0])
+            print("实际:",f)
+            print("飞行时隙能耗：",e)
+            pre_uavs=global_pos
+            uavs=global_pos
+            # fitness.append(f)
+            # fitness.append(global_best[0])
+
+    print("proposed:",fitness)
+    print(r_s)
+    print(g_s)
+    return fitness
+def whole_process_kmeans(U,k,slot1,slot2):
+    users = U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association, p_association,
+                                         slot2)
+    fitness.append(f)
+    j = 1
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j += 1
+        if i % C.r_change_slot == 0 and i != 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot1 != 0:
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            # c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            print(f, rate, gdop)
+            print("悬停时隙能耗：", e)
+            fitness.append(f)
+        else:  # 资源重分配，无人机重部署
+            uavs = tool.getUavInitialPos(k, users)
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            fitness.append(f)
+            print("实际:", f)
+            print("飞行时隙能耗：", e)
+            pre_uavs = uavs
+    print(fitness)
+
+def fixed_position_fixed_resource(U,k,slot1,slot2):
+    users = U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association, p_association,
+                                         slot2)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    fitness.append(f)
+
+    j = 1
+    r_s = []
+    g_s = []
+    r_s.append(r)
+    g_s.append(g)
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j += 1
+        if i % C.r_change_slot == 0 and i != 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                         p_association, slot2)
+        fitness.append(f)
+    print(fitness)
+    return fitness
+
+def whole_process_pso(users,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    e=0
+    for i in range(0,10*slot1,slot2):
+        users_loc = dynamic.gauss_markov_model(users, C.v * slot2, C.max_range, )
+        users = users_loc
+        if i%C.r_change_slot==0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        if i % slot1 != 0:
+            f,e,rate,gdop=OF.real_objective(pre_uavs, uavs, users, r_p, p_all, b_all, c_association, p_association,slot2)
+            print(f,rate,gdop)
+            print("悬停时隙能耗：", e)
+            fitness.append(f)
+        else:#资源重分配，无人机重部署
+            pso.re_init(pre_uavs,users,r_c,r_p,c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f,e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_p, p_all, b_all, c_association, p_association,slot2)
+            print("飞行时隙能耗：",e)
+            pre_uavs=global_pos
+            uavs=global_pos
+            fitness.append(f)
+            # fitness.append(global_best[0])
+
+    print(fitness)
+def run_whole_process():
+    users= Users.getUsers("./data.csv")
+    slot1=30
+    slot2=10
+    U=dynamic.generate_users_pos(users,slot2,30)
+    # whole_process(users,4,slot1,slot2)
+    fitness=[]
+    f_sum = []
+    k=5
+    for j in range(k):
+        for i in range(10,70,10):
+            f=whole_process_new(U, 4, i, slot2)
+            fitness.append(f)
+        f_sum.append(fitness)
+        print(fitness)
+    print(f_sum)
+        # whole_process_kmeans(U,4,i,slot2)
+    # f1_sum=[0 for i in range(19)]
+    # f2_sum = [0 for i in range(19)]
+    # f3_sum = [0 for i in range(19)]
+    # f4_sum = [0 for i in range(19)]
+    # k=3
+    # for i in range(k):
+    #     f1=whole_process_new(U, 4, slot1, slot2)
+    #     f2=fixed_position_dynamic_resource2(U, 4, slot1, slot2)
+    #     f3=fixed_resource_dynamic_position2(U,4,slot1,slot2)
+    #     f4=same_dynamic_resource_dynamic_position2(U,4,slot1,slot2)
+    #     f1_sum=[f1_sum[j]+f1[j] for j in range(len(f1))]
+    #     f2_sum = [f2_sum[j] + f2[j] for j in range(len(f1))]
+    #     f3_sum = [f3_sum[j] + f3[j] for j in range(len(f1))]
+    #     f4_sum = [f4_sum[j] + f4[j] for j in range(len(f1))]
+    # f1_avg=[f1_sum[i]/k for i in range(len(f1_sum))]
+    # f2_avg = [f2_sum[i] / k for i in range(len(f1_sum))]
+    # f3_avg = [f3_sum[i] / k for i in range(len(f1_sum))]
+    # f4_avg = [f4_sum[i] / k for i in range(len(f1_sum))]
+    # print(f1_avg)
+    # print(f2_avg)
+    # print(f3_avg)
+    # print(f4_avg)
+        # fixed_position_fixed_resource(U, 4, slot1, slot2)
+
+
+#只优化资源
+def fixed_position_dynamic_resource(users,k,slot1,slot2):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    fitness = []
+    for i in range(0, 10 * slot1, slot2):
+        users_loc = dynamic.gauss_markov_model(users, C.v * slot2, C.max_range, )
+        users = users_loc
+        if i % C.r_change_slot == 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        f,e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_p, p_all, b_all, c_association, p_association,slot2)
+        print(f, e,rate, gdop)
+        fitness.append(f)
+    print(fitness)
+def fixed_position_dynamic_resource2(U,k,slot1,slot2):
+    users=U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    fitness = []
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association, p_association, slot2)
+    print(f, e, rate, gdop)
+    fitness.append(f)
+    j=1
+    r_s,g_s=[],[]
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j=j+1
+        if i % C.r_change_slot == 0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        f,e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association, p_association,slot2)
+        print(f, e,rate, gdop)
+        fitness.append(f)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+    print("fixed_position_dynamic_resource:",fitness)
+    print("fixed_position_dynamic_resource:", r_s,g_s)
+    return fitness
+
+#只优化位置
+def fixed_resource_dynamic_position(users,k,slot1,slot2):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    fitness = []
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    for i in range(0, 10 * slot1, slot2):
+        users_loc = dynamic.gauss_markov_model(users, C.v * slot2, C.max_range, )
+        users = users_loc
+        if i % C.r_change_slot == 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        # if index==1:
+        #     c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        if i % slot1 == 0:
+            pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+            pre_uavs = global_pos
+            uavs = global_pos
+        else:
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+    print(fitness)
+    return fitness
+def fixed_resource_dynamic_position2(U,k,slot1,slot2):
+    users=U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    fitness = []
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                         p_association, slot2)
+    print(f, e, rate, gdop)
+    fitness.append(f)
+    j=1
+    r_s,g_s=[],[]
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j=j+1
+        if i % C.r_change_slot == 0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        # if index==1:
+        #     c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        if i % slot1 == 0:
+            pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            r, g = OF.cal_satisfaction(global_pos, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            r_s.append(r)
+            g_s.append(g)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+            pre_uavs = global_pos
+            uavs = global_pos
+        else:
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            r_s.append(r)
+            g_s.append(g)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+    print("fixed_resource_dynamic_position",fitness)
+    print("fixed_resource_dynamic_position：", r_s, g_s)
+    return fitness
+#同时优化资源和位置
+def same_dynamic_resource_dynamic_position(users,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    e = 0
+    for i in range(0, 10 * slot1, slot2):
+        users_loc = dynamic.gauss_markov_model(users, C.v * slot2, C.max_range, )
+        users = users_loc
+        if i % C.r_change_slot == 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot1 == 0:
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            print("当前时隙能耗：", e)
+            pre_uavs = global_pos
+            uavs = global_pos
+            fitness.append(f)
+        else:  #计算
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+
+            # fitness.append(global_best[0])
+
+    print(fitness)
+    return fitness
+def same_dynamic_resource_dynamic_position2(U,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    users=U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                         p_association, slot2)
+    fitness.append(f)
+    e = 0
+    j=1
+    r_s,g_s=[],[]
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j=j+1
+        if i % C.r_change_slot == 0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot2 == 0:
+            c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+            pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+            X, V = pso.initParticle(users)
+            global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective,)
+            f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            r, g = OF.cal_satisfaction(global_pos, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            r_s.append(r)
+            g_s.append(g)
+            print("当前时隙能耗：", e)
+            pre_uavs = global_pos
+            uavs = global_pos
+            fitness.append(f)
+        else:  #计算
+            f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                                 p_association, slot2)
+            r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+            r_s.append(r)
+            g_s.append(g)
+            print(f, e, rate, gdop)
+            fitness.append(f)
+
+            # fitness.append(global_best[0])
+
+    print("统一尺度：",fitness)
+    print("统一尺度：",r_s,g_s)
+    return fitness
+
+# run_whole_process()
+
+def cal_avg():
+    result=[[[1328.1765246507357, 1503.4819127657738, 1413.0043866027665, 1463.0694754945252, 1411.3332686120364,
+       1430.8494924488095, 1348.4585284268035, 1425.5665531789396, 1434.6103288936818, 1382.0470220409627,
+       1400.9531567236882, 1430.8877968272081, 1388.6185194154302, 1349.9589680879892, 1331.6482975811632,
+       1349.4196364208437, 1333.4082035862293, 1289.1716043437505],
+      [1328.1765246507357, 1353.1879576378208, 1405.8565440529696, 1432.7524257593964, 1401.1414708835648,
+       1413.2404297641222, 1383.0244353104806, 1424.8984233930191, 1431.198682113639, 1377.8356032938168,
+       1396.959114367267, 1431.17968510067, 1386.0419445035495, 1356.377703925818, 1332.1232554431833,
+       1352.6415842615386, 1334.979935409752, 1287.8872570069313],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1461.5691341367276, 1411.0399445665012,
+       1428.6282956069683, 1342.4394201899177, 1410.6593127134165, 1421.27785518321, 1379.0053611335575,
+       1400.2550306243324, 1433.9203792901133, 1356.75918484904, 1345.2969702372804, 1335.91205104966,
+       1353.492373837457, 1338.6041420862807, 1291.1214993205556],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1397.2181548623369,
+       1416.1632823180864, 1381.329183610079, 1423.0177897257574, 1381.024457636659, 1314.0230128358758,
+       1329.5899423905234, 1388.9476610421257, 1338.3554621772444, 1327.6922803222226, 1313.4015072363025,
+       1355.1396654592534, 1334.929881133014, 1288.7404759786446],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1239.6349292315638, 1256.4686678242904, 1277.2435737396827, 1267.0466827108758, 1178.9368661962508,
+       1368.468264013462, 1423.5411331544783, 1242.4019341077947, 1226.9254448575948, 1186.9326588398535,
+       1344.001558702772, 1337.9391635327902, 1297.4028580414574],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1311.5753634371717, 1388.2120232884542, 1431.8449807730888, 1431.3580503007593, 1378.1452369441306,
+       1396.4141506156877, 1433.377670872926, 1384.1169572273377, 1359.900905300984, 1330.6370094444353,
+       1353.823654014219, 1336.9776868893764, 1288.787931542987],
+      [1328.1765246507357, 1462.1032302319172, 1220.7983757506436, 1250.8351027133883, 1407.6119127563513,
+       1428.0915257023041, 1326.2581462780947, 1429.3390687732622, 1430.3834005472518, 1350.323846827929,
+       1384.6852854906783, 1388.5883879436703, 1384.9288942132264, 1363.9051313106954, 1333.3026983930981,
+       1356.3990159011914, 1336.8379103270627, 1253.635910753905],
+      [1328.1765246507357, 1353.1879576378208, 1391.94935171302, 1419.4723595055307, 1436.012266121132,
+       1448.4136632819252, 1346.110107123306, 1411.6812049560685, 1441.2949816305224, 1380.5627492161482,
+       1312.6260756214342, 1359.85895865934, 1380.3851780875336, 1356.023191076924, 1330.8989844393104,
+       1351.9296714736968, 1356.4851064483519, 1337.4758375470306],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1460.7498660143006, 1410.0728187816906,
+       1427.4464699502457, 1338.1539823621792, 1373.4882526392807, 1377.483347538101, 1373.6246453797514,
+       1387.3376104301349, 1441.5264866455693, 1348.6983324094676, 1338.5720560333045, 1324.1236454249363,
+       1354.1914050001853, 1338.8665448937927, 1291.2684553450981],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1290.7792571204702,
+       1214.308347749437, 1200.58340380531, 1239.501463648965, 1428.4077716261145, 1377.987085647185,
+       1398.1278396468706, 1428.7798719957766, 1385.7469740316235, 1356.5052284818378, 1330.6706853931034,
+       1353.4385628020025, 1335.2583787011524, 1288.6298545399088],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1442.8167958144866, 1406.2259749595107, 1428.1080839534975, 1426.0580976301176, 1381.5838277366659,
+       1397.6065029785466, 1430.6366358870098, 1387.3287325595181, 1359.7125256576942, 1333.3789756932902,
+       1355.1829245789684, 1339.515619602179, 1291.7515379563479],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1311.5753634371717, 1388.1381132403171, 1432.010926615392, 1431.3321798901468, 1378.0565228773867,
+       1396.81940623514, 1432.7982708122322, 1377.8612793152831, 1350.6348893911354, 1327.8024309518744,
+       1345.099570863568, 1328.029161164731, 1280.9350822776496],
+      [1328.1765246507357, 1504.1073057068995, 1219.5661435925442, 1460.274343713098, 1436.3592446035104,
+       1425.1034928610165, 1339.2784050231967, 1443.2730535440444, 1425.0924476853634, 1363.5785187957338,
+       1341.4800219444169, 1423.9137275869386, 1349.7704669791651, 1360.7630386223702, 1333.425113996663,
+       1366.2030511798673, 1337.4231380704002, 1291.6640507163672],
+      [1328.1765246507357, 1353.1879576378208, 1410.579915824099, 1462.9235155522852, 1394.8132884136066,
+       1414.5613788698495, 1392.1522068490503, 1431.5057538173428, 1440.2238046243085, 1387.293910964124,
+       1399.5857248683053, 1435.6166727422476, 1388.969821621961, 1364.2475752956364, 1332.0464144720595,
+       1354.9038046415044, 1339.1743300404082, 1291.630256209334],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1458.4159965010651, 1411.3548799046819,
+       1425.7224684005503, 1391.377051941948, 1431.8159723489114, 1431.278025139241, 1358.8212920663784,
+       1380.2436201750243, 1425.1958017919642, 1384.3256466756068, 1359.07917364794, 1332.9247098888873,
+       1353.1637744754123, 1336.07627725299, 1290.0476381211815],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1393.4337378530465,
+       1409.835132447911, 1391.1722552886845, 1417.0328256395617, 1432.3487735073522, 1378.2772749194908,
+       1396.6008401570577, 1433.1155975041377, 1390.5914406805628, 1362.6765110445174, 1334.5762051476459,
+       1361.4369514102002, 1336.6893145856227, 1288.9669265353398],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1424.8510324458748, 1390.7715313987126, 1431.7204129721713, 1431.9081214684788, 1378.1928653857747,
+       1396.5681148308058, 1430.780621400254, 1384.5714015170224, 1359.6447860912401, 1330.3893264075632,
+       1353.4188754217143, 1336.859504893905, 1288.08174852749],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1311.5753634371717, 1388.375915240571, 1420.9624704974776, 1421.2395781820762, 1367.800240844252,
+       1399.9786499043562, 1424.3801787948573, 1387.408334861284, 1360.107103934737, 1330.7643039541172,
+       1355.5080991940283, 1337.1438609436389, 1290.3414189906262],
+      [1328.1765246507357, 1503.913864640719, 1413.8980639539386, 1468.00534814896, 1422.8104408104573,
+       1423.765114927117, 1324.9175310660078, 1435.7069206999379, 1433.6130926608998, 1381.3226169858867,
+       1356.3795348447263, 1431.3801618130071, 1386.6510817433498, 1361.9492867624042, 1332.7701837350623,
+       1350.1107156688993, 1333.0696822151624, 1329.2298025753437],
+      [1328.1765246507357, 1353.1879576378208, 1409.2744122762876, 1454.3125460261099, 1410.4565982165238,
+       1424.082433572339, 1391.4733702241447, 1430.879428730335, 1414.3890111884998, 1350.34520050958,
+       1394.841280189504, 1429.3744950534692, 1386.4838423235042, 1356.8345977131296, 1332.5345285357314,
+       1354.656448130794, 1336.017705107397, 1290.1994987112023],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1286.0162095769574, 1223.6306854721179,
+       1277.8869653640977, 1354.6916735211269, 1421.0978338505058, 1419.4423878951538, 1357.758685952835,
+       1387.6171511136472, 1429.1792659040157, 1327.5638800212087, 1323.1431279704393, 1316.45311752381,
+       1340.1900930468087, 1333.8143956170393, 1302.8716294862143],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1369.9885881316322,
+       1389.746107250975, 1370.1832958235645, 1402.7731717332395, 1430.2269756772712, 1376.3970040533245,
+       1396.0147842886943, 1431.3398512920298, 1376.3795115633775, 1354.4061186176452, 1322.125898247653,
+       1347.3190083197246, 1357.8289704460617, 1309.05475863672],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1425.0648190120878, 1390.8167346028026, 1432.1377945654822, 1431.7215282770155, 1377.5399714568885,
+       1380.911844832686, 1424.423408758382, 1296.815612973573, 1283.9798136146878, 1256.8194756407995,
+       1355.3359054061746, 1338.4699601111859, 1293.041446825808],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1311.5753634371717, 1388.1670414052744, 1432.0300312924162, 1431.3542356778341, 1378.091812602148,
+       1396.8353951681145, 1432.8116506446752, 1385.8096029856881, 1357.7211709158132, 1330.4374574117403,
+       1353.7555347511545, 1337.7274048862123, 1288.7985560635561],
+      [1328.1765246507357, 1355.524217113136, 1412.9393304787225, 1465.2536583945196, 1410.697993614098,
+       1409.4334667878807, 1392.1943945343658, 1432.1373353197077, 1428.3902744802278, 1376.9823301026836,
+       1376.0674161952127, 1416.117550231582, 1383.412506204135, 1255.0784335917822, 1331.1853834984765,
+       1354.5528533816318, 1335.8822725180155, 1334.9239189752302],
+      [1328.1765246507357, 1353.1879576378208, 1410.814724976499, 1463.2075296132891, 1359.6374526982909,
+       1379.8017091774498, 1339.6702397963766, 1414.80823357494, 1417.2239029395234, 1361.4605045875321,
+       1396.318343095436, 1431.4467641883664, 1383.7222680136367, 1357.68401316966, 1330.5022749791037,
+       1354.0002885613083, 1335.781720673481, 1289.1835604091564],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1458.0227275098182, 1409.1231290530502,
+       1424.7112929163986, 1353.9694749430198, 1425.9473138683177, 1431.1149171600007, 1379.894377567314,
+       1399.26941534447, 1435.5745950310902, 1279.2743553908692, 1265.4957438307756, 1251.3268288661366,
+       1361.1714386001656, 1360.3714394835474, 1337.4519399978253],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1269.3936064664942,
+       1311.6416869961968, 1273.6585777553612, 1313.941461891031, 1432.7823407239148, 1380.7522541180585,
+       1399.1895890144515, 1431.7253108836628, 1384.9934068374707, 1359.608569164655, 1332.9293029520368,
+       1353.6079657716728, 1299.1185666067004, 1268.3723761743663],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1423.4143076476337, 1390.586686099597, 1430.8731140025316, 1431.3352484839065, 1377.530665193921,
+       1396.4032147703758, 1430.013055959338, 1385.216220842132, 1357.4682050836072, 1330.022274655173,
+       1353.691056324455, 1337.2310920781447, 1289.1970135479476],
+      [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+       1311.5753634371717, 1369.5460147787514, 1443.1311137429147, 1440.8509908426902, 1382.9197914159233,
+       1381.0026954392465, 1444.6890646393772, 1353.2513864631362, 1341.0551593686894, 1305.9609304041398,
+       1331.0007656836608, 1328.9668585129314, 1289.3981557047607]], [
+         [1328.1765246507357, 1503.4819127657738, 1413.0043866027665, 1463.0694754945252, 1411.3332686120364,
+          1430.8494924488095, 1348.4585284268035, 1425.5665531789396, 1434.6103288936818, 1382.0470220409627,
+          1400.9531567236882, 1430.8877968272081, 1388.6185194154302, 1349.9589680879892, 1331.6482975811632,
+          1349.4196364208437, 1333.4082035862293, 1289.1716043437505],
+         [1328.1765246507357, 1353.1879576378208, 1405.8565440529696, 1432.7524257593964, 1401.1414708835648,
+          1413.2404297641222, 1383.0244353104806, 1424.8984233930191, 1431.198682113639, 1377.8356032938168,
+          1396.959114367267, 1431.17968510067, 1386.0419445035495, 1356.377703925818, 1332.1232554431833,
+          1352.6415842615386, 1334.979935409752, 1287.8872570069313],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1461.5691341367276, 1411.0399445665012,
+          1428.6282956069683, 1342.4394201899177, 1410.6593127134165, 1421.27785518321, 1379.0053611335575,
+          1400.2550306243324, 1433.9203792901133, 1356.75918484904, 1345.2969702372804, 1335.91205104966,
+          1353.492373837457, 1338.6041420862807, 1291.1214993205556],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1397.2181548623369,
+          1416.1632823180864, 1381.329183610079, 1423.0177897257574, 1381.024457636659, 1314.0230128358758,
+          1329.5899423905234, 1388.9476610421257, 1338.3554621772444, 1327.6922803222226, 1313.4015072363025,
+          1355.1396654592534, 1334.929881133014, 1288.7404759786446],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1239.6349292315638, 1256.4686678242904, 1277.2435737396827, 1267.0466827108758, 1178.9368661962508,
+          1368.468264013462, 1423.5411331544783, 1242.4019341077947, 1226.9254448575948, 1186.9326588398535,
+          1344.001558702772, 1337.9391635327902, 1297.4028580414574],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1311.5753634371717, 1388.2120232884542, 1431.8449807730888, 1431.3580503007593, 1378.1452369441306,
+          1396.4141506156877, 1433.377670872926, 1384.1169572273377, 1359.900905300984, 1330.6370094444353,
+          1353.823654014219, 1336.9776868893764, 1288.787931542987],
+         [1328.1765246507357, 1462.1032302319172, 1220.7983757506436, 1250.8351027133883, 1407.6119127563513,
+          1428.0915257023041, 1326.2581462780947, 1429.3390687732622, 1430.3834005472518, 1350.323846827929,
+          1384.6852854906783, 1388.5883879436703, 1384.9288942132264, 1363.9051313106954, 1333.3026983930981,
+          1356.3990159011914, 1336.8379103270627, 1253.635910753905],
+         [1328.1765246507357, 1353.1879576378208, 1391.94935171302, 1419.4723595055307, 1436.012266121132,
+          1448.4136632819252, 1346.110107123306, 1411.6812049560685, 1441.2949816305224, 1380.5627492161482,
+          1312.6260756214342, 1359.85895865934, 1380.3851780875336, 1356.023191076924, 1330.8989844393104,
+          1351.9296714736968, 1356.4851064483519, 1337.4758375470306],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1460.7498660143006, 1410.0728187816906,
+          1427.4464699502457, 1338.1539823621792, 1373.4882526392807, 1377.483347538101, 1373.6246453797514,
+          1387.3376104301349, 1441.5264866455693, 1348.6983324094676, 1338.5720560333045, 1324.1236454249363,
+          1354.1914050001853, 1338.8665448937927, 1291.2684553450981],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1290.7792571204702,
+          1214.308347749437, 1200.58340380531, 1239.501463648965, 1428.4077716261145, 1377.987085647185,
+          1398.1278396468706, 1428.7798719957766, 1385.7469740316235, 1356.5052284818378, 1330.6706853931034,
+          1353.4385628020025, 1335.2583787011524, 1288.6298545399088],
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+          1384.6852854906783, 1388.5883879436703, 1384.9288942132264, 1363.9051313106954, 1333.3026983930981,
+          1356.3990159011914, 1336.8379103270627, 1253.635910753905],
+         [1328.1765246507357, 1353.1879576378208, 1391.94935171302, 1419.4723595055307, 1436.012266121132,
+          1448.4136632819252, 1346.110107123306, 1411.6812049560685, 1441.2949816305224, 1380.5627492161482,
+          1312.6260756214342, 1359.85895865934, 1380.3851780875336, 1356.023191076924, 1330.8989844393104,
+          1351.9296714736968, 1356.4851064483519, 1337.4758375470306],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1460.7498660143006, 1410.0728187816906,
+          1427.4464699502457, 1338.1539823621792, 1373.4882526392807, 1377.483347538101, 1373.6246453797514,
+          1387.3376104301349, 1441.5264866455693, 1348.6983324094676, 1338.5720560333045, 1324.1236454249363,
+          1354.1914050001853, 1338.8665448937927, 1291.2684553450981],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1290.7792571204702,
+          1214.308347749437, 1200.58340380531, 1239.501463648965, 1428.4077716261145, 1377.987085647185,
+          1398.1278396468706, 1428.7798719957766, 1385.7469740316235, 1356.5052284818378, 1330.6706853931034,
+          1353.4385628020025, 1335.2583787011524, 1288.6298545399088],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1442.8167958144866, 1406.2259749595107, 1428.1080839534975, 1426.0580976301176, 1381.5838277366659,
+          1397.6065029785466, 1430.6366358870098, 1387.3287325595181, 1359.7125256576942, 1333.3789756932902,
+          1355.1829245789684, 1339.515619602179, 1291.7515379563479],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1311.5753634371717, 1388.1381132403171, 1432.010926615392, 1431.3321798901468, 1378.0565228773867,
+          1396.81940623514, 1432.7982708122322, 1377.8612793152831, 1350.6348893911354, 1327.8024309518744,
+          1345.099570863568, 1328.029161164731, 1280.9350822776496],
+         [1328.1765246507357, 1504.1073057068995, 1219.5661435925442, 1460.274343713098, 1436.3592446035104,
+          1425.1034928610165, 1339.2784050231967, 1443.2730535440444, 1425.0924476853634, 1363.5785187957338,
+          1341.4800219444169, 1423.9137275869386, 1349.7704669791651, 1360.7630386223702, 1333.425113996663,
+          1366.2030511798673, 1337.4231380704002, 1291.6640507163672],
+         [1328.1765246507357, 1353.1879576378208, 1410.579915824099, 1462.9235155522852, 1394.8132884136066,
+          1414.5613788698495, 1392.1522068490503, 1431.5057538173428, 1440.2238046243085, 1387.293910964124,
+          1399.5857248683053, 1435.6166727422476, 1388.969821621961, 1364.2475752956364, 1332.0464144720595,
+          1354.9038046415044, 1339.1743300404082, 1291.630256209334],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1458.4159965010651, 1411.3548799046819,
+          1425.7224684005503, 1391.377051941948, 1431.8159723489114, 1431.278025139241, 1358.8212920663784,
+          1380.2436201750243, 1425.1958017919642, 1384.3256466756068, 1359.07917364794, 1332.9247098888873,
+          1353.1637744754123, 1336.07627725299, 1290.0476381211815],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1393.4337378530465,
+          1409.835132447911, 1391.1722552886845, 1417.0328256395617, 1432.3487735073522, 1378.2772749194908,
+          1396.6008401570577, 1433.1155975041377, 1390.5914406805628, 1362.6765110445174, 1334.5762051476459,
+          1361.4369514102002, 1336.6893145856227, 1288.9669265353398],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1424.8510324458748, 1390.7715313987126, 1431.7204129721713, 1431.9081214684788, 1378.1928653857747,
+          1396.5681148308058, 1430.780621400254, 1384.5714015170224, 1359.6447860912401, 1330.3893264075632,
+          1353.4188754217143, 1336.859504893905, 1288.08174852749],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1311.5753634371717, 1388.375915240571, 1420.9624704974776, 1421.2395781820762, 1367.800240844252,
+          1399.9786499043562, 1424.3801787948573, 1387.408334861284, 1360.107103934737, 1330.7643039541172,
+          1355.5080991940283, 1337.1438609436389, 1290.3414189906262],
+         [1328.1765246507357, 1503.913864640719, 1413.8980639539386, 1468.00534814896, 1422.8104408104573,
+          1423.765114927117, 1324.9175310660078, 1435.7069206999379, 1433.6130926608998, 1381.3226169858867,
+          1356.3795348447263, 1431.3801618130071, 1386.6510817433498, 1361.9492867624042, 1332.7701837350623,
+          1350.1107156688993, 1333.0696822151624, 1329.2298025753437],
+         [1328.1765246507357, 1353.1879576378208, 1409.2744122762876, 1454.3125460261099, 1410.4565982165238,
+          1424.082433572339, 1391.4733702241447, 1430.879428730335, 1414.3890111884998, 1350.34520050958,
+          1394.841280189504, 1429.3744950534692, 1386.4838423235042, 1356.8345977131296, 1332.5345285357314,
+          1354.656448130794, 1336.017705107397, 1290.1994987112023],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1286.0162095769574, 1223.6306854721179,
+          1277.8869653640977, 1354.6916735211269, 1421.0978338505058, 1419.4423878951538, 1357.758685952835,
+          1387.6171511136472, 1429.1792659040157, 1327.5638800212087, 1323.1431279704393, 1316.45311752381,
+          1340.1900930468087, 1333.8143956170393, 1302.8716294862143],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1369.9885881316322,
+          1389.746107250975, 1370.1832958235645, 1402.7731717332395, 1430.2269756772712, 1376.3970040533245,
+          1396.0147842886943, 1431.3398512920298, 1376.3795115633775, 1354.4061186176452, 1322.125898247653,
+          1347.3190083197246, 1357.8289704460617, 1309.05475863672],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1425.0648190120878, 1390.8167346028026, 1432.1377945654822, 1431.7215282770155, 1377.5399714568885,
+          1380.911844832686, 1424.423408758382, 1296.815612973573, 1283.9798136146878, 1256.8194756407995,
+          1355.3359054061746, 1338.4699601111859, 1293.041446825808],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1311.5753634371717, 1388.1670414052744, 1432.0300312924162, 1431.3542356778341, 1378.091812602148,
+          1396.8353951681145, 1432.8116506446752, 1385.8096029856881, 1357.7211709158132, 1330.4374574117403,
+          1353.7555347511545, 1337.7274048862123, 1288.7985560635561],
+         [1328.1765246507357, 1355.524217113136, 1412.9393304787225, 1465.2536583945196, 1410.697993614098,
+          1409.4334667878807, 1392.1943945343658, 1432.1373353197077, 1428.3902744802278, 1376.9823301026836,
+          1376.0674161952127, 1416.117550231582, 1383.412506204135, 1255.0784335917822, 1331.1853834984765,
+          1354.5528533816318, 1335.8822725180155, 1334.9239189752302],
+         [1328.1765246507357, 1353.1879576378208, 1410.814724976499, 1463.2075296132891, 1359.6374526982909,
+          1379.8017091774498, 1339.6702397963766, 1414.80823357494, 1417.2239029395234, 1361.4605045875321,
+          1396.318343095436, 1431.4467641883664, 1383.7222680136367, 1357.68401316966, 1330.5022749791037,
+          1354.0002885613083, 1335.781720673481, 1289.1835604091564],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1458.0227275098182, 1409.1231290530502,
+          1424.7112929163986, 1353.9694749430198, 1425.9473138683177, 1431.1149171600007, 1379.894377567314,
+          1399.26941534447, 1435.5745950310902, 1279.2743553908692, 1265.4957438307756, 1251.3268288661366,
+          1361.1714386001656, 1360.3714394835474, 1337.4519399978253],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1269.3936064664942,
+          1311.6416869961968, 1273.6585777553612, 1313.941461891031, 1432.7823407239148, 1380.7522541180585,
+          1399.1895890144515, 1431.7253108836628, 1384.9934068374707, 1359.608569164655, 1332.9293029520368,
+          1353.6079657716728, 1299.1185666067004, 1268.3723761743663],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1423.4143076476337, 1390.586686099597, 1430.8731140025316, 1431.3352484839065, 1377.530665193921,
+          1396.4032147703758, 1430.013055959338, 1385.216220842132, 1357.4682050836072, 1330.022274655173,
+          1353.691056324455, 1337.2310920781447, 1289.1970135479476],
+         [1328.1765246507357, 1353.1879576378208, 1285.3124337035306, 1276.2738975531172, 1272.385208955977,
+          1311.5753634371717, 1369.5460147787514, 1443.1311137429147, 1440.8509908426902, 1382.9197914159233,
+          1381.0026954392465, 1444.6890646393772, 1353.2513864631362, 1341.0551593686894, 1305.9609304041398,
+          1331.0007656836608, 1328.9668585129314, 1289.3981557047607]]]
+    k=5
+    f1_sum=[0 for i in range(18)]
+    f2_sum = [0 for i in range(18)]
+    f3_sum = [0 for i in range(18)]
+    f4_sum = [0 for i in range(18)]
+    f5_sum = [0 for i in range(18)]
+    f6_sum = [0 for i in range(18)]
+    for i in range(k):
+        f1_sum=[f1_sum[j]+result[i][0][j] for j in range(len(result[i][0]))]
+        f2_sum = [f2_sum[j] + result[i][1][j] for j in range(len(result[i][1]))]
+        f3_sum = [f3_sum[j] + result[i][2][j] for j in range(len(result[i][2]))]
+        f4_sum = [f4_sum[j] + result[i][3][j] for j in range(len(result[i][3]))]
+        f5_sum = [f5_sum[j] + result[i][4][j] for j in range(len(result[i][4]))]
+        f6_sum = [f6_sum[j] + result[i][5][j] for j in range(len(result[i][5]))]
+    f1_sum=[f1_sum[j]/k for j in range(len(f1_sum))]
+    f2_sum = [f2_sum[j] / k for j in range(len(f1_sum))]
+    f3_sum = [f3_sum[j] / k for j in range(len(f1_sum))]
+    f4_sum = [f4_sum[j] / k for j in range(len(f1_sum))]
+    f5_sum = [f5_sum[j] / k for j in range(len(f1_sum))]
+    f6_sum = [f6_sum[j] / k for j in range(len(f1_sum))]
+    r=[]
+    r.append(f1_sum)
+    r.append(f2_sum)
+    r.append(f3_sum)
+    r.append(f4_sum)
+    r.append(f5_sum)
+    r.append(f6_sum)
+    print(r)
+
+cal_avg()
\ No newline at end of file

dynamicDeploy/run1.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+import dynamicProcess
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+import constants as C
+import dynamicProcess as dynamic
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+
+def interpolate_points(point1, point2, num_points):
+    # 提取点的坐标
+    x1, y1 = point1
+    x2, y2 = point2
+
+    # 计算每个点之间的间距
+    dx = (x2 - x1) / (num_points + 1)
+    dy = (y2 - y1) / (num_points + 1)
+
+    # 生成等分点坐标
+    interpolated_points = []
+    for i in range(1, num_points + 1):
+        x = x1 + i * dx
+        y = y1 + i * dy
+        interpolated_points.append((x, y))
+
+    return interpolated_points
+#每隔一段时间计算无人机位置
+def cal_uav_pos(U,k,slot):
+    users=U[0]
+    pre_uavs = tool.getUavInitialPos(k, users)
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    num=70
+    t=200
+    pso = PSO(pre_uavs, users, r_c, r_p, k, num, t)
+    X, V = pso.initParticle(users)
+    best_pos_arr=[]
+    best_arr=[]
+    for i in range(0,len(U)-3,3):
+        users=U[i]
+        if i%6==0 and i!=0:
+            index+=1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        c_association, p_association, p_all, b_all = OF.objective_resource_avg(pre_uavs, users, r_c)
+        pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+        # pso.re_init(pre_uavs,)
+        global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective)
+        best_pos_arr.append(global_pos)
+        best_arr.append(global_best)
+        pre_uavs=global_pos
+    print(best_arr)
+    print(best_pos_arr)
+    best_pos_arr=np.array(best_pos_arr)
+    np.save('uavs_pos2.npy', best_pos_arr)
+
+def get_uavs_from_file(U):
+    uavs = np.load('uavs_pos2.npy')
+    users = U[0]
+    pre_uavs = tool.getUavInitialPos(5, users)
+    # 打印加载的数组
+    # print("Loaded Data:")
+    # print(len(uavs))
+    # print(pre_uavs)
+    return uavs
+
+
+def whole_process_new(U,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    users=U[0]
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    e=0
+    j=1
+    r_s=[]
+    g_s=[]
+    UAVS=get_uavs_from_file(U)
+    a=0
+    for i in range(10,3*C.r_change_slot,slot2):
+        if i%C.r_change_slot==0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        #每slot1时隙动一次位置
+        if i%slot1==0 and i!=0:
+            uavs=UAVS[a]
+            a+=1
+        users= U[j-1]#前一个时刻的位置
+        #根据前一个时刻的位置进行资源分配
+        c_association, p_association, p_all, b_all = OF.objective_resource_u(uavs, users, r_c)
+        #作用在这一时刻的用户位置上
+        users=U[j]
+        # interpolated_points = interpolate_points(users_pre,users, 10)
+        # print(interpolated_points)
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                             p_association, slot2)
+        if i%slot1==0 and i!=0:
+            print(i,rate,gdop)
+            break
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        # print(f)
+        fitness.append(f)
+
+        j+=1
+    print("proposed:",fitness)
+    print(r_s)
+    print(g_s)
+    return fitness
+def whole_process(U,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    users=U[0]
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    e=0
+    j=1
+    r_s=[]
+    g_s=[]
+    UAVS=get_uavs_from_file(U)
+    a=0
+    for i in range(10,3*C.r_change_slot,slot2):
+        if i%C.r_change_slot==0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        #每slot1时隙动一次位置
+        if i%slot1==0 and i!=0:
+            uavs=UAVS[a]
+            a+=1
+        users= U[j-1]#前一个时刻的位置
+        #根据前一个时刻的位置进行资源分配
+        c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+        #作用在这一时刻的用户位置上
+        users=U[j]
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                             p_association, slot2)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        fitness.append(f)
+        # print(f)
+
+        j+=1
+    print("proposed:",fitness)
+    print(r_s)
+    print(g_s)
+    return fitness
+#统一尺度
+def whole_process_unified(U,k,slot1,slot2):
+    # users= Users.getUsers("./data.csv")
+    users=U[0]
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness=[]
+    df_c=dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    e=0
+    j=1
+    r_s=[]
+    g_s=[]
+    UAVS=get_uavs_from_file(U)
+    # objective_resource_avg
+    users = U[j - 1]
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    a=0
+    for i in range(10,3*C.r_change_slot,slot2):
+        if i%C.r_change_slot==0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        #每slot1时隙动一次位置
+        # if i%slot1==0 and i!=0:
+        #     uavs=UAVS[a]
+        #     a+=1
+        users= U[j-1]#前一个时刻的位置
+        #根据前一个时刻的位置进行资源分配
+        c_association, p_association, p_all, b_all = OF.objective_resource_u(uavs, users, r_c)
+        #作用在这一时刻的用户位置上
+        users=U[j]
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                             p_association, slot2)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        fitness.append(f)
+        # print(f)
+
+        j+=1
+    print("proposed:",fitness)
+    print(r_s)
+    print(g_s)
+    return fitness
+def whole_process_kmeans(U,k,slot1,slot2):
+    users = U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    e = 0
+    j = 1
+    r_s = []
+    g_s = []
+    a = 0
+    for i in range(10, 3 * C.r_change_slot, slot2):
+        if i % C.r_change_slot == 0 and i != 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        # 每slot1时隙动一次位置
+
+        users = U[j - 1]  # 前一个时刻的位置
+        # 根据前一个时刻的位置进行资源分配
+        c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+        uavs= tool.getUavInitialPos(k, users)
+        # 作用在这一时刻的用户位置上
+        users = U[j]
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                          p_association, slot2)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        fitness.append(f)
+        # print(f)
+
+        j += 1
+    print("proposed:", fitness)
+    print(r_s)
+    print(g_s)
+    return fitness
+
+def fixed_position_fixed_resource(U,k,slot1,slot2):
+    users = U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    fitness = []
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association, p_association,
+                                         slot2)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    fitness.append(f)
+
+    j = 1
+    r_s = []
+    g_s = []
+    r_s.append(r)
+    g_s.append(g)
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        users = U[j]
+        j += 1
+        if i % C.r_change_slot == 0 and i != 0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                         p_association, slot2)
+        fitness.append(f)
+    print(fitness)
+    return fitness
+
+def run_whole_process():
+    users= Users.getUsers("./data.csv")
+    slot1=30
+    slot2=10
+    U=dynamic.get_users_pos_from_file()
+    # whole_process(users,4,slot1,slot2)
+    fitness=[]
+    f_sum = []
+    k=5
+    for j in range(k):
+        for i in range(10,70,10):
+            f=whole_process_new(U, 4, i, slot2)
+            fitness.append(f)
+        f_sum.append(fitness)
+        print(fitness)
+    print(f_sum)
+        # whole_process_kmeans(U,4,i,slot2)
+    # f1_sum=[0 for i in range(19)]
+    # f2_sum = [0 for i in range(19)]
+    # f3_sum = [0 for i in range(19)]
+    # f4_sum = [0 for i in range(19)]
+    # k=3
+    # for i in range(k):
+    #     f1=whole_process_new(U, 4, slot1, slot2)
+    #     f2=fixed_position_dynamic_resource2(U, 4, slot1, slot2)
+    #     f3=fixed_resource_dynamic_position2(U,4,slot1,slot2)
+    #     f4=same_dynamic_resource_dynamic_position2(U,4,slot1,slot2)
+    #     f1_sum=[f1_sum[j]+f1[j] for j in range(len(f1))]
+    #     f2_sum = [f2_sum[j] + f2[j] for j in range(len(f1))]
+    #     f3_sum = [f3_sum[j] + f3[j] for j in range(len(f1))]
+    #     f4_sum = [f4_sum[j] + f4[j] for j in range(len(f1))]
+    # f1_avg=[f1_sum[i]/k for i in range(len(f1_sum))]
+    # f2_avg = [f2_sum[i] / k for i in range(len(f1_sum))]
+    # f3_avg = [f3_sum[i] / k for i in range(len(f1_sum))]
+    # f4_avg = [f4_sum[i] / k for i in range(len(f1_sum))]
+    # print(f1_avg)
+    # print(f2_avg)
+    # print(f3_avg)
+    # print(f4_avg)
+        # fixed_position_fixed_resource(U, 4, slot1, slot2)
+
+
+def fixed_position_dynamic_resource2(U,k,slot1,slot2):
+    users=U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index=0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c,df_p,index)
+    fitness = []
+    j=1
+    r_s,g_s=[],[]
+    for i in range(0, 3 * C.r_change_slot, slot2):
+        if i % C.r_change_slot == 0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        users = U[j - 1]  # 前一个时刻的位置
+        # 根据前一个时刻的位置进行资源分配
+        c_association, p_association, p_all, b_all = OF.objective_resource_u(uavs, users, r_c)
+        users=U[j]
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association, p_association,slot2)
+        print(f, rate, gdop)
+        fitness.append(f)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        j+=1
+    print("fixed_position_dynamic_resource:",fitness)
+    print("fixed_position_dynamic_resource:", r_s,g_s)
+    return fitness
+
+def fixed_resource_dynamic_position2(U,k,slot1,slot2):
+    users=U[0]
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    df_c = dynamic.read_csv_to_df('r_c.csv')
+    df_p = dynamic.read_csv_to_df('r_p.csv')
+    index = 0
+    r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+    fitness = []
+    c_association, p_association, p_all, b_all = OF.objective_resource_u(uavs, users, r_c)
+    j=1
+    a=0
+    UAVS=get_uavs_from_file(U)
+    r_s,g_s=[],[]
+    for i in range(0, 3 * C.r_change_slot, slot2):
+
+        if i % C.r_change_slot == 0 and i!=0:
+            index = index + 1
+            r_c, r_p = dynamic.get_r_c_and_r_p(df_c, df_p, index)
+        if i % slot1 == 0 and i != 0:
+            uavs = UAVS[a]
+            a += 1
+        users = U[j - 1]  # 前一个时刻的位置
+        # 根据前一个时刻的位置进行资源分配
+        # 作用在这一时刻的用户位置上
+        users = U[j]
+        j=j+1
+        f, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c, r_p, p_all, b_all, c_association,
+                                          p_association, slot2)
+        r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+        r_s.append(r)
+        g_s.append(g)
+        fitness.append(f)
+        # print(f)
+    print("fixed_resource_dynamic_position",fitness)
+    print("fixed_resource_dynamic_position：", r_s, g_s)
+    return fitness
+
+
+# run_whole_process()
+if __name__ == '__main__':
+    U = dynamicProcess.get_users_pos_from_file()
+    U=U[:19]
+    # cal_uav_pos(U,5,10)
+    # get_uavs_from_file(U)
+    # whole_process(U, 5, 30, 10)
+    whole_process_new(U, 5, 30, 10)
+    # whole_process_unified(U, 5, 30, 10)
+    # whole_process(U, 5, 30, 10)
+    # whole_process_kmeans(U,5,30,10)
+    # fixed_position_dynamic_resource2(U,5,30,10)
+    # fixed_resource_dynamic_position2(U,5,30,10)
+

