A MEMS gas sensor, gas sensitivity testing method, device and system

Abstract

The present application relates to the field of gas measurement technology, and specifically to a MEMS gas sensor, a gas sensitivity testing method, device and system, the method comprising: determining the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal; determining the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF, and the correlation between each IMF and the humidity response signal, and determining the correction factor of each IMF in combination with the first influencing factor to improve the gas-sensitive characteristics of the gas sensor. The present application compensates for the error caused by humidity by analyzing the differences within different components and the correlation between different components and ambient humidity, thereby improving the accuracy of the target gas concentration measurement.

Description

Technical Field

[0001] The present application relates to the field of gas measurement technology, and in particular to a MEMS gas sensor, a gas sensitivity testing method, a device and a system. Background Art

[0002] MEMS gas sensors are highly integrated micro gas sensors that combine micro-electromechanical processing technology and advanced materials science to detect and quantify gas composition or concentration at a microscopic scale. These gas sensors are usually made of silicon-based materials and use micron or nanometer-level structures to achieve high sensitivity to specific gases. It uses metal oxides as the sensitive layer. When specific gas molecules interact with these materials, it causes changes in resistance, thereby detecting the concentration of the gas.

[0003] The working environment of MEMS gas sensors is complex, and they are often located in environments with different humidity. Changes in humidity can cause baseline drift of gas sensors. Baseline drift can cause changes in the gas sensitivity of MEMS gas sensors, thereby affecting the performance of gas sensors and causing errors in the measured gas concentration. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides a method for testing gas sensitivity of a MEMS gas sensor, the method comprising the following steps:

[0005] Obtaining a gas-sensitive response signal of the gas sensor and a humidity response signal of the humidity sensor within a sampling period;

[0006] Determine the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal;

[0007] Decompose the gas-sensitive response signal into multiple IMFs, and divide each IMF into multiple sub-IMFs with equal frequency intervals;

[0008] Determine the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal;

[0009] A correction factor of each IMF is determined based on the first influencing factor and the second influencing factor to correct the gas-sensitive response signal.

[0010] Preferably, the method for determining the first influencing factor of the humidity response signal is:

[0011] Divide the humidity response signal into multiple sub-signals with equal time intervals, and analyze the standard deviation of all humidity data in each sub-signal in the humidity response signal;

[0012] Analyze the cumulative sum of the standard deviations of all sub-signals in the humidity response signal, and record it as the humidity change amplitude of the humidity response signal;

[0013] Determine the humidity change trend of the humidity response signal based on the change trend of all humidity data in the humidity response signal;

[0014] The sum of the humidity change amplitude and the humidity change trend of the humidity response signal is analyzed, and the product of the sum and the mean value of all humidity data in the humidity response signal is determined as the first influencing factor of the humidity response signal.

[0015] Preferably, the expression of the humidity change trend of the humidity response signal is: Where, Q represents the humidity change trend of the humidity response signal; D i represents the number of humidity data in the ith sub-signal in the humidity response signal that is equal to the mean value of all humidity data in the sub-signal; N represents the number of all sub-signals in the humidity response signal.

[0016] Preferably, the process of determining the second impact factor of each IMF is as follows:

[0017] Determine the energy difference index of each IMF based on the difference between the energies of all adjacent sub-IMFs in each IMF;

[0018] The second influencing factor of each IMF is the result of the fusion of the correlation between each IMF and the humidity response and the energy difference index.

[0019] Preferably, the method for determining the energy difference index of each IMF is:

[0020] Analyze the difference between the energy of each sub-IMF in each IMF and the energy of its previous sub-IMF, and record it as the first difference of each sub-IMF in each IMF;

[0021] Analyze the ratio of the first difference of each sub-IMF in each IMF to the energy of its previous sub-IMF, and record it as the first ratio of each sub-IMF in each IMF;

[0022] The energy difference index of each IMF is the result of the fusion of the first ratios of all sub-IMFs in each IMF.

[0023] Preferably, the correction factor of each IMF is expressed as: σ j =norm(S×C j ), where norm() represents the normalization function; σ j represents the correction factor of the jth IMF; S represents the first influencing factor of the humidity response signal; C j represents the second impact factor of the j-th IMF.

