238 lines
8.1 KiB
Python
238 lines
8.1 KiB
Python
import math
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import yaml
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import numpy as np
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def calculate_max_photos_per_flight(params):
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"""计算每次飞行能拍摄的最大照片数量
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基于以下约束：
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1. 电池能量约束
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2. 计算+传输时间 = 飞行时间
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"""
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# 从参数中提取时间和能量因子
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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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battery_energy_capacity = params['battery_energy_capacity']
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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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# 基于时间约束求解rho：飞行时间 = 计算时间 + 传输时间
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# flight_time_factor * d = comp_time_factor * rho * d + trans_time_factor * (1-rho) * d
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rho_time = (flight_time_factor - trans_time_factor) / (comp_time_factor - trans_time_factor)
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# 基于能量约束求解最大照片数d
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# battery_energy_capacity = flight_energy_factor * d + comp_energy_factor * rho * d + trans_energy_factor * (1-rho) * d
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energy_per_photo = (flight_energy_factor +
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comp_energy_factor * rho_time +
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trans_energy_factor * (1 - rho_time))
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max_photos = math.floor(battery_energy_capacity / energy_per_photo)
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return max_photos, rho_time
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def solve_greedy(params):
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"""使用贪心算法求解任务分配问题"""
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H = params['H']
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W = params['W']
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k = params['num_cars'] # 系统数量
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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_time_factor = params['flight_time_factor']
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# 计算每次飞行能拍摄的最大照片数
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photos_per_flight, rho = calculate_max_photos_per_flight(params)
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print(f"贪心无人机计算的情况下，每次飞行能拍摄的最大照片数: {photos_per_flight}")
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print(f"卸载率 rho: {rho:.3f}")
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# 用较小的边长来划分网格
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min_side = min(H, W)
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next_side = photos_per_flight // min_side
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# 初始化任务分配列表
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tasks = [[] for _ in range(k)]
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if min_side == H:
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grid_h = min_side
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grid_w = next_side
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num_rows = 1
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num_cols = round(W / grid_w)
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current_col = 0
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for i in range(math.ceil(num_cols / k)):
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for j in range(k):
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tasks[j].append((0, current_col))
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current_col += 1
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if current_col == num_cols:
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break
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else:
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grid_w = min_side
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grid_h = next_side
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num_cols = 1
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num_rows = round(H / grid_h)
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current_row = 0
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for i in range(math.ceil(num_rows / k)):
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for j in range(k):
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tasks[j].append((current_row, 0))
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current_row += 1
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if current_row == num_rows:
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break
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print(f"网格大小: {grid_w}x{grid_h}")
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print(f"网格数量: {num_rows}x{num_cols}")
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print(f"任务分配情况: {tasks}")
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# 计算区域中心点
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center_x = W / 2
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center_y = H / 2
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# 为每个系统计算完成时间
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system_times = []
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for i in range(k):
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if not tasks[i]: # 如果该系统没有分配任务
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system_times.append(0)
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continue
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# 生成该系统负责的网格中心坐标
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grids = []
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for row, col in tasks[i]:
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if min_side == H:
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# 如果H是较小边，那么row=0，col递增
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# TODO 最后一个网格的中心点不能这么算
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grid_center_x = (col + 0.5) * grid_w
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grid_center_y = (row + 0.5) * grid_h
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else:
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# 如果W是较小边，那么col=0，row递增
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grid_center_x = (col + 0.5) * grid_w
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grid_center_y = (row + 0.5) * grid_h
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grids.append((grid_center_x, grid_center_y))
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# 计算车辆路径长度（从中心点出发）
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car_distance = math.hypot(center_x - grids[0][0], center_y - grids[0][1]) # 从中心到第一个网格
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for j in range(len(grids)-1):
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car_distance += math.hypot(grids[j+1][0] - grids[j][0],
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grids[j+1][1] - grids[j][1]) # 网格间距离
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car_distance += math.hypot(grids[-1][0] - center_x,
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grids[-1][1] - center_y) # 从最后一个网格回到中心
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# 计算时间
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num_photos = len(grids) * photos_per_flight # 该系统需要拍摄的总照片数
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flight_time = flight_time_factor * num_photos # 飞行时间
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car_time = car_time_factor * car_distance # 车辆移动时间
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bs_time = bs_time_factor * (1 - rho) * num_photos # 基站计算时间
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total_time = max(flight_time + car_time, bs_time)
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system_times.append(total_time)
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print(f"\n系统 {i} 详细信息:")
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print(f"负责的网格数: {len(grids)}")
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print(f"总照片数: {num_photos}")
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print(f"车辆移动距离: {car_distance:.2f}")
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print(f"飞行时间: {flight_time:.2f}")
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print(f"车辆时间: {car_time:.2f}")
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print(f"基站时间: {bs_time:.2f}")
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print(f"总完成时间: {total_time:.2f}")
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# 找出最大完成时间
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max_time = max(system_times)
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print(f"\n最大完成时间: {max_time:.2f}")
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# 准备返回结果
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result = {
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'max_time': max_time,
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'system_times': system_times,
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'photos_per_flight': photos_per_flight,
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'grid_w': grid_w,
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'grid_h': grid_h,
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'num_rows': num_rows,
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'num_cols': num_cols,
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'tasks': tasks,
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'rho': rho
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}
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return result
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def plot_results(params, result):
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"""可视化结果"""
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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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plt.rcParams['font.family'] = ['sans-serif']
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plt.rcParams['font.sans-serif'] = ['SimHei']
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# 创建图形
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
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# 1. 绘制系统完成时间对比
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ax1.bar(range(k), result['system_times'])
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ax1.set_title('各系统完成时间对比')
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ax1.set_xlabel('系统编号')
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ax1.set_ylabel('完成时间(秒)')
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# 2. 绘制网格划分示意图
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ax2.set_xlim(0, W)
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ax2.set_ylim(0, H)
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# 为不同系统的网格使用不同颜色
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colors = plt.cm.rainbow(np.linspace(0, 1, k))
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# 绘制网格和系统分配
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grid_w = result['grid_w']
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grid_h = result['grid_h']
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tasks = result['tasks']
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# 绘制每个系统的网格
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for system_idx, system_tasks in enumerate(tasks):
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for row, col in system_tasks:
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rect = patches.Rectangle(
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(col * grid_w, row * grid_h),
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grid_w, grid_h,
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linewidth=1,
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edgecolor='black',
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facecolor=colors[system_idx],
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alpha=0.3
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)
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ax2.add_patch(rect)
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# 在网格中心添加系统编号
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center_x = (col + 0.5) * grid_w
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center_y = (row + 0.5) * grid_h
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ax2.text(center_x, center_y, str(system_idx),
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ha='center', va='center')
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# 添加中心点标记
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ax2.plot(W/2, H/2, 'r*', markersize=15, label='区域中心')
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ax2.legend()
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ax2.set_title('网格划分和系统分配示意图')
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ax2.set_xlabel('宽度')
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ax2.set_ylabel('高度')
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plt.tight_layout()
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plt.show()
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def main():
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# 读取参数
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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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# 求解
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result = solve_greedy(params)
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# 输出结果
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print("\n求解结果:")
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print(f"最大完成时间: {result['max_time']:.2f} 秒")
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print("\n各系统完成时间:")
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for i, time in enumerate(result['system_times']):
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print(f"系统 {i}: {time:.2f} 秒")
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# 可视化
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plot_results(params, result)
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if __name__ == "__main__":
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main()
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