import matplotlib.pyplot as plt import matplotlib.patches as patches import json import math def visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H, rho_list): region_center = (H / 2.0, W / 2.0) # 创建正方形图像 fig, ax = plt.subplots(figsize=(8, 8)) # 设置固定的正方形大小 ax.set_xlim(0, W) ax.set_ylim(H, 0) # 调整y轴方向，原点在左上角 # 设置英文标题和标签 # ax.set_title("Monte Carlo", fontsize=12) # ax.set_title("Greedy", fontsize=12) ax.set_title("Enumeration-Genetic Algorithm", fontsize=12) # ax.set_title("DQN fine-tuning", fontsize=12) ax.set_xlabel("Region Width", fontsize=10) ax.set_ylabel("Region Height", fontsize=10) # 定义配色方案（使用更专业的配色） colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f'] # 绘制行分割边界 for row in row_boundaries[1:-1]: ax.axhline(y=row * H, color='gray', linestyle='--', alpha=0.5) # 绘制列分割边界 for col in col_boundaries[1:-1]: ax.axvline(x=col * W, color='gray', linestyle='--', alpha=0.5) # 绘制每辆车的轨迹并标注区域序号 for system_id, path in enumerate(car_paths_coords): path = [(region_center[0], region_center[1])] + \ path + [(region_center[0], region_center[1])] y, x = zip(*path) # 使用箭头绘制路径 for i in range(len(path)-1): # 绘制带箭头的线段 ax.annotate('', xy=(x[i+1], y[i+1]), xytext=(x[i], y[i]), arrowprops=dict(arrowstyle='->', color=colors[int(system_id) % len(colors)], lw=2, mutation_scale=15), zorder=1) # 绘制路径点 ax.plot(x, y, 'o', markersize=6, color=colors[int(system_id) % len(colors)], label=f"System {system_id}", zorder=2) # 标注每个区域的序号（将序号向上偏移一点） for idx, (px, py) in enumerate(zip(x[1:-1], y[1:-1])): offset = H * 0.02 # 根据区域高度设置偏移量 ax.text(px, py - offset, str(idx), color='black', fontsize=9, ha='center', va='bottom', bbox=dict( facecolor='none', edgecolor='none', alpha=0.7, pad=0.5)) # 绘制区域中心（设置最高的zorder确保在最上层） ax.plot(region_center[1], region_center[0], 'k*', markersize=12, label="Region Center", zorder=3) # 添加图例 ax.legend(loc='upper right', fontsize=9) # 保持坐标轴比例相等 ax.set_aspect('equal', adjustable='box') # 调整布局，确保所有元素都显示完整 plt.tight_layout() # 显示网格 ax.grid(True, linestyle=':', alpha=0.3) # 在每个矩形区域左上角标注rho值 rho_idx = 0 for i in range(len(row_boundaries) - 1): for j in range(len(col_boundaries) - 1): # 获取矩形左上角坐标 x = col_boundaries[j] * W y = row_boundaries[i] * H # 添加一个小的偏移量，避免完全贴在边界上 offset_x = W * 0.02 offset_y = H * 0.02 # 标注rho值 ax.text(x + offset_x, y + offset_y, f'ρ={rho_list[rho_idx]:.2f}', color='black', fontsize=8, ha='left', va='top', bbox=dict(facecolor='white', edgecolor='none', alpha=0.7, pad=0.5), zorder=2) rho_idx += 1 plt.show() def restore_from_solution(row_boundaries, col_boundaries, car_paths, params): H = params['H'] W = params['W'] k = params['num_cars'] flight_time_factor = params['flight_time_factor'] comp_time_factor = params['comp_time_factor'] trans_time_factor = params['trans_time_factor'] car_time_factor = params['car_time_factor'] bs_time_factor = params['bs_time_factor'] flight_energy_factor = params['flight_energy_factor'] comp_energy_factor = params['comp_energy_factor'] trans_energy_factor = params['trans_energy_factor'] battery_energy_capacity = params['battery_energy_capacity'] rectangles = [] for i in range(len(row_boundaries) - 1): for j in range(len(col_boundaries) - 1): r1 = row_boundaries[i] r2 = row_boundaries[i + 1] c1 = col_boundaries[j] c2 = col_boundaries[j + 1] d = (r2 - r1) * H * (c2 - c1) * W # 任务的照片数量（矩形面积） # 求解rho rho_time_limit = (flight_time_factor - trans_time_factor) / \ (comp_time_factor - trans_time_factor) rho_energy_limit = (battery_energy_capacity - flight_energy_factor * d - trans_energy_factor * d) / \ (comp_energy_factor * d - trans_energy_factor * d) rho = min(rho_time_limit, rho_energy_limit) flight_time = flight_time_factor * d comp_time = comp_time_factor * rho * d trans_time = trans_time_factor * (1 - rho) * d bs_time = bs_time_factor * (1 - rho) * d # 计算任务矩形中心，用于后续车辆移动时间计算 center_r = (r1 + r2) / 2.0 * H center_c = (c1 + c2) / 2.0 * W rectangles.append({ 'rho': rho, 'flight_time': flight_time, 'bs_time': bs_time, 'center': (center_r, center_c) }) system_times = [] # 根据car_paths计算时间 for car_idx in range(k): car_path = car_paths[car_idx] flight_time = sum(rectangles[point]['flight_time'] for point in car_path) bs_time = sum(rectangles[point]['bs_time'] for point in car_path) # 计算车的移动时间，首先在轨迹的首尾添加上大区域中心 car_time = 0 for i in range(len(car_path) - 1): first_point = car_path[i] second_point = car_path[i + 1] car_time += math.dist(rectangles[first_point]['center'], rectangles[second_point]['center']) * \ car_time_factor car_time += math.dist(rectangles[car_path[0]] ['center'], [H / 2, W / 2]) * car_time_factor car_time += math.dist(rectangles[car_path[-1]] ['center'], [H / 2, W / 2]) * car_time_factor system_time = max(flight_time + car_time, bs_time) system_times.append(system_time) print(f"系统{car_idx}的总时间: {system_time}") print(f"最终时间: {max(system_times)}") rho_list = [rectangle['rho'] for rectangle in rectangles] return rectangles, rho_list if __name__ == "__main__": import yaml # --------------------------- # 需要修改的超参数 # --------------------------- params_file = 'params_50_50_3' solution_file = r'solutions\dqn_params_50_50_3_2.json' with open(params_file + '.yml', 'r', encoding='utf-8') as file: params = yaml.safe_load(file) H = params['H'] W = params['W'] k = params['num_cars'] # 读取最佳方案的JSON文件 with open(solution_file, 'r', encoding='utf-8') as f: best_solution = json.load(f) row_boundaries = best_solution['row_boundaries'] col_boundaries = best_solution['col_boundaries'] car_paths = best_solution['car_paths'] rectangles, rho_list = restore_from_solution( row_boundaries, col_boundaries, car_paths, params) # 计算分块区域的中心点坐标 rectangles_centers = [rectangle['center'] for rectangle in rectangles] # 将car_paths里的index换成坐标 car_paths_coords = [[] for _ in range(k)] for car_idx in range(k): car_path = car_paths[car_idx] for point in car_path: car_paths_coords[car_idx].append(rectangles_centers[point]) visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H, rho_list)