[0024] Preferably, the correction of the gas-sensitive response signal comprises:

[0025] The average value of all frequency values ​​in each IMF is taken as the main frequency of each IMF, and the frequency of the first quartile of all frequency values ​​in each IMF sorted from small to large is taken as the cutoff frequency of each IMF, where the cutoff frequency of each IMF is less than the main frequency of the corresponding IMF;

[0026] When the frequency value in each IMF is greater than its main frequency, the frequency value in each IMF remains unchanged;

[0027] When the frequency value in each IMF is less than or equal to its cutoff frequency, the corresponding frequency value in each IMF is set to 0;

[0028] When the frequency value in each IMF is greater than its cutoff frequency and less than or equal to its main frequency, each corrected frequency value in each IMF is the product of each pre-corrected frequency value in each IMF and the correction factor;

[0029] All corrected IMFs are reconstructed to obtain the corrected gas-sensitive response signal.

[0030] In a second aspect, an embodiment of the present application provides a MEMS gas sensor, wherein a characteristic test of the gas sensor adopts the steps of any one of the above-mentioned methods.

[0031] In a third aspect, an embodiment of the present application further provides a MEMS gas sensor gas sensitivity testing device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0032] In a fourth aspect, an embodiment of the present application provides a MEMS gas sensor gas sensitivity testing system, the system comprising:

[0033] The data acquisition module acquires the gas-sensitive response signal of the gas sensor and the humidity response signal of the humidity sensor within the sampling time;

[0034] The data analysis module determines the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal;

[0035] Decompose the gas-sensitive response signal into multiple IMFs, and divide each IMF into multiple sub-IMFs with equal frequency intervals;

[0036] Determine the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal;

[0037] The gas-sensitive data correction module determines a correction factor of each IMF based on the first influencing factor and the second influencing factor to correct the gas-sensitive response signal.

[0038] It can be seen from the above embodiments that the gas sensitivity testing method provided in the embodiments of the present application has at least the following beneficial effects:

[0039] The present application determines the first influencing factor of the humidity response signal according to the discreteness, change trend and average distribution of all humidity data in the humidity response signal to characterize the influence of humidity on the baseline drift; determines the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal; determines the correction factor of each IMF based on the first influencing factor and the second influencing factor, and corrects each IMF component according to the correction factor to obtain the reconstructed gas-sensitive response signal.

[0040] The present application analyzes the changes in ambient humidity and performs EMD decomposition on the response signal of the MEMS gas sensor. The corresponding components are corrected according to the correlation between different components and ambient humidity to avoid affecting components unrelated to humidity, thereby compensating for the errors caused by humidity to improve the gas sensitivity of the gas sensor and enhance the accuracy of the target gas concentration measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flow chart of the steps of a gas sensitivity testing method of a MEMS gas sensor provided in this application;

[0043] Figure 2 A schematic diagram of the correction factor extraction process provided for this application;

[0044] Figure 3 A gas-sensitive response signal calibration flow chart provided for this application;

[0045] Figure 4 Schematic diagram of the gas-sensitive detection process provided for this application;

[0046] Figure 5 Block diagram of the gas sensitivity testing system provided for this application. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of a MEMS gas sensor, gas sensitivity testing method, device and system proposed in the present application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, 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 belongs.

[0049] The specific scheme of a MEMS gas sensor, gas sensitivity testing method, device and system provided by the present application is described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of a method for testing gas sensitivity of a MEMS gas sensor provided by an embodiment of the present application, the method comprising the following steps:

[0051] Step S1: Acquire the gas-sensitive response signal of the gas sensor and the humidity response signal of the humidity sensor within a sampling period.

[0052] The MEMS gas sensor to be tested is installed in the test room, and a humidity sensor is installed in the test room to test the humidity data.

[0053] First, open the gas outlet and introduce dry nitrogen with a relative humidity of 0 to clean the test chamber to ensure that the gas sensor is exposed to an environment without target gas. After cleaning, close the gas outlet, gradually introduce the target gas, and change the ambient humidity to simulate the humidity fluctuation in the real environment, record the gas data of the MEMS gas sensor and the humidity data measured by the humidity sensor, the sampling frequencies of the MEMS gas sensor and the humidity sensor are both the first preset values, the sampling time is T, and the gas response signal and the humidity response signal are obtained.

