A method of pressure measurement for a MEMS pressure sensor

Abstract

The application provides a MEMS pressure sensor pressure measurement method, and relates to the technical field of sensors, which comprises the following steps: collecting pressure output values at a basic sampling interval and preprocessing; constructing a pressure value time window based on the preprocessed pressure output values, calculating the change amplitude in the window, determining the pressure signal state and calculating the change intensity index according to the change amplitude; comparing the change intensity index with a high-frequency trigger intensity threshold and a low-frequency recovery intensity threshold, and determining whether to switch the sampling interval according to the comparison result; when it is determined to switch to a high-frequency sampling interval, constructing a weighted sliding window, inputting the preprocessed pressure output values into the window, and calculating the pressure output value at the current time. Through adaptive sampling interval switching and weighted sliding window filtering, the transient response capability and output value stability of pressure measurement are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of sensor technology, specifically to a pressure measurement method using a MEMS pressure sensor. Background Technology

[0002] MEMS (Micro-Electro-Mechanical Systems) pressure sensors are widely used in industrial process monitoring, environmental meteorological detection, aerospace, and safety protection systems due to their advantages such as small size, low power consumption, fast response, and ease of integration. In these applications, pressure signals often exhibit variability: the signal is relatively smooth under steady-state conditions, while sudden, severe pressure surges occur during startup, shutdown, or abnormal events. To accurately capture the entire pressure change process, the measurement system must be able to respond to rapidly changing signals while maintaining the stability and reliability of the output value to avoid control decision errors or safety protection failures caused by measurement distortion.

[0003] In the prior art, patent CN202411206974.5 discloses a method, system, device and medium for nonlinear correction of MEMS pressure sensors. The technology includes: acquiring the output value of the MEMS pressure sensor; inputting the output value into a pre-established nonlinear correction model to obtain the nonlinear correction value of the MEMS pressure sensor; wherein the nonlinear correction model is pre-established by optimizing the parameters of the support vector machine through the memetic particle swarm optimization algorithm.

[0004] However, the aforementioned existing technologies use a fixed sampling interval to collect pressure output values. To balance endurance, a lower sampling rate is selected, resulting in sparse sampling points during rapid pressure changes. This makes it difficult to fully record the peaks and transition details of the waveform, easily missing key extreme values, and the instantaneous output value cannot accurately reflect the current pressure level. At the same time, this fixed sampling method lacks a means to quantitatively evaluate the severity of pressure signal changes and cannot adjust the sampling strategy according to the actual situation. The risk of measurement distortion increases significantly when facing transient pressure shocks. In addition, the existing technology only performs simple processing on the collected data and does not optimize noise reduction for the characteristics of data during high-speed sampling. The stability and smoothness of the output value are insufficient, making it difficult to meet the actual needs of industrial monitoring, environmental detection, and safety protection for transient response capability and measurement reliability.

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

[0006] The purpose of this invention is to provide a pressure measurement method for MEMS pressure sensors to solve the problems mentioned in the background art. This invention constructs a pressure value time window to calculate the change amplitude and change severity index, and combines dual threshold comparison and duration determination to achieve adaptive switching of the sampling interval. This ensures that during rapid changes, sampling is intensified to completely record the waveform, while during stable periods, low frequencies are restored to balance energy consumption. Simultaneously, during high-speed sampling, a weighted sliding window is used to process dense data, suppressing high-frequency measurement noise and improving the stability and smoothness of the output value, thereby ensuring the accuracy and reliability of the measurement results during dynamic processes.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A pressure measurement method using a MEMS pressure sensor includes the following steps: S1: Acquire the pressure output value of the MEMS pressure sensor at a preset basic sampling interval, and preprocess the acquired pressure output value; S2: Based on the preprocessed pressure output value, construct a pressure value time window; based on the pressure value time window, calculate the change range of the pressure value time window; based on the change range of the pressure value time window, determine the state of the pressure signal; and based on the change range of the pressure value time window, calculate the change intensity index. S3: Compare the change intensity index with a preset high-frequency trigger intensity threshold and a preset low-frequency recovery intensity threshold, and determine whether to switch the sampling interval based on the comparison result. The sampling interval includes a preset high-frequency sampling interval and a preset basic sampling interval. S4: When the preset high-frequency sampling interval is determined, a weighted sliding window is constructed, the preprocessed pressure output value is input into the weighted sliding window, and the pressure output value at the current moment is calculated based on the pressure output value in the weighted sliding window.