dynamicDeploy/run2.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+import constants as C
+import dynamicProcess as dynamic
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+
+def ipso_convex(users,k,r_c,r_p):
+    # users= Users.getUsers("./data.csv")
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    # c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(global_pos, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_c,r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    print("real-f",f)
+    r,g=OF.cal_satisfaction(global_pos,users,p_all,b_all,r_c,r_p, c_association,p_association)
+    print("pso_convex",global_best,r,g)
+    if f>global_best[0]:
+        return f,r,g
+    return global_best[0],r,g
+
+def ipso_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_c, r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(global_pos, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    print("pso_avg",global_best,r,g)
+    if global_best[0]<0:
+        return f,r,g
+    else:
+        return global_best[0],r,g
+
+def pso_convex(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_best)
+
+def pso_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_best)
+
+def kmeans_convex(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c,r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    # pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    # pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    # X, V = pso.initParticle(users)
+    # global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print("km_convex",f,r,g)
+    return f,r,g
+
+def kmeans_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_proportion(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    # pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    # pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    # X, V = pso.initParticle(users)
+    # global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print("km_avg",f,r,g)
+    return f,r,g
+
+#
+# users= Users.getUsers("./data.csv")
+# df_c=dynamic.read_csv_to_df('r_c.csv')
+# df_p = dynamic.read_csv_to_df('r_p.csv')
+# index=0
+# r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+# # pso_convex(users,4,r_c,r_p)
+# # pso_avg(users,4,r_c,r_p)
+# kmeans_convex(users,4,r_c,r_p)
+# kmeans_avg(users,4,r_c,r_p)
+
+def compare():
+    k=4
+    r1,r2,r3,r4=[],[],[],[]
+    r_satisfaction,g_satisfaction=[[] for i in range(4)],[[] for i in range(4)]
+    num=17
+    sum1,sum2,sum3,sum4=0,0,0,0
+    time=10
+    for i in range(0,time):
+        users=Users.generate(num)
+        r_c,r_p=dynamic.random_r(users)
+        s=[0 for i in range(4)]
+        g = [0 for i in range(4)]
+        result1,s[0],g[0]=ipso_convex(users, k, r_c, r_p)
+        result2,s[1],g[1]=ipso_avg(users, k, r_c, r_p)
+        result3,s[2],g[2]=kmeans_convex(users, k, r_c, r_p)
+        result4,s[3],g[3]=kmeans_avg(users, k, r_c, r_p)
+        sum1 += result1
+        sum2 += result2
+        sum3 += result3
+        sum4 += result4
+
+        r1.append(result1)
+        r2.append(result2)
+        r3.append(result3)
+        r4.append(result4)
+        for i in range(4):
+            r_satisfaction[i].append(s[i])
+            g_satisfaction[i].append(g[i])
+    print(r1,r2,r3,r4)
+    print(r_satisfaction,g_satisfaction)
+    print(sum1/time,sum2 /time,sum3 / time,sum4 / time)
+
+
+compare()
+# [[0.8, 0.5714285714285714, 0.5555555555555556, 0.5454545454545454, 0.46153846153846156, 0.6, 0.5294117647058824, 0.42105263157894735], [1.0, 1.0, 1.0, 1.0, 0.8461538461538461, 0.8666666666666667, 0.8823529411764706, 0.7368421052631579], [1.0, 1.0, 1.0, 1.0, 0.9230769230769231, 1.0, 0.8823529411764706, 0.42105263157894735], [1.0, 1.0, 1.0, 0.9090909090909091, 0.9230769230769231, 0.9333333333333333, 0.8823529411764706, 0.8421052631578947]] [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 0.6666666666666666, 0.7272727272727273, 0.7692307692307693, 0.8, 0.8823529411764706, 0.8421052631578947], [1.0, 1.0, 0.6666666666666666, 0.7272727272727273, 0.7692307692307693, 0.8, 0.8823529411764706, 0.8421052631578947]]

dynamicDeploy/solution.py

+class solution:
+    def __init__(self):
+        self.best = 0
+        self.bestIndividual=[]
+        self.convergence = []
+        self.optimizer=""
+        self.objfname=""
+        self.startTime=0
+        self.endTime=0
+        self.executionTime=0
+        self.lb=0
+        self.ub=0
+        self.dim=0
+        self.popnum=0
+        self.maxiers=0
\ No newline at end of file

dynamicDeploy/static_pso.py

+import math
+import Users
+import numpy as np
+import random
+from pprint import pprint
+import constants as C
+import tool
+import ObjectiveFuction as OF
+
+class SPSO:
+    def __init__(self,pre_uavs,users,r_c,r_p,k,N=30):
+        #之后解析JSON，从JSON中读参数
+        self.N=N
+        self.k=k
+        self.pre_uavs=pre_uavs
+        self.users=users
+        self.d = 3  # 可行解维数
+        self.limit = [C.min_range,C.max_range]  # 边界位置限制
+        self.vlimit = [-500, 500]  # 边界速度限制
+        self.vlimit_h=[-10,10]
+        self.r_c = r_c
+        self.r_p=r_p
+        # self.height = 100
+        self.min_height = C.min_height
+        self.max_height = C.max_height
+        self.wmax = 0.9  # 惯性权重
+        self.wmin = 0.4
+        self.c1 = 1.496  # 自我学习因子
+        self.c2 = 1.496  # 群体学习因子
+        # self.c1 = 0.5  # 自我学习因子
+        # self.c2 = 0.5  # 群体学习因子
+        self.maxgen = 200
+        # self.users = Users.getUsers("./data.csv")
+        self.users = users
+        self.init_alloc()
+
+
+    # #todo：防止无人机位置碰撞
+    # def punish_function(self,uavs):
+    #     # user_arr = [1, 2, 5]
+    #     punish=0
+    #     for i in range(len(uavs)):
+    #         for j in range(i+1,len(uavs)):
+    #             dis=self.cal_tool.getDistance(uavs[i],uavs[j])
+    #             if dis<200:
+    #                 punish+=100
+    #
+    #     return punish
+    def punish_f(self,uavs):
+        punish=0
+        max_dis=(C.time_slot-C.run_time)*C.speed
+        for i in range(len(uavs)):
+            dis=tool.calculate_3d_distance_uavs(uavs[i],self.pre_uavs[i])
+            if dis>max_dis:
+                punish=float('-inf')
+        return punish
+
+    def re_init(self, pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all, ):
+        self.pre_uavs = pre_uavs
+        self.user = users
+        self.r_c = r_c
+        self.r_p = r_p
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    # def init_r_c(self):
+    #     r_c={}
+    #     for i in range(len(self.users)):
+    #         r_c[i] = random.randint(1,4)
+    #     return r_c
+    # 初始化粒子群，k是无人机数量
+    def initParticle(self, users):
+        X = []
+        # users=Users.generate(100)#用户位置
+        V = np.random.uniform(self.vlimit[0], self.vlimit[1], (self.N, self.k, 3))
+        V2 = np.random.uniform(self.vlimit_h[0], self.vlimit_h[1], (self.N, self.k, 3))
+        for i in range(self.N):
+            uavs=[]
+
+            for index in range(self.k):
+                x=np.random.randint(self.limit[0],self.limit[1])
+                y=np.random.randint(self.limit[0],self.limit[1])
+                z=50
+                uavs.append([x,y,z])
+
+            X.append(uavs)
+        X = np.array(X)
+        return X, V,
+
+    def init_alloc(self,):
+        c_association, p_association, p_all, b_all = OF.objective_resource_avg(self.pre_uavs, self.users, self.r_c)
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+
+    # 初始化个体和全局最佳历史位置和最佳适应度
+    def initBest(self, X,):
+        p_pos = X.copy()  # 每个个体的历史最佳位置
+        global_pos = np.zeros((self.k, 3))  # 种群的历史最佳位置5*3
+        p_best = np.full((self.N, 1), float('-inf'))  # 每个个体的历史最佳适应度
+        global_best = float('-inf')  # 种群历史最佳适应度
+        return p_pos, global_pos, p_best, global_best
+
+
+    def pso(self, X, V, update_w,get_fitness,):
+        # X, V, users = initParticlePos()
+        p_pos, global_pos, p_best, global_best = self.initBest(X)
+        result = np.zeros(self.maxgen)
+        fitness = np.zeros(self.N)
+        for iter in range(self.maxgen):
+            # fitness = self.getfitness(X, users, k,P)  # 计算所有粒子适应度
+            for i in range(len(X)):
+                # punish = self.punish_f(X[i])
+                # if punish == float('-inf'):
+                #     fitness[i] = punish
+                # else:
+                f, _, _ = get_fitness(self.pre_uavs, X[i], self.users, self.r_c, self.r_p, self.p_all, self.b_all,
+                                      self.c_association, self.p_association)
+                fitness[i] = f
+                # f,_,_=get_fitness(self.pre_uavs,X[i],self.users,self.r_c,self.r_p)
+                # f,_,_=get_fitness(self.pre_uavs, X[i], self.users, self.r_c,self.r_p, self.p_all, self.b_all, self.c_association,
+                #             self.p_association)
+                # fitness[i]= f+self.punish_f(X[i])
+            for i in range(self.N):
+                if fitness[i] > p_best[i]:
+                    p_best[i] = fitness[i]  # 更新个体历史最佳适应度
+                    p_pos[i] = X[i].copy()  # 更新个体历史最佳位置(取值)
+            if max(p_best) > global_best:
+                global_best = max(p_best)  # 更新群体历史最佳适应度
+                max_index = p_best.argmax()
+                global_pos = X[max_index].copy()  # 更新群体历史最佳位置
+
+
+            # 权重更新
+            # w = self.wmax - iter * (self.wmax - self.wmin) / self.maxgen
+            # w = (self.wmax +self.wmin)/2
+            w=update_w(self.wmin,self.wmax,iter,self.maxgen,iter,fitness)
+            for i in range(self.N):
+                V[i] = V[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                        global_pos - X[i])
+                # V2[i] = V2[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                #         global_pos - X[i])
+                # print(V[i])
+                for j in range(len(V[i])):
+                    for p in range(len(V[i][j])):
+                        if V[i][j][p] > self.vlimit[1]:
+                            V[i][j][p] = self.vlimit[1]
+                            # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        elif V[i][j][p] < self.vlimit[0]:
+                            V[i][j][p] = self.vlimit[0]
+                        # if V2[i][j][p] > self.vlimit_h[1]:
+                        #     V2[i][j][p] = self.vlimit_h[1]
+                        #     # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        # elif V2[i][j][p] < self.vlimit_h[0]:
+                        #     V2[i][j][p] = self.vlimit_h[0]
+                # print(V[i])
+
+            # 位置更新
+            X = X + V
+            # 边界位置处理
+            for i in range(self.N):
+                for j in range(len(X[i])):
+                    for p in range(2):
+                        if X[i][j][p] > self.limit[1]:
+                            X[i][j][p] = self.limit[1]
+                        elif X[i][j][p] < self.limit[0]:
+                            X[i][j][p] = self.limit[0]
+                    X[i][j][2] = self.min_height
+                    # if X[i][j][2] > self.max_height:
+                    #     X[i][j][2] = self.max_height
+                    # elif X[i][j][2] < self.min_height:
+                    #     X[i][j][2] = self.min_height
+
+            result[iter] = global_best
+            print(iter,global_pos,global_best)
+
+        # print(global_best)
+        # print(global_pos)
+
+        return global_pos,global_best,result
+
+
+
+if __name__ == '__main__':
+    uavs=[[868.34535881,315.18340086,50.        ],
+          [292.96443022,729.7612657,77.75450997],
+ [732.25467955,927.62861821,50.        ],
+ [303.40107629,240.31208079,150.        ]]
+    # 示例
+    # print(0.012 / 8 * 1.225 * 0.05 * 0.79 * 400 ** 3 * 0.5 ** 3)
+    # print((1+0.1)*100**1.5/math.sqrt(2*1.225*0.79))
+    best=[[335,772,67], [382,739,75], [1758,1683,137], [885,762,133], [403,1196,94], [1049,358,88]]
+
+
+
+
+
+
+

dynamicDeploy/tool.py

+import math
+import scipy.integrate
+import numpy as np
+from sklearn.cluster import KMeans
+
+import GDOP
+from itertools import combinations
+import copy
+
+import Users
+# from Match import Match
+import km
+import constants as C
+
+def calculate_3d_distance(a, b):
+    return math.sqrt(math.pow(a[0] - b[0], 2) + math.pow(a[1] - b[1], 2) + math.pow(a[2] - 0, 2))
+
+def calculate_3d_distance_uavs(a, b):
+    return math.sqrt(math.pow(a[0] - b[0], 2) + math.pow(a[1] - b[1], 2) + math.pow(a[2] - b[2], 2))
+# 计算3d距离
+def calculate_2d_distance(a, b):
+    return math.sqrt(math.pow(a[0] - b[0], 2) + math.pow(a[1] - b[1], 2))
+
+def cal_dis_for_user(uavs,user):
+    dis=[]
+    for uav in uavs:
+        d=calculate_3d_distance(uav,user)
+        dis.append(d)
+    print(dis)
+
+# 计算森林信道路径损耗
+def forest_path_loss(uav, user):
+    dis = calculate_3d_distance(uav, user)
+    # alpha = 3.5
+    alpha = 3
+    A = 0.25
+    C = 0.39
+    E = 0.25
+    G = 0
+    B = 1
+    H = 0.05
+    f_c = 2 * 10 ** 9
+    c = 1 * 10 ** 8
+    path_loss = 10 * alpha * math.log10(dis)
+    theta = math.asin(uav[2] / dis)
+    theta = math.degrees(theta)
+    dis1 = dis  # 无人机和用户间树叶的距离
+    slant = A * math.pow(f_c / 1e6, C) * math.pow(dis1, E) * math.pow(theta + G, H)
+    fspl = 20 * math.log10(4 * math.pi * 1 * f_c / c)
+    L_K_N = slant + fspl + path_loss
+    # print(L_K_N)
+    # L_K_N=expect(L_K_N)
+    return L_K_N
+def forest_path_loss_dis(dis):
+    # alpha = 3.5
+    alpha = 3
+    A = 0.25
+    C = 0.39
+    E = 0.25
+    G = 0
+    B = 1
+    H = 0.05
+    f_c = 2 * 10 ** 9
+    c = 1 * 10 ** 8
+    path_loss = 10 * alpha * math.log10(dis)
+    theta = math.asin(50 / dis)
+    theta = math.degrees(theta)
+    dis1 = dis  # 无人机和用户间树叶的距离
+    slant = A * math.pow(f_c / 1e6, C) * math.pow(dis1, E) * math.pow(theta + G, H)
+    fspl = 20 * math.log10(4 * math.pi * 1 * f_c / c)
+    L_K_N = slant + fspl + path_loss
+    return L_K_N
+
+
+def expect_L(l1):
+    sigma = 3
+    R_u = lambda x1: ((10 ** (-(l1 + x1) / 10))) * 1 / (math.sqrt(2 * math.pi) * sigma) * math.exp(
+        -(x1 ** 2) / (2 * sigma ** 2))
+    R2, err = scipy.integrate.quad(R_u, -float("100"), float("100"))
+    return R2
+
+def expect(l1):
+    sigma = 3
+    R_u = lambda x1: (l1+x1) / (math.sqrt(2 * math.pi) * sigma) * math.exp(
+        -(x1 ** 2) / (2 * sigma ** 2))
+    R2, err = scipy.integrate.quad(R_u, -float("100"), float("100"))
+    # print("阴影衰落：", R2)
+    return R2
+
+def path_loss_dis(dis,h):
+    a = 9.6
+    b = 0.28
+    los = 1
+    nlos = 20
+    freePathLoss = 20 * math.log10(dis) + (20 * math.log10(2000000000)) + 20 * math.log10(4 * (math.pi) / 3 / 100000000)
+    angle = math.asin(h / dis)
+    angle = angle / math.pi * 180
+    plos = 1 / (1 + a * math.exp(-b * (angle - a)))
+    pnlos = 1 - plos
+    pl = plos * los + pnlos * nlos + freePathLoss
+    return pl
+
+def free_path_loss(dis):
+    freePathLoss = 20 * math.log10(dis) + (20 * math.log10(2000000000)) + 20 * math.log10(4 * (math.pi) / 3 / 100000000)
+    return freePathLoss
+
+# 计算snr
+def cal_snr(uav, user, p):
+    # loss = forest_path_loss(uav, user)
+    dis=calculate_3d_distance(uav,user)
+    loss=path_loss_dis(dis,uav[2])
+    noise_power = (10 ** (C.N0 / 10)) / 1000
+    snr = p * (10 ** (-loss / 10)) / noise_power
+    return 10 * math.log10(snr)
+
+
+def cal_snr2(uav, user, p):
+    dis = calculate_3d_distance(uav, user)
+    loss = path_loss_dis(dis, uav[2])
+    noise_power = (10 ** (C.N0 / 10)) / 1000
+    snr = p * (10 ** (-loss / 10)) / noise_power
+    return snr
+
+def cal_snr_forest(uav, user, p):
+    # loss = forest_path_loss(uav, user)
+    # dis=calculate_3d_distance(uav,user)
+    loss=forest_path_loss(uav,user)
+    noise_power = (10 ** (C.noise / 10)) / 1000
+    snr = p * (10 ** (-loss / 10)) / noise_power
+    return 10 * math.log10(snr)
+
+def cal_snr_f(uav, user, p):
+    dis = calculate_3d_distance(uav, user)
+    loss = forest_path_loss(uav, user)
+
+    noise_power = (10 ** (C.noise / 10)) / 1000
+    snr = p * (10 ** (-loss / 10)) / noise_power
+    # snr=p*loss/noise_power
+    return snr
+
+def path_loss(uav,user):
+    dis=calculate_3d_distance(uav,user)
+    h=uav[2]
+    a = 9.6
+    b = 0.28
+    los = 1
+    nlos = 20
+    freePathLoss = 20 * math.log10(dis) + (20 * math.log10(2000000000)) + 20 * math.log10(4 * (math.pi) / 3 / 100000000)
+    angle = math.asin(h / dis)
+    angle = angle / math.pi * 180
+    plos = 1 / (1 + a * math.exp(-b * (angle - a)))
+    pnlos = 1 - plos
+    pl = plos * los + pnlos * nlos + freePathLoss
+    return pl
+
+def cal_u(R,d,h):
+    dis1=math.sqrt(R**2+h**2)
+    dis2=math.sqrt((R+d)**2+h**2)
+    loss1=forest_path_loss_dis(dis1)
+    loss2=forest_path_loss_dis(dis2)
+    noise_power = (10 ** (C.noise / 10)) / 1000
+    sum_u=0
+    snr1 = C.pmax * C.pmax * (10 ** (-loss1 / 10)) / noise_power
+    snr2 = C.pmax* C.pmax * (10 ** (-loss2 / 10)) / noise_power
+    u = math.log(1 + snr1) / math.log(1 + snr2)
+    # for i in range(1,1001):
+    #     snr1 =i/1000*C.pmax * (10 ** (-loss1 / 10)) / noise_power
+    #     snr2=i/1001*C.pmax * (10 ** (-loss2 / 10)) / noise_power
+    #     u=math.log(1+snr1)/math.log(1+snr2)
+    #     sum_u+=u
+    # u=sum_u/1000
+    print(u)
+    return u
+
+def cal_max_radius(Rmin):
+    log=Rmin/C.B_max
+    print(log)
+    c = 1 * 10 ** 8
+    noise_power = (10 ** (C.noise / 10)) / 1000
+    h=(10**log-1)*noise_power/C.pmax
+    L=-10*math.log10(h)
+    r=900
+    H=50
+    dis=math.sqrt(r**2+H**2)
+    l=forest_path_loss_dis(dis)
+    # fspl = 20 * math.log10(4 * math.pi * 1 * C.f_c / c)
+    # r=10**(L-fspl)/30
+    print(l)
+    return r
+
+
+
+#计算速率
+def cal_rate(b,p,uav,user):
+    snr=cal_snr_f(uav,user,p)
+    date_rate = b* math.log2(1 + snr)
+    return date_rate
+
+# 计算数据传输速率
+def cal_data_rate(snr, bandwidth):
+    date_rate = bandwidth * math.log2(1 + snr)
+    return date_rate
+
+
+def dbmToW(p):
+    return pow(10, p / 10) / 1000
+
+#获得每个用户的定位无人机组
+def get_user_loc_uav(users,p_association):
+    user_loc_uav={}
+    for i in range(len(users)):
+        user_loc_uav[i]=[]
+    for i in range(len(p_association)):
+        for j in range(len(p_association[i])):
+            if p_association[i][j]==1:
+                user_loc_uav[j].append(i)
+    return user_loc_uav
+
+
+def getUavInitialPos(k, users):
+    model = KMeans(n_clusters=k,  # 聚类簇数
+                   random_state=1,  # 决定质心初始化的随机数生成，使用int使随机性具有确定性。
+                   max_iter=300,  # 执行一次k-means算法所进行的最大迭代数，默认300
+                   ).fit(users)
+    center = model.cluster_centers_
+    uavs = []
+    for item in center:
+        # height = random.randint(100, 800)
+        height = 50
+        position = [int(item[0]), int(item[1]), height]
+        uavs.append(position)
+    # print(center)
+    return uavs
+
+import skfuzzy as fuzz
+
+def fuzzy_c_means_clustering(points, n_clusters, m=2, error=0.005, max_iter=1000, seed=None):
+    data = np.array(points)
+
+    # 运行模糊均值聚类
+    cntr, _, _, _, _, _, _ = fuzz.cluster.cmeans(
+        data.T, n_clusters, m, error, max_iter, seed=seed)
+
+    return cntr
+def getUavInitialPos_fcm(k, users):
+    center=fuzzy_c_means_clustering(users,k)
+    uavs=[]
+    for item in center:
+        # height = random.randint(100, 800)
+        height = 50
+        position = [int(item[0]), int(item[1]), height]
+        uavs.append(position)
+    # print(center)
+    return uavs
+
+
+def get_c_association(users,uavs_pos,serveMax):
+    user_num = len(users)
+    uavs = [k for k in range(len(uavs_pos))]
+    uav_num = len(uavs)
+    # print(distance_sum)
+    weight = np.zeros((user_num, uav_num), dtype=np.float16)
+    graph = []
+    for i in range(user_num):
+        for j in uavs:
+            # weight[i][j]=-self.distance_c[i][j]/(distance_sum[i]-self.distance_c[i][j])
+            weight[i][j] = -calculate_3d_distance(uavs_pos[j],users[i])
+    dic = {}
+    for i in range(user_num):
+        for j in uavs:
+            for number in range(serveMax):
+                graph.append((i, j + 100 * number, weight[i][j]))
+                dic[j + 100 * number] = j
+
+
+    connection = km.run_kuhn_munkres(graph)
+    c_connection = {}
+    for connect in connection:
+        c_connection[connect[0]] = dic[connect[1]]
+    # print(c_connection)
+    return c_connection
+
+def get_uav_to_user(c_connection,uavs):
+    uav_to_user={}
+    for i in range(len(uavs)):
+        uav_to_user[i]=[]
+    for i in c_connection:
+        uav_to_user[c_connection[i]].append(i)
+    return uav_to_user
+
+# 选择好通信的无人机后，确定其他两架无人机与该无人机对用户获得最优的gdop值
+def get_p_association(users,uavs,c_connection,service_num):
+    p_connection={}
+    for i in c_connection:
+        p_connection[i]=[]
+    if len(uavs)==3:
+        for i in range(len(users)):
+            uavs_all=[i for i in range(len(uavs))]
+            uavs_all.remove(c_connection[i])
+            p_connection[i]=[uavs_all[0],uavs_all[1]]
+    else:
+        uavs_initial = [i for i in range(len(uavs))]
+        uav_group_all = list(combinations(uavs_initial, 3))
+        uav_serive_num = [service_num for i in range(len(uav_group_all))]
+        uav_group_dic={}
+        for i in range(len(uav_group_all)):
+            uav_group_dic[uav_group_all[i]]=i
+        # uav_serive_num=[service_num for i in range(len(uavs))]
+        for i in range(len(users)):
+            #获得所有信道数量大于0的无人机编号
+            # uav_need=[i for i in range(len(uavs)) if uav_serive_num[i]>0]
+            uav_need = [i for i in range(len(uav_group_all)) if uav_serive_num[i] > 0]
+            uav_groups = [uav_group_all[i] for i in uav_need]#所有可用的uav_group
+            #移除通信的无人机
+            uav_group=[]
+            uav_dic={}
+            count=0
+            for group in uav_groups:
+                if c_connection[i] in group:
+                    tmp=[group[0],group[1],group[2]]
+                    tmp.remove(c_connection[i])
+                    uav_group.append(tmp)
+                    uav_dic[count]=group#找到group对饮的下标
+                    count+=1
+                    # uav_need.remove(c_connection[i])
+            # uav_group=list(combinations(uav_need, 2))
+
+            gdop_min = float('inf')
+            index = 0
+            for j in range(len(uav_group)):
+                uavs_pos = [uavs[c_connection[i]], uavs[uav_group[j][0]],uavs[uav_group[j][1]]]
+                gdop_current = GDOP.calculate_gdop(uavs_pos, copy.copy(users[i]))
+                if gdop_current < gdop_min:
+                    gdop_min = gdop_current
+                    index = j
+            # print(gdop_min)
+            p_connection[i].extend([uav_group[index][0], uav_group[index][1]])
+            uav_serive_num[uav_group_dic[uav_dic[index]]]-=1
+    # print(p_connection)
+    return p_connection
+
+# def cal_max_rate():
+
+
+def get_p_by_snr(snr,uav,user,noise):
+    dis = calculate_3d_distance(uav, user)
+    pl= path_loss_dis(dis, uav[2])
+    # pl=path_loss_dis(uav,user)
+    noise=dbmToW(noise)
+    p=(10**(snr/10))*noise/10**(-pl/10)
+    # print(p)
+    return p
+
+def get_p_by_snr_f(snr,uav,user,noise):
+    dis = calculate_3d_distance(uav, user)
+    pl= forest_path_loss(uav, user)
+    # pl=path_loss_dis(uav,user)
+    noise=dbmToW(noise)
+    p=(10**(snr/10))*noise/10**(-pl/10)
+    # print(p)
+    return p
+def get_rest_p(users,uavs,p_connection,snr_thre=C.snr_thre,noise=C.N0,pmax=C.pmax):
+    p_rest=[pmax for i in range(len(uavs))]
+    for user_index in p_connection:
+        uav_group=p_connection[user_index]
+        for uav_index in uav_group:
+            p_need=get_p_by_snr(snr_thre,uavs[uav_index],users[user_index],noise)
+            p_rest[uav_index] -= p_need
+            if p_rest[uav_index]<=0:
+                p_rest[uav_index]=0
+
+    return p_rest
+
+def get_rest_p_f(users,uavs,p_connection,snr_thre=0,noise=C.N0,):
+    p_rest=[C.pmax for i in range(len(uavs))]
+    for user_index in p_connection:
+        uav_group=p_connection[user_index]
+        for uav_index in uav_group:
+            p_need=get_p_by_snr(snr_thre,uavs[uav_index],users[user_index],noise)
+            p_rest[uav_index]-=p_need
+
+    return p_rest
+
+def get_dis(i,uavs_pos,users,uav_to_user):
+    dis=[]
+    for k in uav_to_user[i]:
+        dis.append(calculate_3d_distance(uavs_pos[i],users[k]))
+    return dis
+
+def init_p(uavs_pos,users):
+    c_association=get_c_association(users,uavs_pos,math.ceil(len(users)/len(uavs_pos)))
+    uav_to_user=get_uav_to_user(c_association,uavs_pos)
+    # print(uav_to_user)
+    p_association = get_p_association(users, uavs_pos, c_association, len(users) * 2)
+    p_rest=get_rest_p(users,uavs_pos,p_association)
+    # print(p_rest)
+    p_alloc=[]
+    for i in range(len(uavs_pos)):
+        #归一化生成 功率
+        dis=get_dis(i,uavs_pos,users,uav_to_user)
+        # p=[random.randint(1,len(uav_to_user[i])) for i in range(len(uav_to_user[i]))]
+        p_new=[0 for i in range(len(users))]
+        for j in range(len(uav_to_user[i])):
+            p_new[uav_to_user[i][j]]=dis[j]/sum(dis)*p_rest[i]
+            # p_new.append(p[j]/sum(p)*p_rest[i])
+        p_alloc.append(p_new)
+        # p_alloc.append(np.random.uniform(0, p_rest[i] / len(uav_to_user[i]), (len(uav_to_user[i]))))
+    return p_alloc
+
+def cal_fly_energy(dis,speed):
+    e1=C.P0*(1+3*speed**2/C.U**2)
+    e2=C.P1*math.sqrt(math.sqrt(1+speed**4/4*C.vr**4)-speed**2/2*C.vr**2)
+    e3=0.3*1.225*0.05*0.79*speed**3
+    e=C.P_0 * (1 + (3 * speed**2) / (C.U_tip**2)) + C.P_i * (np.sqrt(1+(speed**4/(4*(C.v_0**4))))-speed**2/(2*(C.v_0**2)))**0.5 + (0.5 * C.d_0 * C.rho * C.sigma * C.A * speed**3)
+
+     # +(0.5 * d_0 * rho * sigma * A * V**3)
+    # return (e1+e2+e3)*dis/speed
+    return e*dis/speed
+
+if __name__ == '__main__':
+    users=Users.getUsers("./data.csv")
+    uavs=getUavInitialPos(4,users)
+    r=cal_max_radius(0.3)
+    u=cal_u(r,10,50)#1.13#1.21
+    # c_connection=get_c_association(users,uavs,math.ceil(len(users)/len(uavs)))
+    # p_connection=get_p_association(users,uavs,c_connection,math.ceil(len(users)*2/len(uavs)))
+    # print(c_connection)
+    # print(p_connection)
+    # print(get_uav_to_user(c_connection,uavs))
+    # print(cal_snr(uavs[0],users[0],0.003))
+    # print(get_p_by_snr(20,uavs[0],users[0],C.N0))
+    # print(get_p_by_snr_f(0, uavs[0], users[0], C.noise))
+    # print(cal_rate(0.19,0.005,uavs[0],users[0]))
+    # print(cal_snr_forest(uavs[0],users[0],0.003))
+    # print((10 ** (C.N0 / 10)) / 1000)
+    # print(get_rest_p(users,uavs,p_connection))
\ No newline at end of file

featureRepository/.idea/featureRepository.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

featureRepository/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

featureRepository/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

featureRepository/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/featureRepository.iml" filepath="$PROJECT_DIR$/.idea/featureRepository.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

featureRepository/DATA_MODEL.py

+import os
+import glob
+import math
+import openpyxl
+from pprint import pprint
+import torch
+import torch.nn as nn
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+import pandas as pd
+import random
+import matplotlib.pyplot as plt
+from torch.cuda.amp import autocast as autocast
+from torch.cuda.amp import GradScaler
+import torch.utils.data as data
+# ranger
+from ranger import Ranger
+from cvae_base import CVAE
+
+
+class GRU_attention(nn.Module):
+    def __init__(self, input_size=10, hidden_layer_size=50, num_layers=1):
+        super().__init__()
+        self.hidden_layer_size = hidden_layer_size
+        self.num_layers = num_layers
+        self.gru = nn.GRU(input_size, hidden_layer_size,num_layers, batch_first=True)
+        self.fc1 = nn.Linear(hidden_layer_size, 30)
+
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+        self.conv3 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+        self.fc2 = nn.Linear(200*10, 30)
+        self.relu = nn.ReLU()
+
+        encoder_layer_sizes = [6, 256, 128]
+        latent_size = 32
+        decoder_layer_sizes = [128, 256, 3]
+        conditional = True
+        num_labels = 3
+
+        # 初始化模型
+        self.cvae = CVAE(encoder_layer_sizes, latent_size, decoder_layer_sizes, conditional, num_labels)
+
+    def forward(self, input_seq, labels):
+        h_0 = torch.zeros(self.num_layers, input_seq.size(0), self.hidden_layer_size).to(input_seq.device)
+        output, _= self.gru(input_seq, h_0)#[16,200,50]
+        output =self.fc1(output[:, -1, :])
+        #print(output.shape)#16,30
+        output = output.view(-1,10,3)
+        #print(output.shape)#16,10,3
+        
+        cnn_input = input_seq[:, :, :]
+        #print(cnn_input.shape)#16,200,10
+        out2 = cnn_input.unsqueeze(1) # 增加一个维度，以匹配卷积层的输入 (batch, channels, height, width)
+        #print(out2.shape)#16,1,200,10
+        out2 = self.relu(self.conv1(out2)) # 16 * 1 * 10 * 
+        #print(out2.shape)#16,16,200,10
+        out2 = self.relu(self.conv2(out2))  # 32 * 1 * 10 * 10
+        #print(out2.shape)#16,32,200,10
+        out2 = self.conv3(out2)  # 1 * 1 * 10 * 10
+        #print(out2.shape)#16,1,200,10
+        out2 = out2.view(-1, 200*10)  # -1表示自动计算该维度的大小
+        #print(out2.shape)#16,2000
+        out2 = self.fc2(out2)
+        #print(out2.shape)#16,30
+        out2 = out2.view(-1,10,3)#16,10,3
+
+        out = torch.cat((output,out2),dim=2)    # [batch size, 10, 6]
+       # print(out.shape)#16,10,20
+        
+        H_yy, means,log_var, z = self.cvae(out, labels)  # [batch size, 10, 3]
+       # print(labels.shape)
+        # recon_y = acc_to_abs(H_yy, cnn_input)
+
+        return H_yy, means,log_var, z
+
+# 将加速度转换为绝对坐标
+def acc_to_abs(acc,obs,delta=1):
+    acc = acc.permute(2,1,0)
+    pred = torch.empty_like(acc)
+    pred[0] = 2*obs[-1] - obs[0] + acc[0]
+    pred[1] = 2*pred[0] - obs[-1] + acc[1]
+    
+    for i in range(2,acc.shape[0]):
+        pred[i] = 2*pred[i-1] - pred[i-2] + acc[i]
+    return pred.permute(2,1,0)
+
+# 计算均方根误差
+def rmse(y1,y2):
+    criterion = nn.MSELoss()
+
+    # return loss
+    return torch.sqrt(criterion(y1, y2))
+
+def mae(y1, y2):
+    criterion = nn.L1Loss()
+    loss = criterion(y1, y2)
+    return loss
+
+def loss_func(recon_y,y,mean,log_var):
+    traj_loss = mae(recon_y,y)
+    KLD = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
+    return traj_loss + KLD, traj_loss
+
+
+def rmse_loss(y_pred, y_true):
+    return torch.sqrt(torch.mean((y_pred - y_true) ** 2))
+
+
+class Mydataset(data.Dataset):
+    def __init__(self, in_len, out_len, scaler_seq, scaler_label):
+        folder_path = "total1"
+        # folder_path = "/home/zzh/test_data"
+        excel_files = glob.glob(os.path.join(folder_path, '*.xlsx'), recursive=True)
+        all_train_seq, all_train_label = list(), list()
+        for file in excel_files:
+            workbook = openpyxl.load_workbook(file)
+            sheet = workbook.active
+            num_rows = sheet.max_row
+            start_index = int(num_rows * 0.15) + 1  # 因为openpyxl的行和列索引从1开始
+            end_index = int(num_rows * 0.95)
+
+            tarEli, tar_x, tar_y, tar_z, position_change = [], [], [], [], []
+            v,v_change,r,eli_change,h_change= [], [], [], [], []
+            for row in sheet.iter_rows(min_row=start_index, max_row=end_index, values_only=True):
+                tarEli.append(row[1])
+                tar_x.append(row[2])
+                tar_y.append(row[3])
+                tar_z.append(row[4])
+                position_change.append(row[5])
+
+                v.append(row[6])
+                v_change.append(row[7])
+                r.append(row[8])
+                eli_change.append(row[9])
+                h_change.append(row[10])
+
+            tarEli = pd.Series(tarEli)
+            tar_x = pd.Series(tar_x)
+            tar_y = pd.Series(tar_y)
+            tar_z = pd.Series(tar_z)
+            position_change = pd.Series(position_change)
+
+            v = pd.Series(v)
+            v_change = pd.Series(v_change)
+            r = pd.Series(r)
+            eli_change = pd.Series(eli_change)
+            h_change = pd.Series(h_change)
+           
+            L = len(tarEli)
+            for i in range(0, L - in_len - out_len, 30):
+                data_tarEli = tarEli[i:i + in_len]
+                data_tar_x = tar_x[i:i + in_len]
+                data_tar_y = tar_y[i:i + in_len]
+                data_tar_z = tar_z[i:i + in_len]
+                data_position_change = position_change[i:i + in_len]
+
+                data_v = v[i:i + in_len]
+                data_v_change = v_change[i:i + in_len]
+                data_r = r[i:i + in_len]
+                data_eli_change = eli_change[i:i + in_len]
+                data_h_change = h_change[i:i + in_len]
+        
+                train_seq = list(zip(data_tarEli,data_tar_x,data_tar_y,data_tar_z,data_position_change,data_v,data_v_change,data_r,data_eli_change,data_h_change))
+
+        
+                tar_x_train_label = tar_x[i + in_len:i + in_len + out_len]
+                tar_y_train_label = tar_y[i + in_len:i + in_len + out_len]
+                tar_z_train_label = tar_z[i + in_len:i + in_len + out_len]
+             
+            
+                train_label = list(zip(tar_x_train_label,tar_y_train_label,tar_z_train_label))
+
+                all_train_seq.append(train_seq)
+                all_train_label.append(train_label)
+
+        #  all_train_seq 是一个三维数组，shape为 (num_samples, num_rows, num_cols)
+        # 将所有训练数据拼接在一起
+        concatenated_data = np.concatenate(all_train_seq, axis=0)  # 在第一个轴上进行拼接
+        normalized_data = scaler_seq.fit_transform(concatenated_data)
+
+        # 将归一化后的数据拆分回原来的样本
+        start_index = 0
+        self.normalized_all_seq = []
+        for train_data in all_train_seq:
+            num_samples = len(train_data)
+            end_index = start_index + num_samples
+            self.normalized_all_seq.append(normalized_data[start_index:end_index, :])
+            start_index = end_index
+
+        # 将归一化后的数据存储在一个新的数组中，形状与 all_train_data 相同
+        self.normalized_all_train_seq = np.array(self.normalized_all_seq)
+
+        concatenated_data = np.concatenate(all_train_label, axis=0)  # 在第一个轴上进行拼接
+        normalized_data = scaler_label.fit_transform(concatenated_data)
+        # 将归一化后的数据拆分回原来的样本
+        start_index = 0
+        self.normalized_all_label = []
+        for train_label in all_train_label:
+            num_samples = len(train_label)
+            end_index = start_index + num_samples
+            self.normalized_all_label.append(normalized_data[start_index:end_index, :])
+            start_index = end_index
+
+        # 将归一化后的数据存储在一个新的数组中，形状与 all_train_data 相同
+        self.normalized_all_train_label = np.array(self.normalized_all_label)
+
+    def __getitem__(self, index):
+        input_data = self.normalized_all_train_seq[index]
+        target = self.normalized_all_train_label[index]
+        return input_data, target
+
+    def __len__(self):
+        return len(self.normalized_all_train_seq)
+
+    
+    
+    
+    
+    
+def evaluate(model, test_loader, device):
+    model.eval()  # 设置模型为评估模式
+    total_loss = 0.0
+    with torch.no_grad():  # 在此模式下，所有计算都不会跟踪梯度
+        for inputs, labels in test_loader:
+            inputs, labels = inputs.to(torch.float32).to(device), labels.to(torch.float32).to(device)
+            y_pred,m,v,_ = model(inputs,labels)
+            y_pred_cpu = y_pred.cpu().detach().numpy()
+            y_pred = y_pred_cpu.reshape(-1, 3)
+               # print(y_pred.shape)
+            y_pred1 = scaler_label.inverse_transform(y_pred)
+               # print(y_pred.shape)
+            labels_cpu = labels.cpu().detach().numpy()
+            labels = labels_cpu.reshape(-1, 3)
+               # print(labels.shape)
+            labels1 = scaler_label.inverse_transform(labels)
+               # print(labels.shape)
+                # single_loss, single_real_loss = loss_func(y_pred, labels,m,v)
+            y_pred = torch.tensor(y_pred1)
+                #print(y_pred.shape)
+            labels = torch.tensor(labels1)
+                #print(labels.shape)
+            y_pred = y_pred.to(torch.float32).to(device)
+            labels = labels.to(torch.float32).to(device)
+            #print("逆归一化后的预测数据",y_pred)
+            #print("逆归一化后的原数据",labels)
+            loss =rmse_loss(y_pred,labels)
+            total_loss += loss.item()
+    return labels1, y_pred1, total_loss / len(test_loader)  
+
+
+
+
+if __name__ == '__main__':
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    print(device)
+    # 归一化
+    scaler_seq, scaler_label = MinMaxScaler(feature_range=(-1, 1)), MinMaxScaler(feature_range=(-1, 1))
+    datasets = Mydataset(200, 10, scaler_seq, scaler_label)
+    print(len(datasets))
+    test_size = int(len(datasets) * 0.3)
+    train_size = len(datasets) - test_size
+    train_dataset, test_dataset = data.random_split(datasets, [train_size, test_size])
+    print(type(train_dataset), len(train_dataset))
+    print(type(test_dataset), len(test_dataset))
+
+    batch_size = 16
+    train_dataloader = data.DataLoader(train_dataset, batch_size=batch_size, num_workers=2, shuffle=True)
+    test_dataloader = data.DataLoader(test_dataset, batch_size=batch_size, num_workers=1, shuffle=True)
+
+    model = GRU_attention().to(device)
+    print(model)
+
+    loss_function = nn.L1Loss()
+    # optimizer = torch.optim.Adam(model.parameters(), lr=0.00008)
+    optimizer = Ranger(model.parameters(), lr = 0.0002)
+
+    print(
+        "enhance data + GRU + CNN + LR = 0.0002 + MAEE + batch_size = 16 + in_len = 200 + out_len = 10 + ranger")
+
+    epochs = 1000
+    scaler2 = GradScaler()
+
+    for epoch in range(epochs):
+        model.train()  # 确保模型处于训练模式
+        running_loss = 0.0
+        running_num = 0
+
+        for batch in train_dataloader:
+            inputs, labels = batch
+            inputs = inputs.to(torch.float16).to(device)
+            labels = labels.to(torch.float16).to(device)
+            optimizer.zero_grad()
+
+            with autocast():
+                y_pred,m,v,_ = model(inputs,labels)
+                single_loss = loss_function(y_pred, labels)
+
+            scaler2.scale(single_loss).backward()
+            scaler2.step(optimizer)
+            scaler2.update()
+            optimizer.zero_grad()  # 重置梯度
+
+            running_loss += single_loss.item()
+            running_num += 1
+        
+        if epoch % 10 == 0:
+        # 在每个epoch结束后计算平均训练损失
+            avg_train_loss = running_loss / running_num
+            print(f'Epoch {epoch+1}/{epochs}, Train Loss: {avg_train_loss:.8f}')
+        # if epoch % 100 == 0:
+        # 在每个epoch结束后在测试集上评估模型
+            labels1,y_pred1,test_loss = evaluate(model, test_dataloader, device)
+            # print("lables:",labels)
+            # print("y_pred:",y_pred)
+            print(f'Epoch {epoch+1}/{epochs}, Test Loss: {test_loss:.8f}')
+
+    #torch.save(model.state_dict(), 'model_CNN_GRU_CVAE.pt')
\ No newline at end of file

