Gas sensor temperature and humidity drift correction method, system, medium and equipment

Abstract

The invention discloses a gas sensor temperature and humidity drift correction method and system based on least square time series fitting, a medium and equipment, and the method comprises the steps: collecting continuous response and temperature and humidity data of a sensor to air in continuous and complete heating and cooling processes; the drift correction method is optimized by using the data acquired in the previous step, so that each sensor achieves the optimal correction effect; and applying the drift correction method optimized in the previous step. The temperature and humidity drift degree of the gas sensor can be remarkably reduced, and the gas component recognition precision based on the gas sensor is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas sensor drift correction, and in particular to a least squares time series fitting gas sensor temperature and humidity drift correction method, system, medium and equipment. Background Art

[0002] During long-term operation, power equipment is prone to partial discharge and other faults caused by insulation defects, which leads to the decomposition of the gas insulation medium inside the equipment. As the most common natural insulating gas, air is widely used in power equipment such as switchgear to ensure insulation performance. When a discharge fault occurs in air-insulated equipment, the solid air insulation medium inside it reacts to generate a variety of gas decomposition products represented by NO2, O3, and CO. Studies have shown that the composition and content of air discharge decomposition products are closely related to the type and severity of equipment failure. Therefore, the fault diagnosis of air-insulated power equipment can be achieved by detecting the composition and content of air discharge decomposition products.

[0003] Air discharge decomposition products can be detected by semiconductor gas sensors. Semiconductor gas sensors have significant advantages such as small size, easy integration, and fast response, and have broad application prospects in power equipment fault diagnosis. However, the actual working environment of air-insulated power equipment is very complex, with many interference factors, such as sudden changes in temperature and humidity, strong airflow, and impurity gases. Gas sensors are prone to drift problems in complex environments, resulting in reduced gas identification accuracy, which further leads to deviations or errors in diagnostic results.

[0004] Due to the relatively lagging development of gas-sensitive materials, it is difficult for existing materials to avoid drift problems. In addition, since the response principle of gas sensors is very complex, it is difficult to establish an accurate mathematical model of gas sensor drift, and it can only be optimized with the help of experience. Therefore, it is necessary to solve the sensor drift problem from the perspective of correcting the sensor response.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0006] The present invention provides a least squares time series fitting gas sensor temperature and humidity drift correction method, system, medium and equipment, which reduce the influence of environmental temperature and humidity changes on sensor response, significantly reduce the degree of temperature and humidity drift of the gas sensor, and improve the gas component identification accuracy based on the gas sensor.

[0007] A least squares time series fitting method for gas sensor temperature and humidity drift correction includes:

[0008] S100: Collect the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process;

[0009] S200: Optimizing a drift correction method based on the temperature and humidity data, which includes:

[0010] S201: Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment,

[0011] S202: Selecting a number of fitted quantities according to the principle of gas sensor detecting gas or the correlation between the response of the gas sensor and the temperature and humidity data,

[0012] S203: For all combinations of power vectors and fitted quantities, use the temperature and humidity data to make temperature and humidity drift corrections, evaluate the correction effects of each combination and select the best combination, wherein:

[0013] S2031: Validity period of selected least squares fitting parameters , when a set of fitting parameters corrects After the sensor response at a certain moment is obtained, it is updated; the data length used to calculate the fitting parameters is selected , is a positive integer and satisfies , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment, select a transition interval length, the transition interval length , the fitting parameters before and after the update will be used for correction at the same time in the transition interval.

[0014] S2032: Calculate the least squares fitting parameters. The length of the temperature and humidity data is ,make , square brackets indicate rounding down, for the uncorrected gas sensor response And every power vector and the fitted quantity The least squares fitting parameters are calculated according to the following steps: :

[0015]

[0016]

[0017] S2033: For each and The corrected sensor response is calculated by combining :

[0018]

[0019] S2034: For each and The corrected sensor response is obtained by combining , calculate two correction effect parameters: root mean square ratio RMSR and drift amplitude ratio CAR. The smaller the correction effect parameter, the better the correction effect.

[0020]

[0021]

[0022] In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value;

[0023] S300: Apply the optimal combination to correct the gas sensor.

[0024] In the least squares time series fitting gas sensor temperature and humidity drift correction method, in S100, the response characteristics and temperature and humidity data of the sensor to the air during the continuous and complete heating process and cooling process are collected.

