Cloud data joint debugging calibration method based on machine learning algorithm

A technology of machine learning and calibration methods, applied in machine learning, instrumentation, computing, etc., can solve problems such as monitoring numerical fluctuations, air pollution, sensor characteristics drift, etc., to automatically correct drift and environmental interference, ensure availability, and solve data problems The effect of data offset

Inactive Publication Date: 2018-08-31
中国科学院计算技术研究所济宁分所 +1
View PDF5 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cloud data joint debugging calibration method based on machine learning algorithm
  • Cloud data joint debugging calibration method based on machine learning algorithm
  • Cloud data joint debugging calibration method based on machine learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0031] Such as Figure 1-3 As shown, the cloud data joint debugging and calibration method based on machine learning algorithm of the present invention includes a gridded air monitoring platform, a cloud server and an equipment management system, and the gridded air monitoring platform is connected with the cloud server and the equipment management system respectively. Connecting to realize data communication, the method includes the following steps:

[0032] S1: The gridded air monitoring platform provides air pollutants and meteorological data collected by air quality monitoring micro-stations and national standard monitoring stations in the grid, and transmits the collected data to the Hbase database in the cloud server;

[0033] S2: The cloud server reads the data of the air quality monitoring micro-station and the national standard monitoring station in the Hbase database in the cloud server, and compares the data consistency to determine whether the data of the air quali...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01D18/00G01D21/02G06N99/00
CPCG01D18/00G01D21/02G06N20/00
Inventor 陈援非周丹丹孟筠旺孙亚洲杨培帅
Owner 中国科学院计算技术研究所济宁分所
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap