汽车停车匹配充电桩随机森林

import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
from matplotlib.colors import get_named_colors_mapping


class ChargingPileMatcher:
    def __init__(self, num_piles=20, lat_range=(30, 32), lon_range=(120, 122)):
        self.num_piles = num_piles  # 充电桩数量改为20
        self.lat_range = lat_range
        self.lon_range = lon_range
        self.pile_coords = None
        self.vehicle_coords = None

        # 使用Matplotlib推荐的颜色映射方式（修复弃用警告）
        self.colors = plt.colormaps['tab20'].colors[:num_piles]  # 取前20种颜色

    def generate_pile_locations(self, random_seed=42):
        np.random.seed(random_seed)
        lats = np.random.uniform(self.lat_range[0], self.lat_range[1], self.num_piles)
        lons = np.random.uniform(self.lon_range[0], self.lon_range[1], self.num_piles)
        self.pile_coords = np.column_stack((lats, lons))
        return self.pile_coords

    def generate_vehicle_data(self, num_vehicles=3000, random_seed=42):
        np.random.seed(random_seed)
        # 80%车辆集中在中心区域，20%分散在周边
        main_lats = np.random.normal(loc=31, scale=0.2, size=int(num_vehicles * 0.8))
        main_lons = np.random.normal(loc=121, scale=0.2, size=int(num_vehicles * 0.8))
        sub_lats = np.random.uniform(self.lat_range[0], self.lat_range[1], int(num_vehicles * 0.2))
        sub_lons = np.random.uniform(self.lon_range[0], self.lon_range[1], int(num_vehicles * 0.2))
        lats = np.concatenate([main_lats, sub_lats])
        lons = np.concatenate([main_lons, sub_lons])
        self.vehicle_coords = np.column_stack((lats, lons))
        return self.vehicle_coords

    def match_nearest_pile(self):
        if self.pile_coords is None or self.vehicle_coords is None:
            raise ValueError("请先生成充电桩和车辆位置")

        knn = NearestNeighbors(n_neighbors=1)
        knn.fit(self.pile_coords)
        distances, indices = knn.kneighbors(self.vehicle_coords)

        self.match_results = {
            'pile_index': indices.flatten(),
            'distance': distances.flatten()
        }
        return self.match_results

    def visualize_matching(self, show_pile_labels=False):
        plt.figure(figsize=(12, 8))

        # 绘制充电桩（大尺寸五星，颜色区分）
        plt.scatter(
            self.pile_coords[:, 1], self.pile_coords[:, 0],  # 经度为X轴，纬度为Y轴
            c=self.colors,
            marker='*',
            s=300,
            label='充电桩',
            edgecolor='black',
            zorder=3
        )

        # 绘制车辆并按匹配充电桩染色（使用对应颜色，半透明显示）
        for idx, (lat, lon) in enumerate(self.vehicle_coords):
            pile_idx = self.match_results['pile_index'][idx]
            plt.scatter(
                lon, lat,
                c=self.colors[pile_idx],  # 直接使用对应索引颜色
                alpha=0.3,
                edgecolor='white',
                s=20
            )

            # 显示前10个充电桩标签
            if show_pile_labels and idx < 10:
                plt.text(lon, lat, f'P{pile_idx}', fontsize=8, color='black', ha='right')

        # 地理信息标注
        plt.xlabel('经度 (°E)', fontsize=12)
        plt.ylabel('纬度 (°N)', fontsize=12)
        plt.title(f'3000辆汽车匹配{self.num_piles}个充电桩', fontsize=14, pad=20)
        plt.xlim(self.lon_range)
        plt.ylim(self.lat_range)
        plt.grid(True, alpha=0.1, linestyle='--')
        plt.legend(bbox_to_anchor=(1, 1), loc='upper left')
        plt.tight_layout()
        plt.show()


if __name__ == "__main__":
    matcher = ChargingPileMatcher(num_piles=20)  # 设置为20个充电桩

    # 生成数据
    matcher.generate_pile_locations()
    matcher.generate_vehicle_data()

    # 执行匹配
    matcher.match_nearest_pile()

    # 输出统计信息
    avg_distance = np.mean(matcher.match_results['distance'])
    print(f"平均匹配距离：{avg_distance:.2f} 度（约{avg_distance * 111:.0f}公里）")

    # 可视化结果
    matcher.visualize_matching(show_pile_labels=True)