import pandas as pd import math import numpy as np from scipy.optimize import minimize

读取数据

data = pd.read_excel(r'D:\pythonProject3\会员信息\附件二：会员信息数据.xlsx')

计算距离函数

def calculate_distance(lat1, lon1, lat2, lon2): R = 6371 # 地球半径，单位为公里

# 将经纬度转换为弧度
lat1 = math.radians(lat1)
lon1 = math.radians(lon1)
lat2 = math.radians(lat2)
lon2 = math.radians(lon2)

dlon = lon2 - lon1
dlat = lat2 - lat1

a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))

distance = R * c
return distance

计算引力子函数

def calculate_gravitational_force(q1, q2, r, k, m, n): return (k * q1 * (q2 ** m)) / (r ** n)

定义目标函数

def objective(x): k, m, n = x y_values = [] for i in range(len(data)): if data.loc[i, '任务执行情况'] == 1: q1 = data.loc[i, '任务标价'] q2 = data.loc[i, '预订任务限额'] lat1 = data.loc[i, '商品GPS纬度'] lon1 = data.loc[i, '商品GPS经度'] lat2 = data.loc[i, '会员GPS纬度'] lon2 = data.loc[i, '会员GPS经度'] r = calculate_distance(lat1, lon1, lat2, lon2) y = calculate_gravitational_force(q1, q2, r, k, m, n) y_values.append(y) min_y = min(y_values) return -min_y

定义约束条件

def constraint(x): k, m, n = x return k, m, n

设置初始值

x0 = [1, 1, 1]

优化求解

bounds = [(0, None), (0, None), (0, None)] constraints = [{'type': 'eq', 'fun': constraint}] result = minimize(objective, x0, bounds=bounds, constraints=constraints)

输出结果

k, m, n = result.x y_min = -result.fun print("最小引力子y的公式为: y = ({} * q1 * (q2^{}) / (r^{}))".format(k, m, n)) print("成功的最小引力子为:", y_min)