Design And Implementation Of Quality Check System For Atmos-pheric Particulate Matter Detectors

With the continuous deterioration of the environment and climate,air pollution is becoming more and more severe,and people are paying more and more attention to air pollution.The monitoring of atmospheric particulate matter(PM2.5,PM10)is also becoming more and more widespread,and the use of atmospheric particulate matter detectors is also increasing.The quality issue of atmospheric particulate matter detectors has always been a concern,therefore the quality check of atmospheric particulate matter detectors plays an important role.Although the national standard quality check method for atmospheric particulate matter detectors has high check accuracy,it has low efficiency and requires high check conditions because of manual operation.Faced with the increasing use of atmospheric particulate matter detectors and increasing check pressure,this article provides a digital quality check scheme for assisted check of atmospheric particulate matter detectors.We have designed and developed a quality check system for atmospheric particulate matter detectors.The work of this paper mainly revolves around the following aspects:Building a Kafka cluster between servers,writing the Kafka producer and consumer program to collect and store raw data,and using abnormal data detection and processing methods to preprocess raw data.A digital quality check scheme for atmospheric particulate matter detectors has been adopted,using certified atmospheric particulate matter detectors as equipment reference to achieve quality check of the equipment to be checked.Referring to the performance index requirements and check methods of national standards for atmospheric particulate matter detectors,the performance indicators and check methods applicable to the check scheme in this article have been provided,and it has been used to check and evaluate the quality of the equipment to be checked.After researching and referring to some papers,it has been found that environmental factors can affect the accuracy of atmospheric particulate matter detectors.The decrease of equipment data accuracy may be a comprehensive result which is affected by complex and multifactorial factors in the actual environment[47].This problem is modeled and machine learning algorithms are used to construct a model of data calibration for atmospheric particulate matter detectors,which is used to predict the calibration values of detectors as a reference,to provide remote assisted quality check services.This article adopts two methods to construct a model of data calibration.The first method is based on multiple linear regression to construct a calibration model for atmospheric particulate matter detectors,and the second method is based on BP(back propagation)neural network to construct a calibration model for atmospheric particulate matter detectors.Finally,based on the above research and work,a quality check system for the atmospheric particulate matter detectors is designed and developed using the development method of Spring Boot and Vue.js front and rear end separation,which is used for the quality check of the atmospheric particulate matter detectors.At the same time,the functions of data collection and storage,equipment management,data import and export,data preprocessing,model construction and data visualization are realized,which has some practical application value.