Cloud data joint debugging calibration method based on machine learning algorithm

Abstract

The invention discloses a cloud data joint debugging calibration method based on a machine learning algorithm, and belongs to the air monitoring technical field; a meshing air monitoring platform, a cloud server and an equipment management system are provided; the meshing air monitoring platform provides air pollutant and meteorology data gathered by meshing air quality monitoring micro-stations and national standard monitoring stations, and sends the gathered data to a Hbase database in the cloud server; the cloud server reads the air quality monitoring micro-station and national standard monitoring station gathered data in the Hbase database in the cloud server, carries out data consistency contrast, and determines whether the air quality monitoring micro station data is deviated or not;if yes, a data calibration model can calibrate the data and outputs calibrated parameters; the cloud server can send the calibrated parameters to the equipment management system via a communication unit, thus effectively solving the air quality monitoring micro station data deviation problems; the cloud automatic calibration can be realized, thus ensuring the monitoring data quality usability.

Description

technical field [0001] The invention relates to a cloud data joint debugging and calibration method based on a machine learning algorithm, and belongs to the technical field of air monitoring. Background technique [0002] In recent years, air pollution has become increasingly serious, especially PM2.5 / PM10 in the air, and the concentration of SO2 / CO / NO2 / O3 in the air has increased, which seriously threatens people's health and daily life. In view of the air quality sensor monitoring technology of the Environmental Protection Bureau Inherent monitoring characteristics, such as the serious drift of its sensing characteristics over time, lead to large fluctuations in the monitoring values ​​of previous calibrations, and the workload of artificial discovery and recalibration is undoubtedly huge. Contents of the invention [0003] The purpose of the present invention is to provide a cloud data joint calibration method based on a machine learning algorithm, which can effectivel...

AI Technical Summary

Problems solved by technology

[0002] In recent years, air pollution has become increasingly serious, especially PM2.5 / PM10 in the air, and the concentration of SO2 / CO / NO2 / O3 in the air has increased, which seriously threatens people's health and daily life. In view of the air quality sensor monitoring technology of the Environmental Protection Bureau Inherent monitoring characteristics, such as the serious drift of its sensing characteristics over time, lead to large fluctuations in the monitoring values ​​of the previous calibration, and the workload of artificial discovery and recalibration is undoubtedly huge

Images