featureRepository/GeodeticTool.cpp

+#include "GeodeticTool.h"
+
+#include <Geodetics/GeodeticTransform.h>
+
+#include <QtCore/QRegExp>
+
+
+namespace F1911
+{
+    GeodeticTool::GeodeticTool(QWidget* parent)
+        : QMainWindow(parent)
+    {
+        ui.setupUi(this);
+
+        connect(ui.toLocalENUButton, &QPushButton::clicked, this, &GeodeticTool::toLocalENUButtonClick);
+        connect(ui.toGeodeticButton, &QPushButton::clicked, this, &GeodeticTool::toGeodeticButtonClick);
+    }
+
+    GeodeticTool::~GeodeticTool()
+    {
+        disconnect(ui.toGeodeticButton, &QPushButton::clicked, this, &GeodeticTool::toGeodeticButtonClick);
+        disconnect(ui.toLocalENUButton, &QPushButton::clicked, this, &GeodeticTool::toLocalENUButtonClick);
+    }
+
+    bool GeodeticTool::parseNumber(const QLineEdit* edit, double& value)
+    {
+        bool ok;
+        value = edit->text().toDouble(&ok);
+        return ok;
+    }
+
+    void GeodeticTool::setNumber(QLineEdit* edit, double value)
+    {
+        auto text = QString::number(value, 'f', 12);
+        text.remove(QRegExp("0+$")); // 移除所有尾部的“0”
+        text.remove(QRegExp("\\.$")); // 如果最后一个字符是“.”，移除它
+        edit->setText(text);
+    }
+
+    void GeodeticTool::toLocalENUButtonClick()
+    {
+        double longitude, latitude, altitude;
+        if (parseNumber(ui.longitudeEdit, longitude) &&
+            parseNumber(ui.latitudeEdit, latitude) &&
+            parseNumber(ui.altitudeEdit, altitude))
+        {
+            double east, north, up;
+            Geodetics::GeodeticTransform::get().geodeticToLocal(
+                east, north, up, longitude, latitude, altitude);
+
+            setNumber(ui.localEastEdit, east);
+            setNumber(ui.localNorthEdit, north);
+            setNumber(ui.localUpEdit, up);
+        }
+    }
+
+    void GeodeticTool::toGeodeticButtonClick()
+    {
+        double east, north, up;
+        if (parseNumber(ui.localEastEdit, east) &&
+            parseNumber(ui.localNorthEdit, north) &&
+            parseNumber(ui.localUpEdit, up))
+        {
+            double longitude, latitude, altitude;
+            Geodetics::GeodeticTransform::get().localToGeodetic(
+                longitude, latitude, altitude, east, north, up);
+
+            setNumber(ui.longitudeEdit, longitude);
+            setNumber(ui.latitudeEdit, latitude);
+            setNumber(ui.altitudeEdit, altitude);
+        }
+    }
+}

featureRepository/Geodetics/CMakeLists.txt

+set(CMAKE_INCLUDE_CURRENT_DIR ON)
+
+file(GLOB_RECURSE SOURCES src/*.cpp src/*.h)
+file(GLOB_RECURSE HEADERS include/*.h)
+
+add_library(Geodetics SHARED ${SOURCES} ${HEADERS})
+
+target_include_directories(Geodetics
+  PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${GDAL_ROOT}
+)
+
+target_compile_definitions(Geodetics PRIVATE GEODETICS_LIB)
+
+target_link_directories(Geodetics PRIVATE ${GDAL_ROOT}/lib/${BUILD_TYPE})
+
+target_link_libraries(Geodetics PRIVATE ${GDAL_LIB_NAME}.lib)
+
+
+add_custom_command(TARGET Geodetics POST_BUILD
+  COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_FILE:Geodetics> ${CLIENTS_BIN_DIR}
+  #COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_PDB_FILE:Geodetics> ${CLIENTS_BIN_DIR}
+  COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_LINKER_FILE:Geodetics> ${CLIENTS_LIB_DIR}
+)
+
+
+install(TARGETS Geodetics RUNTIME DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(FILES ${GDAL_ROOT}/lib/${BUILD_TYPE}/gdal17.dll DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(FILES ${GDAL_ROOT}/lib/${BUILD_TYPE}/proj.dll DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(TARGETS Geodetics LIBRARY DESTINATION "Geodetics/${CMAKE_INSTALL_LIBDIR}")
+install(DIRECTORY include DESTINATION "Geodetics")

featureRepository/Geodetics/include/Geodetics/GeodeticTransform.h

+#pragma once
+
+#include "GeodeticsBase.h"
+
+
+namespace F1911::Geodetics
+{
+    class GeodeticTransformInternal;
+
+    class GEODETICS_API GeodeticTransform final
+    {
+        // Instance ----------------------------------------------------------------------------------------------------
+    public:
+        static GeodeticTransform& get()
+        {
+            static GeodeticTransform t;
+            return t;
+        }
+
+        GeodeticTransform(const GeodeticTransform& other) = delete;
+        GeodeticTransform& operator=(const GeodeticTransform& other) = delete;
+        GeodeticTransform(GeodeticTransform&& other) = delete;
+        GeodeticTransform& operator=(GeodeticTransform&& other) = delete;
+    private:
+        GeodeticTransform();
+        ~GeodeticTransform();
+        GeodeticTransformInternal* _i;
+
+
+        // Transforms --------------------------------------------------------------------------------------------------
+    public:
+        double eastOffset = -643615.753, northOffset = -4400490.578, upOffset = 50;
+
+        bool localToGeodetic(double& longitude, double& latitude,
+            double& altitude, double east, double north, double up) const;
+        bool geodeticToLocal(double& east, double& north, double& up,
+            double longitude, double latitude, double altitude) const;
+    };
+}

featureRepository/Geodetics/include/Geodetics/GeodeticsBase.h

+#pragma once
+
+#if defined(GEODETICS_LIB)
+#   define GEODETICS_API __declspec(dllexport)
+#else
+#   define GEODETICS_API __declspec(dllimport)
+#endif

featureRepository/Geodetics/src/GeodeticTransform.cpp

+#include "Geodetics/GeodeticTransform.h"
+
+#include <string> // 如果不在gdal库之前包含此头文件则gdal库会出现编译错误。
+#include <gdal/ogr_spatialref.h>
+#include <gdal/ogrsf_frmts.h>
+#include <gdal/gdal_alg.h>
+
+
+namespace F1911::Geodetics
+{
+    const char* const WGS84_WKT(
+        "GEOGCS[\"Geographic Coordinate System\",DATUM[\"D_WGS84\",\
+SPHEROID[\"WGS84\",6378137,298.257223560493]],PRIMEM[\"Greenwich\",0],\
+UNIT[\"Degree\",0.017453292519943295]]");
+
+    const char* const XIAN80_WKT(
+        "PROJCS[\"UTM_Zone_51_Northern_Hemisphere\",GEOGCS[\"GCS_XIAN 1980\",\
+DATUM[\"D_XIAN 1980\",SPHEROID[\"Xian_1980\",6378140,298.2569978029111]],\
+PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],\
+PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],\
+PARAMETER[\"central_meridian\",123],PARAMETER[\"scale_factor\",0.9996],\
+PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1]]");
+
+    class GeodeticTransformInternal final
+    {
+    public:
+        GeodeticTransformInternal()
+        {
+            OGRRegisterAll();
+
+            wgs84Reference = OGRSpatialReference(WGS84_WKT);
+            xianReference = OGRSpatialReference(XIAN80_WKT);
+
+            localToGeodetic = OGRCreateCoordinateTransformation(&xianReference, &wgs84Reference);
+            geodeticToLocal = OGRCreateCoordinateTransformation(&wgs84Reference, &xianReference);
+        }
+
+        ~GeodeticTransformInternal()
+        {
+            OGRCoordinateTransformation::DestroyCT(geodeticToLocal);
+            OGRCoordinateTransformation::DestroyCT(localToGeodetic);
+
+            OGRCleanupAll();
+        }
+
+        OGRSpatialReference xianReference;
+        OGRSpatialReference wgs84Reference;
+
+        OGRCoordinateTransformation* geodeticToLocal = nullptr;
+        OGRCoordinateTransformation* localToGeodetic = nullptr;
+    };
+
+    GeodeticTransform::GeodeticTransform()
+    {
+        _i = new GeodeticTransformInternal();
+    }
+
+    GeodeticTransform::~GeodeticTransform()
+    {
+        delete _i;
+        _i = nullptr;
+    }
+
+    bool GeodeticTransform::localToGeodetic(
+        double& longitude, double& latitude, double& altitude, double east, double north, double up) const
+    {
+        longitude = east - eastOffset;
+        latitude = north - northOffset;
+        altitude = up - upOffset;
+        return _i->localToGeodetic->Transform(1, &longitude, &latitude, &altitude);
+    }
+
+    bool GeodeticTransform::geodeticToLocal(
+        double& east, double& north, double& up, double longitude, double latitude, double altitude) const
+    {
+        east = longitude;
+        north = latitude;
+        up = altitude;
+        auto ret = _i->geodeticToLocal->Transform(1, &east, &north, &up);
+        east += eastOffset;
+        north += northOffset;
+        up += upOffset;
+        return ret;
+    }
+}

featureRepository/book.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<books>
+  <book>
+    <id>1</id>
+    <title>Python for beginners</title>
+    <author>Lisa Smith</author>
+    <price>19.99</price>
+    <devices>
+      <tracks>
+        <track>
+          <id>12</id>
+          <ord>1</ord>
+          <tarRng>66096.3437111</tarRng>
+          <tarAzi>0.471221</tarAzi>
+          <tarEli>-0.012623</tarEli>
+        </track>
+        <track>
+          <id>13</id>
+          <ord>1</ord>
+          <tarRng>66096.343750</tarRng>
+          <tarAzi>0.471221</tarAzi>
+          <tarEli>-0.012623</tarEli>
+        </track>
+      </tracks>
+    </devices>
+  </book>
+  <book>
+    <id>2</id>
+    <title>Python tips and tricks</title>
+    <author>Joel Williams</author>
+    <price>24.99</price>
+    <devices>
+      <tracks>
+        <track>
+          <id>14</id>
+          <ord>1</ord>
+          <tarRng>66096.3437222</tarRng>
+          <tarAzi>0.471221</tarAzi>
+          <tarEli>-0.012623</tarEli>
+        </track>
+        <track>
+          <id>15</id>
+          <ord>1</ord>
+          <tarRng>6609</tarRng>
+          <tarAzi>0.471221</tarAzi>
+          <tarEli>-0.012623</tarEli>
+        </track>
+      </tracks>
+    </devices>
+  </book>
+</books>
\ No newline at end of file

featureRepository/cluster_in_file.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import tensorflow as tf
+import os
+
+
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_all=pd.DataFrame()
+
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        
+        file_path = os.path.join(folder_path, file_name)
+#         print(file_path)
+        # 解析CSV文件
+        df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+        data =df.iloc[:,1:] 
+        data_all = pd.concat([data_all, data], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+dataset=data_all.values
+scaler.fit(dataset) ## 生成规则
+dataset = scaler.transform(dataset) ## 将规则应用于训练集
+
+
+
+import numpy as np
+
+def trajectory_split(data,window,offset):
+    child_trajectory= []
+    tr1=[]
+    for i in range(0,len(data)-window-1,offset):
+        if (i + window)<len(data) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            if id1==id2 and id1==0:
+                tr1.append(dataset[i:(i+window),:])
+            if id1==id2:
+                a = dataset[i:(i+window),:-1]
+#                 a = dataset[i:(i+window),1:4]
+                child_trajectory.append(a)
+    return np.array(child_trajectory),np.array(tr1)
+
+trajectorys,tr1=trajectory_split(dataset,300,300)
+print(trajectorys.shape)
+
+
+# In[8]:
+
+
+def cal_fd(l1,l2):
+    dis=l1-l2
+    dis_abs=np.abs(dis)
+    mean = dis_abs.mean(axis=0)  # 计算完之后array的长度等于列数
+    return sum(mean)/len(mean)
+
+# In[10]:
+
+
+def cal_fd_full(trajectorys):
+    fd=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(i+1,len(trajectorys)):
+            x=trajectorys[i]
+            y=trajectorys[j]
+            fd[i][j]=cal_fd(x,y)
+    fd_full=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(len(trajectorys)):
+            if i==j:
+                fd_full[i][j]=0
+            elif i<j:
+                fd_full[i][j]=fd[i][j]
+            else:
+                fd_full[i][j]=fd[j][i]
+    return fd_full
+
+def get_center(cluster):
+    if len(cluster)<=2:
+        return cluster[0]
+    fd=cal_fd_full(cluster)
+    dis=np.sum(fd,axis=0)
+    min_index = np.argmin(dis)
+    return cluster[min_index]
+
+def get_all_center(clusters):
+    centers=[]
+    for i in range(len(clusters)):
+        center=get_center(clusters[i])
+        centers.append(center)
+    return centers
+#n1,n2为轨迹簇中轨迹的数目
+def cal_ward(n1,n2,center1,center2):
+    fd=cal_fd(center1,center2)
+    ward=n1*n2*fd/(n1+n2)
+    return ward
+def cal_all_ward(clusters,centers):
+    ward_arr=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(clusters)):
+        for j in range(i+1,len(clusters)):
+            ward_arr[i][j]=cal_ward(len(clusters[i]),len(clusters[j]),centers[i],centers[j])
+    ward_arr_full=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(ward_arr)):
+        for j in range(len(ward_arr)):
+            if i>=j:
+                ward_arr_full[i][j]=float('inf')
+            else:
+                ward_arr_full[i][j]=ward_arr[i][j]
+    return ward_arr_full
+
+
+
+# In[ ]:
+
+
+def clustering(trajectorys,k):
+    clusters = [[point] for point in trajectorys]
+    count=len(clusters)
+    print(count)
+    index=0
+    dis_arr=[]
+    d_arr=[]
+    while len(clusters)>k:
+        #计算每个簇的中心轨迹
+        if count==len(clusters):
+            centers=[point for point in trajectorys]
+        else:
+            centers=get_all_center(clusters)
+        #计算每个轨迹簇的ward距离
+        ward_all=cal_all_ward(clusters,centers)
+        #合并ward距离最小的两个轨迹簇，使轨迹簇的数目减一
+        min_index = np.unravel_index(np.argmin(ward_all), ward_all.shape)
+        min_dis=ward_all[min_index[0]][[min_index[1]]]
+#         print(min_dis)
+        if index==0:
+            dis=min_dis[0]
+            index+=1
+        d=min_dis[0]-dis
+        print(d)
+        dis_arr.append(min_dis[0])
+        d_arr.append(d)
+        dis=min_dis[0]
+        clusters[min_index[0]].extend(clusters[min_index[1]])
+        clusters.pop(min_index[1])
+    return clusters,dis_arr,d_arr
+
+
+clusters,dis_arr,d_arr=clustering(trajectorys,6599)
+for cluster in clusters:
+    print(len(clusters))
+
+
+# In[ ]:
+
+
+
+

featureRepository/cluster_lstm.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import os
+import numpy as np
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+data_train,data_test
+
+
+# In[28]:
+
+
+from sklearn.preprocessing import MinMaxScaler
+train=data_train.values
+test=data_test.values
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+data_tr,data_te
+
+
+# In[33]:
+
+
+# combined_array = np.vstack((train, test))
+combined_array = np.vstack((data_tr, data_te))
+dataset1=combined_array[:, :3]
+dataset1.shape
+
+
+# In[34]:
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.metrics import calinski_harabasz_score
+from sklearn.metrics import davies_bouldin_score
+def k_means_cluster(k,data):
+    X = data
+    # X, _ = make_blobs(n_samples=300, centers=4, random_state=42, n_features=3)
+    # X.shape
+
+    #使用 k均值算法进行聚类
+    
+    kmeans = KMeans(n_clusters=k, random_state=42)
+    kmeans.fit(X)
+
+
+    # 获取聚类结果和中心点
+    labels = kmeans.labels_
+    centers = kmeans.cluster_centers_
+
+    db_index_1 = davies_bouldin_score(X, labels)
+
+
+    print("Davies-Bouldin Index for Clustering 1:", db_index_1)
+    print(centers)
+    return centers,labels
+
+
+    # 可视化聚类结果
+#     fig = plt.figure()
+#     ax = fig.add_subplot(111, projection='3d')
+#     ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=labels,cmap='viridis', s=2, marker='.')
+#     # ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c='red', marker='X', s=200, label='Cluster Centers')
+#     ax.set_title('K-Means Clustering of 3D Trajectory Points')
+#     ax.set_xlabel('X-axis')
+#     ax.set_ylabel('Y-axis')
+#     ax.set_zlabel('Z-axis')
+#     plt.legend()
+#     plt.show()
+
+centers,labels=k_means_cluster(2,dataset1)
+centers
+
+
+# In[39]:
+
+
+len(labels)
+#给数据集加上labels
+new_col=labels[:, np.newaxis]
+dataset2=np.hstack((combined_array, new_col))
+dataset3=np.copy(dataset2)
+dataset3[:, -1], dataset3[:, -2] = dataset2[:, -2], dataset2[:, -1]
+#拆分数组变成加了标签的训练和测试集
+train_new = dataset3[:1844129, :]
+test_new = dataset3[1844129:, :]
+train_new.shape,test_new.shape
+
+
+# In[41]:
+
+
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+# In[44]:
+
+
+# train_X,train_Y=create_dataset(data_tr,200,100,100)
+# test_X,test_Y=create_dataset(data_te,200,100,100)
+train_X,train_Y=create_dataset(train_new,200,100,100)
+test_X,test_Y=create_dataset(test_new,200,100,100)
+train_X.shape,train_Y.shape,test_X.shape,test_Y.shape
+
+
+# In[ ]:
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+import time
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(70, input_shape=(200, 12)))  # LSTM层
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.2))
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=300, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/lstm_model_feature2.h5')
+# tf.keras.backend.clear_session()
+

featureRepository/clustering.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import os
+from tqdm import tqdm
+
+#只用特征来聚类，按照每条轨迹之间的距离来聚类
+
+folder_path = "E:/replay/1对1/process_data/5"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_all=pd.DataFrame()
+dataY_all=[]
+count=0
+index="5."
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        if count>30:
+            break
+        count+=1
+        file_path = os.path.join(folder_path, file_name)
+        # 解析CSV文件
+        df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+        data =df.iloc[2:,9:] 
+        data['id']=index+str(count)
+        data_all = pd.concat([data_all, data], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+dataset=data_all.values
+scaler.fit(dataset) ## 生成规则
+dataset = scaler.transform(dataset) ## 将规则应用于训练集
+
+
+
+import numpy as np
+
+def trajectory_split(data,window,offset):
+    child_trajectory= []
+    for i in range(0,len(data)-window-1,offset):
+        if (i + window)<len(data) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            if id1==id2:
+                a = dataset[i:(i+window),:-1]
+#                 a = dataset[i:(i+window),1:4]
+                child_trajectory.append(a)
+    return np.array(child_trajectory)
+
+trajectorys=trajectory_split(dataset,200,200)
+
+
+
+def cal_fd(l1,l2):
+    dis=l1-l2
+    dis_abs=np.abs(dis)
+    mean = dis_abs.mean(axis=0)  # 计算完之后array的长度等于列数
+    return sum(mean)/len(mean)
+m=cal_fd(trajectorys[0],trajectorys[1])
+
+
+
+def cal_fd_full(trajectorys):
+    fd=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(i+1,len(trajectorys)):
+            x=trajectorys[i]
+            y=trajectorys[j]
+            fd[i][j]=cal_fd(x,y)
+    fd_full=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(len(trajectorys)):
+            if i==j:
+                fd_full[i][j]=0
+            elif i<j:
+                fd_full[i][j]=fd[i][j]
+            else:
+                fd_full[i][j]=fd[j][i]
+    return fd_full
+fd_full=cal_fd_full(trajectorys)
+dis=np.sum(fd_full,axis=1)
+
+
+
+def get_center(cluster):
+    if len(cluster)<=2:
+        return cluster[0]
+    fd=cal_fd_full(cluster)
+    dis=np.sum(fd,axis=0)
+    min_index = np.argmin(dis)
+    return cluster[min_index]
+
+def get_all_center(clusters):
+    centers=[]
+    for i in range(len(clusters)):
+        center=get_center(clusters[i])
+        centers.append(center)
+    return centers
+#n1,n2为轨迹簇中轨迹的数目
+def cal_ward(n1,n2,center1,center2):
+    fd=cal_fd(center1,center2)
+    ward=n1*n2*fd/(n1+n2)
+    return ward
+def cal_all_ward(clusters,centers):
+    ward_arr=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(clusters)):
+        for j in range(i+1,len(clusters)):
+            ward_arr[i][j]=cal_ward(len(clusters[i]),len(clusters[j]),centers[i],centers[j])
+    ward_arr_full=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(ward_arr)):
+        for j in range(len(ward_arr)):
+            if i>=j:
+                ward_arr_full[i][j]=float('inf')
+            else:
+                ward_arr_full[i][j]=ward_arr[i][j]
+    return ward_arr_full
+
+
+# arr = np.array([[3, 1, 4], [1, 5, 9], [2, 6, 5]])
+# min_index = np.unravel_index(np.argmin(arr), arr.shape)
+
+
+def clustering(trajectorys,k):
+    clusters = [[point] for point in trajectorys]
+    count=len(clusters)
+    index=0
+    while len(clusters)>k:
+        #计算每个簇的中心轨迹
+        if count==len(clusters):
+            centers=[point for point in trajectorys]
+        else:
+            centers=get_all_center(clusters)
+        #计算每个轨迹簇的ward距离
+        ward_all=cal_all_ward(clusters,centers)
+        #合并ward距离最小的两个轨迹簇，使轨迹簇的数目减一
+        min_index = np.unravel_index(np.argmin(ward_all), ward_all.shape)
+        min_dis=ward_all[min_index[0]][[min_index[1]]]
+        print(min_dis)
+        if index==0:
+            dis=min_dis[0]
+            index+=1
+        d=dis-min_dis
+        if d>0.1:
+            break
+        dis=min_dis[0]
+        clusters[min_index[0]].extend(clusters[min_index[1]])
+        clusters.pop(min_index[1])
+        # print(len(clusters))
+    return clusters
+
+
+clusters=clustering(trajectorys,5)
+print(len(clusters))
+
+import matplotlib.pyplot as plt
+
+
+# 画出真实数据和预测数据
+fig = plt.figure()
+ax = fig.gca(projection='3d')
+for k in range(len(clusters)):
+    x, y, z = [], [], []
+    for i in range(len(clusters[k])):
+        for j in range(len(clusters[k][i])):
+            x.append(clusters[k][i][j][1])
+            y.append(clusters[k][i][j][2])
+            z.append(clusters[k][i][j][3])
+    label = "cluster" + str(k)
+    ax.scatter(x, y, z, s=2, label=label)
+plt.legend()
+fig2 = plt.figure()
+ax2 = fig2.gca(projection='3d')
+x2,y2,z2=[],[],[]
+for k in range(len(trajectorys)):
+    for i in range(len(trajectorys[k])):
+        x2.append(trajectorys[k][i][1])
+        y2.append(trajectorys[k][i][2])
+        z2.append(trajectorys[k][i][3])
+ax2.scatter(x2, y2, z2, s=2)
+
+
+plt.show()
\ No newline at end of file

featureRepository/clustering1.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import os
+from tqdm import tqdm
+
+#只用特征来聚类，按照每条轨迹之间的距离来聚类
+
+folder_path = "/home/lxm/dataset/1vs1/5"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_all=pd.DataFrame()
+dataY_all=[]
+count=0
+index="5."
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        if count>30:
+            break
+        count+=1
+        file_path = os.path.join(folder_path, file_name)
+        # 解析CSV文件
+        df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+        data =df.iloc[2:,9:] 
+        data['id']=index+str(count)
+        data_all = pd.concat([data_all, data], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+dataset=data_all.values
+scaler.fit(dataset) ## 生成规则
+dataset = scaler.transform(dataset) ## 将规则应用于训练集
+
+
+
+import numpy as np
+
+def trajectory_split(data,window,offset):
+    child_trajectory= []
+    tr1=[]
+    for i in range(0,len(data)-window-1,offset):
+        if (i + window)<len(data) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            if id1==id2 and id1==0:
+                tr1.append(dataset[i:(i+window),:])
+            if id1==id2:
+                a = dataset[i:(i+window),:-1]
+#                 a = dataset[i:(i+window),1:4]
+                child_trajectory.append(a)
+    return np.array(child_trajectory),np.array(tr1)
+
+trajectorys,tr1=trajectory_split(dataset,200,200)
+
+
+def cal_fd(l1,l2):
+    dis=l1-l2
+    dis_abs=np.abs(dis)
+    mean = dis_abs.mean(axis=0)  # 计算完之后array的长度等于列数
+    return sum(mean)/len(mean)
+m=cal_fd(trajectorys[0],trajectorys[1])
+
+
+
+def cal_fd_full(trajectorys):
+    fd=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(i+1,len(trajectorys)):
+            x=trajectorys[i]
+            y=trajectorys[j]
+            fd[i][j]=cal_fd(x,y)
+    fd_full=np.zeros((len(trajectorys), len(trajectorys)))
+    for i in range(len(trajectorys)):
+        for j in range(len(trajectorys)):
+            if i==j:
+                fd_full[i][j]=0
+            elif i<j:
+                fd_full[i][j]=fd[i][j]
+            else:
+                fd_full[i][j]=fd[j][i]
+    return fd_full
+fd_full=cal_fd_full(trajectorys)
+dis=np.sum(fd_full,axis=1)
+
+
+
+def get_center(cluster):
+    if len(cluster)<=2:
+        return cluster[0]
+    fd=cal_fd_full(cluster)
+    dis=np.sum(fd,axis=0)
+    min_index = np.argmin(dis)
+    return cluster[min_index]
+
+def get_all_center(clusters):
+    centers=[]
+    for i in range(len(clusters)):
+        center=get_center(clusters[i])
+        centers.append(center)
+    return centers
+#n1,n2为轨迹簇中轨迹的数目
+def cal_ward(n1,n2,center1,center2):
+    fd=cal_fd(center1,center2)
+    ward=n1*n2*fd/(n1+n2)
+    return ward
+def cal_all_ward(clusters,centers):
+    ward_arr=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(clusters)):
+        for j in range(i+1,len(clusters)):
+            ward_arr[i][j]=cal_ward(len(clusters[i]),len(clusters[j]),centers[i],centers[j])
+    ward_arr_full=np.zeros((len(clusters), len(clusters)))
+    for i in range(len(ward_arr)):
+        for j in range(len(ward_arr)):
+            if i>=j:
+                ward_arr_full[i][j]=float('inf')
+            else:
+                ward_arr_full[i][j]=ward_arr[i][j]
+    return ward_arr_full
+
+
+def clustering(trajectorys,k):
+    clusters = [[point] for point in trajectorys]
+    count=len(clusters)
+    index=0
+    dis_arr=[]
+    d_arr=[]
+    while len(clusters)>k:
+        #计算每个簇的中心轨迹
+        if count==len(clusters):
+            centers=[point for point in trajectorys]
+        else:
+            centers=get_all_center(clusters)
+        #计算每个轨迹簇的ward距离
+        ward_all=cal_all_ward(clusters,centers)
+        #合并ward距离最小的两个轨迹簇，使轨迹簇的数目减一
+        min_index = np.unravel_index(np.argmin(ward_all), ward_all.shape)
+        min_dis=ward_all[min_index[0]][[min_index[1]]]
+#         print(min_dis)
+        if index==0:
+            dis=min_dis[0]
+            index+=1
+        d=min_dis[0]-dis
+        print(d)
+        dis_arr.append(min_dis[0])
+        d_arr.append(d)
+        dis=min_dis[0]
+        clusters[min_index[0]].extend(clusters[min_index[1]])
+        clusters.pop(min_index[1])
+    return clusters,dis_arr,d_arr
+
+
+# clusters_,dis_arr,d_arr=clustering(trajectorys,1)
+# len(clusters_)
+
+#
+# import matplotlib.pyplot as plt
+# l=[i+1 for i in range(len(dis_arr))]
+# l.reverse()
+# plt.plot(l, dis_arr, marker='.', linestyle='-', label='Line 1')
+# # 设置 x 轴刻度间隔为 5
+# plt.xticks(np.arange(0, 330, 20))
+# plt.xlabel('Cluster Num')
+# plt.ylabel('Minimum Ward Distance')
+# plt.show()
+
+
+def adaptive_clustering(trajectorys):
+    clusters = [[point] for point in trajectorys]
+    count=len(clusters)
+    index=0
+    while len(clusters)>1:
+        #计算每个簇的中心轨迹
+        if count==len(clusters):
+            centers=[point for point in trajectorys]
+        else:
+            centers=get_all_center(clusters)
+        #计算每个轨迹簇的ward距离
+        ward_all=cal_all_ward(clusters,centers)
+        #合并ward距离最小的两个轨迹簇，使轨迹簇的数目减一
+        min_index = np.unravel_index(np.argmin(ward_all), ward_all.shape)
+        min_dis=ward_all[min_index[0]][[min_index[1]]]
+#         print(min_dis)
+        if index==0:
+            dis=min_dis[0]
+            index+=1
+        d=min_dis[0]-dis
+        print(d)
+        if d>0.1:
+            break
+        dis=min_dis[0]
+        clusters[min_index[0]].extend(clusters[min_index[1]])
+        clusters.pop(min_index[1])
+    return clusters
+
+
+clusters=adaptive_clustering(trajectorys)
+
+
+
+import matplotlib.pyplot as plt
+#画出真实数据和预测数据
+fig = plt.figure()
+ax = fig.gca(projection='3d')
+for k in range(len(clusters)):
+    x,y,z=[],[],[]
+    for i in range(len(clusters[k])):
+        for j in range(len(clusters[k][i])):
+            x.append(clusters[k][i][j][1])
+            y.append(clusters[k][i][j][2])
+            z.append(clusters[k][i][j][3])
+    label="cluster"+str(k)
+    ax.scatter(x, y, z, s=2,label=label)
+
+plt.legend()
+plt.show()
+
+
+
+
+fig2 = plt.figure()
+ax2 = fig2.gca(projection='3d')
+x2,y2,z2=[],[],[]
+for k in range(len(trajectorys)):
+    for i in range(len(trajectorys[k])):
+        x2.append(trajectorys[k][i][1])
+        y2.append(trajectorys[k][i][2])
+        z2.append(trajectorys[k][i][3])
+ax2.scatter(x2, y2, z2, s=2)
+    
+plt.show()
+
+

featureRepository/data_process.py

+from tqdm import tqdm
+import concurrent.futures
+import math
+import os
+from csv import reader
+import pandas as pd
+import position_transfer
+import matplotlib as mpl
+from mpl_toolkits.mplot3d import Axes3D
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+def process(file_path,file_name):
+    filename = file_name.split(".")[0]
+    print(filename)
+    df = pd.read_csv(file_path)
+
+    for index,row in df.iterrows():
+        position_change=row['position_change']
+        if position_change==0:
+            if index < len(df) - 1 and index>=1:
+                df.loc[index,'tar_x']=(df.loc[index+1,'tar_x']+df.loc[index-1,'tar_x'])/2
+                df.loc[index, 'position_change'] = (df.loc[index + 1, 'position_change'] + df.loc[
+                    index - 1, 'position_change']) / 2
+                df.loc[index, 'tar_y'] = (df.loc[index + 1, 'tar_y'] + df.loc[index - 1, 'tar_y']) / 2
+                df.loc[index, 'tar_z'] = (df.loc[index + 1, 'tar_z'] + df.loc[index - 1, 'tar_z']) / 2
+                df.loc[index, 'v'] = (df.loc[index + 1, 'v'] + df.loc[index - 1, 'v']) / 2
+                df.loc[index, 'v_change'] = (df.loc[index + 1, 'v_change'] + df.loc[index - 1, 'v_change']) / 2
+                df.loc[index, 'r'] = (df.loc[index + 1, 'r'] + df.loc[index - 1, 'r']) / 2
+                df.loc[index, 'eli_change'] = (df.loc[index + 1, 'eli_change'] + df.loc[index - 1, 'eli_change']) / 2
+                df.loc[index, 'h_change'] = (df.loc[index + 1, 'h_change'] + df.loc[index - 1, 'h_change']) / 2
+            else:
+                df = df.drop(index)
+
+
+    df.to_csv('E:/replay/1对1/process_data/5/'+filename+'.csv', index=False)
+    return filename + 'finish'
+
+def processFiles():
+    folder_path = "E:/replay/1对1/data/5"  # 替换为你的文件夹路径
+    # folder_path = "E:/replay/demo"
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    results = []
+    count = 1
+    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
+        for file_name in tqdm(file_list, desc="Processing files"):
+            if file_name.endswith(".csv"):
+                file_path = os.path.join(folder_path, file_name)
+                # 解析XML文件
+                result = executor.submit(process, file_path, file_name)
+                results.append(result)
+        for future in concurrent.futures.as_completed(results):
+            print(count)
+            print(future.result())
+            count += 1
+
+##数据预处理 load dataset,需要处理0值，0值为异常值，
+def data_preprocess():
+    folder_path = "E:/replay/1对1/process_data/1"  # 替换为你的文件夹路径
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    results = []
+    count = 1
+    data_all = pd.DataFrame()
+    for file_name in tqdm(file_list, desc="Processing files"):
+        if file_name.endswith(".csv"):
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df_temp = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            df_temp = df_temp.iloc[2:, :]  # 不要前两行
+            data_all = pd.concat([data_all, df_temp], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+
+if __name__ == '__main__':
+    folder_path = "E:/replay/1对1/data/1/"
+    file_path=folder_path + "default - 20230601-133944.csv"
+    # process(file_path,"default - 20230601-133944.csv")
+    processFiles()
+    # df = pd.read_csv(file_path)
+    # df = df[['number', 'longitude', 'latitude', 'altitude', 'yaw', 'pitch', 'roll',
+    #                            'tarRng', 'tarAzi', 'tarEli','tar_x','tar_y','tar_z','position_change','v',
+    #                         'v_change','r','eli_change','h_change']]
+    # df.to_csv(file_path, index=False)
+    # process(file_path1,file_name1)
+    # cal_features(file_path, file_name)
+    # processFiles()
+    # cal_features_for_files()
+    # change_colum_for_files()
+
+    # 显示图形
+    # plt.show()

featureRepository/demo.py

+import xml.etree.ElementTree as ET
+import os
+import pandas as pd
+from tqdm import tqdm
+# folder_path = "E:/"  # 替换为你的文件夹路径
+#
+# # 获取文件夹中的所有文件
+# file_list = os.listdir(folder_path)
+tree = ET.parse('book.xml')
+#我们使用tree.getroot()将解析器中的根元素提取出来，并将其存储在命名为“root”的变量中。
+root = tree.getroot()
+
+#我们创建了一个空的DataFrame，包含四个列：id、title、author和price。
+df = pd.DataFrame(columns=['id', 'title', 'author', 'price'])
+# for file_name in file_list:
+#     if file_name.endswith(".xml"):
+#         file_path = os.path.join(folder_path, file_name)
+#
+#         # 解析XML文件
+#         tree = ET.parse(file_path)
+#         root = tree.getroot()
+
+
+for book in root:
+    if book.tag=='book':
+        id = book.find('id').text
+        title = book.find('title').text
+        author = book.find('author').text
+        price = book.find('price').text
+        devices=book.find('devices')
+        tracks=devices.find('tracks')
+        print(devices)
+        print(tracks)
+        track=tracks.find('track')
+        tarRng=track.find('tarRng').text
+        df = df.append(pd.Series([id, title, author, price,tarRng], index=['id', 'title', 'author', 'price','tarRng']), ignore_index=True)
+
+# for book in root.findall("book"):
+#     id = book.find('id').text
+#     title = book.find('title').text
+#     author = book.find('author').text
+#     price = book.find('price').text
+#     df = df.append(pd.Series([id, title, author, price], index=['id', 'title', 'author', 'price']), ignore_index=True)
+
+# df.to_excel('books.xlsx', index=False)
+filename="default - 20230601-133944.replay"
+filename=filename.split(".")[0]
+print(filename)
\ No newline at end of file

featureRepository/demo2.py

+import os
+import xml.etree.ElementTree as ET
+import pandas as pd
+
+folder_path = "C:/xxx/Desktop/2022"  # 替换为你的文件夹路径
+
+# 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+
+# 创建一个空的DataFrame来存储所有XML文件的数据
+all_data = pd.DataFrame()
+
+# 循环处理每个文件
+for file_name in file_list:
+    if file_name.endswith(".xml"):
+        file_path = os.path.join(folder_path, file_name)
+
+        # 解析XML文件
+        tree = ET.parse(file_path)
+        root = tree.getroot()
+
+        # 提取XML数据并转换为DataFrame
+        xml_data = {}
+        for element in root.iter():
+            xml_data[element.tag] = element.text
+
+        df = pd.DataFrame(xml_data, index=[0])
+
+        # 将DataFrame添加到总的数据集中
+        all_data = pd.concat([all_data, df], ignore_index=True)
+
+# 将数据保存为Excel文件
+excel_file_path = "C:/xxx/Desktop/2022/file.xlsx"  # 替换为你的输出文件路径，如：/path/to/output/file.xlsx
+all_data.to_excel(excel_file_path, index=False)
+
+print("转换完成！Excel文件已保存。")
\ No newline at end of file

