Research And Implementation Of Air Quality Index Prediction System Based On STM3

In today’s rapidly developing human society,air pollution has become an increasingly prominent problem,which has an undeniable impact on the ecological environment and public health.To reduce the harm caused by outdoor air pollution,China has done a lot of work in monitoring environmental pollutants and regulating highly polluting industries,gradually improving the quality of outdoor air.However,due to the significant differences in the trends of indoor and outdoor pollutants,the solution to outdoor air pollution is not applicable to indoor environments.In addition,traditional air pollution monitoring systems can only passively control pollution and cannot maximize public health protection.To address these issues,this thesis proposes a predictive method for the indoor air quality index and designs and implements an air quality index prediction system.(1)In response to the problem that the existing monitoring system lacks active pollution control functions,a predictive model for indoor air quality index is proposed.This method is based on long short-term memory networks and is combined with the characteristics of the indoor environment and air quality index.It uses a onedimensional dilated convolution and variable-dimension attention mechanism to improve prediction accuracy.In addition,the model parameters are further optimized through an improved cuckoo search algorithm,achieving high-precision prediction of the indoor air quality index.(2)A prediction system for the air quality index has been designed and implemented.The system consists of three parts: a prediction node,a wireless communication node,and an IoT platform.The prediction node uses STM32F7 series chips as the main control unit,with the prediction model deployed on it.The sensors equipped on the prediction node will collect data and reconstruct it according to the requirements of the prediction model to obtain real-time prediction results.In addition,the prediction node is also equipped with function modules such as alarm and response to achieve a timely response to the prediction results.Finally,the relevant data generated is sent to the IoT platform by the wireless communication node,and data display and remote control are realized in the form of a web interface.The test results show that the system has good air quality index prediction accuracy,and with the equipped response module,it can achieve early treatment of future indoor air pollution to prevent problems before they occur.At the same time,the system can also be transferred for use in similar scenarios,avoiding the need for a long-term collection of training data in new environments,which could lead to the system becoming a monitoring system.