# -*- coding: utf-8 -*-
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import ast
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import multiprocessing
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import sys
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import numpy as np
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import json
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import time
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import redis
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from collections import deque, defaultdict
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radiusLen = None
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# 将字符串转换为浮点型数组
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def convert_to_float_array(str_array):
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if isinstance(str_array, str):
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return np.array(ast.literal_eval(str_array), dtype=float)
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elif isinstance(str_array, list) or isinstance(str_array, np.ndarray):
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return np.array(str_array, dtype=float)
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return np.array([], dtype=float)
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def getWaveScopeByCode_iterative(x, y, codeMatrix, cdaMatrix, radiusLen):
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"""
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使用广度优先搜索（BFS）并跟踪扩展方向，以避免递归深度过大和不必要的资源浪费。
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当遇到 'NONE' 节点时，仅在当前方向上继续扩展。
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"""
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includeList = []
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existNodes = set()
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queue = deque()
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# 初始节点，没有方向
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originNode = {"x": x, "y": y, "dir": None}
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queue.append(originNode)
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existNodes.add((x, y))
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while queue:
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node = queue.popleft()
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node_x, node_y, current_dir = node['x'], node['y'], node['dir']
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# 根据当前方向决定扩展的方向
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if current_dir is None:
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# 如果没有方向，向四个方向扩展
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neighbors = [
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(node_x + 1, node_y, 'right'),
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(node_x - 1, node_y, 'left'),
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(node_x, node_y + 1, 'down'),
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(node_x, node_y - 1, 'up')
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]
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else:
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# 如果有方向，仅在该方向上扩展
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if current_dir == 'right':
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neighbors = [(node_x + 1, node_y, 'right')]
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elif current_dir == 'left':
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neighbors = [(node_x - 1, node_y, 'left')]
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elif current_dir == 'down':
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neighbors = [(node_x, node_y + 1, 'down')]
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elif current_dir == 'up':
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neighbors = [(node_x, node_y - 1, 'up')]
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else:
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neighbors = []
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for nx, ny, direction in neighbors:
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# 检查边界条件
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if (nx < 0 or nx >= codeMatrix.shape[0] or ny < 0 or ny >= codeMatrix.shape[1]):
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continue
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if (nx, ny) in existNodes:
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continue
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existNodes.add((nx, ny))
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neighbor_code = codeMatrix[nx, ny]
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if neighbor_code == 'NONE':
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# 遇到 'NONE' 节点，继续在当前方向上扩展
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queue.append({"x": nx, "y": ny, "dir": direction})
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else:
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# 检查距离条件
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o1Cda = convert_to_float_array(cdaMatrix[x, y])
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o2Cda = convert_to_float_array(cdaMatrix[nx, ny])
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num1 = (o1Cda[0] - o2Cda[0]) ** 2
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num2 = (o1Cda[1] - o2Cda[1]) ** 2
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if num1 + num2 <= radiusLen ** 2:
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includeList.append({
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"x": int(nx),
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"y": int(ny),
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"code": str(codeMatrix[nx, ny])
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})
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# 非 'NONE' 节点，重置方向
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queue.append({"x": nx, "y": ny, "dir": None})
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return includeList
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def find_value_in_matrix(value, codeMatrix):
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indices = np.where(codeMatrix == value)
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return list(zip(indices[0], indices[1]))
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def initWaveMatrix(codeMatrix):
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waveMatrix = np.empty_like(codeMatrix, dtype=object)
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for x in range(codeMatrix.shape[0]):
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for y in range(codeMatrix.shape[1]):
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if codeMatrix[x][y] == 'NONE':
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waveMatrix[x][y] = "-"
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else:
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waveMatrix[x][y] = '[]'
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return waveMatrix
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# 优化版本：使用集合来提高性能
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def mergeWave(originWave, vehicle):
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# 将字符串解析为集合
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try:
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set_data = set(ast.literal_eval(originWave))
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except (ValueError, SyntaxError):
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set_data = set()
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# 如果 vehicle 不在集合中，则添加
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set_data.add(vehicle)
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# 返回序列化后的字符串
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return json.dumps(list(set_data))
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# 将 dynamicMatrix 转换为 numpy 结构化数组
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def convert_to_structured_array(dynamicMatrix):
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# 定义结构化数组的 dtype
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dtype = [('serial', int), ('vehicle', 'U2'), ('time', int)]
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# 确保每个字典包含所有字段
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structured_list = []
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for row in dynamicMatrix:
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for d in row:
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# 提取字段，确保 'time' 存在，否则设置为默认值（例如 0）
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serial = d.get('serial', 0)
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vehicle = d.get('vehicle', '0')
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time_val = d.get('time', 0)
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structured_list.append((serial, vehicle, time_val))
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# 将嵌套的列表转换为结构化数组
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structured_array = np.array(structured_list, dtype=dtype)
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# 重塑为原始的二维形状
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return structured_array.reshape(len(dynamicMatrix), -1)
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# 使用 numpy 加速的代码
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def process_dynamic_matrix(dynamicMatrix, codeMatrix, cdaMatrix, radiusLen, waveMatrix):
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# 将 dynamicMatrix 转换为结构化数组
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dynamicMatrix = convert_to_structured_array(dynamicMatrix)
