An intelligent self-calibration method for temperature and humidity in overlapping blocks of gas sensors

Abstract

The present invention relates to the field of gas sensor measurement, in particular to an intelligent self-calibration method for temperature and humidity in overlapping blocks of gas sensors, comprising steps: S1, measuring the resistance of the gas sensor under different temperatures, humidity and concentrations; S2, obtaining the response resistance and The change law of each variable; S3, rough selection of abnormal points; S4, overlap and divide the data according to the change law of data with temperature, humidity and concentration; S5, do multiple linear regression for each data block separately; S6, repeatedly select abnormal points Point and iterative correction; S7, perform multiple linear regression on the corrected data block; S8, establish the temperature and humidity compensation expression of the concentration through inverse operation; S9, import multiple compensation expressions of the same sensor into the microcontroller, and Obtain multiple predicted values, and take the average of all predicted values ​​as the calibration result of the gas sensor; realize the real-time intelligent self-calibration of the temperature and humidity of the gas sensor.

Description

technical field [0001] The invention relates to the field of gas sensor measurement, in particular to an intelligent self-calibration method for temperature and humidity in overlapping blocks of gas sensors. Background technique [0002] The response of gas sensors is easily affected by temperature and humidity, so it is necessary to calibrate the influence of temperature and humidity. However, many gas sensors on the market do not perform temperature and humidity self-calibration. The existing temperature and humidity calibration The methods mainly include multiple linear regression, Gaussian regression, neural network, etc. Due to Gaussian regression and neural network methods, such as the infrared methane temperature and humidity compensation algorithm based on the Gaussian regression process designed by Tian Zhen, and the backpropagation neural network model temperature and humidity model designed by Wang Hairong et al. Humidity compensation methods, etc., often require ...

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Problems solved by technology

[0002] The response of gas sensors is easily affected by temperature and humidity, so it is necessary to calibrate the influence of temperature and humidity. However, many gas sensors on the market do not perform temperature and humidity self-calibration. The existing temperature and humidity calibration The methods mainly include multiple linear regression, Gaussian regression, neural network, etc. Due to Gaussian regression and neural network methods, such as the infrared methane temperature and humidity compensation algorithm based on the Gaussian regression process designed by Tian Zhen, and the backpropagation neural network model temperature and humidity model designed by Wang Hairong et al. Humidity compensation methods, etc., often require complex calculations such as exponential calculations, so they do not meet the real-time requirements for gas sensor detection, and the existing multiple linear regression methods are only suitable for gas sensors whose data is simply affected by temperature and humidity, such as Zhu Hengjun The multiple linear regression compensation algorithm designed by et al. for vehicle exhaust temperature and humidity compensation will produce large errors if it is used for data with complex laws affected by temperature and humidity

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