A Compensation Method for a Silicon Resonant Pressure Sensor

Abstract

The present invention provides a compensation method for a silicon resonant pressure sensor, which relates to the technical field of pressure measurement and includes: collecting the frequency signal timing output by the silicon resonant pressure sensor and analyzing to obtain the real-time pressure value; obtaining a predetermined compensation item, collecting multi-dimensional compensation features, and obtaining multi-dimensional compensation information; traversing and screening a historical compensation database with the multi-dimensional compensation information as a screening constraint to obtain a target sensor; normalizing and comparing the pressure value deviation obtained from the detected pressure value of the target sensor and a preset pressure value to obtain a multi-dimensional compensation coefficient; performing compensation adjustment to obtain an effective pressure value. The present invention solves the technical problem that most of the existing compensation methods only adjust for a single influencing factor, ignoring the comprehensive influence of multi-dimensional factors, resulting in the inability to effectively cope with multiple error sources under complex environmental conditions, and further leading to insufficient accuracy and reliability of the silicon resonant pressure sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure measurement, and particularly to a compensation method for a silicon resonant pressure sensor. Background Art

[0002] A silicon resonant pressure sensor is a miniaturized, low-power, and high-precision pressure sensor developed specifically for the meteorological field. Its core principle is to measure pressure by the change of the resonant frequency of the silicon material with pressure. However, due to the influence of factors such as temperature, material aging, process variation, and environmental change during the use of the silicon resonant pressure sensor, there are certain errors in its output signal. Therefore, how to accurately compensate the influence of these factors on the performance of the pressure sensor has become a key issue in improving its accuracy and reliability.

[0003] Existing compensation methods for silicon resonant pressure sensors mainly solve the sensor error problem through calibration and simplified compensation algorithms. However, these methods usually ignore the comprehensive influence of multi-dimensional factors. Most traditional compensation methods only adjust for a single factor (such as temperature or pressure range) and cannot effectively cope with multiple error sources under complex environmental conditions, resulting in insufficient accuracy and reliability of silicon resonant pressure sensors. Summary of the Invention

[0004] This application provides a compensation method for a silicon resonant pressure sensor, aiming to solve the technical problem that most of the existing compensation methods only adjust for a single factor, ignoring the comprehensive influence of multi-dimensional factors, resulting in the inability to effectively cope with multiple error sources under complex environmental conditions, and further resulting in insufficient accuracy and reliability of silicon resonant pressure sensors.

[0005] A compensation method for a silicon resonant pressure sensor disclosed in this application includes: collecting the time series of the frequency signal output by the silicon resonant pressure sensor, and analyzing the time series of the frequency signal to obtain the real-time pressure value at the real time; obtaining a predetermined compensation item, and collecting multi-dimensional compensation features of the silicon resonant pressure sensor based on the predetermined compensation item to obtain multi-dimensional compensation information; traversing and screening a historical compensation database with the multi-dimensional compensation information as a screening constraint to obtain a target sensor; normalizing and comparing the pressure value deviation obtained from the detected pressure value and the preset pressure value of the target sensor to obtain a multi-dimensional compensation coefficient; compensating and adjusting the real-time pressure value with the multi-dimensional compensation coefficient as a weight to obtain the effective pressure value of the silicon resonant pressure sensor at the real time.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By collecting the time series of frequency signals in real time and analyzing them, the pressure value of the current sensor can be accurately obtained, which provides the basic data for subsequent compensation and enables the system to quickly respond to pressure changes. By collecting multi-dimensional compensation characteristics of the sensor based on predetermined compensation items, various factors affecting the sensor performance can be comprehensively considered, which effectively integrates the performance of the sensor under different conditions, helps to compensate for errors caused by various environments or manufacturing processes, and provides more accurate compensation data. By screening the target sensor in the historical compensation database, the most matching compensation reference can be found according to the existing data. This process ensures that the known data of historical sensors can be used to provide compensation references for the current sensor, avoiding complex modeling from scratch and improving the efficiency of sensor compensation. By comparing the detected pressure value of the target sensor with the preset pressure value, the pressure deviation can be accurately calculated, and after normalization, multi-dimensional compensation coefficients are obtained, eliminating the deviation caused by sensor errors, further improving the reliability of compensation, and ensuring the accuracy of measurement results. Using the multi-dimensional compensation coefficients as weights to adjust the real-time pressure value, the effective pressure value is finally obtained. This compensation adjustment can dynamically correct the sensor during the real-time measurement process, effectively eliminating various factors affecting the sensor measurement results, making the finally obtained effective pressure value more accurate and adaptable to changes in different environments.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 This is a schematic flow chart of a compensation method for a silicon resonant pressure sensor provided by an embodiment of this application.