[0025] In the least squares time series fitting method for gas sensor temperature and humidity drift correction, the power vector for:

[0026] ,

[0027] , which is due to The power vector formed by the temperature and humidity data at each moment.

[0028] In the least squares time series fitting method for correcting temperature and humidity drift of a gas sensor, the partial pressure or resistance of the gas sensor is used as the fitted quantity.

[0029] In the least squares time series fitting gas sensor temperature and humidity drift correction method, the response characteristic of the gas sensor at a certain moment is , then select and , Representative The sensor response at a given moment.

[0030] In the least squares time series fitting method for gas sensor temperature and humidity drift correction, the fitted quantity ensures that the function The inverse function of exists.

[0031] In the least squares time series fitting gas sensor temperature and humidity drift correction method, the validity period is , .

[0032] In the least squares time series fitting method for gas sensor temperature and humidity drift correction, the transition interval length .

[0033] In the least squares time series fitting gas sensor temperature and humidity drift correction method, the drift amplitude of the gas sensor response after correction does not exceed 0.5V.

[0034] In the least squares time series fitting gas sensor temperature and humidity drift correction method, S300: applying the optimal combination to correct the gas sensor includes:

[0035] S301: Two lengths are Zero matrix array of and ,make , , , The meaning is the total number of data groups currently obtained, the matrix queue No. Position , matrix queue No. Position , their initial values ​​are all zero matrices,

[0036] S302: If ,make , ;like and for An integer multiple of , After obtaining a new set of data, which includes sensor response and temperature and humidity data, let , ;like ,but , ;like ,but , After the value of changes, and Clear to zero, , .like , execute S303, otherwise re-execute S302,

[0037] S303: Calculate the corrected sensor response:

[0038]

[0039] After completion, execute S302 again.

[0040] A gas sensor temperature and humidity drift correction system based on least squares time series fitting includes:

[0041] Collection unit: collects the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process;

[0042] Optimizing unit: optimizing the drift correction method based on the temperature and humidity data, wherein:

[0043] Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment,

[0044] Select several fitted parameters based on the principle of gas sensor detection or the correlation between the response of the gas sensor and the temperature and humidity data. , Sensor response The function of

[0045] For all combinations of power vectors and fitted quantities, the temperature and humidity data are used to make temperature and humidity drift corrections, the correction effects of each combination are evaluated, and the best combination is selected, where:

[0046] Validity period of the selected least squares fitting parameters , when a set of fitting parameters corrects After the sensor response at a certain moment is obtained, it is updated; the data length used to calculate the fitting parameters is selected , is a positive integer and satisfies , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment, select a transition interval length, the transition interval length , the fitting parameters before and after the update will be used for correction at the same time in the transition interval.

[0047] Calculate the least squares fitting parameters. The length of the temperature and humidity data is ,make , square brackets indicate rounding down, for the uncorrected gas sensor response And every power vector and the fitted quantity The least squares fitting parameters are calculated according to the following steps: :

[0048]

[0049] ,

[0050] For each and The corrected sensor response is calculated by combining :

[0051]

[0052] For each and The corrected sensor response is obtained by combining , calculate two correction effect parameters: root mean square ratio RMSR and drift amplitude ratio CAR. The smaller the correction effect parameter, the better the correction effect.

[0053]

[0054]

[0055] In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value.

[0056] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described.

[0057] An electronic device, comprising:

[0058] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0059] When the processor executes the program, the method described is implemented.

[0060] Compared with the prior art, the present invention has the following advantages: the present invention realizes the temperature and humidity drift correction of the gas sensor, so that the sensor can give a relatively stable response when the temperature and humidity change, and optimizes the correction method for a single sensor so that the correction method can achieve the best correction effect for each sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work. Moreover, the same reference numerals are used to represent the same components throughout the drawings.

[0062] In the attached picture:

[0063] Figure 1 It is a flow chart of a method for correcting temperature and humidity drift of a gas sensor according to the present invention;

[0064] Figure 2 It is the response and temperature and humidity data of a sensor during the heating and cooling process provided by an example of the present invention;

[0065] Figure 3 is a comparison chart of the corrected sensor response before and after optimization;

[0066] Figure 4 is a flow chart of the revised method after applying the optimization.