featureRepository/draw.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+
+
+# In[33]:
+
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+
+
+
+
+# In[70]:
+
+
+from keras.models import load_model
+# load model from single file
+model1 = load_model('/home/lxm/model/lstm_model3.h5')
+yhat = model1.predict(test_X)
+
+
+
+# In[83]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error
+
+temp = test_X.reshape(test_X.shape[0]*test_X.shape[1], test_X.shape[2])
+temp=temp[:373700,2:]
+iny_yhat=yhat.reshape(yhat.shape[0]*yhat.shape[1],yhat.shape[2])
+#预测数据逆缩放 invert scaling for forecast
+iny = concatenate((iny_yhat, temp), axis=1)
+inverse_y = scaler.inverse_transform(iny)
+inv_y = inverse_y[:, 0:3]
+# inv_yhat = np.array(inv_yhat)
+inv_y_reshape=inv_y.reshape(int(inv_y.shape[0]/100),100,3)
+
+#真实数据
+tempy=test_Y.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+iny_temp= concatenate((tempy, temp), axis=1)#水平方向合并，为了适配scaler
+iny_temp1=scaler.inverse_transform(iny_temp)
+iny_real=iny_temp1[:,0:3]
+iny_real_reshape=iny_real.reshape(int(iny_real.shape[0]/100),100,3)
+
+# # # calculate RMSE
+rmse = sqrt(mean_squared_error(iny_real_reshape[3],inv_y_reshape[3]))
+print('Test RMSE: %.3f' % rmse)
+
+
+# In[78]:
+
+
+import matplotlib.pyplot as plt
+#画出真实数据和预测数据
+
+predict_x,predict_y,predict_z=[],[],[]
+true_x,true_y,true_z=[],[],[]
+for i in range(len(inv_y_reshape[1])):
+    predict_x.append(inv_y_reshape[1][i][0])
+    predict_y.append(inv_y_reshape[1][i][1])
+    predict_z.append(inv_y_reshape[1][i][2])
+    true_x.append(iny_real_reshape[1][i][0])
+    true_y.append(iny_real_reshape[1][i][1])
+    true_z.append(iny_real_reshape[1][i][2])
+fig = plt.figure()
+ax = fig.gca(projection='3d')
+ax.scatter(true_x, true_y, true_z, s=2,label='true')
+ax.scatter(predict_x, predict_y, predict_z, s=2,label='prediction')
+plt.legend()
+plt.show()
+
+
+# In[80]:
+
+
+
+
+# In[ ]:
+
+
+
+

featureRepository/draw_continuous_predict.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+data_train.shape,data_test.shape
+
+
+
+# In[2]:
+
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+def create_dataset1(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),:-1])
+    return np.array(dataX), np.array(dataY)
+def create_dataset2(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    placeholder=[]
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),0:3]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+                placeholder.append(dataset[i + window:(i + window+predict_length),3:])
+    return np.array(dataX), np.array(dataY),np.array(placeholder)
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+data_tr,data_te
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+
+train_X1,train_Y1=create_dataset1(data_tr,200,100,100)
+test_X1,test_Y1=create_dataset1(data_te,200,100,100)
+
+train_X2,train_Y2,p=create_dataset2(data_tr,200,100,100)
+test_X2,test_Y2,placeholder=create_dataset2(data_te,200,100,100)
+
+train_X2.shape,train_Y2.shape,test_X2.shape,test_Y2.shape
+
+
+# In[69]:
+
+
+from keras.models import load_model
+# load model from single file
+model1 = load_model('/home/lxm/model/lstm_model3.h5')
+
+yhat = model1.predict(test_X)
+
+
+
+# In[132]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+def reverse(yhat,test_X,test_Y):
+    temp = test_X.reshape(test_X.shape[0]*test_X.shape[1], test_X.shape[2])
+    temp=temp[:373700,2:]
+    iny_yhat=yhat.reshape(yhat.shape[0]*yhat.shape[1],yhat.shape[2])
+    #预测数据逆缩放 invert scaling for forecast
+    iny = concatenate((iny_yhat, temp), axis=1)
+    inverse_y = scaler.inverse_transform(iny)
+    inv_y = inverse_y[:, 0:3]
+    # inv_yhat = np.array(inv_yhat)
+    inv_y_reshape=inv_y.reshape(int(inv_y.shape[0]/100),100,3)
+    inv_y_reshape.shape
+    #真实数据
+    tempy=test_Y.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+    iny_temp= concatenate((tempy, temp), axis=1)#水平方向合并，为了适配scaler
+    iny_temp1=scaler.inverse_transform(iny_temp)
+    iny_real=iny_temp1[:,0:3]
+    iny_real_reshape=iny_real.reshape(int(iny_real.shape[0]/100),100,3)
+    iny_real_reshape
+    # # # calculate RMSE
+    mae = sqrt(mean_absolute_error(iny_real,inv_y))
+    print('Test mae: %.3f' % mae)
+    return inv_y_reshape,iny_real_reshape
+
+inv_y_reshape,iny_real_reshape=reverse(yhat,test_X,test_Y)
+
+
+# In[3]:
+
+
+#连续预测
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+from keras.models import load_model
+model=load_model('/home/lxm/model/lstm_model4.h5')
+model2 = load_model('/home/lxm/model/lstm_model_compare1.h5')
+predict=model.predict(test_X1)
+# predict.shape
+predict2=model2.predict(test_X2)
+predict2.shape
+
+
+# In[4]:
+
+
+def reverse2(predict,test_X1,test_Y1,text_X):
+    temp = test_X.reshape(test_X.shape[0]*test_X.shape[1], test_X.shape[2])
+    length=len(predict)
+    temp1=temp[:length*100,2:]
+    temp2=temp[:3737*100,2:]
+    iny_yhat=predict.reshape(predict.shape[0]*predict.shape[1],predict.shape[2])
+    print(iny_yhat.shape)
+    print(temp1.shape)
+    #预测数据逆缩放 invert scaling for forecast
+    iny = concatenate((iny_yhat, temp1), axis=1)
+    inverse_y = scaler.inverse_transform(iny)
+    inv_y = inverse_y[:, 0:3]
+    # inv_yhat = np.array(inv_yhat)
+    inv_y_reshape=inv_y.reshape(length,100,3)
+    inv_y_reshape.shape
+    #真实数据
+    tempy=test_Y.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+    iny_temp= concatenate((tempy, temp2), axis=1)
+    iny_temp1=scaler.inverse_transform(iny_temp)
+    iny_real=iny_temp1[:(length)*100,0:3]
+    iny_real_reshape=iny_real.reshape(length,100,3)
+    print(iny_real_reshape.shape)
+    # # # calculate RMSE
+    mae = sqrt(mean_absolute_error(iny_real,inv_y))
+    print('Test MAE: %.3f' % mae)
+    return inv_y_reshape,iny_real_reshape
+    
+inv_y_reshape3,iny_real_reshape3=reverse2(predict2,test_X2,test_Y2,test_X)
+
+
+# In[87]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+temp = test_X1.reshape(test_X1.shape[0]*test_X1.shape[1], test_X1.shape[2])
+temp1=temp[:373700,-1:]
+temp2=temp[:373700,2:]
+iny_yhat=predict.reshape(predict.shape[0]*predict.shape[1],predict.shape[2])
+#预测数据逆缩放 invert scaling for forecast
+iny = concatenate((iny_yhat, temp1), axis=1)
+iny.shape
+inverse_y = scaler.inverse_transform(iny)
+inv_y = inverse_y[:, 0:3]
+# inv_yhat = np.array(inv_yhat)
+inv_y_reshape=inv_y.reshape(int(inv_y.shape[0]/100),100,3)
+inv_y_reshape.shape
+#真实数据
+tempy=test_Y1.reshape(test_Y1.shape[0]*test_Y1.shape[1], test_Y1.shape[2])
+iny_temp= concatenate((tempy, temp1), axis=1)#水平方向合并，为了适配scaler
+iny_temp1=scaler.inverse_transform(iny_temp)
+iny_real=iny_temp1[:,0:3]
+iny_real_reshape=iny_real.reshape(int(iny_real.shape[0]/100),100,3)
+iny_real_reshape
+# # # calculate RMSE
+mae = sqrt(mean_absolute_error(iny_real,inv_y))
+print('Test mae: %.3f' % mae)
+
+
+# In[8]:
+
+
+def predict_continuous(test_X):
+    test_tr=test_X[0:1]
+
+    # p=model.predict(test_tr)
+    # p.shape
+    tr=[]
+    split_tr=np.vsplit(test_tr[0], 2)
+    # split_tr[1].shape
+    tr.append(split_tr[1])
+    num=5 #连续预测次数
+    for i in range(num):
+        predict_y=model.predict(test_tr)
+        tr.append(predict_y[0])  
+        temp=tr[-2]
+        test_tr=np.vstack((temp,predict_y[0]))
+        test_tr=np.array([test_tr])
+
+    tr=np.array(tr[1:])
+    return tr
+def predict_continuous_compare(test_X):
+    test_tr=test_X[0:1]
+
+    # p=model.predict(test_tr)
+    # p.shape
+    tr=[]
+    split_tr=np.vsplit(test_tr[0], 2)
+    # split_tr[1].shape
+    tr.append(split_tr[1])
+    num=5 #连续预测次数
+    for i in range(num):
+        predict_y=model2.predict(test_tr)
+        tr.append(predict_y[0])  
+        temp=tr[-2]
+        test_tr=np.vstack((temp,predict_y[0]))
+        test_tr=np.array([test_tr])
+
+    tr=np.array(tr[1:])
+    return tr
+tr=predict_continuous(test_X1)
+tr1=predict_continuous_compare(test_X2)
+
+
+# In[9]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+def reverse1(predict,test_X,test_Y):
+    temp = test_X1.reshape(test_X.shape[0]*test_X.shape[1], test_X.shape[2])
+    length=len(predict)
+    temp1=temp[:length*100,-1:]
+    temp2=temp[:3737*100,-1:]
+    iny_yhat=predict.reshape(predict.shape[0]*predict.shape[1],predict.shape[2])
+    #预测数据逆缩放 invert scaling for forecast
+    iny = concatenate((iny_yhat, temp1), axis=1)
+    iny.shape
+    inverse_y = scaler.inverse_transform(iny)
+    inv_y = inverse_y[:, 0:3]
+    # inv_yhat = np.array(inv_yhat)
+    inv_y_reshape=inv_y.reshape(length,100,3)
+    print(inv_y_reshape.shape)
+    #真实数据
+    tempy=test_Y1.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+#     print(tempy.shape)
+    iny_temp= concatenate((tempy, temp2), axis=1)#水平方向合并，为了适配scaler
+    iny_temp1=scaler.inverse_transform(iny_temp)
+    iny_real=iny_temp1[:(length)*100,0:3]
+    iny_real_reshape=iny_real.reshape(length,100,3)
+    print(iny_real_reshape.shape)
+    # # # calculate RMSE
+    mae = sqrt(mean_absolute_error(iny_real,inv_y))
+    print('Test mae: %.3f' % mae)
+    return inv_y_reshape,iny_real_reshape
+
+inv_y_reshape1,iny_real_reshape1=reverse1(tr,test_X1,test_Y1)
+inv_y_reshape4,iny_real_reshape4=reverse2(tr1,test_X2,test_Y2,test_X)
+# inv_y_reshape2,iny_real_reshape2=reverse1(predict,test_X1,test_Y1)
+# inv_y_reshape,iny_real_reshape=reverse(predict,test_X1,test_Y1)
+
+
+# In[197]:
+
+
+import matplotlib.pyplot as plt
+def draw(inv_y_reshape,iny_real_reshape):
+    #画出真实数据和预测数据
+    predict_x,predict_y,predict_z=[],[],[]
+    predict_x2,predict_y2,predict_z2=[],[],[]
+    true_x,true_y,true_z=[],[],[]
+    for k in range(5):
+        for i in range(len(inv_y_reshape[k])):
+            predict_x.append(inv_y_reshape[k][i][0])
+            predict_y.append(inv_y_reshape[k][i][1])
+            predict_z.append(inv_y_reshape[k][i][2])
+            true_x.append(iny_real_reshape[k][i][0])
+            true_y.append(iny_real_reshape[k][i][1])
+            true_z.append(iny_real_reshape[k][i][2])
+    fig = plt.figure()
+    ax = fig.gca(projection='3d')
+    ax.scatter(predict_x, predict_y, predict_z, s=2,label='prediction')
+    ax.scatter(true_x, true_y, true_z, s=2,label='true')
+    plt.legend()
+    plt.show()
+
+
+# In[198]:
+
+
+draw(inv_y_reshape1,iny_real_reshape1)
+
+
+# In[13]:
+
+
+# In[202]:
+
+
+draw(inv_y_reshape4,iny_real_reshape4)
+
+
+# In[ ]:
+
+
+
+

featureRepository/draw_loss.py

+# 打开TXT文件
+import matplotlib.pyplot as plt  # 导入 Matplotlib 工具包
+import numpy as np
+
+plt.style.use('seaborn-whitegrid')
+import matplotlib as mpl
+plt.rcParams['font.family'] = 'Times New Roman'
+mpl.rcParams['axes.labelsize'] = 14
+mpl.rcParams['legend.fontsize'] = 12
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+def drawResultAndIter(line):
+    labels1=["train","test"]
+    f, ax = plt.subplots(1, 1)
+    for i in range(len(line)):
+        plt.plot(line[i],label=labels1[i])
+    # plt.plot(r2,label=labels1[1])
+    # plt.axhline(y=60, color='r', linestyle='--', label='Horizontal Line at y=60')
+    plt.legend()
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('mse')
+    plt.show()
+
+def draw_mae():
+    f, ax = plt.subplots(1, 1)
+    x=[i for i in range(1,6)]
+    r1=[13.212,23.561,31.479,37.121,40.163]
+    r2=[10.979,14.929,16.194,17.517,21.643]
+    r1 = [174.212, 529, 961, 1369, 1600]
+    r2 = [100.979, 196, 256, 289, 441]
+    d=[(r1[i]-r2[i])/r1[i] for i in range(len(r1))]
+    print(sum(d)/len(d))
+    plt.plot(x,r1, label="traditional LSTM",marker="o")
+    plt.plot(x,r2, label="proposed LSTM",marker="^")
+    ax.set_xlabel('number of prediction times')
+    ax.set_ylabel('MAE(m)')
+    plt.xticks(np.arange(1, 6, 1))
+    plt.legend()
+    plt.show()
+
+def draw_num_mae():
+    # 60 0.0078 0.0090 18.347
+    # 70 0.0056 0.0052 12.885
+    # 80 0.0058 0.0080 16.715
+    # 90 0.0077 0.0079 16.813
+    # 100 0.0059 0.0057 13.206
+    # 110 0.0059 0.0060 14.344
+    # 120 0.0175 0.0126 19.897
+    f, ax = plt.subplots(1, 1)
+    x=[i for i in range(60,130,10)]
+    y1=[0.0078,0.0056,0.0058,0.0077,0.0059,0.0060,0.0175]
+    y2=[18.347,12.885,16.715,16.813,13.206,14.344,19.897]
+    plt.plot(x, y1, label="train MAE", marker="o")
+    plt.plot(x, y2, label="real MAE", marker="^")
+    ax.set_xlabel('number of hidden nodes')
+    ax.set_ylabel('MAE(m)')
+    # plt.xticks(np.arange(1, 6, 1))
+    plt.legend()
+    plt.show()
+
+if __name__ == '__main__':
+    draw_mae()
+    # draw_num_mae()
+    # with open("loss.txt", "r", encoding="utf-8") as f:
+    #     text = f.readlines()
+    # with open("val_loss.txt", "r", encoding="utf-8") as f:
+    #     text2 = f.readlines()
+    # l=[]
+    # l1 = [float(line.strip("\n")) for line in text]
+    # l2=[float(line.strip("\n")) for line in text2]
+    # l.append(l1)
+    # l.append(l2)
+    #
+    # drawResultAndIter(l)
+

featureRepository/draw_predict.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+data_train,data_test
+
+
+
+# In[2]:
+
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    placeholder=[]
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),0:3]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+                placeholder.append(dataset[i + window:(i + window+predict_length),3:])
+    return np.array(dataX), np.array(dataY),np.array(placeholder)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+data_tr,data_te
+
+train_X,train_Y,p=create_dataset(data_tr,200,100,100)
+test_X,test_Y,placeholder=create_dataset(data_te,200,100,100)
+train_X.shape,train_Y.shape,test_X.shape,test_Y.shape
+placeholder.shape
+
+
+# In[21]:
+
+
+from keras.models import load_model
+# load model from single file
+model1 = load_model('/home/lxm/model/lstm_model_compare.h5')
+yhat = model1.predict(test_X)
+yhat.shape
+
+
+# In[40]:
+
+
+# In[11]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+temp = placeholder.reshape(placeholder.shape[0]*placeholder.shape[1], placeholder.shape[2])
+iny_yhat=yhat.reshape(yhat.shape[0]*yhat.shape[1],yhat.shape[2])
+#预测数据逆缩放 invert scaling for forecast
+iny = concatenate((iny_yhat, temp), axis=1)
+inverse_y = scaler.inverse_transform(iny)
+inv_y = inverse_y[:, 0:3]
+# inv_yhat = np.array(inv_yhat)
+inv_y_reshape=inv_y.reshape(int(inv_y.shape[0]/100),100,3)
+inv_y_reshape.shape
+
+
+# In[26]:
+
+
+#真实数据
+tempy=test_Y.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+iny_temp= concatenate((tempy, temp), axis=1)
+iny_temp1=scaler.inverse_transform(iny_temp)
+iny_real=iny_temp1[:,0:3]
+iny_real_reshape=iny_real.reshape(int(iny_real.shape[0]/100),100,3)
+print(iny_real_reshape.shape)
+# # # calculate RMSE
+rmse = sqrt(mean_absolute_error(inv_y[5],iny_real[5]))
+print('Test MAE: %.3f' % rmse)
+
+
+# In[42]:
+
+
+import matplotlib.pyplot as plt
+#画出真实数据和预测数据
+predict_x,predict_y,predict_z=[],[],[]
+true_x,true_y,true_z=[],[],[]
+for k in range(10):
+    for i in range(len(inv_y_reshape[k])):
+        predict_x.append(inv_y_reshape[k][i][0])
+        predict_y.append(inv_y_reshape[k][i][1])
+        predict_z.append(inv_y_reshape[k][i][2])
+        true_x.append(iny_real_reshape[k][i][0])
+        true_y.append(iny_real_reshape[k][i][1])
+        true_z.append(iny_real_reshape[k][i][2])
+fig = plt.figure()
+ax = fig.gca(projection='3d')
+ax.scatter(true_x, true_y, true_z, s=2,label='true')
+ax.scatter(predict_x, predict_y, predict_z, s=2,label='prediction')
+plt.legend()
+plt.show()
+
+
+# In[ ]:
+
+
+
+

featureRepository/draw_predict_feature.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:] 
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+data_train,data_test
+
+
+
+# In[20]:
+
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+data_tr,data_te
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+train_X.shape,train_Y.shape,test_X.shape,test_Y.shape
+
+
+# In[19]:
+
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset2(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    placeholder=[]
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),0:3]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+                placeholder.append(dataset[i + window:(i + window+predict_length),3:])
+    return np.array(dataX), np.array(dataY),np.array(placeholder)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+data_tr,data_te
+
+train_X2,train_Y2,p=create_dataset2(data_tr,200,100,100)
+test_X2,test_Y2,placeholder=create_dataset2(data_te,200,100,100)
+train_X2.shape,train_Y2.shape,test_X2.shape,test_Y2.shape
+# placeholder.shape
+
+
+# In[21]:
+
+
+from keras.models import load_model
+# load model from single file
+model1 = load_model('/home/lxm/model/lstm_model3.h5')
+# model2=load_model('/home/lxm/model/lstm_model_compare.h5')
+yhat = model1.predict(test_X)
+# yhat2=model2.predict(test_X2)
+yhat
+
+
+# In[16]:
+
+
+from numpy import concatenate
+from numpy import sqrt
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+temp = test_X.reshape(test_X.shape[0]*test_X.shape[1], test_X.shape[2])
+temp=temp[:373700,2:]
+iny_yhat=yhat.reshape(yhat.shape[0]*yhat.shape[1],yhat.shape[2])
+#预测数据逆缩放 invert scaling for forecast
+iny = concatenate((iny_yhat, temp), axis=1)
+inverse_y = scaler.inverse_transform(iny)
+inv_y = inverse_y[:, 0:3]
+# inv_yhat = np.array(inv_yhat)
+inv_y_reshape=inv_y.reshape(int(inv_y.shape[0]/100),100,3)
+inv_y_reshape.shape
+#真实数据
+tempy=test_Y.reshape(test_Y.shape[0]*test_Y.shape[1], test_Y.shape[2])
+iny_temp= concatenate((tempy, temp), axis=1)#水平方向合并，为了适配scaler
+iny_temp1=scaler.inverse_transform(iny_temp)
+iny_real=iny_temp1[:,0:3]
+iny_real_reshape=iny_real.reshape(int(iny_real.shape[0]/100),100,3)
+iny_real_reshape
+# # # calculate RMSE
+mae = sqrt(mean_absolute_error(iny_real,inv_y))
+print('Test mae: %.3f' % mae)
+
+
+# In[22]:
+
+
+
+import matplotlib.pyplot as plt
+#画出真实数据和预测数据
+predict_x,predict_y,predict_z=[],[],[]
+true_x,true_y,true_z=[],[],[]
+for k in range(30):
+    for i in range(len(inv_y_reshape[k])):
+        predict_x.append(inv_y_reshape[k][i][0])
+        predict_y.append(inv_y_reshape[k][i][1])
+        predict_z.append(inv_y_reshape[k][i][2])
+        true_x.append(iny_real_reshape[k][i][0])
+        true_y.append(iny_real_reshape[k][i][1])
+        true_z.append(iny_real_reshape[k][i][2])
+fig = plt.figure()
+ax = fig.gca(projection='3d')
+ax.scatter(predict_x, predict_y, predict_z, s=2,label='prediction')
+ax.scatter(true_x, true_y, true_z, s=2,label='true')
+plt.legend()
+plt.show()
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

featureRepository/file_process.py

+from tqdm import tqdm
+import concurrent.futures
+import math
+import os
+from csv import reader
+import pandas as pd
+import position_transfer
+import matplotlib as mpl
+from mpl_toolkits.mplot3d import Axes3D
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 第一个文件的第一个有效帧，不用这个
+# < longitude > 124.671000 < / longitude >
+# < latitude > 39.902700 < / latitude >
+# < altitude > 44.743948 < / altitude >
+
+#第一行
+start_long=124.517535
+start_lati=39.947593
+start_altitude=0
+
+
+def cal_objective_position(x,y,z,tarRng,tarAzi,tarEli):
+    tar_x = x + tarRng * math.sin(tarAzi) * math.cos(tarEli)
+    tar_y = y + tarRng * math.cos(tarAzi) * math.cos(tarEli)
+    tar_z = z + tarRng* math.sin(tarEli)
+    return tar_x,tar_y,tar_z
+
+
+def process(file_path,file_name):
+    filename = file_name.split(".")[0]
+    df = pd.read_excel(file_path)
+    df['tar_x']=''
+    df['tar_y']=''
+    df['tar_z']=''
+
+    # df.to_csv('example.csv', index=False)
+    for index,row in df.iterrows():
+        longitude=row['longitude']
+        latitude=row['latitude']
+        altitude=row['altitude']
+        tarRng=row['tarRng']
+        tarEli=row['tarEli']
+        tarAzi=row['tarAzi']
+        result=position_transfer.gps_to_xyz(latitude,longitude,altitude,start_lati,start_long,start_altitude)
+        x,y,z=cal_objective_position(result[0],result[1],result[2],tarRng,tarAzi,tarEli)
+
+    #     print(x,y,z)
+    #     x_arr.append(x)
+    #     y_arr.append(y)
+    #     z_arr.append(z)
+    # return x_arr,y_arr,z_arr
+        df.loc[index, 'tar_x'] = x
+        df.loc[index, 'tar_y'] = y
+        df.loc[index, 'tar_z'] = z
+
+    df.to_csv('E:/replay/1对1/data/1/'+filename+'.csv', index=False)
+    return filename + 'finish'
+
+def processFiles():
+    folder_path = "E:/replay/1对1/original_data/5"  # 替换为你的文件夹路径
+    # folder_path = "E:/replay/demo"
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    results = []
+    count = 1
+    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
+        for file_name in tqdm(file_list, desc="Processing files"):
+            if file_name.endswith(".xlsx"):
+                file_path = os.path.join(folder_path, file_name)
+                # 解析XML文件
+                result = executor.submit(process, file_path, file_name)
+                results.append(result)
+        for future in concurrent.futures.as_completed(results):
+            print(count)
+            print(future.result())
+            count += 1
+
+def cal_features(file_path,file_name):
+    filename = file_name.split(".")[0]
+    df = pd.read_csv(file_path)
+    df['position_change'] = ''
+    df['v'] = ''
+    df['v_change'] = ''
+    df['r']=''
+    df['eli_change']=''
+    df['h_change']=''
+    # df.drop('e_change',axis=1,inplace=True)
+    for index, row in df.iterrows():
+        if index > 0:
+            x = row['tar_x']
+            y = row['tar_y']
+            z = row['tar_z']
+            e=row['tarEli']
+            x_pre=df.loc[index-1, 'tar_x']
+            y_pre=df.loc[index-1, 'tar_y']
+            z_pre=df.loc[index-1, 'tar_z']
+            e_pre=df.loc[index-1, 'tarEli']
+            position_change=math.sqrt((x_pre-x)**2+(y_pre-y)**2+(z_pre-z)**2)
+            h_change=z-z_pre
+            e_change=e-e_pre
+            speed=position_change/0.1
+            df.loc[index, 'position_change'] = position_change
+            df.loc[index,'v']=speed
+            df.loc[index, 'h_change'] = h_change
+            df.loc[index, 'eli_change'] = e_change
+            speed_change=0
+            if index!=1:
+                speed_change=speed-df.loc[index-1,'v']
+            df.loc[index,'v_change']=speed_change
+            if x-x_pre!=0:
+                r=math.atan((y-y_pre)/(x-x_pre))/0.1
+            else:
+                r=0
+            df.loc[index, 'r'] = r
+
+    df.to_csv('E:/replay/1对1/data/1/'+filename+'.csv', index=False)
+    return filename + 'finish'
+
+
+def cal_features_for_files():
+    folder_path = "E:/replay/1对1/data/5"  # 替换为你的文件夹路径
+    # folder_path = "E:/replay/demo"
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    results = []
+    count = 1
+    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
+        for file_name in tqdm(file_list, desc="Processing files"):
+            if file_name.endswith(".csv"):
+                file_path = os.path.join(folder_path, file_name)
+                # 解析XML文件
+                result = executor.submit(cal_features, file_path, file_name)
+                results.append(result)
+        for future in concurrent.futures.as_completed(results):
+            print(count)
+            print(future.result())
+            count += 1
+
+
+def draw():
+    # 设置图例字号
+    folder_path = "E:/replay/1对1/process_data/5"  # 替换为你的文件夹路径
+    # folder_path = "E:/replay/demo"
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    mpl.rcParams['legend.fontsize'] = 10
+    # 方式2：设置三维图形模式
+    fig = plt.figure()
+    ax = fig.gca(projection='3d')
+    count=0
+    for file_name in tqdm(file_list, desc="Processing files"):
+        if count>10:
+            break
+        if file_name.endswith(".csv"):
+            file_path = os.path.join(folder_path, file_name)
+            df = pd.read_csv(file_path)
+            count+=1
+            x_arr = []
+            y_arr = []
+            z_arr = []
+            # df.to_csv('example.csv', index=False)
+            for index, row in df.iterrows():
+                if index>1:
+                    x = row['tar_x']
+                    y = row['tar_y']
+                    z = row['tar_z']
+                    x_arr.append(x)
+                    y_arr.append(y)
+                    z_arr.append(z)
+                # 绘制图形
+            ax.scatter(x_arr, y_arr, z_arr, s=2)
+
+
+def change_colum(file_path):
+    df = pd.read_csv(file_path)
+    df = df[['number', 'longitude', 'latitude', 'altitude', 'yaw', 'pitch', 'roll',
+             'tarRng', 'tarAzi', 'tarEli', 'tar_x', 'tar_y', 'tar_z', 'position_change', 'v',
+             'v_change', 'r', 'eli_change', 'h_change']]
+    df.to_csv(file_path, index=False)
+    return 'finish'
+
+def change_colum_for_files():
+    folder_path = "E:/replay/1对1/data/1"  # 替换为你的文件夹路径
+    # folder_path = "E:/replay/demo"
+    # # 获取文件夹中的所有文件
+    file_list = os.listdir(folder_path)
+    results = []
+    count = 1
+    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
+        for file_name in tqdm(file_list, desc="Processing files"):
+            if file_name.endswith(".csv"):
+                file_path = os.path.join(folder_path, file_name)
+                # 解析XML文件
+                result = executor.submit(change_colum, file_path)
+                results.append(result)
+        for future in concurrent.futures.as_completed(results):
+            print(count)
+            print(future.result())
+            count += 1
+
+
+if __name__ == '__main__':
+    folder_path = "E:/replay/1对1/data/2/"
+    file_path=folder_path + "default - 20230601-134029.csv"
+
+    # df = pd.read_csv(file_path)
+    # df = df[['number', 'longitude', 'latitude', 'altitude', 'yaw', 'pitch', 'roll',
+    #                            'tarRng', 'tarAzi', 'tarEli','tar_x','tar_y','tar_z','position_change','v',
+    #                         'v_change','r','eli_change','h_change']]
+    # df.to_csv(file_path, index=False)
+    # process(file_path1,file_name1)
+    # cal_features(file_path, file_name)
+    # processFiles()
+    # cal_features_for_files()
+    # change_colum_for_files()
+
+    draw()
+    plt.show()
+
+    # 显示图形
+    # plt.show()

featureRepository/loss.txt

+0.16689902544021606
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+0.009228760376572609
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+0.009228064678609371
+0.009227816946804523
+0.009227808564901352
+0.009227623231709003
+0.0092275720089674
+0.009227360598742962
+0.009227430447936058
+0.009227169677615166
+0.009227215312421322
+0.009227119386196136
+0.009226889349520206
+0.009226965717971325
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+0.009225585497915745
+0.009225527755916119
+0.009225485846400261
+0.009225433692336082
+0.009225395508110523
+0.009225340560078621
+0.009225291199982166
+0.009225242771208286

featureRepository/lstm-tf-compare.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_all=pd.DataFrame()
+
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        
+        file_path = os.path.join(folder_path, file_name)
+#         print(file_path)
+        # 解析CSV文件
+        df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+        data =df.iloc[:,1:] 
+        data_all = pd.concat([data_all, data], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+
+from sklearn.preprocessing import MinMaxScaler
+#保证为float ensure all data is float
+value = data_all.values
+value = value.astype('float32')
+#归一化 normalize features
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled = scaler.fit_transform(value)
+
+
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),1:4]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),1:4])
+    return np.array(dataX), np.array(dataY)
+
+
+dataX,dataY=create_dataset(scaled,200,100,100)
+
+
+
+#随机取80%的轨迹为训练集，20%的轨迹为测试集
+#p为百分比
+def split_dataset(dataX,dataY,p=0.8):
+    train_X=[]
+    train_Y=[]
+    test_X=[]
+    test_Y=[]
+#     train_size = int(len(dataset) * 0.8)
+    for i in range(len(dataX)):
+        r=random.random()
+        if r<=p:
+            train_X.append(dataX[i])
+            train_Y.append(dataY[i])
+        else:
+            test_X.append(dataX[i])
+            test_Y.append(dataY[i])
+    return np.array(train_X), np.array(train_Y), np.array(test_X), np.array(test_Y)
+            
+train_X,train_Y,test_X,test_Y=split_dataset(dataX,dataY)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(50, input_shape=(200, 3)))  # LSTM层，50个神经元
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.5))
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_squared_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=200, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/lstm_model_compare.h5')
+
+
+
+
+

featureRepository/lstm-tf-compare1.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),0:3]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(100, input_shape=(200, 3)))  # LSTM层，50个神经元
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.5))
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=200, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/lstm_model_compare1.h5')
+
+
+
+
+

featureRepository/lstm-tf.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_all=pd.DataFrame()
+
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        
+        file_path = os.path.join(folder_path, file_name)
+#         print(file_path)
+        # 解析CSV文件
+        df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+        data =df.iloc[:,1:] 
+        data_all = pd.concat([data_all, data], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+
+
+
+from sklearn.preprocessing import MinMaxScaler
+#保证为float ensure all data is float
+value = data_all.values
+value = value.astype('float32')
+#归一化 normalize features
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled = scaler.fit_transform(value)
+
+
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),1:4])
+    return np.array(dataX), np.array(dataY)
+
+
+dataX,dataY=create_dataset(scaled,200,100,100)
+
+
+
+#随机取80%的轨迹为训练集，20%的轨迹为测试集
+#p为百分比
+def split_dataset(dataX,dataY,p=0.8):
+    train_X=[]
+    train_Y=[]
+    test_X=[]
+    test_Y=[]
+#     train_size = int(len(dataset) * 0.8)
+    for i in range(len(dataX)):
+        r=random.random()
+        if r<=p:
+            train_X.append(dataX[i])
+            train_Y.append(dataY[i])
+        else:
+            test_X.append(dataX[i])
+            test_Y.append(dataY[i])
+    return np.array(train_X), np.array(train_Y), np.array(test_X), np.array(test_Y)
+            
+train_X,train_Y,test_X,test_Y=split_dataset(dataX,dataY)
+
+
+import os
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(50, input_shape=(200, 10)))  # LSTM层，50个神经元
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.5))
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_squared_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=200, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/lstm_model1.h5')
+
+
+
+
+

featureRepository/lstm-tf1.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+#改了训练集和测试集的归一化
+
+
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(100, input_shape=(200, 10)))  # LSTM层
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.2))
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=200, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/lstm_model3.h5')
+# tf.keras.backend.clear_session()
+
+
+
+

featureRepository/lstm-tf2.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+#改了训练集和测试集的归一化
+
+
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),:-1])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+train_X,train_Y=create_dataset(data_tr,200,100,100)
+test_X,test_Y=create_dataset(data_te,200,100,100)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 100帧预测10帧
+
+model = Sequential()
+model.add(LSTM(100, input_shape=(200, 10)))  # LSTM层，50个神经元
+# model.add(LSTM(units=100))  # LSTM层，50个神经元
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+model.add(Dense(1000,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 10 = 100
+# model.add(Dropout(0.2))
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 10)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=100, batch_size=72, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+# with open('loss.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['loss']:
+#         f.write(str(loss) + '\n')
+# with open('val_loss.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['val_loss']:
+#         f.write(str(loss) + '\n')
+
+
+model.save('/home/lxm/model/lstm_model4.h5')
+# tf.keras.backend.clear_session()
+
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+

featureRepository/lstm1.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+#改了训练集和测试集的归一化
+
+
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+train_X,train_Y=create_dataset(data_tr,200,30,100)
+test_X,test_Y=create_dataset(data_te,200,30,100)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(units=70, input_shape=(200, 10)))  # LSTM层
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+model.add(Dense(300,kernel_initializer='normal',activation='relu'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.2))
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=200, batch_size=128, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+# with open('loss_200.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['loss']:
+#         f.write(str(loss) + '\n')
+# with open('val_loss_200.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['val_loss']:
+#         f.write(str(loss) + '\n')
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/real/lstm_model3.h5')
+# tf.keras.backend.clear_session()
+
+
+
+

featureRepository/lstm2.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import time
+
+import pandas as pd
+from tqdm import tqdm
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+from keras.layers import LSTM
+import tensorflow as tf
+from matplotlib import pyplot
+import numpy as np
+import random
+#改了训练集和测试集的归一化
+
+
+from tqdm import tqdm
+import os
+#先分训练集和测试集
+folder_path = "/home/lxm/1vs1/total"  # 替换为你的文件夹路径
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+data_train=pd.DataFrame()
+data_test=pd.DataFrame()
+count=0
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".csv"):
+        count+=1
+        if count<=4:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_train = pd.concat([data_train, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("train",file_path)
+        else:
+            file_path = os.path.join(folder_path, file_name)
+            # 解析CSV文件
+            df = pd.read_csv(file_path)  # 读取csv文件到dataframe中
+            data =df.iloc[:,1:]
+            data1=pd.DataFrame(data,columns=['tar_x','tar_y','tar_z','tarEli','position_change','v','v_change','r','eli_change',
+                                            'h_change','id'])
+            data_test = pd.concat([data_test, data1], axis=0, ignore_index=True)  # 将文件内容合并到data_all中
+#             print("test",file_path)
+
+import numpy as np
+#分别在测试集和训练集中分x和y
+def create_dataset(dataset,window,offset,predict_length):
+    dataX, dataY = [], []
+    for i in range(0,len(dataset)-window-1,offset):
+        if (i + window+predict_length)<len(dataset) :
+            id1=dataset[i,-1]
+            id2=dataset[i+window-1,-1]
+            id3=dataset[i+window+predict_length-1,-1]
+            if id1==id2 and id1==id3:
+                a = dataset[i:(i+window),:-1]
+                dataX.append(a)
+                dataY.append(dataset[i + window:(i + window+predict_length),0:3])
+    return np.array(dataX), np.array(dataY)
+
+
+train=data_train.values
+test=data_test.values
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+scaler.fit(train) ## 生成规则
+data_tr = scaler.transform(train) ## 将规则应用于训练集
+data_te = scaler.transform(test) ## 将规则应用于测试集
+
+train_X,train_Y=create_dataset(data_tr,200,30,100)
+test_X,test_Y=create_dataset(data_te,200,30,100)
+
+
+
+
+start_time = time.time()
+##模型定义 design network 200帧预测100帧
+
+model = Sequential()
+model.add(LSTM(units=128, return_sequences=True,input_shape=(200, 10)))  # LSTM层
+# 第二个隐藏层 LSTM
+model.add(LSTM(units=64,))
+# model.add(Dense(200,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+model.add(Dense(300,kernel_initializer='normal',activation='sigmoid'))  # 输出维度为 10 * 3 = 30
+# model.add(Dropout(0.2))
+# 重塑输出，使其成为[10, 3]
+model.add(tf.keras.layers.Reshape((100, 3)))
+model.compile(loss='mean_absolute_error', optimizer='adam')  # 使用均方误差作为损失函数，Adam优化器
+
+#模型训练 fit network
+history = model.fit(train_X, train_Y, epochs=300, batch_size=128, validation_data=(test_X, test_Y), verbose=2,
+                    shuffle=False)
+
+end_time = time.time()
+print("time:",end_time-start_time)
+#输出 plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
+# with open('loss_200.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['loss']:
+#         f.write(str(loss) + '\n')
+# with open('val_loss_200.txt','a', encoding='utf-8') as f:
+#     for loss in history.history['val_loss']:
+#         f.write(str(loss) + '\n')
+
+# 使用模型进行预测
+yhat = model.predict(test_X)
+
+
+model.save('/home/lxm/model/real/lstm_model2.h5')
+# tf.keras.backend.clear_session()
+
+
+
+

featureRepository/position_transfer.py

+import copy
+from copy import deepcopy
+import math
+import matplotlib.pyplot as plt
+#option是一个字典，确定了坐标系，cor_P是P点相对坐标（形如列表[x,y]），返回P点的经纬度（形如列表[lng,lat]）
+import numpy as np
+
+
+def gps_to_xyz(latitude, longitude, altitude, lat_ref, long_ref, alt_ref):
+    # 将经纬度转换为弧度制
+    latitude_rad = math.radians(latitude)
+    longitude_rad = math.radians(longitude)
+    lat_ref_rad = math.radians(lat_ref)
+    long_ref_rad = math.radians(long_ref)
+
+    # 地球的平均半径，单位：米
+    R = 6371000  # 注意：北纬40度附近
+
+    # 计算纬度和经度的距离差（m）
+    d_lat = (latitude_rad - lat_ref_rad) * R
+    d_long = (longitude_rad - long_ref_rad) * R * math.cos(lat_ref_rad)
+
+    # 将距离差转换为直角坐标系中的x和y坐标
+    x = d_long
+    y = d_lat
+
+    # 计算z轴上的高度差
+    z = altitude - alt_ref
+
+    result = [x, y, z]
+    return result
+
+
+def xyz_to_gps(x, y, z, lat_ref, long_ref, alt_ref):
+    # 地球的平均半径，单位：米
+    R = 6371000  # 注意：北纬40度附近，可调
+
+    # 将经纬度转换为弧度制
+    lat_ref_rad = math.radians(lat_ref)
+    long_ref_rad = math.radians(long_ref)
+
+    # 将 x、y 转换为经纬差
+    d_long = x / (R * math.cos(lat_ref_rad))
+    d_lat = y / R
+
+    # 计算待转换点的纬度和经度弧度制
+    latitude_rad = lat_ref_rad + d_lat
+    longitude_rad = long_ref_rad + d_long
+
+    # 将经纬度转换为度数制
+    latitude = math.degrees(latitude_rad)
+    longitude = math.degrees(longitude_rad)
+
+    # 计算待转换点的高度
+    altitude = z + alt_ref
+
+    result = [latitude, longitude, altitude]
+    return result
+
+
+def draw(data):
+    plt.figure(num=1, figsize=(10, 10))
+    x = data[:, 0]
+    y = data[:, 1]
+    plt.scatter(x, y)
+    # plt.xlim(-1000, 1000)
+    # plt.ylim(-0, 7001)
+    ax = plt.gca()
+    ax.spines['left'].set_position(('data', 0))
+    ax.spines['bottom'].set_position(('data', 0))
+    # x1 = np.linspace(300, 700, 500)
+    # center1 = 500 + np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # center2 = 500 - np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # plt.plot(x1, center1,c='b')
+    # plt.plot(x1, center2,c='b')
+    # x2 = np.linspace(200, 800, 500)
+    # center21 = 500 + np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # center22 = 500 - np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # plt.plot(x2, center21, c='r')
+    # plt.plot(x2, center22, c='r')
+    # x3 = np.linspace(100, 900, 500)
+    # center31 = 500 + np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # center32 = 500 - np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # plt.plot(x3, center31, c='g')
+    # plt.plot(x3, center32, c='g')
+    # model = KMeans(n_clusters=5, random_state=0).fit(data)
+    # center = model.cluster_centers_
+    # for c in center:
+    #     plt.scatter(c[0], c[1],s=200,marker='p')
+    return plt
+
+#主函数为测试样例，仅供参考
+if __name__ == '__main__':
+    point=[115.82869371103702, 39.934012711037056]
+    # users= [[115.81213005558247, 39.91493319843596], [115.80151534346862, 39.89924075328783], [115.81181641633103, 39.91345439036004], [115.81848975759513, 39.89903321215211], [115.85197567, 39.9198535], [115.80020969196244, 39.9189566370977], [115.80196883, 39.92988283], [115.79974025758239, 39.90582839900009], [115.7997408, 39.9058]]
+    users=[[115.82869371103702, 39.934012711037056],
+    [115.82882156385072, 39.93437556385077],
+    [115.82852192424052, 39.9342609242405],
+    [115.83312770598623, 39.92981770598637],
+    [115.83292091918966, 39.929899919189836],
+    [115.83301170598625, 39.92970970598638],
+    [115.8325003116783, 39.93359531167772],
+    [115.83289629147556, 39.93401129147525],
+    [115.83301557069605, 39.934121570695886]]
+    data=[]
+    for user in users:
+        data.append(gps_to_xyz(user[0],user[1],500,point[0],point[1],0))
+    data=np.array(data)
+    # print(data)
+    draw(data).show()
+
+
+    # file = open('相对坐标.txt', 'w')
+    #
+    # point_map={
+    #     "10": [117.4067305, 39.55563215],
+    #     "9": [117.4025971, 39.55762687],
+    #     "8": [117.4051509, 39.55808252],
+    #     "7": [117.4038813, 39.55929924],
+    #     "6": [117.4015306, 39.55961509],
+    #     "5": [117.4028736, 39.56110118],
+    #     "4": [117.401473, 39.5607669],
+    #     "3": [117.4035416, 39.56090042],
+    #     "2": [117.4024371, 39.56174394],
+    #     "1": [117.4018553, 39.56173589],
+    #     "回传点": [117.4049073, 39.55535354],
+    # }
+    # points=[]
+    # for key in point_map:
+    #     points.append(point_map[key])
+    # print(get_option())
+    # # option=determine_coordinate(points)
+    # option=get_option()
+    # file.write("("+str(option["cor_O"][0])+", "+str(option["cor_O"][1])+") 对应 ")
+    # file.write(str(option["ll_O"][0])+", "+str(option["ll_O"][1])+'\n')
+    # file.write("(" + str(option["cor_A"][0]) + ", " + str(option["cor_A"][1]) + ") 对应 ")
+    # file.write(str(option["ll_A"][0]) + ", " + str(option["ll_A"][1]) + '\n')
+    # file.write("(" + str(option["cor_B"][0]) + ", " + str(option["cor_B"][1]) + ") 对应 ")
+    # file.write(str(option["ll_B"][0]) + ", " + str(option["ll_B"][1]) + '\n')
+    # file.write('\n')
+    #
+    # for key in point_map:
+    #     ans=lnglat_to_coordinate(option, point_map[key])
+    #     file.write(key+", "+'('+str(ans[0])+', '+str(ans[1])+')'+'\n')
+    #
+    #
+    #
+    # file.close()
+    # uavs=[[2560.02056799,2086.20168574],
+    #  [4085.42479909,5313.20660584]]
+    # for uav in uavs:
+    #     print(coordinate_to_lnglat(option,uav))
+