[0054] It should be noted that the first preset value and the sampling time length are both manually set. In this embodiment, the first preset value is 1000 Hz and the sampling time length T is 5 hours. The implementer can set them according to the specific situation, and this embodiment does not impose any special restrictions.

[0055] The obtained gas-sensing response signal and humidity response signal are input into a low-pass filter to remove high-frequency noise.

[0056] The working principle and process of the low-pass filter are well-known technologies, and the specific process of filtering out high-frequency noise will not be described in detail.

[0057] Step S2: Determine the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal.

[0058] The baseline drift of a gas sensor is also called zero drift or background drift, which refers to the unexpected change in the gas sensor response signal over time when there is no target gas. The presence of baseline drift will affect the accuracy and reliability of the gas sensor. If the baseline of the gas sensor in clean air is not completely stable, then even if the gas concentration in the environment remains unchanged, the gas sensor may show the illusion of concentration change.

[0059] There are many factors that cause baseline drift, including aging of gas sensor materials, changes in environmental conditions, thermal stability of electronic components, or adsorption of other substances on the surface of the gas sensor. In order to obtain a more accurate target gas concentration, the baseline drift needs to be corrected. This embodiment mainly analyzes the baseline drift of the gas sensor caused by humidity changes in environmental conditions.

[0060] Since MEMS gas sensors are usually highly integrated and miniaturized, they are very sensitive to environmental factors, especially humidity changes. Humidity changes may cause moisture absorption and desorption of gas sensor materials, thereby affecting the performance of gas sensors and causing baseline drift in gas sensors, which in turn affects the gas sensitivity of gas sensors and leads to errors in the detection of target gas concentration.

[0061] (1) Humidity change amplitude of humidity response signal.

[0062] In order to obtain the degree of baseline drift of MEMS gas sensors under different humidity conditions, it is first necessary to obtain the humidity changes in the environment. By quantifying the humidity changes, it is possible to more accurately identify and distinguish whether the cause of the signal change is caused by the change in environmental humidity or by the target gas.

[0063] When the humidity in the air changes and fluctuates, the moisture content in the air will increase or decrease. When the moisture increases, the excess moisture will be absorbed by the gas sensor material, and will be desorbed when the moisture content decreases. Therefore, humidity changes will cause baseline drift in the gas sensor. The degree of baseline drift is related to humidity changes. Humidity changes can be quantified by the amplitude and trend of humidity changes.

[0064] Therefore, in order to quantify the discrete degree of humidity data, the humidity change amplitude of the humidity response signal is determined based on the discrete degree of all humidity data in the humidity response signal, so as to determine the fluctuation degree of ambient humidity in a specific time period, specifically:

[0065] Divide the humidity response signal into sub-signals with equal time intervals, and analyze the standard deviation of all humidity data in each sub-signal in the humidity response signal;

[0066] The cumulative sum of the standard deviations of all sub-signals in the humidity response signal is analyzed and recorded as the humidity change amplitude of the humidity response signal.

[0067] It should be noted that the value of the time interval is manually set. In this embodiment, the value of the time interval is 10 minutes. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0068] The humidity variation amplitude characterizes the volatility of ambient humidity. The larger the humidity variation amplitude, the greater the dispersion of humidity data in time, indicating that the environmental conditions may be unstable. Moreover, the humidity variation amplitude is a measure of the degree of dispersion of humidity data around the average humidity data of each time period. The larger the humidity variation amplitude, the greater the difference between humidity data, which can indirectly reflect the potential frequency of extreme high humidity or low humidity events.

[0069] The larger the humidity change, the more the environmental conditions are changing, which may cause the response signal of the MEMS gas sensor to fluctuate greatly, thereby causing baseline drift. Conversely, the smaller the humidity change, the more stable the environmental conditions are, and the less likely the gas sensor will have baseline drift.

[0070] (2) Humidity change trend of humidity response signal.

