Design And Implementation Of Air Quality Monitoring And Early Warning System In Wuhai Mining Area

With the large-scale and high-intensity mining of coal resources in Wuhai Mining Area,the destruction of soil and the erosion of rock are intensified,which makes a large number of ore body surface soil peeling off,the surface vegetation is seriously damaged,and the dust weather in the mining area is obviously intensified,which has a serious impact on the environmental quality of the mining area and its surrounding areas.Therefore,this paper focuses on the air quality monitoring and early warning research in Wuhai Mining Area,and finally designs and implements an air quality monitoring and early warning system based on the deep learning model.This system mainly includes three parts: data crawling and storage module,air quality prediction module based on deep learning and front and rear development module.In order to better realize the function of the above modules,this paper uses UML to draw the system function use case diagram to sort out the system function content.The system mainly includes personal center module,main interface module,real-time monitoring module,historical data module and air quality prediction module.The data used in this system mainly includes air quality data and meteorological data.Due to the large amount and variety of data needed,the system is designed and implemented by Scrapy data crawling technology and My SQL database technology.SQLalchemy architecture is used as object relational mapping(ORM)to design My SQL database and establish data tables to store user data,air quality data,meteorological data,monitoring site information and web page information.The system also uses the flash framework to build the system back-end server to complete the internal logic processing and data communication,and uses the Vue framework to design the front-end display page.In the aspect of forecasting model research,firstly,preprocess the data,establish the corresponding data training set and test set,then analyze the correlation coefficient of weather factors,and realize the selection of forecasting factors by information entropy and grey correlation analysis.In this paper,the deep learning algorithm RNN and LSTM are selected to study the air quality prediction model.RNN can get the correlation according to the current output and historical input to model the sequence data.However,due to the limited representation ability of ahidden layer,DRNN and DRNN are selected to build prediction model determines the optimal prediction model by changing the number of hidden layers and the number of hidden layer units.The final simulation results show that the prediction results of DLSTM are better than DRNN,and the prediction accuracy is as high as 92.85%,which has good application value.Combined with the developed air quality monitoring and early warning system,it can accurately predict the concentration value and change trend of various air pollutants in Wuhai Mining Area.Finally,the test and deployment of the system are completed.The test results show that all the functions and performance parameters of the system can meet the application standards.The system provides a feasible reference for the environmental protection department to realize the effective control of air pollution,thus providing strong technical support for the ecological security of Wuhai Mining area.It is very important to standardize the production of the mining area and accelerate the ecological recovery of the mining area Significance.