A drift compensation method and system for a MEMS hydrogen sensor

Abstract

The present application relates to the technical field of zero-point drift compensation, and specifically to a drift compensation method and system for a MEMS hydrogen sensor. The method comprises: placing a MEMS hydrogen sensor in a constant temperature and humidity test chamber, collecting the output voltage of the MEMS hydrogen sensor at each preset temperature and at each moment; determining a first distribution characteristic value and a second distribution characteristic value of the output voltage at each preset temperature and at each moment; obtaining a rejection coefficient for the output voltage at each preset temperature and at each moment; using the rejection coefficient to remove noise data from all output voltages collected at each preset temperature; fitting a nonlinear multivariate regression model based on the preset temperature and the humidity corresponding to the output voltage at each moment, and combining the zero-point voltage of the MEMS hydrogen sensor to drift compensate the output voltage of the MEMS hydrogen sensor. This improves the drift compensation accuracy of the MEMS hydrogen sensor.

Description

Technical Field

[0001] This application relates to the field of zero-point drift compensation technology, specifically to a drift compensation method and system for a MEMS hydrogen sensor. Background Art

[0002] MEMS hydrogen sensors are hydrogen sensors manufactured using microprocessor-based electronic systems (MEMS) technology, offering advantages such as small size, low power consumption, and fast response recovery. However, during use, the zero-point value of the hydrogen sensor's output voltage typically drifts due to changes in ambient temperature and humidity. In other words, even without hydrogen input, the output voltage fluctuates. This drift reduces the sensor's measurement accuracy and affects the determination of hydrogen concentration; therefore, drift compensation is necessary for MEMS hydrogen sensors.

[0003] When performing drift compensation on hydrogen sensors, it is typically necessary to establish a fitting model that reflects the relationship between the zero-point value of the hydrogen sensor's output voltage and the ambient temperature and humidity. This model can predict the variation of the zero-point value of the hydrogen sensor's output voltage under different temperature and humidity conditions, thereby correcting the actual output voltage and achieving drift compensation. However, during the data acquisition process of the hydrogen sensor's output voltage, the acquired output voltage is often introduced into the data due to random errors inherent in the hydrogen sensor itself, electromagnetic interference in the measurement environment, and errors during data transmission. This noise affects the accuracy of the final fitting model, causing it to fail to accurately reflect the actual relationship between the zero-point value of the hydrogen sensor's output voltage and the ambient temperature and humidity, thus reducing the effectiveness of drift compensation. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a drift compensation method and system for a MEMS hydrogen sensor, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a drift compensation method for a MEMS hydrogen sensor, the method comprising the following steps:

[0006] The MEMS hydrogen sensor was placed in a constant temperature and humidity test chamber, and the output voltage of the MEMS hydrogen sensor at each preset temperature and time was collected.

[0007] The difference between the output voltage at each time and the output voltage at neighboring times under each preset temperature is analyzed. Combined with the rated output voltage of the MEMS hydrogen sensor, the first distribution characteristic value of the output voltage at each time under each preset temperature is determined.

[0008] Analyze the difference between the output voltage at each moment under any preset temperature and the output voltage with the closest humidity under other preset temperatures, and combine it with the rated output voltage to determine the second distribution characteristic value of the output voltage at each moment under any preset temperature;

[0009] By combining the first distribution feature value and the second distribution feature value, the elimination coefficient of the output voltage at each time at each preset temperature is determined; the elimination coefficient is used to remove noise data from all output voltages collected at each preset temperature.

[0010] The output voltages after removing noise from all preset temperatures are used to form a sample dataset. By combining the preset temperature with the ambient humidity corresponding to the output voltage at each time point, a nonlinear multiple regression model is fitted. Combined with the zero-point voltage of the MEMS hydrogen sensor, the drift compensation of the output voltage of the MEMS hydrogen sensor is performed.

[0011] In one embodiment, at all preset temperatures, the humidity in the constant temperature and humidity test chamber changes continuously with the same preset starting value, the same rate of humidity change, and the same preset ending value.