[0071] The change trend of humidity data represents the overall development direction and continuous change pattern of ambient humidity over a period of time. This trend may be rising, falling or periodic fluctuations. Therefore, based on the change trend of all humidity data in the humidity response signal, the humidity change trend of the humidity response signal is determined to quantify the change of humidity data, specifically:

[0072] The average value of all humidity data in each time period of the humidity response signal is analyzed. Each time the humidity data in each time period reaches the average value of all humidity data in that time period, it is regarded as a change in the humidity change trend.

[0073] The expression of humidity change trend of humidity response signal is: Where, Q represents the humidity change trend of the humidity response signal; D iIt indicates the number of humidity data in the i-th time period that is equal to the mean value of all humidity data in the time period; N indicates the number of all sub-signals in the humidity response signal.

[0074] The humidity change trend reflects the pattern of humidity changes over time in the environment, and the change in humidity will gradually affect the performance of the gas sensor, resulting in baseline drift. If the value of the humidity change trend is larger, it means that the humidity changes more times within a certain period of time, that is, the faster the humidity changes, the greater the humidity fluctuation, and the degree of baseline drift may be more significant; on the contrary, it means that the environmental conditions are more stable, and the degree of baseline drift of the gas sensor may be smaller.

[0075] (3) The first influencing factor of humidity response signal.

[0076] The humidity change amplitude and humidity change trend indicate the fluctuation of humidity change as a whole. The greater the humidity change fluctuation, the greater the impact on the baseline drift of the gas sensor. However, if the humidity change is small, for example, the humidity in the environment has been at a high level, the impact on the baseline drift of the gas sensor is greater than when the humidity in the environment has been at a low level. At this time, the humidity change amplitude and humidity change trend are about the same size, making it difficult to accurately detect humidity changes.

[0077] Therefore, it is necessary to further calculate the first influencing factor of the humidity response signal in combination with the specific humidity data in the environment to characterize the humidity change in the environment and the influence of humidity on the baseline drift of the gas sensor, specifically:

[0078] The sum of the humidity change amplitude and the humidity change trend of the humidity response signal is analyzed, and the product of the sum and the mean value of all humidity data in the humidity response signal is determined as the first influencing factor of the humidity response signal.

[0079] The first influencing factor characterizes the influence of humidity in the environment and humidity changes on the baseline drift of the MEMS gas sensor. Among them, the average value of humidity data, the amplitude of humidity change and the humidity change trend are positively correlated with the first influencing factor. The larger the first influencing factor, the greater the influence of humidity on the gas sensor, and the greater the baseline drift of the gas sensor and the degree of baseline drift. On the contrary, the smaller the first influencing factor, the smaller the influence of humidity on the gas sensor, and the smaller the baseline drift of the gas sensor and the degree of baseline drift.

[0080] Step S3: determining the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal, and determining the correction factor of each IMF in combination with the first influencing factor.

[0081] Baseline drift is usually manifested as a long-term trend or a slowly changing part in the signal. In order to identify baseline drift, this embodiment uses the EMD empirical mode decomposition algorithm to decompose the gas-sensitive response signal of the MEMS gas sensor into different intrinsic mode function components IMF. Among them, each IMF is decomposed from the original signal, they have different frequency characteristics and amplitude changes, and satisfy the unique instantaneous frequency at any time point, so that the low-frequency components related to the baseline drift can be identified separately.

[0082] Among them, the EMD algorithm can decompose nonlinear and non-stationary signals into a set of intrinsic mode function components IMF and trend terms. It is a well-known technology in the field of signal processing. The specific process of decomposing the signal into multiple IMFs will not be repeated here, where the IMF signal is a frequency domain signal.

[0083] The response signal of the MEMS gas sensor is subjected to empirical mode decomposition to obtain multiple IMFs and a trend term. If there is a baseline drift component in the IMF, then there is a low-frequency component. All IMFs are subjected to fast Fourier transform to convert them from the time domain to the frequency domain.

[0084] Furthermore, in order to observe the local characteristics of the IMF signal, each IMF is divided into a preset number of sub-IMFs at equal frequency intervals.

[0085] It should be noted that the value of the preset number is artificially set. In this embodiment, the value of the preset number is 10. The implementer can set it as appropriate according to the specific situation. This embodiment does not impose any special restrictions.