[0067] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0068] The specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0069] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0070] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0071] like Figures 1 to 4 As shown in FIG. 1 , the least squares time series fitting method for correcting the temperature and humidity drift of a gas sensor includes the following steps:

[0072] S100: Collect the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process;

[0073] S200: Optimizing a drift correction method based on the temperature and humidity data, which includes:

[0074] S201: Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment,

[0075] S202: Selecting a number of fitted quantities according to the principle of gas sensor detecting gas or the correlation between the response of the gas sensor and the temperature and humidity data,

[0076] S203: For all combinations of power vectors and fitted quantities, use the temperature and humidity data to make temperature and humidity drift corrections, evaluate the correction effects of each combination and select the best combination, wherein:

[0077] S2031: Validity period of selected least squares fitting parameters , when a set of fitting parameters corrects After the sensor response at a certain moment is obtained, it is updated; the data length used to calculate the fitting parameters is selected , is a positive integer and satisfies , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment, select a transition interval length, the transition interval length , the fitting parameters before and after the update will be used for correction at the same time in the transition interval.

[0078] S2032: Calculate the least squares fitting parameters. The length of the temperature and humidity data is ,make , square brackets indicate rounding down, for the uncorrected gas sensor response And every power vector and the fitted quantity The least squares fitting parameters are calculated according to the following steps: :

[0079]

[0080]

[0081] S2033: For each and The corrected sensor response is calculated by combining :

[0082]

[0083] S2034: For each and The corrected sensor response is obtained by combining , calculate two correction effect parameters: root mean square ratio RMSR and drift amplitude ratio CAR. The smaller the correction effect parameter, the better the correction effect.

[0084]

[0085]

[0086] In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value;

[0087] S300: Apply the optimal combination to correct the gas sensor.

[0088] In a preferred implementation of the least squares time series fitting method for correcting temperature and humidity drift of a gas sensor, in S100, the response characteristics and temperature and humidity data of the sensor to the air during the continuous and complete heating process and cooling process are collected.

[0089] In the preferred embodiment of the least squares time series fitting gas sensor temperature and humidity drift correction method, the power vector for:

[0090] ,

[0091] , which is due to The power vector formed by the temperature and humidity data at each moment.

[0092] In a preferred implementation of the least squares time series fitting method for correcting temperature and humidity drift of a gas sensor, the partial pressure or resistance of the gas sensor is used as the fitted quantity.

[0093] In a preferred embodiment of the least squares time series fitting gas sensor temperature and humidity drift correction method, the response characteristic of the gas sensor at a certain moment is , then select and , Representative The sensor response at a given moment.

[0094] In the preferred embodiment of the least squares time series fitting method for correcting the temperature and humidity drift of a gas sensor, the fitted quantity ensures that the function The inverse function of exists.

[0095] In the preferred embodiment of the least squares time series fitting gas sensor temperature and humidity drift correction method, the validity period is , .

[0096] In the preferred embodiment of the least squares time series fitting gas sensor temperature and humidity drift correction method, the transition interval length .

[0097] In a preferred implementation of the least squares time series fitting method for correcting temperature and humidity drift of a gas sensor, the drift amplitude of the gas sensor response after correction does not exceed 0.5V.

[0098] In a preferred embodiment of the least squares time series fitting gas sensor temperature and humidity drift correction method, S300: applying the optimal combination to correct the gas sensor includes:

[0099] S301: Two lengths are Zero matrix array of and ,make , , , The meaning is the total number of data groups currently obtained, the matrix queue No. Position , matrix queue No. Position , their initial values ​​are all zero matrices,

[0100] S302: If ,make , ;like and for An integer multiple of , After obtaining a new set of data, which includes sensor response and temperature and humidity data, let , ;like ,but , ;like ,but , After the value of changes, and Clear to zero, , .like , execute S303, otherwise re-execute S302,

[0101] S303: Calculate the corrected sensor response:

[0102]

[0103] After completion, execute S302 again.

[0104] In one embodiment, the method includes,

[0105] S100: Collect continuous and complete data on the sensor's response to the air and temperature and humidity during the heating and cooling process, such as Figure 2 As shown;

[0106] S200: Optimize the drift correction method using the data collected in the previous step, so that each sensor achieves its own best correction effect;

[0107] In the method, the optimization of the drift correction method for a single sensor in step S200 includes the following steps:

[0108] S201: Select several power vectors composed of different power combinations of temperature and humidity , in the following steps, we will find the power vector with the best correction effect. Select the following power vector :

[0109]

[0110] Generally speaking, By combining them, we can find the power vector that has the best correction effect without the need for quadratic or higher powers.

