#!/usr/bin/env python # -*- coding: utf-8 -*- ''' # @Time : 2025/9/28 13:36 # @Author : reenrr # @File : plc_data_reader.py # @Description : 通用PLC数据读取线程:按配置动态处理read/read_write类型的区域 # 支持结构化数据解析(real/int/bool等) ''' import threading import time import logging from datetime import datetime from snap7.util import get_real, get_int, get_bool, get_word, get_dint # 导入snap7解析工具 class PLCDataReaderThread(threading.Thread): def __init__(self, plc_client, area_config, update_interval=0.03, output_file_prefix="plc_area_"): """ 初始化PLC数据读取线程(配置驱动,支持多区域) 参数: plc_client: 已连接的Snap7Client实例(来自PLCManager) area_config: 单个区域的配置(来自config.json的plcs[].areas) 示例:{"name":"DB100_Read", "type":"read", "db_number":100, "offset":0, "size":6000, "structure":[...]} update_interval: 读取间隔(秒),默认30ms output_file_prefix: 输出文件前缀,最终文件名为“前缀+区域名.log” """ # 线程名包含区域名,便于日志区分(如"PLCDataReader_DB100_Read") thread_name = f"PLCDataReader_{area_config['name']}" super().__init__(name=thread_name, daemon=True) # 1. 核心依赖(PLC客户端+区域配置) self.plc_client = plc_client self.area_config = area_config # 动态区域配置,不再硬编码DB100 self.area_name = area_config["name"] self.db_number = area_config["db_number"] self.offset = area_config["offset"] self.size = area_config["size"] self.area_type = area_config["type"] # 区分read/read_write/write # 2. 线程与输出配置 self.update_interval = update_interval self.output_file = f"{output_file_prefix}DB{self.db_number}.log" # 每个区域独立文件 # 3. 数据缓存(新增结构化数据存储) self.running = False self._latest_data = None # 格式:(timestamp, data_info, raw_bytes, parsed_data) self._data_lock = threading.Lock() # 线程安全锁 # 4. 日志 self.logger = logging.getLogger(f"PLCDataReader.{self.area_name}") def start(self): """启动线程(验证PLC连接+读写类型适配)""" # 仅处理需要读的区域(read/read_write),write类型不启动 if self.area_type not in ["read", "read_write"]: self.logger.warning(f"跳过启动:区域类型为{self.area_type}(无需循环读取)") return self.running = True super().start() self.logger.info(f"✅ 线程启动成功(DB{self.db_number},{self.area_type})") self.logger.info(f"🔧 配置:间隔{self.update_interval * 1000}ms,读取{self.size}字节,输出{self.output_file}") def stop(self): """停止线程(优雅清理)""" self.running = False if self.is_alive(): self.join(timeout=2.0) if self.is_alive(): self.logger.warning("⚠️ 线程未正常退出,强制终止") self.logger.info(f"🛑 线程已停止(DB{self.db_number})") def get_latest_data(self): """ 线程安全获取最新数据(返回原始字节+解析后的结构化数据) 返回示例: { "timestamp": "2025-09-28 10:00:00.123", "data_info": {"area_name":"DB100_Read", "db_number":100, "offset_range":"0-5999", "actual_length":6000}, "raw_bytes": bytearray(b'\x00\x10...'), "parsed_data": {"temperature":25.5, "pressure":100, "status":True} # 解析后的字段 } """ with self._data_lock: if self._latest_data is None: self.logger.debug("⚠️ 无最新数据缓存") return None timestamp, data_info, raw_bytes, parsed_data = self._latest_data return { "timestamp": timestamp, "data_info": data_info.copy(), "raw_bytes": raw_bytes.copy() } def run(self): """线程主循环:读PLC→解析数据→更新缓存→写文件""" self.logger.debug(f"📌 主循环启动(DB{self.db_number})") while self.running: cycle_start = time.time() try: # 步骤1:读取PLC区域数据(调用Snap7Client的缓存方法) cache_success = self.plc_client.cache_large_data_block( db_number=self.db_number, offset=self.offset, size=self.size ) # 步骤2:处理读取结果(缓存+解析+写文件) if cache_success and self.plc_client.data_cache is not None: raw_data = self.plc_client.data_cache # 原始字节 data_len = len(raw_data) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3] # 构造数据基本信息 data_info = { "area_name": self.area_name, "db_number": self.db_number, "offset_range": f"0-{self.size - 1}", "actual_length": data_len, "area_type": self.area_type } # 步骤3:线程安全更新内存缓存 with self._data_lock: self._latest_data = (timestamp, data_info, raw_data.copy()) # 步骤4:写入文件(含原始字节+解析后数据) self._write_latest_data_to_file(timestamp, data_info, raw_data) else: self.logger.warning(f"⚠️ 数据读取失败(DB{self.db_number}),跳过本次更新") # 步骤6:精确控制读取间隔 cycle_elapsed = time.time() - cycle_start sleep_time = max(0, self.update_interval - cycle_elapsed) if sleep_time > 0: time.sleep(sleep_time) except Exception as e: self.logger.error(f"🔴 循环读取出错: {str(e)}", exc_info=True) time.sleep(self.update_interval) def _write_latest_data_to_file(self, timestamp, data_info, raw_data): """ 写入文件:含原始字节+解析后的结构化数据(每个区域独立文件) """ try: # 处理原始字节为列表(便于查看) data_list = list(raw_data) # 只显示前50字节,避免文件过大 data_str = f"{data_list} (共{len(raw_data)}字节)" # 覆盖写入文件 with open(self.output_file, "w", encoding="utf-8") as f: f.write(f"[{timestamp}] 📝 {self.area_name} 最新数据\n") f.write( f" - 区域信息:DB{data_info['db_number']}({data_info['offset_range']}),类型{data_info['area_type']}\n") f.write(f" - 原始字节数据:{data_str}\n") f.write("=" * 120 + "\n") self.logger.debug(f"📤 最新DB{self.db_number}数据已覆盖写入文件:{self.output_file}") except Exception as e: self.logger.error(f"🔴 写入DB{self.db_number}数据到文件出错: {str(e)}", exc_info=True)