摘要
利用复杂事件处理技术构建了基于事件驱动的煤矿井下安全事件检测与预警触发模式。以井下RFID人员定位数据与环境监测数据为基础应用数据,基于复杂事件处理技术搭建大规模井下安全流数据处理框架,设计离线数据关联规则自学习、在线数据匹配树结构自匹配的双层并行机制,以实现对井下安全预警事件的高效检测与预警触发。测试结果表明,基于事件驱动的煤矿井下安全事件检测与预警模式相对于基于关系型数据库的安全监控模式,在异常事件检测数与检测效率方面均具有显著的优势。
A detection and early warning trigger mode of coal mine underground safety event based on event driven was constructed by using complex event processing technology.By adopting underground RFID personnel positioning data and environmental monitoring data as application data,a large-scale underground safety flow data processing framework based on complex event processing technology was built.And then a double layer parallel mechanism for self learning of offline data association rule and self matching of online data matching tree structure was designed,so as to achieve high efficiency detection and early warning trigger for early warning of underground safety event.The test results show that the detection and early warning trigger mode of coal mine underground safety event based on event driven has significant advantages in abnormal event detection and detection efficiency compared with safety monitoring model based on relational database.
出处
《工矿自动化》
北大核心
2016年第8期33-37,共5页
Journal Of Mine Automation
基金
国家自然科学基金资助项目(61402360)
关键词
煤矿井下安全
复杂事件处理
异常事件检测
预警触发
coal mine underground safety
complex event processing
abnormal event detection
early warning trigger mode
