208 lines
7.9 KiB
Python
208 lines
7.9 KiB
Python
import random
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import math
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import yaml
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import json
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import numpy as np
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# 固定随机种子，便于复现
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random.seed(42)
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num_iterations = 10000
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# ---------------------------
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# 参数设置
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# ---------------------------
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with open('params.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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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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# ---------------------------
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# 蒙特卡洛模拟，寻找最佳方案
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# ---------------------------
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best_T = float('inf')
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best_solution = None
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for iteration in range(num_iterations):
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# 随机生成分区的行分段数与列分段数
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R = random.randint(0, 5) # 行分段数
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C = random.randint(0, 5) # 列分段数
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# 生成随机的行、列分割边界
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horiz = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(R)]
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horiz = sorted(set(horiz))
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horiz = horiz if horiz else []
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row_boundaries = [0] + horiz + [1]
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row_boundaries = [boundary * H for boundary in row_boundaries]
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vert = [np.clip(np.floor(random.random() * 10) /10, 0.0, 0.9) for _ in range(C)]
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vert = sorted(set(vert))
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vert = vert if vert else []
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col_boundaries = [0] + vert + [1]
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col_boundaries = [boundary * W for boundary in col_boundaries]
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# ---------------------------
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# 根据分割边界生成所有矩形任务
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# ---------------------------
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rectangles = []
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valid_partition = True # 标记此分区是否满足所有约束
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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) * (c2 - c1) # 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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if rho_energy_limit < 0:
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valid_partition = False
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break
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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
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center_c = (c1 + c2) / 2.0
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rectangles.append({
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'r1': r1, 'r2': r2, 'c1': c1, 'c2': c2,
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'd': d,
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'rho': rho,
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'flight_time': flight_time,
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'comp_time': comp_time,
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'trans_time': trans_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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if not valid_partition:
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break
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# 如果分区中存在任务不满足电池约束，则跳过该分区
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if not valid_partition:
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continue
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# ---------------------------
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# 随机将所有矩形任务分配给 k 个系统（车-机-巢）
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# ---------------------------
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system_tasks = {i: [] for i in range(k)}
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for rect in rectangles:
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system = random.randint(0, k - 1)
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system_tasks[system].append(rect)
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# ---------------------------
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# 对于每个系统，计算该系统的总完成时间 T_k：
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# T_k = 所有任务的飞行时间之和 + 车辆的移动时间
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# 车辆移动时间：车辆从区域中心出发，依次经过各任务中心（顺序采用距离区域中心的启发式排序）
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# ---------------------------
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region_center = (H / 2.0, W / 2.0)
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T_k_list = []
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for i in range(k):
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tasks = system_tasks[i]
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tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
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r['center'][1] - region_center[1]))
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total_flight_time = sum(task['flight_time'] for task in tasks)
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if tasks:
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# 车辆从区域中心到第一个任务中心
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car_time = math.dist(tasks[0]['center'],
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region_center) * car_time_factor
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# 依次经过任务中心
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for j in range(len(tasks) - 1):
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prev_center = tasks[j]['center']
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curr_center = tasks[j + 1]['center']
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car_time += math.dist(curr_center,
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prev_center) * car_time_factor
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# 回到区域中心
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car_time += math.dist(region_center, curr_center) * car_time_factor
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else:
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car_time = 0
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# 机巢的计算时间
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total_bs_time = sum(task['bs_time'] for task in tasks)
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T_k = max(total_flight_time + car_time, total_bs_time)
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T_k_list.append(T_k)
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T_max = max(T_k_list) # 整体目标 T 为各系统中最大的 T_k
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# TODO 没有限制系统的总能耗
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if T_max < best_T:
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best_T = T_max
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best_solution = {
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'system_tasks': system_tasks,
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'T_k_list': T_k_list,
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'T_max': T_max,
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'iteration': iteration,
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'R': R,
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'C': C,
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'row_boundaries': row_boundaries,
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'col_boundaries': col_boundaries,
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'car_time': car_time,
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'flight_time': total_flight_time,
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'bs_time': total_bs_time
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}
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# ---------------------------
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# 输出最佳方案
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# ---------------------------
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if best_solution is not None:
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print("最佳 T (各系统中最长的完成时间):", best_solution['T_max'])
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print(best_solution['iteration'], "次模拟后找到最佳方案:")
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print("分区情况:")
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print("行分段数:", best_solution['R'])
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print("列分段数:", best_solution['C'])
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print("行分割边界:", best_solution['row_boundaries'])
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print("列分割边界:", best_solution['col_boundaries'])
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print("每辆车的运行轨迹情况:")
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car_paths = {}
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for i in range(k):
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num_tasks = len(best_solution['system_tasks'][i])
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print(
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f"系统 {i}: 完成时间 T = {best_solution['T_k_list'][i]}, 飞行任务数量: {num_tasks}")
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tasks = best_solution['system_tasks'][i]
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tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
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r['center'][1] - region_center[1]))
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if tasks:
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print(
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f"轨迹路线: 区域中心({region_center[0]:.1f}, {region_center[1]:.1f})", end="")
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current_pos = region_center
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car_path = []
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for j, task in enumerate(tasks, 1):
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current_pos = task['center']
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car_path.append(current_pos)
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print(
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f" -> 任务{j}({current_pos[0]:.1f}, {current_pos[1]:.1f})", end="")
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print(" -> 区域中心")
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car_paths[i] = car_path
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# 保存分区边界和车辆轨迹到JSON文件
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output_data = {
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'row_boundaries': [boundary / H for boundary in best_solution['row_boundaries']],
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'col_boundaries': [boundary / W for boundary in best_solution['col_boundaries']],
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'car_paths': car_paths
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}
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with open('./solutions/best_solution_mtkl.json', 'w', encoding='utf-8') as f:
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json.dump(output_data, f, ensure_ascii=False, indent=4)
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else:
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print("在给定的模拟次数内未找到满足所有约束的方案。")
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