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

读取数据文件

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

提取所需列数据

product_lat = data['商品GPS纬度'] product_lon = data['商品GPS经度'] member_lat = data['会员GPS纬度'] member_lon = data['会员GPS经度'] task_limit = data['预订任务限额'] price = data['任务标价'] task_status = data['任务执行情况']

计算距离函数

def calculate_distance(lat1, lon1, lat2, lon2): R = 6371 # 地球半径，单位为千米 lat1_rad = np.radians(lat1) lon1_rad = np.radians(lon1) lat2_rad = np.radians(lat2) lon2_rad = np.radians(lon2) dlon = lon2_rad - lon1_rad dlat = lat2_rad - lat1_rad a = np.sin(dlat / 2) ** 2 + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon / 2) ** 2 c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)) distance = R * c return distance

定义引力子函数

def gravity_function(params): k, m, n = params[0], params[1], params[2] y = k * price * (task_limit ** m) / (distance ** n) return y

定义目标函数

def objective_function(params): y_success = gravity_function(params)[task_status == 1] y_failure = gravity_function(params)[task_status == 0] min_y_success = np.min(y_success) max_y_failure = np.max(y_failure) return max_y_failure - min_y_success

定义约束条件

def constraint(params): return distance - 0.1

定义初始参数值

initial_params = [1, 1, 1]

优化求解最小引力子

result = minimize(objective_function, initial_params, constraints={'type': 'ineq', 'fun': constraint})

输出结果

k_optimal = result.x[0] m_optimal = result.x[1] n_optimal = result.x[2] min_y_success = np.min(gravity_function(result.x)[task_status == 1])

print("最小引力子公式：y = {} * q1 * (q2 ^ {}) / (r ^ {})".format(k_optimal, m_optimal, n_optimal)) print("成功的最小引力子：", min_y_success)