107 lines
3.4 KiB
Python
107 lines
3.4 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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import json
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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) / \
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(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 = battery_energy_capacity / energy_per_photo
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return max_photos
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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'] # 车辆数量
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# 1. 首先将区域均匀切分成k行
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row_ratio = 1 / k
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row_boundaries = [i * row_ratio for i in range(k + 1)]
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# 2. 计算每次飞行能拍摄的最大照片数量
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photos_per_flight = calculate_max_photos_per_flight(params)
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print(f"每次飞行能拍摄的最大照片数: {photos_per_flight}")
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# 3. 针对每一行计算网格划分
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row_start = row_boundaries[0]
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row_end = row_boundaries[1]
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row_height = (row_end - row_start) * H
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# 计算每个网格的宽度
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# 网格面积 = row_height * grid_width = photos_per_flight
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grid_width = photos_per_flight / row_height
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col_ratio = grid_width / W
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col_boundaries = [0]
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ratio = 0
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while (ratio + col_ratio) < 1:
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ratio += col_ratio
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col_boundaries.append(ratio)
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col_boundaries.append(1)
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car_paths = []
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for i in range(k):
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car_path = list(range(i * (len(col_boundaries) - 1),
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(i+1) * (len(col_boundaries) - 1)))
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car_paths.append(car_path)
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return row_boundaries, col_boundaries, car_paths
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def main():
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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_100_100_6'
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# 读取参数
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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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# 求解
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row_boundaries, col_boundaries, car_paths = solve_greedy(params)
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# ---------------------------
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# 输出最佳方案
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# ---------------------------
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output_data = {
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'row_boundaries': row_boundaries,
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'col_boundaries': col_boundaries,
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'car_paths': car_paths
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}
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with open(f'./solutions/greedy_{params_file}.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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if __name__ == "__main__":
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main()
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