[0008] Furthermore, the method for preprocessing the collected pressure output values ​​is as follows: The collected pressure output values ​​are cleaned, including the handling of outliers and missing values. Statistical methods are used to identify outliers in the pressure output values, and outliers are deleted. Linear interpolation is used to fill in missing values ​​in the pressure output values.

[0009] Furthermore, the method for constructing the pressure value time window is as follows: Using the current acquisition time t as the end time of the pressure value time window, backtracking in the reverse direction of time for a preset time length w, the start time of the pressure value time window is determined as tw, and the pressure value time window is constructed using the time range between the start time tw and the end time t. Extract all preprocessed pressure output values ​​whose timestamps fall within the pressure value time window to form a subset of the pressure value time window data.

[0010] Furthermore, the formula used to calculate the variation range of the pressure value within the time window is as follows: ; in, for The magnitude of change in pressure values ​​over a time window; for The preprocessed pressure output value at any given time; The maximum value of all preprocessed pressure output values ​​within the pressure value time window; It is the minimum value of all preprocessed pressure output values ​​within the pressure value time window.

[0011] Furthermore, the determination logic for the state of the pressure signal is as follows: when And the duration exceeds the preset duration for determining the duration. When the current pressure signal is determined to be in a stable state, a stable state signal is output. when And the duration exceeds the preset duration for determining the duration. When the current pressure signal is determined to be in a fluctuating state, a fluctuating state warning signal is output. when However, the duration did not exceed the preset duration for determining the duration. Sometimes, or However, the duration did not exceed the preset duration for determining the duration. At that time, the state determination result of the pressure signal at the previous moment remains unchanged; in, This is the preset threshold for determining a stable state.

[0012] Furthermore, the formula used to calculate the drastic change index is as follows: ; in, for The index of the drastic change in time; This is the preset benchmark fluctuation range.

[0013] Furthermore, the logic for determining whether to switch the sampling interval based on the comparison result is as follows: when And the duration exceeds the preset first duration. When the time is right, the system will switch to the preset high-frequency sampling interval. when And the duration exceeds the preset second duration. When the time is right, the system will switch to the preset base sampling interval. when At that time, or when However, the duration did not exceed the preset first duration. , or when However, the duration did not exceed the preset second duration. At that time, the current sampling interval remains unchanged; in, The preset high-frequency trigger intensity threshold, The preset low-frequency recovery intensity threshold is used, and it satisfies... .

[0014] Furthermore, the method for constructing the weighted sliding window is as follows: Using the current acquisition time t as the end point of the weighted sliding window, backtracking in the reverse direction of time, extract the preprocessed pressure output values ​​corresponding to M consecutive acquisition times to form a weighted sliding window, where M is the preset weighted sliding window length, and M≥3.

[0015] Furthermore, after M preprocessed pressure output values ​​have been input into the weighted sliding window, each time a new preprocessed pressure output value corresponding to a new acquisition time is input, the preprocessed pressure output value corresponding to the new acquisition time is input into the weighted sliding window, while the preprocessed pressure output value corresponding to the earliest acquisition time in the weighted sliding window is removed, so that the weighted sliding window always contains the preprocessed pressure output values ​​corresponding to the most recent M acquisition times. Furthermore, the formula used to calculate the pressure output value at the current time is: ; in, for Pressure output value at any given time; for The preprocessed pressure output value at any given time; The preset high-frequency sampling interval; For the preset first The weighting coefficients corresponding to each pressure output value, and satisfying the following conditions: .

