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修改算法的输出，把可视化模块单独分离出来

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weixin_46229132 2025-03-13 11:18:58 +08:00
parent b1851ac489
commit 1f18d9d96f
3 changed files with 95 additions and 128 deletions
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1
.gitignore vendored
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@ -9,6 +9,7 @@ __pycache__/
# Pytorch weights
weights/
solutions/
# Distribution / packaging
.Python

161
mtkl_sovler.py
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@ -1,8 +1,8 @@
import random
import math
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import yaml
import json
# 固定随机种子，便于复现
random.seed(42)
@ -18,7 +18,6 @@ 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']
@ -117,7 +116,7 @@ for iteration in range(num_iterations):
total_flight_time = sum(task['flight_time'] for task in tasks)
if tasks:
# 车辆从区域中心到第一个任务中心
car_time += math.dist(tasks[0]['center'], region_center) * car_time_factor
car_time = math.dist(tasks[0]['center'], region_center) * car_time_factor
# 依次经过任务中心
for j in range(len(tasks) - 1):
prev_center = tasks[j]['center']
@ -159,131 +158,37 @@ for iteration in range(num_iterations):
if best_solution is not None:
print("最佳 T (各系统中最长的完成时间):", best_solution['T_max'])
print(best_solution['iteration'], "次模拟后找到最佳方案:")
print(best_solution['car_time'], best_solution['flight_time'], best_solution['bs_time'])
print("分区情况:")
print("行分段数:", best_solution['R'])
print("列分段数:", best_solution['C'])
print("行分割边界:", best_solution['row_boundaries'])
print("列分割边界:", best_solution['col_boundaries'])
print("每辆车的运行轨迹情况:")
car_paths = {}
for i in range(k):
num_tasks = len(best_solution['system_tasks'][i])
print(
f"系统 {i}: 完成时间 T = {best_solution['T_k_list'][i]}, 飞行任务数量: {num_tasks}")
print(f"系统 {i}: 完成时间 T = {best_solution['T_k_list'][i]}, 飞行任务数量: {num_tasks}")
tasks = best_solution['system_tasks'][i]
tasks.sort(key=lambda r: math.hypot(r['center'][0] - region_center[0],
r['center'][1] - region_center[1]))
if tasks:
print(f"轨迹路线: 区域中心({region_center[0]:.1f}, {region_center[1]:.1f})", end="")
current_pos = region_center
car_path = []
for j, task in enumerate(tasks, 1):
current_pos = task['center']
car_path.append(current_pos)
print(f" -> 任务{j}({current_pos[0]:.1f}, {current_pos[1]:.1f})", end="")
print(" -> 区域中心")
car_paths[i] = car_path
# 保存分区边界和车辆轨迹到JSON文件
output_data = {
'row_boundaries': [boundary / H for boundary in best_solution['row_boundaries']],
'col_boundaries': [boundary / W for boundary in best_solution['col_boundaries']],
'car_paths': car_paths
}
with open('./solutions/best_solution_mtkl.json', 'w', encoding='utf-8') as f:
json.dump(output_data, f, ensure_ascii=False, indent=4)
else:
print("在给定的模拟次数内未找到满足所有约束的方案。")
# 在输出最佳方案后添加详细信息
if best_solution is not None:
print("\n各系统详细信息:")
region_center = (W / 2.0, H / 2.0)
for system_id, tasks in best_solution['system_tasks'].items():
print(f"\n系统 {system_id} 的任务详情:")
# 按距离区域中心的距离排序任务
tasks_sorted = sorted(tasks, key=lambda r: math.hypot(r['center'][0] - region_center[0],
r['center'][1] - region_center[1]))
if tasks_sorted:
print(
f"轨迹路线: 区域中心({region_center[0]:.1f}, {region_center[1]:.1f})", end="")
current_pos = region_center
total_car_time = 0
total_flight_time = 0
total_flight_energy = 0
total_comp_energy = 0
total_trans_energy = 0
for i, task in enumerate(tasks_sorted, 1):
# 计算车辆移动时间
car_time = math.hypot(task['center'][0] - current_pos[0],
task['center'][1] - current_pos[1]) * car_time_factor
total_car_time += car_time
# 更新当前位置
current_pos = task['center']
print(
f" -> 任务{i}({current_pos[0]:.1f}, {current_pos[1]:.1f})", end="")
# 累加各项数据
total_flight_time += task['flight_time']
total_flight_energy += flight_energy_factor * task['d']
total_comp_energy += comp_energy_factor * \
task['rho'] * task['d']
total_trans_energy += trans_energy_factor * \
(1 - task['rho']) * task['d']
print("\n")
print(f"任务数量: {len(tasks_sorted)}")
print(f"车辆总移动时间: {total_car_time:.2f} 秒")
print(f"无人机总飞行时间: {total_flight_time:.2f} 秒")
print(f"能耗统计:")
print(f" - 飞行能耗: {total_flight_energy:.2f} 分钟")
print(f" - 计算能耗: {total_comp_energy:.2f} 分钟")
print(f" - 传输能耗: {total_trans_energy:.2f} 分钟")
print(
f" - 总能耗: {(total_flight_energy + total_comp_energy + total_trans_energy):.2f} 分钟")
print("\n各任务详细信息:")
for i, task in enumerate(tasks_sorted, 1):
print(f"\n任务{i}:")
print(
f" 位置: ({task['center'][0]:.1f}, {task['center'][1]:.1f})")
print(f" 照片数量: {task['d']}")
print(f" 卸载比率(ρ): {task['rho']:.2f}")
print(f" 飞行时间: {task['flight_time']:.2f} 秒")
print(f" 计算时间: {task['comp_time']:.2f} 秒")
print(f" 传输时间: {task['trans_time']:.2f} 秒")
print(f" -- 飞行能耗: {task['d'] * flight_energy_factor:.2f} 分钟")
print(f" -- 计算能耗: {task['d'] * comp_energy_factor:.2f} 分钟")
print(f" -- 传输能耗: {task['d'] * trans_energy_factor:.2f} 分钟")
print(f" 基站计算时间: {task['bs_time']:.2f} 秒")
else:
print("该系统没有分配任务")
print("-" * 50)
if best_solution is not None:
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
fig, ax = plt.subplots()
ax.set_xlim(0, W)
ax.set_ylim(0, H)
ax.set_title("区域划分与车-机-巢系统覆盖")
ax.set_xlabel("区域宽度")
ax.set_ylabel("区域高度")
# 定义若干颜色以区分不同系统（系统编号从0开始）
colors = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'magenta']
# 绘制区域中心
region_center = (W / 2.0, H / 2.0) # 注意：x对应宽度，y对应高度
ax.plot(region_center[0], region_center[1],
'ko', markersize=8, label="区域中心")
# 绘制每个任务区域（矩形）及在矩形中心标注系统编号与卸载比率 ρ
for system_id, tasks in best_solution['system_tasks'].items():
# 重新按车辆行驶顺序排序（启发式：以任务中心距离区域中心的距离排序）
tasks_sorted = sorted(tasks, key=lambda task: math.hypot(
(task['c1'] + (task['c2'] - task['c1']) / 2.0) - region_center[0],
(task['r1'] + (task['r2'] - task['r1']) / 2.0) - region_center[1]
))
for i, task in enumerate(tasks_sorted, 1):
# 绘制矩形：左下角坐标为 (c1, r1)，宽度为 (c2 - c1)，高度为 (r2 - r1)
rect = patches.Rectangle((task['c1'], task['r1']),
task['c2'] - task['c1'],
task['r2'] - task['r1'],
linewidth=2,
edgecolor=colors[system_id % len(colors)],
facecolor='none')
ax.add_patch(rect)
# 计算矩形中心
center_x = task['c1'] + (task['c2'] - task['c1']) / 2.0
center_y = task['r1'] + (task['r2'] - task['r1']) / 2.0
# 在矩形中心标注：系统编号、执行顺序和卸载比率 ρ
ax.text(center_x, center_y, f"S{system_id}-{i}\nρ={task['rho']:.2f}",
color=colors[system_id % len(colors)],
ha='center', va='center', fontsize=10, fontweight='bold')
# 添加图例
ax.legend()
# 反转 y 轴使得行号从上到下递增（如需，可取消）
ax.invert_yaxis()
plt.show()
else:
print("没有找到满足约束条件的方案，无法进行可视化。")