[0111] S202: Select several fitted quantities according to the principle of gas detection by the sensor or a certain response characteristic of the sensor. In this embodiment, the gas sensor is connected in series with the matching resistor at a voltage of 5V, and the sensor response is the voltage division of the matching resistor. and the ratio of sensor resistance to matching resistance is the fitted quantity. In the following steps, the fitted quantity with the best correction effect will be found.

[0112] S203: For all combinations of power vectors and fitted quantities, use the data in step S100 to make temperature and humidity drift corrections, evaluate the correction effects of each combination and select the best combination.

[0113] In step S203, calculating the sensor response after temperature and humidity drift correction and evaluating the correction effect include the following steps:

[0114] S2031: Validity period of selected least squares fitting parameters , when a set of fitting parameters corrects The sensor response at each moment should be updated; the data length used to calculate a set of fitting parameters is selected ,Pick , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment. Select a transition interval length , in the transition interval, the fitting parameters before and after the update will be used for correction at the same time.

[0115] S2032: Calculate the least squares fitting parameters. Note that the length of the data collected in S100 is ,make , square brackets indicate rounding down. For the uncorrected sensor response and every and The least squares fitting parameters are calculated according to the following steps: :

[0116]

[0117]

[0118] S2033: For each and The corrected sensor response is calculated by combining :

[0119]

[0120] S2034: For each and The corrected sensor response is obtained by combining , two correction effect parameters are calculated: root mean square ratio (RMSR) and drift amplitude ratio (CAR).

[0121]

[0122]

[0123] In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value

[0124] The smaller the two parameters are, the better the correction effect is. and The correction effect parameters of the combination are selected to select the combination with the best correction effect. In this embodiment, several combinations with better correction effects are shown in Table 1 below:

[0125] Table 1 Different and The modified effect parameters of the combination

[0126]

[0127] Therefore, for the sensor in this embodiment, we should choose , .

[0128] S300: Apply the drift correction method optimized in the previous step. In this embodiment, the sensor response before and after correction is as follows: Figure 3 shown.

[0129] In the method, step S300 uses the optimized drift correction method, which includes the following steps. The flowchart is as follows: Figure 4 All equal signs appearing in this step are interpreted as assigned meanings; if the meaning is not declared, the meaning of the letters appearing in this step is consistent with that in step S203.

[0130] S301: Declare two lengths Zero matrix array of and ,make , , . The meaning is the total number of data groups currently obtained. Matrix queue No. Position , matrix queue No. Position , their initial values ​​are all zero matrices.

[0131] S302: If ,make , ;like and for An integer multiple of , After obtaining a new set of data (including sensor response and temperature and humidity data), let , ;like ,but , ;like ,but . After the value of changes, and Clear. , .like , execute S303, otherwise re-execute S302.

[0132] S303: Calculate the corrected sensor response:

[0133]

[0134] After completion, execute S302 again.

[0135] In this embodiment, the root mean square error and drift amplitude (difference between the maximum value and the minimum value) before and after correction are shown in Table 2. The drift amplitude of the sensor response after correction does not exceed 0.5V. It can be considered that the target gas is detected when the change of the sensor response exceeds 0.5V, which greatly reduces the influence of temperature and humidity changes on the detection of the target gas and avoids false alarms.

[0136] Table 2 Comparison of sensor response before and after applying drift correction method

[0137]

[0138] A gas sensor temperature and humidity drift correction system based on least squares time series fitting includes:

[0139] Collection unit: collects the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process;

[0140] Optimizing unit: optimizing the drift correction method based on the temperature and humidity data, wherein:

[0141] Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment,

[0142] Select several fitted parameters based on the principle of gas sensor detection or the correlation between the response of the gas sensor and the temperature and humidity data. , Sensor response The function of

[0143] For all combinations of power vectors and fitted quantities, the temperature and humidity data are used to make temperature and humidity drift corrections, the correction effects of each combination are evaluated, and the best combination is selected, where:

[0144] Validity period of the selected least squares fitting parameters , when a set of fitting parameters corrects After the sensor response at a certain moment is obtained, it is updated; the data length used to calculate the fitting parameters is selected , is a positive integer and satisfies , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment, select a transition interval length, the transition interval length , the fitting parameters before and after the update will be used for correction at the same time in the transition interval.

