Novel calibration method for gas sensors and particulate matter sensors

Abstract

The invention discloses a novel calibration method and a system for gas sensors and particulate matter sensors. The method comprises the steps of acquiring cross-over experiment data of parameters with influence on measurement accuracy of the sensors, and inputting a large number of laboratory data of different parameters to an established calibration model, so as to obtain linear and nonlinear calibration parameters of different sensors. The calibration model adopts a universal artificial-intelligence big data algorithm platform in view of different parameters, linear / nonlinear cross corresponding curves, containing different parameters, of temperature, humidity, wind direction, wind speed, atmospheric pressure, interference gas and sensor element aging of the sensors can be automatically extracted and obtained from experimental tank cross-over experiment parameter data through establishing relatively-deep artificial neural network or intelligent gene algorithm models, and random complicated nonlinear calibration model can be mapped, so that the robustness and adaptability are very high.

Description

technical field [0001] The invention relates to a new calibration method for a gas sensor and a particle sensor. Background technique [0002] Current technology exists for gas sensors (SO 2 , NO 2 , CO, O 3 , VOC, NH 3 , HCHO, H 2 S) and particle sensor (PM 2.5 , PM 10 ) During long-term use, the detection result drifts and the detection limit drops due to temperature, humidity, cross interference of other gases or factors, and sensor element aging. [0003] Because gas sensors and particle sensors are susceptible to temperature, humidity and other gases, the detection results will directly or indirectly cause serious drift, resulting in invalid detection data. At this time, the sensors need to be calibrated effectively. Contents of the invention [0004] In view of the above problems, the present invention provides a new calibration method for gas sensors and particle sensors that improves the stability of the sensors and the validity of the detection results. ...

AI Technical Summary

Problems solved by technology

[0003] Because gas sensors and particle sensors are susceptible to temperature, humidity and other gases, the detection results will directly or indirectly cause serious drift, resulting in invalid detection data. At this time, the sensors need to be calibrated effectively

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