featureRepository/result.txt

+50 0.0064 0.0089 18.280
+60 0.0078 0.0090 18.347
+70 0.0056 0.0052 12.885
+80 0.0058 0.0080 16.715
+90 0.0077 0.0079 16.813
+100 0.0059 0.0057 13.206
+110 0.0059 0.0060 14.344
+120 0.0175 0.0126 19.897
+
+步长
+100 0.0059 0.0069 15.384
+150 0.0057 0.0071 14.678
+200 0.0056 0.0052 12.885
+250 0.0060 0.0064 14.033
+300 0.0078 0.0109 18.321
\ No newline at end of file

featureRepository/trajectory.py

+import xml.etree.ElementTree as ET
+import os
+import pandas as pd
+from tqdm import tqdm
+import concurrent.futures
+
+
+
+
+#我们创建了一个空的DataFrame，包含四个列：id、title、author和price。
+# df = pd.DataFrame(columns=['id','number', 'longitude', 'latitude','altitude', 'yaw', 'pitch', 'roll',
+#                                                     'tarRng', 'tarAzi', 'tarEli'])
+count=1
+
+
+def processFile(file_path,filename):
+    filename=filename.split(".")[0]
+    df = pd.DataFrame(columns=['number', 'longitude', 'latitude', 'altitude', 'yaw', 'pitch', 'roll',
+                               'tarRng', 'tarAzi', 'tarEli'])
+    tree = ET.parse(file_path)
+    replay = tree.getroot()
+    objectsSequences = replay.find('objectsSequences')
+    for sequence in objectsSequences:
+        group = sequence.find('group').text
+        if group == '0':
+            frames = sequence.find('frames')
+            for frame in frames:
+                number = frame.find('number').text
+                item = frame.find('data')
+                if item.get('Type') == '1':
+                    longitude = item.find('longitude').text
+                    if longitude=="0.000000":
+                        continue
+                    latitude = item.find('latitude').text
+                    altitude = item.find('altitude').text
+                    yaw = item.find('yaw').text
+                    pitch = item.find('pitch').text
+                    roll = item.find('roll').text
+
+                    devices = item.find('devices')
+                    tarRng = ''
+                    tarAzi = ''
+                    tarEli = ''
+                    if devices != None:
+                        for device in devices:
+                            if device.get('Type') == "F1911::Radar":
+                                tracks = device.find("tracks")
+                                if tracks != None:
+                                    track = tracks.find("track")
+                                    if track != None:
+                                        tarRng = track.find('tarRng').text
+                                        tarAzi = track.find('tarAzi').text
+                                        tarEli = track.find('tarEli').text
+                    if(tarRng==''):
+                        continue
+                    df = df.append(
+                        pd.Series([number, longitude, latitude, altitude, yaw, pitch, roll, tarRng, tarAzi, tarEli],
+                                  index=['number', 'longitude', 'latitude', 'altitude', 'yaw', 'pitch', 'roll',
+                                         'tarRng', 'tarAzi', 'tarEli']), ignore_index=True)
+    df.to_excel('E:/replay/1对1/original_data/1/'+filename+'.xlsx', index=False)
+    return filename+'finish'
+
+if __name__ == '__main__':
+    folder_path = "E:/replay/replay/20230602_1对1/1"  # 替换为你的文件夹路径
+    filename='default - 20230601-133944.replay'
+    file_path='E:/replay/replay/20230602_1对1/1/default - 20230601-133944.replay'
+    processFile(file_path,filename)
+
+
+    # file_list = os.listdir(folder_path)
+    # results=[]
+    # count=1
+    # with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
+    #     for file_name in tqdm(file_list, desc="Processing files"):
+    #         if file_name.endswith(".replay"):
+    #             file_path = os.path.join(folder_path, file_name)
+    #             # 解析XML文件
+    #             result=executor.submit(processFile,file_path, file_name)
+    #             results.append(result)
+    #     for future in concurrent.futures.as_completed(results):
+    #         print(count)
+    #         print(future.result())
+    #         count+=1
+    #
+    #
+
+
+
+

featureRepository/trajectory_old.py

+import xml.etree.ElementTree as ET
+import os
+import pandas as pd
+from tqdm import tqdm
+
+folder_path = "E:/replay/replay/20230602_1对1/1"  # 替换为你的文件夹路径
+# folder_path ="E:/replay/demo"
+# # 获取文件夹中的所有文件
+file_list = os.listdir(folder_path)
+
+#我们创建了一个空的DataFrame，包含四个列：id、title、author和price。
+df = pd.DataFrame(columns=['id','number', 'longitude', 'latitude','altitude', 'yaw', 'pitch', 'roll',
+                                                    'tarRng', 'tarAzi', 'tarEli'])
+count=1
+for file_name in tqdm(file_list, desc="Processing files"):
+    if file_name.endswith(".replay"):
+        file_path = os.path.join(folder_path, file_name)
+        id = count
+        count += 1
+        # 解析XML文件
+        tree = ET.parse(file_path)
+        replay = tree.getroot()
+        objectsSequences=replay.find('objectsSequences')
+        for sequence in objectsSequences:
+            group=sequence.find('group').text
+            if group=='0':
+                frames= sequence.find('frames')
+                for frame in frames:
+                    number=frame.find('number').text
+                    item=frame.find('data')
+                    if item.get('Type')=='1':
+                        longitude = item.find('longitude').text
+                        latitude = item.find('latitude').text
+                        altitude = item.find('altitude').text
+                        yaw = item.find('yaw').text
+                        pitch = item.find('pitch').text
+                        roll=item.find('roll').text
+
+                        devices = item.find('devices')
+                        tarRng = ''
+                        tarAzi = ''
+                        tarEli = ''
+                        if devices != None:
+                            for device in devices:
+                                if device.get('Type') == "F1911::Radar":
+                                    tracks = device.find("tracks")
+                                    if tracks!=None:
+                                        track =tracks.find("track")
+                                        if track!=None:
+                                            tarRng = track.find('tarRng').text
+                                            tarAzi = track.find('tarAzi').text
+                                            tarEli = track.find('tarEli').text
+                        df = df.append(pd.Series([id,number, longitude, latitude, altitude,yaw, pitch, roll, tarRng, tarAzi, tarEli],
+                                                 index=['id','number', 'longitude', 'latitude','altitude', 'yaw', 'pitch', 'roll',
+                                                        'tarRng', 'tarAzi', 'tarEli']), ignore_index=True)
+
+
+
+
+
+df.to_excel('E:/replay/data/trajectory1.xlsx', index=False)
+
+

featureRepository/val_loss.txt

+0.13156269490718842
+0.08380892127752304
+0.08336402475833893
+0.08330120146274567
+0.06786628067493439
+0.06781288981437683
+0.06777195632457733
+0.06775036454200745
+0.06773287802934647
+0.06773624569177628
+0.06772735714912415
+0.06772556155920029
+0.06772372871637344
+0.06772041320800781
+0.06771867722272873
+0.06771715730428696
+0.06771635264158249
+0.06771714985370636
+0.0677139014005661
+0.06771580129861832
+0.0677129402756691
+0.06771411001682281
+0.06771273165941238
+0.0677129328250885
+0.06771278381347656
+0.06771276146173477
+0.06771276146173477
+0.06771276891231537
+0.009751547127962112
+0.009530491195619106
+0.009525801986455917
+0.009523127228021622
+0.009520863182842731
+0.009519031271338463
+0.00951846968382597
+0.009516251273453236
+0.009514686651527882
+0.009514556266367435
+0.009514677338302135
+0.0095140989869833
+0.009513485245406628
+0.00951301772147417
+0.009512774646282196
+0.009512780234217644
+0.009512978605926037
+0.009513217024505138
+0.00951334647834301
+0.009513288736343384
+0.009513027034699917
+0.009512611664831638
+0.00951218232512474
+0.009511832147836685
+0.00951155461370945
+0.009511313401162624
+0.009511078707873821
+0.009510821662843227
+0.009510600008070469
+0.00951029546558857
+0.00951030757278204
+0.009509805589914322
+0.009510057047009468
+0.009509586729109287
+0.009509543888270855
+0.009509347379207611
+0.009509450756013393
+0.009509209543466568
+0.009509430266916752
+0.00950931292027235
+0.009509033523499966
+0.009509569965302944
+0.009509153664112091
+0.009509089402854443
+0.009509126655757427
+0.009509110823273659
+0.009509342722594738
+0.009509074501693249
+0.009509212337434292
+0.009509410709142685
+0.009509270079433918
+0.009509287774562836
+0.009509256109595299
+0.009509343653917313
+0.009509437717497349
+0.00950941164046526
+0.009509376250207424
+0.009509420022368431
+0.009509126655757427
+0.00950985960662365
+0.009506545960903168
+0.009508593007922173
+0.009508511051535606
+0.00950886495411396
+0.009508886374533176
+0.009508845396339893
+0.009508797898888588
+0.009508750401437283
+0.009508705697953701
+0.009508660063147545
+0.009508607909083366
+0.009508557617664337

multiple_objective_new/.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

multiple_objective_new/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

multiple_objective_new/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

multiple_objective_new/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/multiple_objective.iml" filepath="$PROJECT_DIR$/.idea/multiple_objective.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

multiple_objective_new/.idea/multiple_objective.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

multiple_objective_new/GDOP.py

+import numpy as np
+from numpy.linalg import LinAlgError
+from numpy import seterr
+seterr(all='raise')
+
+
+def get_toa_gdop(bs_list, xyz):
+    """
+    compute the gdop of the toa
+    :param bs_list: the base station coordinate
+    :param xyz: the location coordinate of the tag
+    :return: the gdop value
+    """
+    bs_n = len(bs_list)
+    if bs_n < 3:
+        return None
+    h1 = np.mat(np.zeros((bs_n, 3)))
+
+    for i in range(0, len(bs_list)):
+        ri = np.sqrt((bs_list[i][0] - xyz[0]) ** 2 +
+                     (bs_list[i][1] - xyz[1]) ** 2)
+        if ri == 0:
+            h1[i, 0] = 0
+            h1[i, 1] = 0
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+        else:
+            h1[i, 0] = (bs_list[i][0] - xyz[0]) / ri
+            h1[i, 1] = (bs_list[i][1] - xyz[1]) / ri
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+    m_tmp = h1.T * h1
+    gdop_value = None
+
+    try:
+        g = m_tmp.I
+        gdop_value = np.sqrt(g[0, 0] + g[1, 1])
+        # print(gdop_value)
+    finally:
+        return gdop_value
+
+
+# 计算GDOP 不考虑时钟误差版本即DOP
+def calculate_gdop(uavs, estimate_value):
+    """
+    # :param point: 待定位点真实坐标
+    :param uavs: 无人机坐标集
+    :param estimate_value: 解算出的坐标
+    :return: gdop
+    """
+    # estimate_value.append(0)
+    estimate_value=[estimate_value[0],estimate_value[1],0]
+    var1 = np.array(uavs) - estimate_value
+    # 计算距离列向量
+    d_estimate = (np.sum(var1**2, axis=1)**0.5).reshape(-1, 1)
+    # 计算visible MATRIX
+    H = var1 / d_estimate
+    GDOP = float('inf')
+    try:
+        GDOP = np.trace(np.linalg.inv(np.dot(np.transpose(H), H))) ** 0.5
+    except:
+        GDOP=float('inf')
+    finally:
+        return GDOP
+
+
+
+if __name__ == '__main__':
+    bs_list=[[1,1,1],[2,3,4],[6,6,5]]
+    bs_list2 = [[1, 3, 3], [3, 1, 3], [5, 4, 3]]
+    xyz=[3,3.167,0]
+    print(calculate_gdop(bs_list,xyz))
+    print(get_toa_gdop(bs_list2,xyz))
\ No newline at end of file

multiple_objective_new/Match.py

+import math
+import numpy as np
+from sklearn.cluster import KMeans
+import km
+import GDOP
+from itertools import combinations
+from pprint import pprint
+
+
+import tool
+import copy
+
+
+class Match:
+    def __init__(self,users,uavs):
+        self.id = id
+        # self.cpUsers,self.pUsers,self.cUsers=Users.getAllUsers()
+        self.allUsers =users
+        # 4台无人机，每台服务20个人
+        self.uavs = uavs
+        self.serveMax = 20
+        self.distance= self.getUavAndUserDistance()
+        self.uavConnectNum = [0 for col in range(len(self.uavs))]
+        self.uav_group = list(combinations(self.uavs, 3))
+        self.snr_thre=0
+        self.Pmax=0.1
+
+        self.p_connection, self.c_connection = self.get_all_match()
+        self.uav_to_user,self.uav_to_cuser,self.uav_to_puser = self.get_uav_to_user()
+
+
+    def getUavInitialPos(self, k):
+        model = KMeans(n_clusters=k,  # 聚类簇数
+                       random_state=1,  # 决定质心初始化的随机数生成，使用int使随机性具有确定性。
+                       max_iter=300,  # 执行一次k-means算法所进行的最大迭代数，默认300
+                       ).fit(self.allUsers)
+        center = model.cluster_centers_
+        uavs = []
+        # center=[[250, 250], [250, 750], [750, 250], [750, 750]]
+        for item in center:
+            height = 100
+            position = [int(item[0]), int(item[1]), height]
+            uavs.append(position)
+        # print(center)
+        return uavs
+
+    def getUavAndUserDistance(self):
+        user_num = len(self.allUsers)
+        uav_num = len(self.uavs)
+        distance = np.zeros((user_num, uav_num), dtype=np.float16)
+
+        for i in range(user_num):
+            for j in range(uav_num):
+                distance[i][j] = self.calculateDistance(self.allUsers[i], self.uavs[j])
+
+        return distance
+
+    def calculateDistance(self, user, uav):
+        distanceSquare = (user[0] - uav[0]) ** 2 + (user[1] - uav[1]) ** 2 + uav[2] ** 2
+        return math.sqrt(distanceSquare)
+
+    def get_appropriate_uavs(self, user):
+        uavs = [k for k, v in enumerate(self.uavConnectNum) if v < self.serveMax]
+        appropriate_uavs = []
+        for uav in uavs:
+            snr = tool.cal_snr(self.uavs[uav], user, self.Pmax)
+            if snr > self.snr_thre:
+                appropriate_uavs.append(uav)
+        return appropriate_uavs
+
+
+    # 获取定位匹配
+    def getPositionConnection(self, users):
+        # for user in users:
+        # 计算该user能通信的uav
+        # 获得uav组合
+        # 计算user与uav组合的gdop
+        # 选择最小的gdop组合，用字典保存,并且更新服务次数
+        user_to_uav_group = {}
+        for i in range(len(users)):
+            uavs = self.get_appropriate_uavs(users[i])
+            uav_group = list(combinations(uavs, 3))
+            gdop_min = float('inf')
+            index = 0
+            for j in range(len(uav_group)):
+                uavs_pos = [self.uavs[uav_group[j][0]], self.uavs[uav_group[j][1]], self.uavs[uav_group[j][2]]]
+                gdop_current = GDOP.calculate_gdop(uavs_pos, copy.copy(users[i]))
+                if gdop_current < gdop_min:
+                    gdop_min = gdop_current
+                    index = j
+            user_to_uav_group[i] = uav_group[index]
+            self.update_serve_num(uav_group[index])
+        return user_to_uav_group
+
+    # 纯定位用户的匹配
+    def getPConnection(self):
+        # cp_connection=self.getCPConnection()
+        p_connection = self.getPositionConnection(self.allUsers)
+
+        return p_connection
+
+    def getCommunicationConnection(self):
+        user_num = len(self.allUsers)
+        uavs = [k for k, v in enumerate(self.uavConnectNum) if v < self.serveMax]
+        uav_num = len(self.uavs)
+        distance_sum = self.distance.sum(axis=1)  # 按照行求和
+        # print(distance_sum)
+        weight = np.zeros((user_num, uav_num), dtype=np.float16)
+        graph = []
+        for i in range(user_num):
+            for j in uavs:
+                # weight[i][j]=-self.distance_c[i][j]/(distance_sum[i]-self.distance_c[i][j])
+                weight[i][j] = -self.distance[i][j]
+        dic = {}
+        for i in range(user_num):
+            for j in uavs:
+                for number in range(self.serveMax - self.uavConnectNum[j]):
+                    graph.append((i, j + 100 * number, weight[i][j]))
+                    dic[j + 100 * number] = j
+
+        connection = km.run_kuhn_munkres(graph)
+        c_connection = {}
+        for connect in connection:
+            c_connection[connect[0]] = dic[connect[1]]
+
+        self.update_serve_num_by_c_connection(c_connection)
+
+        return c_connection
+
+    def update_serve_num(self, uavs):
+        for u in uavs:
+            self.uavConnectNum[u] += 1
+
+    # 根据通信连接更新服务次数
+    def update_serve_num_by_c_connection(self, c_connection):
+        for key in c_connection:
+            self.uavConnectNum[c_connection[key]] += 1
+
+    def get_all_match(self):
+        p_connection = self.getPConnection()
+        c_connection = self.getCommunicationConnection()
+        return p_connection, c_connection
+
+    def get_uav_to_user(self):
+        uav_to_user = {}
+        uav_to_cuser={}
+        uav_to_puser={}
+
+        for i in range(len(self.uavs)):
+            uav_to_user[i] = []
+            uav_to_cuser[i]=[]
+            uav_to_puser[i]=[]
+        # 编号规则：定位0-19,通信100-119
+        for key in self.p_connection:
+            for uav_index in self.p_connection[key]:
+                uav_to_user[uav_index].append(key)
+                uav_to_puser[uav_index].append(key)
+
+        for key in self.c_connection:
+            uav_to_user[self.c_connection[key]].append(key + 100)
+            uav_to_cuser[self.c_connection[key]].append(key)
+
+        return uav_to_user,uav_to_cuser,uav_to_puser
+
+    def dbmToW(self, p):
+        return pow(10, p / 10) / 1000
+
+
+if __name__ == '__main__':
+    m = Match()
+    # pprint(m.getPConnection())
+    # print(m.get_uav_to_user())
+    print(m.p_connection,m.c_connection)
+    print(m.uav_to_user)
+    # pprint(m.getPConnection())
+    # pprint(m.getCommunicationConnection())
+    print(m.uavConnectNum)
+    print(m.get_min_speb())

multiple_objective_new/NSGA2.py

+# Program Name: NSGA-II.py
+# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm
+# Author: Haris Ali Khan
+# Supervisor: Prof. Manoj Kumar Tiwari
+
+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+
+
+
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return (sum/len(users))
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        # print(gdop)
+        sum+=gdop
+    return -sum/len(users)
+
+def punish(power,p_rest):
+    f = 0
+    i=0
+    for p in power:
+        dis = sum(p) - p_rest[i]
+        if dis > 0.001:
+            f += 10 ** (dis * 100)
+        for p_singe in p:  # 功率要大于0
+            dis1 = 0 - p_singe
+            if dis1>0:
+                f += 10 ** max(0, dis1 * 100)
+        i+=1
+    return f
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+#Function to sort by values
+def sort_by_values(list1, values):
+    sorted_list = []
+    while(len(sorted_list)!=len(list1)):
+        if index_of(min(values),values) in list1:
+            sorted_list.append(index_of(min(values),values))
+        values[index_of(min(values),values)] = math.inf
+    return sorted_list
+
+#Function to carry out NSGA-II's fast non dominated sort
+def fast_non_dominated_sort(values1, values2):
+    #支配的节点
+    S=[[] for i in range(0,len(values1))]
+    front = [[]]
+    n=[0 for i in range(0,len(values1))]#被支配的数量
+    rank = [0 for i in range(0, len(values1))]#等级
+
+    for p in range(0,len(values1)):
+        S[p]=[]
+        n[p]=0
+        for q in range(0, len(values1)):
+            if (values1[p] > values1[q] and values2[p] > values2[q]) or (values1[p] >= values1[q] and values2[p] > values2[q]) or (values1[p] > values1[q] and values2[p] >= values2[q]):
+                if q not in S[p]:
+                    S[p].append(q)
+            elif (values1[q] > values1[p] and values2[q] > values2[p]) or (values1[q] >= values1[p] and values2[q] > values2[p]) or (values1[q] > values1[p] and values2[q] >= values2[p]):
+                n[p] = n[p] + 1
+        if n[p]==0:
+            rank[p] = 0
+            if p not in front[0]:
+                front[0].append(p)
+
+    i = 0
+    while(front[i] != []):
+        Q=[]
+        for p in front[i]:
+            for q in S[p]:
+                n[q] =n[q] - 1
+                if( n[q]==0):
+                    rank[q]=i+1
+                    if q not in Q:
+                        Q.append(q)
+        i = i+1
+        front.append(Q)
+
+    del front[len(front)-1]
+    return front,rank
+
+#Function to calculate crowding distance
+def crowding_distance(values1, values2, front):
+    distance = [0 for i in range(0,len(front))]
+    #从小到大排序
+    sorted1 = sort_by_values(front, values1[:])
+    sorted2 = sort_by_values(front, values2[:])
+    distance[0] = 4444444444444444
+    distance[len(front) - 1] = 4444444444444444
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values1[sorted1[k+1]] - values1[sorted1[k-1]])/(max(values1)-min(values1))
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values2[sorted2[k+1]] - values2[sorted2[k-1]])/(max(values2)-min(values2))
+    return distance
+
+
+#初始化无人机位置
+def init(pop_size,k,users):
+    min_pos=0
+    max_pos=1000
+    pos_solution=[]
+    p_solution=[]
+    for i in range(pop_size):
+        uavs_pos=[[min_pos+(max_pos-min_pos)*random.random(),min_pos+(max_pos-min_pos)*random.random(),100] for i in range(k)]
+        p_alloc=init_p(uavs_pos,users)
+        pos_solution.append(uavs_pos)
+        p_solution.append(p_alloc)
+    # print(pos_solution)
+    return pos_solution,p_solution
+
+def get_dis(i,uavs_pos,users,uav_to_user):
+    dis=[]
+    for k in uav_to_user[i]:
+        dis.append(tool.calculate_3d_distance(uavs_pos[i],users[k]))
+    return dis
+
+def init_p(uavs_pos,users):
+    c_association=tool.get_c_association(users,uavs_pos,math.ceil(len(users)/len(uavs_pos)))
+    uav_to_user=tool.get_uav_to_user(c_association,uavs_pos)
+    # print(uav_to_user)
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2)
+    p_rest=tool.get_rest_p(users,uavs_pos,p_association)
+    # print(p_rest)
+    p_alloc=[]
+    for i in range(len(uavs_pos)):
+        #归一化生成 功率
+        dis=get_dis(i,uavs_pos,users,uav_to_user)
+        # p=[random.randint(1,len(uav_to_user[i])) for i in range(len(uav_to_user[i]))]
+        p_new=[0 for i in range(len(users))]
+        for j in range(len(uav_to_user[i])):
+            p_new[uav_to_user[i][j]]=dis[j]/sum(dis)*p_rest[i]
+            # p_new.append(p[j]/sum(p)*p_rest[i])
+        p_alloc.append(p_new)
+        # p_alloc.append(np.random.uniform(0, p_rest[i] / len(uav_to_user[i]), (len(uav_to_user[i]))))
+    return p_alloc
+
+def cross_over_old(pos_solution1,pos_solution2):
+    child1 = [pos_solution2[j] if random.random() < 0.5 else pos_solution1[j]
+              for j in range(len(pos_solution2))]
+    return child1
+
+def cross_over_stage(pos_solution1,pos_solution2,iter,iter_max):
+    p_cross=get_beta_stage(iter,iter_max)
+    child1 = [pos_solution2[j] if random.random() < p_cross else pos_solution1[j]
+              for j in range(len(pos_solution2))]
+    return child1
+
+#交叉 pos_solution1=[[],[],[]]
+def cross_over(pos_solution1,pos_solution2,iter,iter_max):
+    p_cross=get_beta(iter,iter_max)
+    child1 = [pos_solution2[j] if random.random() < p_cross else pos_solution1[j]
+              for j in range(len(pos_solution2))]
+    # for i in range(len(pos_solution1)):
+    #     pos1 = []
+    #     pos2=[]
+    #     beta = get_beta()
+    #
+        # for j in range(len(pos_solution1[i])):
+        #     if j<len(pos_solution1[i])-1:
+        #         x1 = (pos_solution1[i][j] + pos_solution2[i][j]) / 2
+        #         x2 = abs((pos_solution1[i][j] - pos_solution2[i][j]) / 2)
+        #         x1=x1 + beta * x2
+        #         x2=abs(x1 - beta * x2)
+        #         pos1.append(x1)
+        #         pos2.append(x2)
+        #     else:
+        #         pos1.append(100)
+        #         pos2.append(100)
+        # child1.append(pos1)
+        # child2.append(pos2)
+    return child1
+
+def cross_over2(pos_solution1,pos_solution2,iter,iter_max):
+    p_cross=get_beta2(iter,iter_max)
+    child1 = [pos_solution2[j] if random.random() < p_cross else pos_solution1[j]
+              for j in range(len(pos_solution2))]
+    return child1
+
+def get_beta_old(crossover_param=2):
+    u = random.random()
+    if u <= 0.5:
+        return (2 * u) ** (1 / (crossover_param + 1))
+    return (2 * (1 - u)) ** (-1 / (crossover_param + 1))
+def get_beta_stage(iter,iter_max):
+    if iter/iter_max<=0.34:
+        return 0.9
+    elif iter/iter_max>0.34 and iter/iter_max<=0.67:
+        return 0.4
+    else:
+        return 0.1
+def get_beta(iter,iter_max,p_max=0.9,p_min=0.1,crossover_param=2):
+    return p_max-(p_max-p_min)*iter/iter_max
+    # u = random.random()
+    # if u <= 0.5:
+    #     return (2 * u) ** (1 / (crossover_param + 1))
+    # return (2 * (1 - u)) ** (-1 / (crossover_param + 1))
+def get_beta2(iter,iter_max,p_max=0.9,p_min=0.1,crossover_param=2):
+    return p_min+(p_max-p_min)/2*(1+math.cos(iter/iter_max*math.pi))
+
+def get_delta(iter,iter_max,p_max=0.1,p_min=0.01,mutation_param=5):
+    u = random.random()
+    a=random.random()
+    delta=p_max-(p_max-p_min)*iter/iter_max
+    if a<0.5:
+        return u,delta
+    return u,-delta
+    # if u < 0.5:
+    #     return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    # return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+
+def get_delta2(iter,iter_max,p_max=0.1,p_min=0.01,mutation_param=5):
+    u = random.random()
+    a=random.random()
+    delta=p_min+(p_max-p_min)/2*(1+math.cos(iter/iter_max*math.pi))
+    if a<0.5:
+        return u,delta
+    return u,-delta
+
+def get_delta_stage(iter,iter_max):
+    u = random.random()
+    a=random.random()
+    if iter / iter_max <= 0.34:
+        delta = 0.1
+    elif iter / iter_max > 0.34 and iter / iter_max <= 0.67:
+        delta = 0.05
+    else:
+        delta = 0.01
+    if a<0.5:
+        return u,delta
+    return u,-delta
+    # if u < 0.5:
+    #     return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    # return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+def get_delta_old(mutation_param=5):
+    u = random.random()
+    return u,0.05
+    # if u < 0.5:
+    #     return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    # return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+
+def mutate_old(child,variables_range):
+    u, delta = get_delta_old()
+    child_new = []
+    if u < 0.5:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x = child[i][j] + delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x = child[i][j] + delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j < len(child_new[i]) - 1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+def mutate_stage(child,variables_range,iter,iter_max):
+    u, delta = get_delta_stage(iter,iter_max)
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+def mutate(child,variables_range,iter,iter_max):
+    u, delta = get_delta(iter,iter_max)
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+def mutate2(child,variables_range,iter,iter_max):
+    u, delta = get_delta2(iter,iter_max)
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+
+def get_function_values(pop_size,pos_solution,p_solution,users):
+    function1_values = []
+    function2_values = []
+    punish_values = []
+    for i in range(pop_size):
+        uavs_pos = pos_solution[i]
+        c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+        p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+        p_rest = tool.get_rest_p(users, uavs_pos, p_association)
+        function1_values.append(function_snr(users, pos_solution[i], c_association, p_solution[i]))
+        function2_values.append(function_gdop(users, uavs_pos, c_association, p_association, ))
+        punish_values.append(punish(p_solution[i], p_rest))
+    return function1_values,function2_values,punish_values
+
+
+def tournament(pos_solution,non_dominated_sorted_solution,rank,distance):
+    a1 = random.randint(0, len(pos_solution) - 1)
+    b1=a1
+    while b1==a1:
+        b1 = random.randint(0, len(pos_solution) - 1)
+    participants = [pos_solution[a1],pos_solution[b1]]
+    best = None
+    for participant in participants:
+        if best is None or crowding_operator(a1,b1,non_dominated_sorted_solution,rank,distance) == 1:
+            best = participant
+    return best
+
+def get_f_by_pos(pos,users):
+    c_association = tool.get_c_association(users, pos, math.ceil(len(users) / len(pos)))
+    p_association = tool.get_p_association(users, pos, c_association, len(users) * 2 / len(pos))
+    p = init_p(pos, users)
+    function1_value=function_snr(users, pos, c_association, p)
+    function2_value=function_gdop(users, pos, c_association, p, )
+    return function1_value,function2_value
+
+def crowding_operator(index1,index2,non_dominated_sorted_solution,rank,distance):
+    rank1=rank[index1]#index1的等级
+    rank2=rank[index2]
+    distance1=0
+    distance2=0
+    for i in range(len(non_dominated_sorted_solution[rank1])):
+        if non_dominated_sorted_solution[rank1][i]==index1:
+            distance1=distance[rank1][i]
+    for i in range(len(non_dominated_sorted_solution[rank2])):
+        if non_dominated_sorted_solution[rank2][i]==index2:
+            distance2=distance[rank2][i]
+    if rank1<rank2 or (rank1==rank2 and distance1>distance2):
+        return 1
+    else:
+        return 0
+
+def drawResultAndIter(resultAndIter):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(resultAndIter)
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('fitness')
+    plt.show()
+
+if __name__ == '__main__':
+    num=[5, 7, 4, 4, 4, 4, 4, 8, 9, 7, 9, 8, 8, 9, 9, 9, 11, 13, 14, 12, 15, 14, 15, 14, 14, 14, 12, 14, 14, 15, 15, 15, 19, 19, 19, 19, 18, 18, 19, 15, 17, 17, 17, 17, 17, 18, 19, 19, 17, 17, 18, 20, 15, 16, 16, 16, 17, 17, 17, 18, 18, 19, 19, 19, 20, 22, 25, 25, 25, 26, 26, 26, 26, 27, 28, 30, 21, 22, 23, 24, 25, 26, 27, 27, 28, 29, 29, 29, 28, 28, 29, 29, 29, 29, 31, 30, 30, 26, 26, 27, 27, 27, 27, 27, 28, 29, 29, 29, 30, 33, 35, 35, 34, 36, 36, 39, 41, 40, 40, 41, 40, 42, 42, 42, 43, 43, 43, 46, 46, 48, 48, 52, 54, 54, 58, 59, 59, 60]
+    drawResultAndIter(num)
+

multiple_objective_new/NSGA2_TS.py

+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+import NSGA2 as ns
+import TS as ts
+
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=ns.init(pop_size,k,users)
+    # print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    function1_best=[]
+    function2_best=[]
+    solution_num=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=ns.get_function_values(pop_size,pos_solution,p_solution,users)
+        non_dominated_sorted_solution,rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(ns.crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        #todo 采用TS对父代每个个体进行搜索
+        # for index in range(len(pos_solution)):
+        #     pos_solution[index]=ts.tabu_search(pos_solution[index],users,10)
+        #     p_solution[index]=ns.init_p(pos_solution[index],users)
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            # child = ns.cross_over_old(parent1, parent2)
+            # child_mute = ns.mutate_old(child, pos_range)
+            child = ns.cross_over(parent1, parent2, gen_no, max_gen)
+            child_mute = ns.mutate(child, pos_range, gen_no, max_gen)
+            child_p=ns.init_p(child_mute,users)
+            #todo 对每个子代进行TS
+            # child_mute=ts.tabu_search(child_mute,users,10)
+            # child_p=ns.init_p(child_mute,users)
+            pos_solution2.append(child_mute)
+            p_solution2.append(child_p)
+
+        function1_values2, function2_values2, punish_values2 = ns.get_function_values(2*pop_size, pos_solution2, p_solution2,users)
+        non_dominated_sorted_solution2,rank2 = ns.fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                ns.crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = ns.sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+
+        function1_best,function2_best,_=ns.get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution,users)
+
+        best_pos,rank=ns.fast_non_dominated_sort(function1_best[:], function2_best[:])
+        solution_num.append(len(best_pos[0]))
+
+        if len(best_pos[0]) >= pop_size:
+            break
+
+        # if len(best_pos[0])> 30:
+        # for index in range(len(pos_solution)):
+        #     pos_solution[index] = ts.tabu_search(pos_solution[index], users, 5, rank[index], len(best_pos),gen_no,max_gen)
+        #     p_solution[index] = ns.init_p(pos_solution[index], users)
+            # for r in range(1,len(best_pos)):#取所有支配解
+            #     for index in best_pos[r]:
+            #         pos_solution[index] = ts.tabu_search(pos_solution[index], users, 5, rank[index], len(best_pos))
+            #         p_solution[index] = ns.init_p(pos_solution[index], users)
+        if len(best_pos[0])< pop_size/2:
+            # for r in range(0, len(best_pos)):
+            #     for index in best_pos[r]:
+            #         pos_solution[index] = ts.tabu_search(pos_solution[index], users, 1, len(best_pos[0]), 60,r,len(best_pos))
+            #         p_solution[index] = ns.init_p(pos_solution[index], users)
+            for index in range(len(pos_solution)):
+                pos_solution[index] = ts.tabu_search(pos_solution[index], users, 1, len(best_pos[0]), pop_size,1,len(best_pos))
+                p_solution[index] = ns.init_p(pos_solution[index], users)
+
+        gen_no+=1
+    for i in range(len(best_pos[0])):
+        f1_best.append(function1_best[best_pos[0][i]])
+        f2_best.append(function2_best[best_pos[0][i]])
+
+    return f1_best,f2_best,solution_num
+
+def compare_pop():
+    pop=[i for i in range(10,110,10)]
+    filename = "./data.csv"
+    print(pop)
+    times=[]
+    for i in pop:
+        run_time=run_pop(filename,i,150,3)
+        times.append(run_time)
+    print(times)
+
+
+def run_pop(filename,pop_size,iter_max,uav_num):
+    users = Users.getUsers(filename)
+    start_time = time.time()
+    f1, f2, solution_num = main(users, pop_size, iter_max, uav_num)
+    end_time = time.time()
+    run_time = end_time - start_time
+    return run_time
+
+def run(filename,pop_size,iter_max,uav_num):
+    users = Users.getUsers(filename)
+    start_time = time.time()
+    f1, f2, solution_num = main(users, pop_size, iter_max, uav_num)
+    end_time = time.time()
+    run_time = end_time - start_time
+    print(f1, f2)
+    print("程序运行时间为：", run_time)
+    print(solution_num)
+    plt.xlabel('Average snr', fontsize=12)
+    plt.ylabel('Average -gdop', fontsize=12)
+
+    plt.scatter(f1, f2, )
+
+    plt.show()
+def run_ts():
+    filename = "./data.csv"
+    times=[]
+    for i in range(10):
+        run_time = run_pop(filename, 30, 150, 3)
+        print("time-",run_time)
+        times.append(run_time)
+    print(times)
+
+
+if __name__ == '__main__':
+    # compare_pop()
+    filename="./data.csv"
+    # run_ts()
+    # print(run_pop(filename,60,200,3))
+    run(filename,60,200,3)
+    # users = Users.getUsers("./data.csv")
+    # start_time = time.time()
+    # f1,f2,solution_num=main(users,60,120,3)
+    # end_time = time.time()
+    # run_time = end_time - start_time
+    # print(f1, f2)
+    # print("程序运行时间为：", run_time)
+    # print(solution_num)
+    # plt.xlabel('Average snr', fontsize=12)
+    # plt.ylabel('Average -gdop', fontsize=12)
+    #
+    # plt.scatter(f1, f2, )
+    #
+    # plt.show()
+

multiple_objective_new/NSGA2_TS_avg.py

+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+import NSGA2 as ns
+import TS as ts
+
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+#初始化无人机位置
+def init(pop_size,k,users):
+    min_pos=0
+    max_pos=1000
+    pos_solution=[]
+    p_solution=[]
+    for i in range(pop_size):
+        uavs_pos=[[min_pos+(max_pos-min_pos)*random.random(),min_pos+(max_pos-min_pos)*random.random(),100] for i in range(k)]
+        p_alloc=init_p(uavs_pos,users)
+        pos_solution.append(uavs_pos)
+        p_solution.append(p_alloc)
+    # print(pos_solution)
+    return pos_solution,p_solution
+
+def init_p(uavs_pos,users):
+    c_association=tool.get_c_association(users,uavs_pos,math.ceil(len(users)/len(uavs_pos)))
+    uav_to_user=tool.get_uav_to_user(c_association,uavs_pos)
+    # print(uav_to_user)
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2)
+    p_rest=tool.get_rest_p(users,uavs_pos,p_association)
+    # print(p_rest)
+    p_alloc=[]
+    for i in range(len(uavs_pos)):
+        #平分功率
+        # p=[random.randint(1,len(uav_to_user[i])) for i in range(len(uav_to_user[i]))]
+        p_new=[0 for i in range(len(users))]
+        for j in range(len(uav_to_user[i])):
+            p_new[uav_to_user[i][j]]=1/len(uav_to_user[i])*p_rest[i]
+            # p_new.append(p[j]/sum(p)*p_rest[i])
+        p_alloc.append(p_new)
+        # p_alloc.append(np.random.uniform(0, p_rest[i] / len(uav_to_user[i]), (len(uav_to_user[i]))))
+    return p_alloc
+def get_beta(crossover_param=2):
+    u = random.random()
+    if u <= 0.5:
+        return (2 * u) ** (1 / (crossover_param + 1))
+    return (2 * (1 - u)) ** (-1 / (crossover_param + 1))
+
+def mutate(child,variables_range):
+    u, delta = get_delta()
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+def get_delta(mutation_param=5):
+    u = random.random()
+    if u < 0.5:
+        return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+def cross_over(pos_solution1,pos_solution2):
+    child1=[]
+    child2=[]
+    for i in range(len(pos_solution1)):
+        pos1 = []
+        pos2=[]
+        beta = get_beta()
+        for j in range(len(pos_solution1[i])):
+            if j<len(pos_solution1[i])-1:
+                x1 = (pos_solution1[i][j] + pos_solution2[i][j]) / 2
+                x2 = abs((pos_solution1[i][j] - pos_solution2[i][j]) / 2)
+                x1=x1 + beta * x2
+                x2=abs(x1 - beta * x2)
+                pos1.append(x1)
+                pos2.append(x2)
+            else:
+                pos1.append(100)
+                pos2.append(100)
+        child1.append(pos1)
+        child2.append(pos2)
+    u=random.random()
+    if u>0.5:
+        return child1
+    else:
+        return child2
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=init(pop_size,k,users)
+    # print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    function1_best=[]
+    function2_best=[]
+    solution_num=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=ns.get_function_values(pop_size,pos_solution,p_solution,users)
+        non_dominated_sorted_solution,rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(ns.crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            # child = ns.cross_over_old(parent1, parent2)
+            # child_mute = ns.mutate_old(child, pos_range)
+            # child = cross_over(parent1, parent2,)
+            # child_mute = mutate(child, pos_range)
+            child = ns.cross_over(parent1, parent2, gen_no, max_gen)
+            child_mute = ns.mutate(child, pos_range, gen_no, max_gen)
+            child_p=init_p(child_mute,users)
+            pos_solution2.append(child_mute)
+            p_solution2.append(child_p)
+
+        function1_values2, function2_values2, punish_values2 = ns.get_function_values(2*pop_size, pos_solution2, p_solution2,users)
+        non_dominated_sorted_solution2,rank2 = ns.fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                ns.crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = ns.sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+
+        function1_best,function2_best,_=ns.get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution,users)
+
+        best_pos,rank=ns.fast_non_dominated_sort(function1_best[:], function2_best[:])
+        solution_num.append(len(best_pos[0]))
+
+        if len(best_pos[0]) == 60:
+            break
+
+        # if len(best_pos[0])> 30:
+        # for index in range(len(pos_solution)):
+        #     pos_solution[index] = ts.tabu_search(pos_solution[index], users, 5, rank[index], len(best_pos),gen_no,max_gen)
+        #     p_solution[index] = ns.init_p(pos_solution[index], users)
+            # for r in range(1,len(best_pos)):#取所有支配解
+            #     for index in best_pos[r]:
+            #         pos_solution[index] = ts.tabu_search(pos_solution[index], users, 5, rank[index], len(best_pos))
+            #         p_solution[index] = ns.init_p(pos_solution[index], users)
+        if len(best_pos[0])< 30:
+            for r in range(1, len(best_pos)):
+                for index in best_pos[r]:
+                    pos_solution[index] = ts.tabu_search(pos_solution[index], users, 1, len(best_pos[0]), 60)
+                    p_solution[index] = ns.init_p(pos_solution[index], users)
+            # for index in range(len(pos_solution)):
+            #     pos_solution[index] = ts.tabu_search(pos_solution[index], users, 5, gen_no, max_gen)
+            #     p_solution[index] = ns.init_p(pos_solution[index], users)
+
+        gen_no+=1
+    for r in range(len(best_pos)):
+        for i in range(len(best_pos[r])):
+            f1_best.append(function1_best[best_pos[0][i]])
+            f2_best.append(function2_best[best_pos[0][i]])
+
+    return f1_best,f2_best,solution_num
+
+
+
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    start_time = time.time()
+    f1,f2,solution_num=main(users,60,150,3)
+    end_time = time.time()
+    run_time = end_time - start_time
+    print(f1, f2)
+    print("程序运行时间为：", run_time)
+    print(solution_num)
+    plt.xlabel('average snr', fontsize=12)
+    plt.ylabel('average -gdop', fontsize=12)
+    # plt.xlim(70, 81)
+    # plt.ylim(0,0.1)
+    plt.scatter(f1, f2, )
+
+    plt.show()
+

