234 lines
8.5 KiB
Python
234 lines
8.5 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import json
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import math
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def visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H, rho_list):
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region_center = (H / 2.0, W / 2.0)
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# 创建正方形图像
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fig, ax = plt.subplots(figsize=(8, 8)) # 设置固定的正方形大小
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ax.set_xlim(0, W)
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ax.set_ylim(H, 0) # 调整y轴方向，原点在左上角
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# 设置英文标题和标签
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# ax.set_title("Monte Carlo", fontsize=12)
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# ax.set_title("Greedy", fontsize=12)
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ax.set_title("Enumeration-Genetic Algorithm", fontsize=12)
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# ax.set_title("DQN fine-tuning", fontsize=12)
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ax.set_xlabel("Region Width", fontsize=10)
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ax.set_ylabel("Region Height", fontsize=10)
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# 定义配色方案（使用更专业的配色）
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colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
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'#9467bd', '#8c564b', '#e377c2', '#7f7f7f']
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# 绘制行分割边界
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for row in row_boundaries[1:-1]:
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ax.axhline(y=row * H, color='gray', linestyle='--', alpha=0.5)
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# 绘制列分割边界
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for col in col_boundaries[1:-1]:
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ax.axvline(x=col * W, color='gray', linestyle='--', alpha=0.5)
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# 绘制每辆车的轨迹并标注区域序号
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for system_id, path in enumerate(car_paths_coords):
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path = [(region_center[0], region_center[1])] + \
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path + [(region_center[0], region_center[1])]
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y, x = zip(*path)
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# 使用箭头绘制路径
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for i in range(len(path)-1):
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# 绘制带箭头的线段
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ax.annotate('',
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xy=(x[i+1], y[i+1]),
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xytext=(x[i], y[i]),
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arrowprops=dict(arrowstyle='->',
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color=colors[int(system_id) % len(colors)],
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lw=2,
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mutation_scale=15),
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zorder=1)
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# 绘制路径点
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ax.plot(x, y, 'o', markersize=6,
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color=colors[int(system_id) % len(colors)],
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label=f"System {system_id}",
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zorder=2)
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# 标注每个区域的序号（将序号向上偏移一点）
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for idx, (px, py) in enumerate(zip(x[1:-1], y[1:-1])):
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offset = H * 0.02 # 根据区域高度设置偏移量
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ax.text(px, py - offset, str(idx),
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color='black',
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fontsize=9,
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ha='center',
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va='bottom',
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bbox=dict(
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facecolor='none',
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edgecolor='none',
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alpha=0.7,
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pad=0.5))
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# 绘制区域中心（设置最高的zorder确保在最上层）
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ax.plot(region_center[1], region_center[0],
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'k*', markersize=12, label="Region Center",
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zorder=3)
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# 添加图例
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ax.legend(loc='upper right', fontsize=9)
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# 保持坐标轴比例相等
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ax.set_aspect('equal', adjustable='box')
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# 调整布局，确保所有元素都显示完整
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plt.tight_layout()
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# 显示网格
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ax.grid(True, linestyle=':', alpha=0.3)
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# 在每个矩形区域左上角标注rho值
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rho_idx = 0
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for i in range(len(row_boundaries) - 1):
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for j in range(len(col_boundaries) - 1):
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# 获取矩形左上角坐标
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x = col_boundaries[j] * W
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y = row_boundaries[i] * H
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# 添加一个小的偏移量，避免完全贴在边界上
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offset_x = W * 0.02
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offset_y = H * 0.02
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# 标注rho值
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ax.text(x + offset_x, y + offset_y,
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f'ρ={rho_list[rho_idx]:.2f}',
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color='black',
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fontsize=8,
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ha='left',
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va='top',
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bbox=dict(facecolor='white',
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edgecolor='none',
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alpha=0.7,
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pad=0.5),
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zorder=2)
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rho_idx += 1
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plt.show()
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def restore_from_solution(row_boundaries, col_boundaries, car_paths, params):
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H = params['H']
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W = params['W']
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k = params['num_cars']
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flight_time_factor = params['flight_time_factor']