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention fundamentally solves the problem of fixed low-frequency sampling failing to fully capture dynamic processes during rapid pressure changes by adaptively switching the sampling interval, ensuring that the instantaneous value output by the system at any given moment accurately reflects the current pressure level. When pressure changes drastically, it automatically switches to high-frequency sampling to densely collect and record the peak values ​​and transition details of the pressure waveform, avoiding missing critical extreme values ​​due to sparse sampling points. When the pressure stabilizes, it restores the basic sampling interval, ensuring measurement accuracy while considering overall system energy consumption, making it suitable for various power supply scenarios. Simultaneously, by constructing a pressure value time window and calculating the change amplitude and drasticness index, combined with dual threshold comparison and duration determination, it effectively eliminates transient noise interference, ensuring the accuracy and reliability of frequency switching. Furthermore, during high-speed sampling, a weighted sliding window is used to process dense data, allocating weights according to time proximity, effectively suppressing high-frequency measurement noise, improving the stability and smoothness of the output value, and reducing the interference of random noise on the measurement results. This is particularly suitable for applications with stringent requirements for transient response capabilities, such as industrial monitoring, environmental detection, and security protection. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall process of a pressure measurement method using a MEMS pressure sensor. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: Please see Figure 1 The present invention provides a technical solution: A pressure measurement method using a MEMS pressure sensor includes the following steps: S1: Acquire the pressure output value of the MEMS pressure sensor at a preset basic sampling interval, and preprocess the acquired pressure output value. Specifically, according to a pre-set fixed time period, read the raw pressure signal value output by the sensor at the current moment, forming a discrete sampling sequence continuously distributed over time. Each data point in this sequence directly reflects the instantaneous pressure response of the sensor at that sampling moment. Due to the coupling effect of the sensor's own characteristics and external environmental factors, the acquired raw pressure output value inevitably contains various interference components, including random fluctuations caused by circuit noise, quantization errors, temperature drift, etc., as well as numerical mutations and omissions caused by communication packet loss, data misalignment, or occasional abnormal output of the sensor. Therefore, before using the acquired data for subsequent window construction and pressure state determination, a systematic preprocessing operation needs to be performed on the raw sampling sequence. The purpose of preprocessing is to remove or correct these abnormal components so that the data sequence of pressure output values ​​can truly and accurately represent the actual pressure change trend. At the same time, it ensures that the data used in subsequent analysis has sufficient credibility and consistency, and avoids the distortion of window change amplitude calculation, error in state judgment logic and deviation of severity index evaluation caused by the introduction of abnormal data points. This provides an accurate and reliable data foundation for the stable operation of the entire pressure measurement method. The method for preprocessing the collected pressure output values ​​is as follows: Data cleaning is performed on the collected pressure output values. This data cleaning includes handling outliers and missing values. First, for each collected pressure output value, statistical methods are used to identify outliers that significantly deviate from the normal fluctuation range. Specifically, the lower and upper quartiles of all pressure output values ​​are calculated to determine the main distribution range of the data. The interquartile range is then expanded outwards by a preset multiple to obtain reasonable upper and lower boundaries of numerical fluctuations. Pressure output values ​​exceeding these boundaries are identified as outliers. These outliers originate from occasional sensor interference, transient environmental shocks, or data distortion during the acquisition process. Their values ​​significantly deviate from the normal trend of pressure output values ​​at adjacent times. Retaining them would directly affect the accuracy of subsequent window variation calculations. Outliers are removed to ensure that all pressure output values ​​used in subsequent analysis are valid data that conform to normal fluctuation patterns, avoiding interference from outliers in extreme value extraction and amplitude calculation within the window. Meanwhile, for pressure output values ​​missing during the acquisition process due to momentary communication interruptions, buffer overflows, or sampling time offsets, linear interpolation is used to fill in the gaps. This involves linearly calculating the approximate pressure output value at the missing time based on the valid pressure output values ​​of adjacent times before and after the missing time, according to the proportional relationship of the time intervals. This ensures that the pressure output values ​​at each time point form a continuous, equally spaced, and uninterrupted complete dataset on the time axis, providing a reliable and complete data foundation for constructing subsequent pressure value time windows, and guaranteeing the continuity and accuracy of the amplitude calculation within the window.

[0021] S2: Based on the preprocessed pressure output value, a pressure value time window is constructed. Based on the pressure value time window, the change amplitude of the pressure value time window is calculated. This change amplitude can comprehensively reflect the overall intensity of pressure fluctuation within the pressure value time window period and is the core basis for subsequent judgment and calculation. Based on the change amplitude of the pressure value time window, the state of the pressure signal is determined to clarify whether the signal is in a relatively stable state or exhibits a fluctuating change state. Based on the change amplitude of the pressure value time window, the change intensity index is calculated. This change intensity index quantitatively characterizes the relative strength of pressure fluctuation through normalization, providing a quantitative comparison basis for subsequent sampling interval switching judgment. The method for constructing the pressure value time window is as follows: The pressure value time window ends at the current sampling time t, meaning that the pressure value time window is closed on the time axis with the latest sampling time as the boundary. A preset time length w is traced back in the reverse direction of time to determine the start time tw of the pressure value time window. The pressure value time window is constructed using the time range between the start time tw and the end time t. The preset time length w determines the range of historical data covered by the window, and its value directly affects the number of pressure output values ​​included in the window and the time span on which the calculation of the change amplitude is based. Based on this, all preprocessed pressure output values ​​with timestamps within the pressure value time window are extracted. That is, from the complete dataset that has undergone cleaning, all valid pressure output values ​​with sampling times between tw and t (including endpoints) are selected and organized in chronological order to form a data subset of the pressure value time window. This data subset centrally reflects the continuous change trajectory of pressure within a period of time prior to the current moment.