[0012] In one embodiment, determining the first distribution feature value includes:

[0013] The average difference between the output voltage at each time point and the output voltage at all adjacent times under each preset temperature is calculated, and the first distribution characteristic value is determined by combining it with the rated maximum output voltage of the MEMS hydrogen sensor.

[0014] In one embodiment, the first distribution characteristic value is the ratio of the mean to the rated maximum output voltage.

[0015] In one embodiment, determining the second distribution characteristic value includes:

[0016] The difference between the output voltage at each moment under any preset temperature and the output voltage with the closest humidity under other preset temperatures is denoted as the first difference. The ratio of the first difference at each moment under any preset temperature to the rated maximum output voltage of the MEMS hydrogen sensor is calculated and denoted as the first ratio.

[0017] Calculate the average difference between the first ratio at each time point under any preset temperature and the first ratio at all times under any preset temperature;

[0018] The second distribution characteristic value is the mean of all the average values ​​corresponding to each time point at any preset temperature.

[0019] In one embodiment, the elimination coefficient is the mean of the first distribution feature value and the second distribution feature value.

[0020] In one embodiment, removing noise data from all output voltages collected at each preset temperature using the rejection coefficient includes:

[0021] The output voltage at all times under each preset temperature is sorted in descending order by the elimination coefficients, and the output voltages corresponding to the first preset percentage elimination coefficients are treated as noise data and removed.

[0022] In one embodiment, the fitting of the nonlinear multiple regression model includes: taking the output voltage at each moment in the sample dataset as the dependent variable, and taking the temperature and humidity in the constant temperature and humidity test chamber corresponding to the output voltage at each moment in the sample dataset as independent variables, and obtaining the nonlinear multiple regression model through nonlinear fitting.

[0023] In one embodiment, the drift compensation of the output voltage of the MEMS hydrogen sensor includes:

[0024] The ambient temperature and humidity at the time corresponding to the actual output voltage of the MEMS hydrogen sensor are used as inputs to the nonlinear multiple regression model. The output of the nonlinear multiple regression model is obtained, and the difference between the output and the zero-point voltage of the MEMS hydrogen sensor is calculated.

[0025] The output voltage of the MEMS hydrogen sensor after drift compensation is the difference between the actual output voltage and the difference.

[0026] Secondly, embodiments of this application also provide a drift compensation system for a MEMS hydrogen sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0027] This application has at least the following beneficial effects:

[0028] This application places a MEMS hydrogen sensor in a constant temperature and humidity test chamber and collects the output voltage of the MEMS hydrogen sensor at various preset temperatures and times. It analyzes the difference between the output voltage at each preset temperature and its neighboring times, and combines this with the rated output voltage of the MEMS hydrogen sensor to determine the first distribution characteristic value of the output voltage at each preset temperature and times. This quantifies the distribution characteristics of the output voltage between different times under the same preset temperature, which helps to identify noise data in the collected output voltage. Furthermore, it analyzes the difference between the output voltage at any preset temperature and the output voltage with the closest humidity at other preset temperatures, and combines this with the rated output voltage to determine the second distribution characteristic value of the output voltage at any preset temperature and times. This quantifies the distribution characteristics of the output voltage at different preset temperatures and times. The distribution characteristics of output voltage within the same time sequence improve the accuracy and reliability of subsequent output voltage anomaly identification. By fusing the first and second distribution characteristic values, the elimination coefficient of output voltage at each time point under each preset temperature is determined. This elimination coefficient is used to remove noise data from all output voltages collected at each preset temperature, effectively removing noise from the collected data, ensuring the quality of the final output sample dataset, improving the fitting effect of the subsequent regression model, and reducing error sources. The output voltages after noise removal at all preset temperatures are combined into a sample dataset. Combined with the preset temperature and the humidity corresponding to the output voltage at each time point, a nonlinear multivariate regression model is fitted. In conjunction with the zero-point voltage of the MEMS hydrogen sensor, drift compensation is performed on the output voltage of the MEMS hydrogen sensor. By fusing the first and second distribution characteristic values, the model can more accurately compensate for drift, improving the accuracy and reliability of the sensor under various environments. This application, by compensating for output voltage drift, can effectively reduce the output instability caused by temperature and humidity changes during long-term use of the MEMS hydrogen sensor, thereby improving the long-term stability and reliability of the sensor. Attached Figure Description

[0029] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating the steps of a drift compensation method for a MEMS hydrogen sensor provided in one embodiment of this application;

[0031] Figure 2 Flowchart for determining the characteristic values ​​of the second distribution. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a drift compensation method and system for a MEMS hydrogen sensor proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] The following description, in conjunction with the accompanying drawings, details a specific scheme for a drift compensation method and system for a MEMS hydrogen sensor provided in this application.

