Design And Implementation Of Air Quality Sensor Data Monitoring System

In recent years,with the rapid economic development,industrialisation and urbanisation have accelerated and the quality of people’s life has improved significantly,but at a huge environmental cost.With the intensification of environmental pollution,especially air pollution,more and more cities are experiencing severe hazy weather,which has a great impact on social production and people’s health.In response to the above problems,the Party and the State have actively taken various measures to prevent and combat air pollution problems,and hence the air pollution situation has gradually improved.In the process of dealing with air pollution problems,monitoring of air pollutant concentration is the basis for a series of follow-up measures.Only with timely and accurate monitoring of real data on the concentration of various pollutants in the air can relevant researchers accurately analyse the problem and propose relevant preventive solutions.In the above context,this paper designs and develops a data monitoring system for air quality sensor to achieve effective real-time collection and visual analysis of air pollutant data.The main modules include sensor device management,data collection,data visualisation and analysis,real-time data anomaly warning,historical data anomaly rate detection and PM2.5 concentration prediction.The sensor device management module enables the management and maintenance of the basic information,location information and manufacturer information of air quality sensors,and also supports users to set the parameters of the sensors remotely.The data collection module establishes a stable communication connection between the system and the air quality sensors by referring to the data transmission standard of pollutant online monitoring system,HJ 212-2017.It receives the raw data packets sent by the sensors in real time,and then parses them to obtain the pollutant concentrations.The module for data visualisation and analysis enables the visualisation of real-time data and historical data of pollutant concentrations,facilitating users to monitor and analyse the air quality situation.The module for real-time data anomaly warning determines whether the real-time data is anomalous such as threshold-exceeded pollutant concentrations and constant real-time data based on the pre-defined strategy for early warning.If any anomaly is detected,the module will inform the persons in charge by in-system notification,SMS or WeChat messages.The module for PM2.5 concentration prediction enables the prediction of future PM2.5 concentrations on the time-scales of days and hours.With experimental analysises,this module proposes a prediction model based on SARIMA algorithm to predict daily PM2.5 concentrations as well as a combined prediction model based on LSTM and XGBoost to predict hourly PM2.5 concentrations,so that users can propose preventive measures for the predicted pollutant concentrations timely.The front-end of the system uses Vue.js framework and the back-end uses SpringBoot+JPA framework.The overall functionality has been implemented and can meet the current needs of users.