[0022] The formula used to calculate the variation range of the pressure value within the time window is as follows: ; in, for The magnitude of change in pressure values ​​over a time window; for The preprocessed pressure output value at any given time; The maximum value of all preprocessed pressure output values ​​within the pressure value time window; It is the minimum value of all preprocessed pressure output values ​​within the pressure value time window.

[0023] when The larger the value, the more significant the pressure signal has changed, indicating a substantial increase or decrease within that time period. Physically, this corresponds to a rapid surge, step, or oscillation in the pressure of the measured medium. In this case, the sensor output value has significantly deviated from its stable range, and the system needs to pay close attention to subsequent trends. Conversely, when... The smaller the value, the more concentrated the pressure output value is within a narrow amplitude range at each moment within the window. The pressure signal tends to be stable, and the measured object is in a relatively constant working state, so high-frequency tracking is not required.

[0024] The logic for determining the state of the pressure signal is as follows: when And the duration exceeds the preset duration for determining the duration. When the pressure signal is in a stable state, the system determines that the current pressure signal is in a stable state and outputs a stable state signal. This determination logic requires that the change amplitude be below the threshold for a sufficiently long time, rather than making a stable determination as soon as it is below the threshold. The purpose is to eliminate misjudgments caused by brief noise disturbances or occasional numerical drops, and to ensure that the pressure signal has indeed transitioned from a fluctuating state to a stable range. Only when the change amplitude of the window remains below the stable threshold for a sufficient duration in multiple consecutive acquisition times is the system considered to be in a stable working state and the corresponding signal is output. when And the duration exceeds the preset duration for determining the duration. When the current pressure signal is determined to be in a fluctuating state, a fluctuating state warning signal is output. This determination logic requires that the change amplitude reaches or exceeds the threshold for a sufficiently long time, rather than triggering the fluctuation warning immediately due to a momentary jump. This is to distinguish between continuous fluctuations caused by physical pressure changes and occasional interference pulses. Only when the change amplitude continuously exceeds the threshold for a time span that is sufficient to indicate that the pressure signal has deviated from the stable range is the existence of the fluctuating state confirmed and a warning signal is output, providing a reliable decision basis for the adaptive switching of the subsequent sampling interval. when However, the duration did not exceed the preset duration for determining the duration. Sometimes, or However, the duration did not exceed the preset duration for determining the duration. At the same time, the pressure signal state determination result of the previous moment is kept unchanged. This processing method introduces a determination lag mechanism to maintain the historical determination result within the transition interval of state determination. This avoids repeated jumps in the state signal caused by the change amplitude frequently crossing the threshold. This eliminates the instability of the state determination result during high-frequency switching, keeps the pressure signal state output continuous and smooth, and provides a sufficient observation window for state transition. Only after the change trend has sufficient continuity and directional certainty is the state determination result allowed to change. in, This is the preset threshold for determining a stable state.

[0025] The formula used to calculate the drastic change index is as follows: ; in, for The index of the drastic change in time; This is the preset benchmark fluctuation range.

[0026] When the preset benchmark fluctuation range The larger the value, the greater the drastic change index. The smaller the value, the higher the system's tolerance to fluctuations. The severity of the impact of the same actual pressure change will be assessed accordingly, making it suitable for measurement scenarios with strong background noise or where the pressure itself fluctuates slightly under normal operating conditions. Conversely, a larger value indicates a higher tolerance. The smaller, The larger the value, the higher the system's sensitivity to fluctuations. Even small pressure changes can be amplified into significant and drastic responses, making it suitable for precision measurement applications where pressure stability is extremely important.