[0035] Please see Figure 1 The diagram illustrates a flowchart of a drift compensation method for a MEMS hydrogen sensor according to an embodiment of this application. The method includes the following steps:

[0036] S1. Place the MEMS hydrogen sensor in a constant temperature and humidity test chamber and collect the output voltage of the MEMS hydrogen sensor at each preset temperature and time.

[0037] In this embodiment, the MEMS hydrogen sensor is first placed in a constant temperature and humidity test chamber. The gas in the constant temperature and humidity test chamber is uncontaminated air, which is used to collect the output voltage data of the MEMS hydrogen sensor under different temperature and humidity conditions without hydrogen input. That is, the actual zero-point output voltage data of the MEMS hydrogen sensor under different temperature and humidity conditions.

[0038] The temperature in the constant temperature and humidity test chamber was set to eight preset temperatures: 5℃, 10℃, 15℃, 20℃, 25℃, 30℃, 35℃, and 40℃. The MEMS hydrogen sensor in the test chamber was tested at each preset temperature. In this embodiment, the test duration at each preset temperature was set to 8 minutes. The number and values ​​of the preset temperatures, as well as the test duration, can be set by the implementer according to actual conditions; this embodiment does not impose any restrictions on this.

[0039] Secondly, regarding the humidity in the constant temperature and humidity test chamber, during each temperature test of the MEMS hydrogen sensor, the relative humidity of the test chamber was continuously varied from a starting value of 20%RH to an ending value of 60%RH, and the same rate of humidity change was used throughout all temperature tests of the MEMS hydrogen sensor. During each temperature test, the output voltage data of the MEMS hydrogen sensor was collected at various times, with a sampling frequency of 0.5Hz. The starting and ending values ​​of the relative humidity, as well as the sampling frequency of the output voltage, can be set by the implementer according to the actual situation.

[0040] At this point, the output voltage dataset of the MEMS hydrogen sensor at each preset temperature can be obtained.

[0041] S2, analyze the difference between the output voltage at each time and the output voltage at the nearest time under each preset temperature, and determine the first distribution characteristic value of the output voltage at each time under each preset temperature in combination with the rated output voltage of the MEMS hydrogen sensor.

[0042] Because the humidity in the constant temperature and humidity test chamber changes continuously during each temperature test of the MEMS hydrogen sensor, the ambient humidity of the MEMS hydrogen sensor has a small variation range between adjacent moments during each temperature test. Therefore, the change in the output voltage data of the MEMS hydrogen sensor caused by the change in ambient humidity between adjacent moments during each temperature test will usually also have a small variation range due to the continuous change in ambient humidity. Furthermore, each output voltage data should have a relatively close numerical distribution with the output voltage data collected under similar ambient humidity conditions. However, the output voltage data affected by noise interference usually does not have this data distribution characteristic due to the randomness of the noise.

[0043] Based on the above analysis, for the output voltage dataset at each preset temperature, the difference between each output voltage and the output voltage at adjacent times is calculated. It should be noted that the difference represents the degree of difference between two variables, and can be calculated using methods such as the absolute value of the difference, the square of the difference, or the ratio. This embodiment does not impose any restrictions on this method.

[0044] In this embodiment, for the output voltage dataset at each preset temperature, the mean of the absolute values ​​of the differences between each output voltage and its M nearest neighboring output voltages is calculated and denoted as the first mean. The ratio of the first mean to the rated maximum output voltage of the MEMS hydrogen sensor is then calculated as the first distribution characteristic value of the output voltage at each time point at each preset temperature. In this embodiment, M=6, but the implementer can set it according to actual conditions; this embodiment does not impose any restrictions on this. Furthermore, in this embodiment, the rated maximum output voltage of the MEMS hydrogen sensor is 5V.