multiple_objective_new/NSGA2_adapted.py

+# Program Name: NSGA-II.py
+# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm
+# Author: Haris Ali Khan
+# Supervisor: Prof. Manoj Kumar Tiwari
+
+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+import NSGA2 as ns
+
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=ns.init(pop_size,k,users)
+    print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    function1_best=[]
+    function2_best=[]
+    solution_num=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=ns.get_function_values(pop_size,pos_solution,p_solution,users)
+        # print(function1_values)
+        # print(function2_values)
+        # print(punish_values)
+        non_dominated_sorted_solution,rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        # print("等级")
+        # print(rank)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(ns.crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            # child = ns.cross_over_old(parent1, parent2)
+            # child_mute = ns.mutate_old(child, pos_range)
+            child = ns.cross_over(parent1, parent2, gen_no, max_gen)
+            child_mute = ns.mutate(child, pos_range, gen_no, max_gen)
+            child_p=ns.init_p(child_mute,users)
+            pos_solution2.append(child_mute)
+            p_solution2.append(child_p)
+
+            # child1,child2=cross_over(parent1,parent2)
+            # child1_mute=mutate(child1,pos_range)
+            # child2_mute=mutate(child2,pos_range)
+            # child1_p=init_p(child1_mute,users)
+            # child2_p=init_p(child2_mute,users)
+            # pos_solution2.append(child1_mute)
+            # pos_solution2.append(child2_mute)
+            # p_solution2.append(child1_p)
+            # p_solution2.append(child2_p)
+        function1_values2, function2_values2, punish_values2 = ns.get_function_values(2*pop_size, pos_solution2, p_solution2,users)
+        non_dominated_sorted_solution2,rank2 = ns.fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                ns.crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = ns.sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+        function1_best,function2_best,_=ns.get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution,users)
+
+        best_pos,_=ns.fast_non_dominated_sort(function1_best[:], function2_best[:])
+        solution_num.append(len(best_pos[0]))
+
+        # if len(best_pos[0])==60:
+        #     break
+
+        gen_no+=1
+    for r in range(len(best_pos)):
+        for i in range(len(best_pos[r])):
+            f1_best.append(function1_best[best_pos[r][i]])
+            f2_best.append(function2_best[best_pos[r][i]])
+    # for i in range(len(best_pos[0])):
+    #     f1_best.append(function1_best[best_pos[0][i]])
+    #     f2_best.append(function2_best[best_pos[0][i]])
+
+    return f1_best,f2_best,solution_num
+
+def run_pop(filename,pop_size,iter_max,uav_num):
+    users = Users.getUsers(filename)
+    start_time = time.time()
+    f1, f2, solution_num = main(users, pop_size, iter_max, uav_num)
+    end_time = time.time()
+    run_time = end_time - start_time
+    return run_time
+
+def run_linear():
+    filename = "./data.csv"
+    times=[]
+    for i in range(10):
+        run_time = run_pop(filename, 30, 150, 3)
+        print("time-",run_time)
+        times.append(run_time)
+    print(times)
+
+def run(filename,pop_size,iter_max,uav_num):
+    users = Users.getUsers(filename)
+    start_time = time.time()
+    f1, f2, solution_num = main(users, pop_size, iter_max, uav_num)
+    end_time = time.time()
+    run_time = end_time - start_time
+    print(f1, f2)
+    print("程序运行时间为：", run_time)
+    print(solution_num)
+    plt.xlabel('Average snr', fontsize=12)
+    plt.ylabel('Average -gdop', fontsize=12)
+
+    plt.scatter(f1, f2, )
+    plt.show()
+
+if __name__ == '__main__':
+    #60 200 3 3架无人机
+    #30 200 4 4架无人机
+    # run_linear()
+    filename = "./data.csv"
+    run(filename,60,200,3)
+    # users = Users.getUsers("./data-20.csv")
+    # start_time = time.time()
+    # f1,f2,solution_num=main(users,30,200,3)
+    # end_time = time.time()
+    # run_time = end_time - start_time
+    # print(f1, f2)
+    # print("程序运行时间为：", run_time)
+    # print(solution_num)
+    # plt.xlabel('average snr', fontsize=12)
+    # plt.ylabel('average -gdop', fontsize=12)
+    # # plt.xlim(70, 81)
+    # # plt.ylim(0,0.1)
+    # plt.scatter(f1, f2, )
+    #
+    # plt.show()
+

multiple_objective_new/NSGA2_avg.py

+# Program Name: NSGA-II.py
+# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm
+# Author: Haris Ali Khan
+# Supervisor: Prof. Manoj Kumar Tiwari
+
+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+
+#First function to optimize
+def function1(x):
+    value = -x**2
+    return value
+
+#Second function to optimize
+def function2(x):
+    value = -(x-2)**2
+    return value
+
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return (sum/len(users))
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        # print(gdop)
+        sum+=gdop
+    return -sum/len(users)
+
+def punish(power,p_rest):
+    f = 0
+    i=0
+    for p in power:
+        dis = sum(p) - p_rest[i]
+        if dis > 0.001:
+            f += 10 ** (dis * 100)
+        for p_singe in p:  # 功率要大于0
+            dis1 = 0 - p_singe
+            if dis1>0:
+                f += 10 ** max(0, dis1 * 100)
+        i+=1
+    return f
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+#Function to sort by values
+def sort_by_values(list1, values):
+    sorted_list = []
+    while(len(sorted_list)!=len(list1)):
+        if index_of(min(values),values) in list1:
+            sorted_list.append(index_of(min(values),values))
+        values[index_of(min(values),values)] = math.inf
+    return sorted_list
+
+#Function to carry out NSGA-II's fast non dominated sort
+def fast_non_dominated_sort(values1, values2):
+    #支配的节点
+    S=[[] for i in range(0,len(values1))]
+    front = [[]]
+    n=[0 for i in range(0,len(values1))]#被支配的数量
+    rank = [0 for i in range(0, len(values1))]#等级
+
+    for p in range(0,len(values1)):
+        S[p]=[]
+        n[p]=0
+        for q in range(0, len(values1)):
+            if (values1[p] > values1[q] and values2[p] > values2[q]) or (values1[p] >= values1[q] and values2[p] > values2[q]) or (values1[p] > values1[q] and values2[p] >= values2[q]):
+                if q not in S[p]:
+                    S[p].append(q)
+            elif (values1[q] > values1[p] and values2[q] > values2[p]) or (values1[q] >= values1[p] and values2[q] > values2[p]) or (values1[q] > values1[p] and values2[q] >= values2[p]):
+                n[p] = n[p] + 1
+        if n[p]==0:
+            rank[p] = 0
+            if p not in front[0]:
+                front[0].append(p)
+
+    i = 0
+    while(front[i] != []):
+        Q=[]
+        for p in front[i]:
+            for q in S[p]:
+                n[q] =n[q] - 1
+                if( n[q]==0):
+                    rank[q]=i+1
+                    if q not in Q:
+                        Q.append(q)
+        i = i+1
+        front.append(Q)
+
+    del front[len(front)-1]
+    return front,rank
+
+#Function to calculate crowding distance
+def crowding_distance(values1, values2, front):
+    distance = [0 for i in range(0,len(front))]
+    #从小到大排序
+    sorted1 = sort_by_values(front, values1[:])
+    sorted2 = sort_by_values(front, values2[:])
+    distance[0] = 4444444444444444
+    distance[len(front) - 1] = 4444444444444444
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values1[sorted1[k+1]] - values1[sorted1[k-1]])/(max(values1)-min(values1))
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values2[sorted2[k+1]] - values2[sorted2[k-1]])/(max(values2)-min(values2))
+    return distance
+
+
+#初始化无人机位置
+def init(pop_size,k,users):
+    min_pos=0
+    max_pos=1000
+    pos_solution=[]
+    p_solution=[]
+    for i in range(pop_size):
+        uavs_pos=[[min_pos+(max_pos-min_pos)*random.random(),min_pos+(max_pos-min_pos)*random.random(),100] for i in range(k)]
+        p_alloc=init_p(uavs_pos,users)
+        pos_solution.append(uavs_pos)
+        p_solution.append(p_alloc)
+    # print(pos_solution)
+    return pos_solution,p_solution
+
+def get_dis(i,uavs_pos,users,uav_to_user):
+    dis=[]
+    for k in uav_to_user[i]:
+        dis.append(tool.calculate_3d_distance(uavs_pos[i],users[k]))
+    return dis
+
+def init_p(uavs_pos,users):
+    c_association=tool.get_c_association(users,uavs_pos,math.ceil(len(users)/len(uavs_pos)))
+    uav_to_user=tool.get_uav_to_user(c_association,uavs_pos)
+    print(uav_to_user)
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2)
+    p_rest=tool.get_rest_p(users,uavs_pos,p_association)
+    # print(p_rest)
+    p_alloc=[]
+    for i in range(len(uavs_pos)):
+        #归一化生成 功率
+        dis=get_dis(i,uavs_pos,users,uav_to_user)
+        # p=[random.randint(1,len(uav_to_user[i])) for i in range(len(uav_to_user[i]))]
+        p_new=[0 for i in range(len(users))]
+        for j in range(len(uav_to_user[i])):
+            p_new[uav_to_user[i][j]]=1/len(uav_to_user[i])*p_rest[i]
+            # p_new.append(p[j]/sum(p)*p_rest[i])
+        p_alloc.append(p_new)
+        # p_alloc.append(np.random.uniform(0, p_rest[i] / len(uav_to_user[i]), (len(uav_to_user[i]))))
+    return p_alloc
+
+#交叉 pos_solution1=[[],[],[]]
+def cross_over(pos_solution1,pos_solution2):
+    child1=[]
+    child2=[]
+    for i in range(len(pos_solution1)):
+        pos1 = []
+        pos2=[]
+        beta = get_beta()
+        for j in range(len(pos_solution1[i])):
+            if j<len(pos_solution1[i])-1:
+                x1 = (pos_solution1[i][j] + pos_solution2[i][j]) / 2
+                x2 = abs((pos_solution1[i][j] - pos_solution2[i][j]) / 2)
+                x1=x1 + beta * x2
+                x2=abs(x1 - beta * x2)
+                pos1.append(x1)
+                pos2.append(x2)
+            else:
+                pos1.append(100)
+                pos2.append(100)
+        child1.append(pos1)
+        child2.append(pos2)
+    return child1,child2
+
+def cross_over_p(p_solution1,p_solution2):
+    child1 = []
+    child2 = []
+    for i in range(len(p_solution1)):
+        p1 = []
+        p2 = []
+        beta = get_beta()
+        for j in range(len(p_solution1[i])):
+            x1 = (p_solution1[i][j] + p_solution2[i][j]) / 2
+            x2 = abs((p_solution1[i][j] - p_solution2[i][j]) / 2)
+            x1 = x1 + beta * x2
+            x2 = abs(x1 - beta * x2)
+            p1.append(x1)
+            p2.append(x2)
+
+        child1.append(p1)
+        child2.append(p2)
+    return child1,child2
+
+
+def get_beta(crossover_param=2):
+    u = random.random()
+    if u <= 0.5:
+        return (2 * u) ** (1 / (crossover_param + 1))
+    return (2 * (1 - u)) ** (-1 / (crossover_param + 1))
+
+def mutate(child,variables_range):
+    u, delta = get_delta()
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+def get_delta(mutation_param=5):
+    u = random.random()
+    if u < 0.5:
+        return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+
+def get_function_values(pop_size,pos_solution,p_solution):
+    function1_values = []
+    function2_values = []
+    punish_values = []
+    for i in range(pop_size):
+        uavs_pos = pos_solution[i]
+        c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+        p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+        p_rest = tool.get_rest_p(users, uavs_pos, p_association)
+        function1_values.append(function_snr(users, pos_solution[i], c_association, p_solution[i]))
+        function2_values.append(function_gdop(users, uavs_pos, c_association, p_association, ))
+        punish_values.append(punish(p_solution[i], p_rest))
+    return function1_values,function2_values,punish_values
+
+
+def tournament(pos_solution,non_dominated_sorted_solution,rank,distance):
+    a1 = random.randint(0, len(pos_solution) - 1)
+    b1=a1
+    while b1==a1:
+        b1 = random.randint(0, len(pos_solution) - 1)
+    participants = [pos_solution[a1],pos_solution[b1]]
+    best = None
+    for participant in participants:
+        if best is None or crowding_operator(a1,b1,non_dominated_sorted_solution,rank,distance) == 1:
+            best = participant
+    return best
+
+def get_f_by_pos(pos):
+    c_association = tool.get_c_association(users, pos, math.ceil(len(users) / len(pos)))
+    p_association = tool.get_p_association(users, pos, c_association, len(users) * 2 / len(pos))
+    p = init_p(pos, users)
+    function1_value=function_snr(users, pos, c_association, p)
+    function2_value=function_gdop(users, pos, c_association, p, )
+    return function1_value,function2_value
+
+def crowding_operator(index1,index2,non_dominated_sorted_solution,rank,distance):
+    rank1=rank[index1]#index1的等级
+    rank2=rank[index2]
+    distance1=0
+    distance2=0
+    for i in range(len(non_dominated_sorted_solution[rank1])):
+        if non_dominated_sorted_solution[rank1][i]==index1:
+            distance1=distance[rank1][i]
+    for i in range(len(non_dominated_sorted_solution[rank2])):
+        if non_dominated_sorted_solution[rank2][i]==index2:
+            distance2=distance[rank2][i]
+    if rank1<rank2 or (rank1==rank2 and distance1>distance2):
+        return 1
+    else:
+        return 0
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=init(pop_size,k,users)
+    print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=get_function_values(pop_size,pos_solution,p_solution)
+        # for i in range(pop_size):
+        #     uavs_pos=pos_solution[i]
+        #     c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+        #     p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2/len(uavs_pos))
+        #     p_rest=tool.get_rest_p(users, uavs_pos, p_association)
+        #     function1_values.append(function_snr(users,pos_solution[i],c_association,p_solution[i]))
+        #     function2_values.append(function_gdop(users,uavs_pos,c_association,p_association,))
+        #     punish_values.append(punish(p_solution[i],p_rest))
+        print(function1_values)
+        print(function2_values)
+        print(punish_values)
+        non_dominated_sorted_solution,rank = fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        print("等级")
+        print(rank)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            child1,child2=cross_over(parent1,parent2)
+
+            child1_mute=mutate(child1,pos_range)
+            child2_mute=mutate(child2,pos_range)
+            child1_p=init_p(child1_mute,users)
+            child2_p=init_p(child2_mute,users)
+            pos_solution2.append(child1_mute)
+            pos_solution2.append(child2_mute)
+            p_solution2.append(child1_p)
+            p_solution2.append(child2_p)
+        function1_values2, function2_values2, punish_values2 = get_function_values(2*pop_size, pos_solution2, p_solution2)
+        print(function1_values2)
+        print(function2_values2)
+        print(punish_values2)
+        non_dominated_sorted_solution2,rank2 = fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        print(non_dominated_sorted_solution2)
+        print(rank2)
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+        # function1_last, function2_last, _ = get_function_values(len(solution), solution,
+        #                                                         p_solution_)
+        # non_dominated_sorted_solution = fast_non_dominated_sort(function1_last[:], function2_last[:])
+        # print(non_dominated_sorted_solution[0])
+        # pos_now=[solution[i] for i in non_dominated_sorted_solution[0]]
+        # p_now=[p_solution_[i] for i in non_dominated_sorted_solution[0]]
+        # best_pos.append(pos_now)
+        # best_p.append(p_now)
+        function1_best,function2_best,_=get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution)
+        non_dominated_sorted_solution3,_ = fast_non_dominated_sort(function1_best[:], function2_best[:])
+        function1_best1=[]
+        function2_best1 = []
+        for i in range(len(non_dominated_sorted_solution3[0])):
+            # print(i)
+            function1_best1.append(function1_best[non_dominated_sorted_solution3[0][i]])
+            function2_best1.append(function2_best[non_dominated_sorted_solution3[0][i]])
+
+        # f1_best.extend(function1_best1)
+        # f2_best.extend(function2_best1)
+        f1_best=function1_best1
+        f2_best=function2_best1
+        gen_no+=1
+
+    # plt.xlabel('average snr', fontsize=15)
+    # plt.ylabel('average 1/gdop', fontsize=15)
+    # plt.scatter(function1_best, function2_best)
+    # plt.show()
+    return f1_best,f2_best,best_pos,best_p
+
+
+
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    f1,f2,_,_=main(users,60,300,3)
+    print(f1, f2)
+    plt.xlabel('average snr', fontsize=12)
+    plt.ylabel('average -gdop', fontsize=12)
+    # plt.xlim(70, 81)
+    # plt.ylim(0,0.1)
+    plt.scatter(f1, f2, )
+
+    plt.show()
+

multiple_objective_new/NSGA2_mutate_in_stage.py

+# Program Name: NSGA-II.py
+# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm
+# Author: Haris Ali Khan
+# Supervisor: Prof. Manoj Kumar Tiwari
+
+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+import NSGA2 as ns
+
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=ns.init(pop_size,k,users)
+    print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    function1_best=[]
+    function2_best=[]
+    solution_num=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=ns.get_function_values(pop_size,pos_solution,p_solution,users)
+        # print(function1_values)
+        # print(function2_values)
+        # print(punish_values)
+        non_dominated_sorted_solution,rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        # print("等级")
+        # print(rank)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(ns.crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            # child = ns.cross_over_old(parent1, parent2)
+            # child_mute = ns.mutate_old(child, pos_range)
+            child = ns.cross_over_stage(parent1, parent2, gen_no, max_gen)
+            child_mute = ns.mutate_stage(child, pos_range, gen_no, max_gen)
+            child_p=ns.init_p(child_mute,users)
+            pos_solution2.append(child_mute)
+            p_solution2.append(child_p)
+
+            # child1,child2=cross_over(parent1,parent2)
+            # child1_mute=mutate(child1,pos_range)
+            # child2_mute=mutate(child2,pos_range)
+            # child1_p=init_p(child1_mute,users)
+            # child2_p=init_p(child2_mute,users)
+            # pos_solution2.append(child1_mute)
+            # pos_solution2.append(child2_mute)
+            # p_solution2.append(child1_p)
+            # p_solution2.append(child2_p)
+        function1_values2, function2_values2, punish_values2 = ns.get_function_values(2*pop_size, pos_solution2, p_solution2,users)
+        non_dominated_sorted_solution2,rank2 = ns.fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                ns.crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = ns.sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+        function1_best,function2_best,_=ns.get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution,users)
+
+        best_pos,_=ns.fast_non_dominated_sort(function1_best[:], function2_best[:])
+        solution_num.append(len(best_pos[0]))
+
+        # if len(best_pos[0])==60:
+        #     break
+
+        gen_no+=1
+    for r in range(len(best_pos)):
+        for i in range(len(best_pos[r])):
+            f1_best.append(function1_best[best_pos[r][i]])
+            f2_best.append(function2_best[best_pos[r][i]])
+    # for i in range(len(best_pos[0])):
+    #     f1_best.append(function1_best[best_pos[0][i]])
+    #     f2_best.append(function2_best[best_pos[0][i]])
+
+    return f1_best,f2_best,solution_num
+
+
+
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    start_time = time.time()
+    f1,f2,solution_num=main(users,60,200,3)
+    end_time = time.time()
+    run_time = end_time - start_time
+    print(f1, f2)
+    print("程序运行时间为：", run_time)
+    print(solution_num)
+    plt.xlabel('average snr', fontsize=12)
+    plt.ylabel('average -gdop', fontsize=12)
+    # plt.xlim(70, 81)
+    # plt.ylim(0,0.1)
+    plt.scatter(f1, f2, )
+
+    plt.show()
+

multiple_objective_new/NSGA2_new.py

+# Program Name: NSGA-II.py
+# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm
+# Author: Haris Ali Khan
+# Supervisor: Prof. Manoj Kumar Tiwari
+
+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import time
+import NSGA2 as ns
+
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=ns.init(pop_size,k,users)
+    print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    function1_best=[]
+    function2_best=[]
+    solution_num=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=ns.get_function_values(pop_size,pos_solution,p_solution,users)
+        # print(function1_values)
+        # print(function2_values)
+        # print(punish_values)
+        non_dominated_sorted_solution,rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        # print("等级")
+        # print(rank)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(ns.crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = ns.tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            child = ns.cross_over_old(parent1, parent2)
+            child_mute = ns.mutate_old(child, pos_range)
+            # child = ns.cross_over(parent1, parent2, gen_no, max_gen)
+            # child_mute = ns.mutate(child, pos_range, gen_no, max_gen)
+            child_p=ns.init_p(child_mute,users)
+            pos_solution2.append(child_mute)
+            p_solution2.append(child_p)
+
+            # child1,child2=cross_over(parent1,parent2)
+            # child1_mute=mutate(child1,pos_range)
+            # child2_mute=mutate(child2,pos_range)
+            # child1_p=init_p(child1_mute,users)
+            # child2_p=init_p(child2_mute,users)
+            # pos_solution2.append(child1_mute)
+            # pos_solution2.append(child2_mute)
+            # p_solution2.append(child1_p)
+            # p_solution2.append(child2_p)
+        function1_values2, function2_values2, punish_values2 = ns.get_function_values(2*pop_size, pos_solution2, p_solution2,users)
+        non_dominated_sorted_solution2,rank2 = ns.fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                ns.crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = ns.sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+        function1_best,function2_best,_=ns.get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution,users)
+
+        best_pos,_=ns.fast_non_dominated_sort(function1_best[:], function2_best[:])
+        solution_num.append(len(best_pos[0]))
+
+        # if len(best_pos[0])==60:
+        #     break
+
+        gen_no+=1
+    # for r in range(len(best_pos)):
+    #     for i in range(len(best_pos[r])):
+    #         f1_best.append(function1_best[best_pos[r][i]])
+    #         f2_best.append(function2_best[best_pos[r][i]])
+    for i in range(len(best_pos[0])):
+        f1_best.append(function1_best[best_pos[0][i]])
+        f2_best.append(function2_best[best_pos[0][i]])
+
+    return f1_best,f2_best,solution_num
+
+
+
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    # start_time = time.time()
+    # f1,f2,solution_num=main(users,60,200,3)
+    # end_time = time.time()
+    # run_time = end_time - start_time
+    # print(f1, f2)
+    # print("程序运行时间为：", run_time)
+    # print(solution_num)
+    # plt.xlabel('average snr', fontsize=12)
+    # plt.ylabel('average -gdop', fontsize=12)
+    # # plt.xlim(70, 81)
+    # # plt.ylim(0,0.1)
+    # plt.scatter(f1, f2, )
+    #
+    # plt.show()
+
+    f11=[44.01293032115052, 47.83374754674163, 47.85528465884196, 44.01293032115052, 44.01293032115052, 46.63593006080242, 39.62478182480577, 44.01293032115052, 47.17679025658907, 44.01293032115052, 47.82808413404777, 47.82808413404777, 46.76029845544239, 47.17679025658907, 45.044452988184645, 47.17679025658907, 47.543896627698835, 46.8849173446886, 44.01293032115052, 46.76029845544239, 44.01293032115052, 47.85528465884196, 46.76029845544239, 44.01293032115052, 44.01293032115052, 47.3229745773721, 44.01293032115052, 46.76029845544239, 47.599713025764835, 43.38306806385106, 47.17679025658907, 47.17679025658907, 44.01293032115052, 46.76029845544239, 47.599713025764835, 46.76029845544239, 47.17239267042667, 47.82808413404777, 46.76029845544239, 47.83374754674163, 42.01969688660906, 44.01293032115052, 47.85528465884196, 47.82808413404777, 46.76029845544239, 47.82808413404777, 47.82808413404777, 47.599713025764835, 47.71696642889315, 47.17679025658907, 47.85528465884196, 44.01293032115052, 44.01293032115052, 46.76029845544239, 47.22576564590512, 46.67715661386702, 44.67828196719196, 46.2996992724134, 46.67715661386702, 44.01293032115052]
+    f12=[-2.9306299727420435, -6.746681686929208, -7.620343115050427, -2.9306299727420435, -2.9306299727420435, -3.640093821617248, -2.821788441120495, -2.9306299727420435, -4.170921680968133, -2.9306299727420435, -5.907046245460523, -5.907046245460523, -3.9462164822262786, -4.170921680968133, -2.9749259469999108, -4.170921680968133, -4.651331808328007, -3.9668788407598825, -2.9306299727420435, -3.9462164822262786, -2.9306299727420435, -7.620343115050427, -3.9462164822262786, -2.9306299727420435, -2.9306299727420435, -4.298700541930365, -2.9306299727420435, -3.9462164822262786, -5.338312872832227, -2.882918744052549, -4.170921680968133, -4.170921680968133, -2.9306299727420435, -3.9462164822262786, -5.338312872832227, -3.9462164822262786, -4.154760990048628, -5.907046245460523, -3.9462164822262786, -6.746681686929208, -2.8372490343870385, -2.9306299727420435, -7.620343115050427, -5.907046245460523, -3.9462164822262786, -5.907046245460523, -5.907046245460523, -5.338312872832227, -5.374574095972783, -4.170921680968133, -7.620343115050427, -2.9306299727420435, -2.9306299727420435, -3.9462164822262786, -4.202109252160319, -3.7840000822410667, -2.9530523643591864, -3.5360407260131788, -3.7840000822410667, -2.9306299727420435]
+    plt.scatter(f11, f12, )
+    plt.show()
+    print(len(f11))
\ No newline at end of file

multiple_objective_new/NSGA2_p_first.py

+#Importing required modules
+import math
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+import Users
+import tool
+import GDOP
+import p_tool
+from itertools import combinations
+
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return (sum/len(users))
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        sum+=gdop
+    return -sum/len(users)
+
+def punish(power,p_rest):
+    f = 0
+    i=0
+    for p in power:
+        dis = sum(p) - p_rest[i]
+        if dis > 0.001:
+            f += 10 ** (dis * 100)
+        for p_singe in p:  # 功率要大于0
+            dis1 = 0 - p_singe
+            if dis1>0:
+                f += 10 ** max(0, dis1 * 100)
+        i+=1
+    return f
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+#Function to sort by values
+def sort_by_values(list1, values):
+    sorted_list = []
+    while(len(sorted_list)!=len(list1)):
+        if index_of(min(values),values) in list1:
+            sorted_list.append(index_of(min(values),values))
+        values[index_of(min(values),values)] = math.inf
+    return sorted_list
+
+#Function to carry out NSGA-II's fast non dominated sort
+def fast_non_dominated_sort(values1, values2):
+    #支配的节点
+    S=[[] for i in range(0,len(values1))]
+    front = [[]]
+    n=[0 for i in range(0,len(values1))]#被支配的数量
+    rank = [0 for i in range(0, len(values1))]#等级
+
+    for p in range(0,len(values1)):
+        S[p]=[]
+        n[p]=0
+        for q in range(0, len(values1)):
+            if (values1[p] > values1[q] and values2[p] > values2[q]) or (values1[p] >= values1[q] and values2[p] > values2[q]) or (values1[p] > values1[q] and values2[p] >= values2[q]):
+                if q not in S[p]:
+                    S[p].append(q)
+            elif (values1[q] > values1[p] and values2[q] > values2[p]) or (values1[q] >= values1[p] and values2[q] > values2[p]) or (values1[q] > values1[p] and values2[q] >= values2[p]):
+                n[p] = n[p] + 1
+        if n[p]==0:
+            rank[p] = 0
+            if p not in front[0]:
+                front[0].append(p)
+
+    i = 0
+    while(front[i] != []):
+        Q=[]
+        for p in front[i]:
+            for q in S[p]:
+                n[q] =n[q] - 1
+                if( n[q]==0):
+                    rank[q]=i+1
+                    if q not in Q:
+                        Q.append(q)
+        i = i+1
+        front.append(Q)
+
+    del front[len(front)-1]
+    return front,rank
+
+#Function to calculate crowding distance
+def crowding_distance(values1, values2, front):
+    distance = [0 for i in range(0,len(front))]
+    #从小到大排序
+    sorted1 = sort_by_values(front, values1[:])
+    sorted2 = sort_by_values(front, values2[:])
+    distance[0] = 4444444444444444
+    distance[len(front) - 1] = 4444444444444444
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values1[sorted1[k+1]] - values1[sorted1[k-1]])/(max(values1)-min(values1))
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values2[sorted2[k+1]] - values2[sorted2[k-1]])/(max(values2)-min(values2))
+    return distance
+
+
+#初始化无人机位置
+def init(pop_size,k,users):
+    min_pos=0
+    max_pos=1000
+    pos_solution=[]
+    p_solution=[]
+    for i in range(pop_size):
+        uavs_pos=[[min_pos+(max_pos-min_pos)*random.random(),min_pos+(max_pos-min_pos)*random.random(),100] for i in range(k)]
+        p_alloc=init_p(uavs_pos,users)
+        pos_solution.append(uavs_pos)
+        p_solution.append(p_alloc)
+    # print(pos_solution)
+    return pos_solution,p_solution
+
+def get_dis(i,uavs_pos,users,uav_to_user):
+    dis=[]
+    for k in uav_to_user[i]:
+        dis.append(tool.calculate_3d_distance(uavs_pos[i],users[k]))
+    return dis
+
+def init_p(uavs_pos,users):
+    uavs_initial = [i for i in range(len(uavs_pos))]
+    combination = list(combinations(uavs_initial, 3))
+    c_association, p_association = p_tool.get_p_association_first(users, uavs_pos,
+                                                                  math.ceil(len(users) / len(combination)),
+                                                                  math.ceil(len(users) / len(uavs_pos)))
+    uav_to_user=tool.get_uav_to_user(c_association,uavs_pos)
+    print(uav_to_user)
+    p_rest=tool.get_rest_p(users,uavs_pos,p_association)
+    # print(p_rest)
+    p_alloc=[]
+    for i in range(len(uavs_pos)):
+        #归一化生成 功率
+        dis=get_dis(i,uavs_pos,users,uav_to_user)
+        # p=[random.randint(1,len(uav_to_user[i])) for i in range(len(uav_to_user[i]))]
+        p_new=[0 for i in range(len(users))]
+        for j in range(len(uav_to_user[i])):
+            p_new[uav_to_user[i][j]]=dis[j]/sum(dis)*p_rest[i]
+            # p_new.append(p[j]/sum(p)*p_rest[i])
+        p_alloc.append(p_new)
+        # p_alloc.append(np.random.uniform(0, p_rest[i] / len(uav_to_user[i]), (len(uav_to_user[i]))))
+    return p_alloc
+
+#交叉 pos_solution1=[[],[],[]]
+def cross_over(pos_solution1,pos_solution2):
+    child1=[]
+    child2=[]
+    for i in range(len(pos_solution1)):
+        pos1 = []
+        pos2=[]
+        beta = get_beta()
+        for j in range(len(pos_solution1[i])):
+            if j<len(pos_solution1[i])-1:
+                x1 = (pos_solution1[i][j] + pos_solution2[i][j]) / 2
+                x2 = abs((pos_solution1[i][j] - pos_solution2[i][j]) / 2)
+                x1=x1 + beta * x2
+                x2=abs(x1 - beta * x2)
+                pos1.append(x1)
+                pos2.append(x2)
+            else:
+                pos1.append(100)
+                pos2.append(100)
+        child1.append(pos1)
+        child2.append(pos2)
+    return child1,child2
+
+def cross_over_p(p_solution1,p_solution2):
+    child1 = []
+    child2 = []
+    for i in range(len(p_solution1)):
+        p1 = []
+        p2 = []
+        beta = get_beta()
+        for j in range(len(p_solution1[i])):
+            x1 = (p_solution1[i][j] + p_solution2[i][j]) / 2
+            x2 = abs((p_solution1[i][j] - p_solution2[i][j]) / 2)
+            x1 = x1 + beta * x2
+            x2 = abs(x1 - beta * x2)
+            p1.append(x1)
+            p2.append(x2)
+
+        child1.append(p1)
+        child2.append(p2)
+    return child1,child2
+
+
+def get_beta(crossover_param=2):
+    u = random.random()
+    if u <= 0.5:
+        return (2 * u) ** (1 / (crossover_param + 1))
+    return (2 * (1 - u)) ** (-1 / (crossover_param + 1))
+
+def mutate(child,variables_range):
+    u, delta = get_delta()
+    child_new=[]
+    if u < 0.5:
+        for i in range(len(child)):
+            pos=[]
+            for j in range(len(child[i])):
+                if j < len(child[i]) - 1:
+                    x=child[i][j]+ delta * (child[i][j] - variables_range[0])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    else:
+        for i in range(len(child)):
+            pos = []
+            for j in range(len(child[i])):
+                if j<len(child[i])-1:
+                    x= child[i][j] +delta * (variables_range[1] - child[i][j])
+                    pos.append(x)
+                else:
+                    pos.append(100)
+            child_new.append(pos)
+    for i in range(len(child_new)):
+        for j in range(len(child_new[i])):
+            if j<len(child_new[i])-1:
+                if child_new[i][j] < variables_range[0]:
+                    child_new[i][j] = variables_range[0]
+                elif child_new[i][j] > variables_range[1]:
+                    child_new[i][j] = variables_range[1]
+    return child_new
+
+def mutate_p(child,variables_range):
+    return
+
+def get_delta(mutation_param=5):
+    u = random.random()
+    if u < 0.5:
+        return u, (2 * u) ** (1 / (mutation_param + 1)) - 1
+    return u, 1 - (2 * (1 - u)) ** (1 / (mutation_param + 1))
+
+def get_function_values(pop_size,pos_solution,p_solution):
+    function1_values = []
+    function2_values = []
+    punish_values = []
+    for i in range(pop_size):
+        uavs_pos = pos_solution[i]
+        uavs_initial = [i for i in range(len(uavs_pos))]
+        combination = list(combinations(uavs_initial, 3))
+        c_association,p_association=p_tool.get_p_association_first(users, uavs_pos, math.ceil(len(users) / len(combination)),
+                                math.ceil(len(users) / len(uavs_pos)))
+        # c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+        # p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+        p_rest = tool.get_rest_p(users, uavs_pos, p_association)
+        function1_values.append(function_snr(users, pos_solution[i], c_association, p_solution[i]))
+        function2_values.append(function_gdop(users, uavs_pos, c_association, p_association, ))
+        punish_values.append(punish(p_solution[i], p_rest))
+    return function1_values,function2_values,punish_values
+
+
+def tournament(pos_solution,non_dominated_sorted_solution,rank,distance):
+    a1 = random.randint(0, len(pos_solution) - 1)
+    b1=a1
+    while b1==a1:
+        b1 = random.randint(0, len(pos_solution) - 1)
+    participants = [pos_solution[a1],pos_solution[b1]]
+    best = None
+    for participant in participants:
+        if best is None or crowding_operator(a1,b1,non_dominated_sorted_solution,rank,distance) == 1:
+            best = participant
+    return best
+
+def get_f_by_pos(pos):
+    c_association = tool.get_c_association(users, pos, math.ceil(len(users) / len(pos)))
+    p_association = tool.get_p_association(users, pos, c_association, len(users) * 2 / len(pos))
+    p = init_p(pos, users)
+    function1_value=function_snr(users, pos, c_association, p)
+    function2_value=function_gdop(users, pos, c_association, p, )
+    return function1_value,function2_value
+
+def crowding_operator(index1,index2,non_dominated_sorted_solution,rank,distance):
+    rank1=rank[index1]#index1的等级
+    rank2=rank[index2]
+    distance1=0
+    distance2=0
+    for i in range(len(non_dominated_sorted_solution[rank1])):
+        if non_dominated_sorted_solution[rank1][i]==index1:
+            distance1=distance[rank1][i]
+    for i in range(len(non_dominated_sorted_solution[rank2])):
+        if non_dominated_sorted_solution[rank2][i]==index2:
+            distance2=distance[rank2][i]
+    if rank1<rank2 or (rank1==rank2 and distance1>distance2):
+        return 1
+    else:
+        return 0
+
+def main(users,pop_size,max_gen,k=3):
+    pos_solution,p_solution=init(pop_size,k,users)
+    print(p_solution)
+    # max_gen = 100
+    gen_no = 0
+    pos_range=[0,1000]
+    best_pos=[]
+    best_p=[]
+    f1_best=[]
+    f2_best=[]
+    while(gen_no<max_gen):
+        function1_values,function2_values,punish_values=get_function_values(pop_size,pos_solution,p_solution)
+        # for i in range(pop_size):
+        #     uavs_pos=pos_solution[i]
+        #     c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+        #     p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2/len(uavs_pos))
+        #     p_rest=tool.get_rest_p(users, uavs_pos, p_association)
+        #     function1_values.append(function_snr(users,pos_solution[i],c_association,p_solution[i]))
+        #     function2_values.append(function_gdop(users,uavs_pos,c_association,p_association,))
+        #     punish_values.append(punish(p_solution[i],p_rest))
+        print(function1_values)
+        print(function2_values)
+        print(punish_values)
+        non_dominated_sorted_solution,rank = fast_non_dominated_sort(function1_values[:], function2_values[:])
+        print(non_dominated_sorted_solution)
+        print("等级")
+        print(rank)
+        crowding_distance_values=[]
+        for i in range(0,len(non_dominated_sorted_solution)):
+            crowding_distance_values.append(crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+        pos_solution2 = pos_solution[:]
+        p_solution2=p_solution[:]
+        #排序要考虑惩罚项
+        #交叉变异生成子代
+        while (len(pos_solution2) != 2 * pop_size):
+            #锦标赛选择
+            parent1 = tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            parent2 = parent1
+            while parent1== parent2:
+                parent2 = tournament(pos_solution,non_dominated_sorted_solution,rank,crowding_distance_values)
+            #交叉变异
+            child1,child2=cross_over(parent1,parent2)
+
+            child1_mute=mutate(child1,pos_range)
+            child2_mute=mutate(child2,pos_range)
+            child1_p=init_p(child1_mute,users)
+            child2_p=init_p(child2_mute,users)
+            pos_solution2.append(child1_mute)
+            pos_solution2.append(child2_mute)
+            p_solution2.append(child1_p)
+            p_solution2.append(child2_p)
+        function1_values2, function2_values2, punish_values2 = get_function_values(2*pop_size, pos_solution2, p_solution2)
+        print(function1_values2)
+        print(function2_values2)
+        print(punish_values2)
+        non_dominated_sorted_solution2,rank2 = fast_non_dominated_sort(function1_values2[:], function2_values2[:])
+        print(non_dominated_sorted_solution2)
+        print(rank2)
+        crowding_distance_values2 = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            crowding_distance_values2.append(
+                crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
+        new_solution = []
+        for i in range(0, len(non_dominated_sorted_solution2)):
+            non_dominated_sorted_solution2_1 = [
+                index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
+                range(0, len(non_dominated_sorted_solution2[i]))]
+            front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+            front = [non_dominated_sorted_solution2[i][front22[j]] for j in
+                     range(0, len(non_dominated_sorted_solution2[i]))]
+            front.reverse()
+            for value in front:
+                new_solution.append(value)
+                if (len(new_solution) == pop_size):
+                    break
+            if (len(new_solution) == pop_size):
+                break
+        pos_solution = [pos_solution2[i] for i in new_solution]
+        p_solution=[p_solution2[i] for i in new_solution]
+        # function1_last, function2_last, _ = get_function_values(len(solution), solution,
+        #                                                         p_solution_)
+        # non_dominated_sorted_solution = fast_non_dominated_sort(function1_last[:], function2_last[:])
+        # print(non_dominated_sorted_solution[0])
+        # pos_now=[solution[i] for i in non_dominated_sorted_solution[0]]
+        # p_now=[p_solution_[i] for i in non_dominated_sorted_solution[0]]
+        # best_pos.append(pos_now)
+        # best_p.append(p_now)
+        function1_best,function2_best,_=get_function_values(len(pos_solution),pos_solution,
+                                                                p_solution)
+        non_dominated_sorted_solution3,_ = fast_non_dominated_sort(function1_best[:], function2_best[:])
+        function1_best1=[]
+        function2_best1 = []
+        for i in range(len(non_dominated_sorted_solution3[0])):
+            # print(i)
+            function1_best1.append(function1_best[non_dominated_sorted_solution3[0][i]])
+            function2_best1.append(function2_best[non_dominated_sorted_solution3[0][i]])
+
+        # f1_best.extend(function1_best1)
+        # f2_best.extend(function2_best1)
+        f1_best=function1_best1
+        f2_best=function2_best1
+        gen_no+=1
+
+    # plt.xlabel('average snr', fontsize=15)
+    # plt.ylabel('average 1/gdop', fontsize=15)
+    # plt.scatter(function1_best, function2_best)
+    # plt.show()
+    return f1_best,f2_best,best_pos,best_p
+
+
+
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    f1,f2,_,_=main(users,60,500,4)
+    print(f1, f2)
+    plt.xlabel('average snr', fontsize=12)
+    plt.ylabel('average -gdop', fontsize=12)
+
+    plt.scatter(f1, f2,)
+    plt.show()
+