[0086] (1)Energy differential index of each IMF.

[0087] In order to identify the baseline drift of the gas sensor and evaluate the quality of the gas sensing data, the energy difference index of each IMF is determined based on the energy difference between all adjacent sub-IMFs in each IMF to identify and correct the baseline drift and improve the accuracy and readability of the gas sensing response signal. Specifically:

[0088] Analyze the difference between the energy of each sub-IMF in each IMF and the energy of its previous sub-IMF, and record it as the first difference of each sub-IMF in each IMF;

[0089] Analyze the ratio of the first difference of each sub-IMF in each IMF to the energy of its previous sub-IMF, and record it as the first ratio of each sub-IMF in each IMF;

[0090] The energy difference index of each IMF is the result of the fusion of the first ratios of all sub-IMFs in each IMF.

[0091] It should be noted that the calculation process of signal energy is a well-known technology, and its specific calculation process will not be repeated here.

[0092] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0093] Preferably, in the present embodiment, the energy difference index of each IMF is the cumulative sum of the first ratios of all sub-IMFs in each IMF; in actual application, as other implementation methods, the implementer may use an exponential function value with a natural constant as the base and the cumulative sum of the first ratios of all sub-IMFs in each IMF as the independent variable as the energy difference index of each IMF.

[0094] The energy difference index characterizes the change of low-frequency energy intensity over time. The larger the value of the energy difference index, the greater the degree of baseline drift between different time windows, indicating that the degree of drift is greater; conversely, the smaller the value of the energy difference index, the smaller the degree of drift.

[0095] (2) The second impact factor of each IMF.

[0096] Furthermore, in order to more accurately evaluate the impact of baseline drift on the quality of gas-sensing data, the second influencing factor of each IMF is determined based on the energy difference index and the correlation between each IMF and the humidity response signal to detect the change of baseline drift, so as to take corresponding measures to improve the quality of gas-sensing data, specifically:

[0097] The second influencing factor of each IMF is the result of the fusion of the correlation between each IMF and the humidity response signal and the energy difference index of the corresponding IMF.

[0098] It should be noted that there are many methods for measuring correlation. In this embodiment, the Pearson correlation coefficient is used to measure the correlation between the IMF and the humidity response signal. The implementer may also use methods such as the Spearman correlation coefficient or the Kendall correlation coefficient to measure the correlation according to the specific situation. This embodiment does not impose any special restrictions.

[0099] Preferably, in the present embodiment, the second influencing factor of each IMF is the product of the correlation between each IMF and the humidity response signal and the energy difference index of the corresponding IMF; in actual application, as other implementation methods, the implementer may use an exponential function value with a natural constant as the base and the sum of the correlation between each IMF and the humidity response signal and the energy difference index of the corresponding IMF as the independent variable as the second influencing factor of each IMF.

[0100] The larger the second impact factor is, the greater the correlation between the current IMF and the humidity response signal is, and the larger the energy difference index is, the more low-frequency components there are in the IMF, and the more likely it is that the current IMF contains baseline drift; on the contrary, the smaller the second impact factor is, the smaller the correlation between the current IMF and the humidity response signal is, and the smaller the energy difference index is, the fewer low-frequency components there are in the IMF, and the less likely it is to contain baseline drift.

[0101] (3) Correction factor for each IMF.

[0102] Furthermore, based on the first influencing factor and the second influencing factor, the correction factor of each IMF is determined, and the baseline drift in the IMF is removed according to the correction factor. Finally, each corrected IMF is reconstructed to achieve the purpose of removing the baseline drift in the MEMS gas sensor and thus complete the correction of the gas sensitive data, specifically:

[0103] The expression of the correction factor for each IMF is: j =norm(S×C j ), where norm() represents the normalization function; σ j represents the correction factor of the jth IMF; S represents the first influencing factor of the humidity response signal; C j represents the second impact factor of the j-th IMF.

[0104] The more serious the baseline drift of the gas sensor, the larger the corresponding first response factor and second response factor, which makes the correction factor of each IMF larger and the degree of correction of the gas-sensitive response signal greater; conversely, if the gas sensor is not greatly affected by humidity, the baseline drift phenomenon is weaker, the corresponding first response factor and second response factor are smaller, and the correction factor of each IMF is also smaller.

