Design Of Community Gas Facilities Risk Monitoring And Early Warning System

Gas is a necessity for the residents of the community in our community.In recent years,the use of gas in our country has been increasing.The safety and stability of gas have become more and more important.The leakage warning of gas facilities is an important means to ensure the safe operation of gas facilities.But the traditional leakage warning methods of gas facilities mainly rely on experts’ experience,the level of real-time and intelligence is insufficiently.This article combines data collection,video monitoring,machine learning and visualization technology to design a community gas facility risk monitoring and early warning system.Mainly carried out the following four aspects of work:(1)A wireless sensor network is developed based on the 6Lo WPAN protocol to collect community gas data and a smart gateway is created based on the Raspberry Pi to gather community gas data.Meanwhile,the MQTT protocol is used to realize the transmission of community gas data from the wireless sensor network child nodes to the smart gateway and from the smart gateway to the cloud platform.(2)A video monitoring module is built based on FFmpeg,Nginx,RTMP and Flv.js to realize the video monitoring function of the surrounding environment of community gas facilities.FFmpeg is used to read the webcam video stream,encode and push the stream to the streaming media server.A streaming media server is established through Nginx to realize the function of proxying and forwarding video streams.Flv.js is applyed to design video display module to realize the function of displaying the video surveillance screen on web pages.(3)The collected historical data is deemed as the data set,the fuzzy control algorithm is employed to optimize the candidate features in the data set to reduce the interference of less important features.The optimized gas features is used as the input of the random forest algorithm with the risk level as the output to establish the fuzzy-random forest model.The real-time data collected by the sensor is applyed as the input of the fuzzy-random forest model to obtain the gas leakage level of the monitoring location,and realize the early warning of gas leakage in the community.(4)The front-end display interface is developed by HTML,CSS and Java Script,combined with Baidu Map API to realize the function of displaying online monitoring data,gas leakage warning level,video monitoring screen and historical curves in the web page,and Ajax method is used to dynamically update monitoring information.The back-end server is developed based on Node.js to interact with My SQL database,and the Post method is utilized to push the data to the front-end interface.This paper uses wireless sensor networks and machine learning algorithms to improve the real-time performance of the gas facility leakage warning method and the intelligence level of the gas leakage early warning system.The visual display of online monitoring data,video monitoring information and gas leakage warning level of community gas facilities is realized through the Web interface.It has certain reference significance to the design of actual gas leakage monitoring and early warning system.