Gas sensor temperature and humidity drift correction methods, systems, media and equipment

Abstract

A method, system, medium, and device for correcting temperature and humidity drift in gas sensors using least-squares time-series fitting are disclosed. This method involves acquiring continuous and complete data on the sensor's response to air and temperature and humidity during heating and cooling processes. The drift correction method is optimized using the acquired data to achieve the best correction effect for each sensor. The optimized drift correction method is then applied. This significantly reduces the degree of temperature and humidity drift in gas sensors and improves the accuracy of gas component identification based on gas sensors.

Description

Technical Field

[0001] This invention relates to the field of gas sensor drift correction technology, and in particular to a gas sensor temperature and humidity drift correction method, system, medium, and device based on least squares time series fitting. Background Technology

[0002] During long-term operation, electrical equipment is prone to faults such as partial discharge caused by insulation defects, leading to the decomposition of the gaseous insulating medium inside the equipment. Air, as the most common natural insulating gas, is widely used in electrical equipment such as switchgear to ensure insulation performance. When a discharge fault occurs in air-insulated equipment, the solid air insulating medium inside reacts, generating various gaseous decomposition products, such as 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 faults. Therefore, fault diagnosis of air-insulated electrical equipment can be achieved by detecting the composition and content of air discharge decomposition products.

[0003] Air discharge decomposition products can be detected using semiconductor gas sensors. Semiconductor gas sensors offer significant advantages such as small size, easy integration, and fast response, making them promising for applications in power equipment fault diagnosis. However, the actual operating environment of air-insulated power equipment is highly complex, with numerous interfering factors, such as sudden changes in temperature and humidity, strong airflow, and impurity gases. Gas sensors are prone to drift in complex environments, leading to decreased gas identification accuracy and further resulting in biased or erroneous diagnostic results.

[0004] Due to the relatively slow development of gas-sensitive materials, existing materials are unable to avoid drift problems. Furthermore, because the response principle of gas sensors is highly complex, establishing an accurate mathematical model of gas sensor drift is difficult, and optimization can only be achieved through experience. Therefore, it is necessary to address the sensor drift problem from the perspective of correcting the sensor response.

[0005] The information disclosed in the background section is only for enhancing the understanding of the background of this invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method, system, medium, and device for correcting temperature and humidity drift of a gas sensor using least-squares time series fitting. This reduces the impact of environmental temperature and humidity changes on the sensor response, significantly reduces the degree of temperature and humidity drift of the gas sensor, and improves the accuracy of gas component identification based on the gas sensor.

[0007] A method for correcting temperature and humidity drift in gas sensors using least-squares time-series fitting includes:

[0008] S100: Collects gas sensor data on air response and temperature and humidity during continuous heating and cooling processes;

[0009] S200: Optimize the drift correction method based on the temperature and humidity data, including,

[0010] S201: Select several power vectors composed of power combinations of different temperatures and humidity. Power vector for The combination, in which and It refers to the temperature and humidity at a certain moment.

[0011] S202: Select several fitted quantities based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data.

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

[0013] S2031: Select the validity period of the least squares fitting parameters When a set of fitting parameters is corrected Update the data after the sensor response at each time step; select the data length used to calculate the fitting parameters. , are positive integers and satisfy The updated fitting parameters will use the previously continuous parameters. The sensor response and temperature / humidity data at each moment are used to select a transition interval length. During the transition interval, the fitted parameters before and after the update will be used simultaneously for correction.

[0014] S2032: Calculate the least squares fitting parameters, where the length of the temperature and humidity data is... ,make Square brackets indicate rounding down for the uncorrected gas sensor response. and each power vector and fitted quantity The combination of these parameters is used to calculate the least squares fitting parameters following these steps. :

[0015]

[0016]

[0017] S2033: For each and The combination of these factors is used to calculate the corrected sensor response. :

[0018]

[0019] S2034: For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: 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, For the minimum value, similarly, for The maximum value in, It is the minimum value;

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

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

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

[0026] ,

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

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

[0029] In the least squares time series fitting method for correcting temperature and humidity drift in gas sensors, the response characteristics of the gas sensor at a certain moment are as follows: Then choose and , Representing the Sensor response at each moment.

[0030] In the least squares time series fitting method for correcting temperature and humidity drift in gas sensors, the fitted quantity ensures the function The inverse function exists.

[0031] In the aforementioned least-squares time series fitting method for correcting temperature and humidity drift in gas sensors, the effective period is... , .

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

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

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

[0035] S301: Two lengths are Zero matrix queue and ,make , , , The meaning is the total number of data sets currently obtained; matrix queue. The Position Matrix queue The Position Their initial values ​​are all zero matrices.

