95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
import numpy as np
|
||
from ga import GA
|
||
|
||
|
||
def if_valid_partition(row_boundaries, col_boundaries, params):
|
||
H = params['H']
|
||
W = params['W']
|
||
k = params['num_cars']
|
||
|
||
flight_time_factor = params['flight_time_factor']
|
||
comp_time_factor = params['comp_time_factor']
|
||
trans_time_factor = params['trans_time_factor']
|
||
car_time_factor = params['car_time_factor']
|
||
bs_time_factor = params['bs_time_factor']
|
||
|
||
flight_energy_factor = params['flight_energy_factor']
|
||
comp_energy_factor = params['comp_energy_factor']
|
||
trans_energy_factor = params['trans_energy_factor']
|
||
battery_energy_capacity = params['battery_energy_capacity']
|
||
|
||
# 根据分割边界生成所有矩形任务
|
||
rectangles = []
|
||
for i in range(len(row_boundaries) - 1):
|
||
for j in range(len(col_boundaries) - 1):
|
||
r1 = row_boundaries[i]
|
||
r2 = row_boundaries[i + 1]
|
||
c1 = col_boundaries[j]
|
||
c2 = col_boundaries[j + 1]
|
||
d = (r2 - r1) * H * (c2 - c1) * W # 任务的照片数量（矩形面积）
|
||
|
||
# 求解rho
|
||
rho_time_limit = (flight_time_factor - trans_time_factor) / \
|
||
(comp_time_factor - trans_time_factor)
|
||
rho_energy_limit = (battery_energy_capacity - flight_energy_factor * d - trans_energy_factor * d) / \
|
||
(comp_energy_factor * d - trans_energy_factor * d)
|
||
if rho_energy_limit < 0:
|
||
return []
|
||
|
||
rho = min(rho_time_limit, rho_energy_limit)
|
||
flight_time = flight_time_factor * d
|
||
comp_time = comp_time_factor * rho * d
|
||
trans_time = trans_time_factor * (1 - rho) * d
|
||
bs_time = bs_time_factor * (1 - rho) * d
|
||
|
||
# 计算任务矩形中心，用于后续车辆移动时间计算
|
||
center_r = (r1 + r2) / 2.0 * H
|
||
center_c = (c1 + c2) / 2.0 * W
|
||
|
||
rectangles.append({
|
||
# 'r1': r1, 'r2': r2, 'c1': c1, 'c2': c2,
|
||
'd': d,
|
||
'rho': rho,
|
||
'flight_time': flight_time,
|
||
'comp_time': comp_time,
|
||
'trans_time': trans_time,
|
||
'bs_time': bs_time,
|
||
'center': (center_r, center_c)
|
||
})
|
||
return rectangles
|
||
|
||
|
||
def GA_solver(rectangles, params):
|
||
num_city = len(rectangles) + 1 # 划分好的区域中心点+整个区域的中心
|
||
k = params['num_cars']
|
||
|
||
# 初始化坐标 (第一个点是整个区域的中心)
|
||
center_data = [[params['H'] / 2.0, params['W'] / 2.0]]
|
||
for rec in rectangles:
|
||
center_data.append(rec['center'])
|
||
center_data = np.array(center_data)
|
||
|
||
# 关键：有k架无人机，则再增加N-1个`点` (坐标是起始点)，这些点之间的距离是inf
|
||
for d in range(k - 1):
|
||
center_data = np.vstack([center_data, center_data[0]])
|
||
num_city += 1 # 增加欺骗城市
|
||
|
||
to_process_idx = [0]
|
||
# print("start point:", location[0])
|
||
for d in range(1, k): # 1, ... drone-1
|
||
# print("added base point:", location[num_city - d])
|
||
to_process_idx.append(num_city - d)
|
||
|
||
model = GA(num_drones=k, num_city=num_city, num_total=20, data=center_data.copy(
|
||
), to_process_idx=to_process_idx, rectangles=rectangles)
|
||
Best_path, Best = model.run()
|
||
|
||
# 根据最佳路径计算各系统任务分配
|
||
if Best_path[0] not in to_process_idx:
|
||
Best_path.insert(0, 0)
|
||
|
||
if Best_path[-1] not in to_process_idx:
|
||
Best_path.append(0)
|
||
|
||
return Best_path, Best, to_process_idx
|