[0027] S3: Compare the change intensity index with a preset high-frequency trigger intensity threshold and a preset low-frequency recovery intensity threshold, and determine whether to switch the sampling interval based on the comparison result. The sampling interval includes a preset high-frequency sampling interval and a preset basic sampling interval. The logic for determining whether to switch the sampling interval based on the comparison result is as follows: when And the duration exceeds the preset first duration. When the signal is strong and continuous, the energy of the fluctuation is sufficient and there is no sign of weakening. Therefore, it is determined to switch to the preset high-frequency sampling interval so as to capture the rapid evolution details of the pressure waveform with a more intensive sampling rhythm in the subsequent period. when And the duration exceeds the preset second duration. When the current pressure signal has been in a sufficiently stable range and the fluctuation amplitude has remained at a low level for a long time, there is no longer a need for high-frequency sampling. Therefore, it is determined to switch to the preset basic sampling interval to reduce redundant acquisition load. when This indicates that the current fluctuation level is neither weak enough to trigger a frequency reduction nor strong enough to trigger a frequency increase, therefore the current sampling interval will remain unchanged. when However, the duration did not exceed the preset first duration. This indicates that the sharp fluctuations are not yet sufficiently sustained and may be temporary spikes, or... However, the duration did not exceed the preset second duration. If this occurs, it indicates that the low-fluctuation state has not been fully verified and there is still a possibility of a rapid rebound. In both of these cases, the current sampling interval will remain unchanged. in, The preset high-frequency trigger intensity threshold, The preset low-frequency recovery intensity threshold is used, and it satisfies... .

[0028] S4: When the preset high-frequency sampling interval is switched, a weighted sliding window is constructed, the preprocessed pressure output value is input into the weighted sliding window, and the pressure output value at the current moment is calculated based on the pressure output value in the weighted sliding window; The method for constructing the weighted sliding window is as follows: Using the current acquisition time t as the endpoint of the weighted sliding window, backtracking in the reverse direction of time, the preprocessed pressure output values ​​corresponding to the M consecutive acquisition times closest to the current time are extracted in sequence to form a weighted sliding window. Here, M is the preset weighted sliding window length, and M≥3, to ensure that the window contains a sufficient number of samples for subsequent weighted calculations, thereby ensuring the representativeness and stability of the output results.

[0029] After M preprocessed pressure output values ​​have been input into the weighted sliding window, each time a new preprocessed pressure output value corresponding to a new acquisition time is input, the preprocessed pressure output value corresponding to the new acquisition time is input into the weighted sliding window. At the same time, the preprocessed pressure output value corresponding to the earliest acquisition time in the weighted sliding window is removed. This ensures that the weighted sliding window always contains the preprocessed pressure output values ​​corresponding to the most recent M acquisition times. Through this first-in-first-out update mechanism, the weighted sliding window always retains only the values ​​closest to the current time when new data arrives. The preprocessed pressure output value corresponding to each acquisition moment is used to ensure that the window content is always updated in real time with the acquisition process, providing a historical data basis that best fits the current state for subsequent pressure output value calculation.

[0030] The formula used to calculate the pressure output value at the current moment is: ; in, for Pressure output value at any given time; for The preprocessed pressure output value at any given time; The preset high-frequency sampling interval; For the preset first The weighting coefficients corresponding to each pressure output value, and satisfying the following conditions: , ; Follow The weighting decreases as the data point increases, meaning that data points closer to the current moment are assigned a larger weight, and data points farther away are assigned a smaller weight. This implies that recent pressure trends have a more significant impact on current measurement results, while historical data gradually becomes less representative of the current moment. When the pressure output value at a certain historical moment experiences a sudden jump due to random noise, this outlier is only represented by its corresponding weight coefficient during the weighted summation process. The proportion of contributions made, while The value of is designed to be less than 1 and the sum of all weights is normalized to 1. Therefore, the influence of the outlier is effectively compressed to its weight ratio range and will not dominate the entire weighted summation result, thereby suppressing the interference of high-frequency random noise on the output value. At the same time, since the pressure output value of the adjacent time is given a higher weight, the real pressure change trend at the current time can be quickly transmitted to the output result through the high weight term, ensuring that the output value maintains sufficient tracking sensitivity to the actual change of physical pressure.

[0031] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0032] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0033] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0034] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A pressure measurement method using a MEMS pressure sensor, characterized in that, Includes the following steps: S1: Acquire the pressure output value of the MEMS pressure sensor at a preset basic sampling interval, and preprocess the acquired pressure output value; S2: Based on the preprocessed pressure output value, construct a pressure value time window; based on the pressure value time window, calculate the change range of the pressure value time window; based on the change range of the pressure value time window, determine the state of the pressure signal; and based on the change range of the pressure value time window, calculate the change intensity index. S3: Compare the change intensity index with a preset high-frequency trigger intensity threshold and a preset low-frequency recovery intensity threshold, and determine whether to switch the sampling interval based on the comparison result. The sampling interval includes a preset high-frequency sampling interval and a preset basic sampling interval. S4: When the preset high-frequency sampling interval is determined, a weighted sliding window is constructed, the preprocessed pressure output value is input into the weighted sliding window, and the pressure output value at the current moment is calculated based on the pressure output value in the weighted sliding window.