61
visualization.py Normal file
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@ -0,0 +1,61 @@
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import json
def visualize_solution(row_boundaries, col_boundaries, car_paths, W, H):
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
fig, ax = plt.subplots()
ax.set_xlim(0, W)
ax.set_ylim(0, H)
ax.set_title("区域划分与车-机-巢系统覆盖")
ax.set_xlabel("区域宽度")
ax.set_ylabel("区域高度")
# 定义若干颜色以区分不同系统（系统编号从0开始）
colors = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'magenta']
# 绘制区域中心
region_center = (H / 2.0, W / 2.0) # 注意：x对应宽度，y对应高度
ax.plot(region_center[1], region_center[0],
'ko', markersize=8, label="区域中心")
# 绘制行分割边界
for row in row_boundaries:
ax.axhline(y=row * H, color='black', linestyle='--')
# 绘制列分割边界
for col in col_boundaries:
ax.axvline(x=col * W, color='black', linestyle='--')
# 绘制每辆车的轨迹
for system_id, path in car_paths.items():
path = [(region_center[0], region_center[1])] + path + [(region_center[0], region_center[1])]
y, x = zip(*path)
ax.plot(x, y, marker='o', color=colors[int(system_id) % len(colors)], label=f"系统 {system_id}")
# 添加图例
ax.legend()
# 反转 y 轴使得行号从上到下递增（如需，可取消）
ax.invert_yaxis()
plt.show()
if __name__ == "__main__":
import yaml
# 读取参数
with open('params.yml', 'r', encoding='utf-8') as file:
params = yaml.safe_load(file)
H = params['H']
W = params['W']
# 读取最佳方案的JSON文件
with open('./solutions/best_solution_mtkl.json', 'r', encoding='utf-8') as f:
best_solution = json.load(f)
row_boundaries = best_solution['row_boundaries']
col_boundaries = best_solution['col_boundaries']
car_paths = best_solution['car_paths']
visualize_solution(row_boundaries, col_boundaries, car_paths, W, H)