Intelligent self-calibration method for temperature and humidity of overlapped blocks of gas sensor

Abstract

The invention relates to the field of gas sensor measurement, in particular to a gas sensor overlapped block temperature and humidity intelligent self-calibration method, which comprises the following steps: S1, measuring the resistance of a gas sensor under different temperature, humidity and concentration conditions; s2, acquiring a change rule of response resistance and each variable; s3, roughing abnormal points; s4, overlapping and blocking the data according to the change rule of the data along with temperature, humidity and concentration; s5, performing multiple linear regression on each data block; s6, repeatedly and finely selecting abnormal points and iteratively correcting the abnormal points; s7, performing multiple linear regression on the corrected data block; s8, establishing a temperature and humidity compensation expression of the concentration through inverse operation; s9, importing a plurality of compensation expressions of the same kind of sensors into the microcontroller, obtaining a plurality of predicted values, and taking the average value of all the predicted values as the calibration result of the gas sensor. According to the method, the temperature and humidity real-time intelligent self-calibration of the gas sensor is realized.

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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