Dynamic Risk Assessment Of Community Gas System Based On Machine Learning

With the increasing demand of urban construction residents,various risk factors related to gas are also increasing.In order to strengthen the supervision of community indoor gas system and ensure the safety of community residents,it is necessary to carry out risk assessment on indoor gas system,so as to quickly and comprehensively understand the risk level of indoor gas system.However,the existing risk assessment methods still have some problems.First of all,at present,the risk assessment of indoor gas system is mostly static analysis method.These methods can only roughly estimate the long-term safety risk status of indoor gas system,which is difficult to adapt to the dynamic characteristics of risk,and cannot timely predict the development trend and consequences of risk events.Secondly,the existing methods generally only model the individual components of the system,ignoring the complex coupling relationship of indoor gas system risk.In addition,due to the lack of intelligent sensing system and immature monitoring sensors and functional software,the indoor gas system is lack of monitoring data,and it is difficult to trace the risk of gas facilities,which makes it impossible to realize the comprehensive monitoring of the operation status and risk events of the indoor gas system.In order to solve the above problems,the research contents of this paper are as follows:(1)Firstly,through the analysis of the risk factors of community indoor gas system,the index system of indoor gas risk assessment is constructed.Secondly,the knowledge graph is applied to the scene construction of indoor gas system,and the knowledge graph is used to show the complex coupling relationship of indoor gas system risk.Then,a dynamic risk assessment method of community indoor gas based on graph neural network is proposed in the knowledge graph.By aggregating the entity features in the knowledge graph and mining the deep information of the entity,the dynamic risk assessment of community indoor gas system is realized,which provides a theoretical reference for the safety management of community gas users.(2)A community indoor gas risk monitoring system based on "end-edge-cloud" is developed.In the end layer,6LoWPAN sensor nodes are developed to realize real-time data collection of indoor gas operation environment;in the edge layer,6LoWPAN boundary routing is developed to realize data aggregation of 6LoWPAN sensor nodes.At the same time,in order to realize the whole life cycle management and risk traceability of indoor gas facilities,a security mechanism based on blockchain is constructed on 6LoWPAN boundary routing;in the cloud layer,based on the open source Internet of things access platform Things Board and indoor gas dynamic risk assessment model API interface,the operation environment data and risk assessment results of indoor gas system are visually displayed and realize real-time monitoring of indoor gas risk events.