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

[0037] S303: Calculate the corrected sensor response:

[0038]

[0039] After completion, re-execute S302.

[0040] A least-squares time-series fitting gas sensor temperature and humidity drift correction system includes,

[0041] Acquisition Unit: Acquires gas sensor data to measure air response and temperature and humidity during continuous heating and cooling processes;

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

[0043] Choose several power vectors composed of power combinations of different temperatures and humidity. power vector for The combination, in which and It refers to the temperature and humidity at a certain moment.

[0044] Based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data, several fitted data points are selected. , For sensor response The function,

[0045] For all combinations of power vectors and fitted variables, temperature and humidity drift correction is performed using the aforementioned temperature and humidity data. The correction effect of each combination is evaluated, and the optimal combination is selected.

[0046] The validity period of selecting least squares fitting parameters When a set of fitting parameters is corrected Update the data after the sensor response at each time step; select the data length used to calculate the fitting parameters. , are positive integers and satisfy The updated fitting parameters will use the previously continuous parameters. The sensor response and temperature / humidity data at each moment are used to select a transition interval length. During the transition interval, the fitted parameters before and after the update will be used simultaneously for correction.

[0047] Calculate the least squares fitting parameters, where the length of the temperature and humidity data is [length missing]. ,make Square brackets indicate rounding down for the uncorrected gas sensor response. and each power vector and fitted quantity The combination of these parameters is used to calculate the least squares fitting parameters following these steps. :

[0048]

[0049] ,

[0050] For each and The combination of these factors is used to calculate the corrected sensor response. :

[0051]

[0052] For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: 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, For the minimum value, similarly, for The maximum value in, It is the minimum value.

[0056] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0057] An electronic device, the electronic device comprising:

[0058] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0059] The processor implements the method when executing the program.

[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. The correction method is optimized for a single sensor, so that the correction method can achieve the best correction effect for each sensor. Attached Figure Description

[0061] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0062] In the attached diagram:

[0063] Figure 1 This is a flowchart illustrating a method for correcting temperature and humidity drift in a gas sensor according to the present invention.

[0064] Figure 2 This is an example of the present invention, which provides the response of a sensor and temperature and humidity data during the heating and cooling process;

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

[0066] Figure 4 This is a flowchart of the optimized correction method.

[0067] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0068] Specific embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0069] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0070] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0071] like Figures 1 to 4 As shown, the gas sensor temperature and humidity drift correction method based on least squares time series fitting includes the following steps:

[0072] S100: Collects gas sensor data on air response and temperature and humidity during continuous heating and cooling processes;

[0073] S200: Optimize the drift correction method based on the temperature and humidity data, including,

[0074] S201: Select several power vectors composed of power combinations of different temperatures and humidity. power vector for The combination, in which and It refers to the temperature and humidity at a certain moment.

[0075] S202: Select several fitted quantities based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data.

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

[0077] S2031: Select the validity period of the least squares fitting parameters When a set of fitting parameters is corrected Update the data after the sensor response at each time step; select the data length used to calculate the fitting parameters. , are positive integers and satisfy The updated fitting parameters will use the previously continuous parameters. The sensor response and temperature / humidity data at each moment are used to select a transition interval length. During the transition interval, the fitted parameters before and after the update will be used simultaneously for correction.

[0078] S2032: Calculate the least squares fitting parameters, where the length of the temperature and humidity data is... ,make Square brackets indicate rounding down for the uncorrected gas sensor response. and each power vector and fitted quantity The combination of these parameters is used to calculate the least squares fitting parameters following these steps. :

[0079]

[0080]

[0081] S2033: For each and The combination of these factors is used to calculate the corrected sensor response. :

[0082]

[0083] S2034: For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: 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, For the minimum value, similarly, for The maximum value in, It is the minimum value;

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

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

[0089] In a preferred embodiment of the least-squares time series fitting method for correcting temperature and humidity drift in gas sensors, the power vector... for:

[0090] ,

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

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

[0093] In a preferred embodiment of the least-squares time-series fitting method for correcting temperature and humidity drift in a gas sensor, the response characteristics of the gas sensor at a certain moment are as follows: Then choose and , Representing the Sensor response at each moment.

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

[0095] In a preferred embodiment of the least squares time series fitting method for correcting temperature and humidity drift in gas sensors, the effective period is... , .

[0096] In a preferred embodiment of the least-squares time series fitting method for correcting temperature and humidity drift in gas sensors, the transition interval length is... .

