import collections import math coordinate_X = [3, 8, 2, 6, 8] coordinate_Y = [8, 2, 5, 4, 8] Rate = [5, 5, 7.5, 7.5, 7.5] Volumn = [20, 30, 25, 10, 15] length = len(coordinate_X) temp_x = [] temp_y = [] temp_z = [] # 第一次坐标值计算 for i in range(length): temp_x.append(Rate[i] * Volumn[i] * coordinate_X[i]) temp_y.append(Rate[i] * Volumn[i] * coordinate_Y[i]) temp_z.append(Rate[i] * Volumn[i]) sigma_x = 0 sigma_y = 0 sigma_z = 0 for i in range(length): sigma_x += temp_x[i] sigma_y += temp_y[i] sigma_z += temp_z[i] before_x = sigma_x / sigma_z before_y = sigma_y / sigma_z print("第一次的坐标值", before_x, before_y) # 继续优化坐标值 flag = 1 while flag != 0: before_dis = [] for i in range(length): before_dis.append( math.sqrt((before_x - coordinate_X[i]) ** 2 + (before_y - coordinate_Y[i]) ** 2)) temp_xm = [] temp_ym = [] temp_zm = [] for i in range(length): temp_xm.append(Rate[i] * Volumn[i] * coordinate_X[i] / before_dis[i]) temp_ym.append(Rate[i] * Volumn[i] * coordinate_Y[i] / before_dis[i]) temp_zm.append(Rate[i] * Volumn[i] / before_dis[i]) sigma_xm = 0 sigma_ym = 0 sigma_zm = 0 for i in range(length): sigma_xm += temp_xm[i] sigma_ym += temp_ym[i] sigma_zm += temp_zm[i] after_x = sigma_xm / sigma_zm after_y = sigma_ym / sigma_zm after_dis = [] for i in range(length): after_dis.append(math.sqrt((after_x - coordinate_X[i]) ** 2 + (after_y - coordinate_Y[i]) ** 2)) before_TC = 0 after_TC = 0 for i in range(length): before_TC += (Rate[i] * Volumn[i] * before_dis[i]) after_TC += (Rate[i] * Volumn[i] * after_dis[i]) if before_TC > after_TC and before_TC-after_TC<0.002: print("第", flag, "次坐标值是", after_x, after_y) flag += 1 before_x = after_x before_y = after_y else: flag = 0这个代码如何在迭代结果=0.002时停止并输出

可以在代码中加入一个判断条件，在 before_TC 和 after_TC 差值小于等于 0.002 时停止迭代并输出最终结果。以下是修改后的代码：

import collections
import math

coordinate_X = [3, 8, 2, 6, 8]
coordinate_Y = [8, 2, 5, 4, 8]
Rate = [5, 5, 7.5, 7.5, 7.5]
Volumn = [20, 30, 25, 10, 15]

length = len(coordinate_X)
temp_x = []
temp_y = []
temp_z = []

# 第一次坐标值计算
for i in range(length):
    temp_x.append(Rate[i] * Volumn[i] * coordinate_X[i])
    temp_y.append(Rate[i] * Volumn[i] * coordinate_Y[i])
    temp_z.append(Rate[i] * Volumn[i])

sigma_x = 0
sigma_y = 0
sigma_z = 0
for i in range(length):
    sigma_x += temp_x[i]
    sigma_y += temp_y[i]
    sigma_z += temp_z[i]

before_x = sigma_x / sigma_z
before_y = sigma_y / sigma_z

print("第一次的坐标值", before_x, before_y)

# 继续优化坐标值
flag = 1
while flag != 0:
    before_dis = []
    for i in range(length):
        before_dis.append(
            math.sqrt((before_x - coordinate_X[i]) ** 2 + (before_y - coordinate_Y[i]) ** 2))

    temp_xm = []
    temp_ym = []
    temp_zm = []
    for i in range(length):
        temp_xm.append(Rate[i] * Volumn[i] * coordinate_X[i] / before_dis[i])
        temp_ym.append(Rate[i] * Volumn[i] * coordinate_Y[i] / before_dis[i])
        temp_zm.append(Rate[i] * Volumn[i] / before_dis[i])

    sigma_xm = 0
    sigma_ym = 0
    sigma_zm = 0
    for i in range(length):
        sigma_xm += temp_xm[i]
        sigma_ym += temp_ym[i]
        sigma_zm += temp_zm[i]

    after_x = sigma_xm / sigma_zm
    after_y = sigma_ym / sigma_zm

    after_dis = []
    for i in range(length):
        after_dis.append(math.sqrt((after_x - coordinate_X[i]) ** 2 + (after_y - coordinate_Y[i]) ** 2))

    before_TC = 0
    after_TC = 0
    for i in range(length):
        before_TC += (Rate[i] * Volumn[i] * before_dis[i])
        after_TC += (Rate[i] * Volumn[i] * after_dis[i])

    if before_TC - after_TC <= 0.002:
        print("第", flag, "次坐标值是", after_x, after_y)
        flag = 0
    else:
        print("第", flag, "次坐标值是", after_x, after_y)
        flag += 1
        before_x = after_x
        before_y = after_y

在修改后的代码中，增加了一个判断条件 `if before_TC - after_TC <= 0.002:`，当 before_TC 和 after_TC 差值小于等于 0.002 时，停止迭代并输出最终结果。同时，在迭代过程中，增加了输出每次迭代结果的代码，方便查看。