2. The pressure measurement method of a MEMS pressure sensor according to claim 1, characterized in that: The method for preprocessing the collected pressure output values ​​is as follows: The collected pressure output values ​​are cleaned, including the handling of outliers and missing values. Statistical methods are used to identify outliers in the pressure output values, and outliers are deleted. Linear interpolation is used to fill in missing values ​​in the pressure output values.

3. The pressure measurement method of a MEMS pressure sensor according to claim 2, characterized in that: The method for constructing the pressure value time window is as follows: Using the current acquisition time t as the end time of the pressure value time window, backtracking in the reverse direction of time for a preset time length w, the start time of the pressure value time window is determined as tw, and the pressure value time window is constructed using the time range between the start time tw and the end time t. Extract all preprocessed pressure output values ​​whose timestamps are located within the pressure value time window to form a subset of the pressure value time window data.

4. The pressure measurement method of a MEMS pressure sensor according to claim 3, characterized in that: The formula used to calculate the variation range of the pressure value within the time window is as follows: ； in, for The magnitude of change in pressure values ​​over a time window; for The preprocessed pressure output value at any given time; The maximum value of all preprocessed pressure output values ​​within the pressure value time window; It is the minimum value of all preprocessed pressure output values ​​within the pressure value time window.

5. The pressure measurement method of a MEMS pressure sensor according to claim 4, characterized in that: The logic for determining the state of the pressure signal is as follows: when And the duration exceeds the preset duration for determining the duration. When the current pressure signal is determined to be in a stable state, a stable state signal is output. when And the duration exceeds the preset duration for determining the duration. When the current pressure signal is determined to be in a fluctuating state, a fluctuating state warning signal is output. when However, the duration did not exceed the preset duration for determining the duration. Sometimes, or However, the duration did not exceed the preset duration for determining the duration. At that time, the state determination result of the pressure signal at the previous moment remains unchanged; in, This is the preset threshold for determining a stable state.

6. The pressure measurement method of a MEMS pressure sensor according to claim 5, characterized in that: The formula used to calculate the drastic change index is as follows: ； in, for The index of the drastic change in time; This is the preset benchmark fluctuation range.

7. The pressure measurement method of a MEMS pressure sensor according to claim 6, characterized in that: The logic for determining whether to switch the sampling interval based on the comparison result is as follows: when And the duration exceeds the preset first duration. When the time is right, the system will switch to the preset high-frequency sampling interval. when And the duration exceeds the preset second duration. When the time is right, the system will switch to the preset base sampling interval. when At that time, or when However, the duration did not exceed the preset first duration. , or when However, the duration did not exceed the preset second duration. At that time, the current sampling interval remains unchanged; in, The preset high-frequency trigger intensity threshold, The preset low-frequency recovery intensity threshold is used, and it satisfies... .

8. The pressure measurement method of a MEMS pressure sensor according to claim 2, characterized in that: The method for constructing the weighted sliding window is as follows: Using the current acquisition time t as the end point of the weighted sliding window, backtracking in the reverse direction of time, extract the preprocessed pressure output values ​​corresponding to M consecutive acquisition times to form a weighted sliding window, where M is the preset weighted sliding window length, and M≥3.

9. A pressure measurement method for a MEMS pressure sensor according to claim 8, characterized in that: After M preprocessed pressure output values ​​have been input into the weighted sliding window, each time a new preprocessed pressure output value corresponding to a new acquisition time is input, the preprocessed pressure output value corresponding to the new acquisition time is input into the weighted sliding window, and at the same time, the preprocessed pressure output value corresponding to the earliest acquisition time in the weighted sliding window is removed, so that the weighted sliding window always contains the preprocessed pressure output values ​​corresponding to the most recent M acquisition times.

10. A pressure measurement method for a MEMS pressure sensor according to claim 9, characterized in that: The formula used to calculate the pressure output value at the current moment is: ； in, for Pressure output value at any given time; for The preprocessed pressure output value at any given time; The preset high-frequency sampling interval; For the preset first The weighting coefficients corresponding to each pressure output value, and satisfying the following conditions: .

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