[0105] Preferably, the schematic diagram of the correction factor extraction process provided in the present application is as follows: Figure 2 shown.

[0106] Step S4: Correcting the gas-sensitive response signal based on the correction factor of each IMF.

[0107] The change of humidity in the environment is usually presented in the signal as low-frequency components. Therefore, in order to eliminate the baseline drift caused by humidity on the gas sensor and avoid unnecessary errors caused by humidity when testing the gas sensor data, the IMF is corrected based on the correction factor, specifically:

[0108] The average of all frequency values ​​in each IMF is taken as the main frequency of each IMF, and the frequency of the first quartile of all frequency values ​​in each IMF sorted from small to large is taken as the cutoff frequency of each IMF;

[0109] When the frequency value in each IMF is greater than its main frequency, the frequency value in each IMF does not need to be corrected;

[0110] When the frequency value in each IMF is less than or equal to its cutoff frequency, the corresponding frequency value in each IMF is set to 0;

[0111] When the frequency value in each IMF is greater than its cutoff frequency and less than or equal to its main frequency, each corrected frequency value in each IMF is the product of each pre-corrected frequency value in each IMF and the correction factor.

[0112] It should be noted that since the frequency of each IMF increases gradually, the average value of all frequencies in each IMF is always greater than its first quartile, that is, the cutoff frequency of each IMF is always less than the main frequency of the corresponding IMF.

[0113] After completing the correction of each IMF according to the above steps, perform inverse Fourier transform on each IMF, convert it into a time domain signal, and then reconstruct all IMFs to obtain the corrected MEMS gas-sensitive response signal.

[0114] Preferably, the gas-sensitive response signal correction flow chart provided in the present application is as follows: Figure 3 shown.

[0115] In particular, the trend term obtained by EMD decomposition of the response signal of the MEMS gas sensor should not be corrected, because the trend term contains the long-term variation characteristics of the signal. Correcting the trend term may destroy this feature and cause the signal to be unable to be accurately reconstructed.

[0116] The corrected MEMS gas-sensitive response signal reduces the impact of humidity changes, avoids changes in the gas-sensitive characteristics of the gas sensor, and avoids the impact of baseline drift caused by humidity changes on subsequent gas sensor test gas-sensitive data, thereby improving the performance of the gas sensor and thus obtaining a more accurate target gas concentration.

[0117] At this point, based on the correction factor of each IMF, the gas-sensitive response signal is corrected, thereby improving the performance of the gas sensor.

[0118] Preferably, the schematic diagram of the gas-sensitive detection process provided in the present application is as follows: Figure 4 shown.

[0119] Based on the same inventive concept as the above method, an embodiment of the present application further provides a MEMS gas sensor, wherein a characteristic test of the gas sensor adopts the steps of any one of the above-mentioned gas sensitivity test methods of the MEMS gas sensor.

[0120] Based on the same inventive concept as the above method, an embodiment of the present application also provides a MEMS gas sensor gas sensitivity testing device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned MEMS gas sensor gas sensitivity testing methods are implemented.

[0121] Based on the same inventive concept as the above method, the embodiment of the present application also provides a MEMS gas sensor gas sensitivity testing system, including:

[0122] The data acquisition module acquires the gas-sensitive response signal of the gas sensor and the humidity response signal of the humidity sensor within the sampling time;

[0123] The data analysis module determines the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal;

[0124] Decompose the gas-sensitive response signal into multiple IMFs, and divide each IMF into multiple sub-IMFs with equal frequency intervals;

[0125] Determine the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal;

[0126] The gas-sensitive data correction module determines a correction factor of each IMF based on the first influencing factor and the second influencing factor to correct the gas-sensitive response signal.

[0127] The gas sensitivity test system block diagram provided in this application is as follows: Figure 5 shown.