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# 获取 dynamicMatrix 的形状
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rows, cols = dynamicMatrix.shape
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# 创建一个布尔掩码，用于筛选出 vehicle 不为 '0' 和 '-1' 的元素
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mask = (dynamicMatrix['vehicle'] != '0') & (dynamicMatrix['vehicle'] != '-1')
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# 获取满足条件的 x 和 y 坐标
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x_indices, y_indices = np.where(mask)
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# 遍历满足条件的坐标
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for x, y in zip(x_indices, y_indices):
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vehicle = dynamicMatrix[x][y]['vehicle']
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includeList = getWaveScopeByCode_iterative(x, y, codeMatrix, cdaMatrix, radiusLen)
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for include in includeList:
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originWave = waveMatrix[include['x']][include['y']]
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waveMatrix[include['x']][include['y']] = mergeWave(originWave, vehicle)
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return waveMatrix
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def process_chunk(chunk, dynamicMatrix, codeMatrix, cdaMatrix, radiusLen):
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"""处理数据块的函数，返回需要合并的 (x, y, vehicle) 列表"""
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local_wave_updates = []
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for data in chunk:
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x, y = data # 假设每个数据项包含x和y坐标
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vehicle = dynamicMatrix[x][y]['vehicle']
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includeList = getWaveScopeByCode_iterative(x, y, codeMatrix, cdaMatrix, radiusLen)
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for include in includeList:
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local_wave_updates.append((include['x'], include['y'], vehicle))
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return local_wave_updates
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def process_dynamic_matrix_parallel(dynamicMatrix, codeMatrix, cdaMatrix, radiusLen):
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# 将 dynamicMatrix 转换为结构化数组
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dynamicMatrix = convert_to_structured_array(dynamicMatrix)
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# 创建布尔掩码
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mask = (dynamicMatrix['vehicle'] != '0') & (dynamicMatrix['vehicle'] != '-1')
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x_indices, y_indices = np.where(mask)
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# 将满足条件的坐标组合成任务，每个任务包含一个数据块
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tasks = list(zip(x_indices, y_indices))
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# num_processes = multiprocessing.cpu_count()
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num_processes = 5
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chunk_size = max(1, len(tasks) // num_processes)
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chunks = [tasks[i:i + chunk_size] for i in range(0, len(tasks), chunk_size)]
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all_results = [] # 存储所有进程的结果
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# 设置进程池
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with multiprocessing.Pool(processes=num_processes) as pool:
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# 使用map方法并行处理任务
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results = pool.starmap(process_chunk, [(chunk, dynamicMatrix, codeMatrix, cdaMatrix, radiusLen) for chunk in chunks])
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for result in results:
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all_results.extend(result)
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# 使用 defaultdict 来收集每个 (x, y) 对应的所有 vehicle
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wave_updates = defaultdict(set)
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for x, y, vehicle in all_results:
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wave_updates[(x, y)].add(vehicle)
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return wave_updates
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def main():
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global radiusLen, codeMatrix, cdaMatrix, waveMatrix # 声明为全局变量
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if len(sys.argv) != 6:
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print("Usage: python script.py <radiusLen> <redisHost> <redisPwd> <redisPort> <redisIdx>")
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sys.exit(1)
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radiusLenStr = sys.argv[1]
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try:
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radiusLen = float(radiusLenStr)
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except ValueError:
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print("Error: radiusLen must be a float.")
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sys.exit(1)
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redisHost = sys.argv[2]
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redisPwd = sys.argv[3]
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redisPort = sys.argv[4]
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redisIdx = sys.argv[5]
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startTime = time.perf_counter()
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try:
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# 创建一个连接池
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pool = redis.ConnectionPool(host=redisHost, port=int(redisPort), password=redisPwd, db=int(redisIdx))
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r = redis.Redis(connection_pool=pool)
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# 获取并加载 codeMatrix
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codeMatrixStr = r.get('KV.AGV_MAP_ASTAR_CODE_FLAG.1')
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if codeMatrixStr is None:
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print("Error: 'KV.AGV_MAP_ASTAR_CODE_FLAG.1' not found in Redis.")
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sys.exit(1)
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codeMatrix = np.array(json.loads(codeMatrixStr.decode('utf-8')), dtype=str)
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# 获取并加载 cdaMatrix
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cdaMatrixStr = r.get('KV.AGV_MAP_ASTAR_CDA_FLAG.1')
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if cdaMatrixStr is None:
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print("Error: 'KV.AGV_MAP_ASTAR_CDA_FLAG.1' not found in Redis.")
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sys.exit(1)
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cdaMatrix = np.array(json.loads(cdaMatrixStr.decode('utf-8')), dtype=object)
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# 获取并加载 dynamicMatrix
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dynamicMatrixStr = r.get('KV.AGV_MAP_ASTAR_DYNAMIC_FLAG.1')
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if dynamicMatrixStr is None:
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print("Error: 'KV.AGV_MAP_ASTAR_DYNAMIC_FLAG.1' not found in Redis.")
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sys.exit(1)
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dynamicMatrix = np.array(json.loads(dynamicMatrixStr.decode('utf-8')), dtype=object)
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# 初始化 waveMatrix
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waveMatrix = initWaveMatrix(codeMatrix)
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# 调用并行处理的函数
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wave_updates = process_dynamic_matrix_parallel(dynamicMatrix, codeMatrix, cdaMatrix, radiusLen)
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# 应用所有更新到 waveMatrix
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for (x, y), vehicles in wave_updates.items():
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originWave = waveMatrix[x][y]
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for vehicle in vehicles:
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originWave = mergeWave(originWave, vehicle)
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waveMatrix[x][y] = originWave
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# 将 numpy.ndarray 转换为嵌套列表
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waveMatrixList = waveMatrix.tolist()
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# 将嵌套列表转换为 JSON 字符串
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waveMatrixJsonStr = json.dumps(waveMatrixList)
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# 将结果保存回 Redis
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r.set("KV.AGV_MAP_ASTAR_WAVE_FLAG.1", waveMatrixJsonStr)
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end = time.perf_counter()
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# 打印程序运行时间
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# print(f"程序运行时间为: {end - startTime} Seconds")
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print("1")
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except Exception as e:
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print(f"An error occurred: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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