Design And Implementation Of Real-time Monitoring And Anomaly Early Warning System For Smart Corridor

In recent years,with the continuous expansion and deepening of the"smart city" construction scale,the problem of low governance efficiency in information processing,operation and maintenance,and emergency disaster prevention of the traditional underground comprehensive pipe corridor has gradually emerged.Smart pipe corridors use emerging technologies such as 5G(fifth generation mobile communication),Internet of Things,big data,and artificial intelligence,to realize the information,automation,intelligent management mode,and improve the comprehensive management capabilities and operating efficiency of underground pipe corridors.The real-time monitoring and anomaly warning system is a subsystem of the smart pipe corridor project,realized by SSM(SpringMVC,Spring,Mybatis)framework.Based on Drools rule engine and recursive principal component analysis(R-PCA)algorithm,the system design and implement the real-time monitoring module.Based on Activiti process engine and Drools rules engine,the system design and implement the abnormal warning module.The real-time monitoring and anomaly warning system,as the "eyes and brain" of the intelligent pipe gallery,can realize automatic monitoring of the underground pipe gallery environment and early warning of hidden dangers.The main work is as follows:1)The system implements a real-time monitoring module,which provides two complementary real-time monitoring modes:Standardized monitoring is based on the Drools rule engine.By dynamically deploying simple,efficient,flexible and scalable real-time monitoring rules during system operation,execute rule pattern matching to sense the abnormality of the smart pipe corridor environment and the device.While.improving the flexibility and scalability of the system,it effectively saves the rule matching time and improves the real-time monitoring efficiency.This method requires configuration rules and no constraints.Automatic monitoring is based on the R-PCA algorithm.The mode uses PCA(Principal Component Analysis)transformation to calculate the SPE(mean square prediction error)score of multidimensional data,automatically generates the dynamic threshold that changes with time.Then the mode start to iterate detection parameters by recursively,and executes real-data anomaly detection.While reducing system complexity and operating costs,the mode has high anomaly detection accuracy.This method does not require configuration rules,but requires spatial correlation between multidimensional data.2)Designed and implemented the abnormal warning module.In response to the abnormal characteristics of multiple dimensions in the smart pipe gallery application scenario,the module use Drools rule engine to systematically evaluates the risk of hidden accidents,and combined Activiti process engine to generate a flexible exception warning strategy to reasonably dispatch message notification plugin,remote control equipment,and system automation operating.