multiple_objective_new/TS.py

+import random
+import NSGA2 as ns
+
+def objective_function(function1,function2,x):
+    # 定义优化的目标函数，此处以一个简单的二维函数为例
+    return function1(x),function2(x)
+
+#todo 按照非支配解个数来动态修改生成位置范围
+def generate_neighbor(pos,iter,iter_max):
+    # 在当前解的邻域内随机生成新解
+    new_pos=[]
+    pos_range=[0,1000]
+    for loc in pos:
+        k=[]
+        for i in range(0,len(loc)-1):
+            # x = loc[i] + random.uniform(-20 + 10 * iter / iter_max, 20 -10 * iter / iter_max)
+            # x = loc[i] + random.uniform(-10, 10)
+            if iter<10:
+                x=loc[i] + random.uniform(-40+10*iter/iter_max, 40-10*iter/iter_max)
+            elif iter>=10 and iter<20:
+                x = loc[i] + random.uniform(-30 + 10 * iter / iter_max, 30 - 10 * iter / iter_max)
+            elif iter>=20 and iter<30:
+                x = loc[i] + random.uniform(-20 + 10 * iter / iter_max, 20 -10 * iter / iter_max)
+            else:
+                x = loc[i] + random.uniform(-10 + 5 * iter / iter_max, 10 - 5 * iter / iter_max)
+            if x>pos_range[1]:
+                x=pos_range[1]
+            if x<pos_range[0]:
+                x=pos_range[0]
+            k.append(x)
+        k.append(loc[2])
+        new_pos.append(k)
+
+    return new_pos
+
+def generate_neighbor2(pos,rank,rank_max,iter_,iter_max):
+    # 在当前解的邻域内随机生成新解
+    new_pos=[]
+    pos_range=[0,1000]
+    for loc in pos:
+        k=[]
+        for i in range(0,len(loc)-1):
+            r1=-10-20 * (rank+1) / rank_max+10*iter_/iter_max
+            r2=10+ 20 * (rank+1) / rank_max-10*iter_/iter_max
+            x = loc[i] + random.uniform(r1,r2)
+
+            if x>pos_range[1]:
+                x=pos_range[1]
+            if x<pos_range[0]:
+                x=pos_range[0]
+            k.append(x)
+        k.append(loc[2])
+        new_pos.append(k)
+
+    return new_pos
+
+#todo：根据当前解的等级调整领域范围生成领域解
+def generate_neighbor3(pos,rank,rank_max):
+    new_pos=[]
+    pos_range=[0,1000]
+    for loc in pos:
+        k=[]
+        for i in range(0,len(loc)-1):
+            if rank_max>10:
+                r1=-50-20 * (rank+1) / rank_max
+                r2=50+ 20 * (rank+1) / rank_max
+                x = loc[i] + random.uniform(r1,r2)
+            elif rank_max<=10 and rank_max>5:
+                r1 = -20 - 20 * (rank + 1) / rank_max
+                r2 = 20 + 10 * (rank + 1) / rank_max
+                x = loc[i] + random.uniform(r1, r2)
+            else:
+                r1 = -20 - 10 * (rank + 1) / rank_max
+                r2 = 20 + 10 * (rank + 1) / rank_max
+                x = loc[i] + random.uniform(r1, r2)
+            if x>pos_range[1]:
+                x=pos_range[1]
+            if x<pos_range[0]:
+                x=pos_range[0]
+            k.append(x)
+        k.append(loc[2])
+        new_pos.append(k)
+
+    return new_pos
+def get_best(neighbors,neighbors_p,users):
+    function1_values, function2_values, _ = ns.get_function_values(len(neighbors), neighbors,
+                                                                   neighbors_p, users)
+    non_dominated_sorted_solution, rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+    best=non_dominated_sorted_solution[0][0]
+    return neighbors[best]
+
+def tabu_search(pos,users,max_iter,iter,iterMax,rank,rank_max):
+    individual=pos
+    current_solution = pos  # 初始解
+    best_solution = current_solution  # 记录最优解
+    best=[]
+    tabu_list = []  # 禁忌表，避免重复搜索
+    tabu_tenure = 4  # 禁忌期限，即禁忌解在禁忌表中停留的迭代次数
+
+    for i in range(max_iter):
+        # neighbors = [generate_neighbor2(current_solution,rank,rank_max,iter,iterMax) for _ in range(10)]  # 随机生成10个邻域解
+        neighbors = [generate_neighbor(current_solution, iter,iterMax) for _ in range(10)]
+        # neighbors = [generate_neighbor3(current_solution, rank, rank_max) for _ in range(10)]
+        neighbors_p=[]#根据领域解生成的功率分配
+        for j in range(len(neighbors)):
+            neighbors_p.append(ns.init_p(neighbors[j],users))
+        # 在邻域解中选择一个非禁忌的最优解,找到支配等级最高的解，
+
+        candidate = get_best(neighbors,neighbors_p,users)
+        while candidate in tabu_list:
+            neighbors.remove(candidate)
+            candidate = get_best(neighbors,neighbors_p,users)
+
+        # 更新当前解和最优解
+        current_solution = candidate
+        compare_group=[candidate,individual]
+        compare_group_p=[]
+        for i in range(len(compare_group)):
+            compare_group_p.append(ns.init_p(compare_group[i],users))
+        function1_values, function2_values, _ = ns.get_function_values(len(compare_group), compare_group,
+                                                                       compare_group_p, users)
+        non_dominated_sorted_solution, rank = ns.fast_non_dominated_sort(function1_values[:], function2_values[:])
+        # crowding_distance_values = []
+        # for i in range(0, len(non_dominated_sorted_solution)):
+        #     crowding_distance_values.append(
+        #         ns.crowding_distance(function1_values[:], function2_values[:], non_dominated_sorted_solution[i][:]))
+
+        if rank[0]<rank[1]:
+            best_solution = candidate
+            return best_solution
+
+        tabu_list.append(candidate)  # 将当前解加入禁忌表
+        if len(tabu_list) > tabu_tenure:  # 维护禁忌表的长度不超过禁忌期限
+            tabu_list.pop(0)
+
+    return best_solution
+
+pos=[[313.99873528 ,416.45594562, 100.        ],
+ [842.85965065 ,611.07755542, 100.        ],
+ [810.63437665 ,371.17088506 ,100.        ]]
+
+# print(generate_neighbor(pos))
\ No newline at end of file

multiple_objective_new/Users.py

+import math
+import random
+
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+import pandas as pd
+
+
+# 随机生成U个用户的位置
+def generate(num,filename):
+    data = np.random.randint(1, 1000, (num, 2))
+    f = open(filename, 'w', encoding='utf-8', newline="")
+    #  2.基于文件对象构建csv写入对象
+    csv_write = csv.writer(f)
+    #  4.写入csv文件
+    for d in data:
+        csv_write.writerow(d)
+    return data
+
+
+def generateNormalDistribution(num):
+    mu, sigma = 500, 170  # 有关于正态分布的值μ，σ
+    x = sigma * np.random.randn(1, num) + mu
+    x = ([i for item in x for i in item])  # 在这个循环中我们能把二维的数组转换成一维的数组，y2同理
+    y = sigma * np.random.randn(1, num) + mu
+    y = ([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x[i], y[i]])
+    f = open('../data_normal.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+# 用户按集群分布
+def generateGroupDistribution(num):
+    centers = [[500, 500], [1600, 400], [700, 1200], [1400, 1500]]
+    sigma = 100  # 有关于正态分布的值σ
+    group_num = 4
+    x_ = []
+    y_ = []
+    for i in range(group_num):
+        number = int(num / group_num)
+        print(centers[i])
+        x = sigma * np.random.randn(1, number) + centers[i][0]
+        y = sigma * np.random.randn(1, number) + centers[i][1]
+        x_.extend([i for item in x for i in item])
+        y_.extend([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x_[i], y_[i]])
+    f = open('../data_group.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+def generateNormal(mean, sigma):
+    x = sigma * np.random.randn(1) + mean
+    return x
+
+
+# 从csv文件读用户位置
+def getUsers(filename):
+    data = pd.read_csv(filename, header=None)
+    list = data.values.tolist()
+    array = np.array(list)
+    return array
+
+
+def getDistanceFromCenter(user, x, y):
+    return math.sqrt((user[0] - x) ** 2 + (user[1] - y) ** 2)
+
+
+def draw(data):
+    plt.figure(num=1, figsize=(10, 10))
+    x = data[:, 0]
+    y = data[:, 1]
+    plt.scatter(x, y)
+    plt.xlim(0, 7001)
+    plt.ylim(0, 7001)
+    ax = plt.gca()
+    ax.spines['left'].set_position(('data', 0))
+    ax.spines['bottom'].set_position(('data', 0))
+    # x1 = np.linspace(300, 700, 500)
+    # center1 = 500 + np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # center2 = 500 - np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # plt.plot(x1, center1,c='b')
+    # plt.plot(x1, center2,c='b')
+    # x2 = np.linspace(200, 800, 500)
+    # center21 = 500 + np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # center22 = 500 - np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # plt.plot(x2, center21, c='r')
+    # plt.plot(x2, center22, c='r')
+    # x3 = np.linspace(100, 900, 500)
+    # center31 = 500 + np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # center32 = 500 - np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # plt.plot(x3, center31, c='g')
+    # plt.plot(x3, center32, c='g')
+    # model = KMeans(n_clusters=5, random_state=0).fit(data)
+    # center = model.cluster_centers_
+    # for c in center:
+    #     plt.scatter(c[0], c[1],s=200,marker='p')
+    return plt
+
+
+if __name__ == '__main__':
+    data=generate(20,"./data-20.csv")
+    # list=getUsers("./data2.csv")
+    # print(list)
+
+    # data=generateGroupDistribution(100)
+    # list = getUsers("../data_group.csv")
+    # draw(list).show()
+

multiple_objective_new/compare1.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+
+import Users
+from pso import PSO
+import tool
+import p_tool
+import GDOP
+import NSGA2 as ns
+import draw
+import NSGA2_p_first as ns_p
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return (sum/len(users))
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        sum+=gdop
+    return -sum/len(users)
+
+def fitness(users,uavs_pos):
+    c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    p_solution=ns.init_p(uavs_pos,users)
+    snr=function_snr(users, uavs_pos, c_association, p_solution)
+    gdop=function_gdop(users, uavs_pos, c_association, p_association, )
+    return snr,gdop,snr+gdop
+
+def fitness_p(users,uavs_pos):
+    uavs_initial = [i for i in range(len(uavs_pos))]
+    combination = list(combinations(uavs_initial, 3))
+    c_association, p_association = p_tool.get_p_association_first(users, uavs_pos,
+                                                                  math.ceil(len(users) / len(combination)),
+                                                                  math.ceil(len(users) / len(uavs_pos)))
+    p_solution = ns_p.init_p(uavs_pos, users)
+    snr = function_snr(users, uavs_pos, c_association, p_solution)
+    gdop = function_gdop(users, uavs_pos, c_association, p_association, )
+    return snr, gdop, snr + gdop
+
+def run(users, k):
+    pso=PSO(users,[],)
+    X, V = pso.initParticle(k, users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, k,fitness)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    # draw.drawResultAndIter(result)
+    # plt.show()
+    return global_pos, global_best, result
+
+def run_p(users, k):
+    pso = PSO(users, [], )
+    X, V = pso.initParticle(k, users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, k, fitness_p)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    # draw.drawResultAndIter(result)
+    # plt.show()
+    return global_pos, global_best, result
+
+if __name__ == '__main__':
+    start_time = time.time()
+    users = Users.getUsers("./data.csv")
+    # global_pos, global_best,result=run(users,3)
+    # print(fitness(users,global_pos))
+
+    global_pos, global_best, result = run(users, 3)
+    print(global_pos)
+    print(fitness_p(users, global_pos))
+    end_time = time.time()
+    run_time = end_time - start_time
+    print("程序运行时间为：", run_time)
+
+

multiple_objective_new/compare2.py

+#主要目标法pso
+import math
+import time
+from itertools import combinations
+
+import Users
+import p_tool
+from pso import PSO
+import tool
+import GDOP
+import NSGA2 as ns
+import draw
+import NSGA2_p_first as ns_p
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return sum/len(users)
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        sum+=gdop
+    return sum/len(users)
+
+def punish_function(gdop,gdop_thre=3):
+    if gdop>gdop_thre:
+        return -100
+    else:
+        return 0
+
+def fitness(users,uavs_pos):
+    c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    p_solution=ns.init_p(uavs_pos,users)
+    snr=function_snr(users, uavs_pos, c_association, p_solution)
+    gdop=function_gdop(users, uavs_pos, c_association, p_association, )
+    punish=punish_function(gdop)
+    fitness=snr+punish
+    return snr,gdop,fitness
+
+def run(users, k):
+    pso=PSO(users,[],)
+    X, V = pso.initParticle(k, users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, k,fitness)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    # draw.drawResultAndIter(result)
+    # plt.show()
+    return global_pos, global_best, result
+
+def fitness_p(users,uavs_pos):
+    uavs_initial = [i for i in range(len(uavs_pos))]
+    combination = list(combinations(uavs_initial, 3))
+    c_association, p_association = p_tool.get_p_association_first(users, uavs_pos,
+                                                                  math.ceil(len(users) / len(combination)),
+                                                                  math.ceil(len(users) / len(uavs_pos)))
+    p_solution = ns_p.init_p(uavs_pos, users)
+    snr = function_snr(users, uavs_pos, c_association, p_solution)
+    gdop = function_gdop(users, uavs_pos, c_association, p_association, )
+    punish = punish_function(gdop)
+    fitness = snr + punish
+    return snr, gdop, fitness
+
+def run_p(users, k):
+    pso = PSO(users, [], )
+    X, V = pso.initParticle(k, users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, k, fitness_p)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    # draw.drawResultAndIter(result)
+    # plt.show()
+    return global_pos, global_best, result
+
+if __name__ == '__main__':
+    start_time = time.time()
+    users = Users.getUsers("./data.csv")
+    global_pos, global_best, result = run(users, 3)
+    print(fitness(users, global_pos))
+    end_time = time.time()
+    run_time = end_time - start_time
+    print("程序运行时间为：", run_time)
+
+

multiple_objective_new/compare3.py

+#随机位置,k-means
+import math
+import random
+
+import Users
+from pso import PSO
+import tool
+import GDOP
+import NSGA2 as ns
+from sklearn.cluster import KMeans
+import draw
+#计算用户平均snr
+def function_snr(users,uavs_pos,c_association,power):
+    sum=0
+    for user_index in range(len(c_association)):
+        snr=tool.cal_snr(uavs_pos[c_association[user_index]],users[user_index],power[c_association[user_index]][user_index])
+        sum+=snr
+    return sum/len(users)
+
+#计算用户平均gdop
+def function_gdop(users,uavs_pos,c_association,p_association):
+    # p_association=tool.get_p_association(users,uavs_pos,c_association,len(users)*2)
+    sum=0
+    for user_index in p_association:
+        uavs=[]
+        uavs.append(uavs_pos[c_association[user_index]])
+        for uav_index in p_association[user_index]:
+            uavs.append(uavs_pos[uav_index])
+        gdop=GDOP.calculate_gdop(uavs,users[user_index])
+        sum+=gdop
+    return -sum/len(users)
+
+def punish_function(gdop,gdop_thre=-3):
+    if gdop<gdop_thre:
+        return -100
+    else:
+        return 0
+
+def fitness(users,uavs_pos):
+    c_association = tool.get_c_association(users, uavs_pos, math.ceil(len(users) / len(uavs_pos)))
+    p_association = tool.get_p_association(users, uavs_pos, c_association, len(users) * 2 / len(uavs_pos))
+    p_solution=ns.init_p(uavs_pos,users)
+    snr=function_snr(users, uavs_pos, c_association, p_solution)
+    gdop=function_gdop(users, uavs_pos, c_association, p_association, )
+    punish=punish_function(gdop)
+    fitness=snr+punish
+    return snr,gdop,fitness
+
+def random_pos(k):
+    min_pos=0
+    max_pos=1000
+    uavs=[[min_pos + (max_pos - min_pos) * random.random(), min_pos + (max_pos - min_pos) * random.random(), 100] for i in range(k)]
+    return uavs
+
+def k_means(k):
+    uavs=[]
+    if k > 1:
+        model = KMeans(n_clusters=k,  # 聚类簇数
+                       random_state=1,  # 决定质心初始化的随机数生成，使用int使随机性具有确定性。
+                       max_iter=300,  # 执行一次k-means算法所进行的最大迭代数，默认300
+                       ).fit(users)
+        center = model.cluster_centers_
+        for item in center:
+            # height = random.randint(100, 800)
+            height = 100
+            position = [int(item[0]), int(item[1]), height]
+            uavs.append(position)
+        return uavs
+
+if __name__ == '__main__':
+    users = Users.getUsers("./data.csv")
+    # uav_pos = random_pos(3)
+    # print(fitness(users, uav_pos))
+    uavs=k_means(3)
+    print(fitness(users, uavs))
+
+

multiple_objective_new/data-10.csv

+763,553
+215,366
+996,343
+968,812
+472,189
+869,322
+914,650
+621,455
+259,634
+52,66

multiple_objective_new/data-20.csv

+8,6
+661,93
+708,567
+171,53
+221,858
+822,518
+919,967
+433,827
+478,702
+191,953
+499,68
+497,788
+772,72
+731,292
+929,442
+50,18
+900,691
+265,55
+66,543
+275,407

multiple_objective_new/data.csv

+330,520
+347,688
+179,914
+688,83
+700,447
+606,416

multiple_objective_new/data2.csv

+463,50
+413,347
+722,696
+301,271
+274,739
+325,969
+604,199
+497,630
+436,522
+844,378
+181,633
+19,455
+478,364
+622,231
+649,383

multiple_objective_new/data3.csv

+70,337
+142,901
+274,86
+694,316
+839,58
+75,363
+700,136
+303,523
+508,732
+407,275
+165,881
+456,391
+479,530
+996,261
+217,73
+617,137
+416,996
+520,480
+208,653
+503,280
+842,995
+979,720
+830,25
+504,703
+878,984
+223,20
+542,267
+464,813
+279,58
+384,874

multiple_objective_new/demo.py

+import math
+import random
+import matplotlib.pyplot as plt
+
+#First function to optimize
+def function1(x):
+    value = -x**2
+    return value
+
+#Second function to optimize
+def function2(x):
+    value = -(x-2)**2
+    return value
+
+#Function to find index of list
+def index_of(a,list):
+    for i in range(0,len(list)):
+        if list[i] == a:
+            return i
+    return -1
+
+#Function to sort by values
+def sort_by_values(list1, values):
+    sorted_list = []
+    while(len(sorted_list)!=len(list1)):
+        if index_of(min(values),values) in list1:
+            sorted_list.append(index_of(min(values),values))
+        values[index_of(min(values),values)] = math.inf
+    return sorted_list
+
+#Function to carry out NSGA-II's fast non dominated sort
+def fast_non_dominated_sort(values1, values2):
+    S=[[] for i in range(0,len(values1))]
+    front = [[]]
+    n=[0 for i in range(0,len(values1))]
+    rank = [0 for i in range(0, len(values1))]
+
+    for p in range(0,len(values1)):
+        S[p]=[]
+        n[p]=0
+        for q in range(0, len(values1)):
+            if (values1[p] > values1[q] and values2[p] > values2[q]) or (values1[p] >= values1[q] and values2[p] > values2[q]) or (values1[p] > values1[q] and values2[p] >= values2[q]):
+                if q not in S[p]:
+                    S[p].append(q)
+            elif (values1[q] > values1[p] and values2[q] > values2[p]) or (values1[q] >= values1[p] and values2[q] > values2[p]) or (values1[q] > values1[p] and values2[q] >= values2[p]):
+                n[p] = n[p] + 1
+        if n[p]==0:
+            rank[p] = 0
+            if p not in front[0]:
+                front[0].append(p)
+
+    i = 0
+    while(front[i] != []):
+        Q=[]
+        for p in front[i]:
+            for q in S[p]:
+                n[q] =n[q] - 1
+                if( n[q]==0):
+                    rank[q]=i+1
+                    if q not in Q:
+                        Q.append(q)
+        i = i+1
+        front.append(Q)
+
+    del front[len(front)-1]
+    return front
+
+#Function to calculate crowding distance
+def crowding_distance(values1, values2, front):
+    distance = [0 for i in range(0,len(front))]
+    sorted1 = sort_by_values(front, values1[:])
+    sorted2 = sort_by_values(front, values2[:])
+    distance[0] = 4444444444444444
+    distance[len(front) - 1] = 4444444444444444
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values1[sorted1[k+1]] - values2[sorted1[k-1]])/(max(values1)-min(values1))
+    for k in range(1,len(front)-1):
+        distance[k] = distance[k]+ (values1[sorted2[k+1]] - values2[sorted2[k-1]])/(max(values2)-min(values2))
+    return distance
+
+#Function to carry out the crossover
+def crossover(a,b):
+    r=random.random()
+    if r>0.5:
+        return mutation((a+b)/2)
+    else:
+        return mutation((a-b)/2)
+
+#Function to carry out the mutation operator
+def mutation(solution):
+    mutation_prob = random.random()
+    if mutation_prob <1:
+        solution = min_x+(max_x-min_x)*random.random()
+    return solution
+
+#Main program starts here
+pop_size = 20
+max_gen = 921
+
+#Initialization
+min_x=-55
+max_x=55
+solution=[min_x+(max_x-min_x)*random.random() for i in range(0,pop_size)]
+gen_no=0
+while(gen_no<max_gen):
+    function1_values = [function1(solution[i])for i in range(0,pop_size)]
+    function2_values = [function2(solution[i])for i in range(0,pop_size)]
+    non_dominated_sorted_solution = fast_non_dominated_sort(function1_values[:],function2_values[:])
+    print("The best front for Generation number ",gen_no, " is")
+    for valuez in non_dominated_sorted_solution[0]:
+        print(round(solution[valuez],3),end=" ")
+    print("\n")
+    crowding_distance_values=[]
+    for i in range(0,len(non_dominated_sorted_solution)):
+        crowding_distance_values.append(crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))
+    solution2 = solution[:]
+    #Generating offsprings
+    while(len(solution2)!=2*pop_size):
+        a1 = random.randint(0,pop_size-1)
+        b1 = random.randint(0,pop_size-1)
+        solution2.append(crossover(solution[a1],solution[b1]))
+    function1_values2 = [function1(solution2[i])for i in range(0,2*pop_size)]
+    function2_values2 = [function2(solution2[i])for i in range(0,2*pop_size)]
+    non_dominated_sorted_solution2 = fast_non_dominated_sort(function1_values2[:],function2_values2[:])
+    crowding_distance_values2=[]
+    for i in range(0,len(non_dominated_sorted_solution2)):
+        crowding_distance_values2.append(crowding_distance(function1_values2[:],function2_values2[:],non_dominated_sorted_solution2[i][:]))
+    new_solution= []
+    for i in range(0,len(non_dominated_sorted_solution2)):
+        non_dominated_sorted_solution2_1 = [index_of(non_dominated_sorted_solution2[i][j],non_dominated_sorted_solution2[i] ) for j in range(0,len(non_dominated_sorted_solution2[i]))]
+        front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])
+        front = [non_dominated_sorted_solution2[i][front22[j]] for j in range(0,len(non_dominated_sorted_solution2[i]))]
+        front.reverse()
+        for value in front:
+            new_solution.append(value)
+            if(len(new_solution)==pop_size):
+                break
+        if (len(new_solution) == pop_size):
+            break
+    solution = [solution2[i] for i in new_solution]
+    gen_no = gen_no + 1
+
+#Lets plot the final front now
+function1 = [i * -1 for i in function1_values]
+print(function1_values)
+print(function2_values)
+function2 = [j * -1 for j in function2_values]
+plt.xlabel('Function 1', fontsize=15)
+plt.ylabel('Function 2', fontsize=15)
+plt.scatter(function1, function2)
+plt.show()
\ No newline at end of file

multiple_objective_new/draw.py

+import matplotlib.pyplot as plt  # 导入 Matplotlib 工具包
+import networkx as nx  # 导入 NetworkX 工具包
+
+
+
+def example():
+    # 问题 2：无向图的最短路问题（司守奎，数学建模算法与应用，P43，例4.3）
+    G2 = nx.Graph()  # 创建无向图
+    G2.add_weighted_edges_from([(1, 2, 2), (1, 3, 8), (1, 4, 1),
+                                (2, 3, 6), (2, 5, 1),
+                                (3, 4, 7), (3, 5, 5), (3, 6, 1), (3, 7, 2),
+                                (4, 7, 9),
+                                (5, 6, 3), (5, 8, 2), (5, 9, 9),
+                                (6, 7, 4), (6, 9, 6),
+                                (7, 9, 3), (7, 10, 1),
+                                (8, 9, 7), (8, 11, 9),
+                                (9, 10, 1), (9, 11, 2),
+                                (10, 11, 4)])  # 向图中添加多条赋权边: (node1,node2,weight)
+
+    # 两个指定顶点之间的最短加权路径
+    minWPath_v1_v5 = nx.dijkstra_path(G2, source=1, target=5)  # 顶点 0 到 顶点 3 的最短加权路径
+    print("顶点 v1 到 顶点 v5 的最短加权路径: ", minWPath_v1_v5)
+    # 两个指定顶点之间的最短加权路径的长度
+    lMinWPath_v1_v5 = nx.dijkstra_path_length(G2, source=1, target=5)  # 最短加权路径长度
+    print("顶点 v1 到 顶点 v5 的最短加权路径长度: ", lMinWPath_v1_v5)
+    # === 关注 Youcans 原创系列（https://blog.csdn.net/youcans）===
+    pos = nx.spring_layout(G2)  # 用 FR算法排列节点
+    nx.draw(G2, pos, with_labels=True, alpha=0.5)
+    labels = nx.get_edge_attributes(G2, 'weight')
+    nx.draw_networkx_edge_labels(G2, pos, edge_labels=labels)
+    plt.show()
+
+def example2():
+    x = [1, 3, 4, 5, 7, 6]
+    y = [4, 5, 2, 8, 5, 10]
+    pos = {i + 1: [x[i], y[i]] for i in range(len(x))}
+    G = nx.Graph()
+    G.add_nodes_from(pos.keys())
+    for n, p in pos.items():
+        G._node[n]['pos'] = p
+    G.add_edges_from([(1, 3), (1, 5), (2, 4), (3, 6), (4, 6)])
+    nx.draw(G, pos=pos, with_labels=True, font_size=8, node_size=150)
+    plt.show()
+
+def example3():
+    G = nx.Graph()
+    # 添加对应的边和点
+    for i in range(1, 10):
+        G.add_node(i, desc=str(i))  # 结点名称不能为str,desc为标签即结点名称
+    G.add_edge(1, 2, name='6')  # 添加边， 参数name为边权值
+    G.add_edge(1, 3, name='4')
+    G.add_edge(1, 4, name='5')
+    G.add_edge(2, 5, name='1')
+    G.add_edge(3, 5, name='1')
+    G.add_edge(4, 6, name='2')
+    G.add_edge(5, 7, name='9')
+    G.add_edge(5, 8, name='7')
+    G.add_edge(6, 8, name='4')
+    G.add_edge(7, 9, name='2')
+    G.add_edge(8, 9, name='4')
+
+    pos = [(1, 3), (1, 3), (2, 4), (2, 2), (2, 1), (3, 3), (4, 1), (5, 4), (5, 2),
+           (6, 3)]  # pos列表从第0位开始，但我定义是从结点1开始，这里令前两个坐标相同
+    # 按pos所定位置画出节点,无标签无权值
+    nx.draw_networkx(G, pos, with_labels=None)
+    # 画出标签
+    node_labels = nx.get_node_attributes(G, 'desc')
+    nx.draw_networkx_labels(G, pos, labels=node_labels)
+    # 画出边权值
+    edge_labels = nx.get_edge_attributes(G, 'name')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+
+    plt.show()
+
+def draw_topology_before(users,graph,g):
+    plt.figure()
+    pos = {i + 1: [users[i][0], users[i][1]] for i in range(len(users))}
+    pos[0]=g
+
+    G = nx.Graph()
+    # 添加对应的边和点
+    for i in range(0, len(users)+1):
+        G.add_node(i, desc=str(i))  # 结点名称不能为str,desc为标签即结点名称
+
+    for edge in graph:
+        G.add_edge(edge[0], edge[1], name=edge[2])  # 添加边， 参数name为边权值
+
+    # 按pos所定位置画出节点,无标签无权值
+    nx.draw_networkx(G, pos, with_labels=None)
+    # 画出标签
+    node_labels = nx.get_node_attributes(G, 'desc')
+    nx.draw_networkx_labels(G, pos, labels=node_labels)
+    # 画出边权值
+    edge_labels = nx.get_edge_attributes(G, 'name')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+
+
+
+
+def draw_topology_after(uavs,users,graph,g):
+    plt.figure()
+    pos = {i + 1: [users[i][0], users[i][1]] for i in range(len(users))}
+    pos[0]=g
+    for i in range(len(uavs)):
+        pos[i+100]=[uavs[i][0],uavs[i][1]]
+    G = nx.Graph()
+    # 添加对应的边和点
+    for i in range(0, len(users)+1):
+        G.add_node(i, desc=str(i))  # 结点名称不能为str,desc为标签即结点名称
+    for i in range(100,100+len(uavs)):
+        G.add_node(i, desc=str(i))  # 结点名称不能为str,desc为标签即结点名称
+    for edge in graph:
+        G.add_edge(edge[0], edge[1], name=edge[2])  # 添加边， 参数name为边权值
+
+    # 按pos所定位置画出节点,无标签无权值
+    nx.draw_networkx(G, pos, with_labels=None)
+    # 画出标签
+    node_labels = nx.get_node_attributes(G, 'desc')
+    nx.draw_networkx_labels(G, pos, labels=node_labels)
+    # 画出边权值
+    edge_labels = nx.get_edge_attributes(G, 'name')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+
+    # plt.title('AOE_CPM', fontsize=10)
+
+def drawResultAndIter(resultAndIter):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(resultAndIter)
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('fitness')
+    plt.show()
+
+
+if __name__ == '__main__':
+    f11=[78.55335494041263, 78.4925571817822, 78.12553455940584, 74.64405628626322, 74.81648579421885, 78.38827173203428,
+     74.55140845209807, 74.25000849350343, 78.53516476576708, 78.62622103565846, 77.80989106559173, 78.49073674312767,
+     74.56187387317372, 73.8567018719367, 78.33811448348496, 78.39409737006262, 78.45073133117388, 77.40271093532404,
+     78.14754301619742, 78.29073591677286, 78.20984708408737, 77.58599589624374, 77.50354215075774, 74.49757141289503,
+     77.7276672803666, 74.37167194076233, 74.04065468827585, 78.18592122242832, 78.63426482129661, 74.59491249776092]
+    f21=[0.007917562416945053, 0.008151915801430798, 0.008694667137021531, 0.009298857060978374, 0.009168856063992566, 0.008393988454835794, 0.009356937434857335, 0.00944301517076346, 0.008052155456294353, 0.007891764380788865, 0.008955166010251241, 0.00823125303080821, 0.009324932673847845, 0.009564614590386999, 0.008460548514006244, 0.008326270775816673, 0.00829068236337162, 0.009167415032401014, 0.008674229967017484, 0.008570277672074586, 0.008634748200016893, 0.009081593338821761, 0.009103629944510878, 0.009379318906272775, 0.00897481373615487, 0.009419446727519166, 0.009517387594754897, 0.008666954147779366, 0.007889234462668473, 0.00931283329983273]
+
+    f1=[76.09611889350377, 78.77730268287569, 77.27692095290696, 78.66804524192476, 78.05854827300608, 78.72977555318688,
+     77.86763080696251, 78.47870754906403, 78.2432740754877, 79.0176228328904, 78.78766830591486, 77.79220146161504,
+     78.0416057000793, 78.91770303778918, 78.10175093982203, 78.69063008870197, 77.8448065926295, 78.89257481607883,
+     78.84616875980842, 78.54240776126834, 77.92089708265037, 78.27069703784417, 78.89814401504553, 77.26379035039697,
+     78.4852605896078, 78.4483362995136, 78.62594386393594, 76.95302464521706, 78.07787701339166, 78.54801211464334]
+    f2=[0.008957420243593904, 0.008201290740429946, 0.008927274457396635, 0.008288478456987991, 0.00876020337183325, 0.00821775769307678, 0.00884697549908514, 0.008564012376663105, 0.008693185801597466, 0.00735141379320691, 0.008121597040711687, 0.008870144914246, 0.008814175569114886, 0.007816144483473175, 0.008752339154269444, 0.008251089419735406, 0.008854797069740515, 0.00797346990413977, 0.00810949970682477, 0.008479282602830965, 0.0088147904312125, 0.008656288021137467, 0.007818774436980862, 0.008933195248335789, 0.008530920794556343, 0.008610826245068343, 0.008429900737165834, 0.008944929590962195, 0.008752409225371844, 0.008464885127027401]
+
+    f11 = [78.13158237224876, 79.67913916248689, 79.40202899951338, 78.77272197444738, 79.59917444374956,
+           80.09035223332909, 79.6681397124661, 78.34080376201238, 78.12819077368123, 79.85563519006915,
+           80.52055147165817, 79.30631771622784, 79.8274288624724, 79.56041457679224, 80.21400194139083,
+           79.73638571878287, 79.84995062609373, 78.26840266326194, 78.3831285983927, 79.52416434639552,
+           79.33923792160981, 79.05746289378486, 79.11942123132677, 79.42490159278476, 79.89660802047895,
+           78.79239337163062, 77.47565821766244, 79.29407602757921, 79.66940798370524, 79.77125064402064]
+    f12 = [0.22231773601132473, 0.16350837224085044, 0.17312398799855006, 0.18963403819980326, 0.16713480177497708,
+           0.14600814666994671, 0.16578890122242448, 0.19839836350150053, 0.22423854376689586, 0.1571067600159749,
+           0.12792181499374417, 0.1747848367451167, 0.15790084421713313, 0.16762497001543297, 0.1444292252839702,
+           0.16218682974741622, 0.15757268651940703, 0.2155643486159565, 0.19023432849851585, 0.16929884872510073,
+           0.17450508391491218, 0.1832180145938337, 0.18271473993899678, 0.1707995960118773, 0.15515245415593762,
+           0.1894473149044883, 0.23583166359095892, 0.1765863721724546, 0.16394723572377376, 0.16163081747436026]
+    f21 = [78.31098477060134, 78.82560236305012, 78.57826894095565, 78.3108960440399, 78.5854728828511,
+           77.30031160364588, 78.11383167173456, 78.25213341781318, 78.04134358140921, 78.77412389989188,
+           77.9454224332219, 78.97431294265247, 79.09061910039671, 78.48348634443312, 78.21024906731701,
+           78.4721077341294, 78.97911928203568, 77.55794714768187, 77.08805834582111, 78.53105259648503,
+           77.71578342289433, 77.50550382969944, 78.43462896476744, 78.9513392331249, 79.11843761386997,
+           78.9294332432202, 78.1978485303893, 78.7511370236766, 78.58663330340367, 77.7955757103145]
+    f22 = [0.2911141433778622, 0.27823663193938153, 0.28568828153083287, 0.2913045174684795, 0.28494162087279007,
+           0.30198200261447683, 0.2936150040593367, 0.2920916867844547, 0.2959621683803247, 0.2807112259494087,
+           0.29615233759813037, 0.27271478371269403, 0.26918049567143515, 0.287245782388438, 0.29221192743690416,
+           0.28865182933677797, 0.2711034108106382, 0.300264700268352, 0.30252685474214913, 0.286811199780341,
+           0.2994874545333383, 0.300273046692719, 0.2887174952405154, 0.27418992817388244, 0.26076702354427583,
+           0.2777877052773878, 0.2923068759498851, 0.2817938400732662, 0.2844466978672097, 0.2976794824343081]
+    f31 = [80.40384249037861, ]
+    f32 = [0.14848535240622637]
+    labels = ["proposed NSGA2-communication first", "proposed NSGA2-localization first", "pso-weighting method",
+              "pso-main objective method","pso-weighting method-localization first","pso-main objective method-localization-first"]
+
+    f41 = [81.46866648101934, ]
+    f42 = [0.1379323588640624]
+    f51 = [81.20664927884438, ]
+    f52 = [0.269883203999093]
+    f61 = [81.21235572744315, ]
+    f62 = [0.2577773453056189]
+    plt.xlabel('average snr', fontsize=12)
+    plt.ylabel('average 1/gdop', fontsize=12)
+    plt.scatter(f11, f12, label=labels[0])
+    plt.scatter(f21, f22, label=labels[1])
+    plt.scatter(f31, f32, label=labels[2])
+    plt.scatter(f41, f42, label=labels[3])
+    plt.scatter(f51, f52, label=labels[4])
+    plt.scatter(f61, f62, label=labels[5])
+    plt.legend()
+
+    plt.show()
+
+
+
+

multiple_objective_new/km.py

+import numpy as np
+import random
+import time
+
+
+zero_threshold = 0.00000001
+
+
+class KMNode(object):
+    def __init__(self, id, exception=0, match=None, visit=False):
+        self.id = id
+        self.exception = exception
+        self.match = match
+        self.visit = visit
+
+
+class KuhnMunkres(object):
+    def __init__(self):
+        self.matrix = None
+        self.x_nodes = []
+        self.y_nodes = []
+        self.minz = float('inf')
+        self.x_length = 0
+        self.y_length = 0
+        self.index_x = 0
+        self.index_y = 1
+
+    def __del__(self):
+        pass
+
+    def set_matrix(self, x_y_values):
+        xs = set()
+        ys = set()
+        for x, y, value in x_y_values:
+            xs.add(x)
+            ys.add(y)
+
+        #选取较小的作为x
+        if len(xs) < len(ys):
+            self.index_x = 0
+            self.index_y = 1
+        else:
+            self.index_x = 1
+            self.index_y = 0
+            xs, ys = ys, xs
+
+        x_dic = {x: i for i, x in enumerate(xs)}
+        y_dic = {y: j for j, y in enumerate(ys)}
+        self.x_nodes = [KMNode(x) for x in xs]
+        self.y_nodes = [KMNode(y) for y in ys]
+        self.x_length = len(xs)
+        self.y_length = len(ys)
+
+        self.matrix = np.zeros((self.x_length, self.y_length))
+        for row in x_y_values:
+            x = row[self.index_x]
+            y = row[self.index_y]
+            value = row[2]
+            x_index = x_dic[x]
+            y_index = y_dic[y]
+            self.matrix[x_index, y_index] = value
+
+        for i in range(self.x_length):
+            self.x_nodes[i].exception = max(self.matrix[i, :])
+
+
+    def km(self):
+        for i in range(self.x_length):
+            while True:
+                self.minz = float('inf')
+                self.set_false(self.x_nodes)
+                self.set_false(self.y_nodes)
+
+                if self.dfs(i):
+                    break
+
+                self.change_exception(self.x_nodes, -self.minz)
+                self.change_exception(self.y_nodes, self.minz)
+
+    def dfs(self, i):
+        x_node = self.x_nodes[i]
+        x_node.visit = True
+        for j in range(self.y_length):
+            y_node = self.y_nodes[j]
+            if not y_node.visit:
+                t = x_node.exception + y_node.exception - self.matrix[i][j]
+                if abs(t) < zero_threshold:
+                    y_node.visit = True
+                    if y_node.match is None or self.dfs(y_node.match):
+                        x_node.match = j
+                        y_node.match = i
+                        return True
+                else:
+                    if t >= zero_threshold:
+                        self.minz = min(self.minz, t)
+        return False
+
+    def set_false(self, nodes):
+        for node in nodes:
+            node.visit = False
+
+    def change_exception(self, nodes, change):
+        for node in nodes:
+            if node.visit:
+                node.exception += change
+
+    def get_connect_result(self):
+        ret = []
+        for i in range(self.x_length):
+            x_node = self.x_nodes[i]
+            j = x_node.match
+            y_node = self.y_nodes[j]
+            x_id = x_node.id
+            y_id = y_node.id
+            value = self.matrix[i][j]
+
+            if self.index_x == 1 and self.index_y == 0:
+                x_id, y_id = y_id, x_id
+            ret.append((x_id, y_id, value))
+
+        return ret
+
+    def get_max_value_result(self):
+        ret = 0
+        for i in range(self.x_length):
+            j = self.x_nodes[i].match
+            ret += self.matrix[i][j]
+
+        return ret
+
+
+def run_kuhn_munkres(x_y_values):
+    process = KuhnMunkres()
+    process.set_matrix(x_y_values)
+    process.km()
+    return process.get_connect_result()
+
+
+# def test():
+#     values = []
+#     random.seed(0)
+#     for i in range(500):
+#         for j in range(1000):
+#             value = random.random()
+#             values.append((i, j, value))
+#
+#     return run_kuhn_munkres(values)
+
+if __name__ == '__main__':
+    s_time = time.time()
+    # ret = test()
+    # print("time usage: %s " % str(time.time() - s_time))
+    values = [
+        (1, (1,1), 3),
+        (1, (3,3), 4),
+        (2, (1,1), 2),
+        (2, (2,2), 1),
+        (2, (3,3), 3),
+        (3, (2,2), 4),
+        (3, (3,3), 5)
+    ]
+
+    print(run_kuhn_munkres(values))
\ No newline at end of file