[0045] The first distribution characteristic value is used to evaluate whether the output voltage data of the MEMS hydrogen sensor at the same ambient temperature have a small difference in value from the output voltage data collected under similar humidity conditions. That is, the output voltage changes slightly due to continuous changes in ambient humidity at the same ambient temperature, and the distribution characteristics of the output voltage data are similar under similar humidity conditions.

[0046] S3, analyze the difference between the output voltage at each moment under any preset temperature and the output voltage with the closest humidity under other preset temperatures, and determine the second distribution characteristic value of the output voltage at each moment under any preset temperature by combining the rated output voltage of the MEMS hydrogen sensor.

[0047] Since the ambient temperature remains constant during the same temperature test of the MEMS hydrogen sensor, the degree of zero-point drift of the output voltage caused by changes in ambient temperature is constant throughout each temperature test. Furthermore, because the range and rate of change of ambient humidity are the same during different temperature tests of the MEMS hydrogen sensor, the response pattern of the output voltage data of the MEMS hydrogen sensor to changes in ambient humidity is similar during different temperature tests. That is, under different ambient temperatures, the output voltage data of the MEMS hydrogen sensor should have a relatively consistent degree of data variation at various ambient humidity levels. However, the output voltage data affected by noise interference usually does not exhibit this data distribution characteristic under different ambient temperatures due to the randomness of the noise.

[0048] Based on the above analysis, taking the experimental process of the MEMS hydrogen sensor at the i-th temperature as an example, for the output voltage data a1 at the j-th time at the i-th temperature, in the remaining j-th temperatures other than the i-th temperature... During the temperature test, the output voltage data a2 corresponding to the humidity closest to the j-th time is obtained, and the difference between the output voltage data a1 and the output voltage data a2 is recorded as the first difference. In this embodiment, the absolute value of the difference between the output voltage data a1 and the output voltage data a2 is calculated and compared with the rated maximum output voltage of the MEMS hydrogen sensor, and recorded as the first ratio.

[0049] Further, the average of the absolute values ​​of the differences between the first ratio at time j and the first ratio at all times under temperature i is calculated and denoted as the second mean. For temperature i and all other temperatures, the mean of all the second means corresponding to time j is calculated and used as the second distribution characteristic value of the output voltage at time j under temperature i. The flowchart for determining the second distribution characteristic value is as follows: Figure 2 As shown.

[0050] The first ratio is used to evaluate the MEMS hydrogen sensor at the ambient humidity corresponding to the output voltage data a1, and at the ambient temperature of the i-th temperature. The degree of variation in output voltage data between different temperatures.

[0051] The second mean is used to evaluate the i-th temperature and the... Under the two ambient temperatures (i.e., temperature 1) and the corresponding ambient humidity (a1), the variation of the MEMS hydrogen sensor's output voltage between these two ambient temperatures is examined to determine whether it exhibits a consistent degree of variation with the variation of the MEMS hydrogen sensor's output voltage under other ambient humidity conditions between these two ambient temperatures. The second distribution feature value integrates the output voltage variation characteristics at each time point under the i-th temperature and all other temperatures.

[0052] S4, combine the first distribution feature value and the second distribution feature value to determine the elimination coefficient of the output voltage at each time under each preset temperature; use the elimination coefficient to remove noise data from all output voltages collected under each preset temperature.

[0053] The average of the first and second distribution characteristic values ​​of the output voltage at each time point under each preset temperature is used as the elimination coefficient of the output voltage at each time point under each preset temperature. This reflects the degree of abnormality of the output voltage at each time point under each preset temperature, that is, the degree of noise interference on the output voltage. The larger the elimination coefficient, that is, the larger the first and second distribution characteristic values, the less the output voltage at the corresponding time point has the data distribution characteristics of the actual zero-point output voltage data of the MEMS hydrogen sensor under the same ambient temperature and under different ambient temperatures. This indicates that the output voltage at the corresponding time point is more severely affected by noise interference, and the output voltage should be eliminated to avoid reducing the drift compensation effect of the MEMS hydrogen sensor.

