基于大数据分析的大气网格化监测质控技术研究

基于对现阶段中国环境空气的污染特征及监测手段进行分析,研究了如何采用传感器技术进行大气污染防治网格化监测。对标物校准、训化校准、自适应校准、传递校准等校准质控技术进行了充分研究并基于此建立了自主学习神经网络算法的校准体系。为解决传感器应用过程中零点漂移、温度和湿度漂移、时间漂移等问题,利用大数据、基因算法成功开发出了智能数据修正模型,实现了低成本、高稳定、具有相当精度且可自动化运行的网格化监测体系。

Research on Quality Control of Atmospheric Grid Monitoring Based on Large Data Analysis

The pollution characteristics and monitoring methods of environmental air in China are analyzed, and how to adopt the sensor technology to monitor the air pollution prevention and control is studied. The calibration quality control technology, such as calibration, acclimation, adaptive calibration and transfer calibration, has been fully researched and a calibration system for autonomous learning neural network algorithm is established. In order to solve the problem of zero drift, temperature and humidity drift and time drift in the process of sensor application, this paper develops an intelligent data correction model using large data and genetic algorithm, and achieves low cost, high stability, accuracy and automatic operation of the grid monitoring system.