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comp_time_factor = params['comp_time_factor']
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trans_time_factor = params['trans_time_factor']
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car_time_factor = params['car_time_factor']
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bs_time_factor = params['bs_time_factor']
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flight_energy_factor = params['flight_energy_factor']
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comp_energy_factor = params['comp_energy_factor']
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trans_energy_factor = params['trans_energy_factor']
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battery_energy_capacity = params['battery_energy_capacity']
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rectangles = []
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for i in range(len(row_boundaries) - 1):
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for j in range(len(col_boundaries) - 1):
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r1 = row_boundaries[i]
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r2 = row_boundaries[i + 1]
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c1 = col_boundaries[j]
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c2 = col_boundaries[j + 1]
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d = (r2 - r1) * H * (c2 - c1) * W # 任务的照片数量（矩形面积）
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# 求解rho
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rho_time_limit = (flight_time_factor - trans_time_factor) / \
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(comp_time_factor - trans_time_factor)
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rho_energy_limit = (battery_energy_capacity - flight_energy_factor * d - trans_energy_factor * d) / \
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(comp_energy_factor * d - trans_energy_factor * d)
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rho = min(rho_time_limit, rho_energy_limit)
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flight_time = flight_time_factor * d
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comp_time = comp_time_factor * rho * d
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trans_time = trans_time_factor * (1 - rho) * d
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bs_time = bs_time_factor * (1 - rho) * d
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# 计算任务矩形中心，用于后续车辆移动时间计算
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center_r = (r1 + r2) / 2.0 * H
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center_c = (c1 + c2) / 2.0 * W
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rectangles.append({
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'rho': rho,
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'flight_time': flight_time,
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'bs_time': bs_time,
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'center': (center_r, center_c)
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})
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system_times = []
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# 根据car_paths计算时间
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for car_idx in range(k):
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car_path = car_paths[car_idx]
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flight_time = sum(rectangles[point]['flight_time']
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for point in car_path)
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bs_time = sum(rectangles[point]['bs_time'] for point in car_path)
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# 计算车的移动时间，首先在轨迹的首尾添加上大区域中心
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car_time = 0
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for i in range(len(car_path) - 1):
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first_point = car_path[i]
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second_point = car_path[i + 1]
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car_time += math.dist(rectangles[first_point]['center'], rectangles[second_point]['center']) * \
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car_time_factor
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car_time += math.dist(rectangles[car_path[0]]
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['center'], [H / 2, W / 2]) * car_time_factor
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car_time += math.dist(rectangles[car_path[-1]]
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['center'], [H / 2, W / 2]) * car_time_factor
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system_time = max(flight_time + car_time, bs_time)
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system_times.append(system_time)
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print(f"系统{car_idx}的总时间: {system_time}")
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print(f"最终时间: {max(system_times)}")
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rho_list = [rectangle['rho'] for rectangle in rectangles]
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return rectangles, rho_list
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if __name__ == "__main__":
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import yaml
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# ---------------------------
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# 需要修改的超参数
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# ---------------------------
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params_file = 'params_50_50_3'
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solution_file = r'solutions\finetune_params_50_50_3.json'
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with open(params_file + '.yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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H = params['H']
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W = params['W']
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k = params['num_cars']
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# 读取最佳方案的JSON文件
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with open(solution_file, 'r', encoding='utf-8') as f:
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best_solution = json.load(f)
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row_boundaries = best_solution['row_boundaries']
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col_boundaries = best_solution['col_boundaries']
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car_paths = best_solution['car_paths']
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rectangles, rho_list = restore_from_solution(
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row_boundaries, col_boundaries, car_paths, params)
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# 计算分块区域的中心点坐标
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rectangles_centers = [rectangle['center'] for rectangle in rectangles]
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# 将car_paths里的index换成坐标
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car_paths_coords = [[] for _ in range(k)]
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for car_idx in range(k):
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car_path = car_paths[car_idx]
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for point in car_path:
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car_paths_coords[car_idx].append(rectangles_centers[point])
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visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H, rho_list)
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