Research On Multi-Parameter Network Monitoring System For Atmospheric Environment

In recent years,the problem of atmospheric environmental pollution has become increasingly serious,and smoggy weather has frequently appeared in more and more places,which has caused serious harm to people’s physical health and living environment.The monitoring of atmospheric environmental quality and the trend of changes in particulate matter have attracted widespread attention.Real-time monitoring of atmospheric environmental quality and effective analysis of changes in particulate matter concentration are of great importance to environmental protection work and people’s health.In this context,the existing monitoring equipment for the existing atmospheric environment monitoring system is costly,bulky,difficult to meet the needs of refined monitoring and data is not timely enough.This paper designs a multi-parameter network monitoring system for the atmospheric environment to monitor and accurately manage the atmospheric environmental quality changes in various regions.And the GA-Elman neural network particle concentration prediction model was constructed to predict the PM2.5 and PM10 concentrations in the future.The main work of this paper is as follows:(1)Analyze the current domestic and international status of the application of Internet of Things technology in atmospheric environment monitoring,and combines LoRa wireless communication technology with low power consumption and long transmission distance.The LoRa wireless communication technology is applied to a multi-parameter atmospheric environment monitoring system.(2)A multi-parameter network monitoring system for the atmospheric environment was designed and implemented.Firstly,the hardware circuit design of the multi-parameter atmospheric environment monitoring equipment and the gateway backplane is completed,and the main circuits of each part are introduced in detail and the hardware objects are produced.Then,based on the hardware,the related software design is carried out according to the LoRaWAN protocol standard.Several atmospheric environment monitoring nodes and one gateway node are networked in a star network,and finally a Web server is set up to store and display the monitoring data.Real-time monitoring of relevant elements such as NO2,SO2,O3,CO,PM2.5,PM10,temperature and humidity,wind speed,wind direction,rainfall and atmospheric pressure in the atmospheric environment of each region is realized.(3)The particle concentration data collected by the monitoring site is used as sample data.Aiming at the problem that Elman neural network is easy to fall into local minimum value during training,the genetic algorithm is used to improve Elman neural network and the particle concentration prediction model based on GA-Elman neural network is constructed.The prediction effect is better.(4)The stability and data reliability of the multi-parameter atmospheric environment monitoring system developed in this paper are verified by systematic test and comparison of data results and the comparison experiments show that the prediction accuracy of the GAElman neural network particle concentration prediction model is higher than that of the traditional BP prediction model and the Elamn prediction model,has a certain application prospects.