multiple_objective_new/p_tool.py

+from itertools import combinations
+
+import Users
+import tool
+import GDOP
+import copy
+import math
+
+def get_p_association_first(users,uavs,service_num_p,service_num_c):
+    p_connection = {}
+    for i in range(len(users)):
+        p_connection[i]=[]
+    uavs_initial=[i for i in range(len(uavs))]
+    uav_group_all=list(combinations(uavs_initial, 3))
+    uav_serive_num = [service_num_p for i in range(len(uav_group_all))]
+    uav_serive_num_c=[service_num_c for i in range(len(uavs))]
+    # 获得所有信道数量大于0的无人机编号
+    for i in range(len(users)):
+        uav_need = [i for i in range(len(uav_group_all)) if uav_serive_num[i] > 0]
+        uav_group = [uav_group_all[i] for i in uav_need]
+        gdop_min = float('inf')
+        index = 0
+        for j in range(len(uav_group)):
+            uavs_pos = [uavs[uav_group[j][0]], uavs[uav_group[j][1]], uavs[uav_group[j][2]]]
+            gdop_current = GDOP.calculate_gdop(uavs_pos, copy.copy(users[i]))
+            if gdop_current < gdop_min:
+                gdop_min = gdop_current
+                index = j
+        print(gdop_min)
+        # for k in range(len(uav_group[index])):
+        p_connection[i].extend([uav_group[index][0],uav_group[index][1],uav_group[index][2]])
+        uav_serive_num[index] -= 1
+    c_connection={}
+    for i in range(len(users)):
+        uav_need = [i for i in range(len(uavs)) if uav_serive_num_c[i] > 0]
+        min=float('inf')
+        node=-1
+        for j in range(len(p_connection[i])):
+            if p_connection[i][j] in uav_need:
+                dis=tool.calculate_3d_distance(uavs[p_connection[i][j]],users[i])
+                if min>dis:
+                    min=dis
+                    node=p_connection[i][j]
+        c_connection[i]=node
+        uav_serive_num_c[node]-=1
+    # print(c_connection)
+    for i in range(len(p_connection)):
+        p_connection[i].remove(c_connection[i])
+    # print(p_connection)
+    return c_connection,p_connection
+
+if __name__ == '__main__':
+    users=Users.getUsers("./data.csv")
+    uavs=tool.getUavInitialPos(3,users)
+    uavs_initial = [i for i in range(len(uavs))]
+    combination=list(combinations(uavs_initial, 3))
+    print(get_p_association_first(users,uavs,math.ceil(len(users)/len(combination)),math.ceil(len(users)/len(uavs))))
+
+    # uav_group = list(combinations(uav_need, 1))
+    # print(uav_group)
\ No newline at end of file

multiple_objective_new/pso.py

+import math
+import Users
+import numpy as np
+import random
+from pprint import pprint
+
+
+class PSO:
+    def __init__(self,users,cal_tool,N=60):
+        #之后解析JSON，从JSON中读参数
+        self.N=N
+        self.cal_tool=cal_tool
+        self.d = 3  # 可行解维数
+        self.limit = [0, 1000]  # 边界位置限制
+        self.vlimit = [-100, 100]  # 边界速度限制
+        self.height = 100
+        self.wmax = 1.5  # 惯性权重
+        self.wmin = 0.3
+        self.c1 = 2  # 自我学习因子
+        self.c2 = 2  # 群体学习因子
+        # self.c1 = 0.5  # 自我学习因子
+        # self.c2 = 0.5  # 群体学习因子
+        self.maxgen = 200
+        # self.users = Users.getUsers("./data.csv")
+        self.users = users
+
+
+    # #todo：防止无人机位置碰撞
+    # def punish_function(self,uavs):
+    #     # user_arr = [1, 2, 5]
+    #     punish=0
+    #     for i in range(len(uavs)):
+    #         for j in range(i+1,len(uavs)):
+    #             dis=self.cal_tool.getDistance(uavs[i],uavs[j])
+    #             if dis<200:
+    #                 punish+=100
+    #
+    #     return punish
+
+
+    # 初始化粒子群，k是无人机数量
+    def initParticle(self, k, users):
+        X = []
+        # users=Users.generate(100)#用户位置
+        V = np.random.uniform(self.vlimit[0], self.vlimit[1], (self.N, k, 3))
+        for i in range(self.N):
+            uavs=[]
+            for index in range(k):
+                x=np.random.randint(self.limit[0],self.limit[1])
+                y=np.random.randint(self.limit[0],self.limit[1])
+                z=self.height
+                uavs.append([x,y,z])
+            # uavs = self.cal_tool.getUavInitialPos(k, users,self.height[0])
+            X.append(uavs)
+        X = np.array(X)
+        return X, V
+
+    # 初始化个体和全局最佳历史位置和最佳适应度
+    def initBest(self, X, k):
+        p_pos = X.copy()  # 每个个体的历史最佳位置
+        global_pos = np.zeros((k, 3))  # 种群的历史最佳位置5*3
+        p_best = np.full((self.N, 1), float('-inf'))  # 每个个体的历史最佳适应度
+        global_best = float('-inf')  # 种群历史最佳适应度
+        return p_pos, global_pos, p_best, global_best
+
+
+    def pso(self, X, V, k,get_fitness):
+        # X, V, users = initParticlePos()
+        p_pos, global_pos, p_best, global_best = self.initBest(X, k)
+        result = np.zeros(self.maxgen)
+
+        for iter in range(self.maxgen):
+            # fitness = self.getfitness(X, users, k,P)  # 计算所有粒子适应度
+            fitness = np.zeros(self.N)
+            for i in range(len(X)):
+                _,_,fitness[i]= get_fitness(self.users,X[i])
+            for i in range(self.N):
+                if fitness[i] > p_best[i]:
+                    p_best[i] = fitness[i]  # 更新个体历史最佳适应度
+                    p_pos[i] = X[i].copy()  # 更新个体历史最佳位置(取值)
+            if max(p_best) > global_best:
+                global_best = max(p_best)  # 更新群体历史最佳适应度
+                max_index = p_best.argmax()
+                global_pos = X[max_index].copy()  # 更新群体历史最佳位置
+
+
+            # 权重更新
+            w = self.wmax - iter * (self.wmax - self.wmin) / self.maxgen
+            for i in range(self.N):
+                V[i] = V[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                        global_pos - X[i])
+                # print(V[i])
+                for j in range(len(V[i])):
+                    for p in range(len(V[i][j])):
+                        if V[i][j][p] > self.vlimit[1]:
+                            V[i][j][p] = self.vlimit[1]
+                            # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        elif V[i][j][p] < self.vlimit[0]:
+                            V[i][j][p] = self.vlimit[0]
+                # print(V[i])
+
+            # 位置更新
+            X = X + V
+            # 边界位置处理
+            for i in range(self.N):
+                for j in range(len(X[i])):
+                    for p in range(2):
+                        if X[i][j][p] > self.limit[1]:
+                            X[i][j][p] = self.limit[1]
+                        elif X[i][j][p] < self.limit[0]:
+                            X[i][j][p] = self.limit[0]
+                    if X[i][j][2] > self.height:
+                        X[i][j][2] = self.height
+                    elif X[i][j][2] < self.height:
+                        X[i][j][2] = self.height
+
+            result[iter] = global_best
+            # print(iter,global_pos,global_best)
+
+        # print(global_best)
+        # print(global_pos)
+
+        return global_pos,global_best,result
+
+
+
+
+
+
+if __name__ == '__main__':
+    print()
+
+
+
+

multiple_objective_new/result.txt

+30 200 4 15
+ts  136.61536121368408s
+f11=[54.0507626287143, 52.96308084332225, 53.81164827066072, 52.662348665211766, 53.058950945849816, 54.19019342328689, 53.9299688459228, 53.11426657277529, 53.53448095960305, 53.41244012096225, 52.161071436391765, 53.63152603890542, 52.60717139811691, 52.9419652152142, 53.12691669130094, 54.769726980442876, 53.221038636189846, 53.273098613877536, 54.813441694286276, 52.444405907733056, 52.07495261632682, 53.65582251950728, 54.572560522519765, 53.34200366227426, 52.951982284304776, 54.73271397453529, 54.740843042254234, 54.62014017494, 54.65028166545009, 54.058041455437795]
+f12=[-2.6593781859878236, -2.543101161647058, -2.629703840153344, -2.5232018979680153, -2.5456278171312903, -2.6935826769370554, -2.6471125403460376, -2.553281887750045, -2.5912253989127305, -2.575347927941496, -2.507729928482692, -2.6029178154110104, -2.519878280391824, -2.5396945038293763, -2.554082938296093, -2.8538301515033497, -2.5609278449877055, -2.566614987963588, -2.9048977606725277, -2.5105198789979197, -2.491221788958603, -2.622021879332393, -2.772197542173876, -2.5684940830071743, -2.540468767759957, -2.827654957571949, -2.8307525713156143, -2.7958452945775663, -2.808613976366928, -2.6603893135495023]
+linear
+f21=[54.050685006781094, 54.283393761916976, 54.47656291210348, 54.74324070888281, 53.99842379062622, 51.528636671309236, 52.75218963603528, 54.84114777226443, 54.273369607211556, 53.58254967717716, 54.58375169247761, 51.284494357009486, 54.89359217394771, 53.600798991323046, 50.86788233140609, 54.476286152752024, 52.255896253350805, 54.65071141233971, 53.95519536414517, 53.282677590253826, 53.64150189178759, 54.134345252288654, 53.76200008453646, 51.57929194812203, 54.901465412391964, 54.05680886961717, 51.37917279898564, 52.04171632091941, 54.39133221686892, 54.74753796495144]
+f22=[-2.654986150981363, -2.693018355944383, -2.7210723464506525, -2.7864337596677493, -2.6518544908865724, -2.444966870948845, -2.5464415029504903, -2.8258801390793424, -2.692146096562049, -2.5976241860819047, -2.7424514298711387, -2.438197829313505, -2.8528428455891137, -2.599653195343768, -2.431283621244636, -2.718041590820195, -2.485692740495766, -2.7714866271755527, -2.641513633880053, -2.5780465532926784, -2.6057845459446995, -2.6672188815808133, -2.6210307516417344, -2.445576451080987, -2.880499673026878, -2.660973875292016, -2.440809680204474, -2.4685965016751674, -2.705178447946934, -2.788315779933751]
+
+30 200 5 30
+f21=[51.334550890077836, 51.439931078634245, 51.683248011667956, 51.79320986700474, 51.74773078781322, 51.066584115404, 51.465600927140144, 50.91638741289003, 51.435648773245056, 50.94435567401892, 50.80237756584818, 51.11468577768979, 50.924785079829604, 51.578759424591475, 50.05252986777546, 51.075165500431204, 50.92063662520722, 50.80907917792338, 50.80582860326489, 51.246941894012174, 51.72772609250578, 51.08659894862157, 50.99738248640082, 51.46728162136219, 50.89467711194264, 51.87174348917633, 51.82639082365849, 50.993598263077686, 51.1913242679079, 51.21836600037675]
+f22=[-2.566384248015569, -2.592968131062478, -2.696891979673827, -2.745844761338047, -2.728883383157762, -2.5121329706826527, -2.6112407203328982, -2.491221796089005, -2.591229007662642, -2.4984741419357577, -2.4806567659555645, -2.524850247488119, -2.4921996101705193, -2.6676583378520307, -2.409158020041916, -2.516711583016044, -2.4917306582487626, -2.4811503760525326, -2.4811211415350547, -2.550114654729561, -2.7031389569361743, -2.519740686177889, -2.502019524544895, -2.6142072933500042, -2.4905090225082773, -2.8293998979711334, -2.7980034318716367, -2.501470477979192, -2.540907235037267, -2.5448328297185085]
+
+30 200 3 6
+f31=[56.46907510714308, 56.31152006835182, 56.5069364355453, 56.65546579871943, 57.65572244421892, 56.537074313489654, 54.728001286126414, 56.71257763559322, 55.8790430205371, 56.31392385285384, 56.252242015120736, 55.72214664981578, 56.73305359657204, 54.73482564422065, 57.707600420188705, 55.73433913222626, 55.691417796384265, 56.665708111562225, 56.70578493156345, 56.62844599901684, 56.57821174331196, 56.76137252714323, 55.452059302856135, 57.49377772545475, 54.920993137552166, 56.614387469145534, 57.418712640751615, 56.04918950267493, 55.95185466451161, 57.51701412781926]
+f32=[-3.027839142216957, -2.985878123714967, -3.0587403682717293, -3.1735775860398334, -4.1253845778201095, -3.066336190194695, -2.794401394953883, -3.21329609374846, -2.8850163579446826, -2.986885978939206, -2.9712895163027397, -2.866333453680601, -3.2350971885172064, -2.7947129156080197, -4.142099402500488, -2.874634676486394, -2.8563855146463712, -3.188822319243743, -3.210607568911858, -3.138741942898081, -3.0963794118572636, -3.2542231629655984, -2.8533669810824875, -3.8719649467160626, -2.8155056230211994, -3.1174818497736854, -3.8596923965708885, -2.9166464040747044, -2.9074874484447832, -3.8914722244903897]
+
+3 10  15.384773015975952s
+f11[53.15701374063989, 53.121439465960066, 50.80182203069932, 52.57704773190993, 52.116421205820686, 52.09329273740436, 52.399972462185715, 53.01678997498576, 50.807170387786, 53.326298012750634, 53.34190566809375, 50.81184773390719, 52.03451804459312, 51.9861301449006, 51.28067896102526, 53.325828586097074, 52.49847609177091, 49.292408973950806, 50.02228891711617, 51.72293287997159, 53.377990911067855, 52.08613787626742, 52.25767760436143, 53.38191040988062, 48.85624274835523, 52.25767760436143, 48.271449563838516, 52.18120069954898, 51.51698294628223, 53.19965725636094]
+f12=[-4.7424166355797785, -4.513453375218427, -3.5943890061967876, -4.167452148384593, -3.8484763795448247, -3.8029009898454946, -4.037622926103712, -4.308518223735279, -3.5983506584093816, -5.287712830251175, -5.288431056864009, -3.6024041003298928, -3.793501824363529, -3.7683812816092397, -3.6202386010254024, -5.282493280214469, -4.104132449665276, -3.410528994407205, -3.4687279171780445, -3.725358712425691, -5.6681441205003775, -3.801806630712472, -3.972890625225026, -6.021619944112665, -3.4011907565203017, -3.972890625225026, -3.3791900861821937, -3.853850446523785, -3.645392363317767, -4.905403808886767]
+
+3 20 65.87500405311584
+f21=[45.834016331970325, 46.842404026080104, 45.94045152723605, 46.42827224025241, 46.251855922061814, 44.62584487748428, 46.89676226877265, 44.57046417411123, 47.41368813518143, 45.349395457193516, 47.08118934629392, 44.261763305736466, 46.401612819374726, 45.73940940353238, 46.835575183380456, 45.77541441143831, 47.3008955908348, 47.11031927291376, 44.679187273276305, 47.39251804257596, 47.20097887444749, 46.68052902779725, 47.50215603042076, 46.91697172969312, 46.16228709110786, 44.809257534469836, 45.926227268456664, 45.81400260193812, 47.44277260841224, 46.42827224025241]
+f22=[-3.602227997889564, -3.7693855290562523, -3.632090146991377, -3.669691119481263, -3.637898796537504, -3.5718140421956512, -3.7988305944632748, -3.5644165862648656, -4.007652260162592, -3.5773993228856917, -3.8420823362404994, -3.547425831167228, -3.66454936187507, -3.5919482349036813, -3.725333399844625, -3.594369753236859, -3.8975035520421812, -3.8773725839117015, -3.5720637484629956, -3.9760226708214175, -3.883487728725116, -3.70330819927777, -4.10293633804125, -3.8399525976708935, -3.6340000246124724, -3.573425351082262, -3.6092631849525105, -3.599299254708529, -4.017091540549122, -3.669691119481263]
+linear  91.6108136177063
+f21_l=[46.79618184949475, 46.66173057329805, 47.048035449057245, 46.69420057121491, 46.53565738757049, 46.66161883107671, 46.1235761225788, 47.241066700521785, 46.611503519410846, 46.5229736380701, 47.09594822228021, 47.12455615201715, 46.003817954398684, 46.46336942734452, 47.04083977120062, 46.986035176967526, 47.07780200662248, 47.11707528453678, 47.28482107224288, 47.057084907593236, 46.71413609531813, 46.95838300559289, 46.98060186587708, 46.909028521789125, 47.092370130946236, 46.131922988760394, 46.75734538408214, 46.04274075369345, 47.14300506574648, 46.54637252224787]
+f22_l=[-3.709122162038647, -3.6847169991471787, -3.7857050799838134, -3.6897570032031304, -3.66527004040039, -3.68469729515044, -3.6170565700424824, -3.8622391074070967, -3.676220943137094, -3.6636864028390064, -3.8055132164666987, -3.811329737079722, -3.605462567892802, -3.6567498090628563, -3.7851108435773755, -3.7619491560879013, -3.788187839985887, -3.8081523457403312, -3.8807695502446364, -3.78695250942519, -3.6976590423082216, -3.753304317282158, -3.760147474280349, -3.741238771314687, -3.804596894111487, -3.6327395231058985, -3.70536316854747, -3.615544928590912, -3.8182133648282433, -3.6666538282669876]
+
+4 10  59.793779134750366
+f31=[54.52245664123465, 55.959013088613474, 55.90919524888748, 56.04323778793106, 56.62688910064067, 56.527310681276084, 56.97600661134239, 56.804251461885976, 55.89222842050642, 55.51308351465983, 55.581478667918034, 54.42201774010683, 56.18377711686112, 56.4518813332583, 56.15200217930927, 57.08709543815009, 55.07239767017169, 54.633243985137916, 56.97454773954313, 56.17561419615326, 54.858042350200506, 56.56923529266034, 54.8471048892334, 56.61624965775959, 56.18377711686112, 56.46129792339117, 55.04642864123899, 55.77653197200065, 54.41935176901326, 56.183162384285275]
+f32=[-2.3894560859047718, -2.503557193006404, -2.496198934326645, -2.5361925139069315, -2.73527987814952, -2.6801974621808853, -2.8784200648565887, -2.8044862060003446, -2.4905777651047996, -2.437403277202888, -2.460102424252358, -2.382228126320782, -2.6355501399682764, -2.6427826731164634, -2.5736419648400872, -2.9360719176142496, -2.4174280695654993, -2.394797245270132, -2.8291712528173045, -2.592587372358513, -2.4042744982395474, -2.7019810214304445, -2.3957679672104257, -2.7280756174286314, -2.6355501399682764, -2.657128100796301, -2.4120154234175217, -2.4834794164187377, -2.362570181158639, -2.6193303855868253]
+
+4 20 288.8421359062195
+f41=[51.741931566142526, 51.769393918636595, 51.4289451536845, 51.98822686920067, 50.461344139017676, 52.07141903589688, 51.20667675520541, 51.66049358343737, 51.68361280228959, 51.92241969980686, 51.4372372254511, 51.68389406273518, 51.37708272396709, 51.61591325907106, 51.71059140008647, 49.691927796142025, 51.61740030254972, 51.9523760025977, 50.91915418812668, 51.983748122984935, 51.04638364116797, 49.719832111034364, 51.34478938510604, 51.83586291877852, 51.99810830317019, 51.60888878340478, 51.29566477611089, 51.881135193618526, 51.98866858853559, 50.192225612156186]
+f42=[-2.807628072853997, -2.8115086214045144, -2.7473289441363424, -2.895768951777114, -2.659690550988313, -2.964548645290694, -2.6990415981837326, -2.797083370049629, -2.8029268344139178, -2.849226454640806, -2.756608871451027, -2.80406672890553, -2.73197709613222, -2.7861828793299486, -2.80599414790823, -2.6372127023852006, -2.789837667382157, -2.859644002149485, -2.670784257045365, -2.887935524744255, -2.6919251396932364, -2.6541055308766763, -2.7115026939622626, -2.829755867138337, -2.9014524787009863, -2.7569856303387557, -2.709519873808802, -2.832857043616753, -2.898140230132415, -2.657812119234906]
+
+
+[2.469273328781128, 7.421365737915039, 15.89266562461853, 30.616859912872314, 41.11850118637085, 48.21879506111145, 90.0976836681366, 85.44814968109131, 100.62214803695679, 140.64940476417542]
+ts
+[11.955068588256836, 18.25909113883972, 14.64045238494873, 16.398587226867676, 12.18566083908081, 10.024715662002563, 16.576231479644775, 12.345167398452759, 19.49023723602295, 15.916483640670776]
+linear
+[17.869938373565674, 19.484696865081787, 18.30145573616028, 17.97358202934265, 17.8395037651062, 17.644514799118042, 20.304096460342407, 16.842082262039185, 19.157524585723877, 17.43966555595398]

multiple_objective_new/tool.py

+import math
+import scipy.integrate
+import numpy as np
+from sklearn.cluster import KMeans
+
+import GDOP
+from itertools import combinations
+import copy
+
+import Users
+# from Match import Match
+import km
+
+def calculate_3d_distance(a, b):
+    return math.sqrt(math.pow(a[0] - b[0], 2) + math.pow(a[1] - b[1], 2) + math.pow(a[2] - 0, 2))
+
+
+# 计算3d距离
+def calculate_2d_distance(a, b):
+    return math.sqrt(math.pow(a[0] - b[0], 2) + math.pow(a[1] - b[1], 2))
+
+
+# 计算森林信道路径损耗
+def forest_path_loss(uav, user):
+    dis = calculate_3d_distance(uav, user)
+    alpha = 3.5
+    A = 0.25
+    C = 0.39
+    E = 0.25
+    G = 0
+    B = 1
+    H = 0.05
+    f_c = 2 * 10 ** 9
+    c = 1 * 10 ** 8
+    path_loss = 10 * alpha * math.log10(dis)
+    theta = math.asin(uav[2] / dis)
+    theta = math.degrees(theta)
+    dis1 = 5 * dis / uav[2]  # 树叶的高度
+    slant = A * math.pow(f_c / 1e6, C) * math.pow(dis1, E) * math.pow(theta + G, H)
+    fspl = 20 * math.log10(4 * math.pi * 1 * f_c / c)
+    L_K_N = slant + fspl + path_loss
+    return L_K_N
+
+
+def expect_L(l1):
+    sigma = 3
+    R_u = lambda x1: ((10 ** (-(l1 + x1) / 10))) * 1 / (math.sqrt(2 * math.pi) * sigma) * math.exp(
+        -(x1 ** 2) / (2 * sigma ** 2))
+    R2, err = scipy.integrate.quad(R_u, -float("100"), float("100"))
+    return R2
+
+def path_loss_dis(dis,h):
+    a = 9.6
+    b = 0.28
+    los = 1
+    nlos = 20
+    freePathLoss = 20 * math.log10(dis) + (20 * math.log10(2000000000)) + 20 * math.log10(4 * (math.pi) / 3 / 100000000)
+    angle = math.asin(h / dis)
+    angle = angle / math.pi * 180
+    plos = 1 / (1 + a * math.exp(-b * (angle - a)))
+    pnlos = 1 - plos
+    pl = plos * los + pnlos * nlos + freePathLoss
+    return pl
+
+def free_path_loss(dis):
+    freePathLoss = 20 * math.log10(dis) + (20 * math.log10(2000000000)) + 20 * math.log10(4 * (math.pi) / 3 / 100000000)
+    return freePathLoss
+
+# 计算snr
+def cal_snr(uav, user, p):
+    # loss = forest_path_loss(uav, user)
+    dis=calculate_3d_distance(uav,user)
+    loss=path_loss_dis(dis,uav[2])
+    noise_power = (10 ** (-117 / 10)) / 1000
+    snr = p * (10 ** (-loss / 10)) / noise_power
+    return 10 * math.log10(snr)
+
+
+# 计算数据传输速率
+def cal_data_rate(snr, bandwidth):
+    date_rate = bandwidth * math.log2(1 + snr)
+    return date_rate
+
+
+def dbmToW(p):
+    return pow(10, p / 10) / 1000
+
+#获得每个用户的定位无人机组
+def get_user_loc_uav(users,p_association):
+    user_loc_uav={}
+    for i in range(len(users)):
+        user_loc_uav[i]=[]
+    for i in range(len(p_association)):
+        for j in range(len(p_association[i])):
+            if p_association[i][j]==1:
+                user_loc_uav[j].append(i)
+    return user_loc_uav
+
+#贪心关联
+def get_association(users,uavs):
+
+    return
+
+
+def getUavInitialPos(k, users):
+    model = KMeans(n_clusters=k,  # 聚类簇数
+                   random_state=1,  # 决定质心初始化的随机数生成，使用int使随机性具有确定性。
+                   max_iter=300,  # 执行一次k-means算法所进行的最大迭代数，默认300
+                   ).fit(users)
+    center = model.cluster_centers_
+    uavs = []
+    for item in center:
+        # height = random.randint(100, 800)
+        height = 100
+        position = [int(item[0]), int(item[1]), height]
+        uavs.append(position)
+    # print(center)
+    return uavs
+
+
+def get_c_association(users,uavs_pos,serveMax):
+    user_num = len(users)
+    uavs = [k for k in range(len(uavs_pos))]
+    uav_num = len(uavs)
+    # print(distance_sum)
+    weight = np.zeros((user_num, uav_num), dtype=np.float16)
+    graph = []
+    for i in range(user_num):
+        for j in uavs:
+            # weight[i][j]=-self.distance_c[i][j]/(distance_sum[i]-self.distance_c[i][j])
+            weight[i][j] = -calculate_3d_distance(uavs_pos[j],users[i])
+    dic = {}
+    for i in range(user_num):
+        for j in uavs:
+            for number in range(serveMax):
+                graph.append((i, j + 100 * number, weight[i][j]))
+                dic[j + 100 * number] = j
+
+
+    connection = km.run_kuhn_munkres(graph)
+    c_connection = {}
+    for connect in connection:
+        c_connection[connect[0]] = dic[connect[1]]
+    # print(c_connection)
+    return c_connection
+
+def get_uav_to_user(c_connection,uavs):
+    uav_to_user={}
+    for i in range(len(uavs)):
+        uav_to_user[i]=[]
+    for i in c_connection:
+        uav_to_user[c_connection[i]].append(i)
+    return uav_to_user
+
+# 选择好通信的无人机后，确定其他两架无人机与该无人机对用户获得最优的gdop值
+def get_p_association(users,uavs,c_connection,service_num):
+    p_connection={}
+    for i in c_connection:
+        p_connection[i]=[]
+    if len(uavs)==3:
+        for i in range(len(users)):
+            uavs_all=[i for i in range(len(uavs))]
+            uavs_all.remove(c_connection[i])
+            p_connection[i]=[uavs_all[0],uavs_all[1]]
+    else:
+        uavs_initial = [i for i in range(len(uavs))]
+        uav_group_all = list(combinations(uavs_initial, 3))
+        uav_serive_num = [service_num for i in range(len(uav_group_all))]
+        uav_group_dic={}
+        for i in range(len(uav_group_all)):
+            uav_group_dic[uav_group_all[i]]=i
+        # uav_serive_num=[service_num for i in range(len(uavs))]
+        for i in range(len(users)):
+            #获得所有信道数量大于0的无人机编号
+            # uav_need=[i for i in range(len(uavs)) if uav_serive_num[i]>0]
+            uav_need = [i for i in range(len(uav_group_all)) if uav_serive_num[i] > 0]
+            uav_groups = [uav_group_all[i] for i in uav_need]#所有可用的uav_group
+            #移除通信的无人机
+            uav_group=[]
+            uav_dic={}
+            count=0
+            for group in uav_groups:
+                if c_connection[i] in group:
+                    tmp=[group[0],group[1],group[2]]
+                    tmp.remove(c_connection[i])
+                    uav_group.append(tmp)
+                    uav_dic[count]=group#找到group对饮的下标
+                    count+=1
+                    # uav_need.remove(c_connection[i])
+            # uav_group=list(combinations(uav_need, 2))
+
+            gdop_min = float('inf')
+            index = 0
+            for j in range(len(uav_group)):
+                uavs_pos = [uavs[c_connection[i]], uavs[uav_group[j][0]],uavs[uav_group[j][1]]]
+                gdop_current = GDOP.calculate_gdop(uavs_pos, copy.copy(users[i]))
+                if gdop_current < gdop_min:
+                    gdop_min = gdop_current
+                    index = j
+            # print(gdop_min)
+            p_connection[i].extend([uav_group[index][0], uav_group[index][1]])
+            uav_serive_num[uav_group_dic[uav_dic[index]]]-=1
+    # print(p_connection)
+    return p_connection
+
+
+def get_p_by_snr(snr,uav,user,noise):
+    dis = calculate_3d_distance(uav, user)
+    pl= path_loss_dis(dis, uav[2])
+    # pl=path_loss_dis(uav,user)
+    noise=dbmToW(noise)
+    p=(10**(snr/10))*noise/10**(-pl/10)
+    # print(p)
+    return p
+
+def get_rest_p(users,uavs,p_connection,snr_thre=0,noise=-117,pmax=0.1):
+    p_rest=[pmax for i in range(len(uavs))]
+    for user_index in p_connection:
+        uav_group=p_connection[user_index]
+        for uav_index in uav_group:
+            p_need=get_p_by_snr(snr_thre,uavs[uav_index],users[user_index],noise)
+            p_rest[uav_index]-=p_need
+
+    return p_rest
+
+
+if __name__ == '__main__':
+    users=Users.getUsers("./data.csv")
+    uavs=getUavInitialPos(3,users)
+    c_connection=get_c_association(users,uavs,math.ceil(len(users)/len(uavs)))
+    p_connection=get_p_association(users,uavs,c_connection,math.ceil(len(users)*2/len(uavs)))
+    print(get_p_by_snr(20,uavs[2],users[0],-117))
+    # print(get_rest_p(users,uavs,p_connection))
\ No newline at end of file

multiple_objective_new/tsDemo.py

+import random
+import math
+
+from matplotlib import pyplot as plt
+
+
+def objective_function(x):
+    # 定义优化的目标函数，此处以一个简单的二维函数为例
+    return (x[0] - 2) ** 2 + (x[1] - 3) ** 2
+
+def generate_neighbor(x):
+    # 在当前解的邻域内随机生成新解
+    return [x[0] + random.uniform(-0.1, 0.1), x[1] + random.uniform(-0.1, 0.1)]
+
+def tabu_search(max_iter):
+    current_solution = [0, 0]  # 初始解
+    best_solution = current_solution  # 记录最优解
+    best=[]
+    tabu_list = []  # 禁忌表
+    tabu_tenure = 10  # 禁忌期限，即禁忌解在禁忌表中停留的迭代次数
+
+    for i in range(max_iter):
+        neighbors = [generate_neighbor(current_solution) for _ in range(10)]  # 随机生成10个邻域解
+
+        # 在邻域解中选择一个非禁忌的最优解
+        candidate = min(neighbors, key=objective_function)
+        while candidate in tabu_list:
+            neighbors.remove(candidate)
+            candidate = min(neighbors, key=objective_function)
+
+        # 更新当前解和最优解
+        current_solution = candidate
+        if objective_function(candidate) < objective_function(best_solution):
+            best_solution = candidate
+        best.append(objective_function(best_solution))
+        tabu_list.append(candidate)  # 将当前解加入禁忌表
+
+        if len(tabu_list) > tabu_tenure:  # 维护禁忌表的长度不超过禁忌期限
+            tabu_list.pop(0)
+
+    return best_solution,best
+
+def drawResultAndIter(r1,r2,r3):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(r1)
+    plt.plot(r2)
+    plt.plot(r3)
+    plt.axhline(y=60, color='r', linestyle='--', label='Horizontal Line at y=60')
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('number of non-dominated solutions')
+    plt.show()
+
+if __name__ == "__main__":
+    # num2=[2, 3, 9, 11, 7, 10, 14, 11, 12, 9, 14, 15, 16, 16, 15, 14, 15, 15, 15, 16, 19, 20, 20, 19, 23, 21, 21, 23, 26, 28,
+    #  28, 28, 29, 31, 32, 35, 34, 34, 34, 36, 37, 37, 37, 38, 38, 39, 39, 39, 39, 40, 40, 44, 45, 45, 45, 45, 45, 41, 41,
+    #  42, 41, 43, 44, 45, 44, 47, 46, 47, 48, 49, 50, 53, 53, 53, 52, 54, 55, 56, 57, 57, 58, 58, 59, 60]
+    # num1=[1, 3, 4, 4, 4, 5, 5, 5, 6, 5, 5, 5, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 12, 13, 16, 18, 19, 20, 15, 14,
+    #  15, 14, 15, 15, 16, 17, 17, 17, 17, 17, 18, 17, 17, 19, 20, 21, 17, 18, 18, 19, 20, 22, 24, 25, 25, 26, 27, 28, 28,
+    #  27, 26, 27, 29, 29, 31, 30, 31, 29, 31, 32, 32, 34, 34, 35, 35, 35, 36, 35, 35, 33, 29, 29, 29, 29, 29, 33, 36, 35,
+    #  35, 35, 35, 38, 40, 41, 41, 43, 45, 43, 45, 45, 45, 43, 44, 44, 44, 45, 47, 47, 47, 43, 44, 46, 46, 49, 47, 48, 46,
+    #  46, 47, 48, 49, 47, 47, 46, 48, 49, 50, 50, 51, 51, 50, 50, 48, 46, 48, 49, 50, 53, 52, 54, 54, 55, 56, 56, 57, 57,
+    #  58, 59, 60]
+    # num2=[8, 7, 5, 8, 9, 6, 9, 11, 15, 12, 17, 21, 22, 22, 21, 20, 21, 23, 21, 22, 22, 24, 24, 25, 27, 26, 28, 30, 33, 34,
+    #  35, 37, 38, 39, 39, 41, 42, 42, 44, 46, 47, 48, 49, 51, 50, 51, 53, 56, 56, 58, 58, 58, 59, 60]
+    #
+    # num1=[4, 6, 5, 6, 7, 9, 9, 9, 11, 13, 9, 10, 9, 9, 9, 11, 10, 10, 12, 12, 13, 13, 13, 14, 14, 15, 16, 14, 14, 14, 13, 14,
+    #  14, 16, 20, 21, 22, 24, 26, 27, 28, 22, 22, 24, 25, 25, 26, 29, 29, 31, 26, 27, 27, 26, 29, 28, 26, 27, 29, 27, 29,
+    #  30, 32, 32, 34, 36, 37, 33, 35, 36, 39, 39, 39, 40, 41, 43, 43, 43, 45, 52, 52, 55, 52, 54, 55, 57, 57, 56, 57, 55,
+    #  56, 57, 59, 60, 60, 60, 58, 57, 60, 57, 59, 60, 60, 60, 60, 60, 57, 59, 60, 58, 58, 58, 59, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 56, 56, 60, 60, 53, 55, 56, 56, 56, 55, 58, 58, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60]
+    # num2=[6, 8, 11, 12, 12, 9, 10, 11, 18, 21, 20, 22, 25, 27, 28, 29, 31, 32, 33, 35, 34, 36, 36, 37, 37, 36, 36, 36, 38,
+    #  39, 40, 40, 39, 44, 45, 46, 46, 48, 48, 50, 48, 48, 49, 51, 51, 51, 50, 51, 52, 52, 54, 55, 57, 57, 59, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 55, 56, 56, 57, 57, 58, 55, 57,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 58, 59, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60]
+    #
+    # num1=[8, 9, 8, 10, 3, 4, 4, 5, 6, 7, 5, 6, 6, 7, 8, 6, 7, 7, 6, 6, 6, 6, 6, 7, 8, 7, 8, 10, 10, 10, 10, 11, 11, 13, 16,
+    #  14, 14, 14, 13, 13, 15, 15, 16, 17, 17, 20, 20, 20, 21, 23, 20, 20, 21, 23, 23, 24, 22, 21, 23, 24, 27, 22, 23, 22,
+    #  14, 15, 17, 19, 19, 21, 21, 18, 20, 19, 19, 19, 20, 21, 22, 21, 23, 23, 23, 24, 25, 27, 27, 28, 28, 30, 30, 31, 33,
+    #  33, 35, 36, 36, 36, 37, 36]
+    #
+    # num2=[4, 3, 6, 5, 8, 12, 12, 16, 22, 19, 22, 22, 25, 28, 30, 30, 34, 35, 34, 35, 36, 37, 37, 36, 37, 39, 40, 41, 42, 43,
+    #  45, 47, 43, 44, 44, 44, 45, 46, 47, 47, 47, 47, 50, 52, 51, 52, 54, 56, 58, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  59, 60, 60, 60, 58, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
+    #  60, 60, 60, 60, 59, 60, 60, 60, 60, 60, 60, 60]
+    #
+    # num1=[6, 4, 5, 8, 9, 10, 10, 11, 12, 12, 11, 11, 11, 11, 9, 7, 8, 5, 6, 6, 6, 8, 9, 11, 11, 12, 12, 12, 12, 14, 16, 16,
+    #  16, 17, 18, 14, 14, 14, 15, 16, 16, 17, 18, 13, 13, 14, 13, 13, 13, 13, 13, 13, 13, 14, 18, 19, 23, 24, 27, 27, 29,
+    #  29, 30, 29, 30, 31, 33, 33, 33, 32, 33, 33, 40, 40, 40, 41, 43, 45, 46, 47, 40, 42, 42, 43, 46, 46, 47, 47, 48, 50,
+    #  42, 40, 40, 39, 40, 40, 37, 37, 37, 37, 37, 38, 40, 38, 33, 34, 34, 36, 36, 36, 36, 36, 36, 36, 35, 38, 34, 34, 36,
+    #  36, 37, 37, 38, 39, 37, 36, 36, 37, 38, 36, 38, 37, 38, 35, 35, 31, 31, 31, 31, 32, 32, 32, 33, 34, 34, 34, 36, 36,
+    #  37, 38, 40, 42, 42, 43, 46, 47, 48, 48, 48, 48, 50, 48, 48, 47, 49, 50, 52, 52, 53, 54, 53, 55, 58, 59, 60]
+    #
+    # num2=[4, 7, 7, 10, 13, 15, 8, 7, 13, 8, 13, 14, 15, 16, 24, 23, 29, 38, 39, 40, 40, 40, 42, 43, 43, 45, 47, 46, 47, 49,
+    #  50, 52, 51, 50, 49, 51, 50, 51, 56, 59, 60]
+    #
+    # num2=[4, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 8, 5, 7, 6, 7, 7, 7, 9, 9, 9, 6, 6, 7, 7, 7, 8, 10, 11, 11, 12, 14,
+    #  15, 12, 12, 12, 12, 15, 15, 15, 17, 16, 17, 19, 20, 21, 21, 24, 22, 22, 25, 26, 26, 26, 27, 27, 27, 27, 28, 29, 30,
+    #  27, 29, 30, 35, 34, 38, 39, 40, 40, 41, 37, 40, 46, 50, 51, 50, 51, 52, 53, 57, 58, 57, 59, 60]
+    #
+    num1=[2, 2, 2, 3, 4, 4, 6, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 6, 6, 6, 6, 6, 7, 8, 9, 9, 11, 11, 11, 12, 13, 13, 13, 13,
+     13, 15, 17, 17, 16, 13, 13, 13, 12, 13, 15, 16, 17, 18, 20, 21, 21, 22, 23, 22, 22, 22, 24, 25, 25, 26, 28, 29, 31,
+     34, 35, 36, 36, 38, 41, 42, 43, 44, 45, 45, 45, 48, 52, 54, 55, 51, 50, 51, 52, 50, 51, 52, 56, 59, 59, 60]
+
+    num2=[3, 8, 7, 7, 12, 8, 8, 12, 10, 12, 16, 15, 24, 24, 26, 25, 32, 34, 37, 35, 38, 39, 40, 43, 41, 43, 44, 47, 50, 55,
+     55, 55, 55, 53, 49, 49, 49, 48, 50, 52, 53, 55, 56, 56, 56, 54, 56, 59, 60]
+    num2=[3, 5, 4, 6, 6, 11, 13, 8, 9, 9, 9, 10, 9, 10, 10, 9, 8, 3, 6, 6, 7, 9, 10, 11, 12, 13, 13, 14, 15, 19, 21, 16, 17, 23, 24, 23, 23, 24, 25, 27, 28, 27, 27, 29, 24, 24, 26, 29, 29, 32, 40, 45, 40, 38, 42, 45, 47, 53, 50, 49, 50, 52, 49, 50, 48, 51, 52, 50, 51, 55, 56, 57, 56, 59, 58, 57, 58, 59, 60]
+    num1=[3, 4, 7, 5, 8, 10, 6, 7, 6, 7, 6, 3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 7, 7, 9, 10, 11, 12, 12, 13, 12, 13,
+     13, 9, 9, 9, 10, 10, 10, 11, 13, 10, 10, 10, 11, 11, 11, 11, 14, 15, 16, 17, 17, 16, 16, 17, 17, 15, 14, 18, 18,
+     18, 17, 17, 18, 18, 19, 21, 21, 22, 22, 23, 24, 26, 26, 26, 22, 22, 22, 25, 26, 27, 28, 28, 29, 30, 30, 31, 32, 32,
+     31, 32, 31, 31, 32, 34, 31, 32, 34, 34, 36, 36, 37, 38, 34, 36, 38, 37, 39, 41, 43, 42, 44, 44, 44, 46, 46, 46, 48,
+     49, 49, 51, 53, 50, 50, 51, 51, 49, 53, 56, 57, 58, 55, 58, 60]
+    num3=[4, 4, 3, 5, 8, 8, 8, 9, 12, 7, 8, 9, 9, 9, 10, 10, 12, 12, 13, 14, 14, 14, 16, 17, 12, 13, 14, 12, 13, 11, 12, 13,
+     16, 16, 16, 16, 16, 22, 23, 23, 26, 24, 26, 26, 28, 30, 30, 34, 37, 38, 40, 41, 42, 42, 35, 35, 38, 41, 40, 42, 43,
+     43, 44, 41, 38, 39, 41, 43, 47, 45, 46, 48, 48, 49, 50, 51, 52, 52, 56, 60]
+
+
+    drawResultAndIter(num1,num2,num3)
+
+    print((0.8)**(1/6)-1)