[0128] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0130] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for testing gas sensitivity of a MEMS gas sensor, characterized in that: The method comprises the following steps: Obtaining a gas-sensitive response signal of the gas sensor and a humidity response signal of the humidity sensor within a sampling period; Determine the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal; Decompose the gas-sensitive response signal into multiple IMFs, and divide each IMF into multiple sub-IMFs with equal frequency intervals; Determine the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal; Determining a correction factor for each IMF based on the first influencing factor and the second influencing factor to correct the gas-sensitive response signal; The method for determining the first influencing factor of the humidity response signal is: Divide the humidity response signal into multiple sub-signals with equal time intervals, and analyze the standard deviation of all humidity data in each sub-signal in the humidity response signal; Analyze the cumulative sum of the standard deviations of all sub-signals in the humidity response signal, and record it as the humidity change amplitude of the humidity response signal; Determine the humidity change trend of the humidity response signal based on the change trend of all humidity data in the humidity response signal; Analyze the sum of the humidity change amplitude and the humidity change trend of the humidity response signal, and determine the product of the sum and the mean value of all humidity data in the humidity response signal as the first influencing factor of the humidity response signal; The determination process of the second impact factor of each IMF is as follows: Determine the energy difference index of each IMF based on the difference between the energies of all adjacent sub-IMFs in each IMF; The second influencing factor of each IMF is the result of the fusion of the correlation between each IMF and the humidity response and the energy difference index; The method for determining the energy difference index of each IMF is: Analyze the difference between the energy of each sub-IMF in each IMF and the energy of its previous sub-IMF, and record it as the first difference of each sub-IMF in each IMF; Analyze the ratio of the first difference of each sub-IMF in each IMF to the energy of its previous sub-IMF, and record it as the first ratio of each sub-IMF in each IMF; The energy difference index of each IMF is the result of the fusion of the first ratios of all sub-IMFs in each IMF; The expression of the correction factor of each IMF is: j =norm(S×C j ), where norm() represents the normalization function; σ j represents the correction factor of the jth IMF; S represents the first influencing factor of the humidity response signal; C j represents the second impact factor of the j-th IMF.

2. A method for testing gas sensitivity of a MEMS gas sensor as claimed in claim 1, characterized in that: The expression of the humidity change trend of the humidity response signal is: Where, Q represents the humidity change trend of the humidity response signal; D i represents the number of humidity data in the ith sub-signal in the humidity response signal that is equal to the mean value of all humidity data in the sub-signal; N represents the number of all sub-signals in the humidity response signal.

3. A method for testing gas sensitivity of a MEMS gas sensor as claimed in claim 1, characterized in that: The step of correcting the gas-sensitive response signal comprises: The average value of all frequency values ​​in each IMF is taken as the main frequency of each IMF, and the frequency of the first quartile of all frequency values ​​in each IMF sorted from small to large is taken as the cutoff frequency of each IMF, where the cutoff frequency of each IMF is less than the main frequency of the corresponding IMF; When the frequency value in each IMF is greater than its main frequency, the frequency value in each IMF remains unchanged; When the frequency value in each IMF is less than or equal to its cutoff frequency, the corresponding frequency value in each IMF is set to 0; When the frequency value in each IMF is greater than its cutoff frequency and less than or equal to its main frequency, each corrected frequency value in each IMF is the product of each pre-corrected frequency value in each IMF and the correction factor; All corrected IMFs are reconstructed to obtain the corrected gas-sensitive response signal.

4. A gas sensitivity testing device for a MEMS gas 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, the steps of the gas sensitivity testing method of a MEMS gas sensor as described in any one of claims 1 to 3 are implemented.

5. A MEMS gas sensor gas sensitivity test system, implementing a MEMS gas sensor gas sensitivity test method as claimed in claim 1, characterized in that: The system comprises: The data acquisition module acquires the gas-sensitive response signal of the gas sensor and the humidity response signal of the humidity sensor within the sampling time; The data analysis module determines the first influencing factor of the humidity response signal according to the discrete degree, change trend and average distribution of all humidity data in the humidity response signal; Decompose the gas-sensitive response signal into multiple IMFs, and divide each IMF into multiple sub-IMFs with equal frequency intervals; Determine the second influencing factor of each IMF according to the difference between the energies of all adjacent sub-IMFs in each IMF and the correlation between each IMF and the humidity response signal; The gas-sensitive data correction module determines a correction factor of each IMF based on the first influencing factor and the second influencing factor to correct the gas-sensitive response signal.

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