[0145] Calculate the least squares fitting parameters. The length of the temperature and humidity data is ,make , square brackets indicate rounding down, for the uncorrected gas sensor response And every power vector and the fitted quantity The least squares fitting parameters are calculated according to the following steps: :

[0146]

[0147] ,

[0148] For each and The corrected sensor response is calculated by combining :

[0149]

[0150] For each and The corrected sensor response is obtained by combining , calculate two correction effect parameters: root mean square ratio RMSR and drift amplitude ratio CAR. The smaller the correction effect parameter, the better the correction effect.

[0151]

[0152]

[0153] In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value.

[0154] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described.

[0155] An electronic device, comprising:

[0156] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0157] When the processor executes the program, the method described is implemented.

[0158] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A gas sensor temperature and humidity drift correction method based on least squares time series fitting, characterized in that: The steps include: S100: Collect the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process; S200: Optimizing a drift correction method based on the temperature and humidity data, which includes: S201: Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment, S202: Selecting a number of fitted parameters according to the principle of gas sensor detection of gas or the correlation between the response of the gas sensor and the temperature and humidity data , Sensor response The function of S203: For all combinations of power vectors and fitted quantities, use the temperature and humidity data to make temperature and humidity drift corrections, evaluate the correction effects of each combination and select the best combination. S300: Apply the optimal combination to correct the gas sensor.

2. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1 is characterized in that: Preferably, in S100, the response characteristics and temperature and humidity data of the sensor to the air during the continuous and complete heating process and cooling process are collected.

3. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1 is characterized in that: Power Vector for: ， , which is due to The power vector formed by the temperature and humidity data at each moment.

4. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1, characterized in that: The partial pressure or resistance of the gas sensor is used as the fitted quantity.

5. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1, characterized in that: The response characteristic of the gas sensor at a certain moment is , then select and , Representative The sensor response at a given moment.

6. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1, characterized in that: The fitted quantity ensures that the function The inverse function of exists.

7. The method for correcting temperature and humidity drift of a gas sensor by least squares time series fitting according to claim 1, characterized in that S3 00: Apply the best combination of corrections to the gas sensor including, S301: Two lengths are Zero matrix array of and ,make , , , The meaning is the total number of data groups currently obtained, the matrix queue No. Position , matrix queue No. Position , their initial values ​​are all zero matrices, S302: If ,make , ;like and for An integer multiple of , , after obtaining a new set of data, which includes sensor response and temperature and humidity data, let , ;like ,but , ;like ,but , After the value of changes, and Clear to zero, , ,like , execute S303, otherwise re-execute S302, S303: Calculate the corrected sensor response: ， After completion, execute S302 again.

8. A gas sensor temperature and humidity drift correction system based on least squares time series fitting, characterized in that: These include, Collection unit: collects the response and temperature and humidity data of the gas sensor to the air during the continuous heating and cooling process; Optimizing unit: optimizing the drift correction method based on the temperature and humidity data, wherein: Select several power vectors composed of different power combinations of temperature and humidity , the power vector for A combination of and is the temperature and humidity at a certain moment, Select several fitted parameters based on the principle of gas sensor detection or the correlation between the response of the gas sensor and the temperature and humidity data. , Sensor response The function of For all combinations of power vectors and fitted quantities, the temperature and humidity data are used to make temperature and humidity drift corrections, the correction effects of each combination are evaluated, and the best combination is selected, where: Validity period of the selected least squares fitting parameters , when a set of fitting parameters corrects After the sensor response at each moment is obtained, it is updated; the data length used to calculate the fitting parameters is selected , is a positive integer and satisfies , updating the fitting parameters will use the previous continuous The sensor response and temperature and humidity data at each moment, select a transition interval length, the transition interval length , the fitting parameters before and after the update will be used for correction at the same time in the transition interval. Calculate the least squares fitting parameters. The length of the temperature and humidity data is ,make , square brackets indicate rounding down, for the uncorrected gas sensor response And every power vector and the fitted quantity The least squares fitting parameters are calculated according to the following steps: : ， ， For each and The corrected sensor response is calculated by combining : ， For each and The corrected sensor response is obtained by combining , calculate two correction effect parameters: root mean square ratio RMSR and drift amplitude ratio CAR. The smaller the correction effect parameter, the better the correction effect. ， ， In the formula for The maximum value in is the minimum value, and similarly, for The maximum value in is the minimum value.

9. A computer storage medium, characterized in that: The storage medium includes computer instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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