[0097] In a preferred embodiment of the least squares time series fitting method for correcting temperature and humidity drift of a gas sensor, the drift amplitude of the corrected gas sensor response 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 queue and ,make , , , The meaning is the total number of data sets currently obtained; matrix queue. The Position Matrix queue The Position Their initial values ​​are all zero matrices.

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

[0101] S303: Calculate the corrected sensor response:

[0102]

[0103] After completion, re-execute S302.

[0104] In one embodiment, the method includes,

[0105] S100: Collects continuous and complete data on the sensor's response to air and temperature and humidity during heating and cooling processes, 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 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 power combinations of different temperatures and humidity. In the following steps, we will find the power vector that provides the best correction effect. We will select the following power vector. :

[0109]

[0110] Generally speaking, By combining the vectors, we can find the power vector that provides the best correction effect without needing a quadratic or higher power.

[0111] S202: Select several fitted quantities based on the principle of gas detection by the sensor or a certain response characteristic of the sensor. In this embodiment, the gas sensor and the matching resistor are connected in series at 5V, and the sensor response is the voltage division of the matching resistor. Therefore, the sensor response is selected... and the ratio of sensor resistance to matching resistance This is the fitted variable. In later steps, we will find the fitted variable that provides the best correction.

[0112] S203: For all combinations of power vectors and fitted quantities, use the data from step S100 to perform temperature and humidity drift correction, evaluate the correction effect 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 includes the following steps:

[0114] S2031: Select the validity period of the least squares fitting parameters When a set of fitting parameters is corrected After the sensor response at each time point, it should be updated; the data length used to calculate a set of fitting parameters should be selected. ,Pick The updated fitting parameters will use the previously continuous parameters. Sensor response and temperature / humidity data at each moment. A transition interval length is selected. During the transition interval, the fitted parameters before and after the update will be used for correction simultaneously.

[0115] S2032: Calculate the least squares fitting parameters. Let the length of the data collected in S100 be... ,make Square brackets indicate rounding down. For uncorrected sensor response. And each and The combination of these parameters is used to calculate the least squares fitting parameters following these steps. :

[0116]

[0117]

[0118] S2033: For each and The combination of these factors is used to calculate the corrected sensor response. :

[0119]

[0120] S2034: For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: root mean square ratio (RMSR) and drift magnitude ratio (CAR).

[0121]

[0122]

[0123] In the formula for The maximum value in, For the minimum value, similarly, for The maximum value in, Minimum value

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

[0125] Table 1 Differences and The combined correction effect parameters

[0126]

[0127] Therefore, for the sensor in this embodiment, a suitable sensor should be selected. , .

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

[0129] In the method described above, step S300, applying the optimized drift correction method, includes the following steps, as shown in the flowchart below. Figure 4 As shown. All equal signs appearing in this step are interpreted according to their assigned meaning; if no meaning is declared, the meaning of the letters appearing in this step is consistent with that in step S203.

[0130] S301: Declare two lengths of Zero matrix queue and ,make , , . This refers to the total number of data sets currently retrieved. (Matrix queue) The Position Matrix queue The Position Their initial values ​​are all zero matrices.

[0131] S302: If ,make , ;like and for If it is an integer multiple of , then let , After acquiring a new set of data (including sensor response and temperature and humidity data), let... , ;like ,but , ;like ,but . After the value changes, and Reset to zero. , .like Execute S303; otherwise, re-execute S302.

[0132] S303: Calculate the corrected sensor response:

[0133]

[0134] After completion, re-execute S302.

[0135] In this embodiment, the root mean square error and drift amplitude (difference between the maximum and minimum values) 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 in the sensor response exceeds 0.5V, which greatly reduces the impact 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 the drift correction method

[0137]

[0138] A least-squares time-series fitting gas sensor temperature and humidity drift correction system includes,

[0139] Acquisition Unit: Acquires gas sensor data to measure air response and temperature and humidity during continuous heating and cooling processes;

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

[0141] Choose several power vectors composed of power combinations of different temperatures and humidity. power vector for The combination, in which and It refers to the temperature and humidity at a certain moment.

[0142] Based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data, several fitted data points are selected. , For sensor response The function,

[0143] For all combinations of power vectors and fitted variables, temperature and humidity drift correction is performed using the aforementioned temperature and humidity data. The correction effect of each combination is evaluated, and the optimal combination is selected.

[0144] The validity period of selecting least squares fitting parameters When a set of fitting parameters is corrected Update the data after the sensor response at each time step; select the data length used to calculate the fitting parameters. , are positive integers and satisfy The updated fitting parameters will use the previously continuous parameters. The sensor response and temperature / humidity data at each moment are used to select a transition interval length. During the transition interval, the fitted parameters before and after the update will be used simultaneously for correction.