[0010] Figure 2 This is a schematic flow chart of the real-time pressure value verification process in a compensation method for a silicon resonant pressure sensor provided by an embodiment of this application. Detailed Description of the Embodiments

[0011] By providing a compensation method for a silicon resonant pressure sensor in an embodiment of this application, the technical problem that most of the existing compensation methods only adjust for single factors, ignoring the comprehensive influence of multi-dimensional factors, resulting in the inability to effectively cope with multiple error sources under complex environmental conditions, and further leading to insufficient accuracy and reliability of silicon resonant pressure sensors is solved.

[0012] After introducing the basic principle of this application, the various non-restrictive embodiments of this application will be specifically introduced below in conjunction with the drawings of the specification.

[0013] As Figure 1 shown, an embodiment of the present application provides a compensation method for a silicon resonant pressure sensor, and the method includes:

[0014] Collect the time sequence of the frequency signal output by the silicon resonant pressure sensor, and analyze the time sequence of the frequency signal to obtain the real-time pressure value at the real-time moment.

[0015] The silicon resonant pressure sensor is a miniaturized, low-power, and high-precision pressure sensor developed specifically for the meteorological field. A high-speed data acquisition card or a dedicated interface device is used to collect the frequency output of the silicon resonant pressure sensor in real time. The working principle of the silicon resonant pressure sensor is that the resonance frequency of the resonant beam changes due to pressure changes, and the frequency change has a certain relationship with the pressure. The acquisition process should ensure the high precision and high frequency of the signal to avoid errors caused by too low sampling rate. Perform time-domain analysis on the collected time sequence of the frequency signal, extract relevant features of the signal, such as statistical quantities such as maximum value, minimum value, average value, and standard deviation. The time-domain features are used to preliminarily judge the change trend of the signal. Through the known mathematical relationship between frequency and pressure, the collected frequency signal is converted into a real-time pressure value. A linear function between pressure and frequency can be used for calculation, that is, the frequency change amount is linearly proportional to the pressure change, and the real-time pressure value is directly calculated based on the frequency deviation.

[0016] Obtain a predetermined compensation item, and collect multi-dimensional compensation features for the silicon resonant pressure sensor based on the predetermined compensation item to obtain multi-dimensional compensation information.

[0017] Obtain a predetermined compensation item. The predetermined compensation item is set according to the factors affecting the monitoring accuracy of the silicon resonant pressure sensor, including design rationality compensation, material property compensation, process adaptability compensation, and environmental suitability compensation. Collect relevant compensation features for each compensation item. Specifically, for design rationality compensation, consider the modal coupling effect of the resonant beam and collect the frequency response features under different design schemes; for material property compensation, consider the influence of temperature change on the Young's modulus of silicon material and collect the frequency data at different temperatures; for process adaptability compensation, collect the influence of errors occurring in the manufacturing process on the frequency signal, especially the changes in geometric shape or surface roughness occurring in the microfabrication process; for environmental suitability compensation, record the changes in the frequency signal under different environmental conditions, especially in the case of temperature or humidity changes. Integrate the characteristic information of each collected compensation item to obtain multi-dimensional compensation information. This information set can describe various factors affecting the accuracy of the silicon resonant pressure sensor in detail and provide support for subsequent compensation calculations.