[0054] Based on the above analysis, the elimination coefficients of the output voltages at all times under each preset temperature are sorted in descending order, and the output voltages corresponding to the first preset percentage of elimination coefficients are treated as noise data and removed. In this embodiment, the preset percentage is 10%, but implementers can set it according to actual conditions; this embodiment does not impose any restrictions on it.

[0055] S5: The output voltages after removing noise from all preset temperatures are combined into a sample dataset. The preset temperature and the humidity corresponding to the output voltage at each time are combined with the humidity to fit a nonlinear multiple regression model. Combined with the zero-point voltage of the MEMS hydrogen sensor, the drift compensation of the output voltage of the MEMS hydrogen sensor is performed.

[0056] Furthermore, a sample dataset is constructed by taking the remaining output voltages after removing noise from all preset temperature data. The output voltage data at each moment in the sample dataset of the MEMS hydrogen sensor is used as the dependent variable, and the temperature and humidity in the constant temperature and humidity test chamber corresponding to the output voltage at each moment in the sample dataset are used as independent variables. A nonlinear multiple regression model is constructed to fit the relationship between the zero point value of the output voltage of the MEMS hydrogen sensor and the temperature and humidity. Specifically, it can be expressed as S=f(t,h), where t and h represent the ambient temperature and humidity of the MEMS hydrogen sensor, respectively, and S represents the zero point value of the actual output voltage of the MEMS hydrogen sensor under the environment of temperature t and humidity h. The construction of the nonlinear multiple regression model is a well-known technique, and the specific process will not be described in detail.

[0057] Finally, the output voltage of the MEMS hydrogen sensor after drift compensation is obtained, and the specific expression is as follows: In the formula, V is the output voltage of the MEMS hydrogen sensor after drift compensation, and V is the actual output voltage of the MEMS hydrogen sensor. The output result is obtained by using the ambient temperature and humidity at the time corresponding to the actual output voltage as input to the nonlinear multiple regression model. This is the zero-point voltage of the MEMS hydrogen sensor, in this embodiment... .

[0058] This completes the drift compensation for the MEMS hydrogen sensor.

[0059] Based on the same inventive concept as the above method, this application embodiment also provides a drift compensation system for a MEMS hydrogen sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described drift compensation methods for a MEMS hydrogen sensor.

[0060] In summary, this application places a MEMS hydrogen sensor in a constant temperature and humidity test chamber and collects the output voltage of the MEMS hydrogen sensor at various preset temperatures and times. It analyzes the difference between the output voltage at each preset temperature and its neighboring times, and combines this with the rated output voltage of the MEMS hydrogen sensor to determine the first distribution characteristic value of the output voltage at each preset temperature and times. This quantifies the distribution characteristics of the output voltage between different times under the same preset temperature, which helps to identify noise data in the collected output voltage. Furthermore, it analyzes the difference between the output voltage at any preset temperature and the output voltage with the closest humidity at other preset temperatures, and combines this with the rated output voltage to determine the second distribution characteristic value of the output voltage at any preset temperature and times. This quantifies the distribution characteristics of the output voltage between different preset temperatures and times. The distribution characteristics of output voltage at the same time interval under different temperatures improve the accuracy and reliability of subsequent output voltage anomaly identification. By fusing the first and second distribution characteristic values, the elimination coefficient of output voltage at each time point under each preset temperature is determined. This elimination coefficient is used to remove noise data from all output voltages collected at each preset temperature, effectively removing noise from the collected data, ensuring the quality of the final output sample dataset, improving the fitting effect of the subsequent regression model, and reducing error sources. The output voltages after noise removal at all preset temperatures are combined into a sample dataset. Combined with the preset temperature and the humidity corresponding to the output voltage at each time point, a nonlinear multivariate regression model is fitted. In conjunction with the zero-point voltage of the MEMS hydrogen sensor, drift compensation is performed on the output voltage of the MEMS hydrogen sensor. By fusing the first and second distribution characteristic values, the model can more accurately compensate for drift, improving the accuracy and reliability of the sensor in various environments. This application, by compensating for output voltage drift, can effectively reduce the output instability caused by temperature and humidity changes during long-term use of the MEMS hydrogen sensor, thereby improving the long-term stability and reliability of the sensor.

