Research on Data Processing Method of Air Quality Monitoring System

In order to improve the efficiency of air quality monitoring system processing and analyzing large amounts of data, an air quality monitoring system based on BP neural network is constructed to intelligently process the data collected in real time. A new data processing method integrating ROLAP technology and BP neural network was proposed. The method uses the ROLAP system to combine multiple relational databases into multi-dimensional star structures. The BP neural network accurately classify air quality according to AQI to the corresponding level. The uncached data is classified into cache cells with the lowest degree of dissimilarity and then followed by a linear regression function to monitor the rate of change. The experimental results show that two technologies are successfully integrated, and the data storage and classification dimensions are increased. Compared with the traditional device, the information query rate and accuracy are improved, and the average response time of the system is reduced from 60ms to less than 20ms. The sensitivity of the system is improved by monitoring the data change rate. The function of warning abnormal data was achieved.