[0145] Calculate the least squares fitting parameters, where the length of the temperature and humidity data is [length missing]. ,make Square brackets indicate rounding down for the uncorrected gas sensor response. and each power vector and fitted quantity The combination of these parameters is used to calculate the least squares fitting parameters following these steps. :

[0146]

[0147] ,

[0148] For each and The combination of these factors is used to calculate the corrected sensor response. :

[0149]

[0150] For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: 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, For the minimum value, similarly, for The maximum value in, It is the minimum value.

[0154] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0155] An electronic device, the electronic device comprising:

[0156] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0157] The processor implements the method when executing the program.

[0158] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A method for correcting temperature and humidity drift in gas sensors using least-squares time-series fitting, characterized in that, Includes the following steps: S100: Collects gas sensor data on air response and temperature and humidity during continuous heating and cooling processes; S200: Optimize the drift correction method based on the temperature and humidity data, including, S201: Select several power vectors composed of power combinations of different temperatures and humidity. Power vector for The combination, in which and It refers to the temperature and humidity at a certain moment. S202: Based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data, select several fitted data points. , For sensor response The function of the gas sensor, the response characteristic of the gas sensor at a certain moment is: Then choose and , Representing the The sensor response at each moment is used, with the partial pressure or resistance of the gas sensor as the fitted quantity. S203: For all combinations of power vectors and fitted variables, use the aforementioned temperature and humidity data to perform temperature and humidity drift correction, evaluate the correction effect of each combination, and select the best combination. S300: Utilizing the aforementioned optimal combination of corrected gas sensors, including, S301: Two lengths are Zero matrix queue and ,make , , , The meaning is the total number of data sets currently obtained; matrix queue The Position Matrix queue The Position The initial values ​​are all zero matrices; S302: If ,make , ;like and for If it is an integer multiple of , then let , After acquiring a new set of data, which includes sensor response and temperature and humidity data, let , ;like ,but , ;like ,but , After the value changes, and Clear to zero, make , ;like If S303 is executed, otherwise S302 is executed again; S303: Calculate the corrected sensor response: ， After completion, re-execute S302.

2. The gas sensor temperature and humidity drift correction method for least squares time series fitting according to claim 1, characterized in that, In S100, the sensor's response characteristics to air and temperature and humidity data are collected, including continuous and complete heating and cooling processes.

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

4. The gas sensor temperature and humidity drift correction method for least squares time series fitting according to claim 1, characterized in that, The fitted quantity ensures the function The inverse function exists.

5. A gas sensor temperature and humidity drift correction system based on least squares time series fitting, characterized in that, It includes, Acquisition Unit: Acquires gas sensor data to measure air response and temperature and humidity during continuous heating and cooling processes; Optimization unit: Optimizes the drift correction method based on the temperature and humidity data, wherein, Choose several power vectors composed of power combinations of different temperatures and humidity. Power vector for The combination, in which and It refers to the temperature and humidity at a certain moment. Based on the principle of gas sensor detection or the correlation between gas sensor response and temperature and humidity data, several fitted data points are selected. , For sensor response The function, For all combinations of power vectors and fitted variables, temperature and humidity drift correction is performed using the aforementioned temperature and humidity data. The correction effect of each combination is evaluated, and the optimal combination is selected. The validity period of selecting least squares fitting parameters When a set of fitting parameters is corrected Update the data after the sensor response at each time step; select the data length used to calculate the fitting parameters. , are positive integers and satisfy The updated fitting parameters will use the previously continuous parameters. The sensor response and temperature / humidity data at each moment are used to select a transition interval length. During the transition interval, the fitted parameters before and after the update will be used simultaneously for correction. Calculate the least squares fitting parameters, where the length of the temperature and humidity data is [length missing]. ,make Square brackets indicate rounding down for the uncorrected gas sensor response. and each power vector and fitted quantity The combination of these parameters is used to calculate the least squares fitting parameters following these steps. : ， ， For each and The combination of these factors is used to calculate the corrected sensor response. : ， For each and The corrected sensor response derived from the combination of these factors Two correction effect parameters are calculated: 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, For the minimum value, similarly, for The maximum value in, It is the minimum value.

6. A computer storage medium, characterized in that, The storage medium includes computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-4.

7. An electronic device, characterized in that, The electronic device includes: Memory, processor, and computer programs stored in memory and executable on the processor, wherein, When the processor executes the program, it implements the method as described in any one of claims 1-4.

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