Design And Implementation Of Distributed Multi-modal Air Quality Monitoring System Based On Cloud Platform

With the development of the national economy and the acceleration of industrialization,the problem of air pollution is becoming more and more serious,and the monitoring of air quality can no longer wait.In recent years,the state has paid more attention to air quality monitoring,the construction of monitoring stations has become more and more sound,and the overall air quality monitoring technology and infrastructure is basically perfect.However,there are still some problems,such as: limited monitoring range,low monitoring accuracy,unreliable data transmission,etc.In view of this,a distributed multi-modal air quality monitoring system is designed using multi-modal self-organizing network remote communication technology and "cloud,tube,end" edge IOT architecture.This system can transmit the data information collected by the equipment to the database for storage and display in a stable manner to realize local area air quality monitoring and analysis.The main research work and contributions can be summarized as following:(1)In order to solve the problems of monitoring scope and accuracy of traditional air quality monitoring systems,the functional and non-functional needs is analyzed.A general framework of a distributed multimodal air quality monitoring system based on a cloud platform is proposed,and the physical device layer,network transmission layer,data resource layer and system application layer are analyzed in detail.(2)To ensure the reliability of data remote transmission,the multi-modal self-organizing network remote communication method of air quality monitoring system is studied.A wireless communication data transmission scheme combining NB-IoT and IEEE 802.15.4 technology is designed.It is also designed and implemented in layers from hardware abstraction layer,physical layer and network layer.(3)For the problem of improving the accuracy of air quality prediction,the optimal weights and thresholds of the BP neural network are based on the pre-processing of the data and the result of the genetic algorithm for finding the optimal values.A genetic algorithm-based BP neural network air quality prediction model is designed to improve the deficiency that a single BP neural network optimization is easy to fall into local minima.The prediction accuracy of air quality is improved,and the experimental results verify the validity of the model.(4)By using the SSM framework,a prototype system based on the cloud platform is built for air quality monitoring.According to the storage and display of air quality monitoring data information,the software functions which include public interface module,data storage module,data management module,device management module,and system management module were designed.The results of the Alibaba Cloud deployment test of the air quality monitoring software show that the system modules operate stably and can meet the functional requirements in air quality monitoring.