HPCC2025/greedy_solver.py

import math
import yaml
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import json


def calculate_max_photos_per_flight(params):
    """计算每次飞行能拍摄的最大照片数量
    基于以下约束：
    1. 电池能量约束
    2. 计算+传输时间 = 飞行时间
    """
    # 从参数中提取时间和能量因子
    flight_time_factor = params['flight_time_factor']
    comp_time_factor = params['comp_time_factor']
    trans_time_factor = params['trans_time_factor']
    battery_energy_capacity = params['battery_energy_capacity']
    flight_energy_factor = params['flight_energy_factor']
    comp_energy_factor = params['comp_energy_factor']
    trans_energy_factor = params['trans_energy_factor']

    # 基于时间约束求解rho：飞行时间 = 计算时间 + 传输时间
    # flight_time_factor * d = comp_time_factor * rho * d + trans_time_factor * (1-rho) * d
    rho_time = (flight_time_factor - trans_time_factor) / \
        (comp_time_factor - trans_time_factor)

    # 基于能量约束求解最大照片数d
    # battery_energy_capacity = flight_energy_factor * d + comp_energy_factor * rho * d + trans_energy_factor * (1-rho) * d
    energy_per_photo = (flight_energy_factor +
                        comp_energy_factor * rho_time +
                        trans_energy_factor * (1 - rho_time))

    max_photos = battery_energy_capacity / energy_per_photo

    return max_photos


def solve_greedy(params):
    """使用贪心算法求解任务分配问题"""
    H = params['H']
    W = params['W']
    k = params['num_cars']  # 车辆数量

    # 1. 首先将区域均匀切分成k行
    row_ratio = 1 / k
    row_boundaries = [i * row_ratio for i in range(k + 1)]

    # 2. 计算每次飞行能拍摄的最大照片数量
    photos_per_flight = calculate_max_photos_per_flight(params)
    print(f"每次飞行能拍摄的最大照片数: {photos_per_flight}")

    # 3. 针对每一行计算网格划分
    row_start = row_boundaries[0]
    row_end = row_boundaries[1]
    row_height = (row_end - row_start) * H

    # 计算每个网格的宽度
    # 网格面积 = row_height * grid_width = photos_per_flight
    grid_width = photos_per_flight / row_height
    col_ratio = grid_width / W

    col_boundaries = [0]
    ratio = 0
    while (ratio + col_ratio) < 1:
        ratio += col_ratio
        col_boundaries.append(ratio)
    col_boundaries.append(1)

    car_paths = []
    for i in range(k):
        car_path = list(range(i * (len(col_boundaries) - 1),
                        (i+1) * (len(col_boundaries) - 1)))
        car_paths.append(car_path)

    return row_boundaries, col_boundaries, car_paths


def main():
    # ---------------------------
    # 需要修改的超参数
    # ---------------------------
    params_file = 'params_50_50_3'

    # 读取参数
    with open(params_file + '.yml', 'r', encoding='utf-8') as file:
        params = yaml.safe_load(file)

    # 求解
    row_boundaries, col_boundaries, car_paths = solve_greedy(params)

    # ---------------------------
    # 输出最佳方案
    # ---------------------------
    output_data = {
        'row_boundaries': row_boundaries,
        'col_boundaries': col_boundaries,
        'car_paths': car_paths
    }
    with open(f'./solutions/greedy_{params_file}.json', 'w', encoding='utf-8') as f:
        json.dump(output_data, f, ensure_ascii=False, indent=4)


if __name__ == "__main__":
    main()