[0061] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0063] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A drift compensation method for a MEMS hydrogen sensor, characterized in that, The method includes the following steps: The MEMS hydrogen sensor was placed in a constant temperature and humidity test chamber, and the output voltage of the MEMS hydrogen sensor at each preset temperature and time was collected. The difference between the output voltage at each time and the output voltage at neighboring times under each preset temperature is analyzed. Combined with the rated output voltage of the MEMS hydrogen sensor, the first distribution characteristic value of the output voltage at each time under each preset temperature is determined. Analyze the difference between the output voltage at each moment under any preset temperature and the output voltage with the closest humidity under other preset temperatures, and combine it with the rated output voltage to determine the second distribution characteristic value of the output voltage at each moment under any preset temperature; By combining the first distribution feature value and the second distribution feature value, the elimination coefficient of the output voltage at each time at each preset temperature is determined; the elimination coefficient is used to remove noise data from all output voltages collected at each preset temperature. The output voltages after removing noise from all preset temperatures are used to form a sample dataset. The preset temperatures and the ambient humidity corresponding to the output voltages at each time are combined to fit a nonlinear multiple regression model. The zero-point voltage of the MEMS hydrogen sensor is then used to compensate for the drift of the output voltage of the MEMS hydrogen sensor. The determination of the second distribution characteristic value includes: The difference between the output voltage at each moment under any preset temperature and the output voltage with the closest humidity under other preset temperatures is denoted as the first difference. The ratio of the first difference at each moment under any preset temperature to the rated maximum output voltage of the MEMS hydrogen sensor is calculated and denoted as the first ratio. Calculate the average difference between the first ratio at each time point under any preset temperature and the first ratio at all times under any preset temperature; The second distribution characteristic value is the mean of all the average values ​​corresponding to each time point at any preset temperature; The step of removing noise data from all output voltages collected at each preset temperature using the elimination coefficient includes: The output voltage at all times under each preset temperature is sorted in descending order by the elimination coefficients, and the output voltages corresponding to the first preset percentage elimination coefficients are treated as noise data and removed.

2. The drift compensation method for a MEMS hydrogen sensor as described in claim 1, characterized in that, At all preset temperatures, the humidity inside the constant temperature and humidity test chamber changes continuously with the same preset starting value, the same rate of humidity change, and the same preset ending value.

3. The drift compensation method for a MEMS hydrogen sensor as described in claim 1, characterized in that, The determination of the first distribution characteristic value includes: The average difference between the output voltage at each time point and the output voltage at all adjacent times under each preset temperature is calculated, and the first distribution characteristic value is determined by combining it with the rated maximum output voltage of the MEMS hydrogen sensor.

4. The drift compensation method for a MEMS hydrogen sensor as described in claim 3, characterized in that, The first distribution characteristic value is the ratio of the mean to the rated maximum output voltage.

5. The drift compensation method for a MEMS hydrogen sensor as described in claim 1, characterized in that, The elimination coefficient is the mean of the first distribution feature value and the second distribution feature value.

6. The drift compensation method for a MEMS hydrogen sensor as described in claim 1, characterized in that, The fitting of the nonlinear multiple regression model includes: taking the output voltage at each moment in the sample dataset as the dependent variable, and taking the temperature and humidity in the constant temperature and humidity test chamber corresponding to the output voltage at each moment in the sample dataset as independent variables, and obtaining the nonlinear multiple regression model through nonlinear fitting.

7. The drift compensation method for a MEMS hydrogen sensor as described in claim 1, characterized in that, The drift compensation for the output voltage of the MEMS hydrogen sensor includes: The ambient temperature and humidity at the time corresponding to the actual output voltage of the MEMS hydrogen sensor are used as inputs to the nonlinear multiple regression model. The output of the nonlinear multiple regression model is obtained, and the difference between the output and the zero-point voltage of the MEMS hydrogen sensor is calculated. The output voltage of the MEMS hydrogen sensor after drift compensation is the difference between the actual output voltage and the difference.

8. A drift compensation system for a MEMS hydrogen sensor, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

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