[0018] Traverse and screen the historical compensation database with the multi-dimensional compensation information as the screening constraint to obtain the target sensor.

[0019] The historical compensation database contains compensation records of multiple known sensors. Each compensation record contains compensation data in multiple dimensions of the sensor, and these data can describe the compensation effects under different environments or usage scenarios. Taking the multi-dimensional compensation information extracted from the current sensor as a constraint condition, the data in the historical compensation database is screened. By calculating the similarity between the current multi-dimensional compensation information and each compensation data in the historical database, it is evaluated which historical compensation data is most suitable for the current sensor. For example, methods such as Euclidean distance or cosine similarity are used to measure the similarity between two sets of compensation features. Through screening, the target sensor that best matches the current sensor is found, and the historical compensation data of this target sensor can provide an effective compensation scheme for the current sensor.

[0020] Normalize the pressure value deviation obtained by comparing the detected pressure value of the target sensor with the preset pressure value to obtain a multi-dimensional compensation coefficient.

[0021] The historical compensation data of the target sensor contains the relationship with the corresponding pressure value. In this step, the actual detected pressure value of the target sensor in the historical data is extracted; the preset pressure value is a standardized reference pressure value, which represents the pressure value under ideal conditions. Compare the detected pressure value of the target sensor with the preset pressure value and calculate the deviation between them.

[0022] To eliminate the influence brought by different pressure ranges, units or dimensions, the deviation is normalized. For example, the min-max normalization method is adopted. By subtracting the minimum value and dividing by the value range, the deviation is compressed into a fixed range, usually between 0 and 1. Through the comparison analysis and normalization process, the multi-dimensional compensation coefficient is finally obtained. This coefficient is an adjustment factor for multiple compensation items (such as design, process, temperature, etc.).

[0023] Compensate and adjust the real-time pressure value with the multi-dimensional compensation coefficient as the weight to obtain the effective pressure value of the silicon resonant pressure sensor at the real-time moment.

[0024] The real-time pressure value is calculated based on the output frequency signal of the silicon resonant pressure sensor, and this pressure value may have deviations caused by factors such as sensor design and environmental impact. Use the obtained multi-dimensional compensation coefficient to compensate and adjust the real-time pressure value. The specific operation is to apply the compensation coefficient to the calculation formula of the real-time pressure value. That is, the compensation coefficient is combined with the real-time pressure value in a weighted manner to calculate the final effective pressure value. For example, assuming a certain compensation coefficient is 0.1, then the real-time pressure value is multiplied by (1 + 0.1) to achieve the adjustment. The compensated pressure value is the effective pressure value of the silicon resonant pressure sensor at the current moment. This value can provide a more accurate pressure measurement result through the correction of multiple factors such as design defects, material properties, and process impacts.

[0025] Furthermore, as Figure 2 shown, after collecting the time sequence of the frequency signal output by the silicon resonant pressure sensor and analyzing the time sequence of the frequency signal to obtain the real-time pressure value at the real-time moment, it further includes:

[0026] Collecting the time-domain characteristic parameters of the time sequence of the frequency signal; performing Fourier transform on the time sequence of the frequency signal to obtain the frequency signal spectrum; collecting the frequency-domain characteristic parameters of the frequency signal spectrum; performing modal decomposition on the time sequence of the frequency signal to obtain a set of modal components, where the set of modal components includes target components; collecting the component characteristic parameters of the target components and collaborating with the time-domain characteristic parameters and the frequency-domain characteristic parameters to form a frequency parameter set; using the frequency parameter set as the processing data of the pressure processing predictor and obtaining a predicted pressure value through the pressure processing predictor; and verifying the real-time pressure value through the predicted pressure value.

[0027] Performing time-domain analysis on the time sequence of the frequency signal. Time-domain analysis is to analyze the changes of the frequency signal over time, including the analysis of aspects such as the amplitude, frequency, and waveform of the signal. Time-domain analysis helps to understand the basic change trend of the signal. Through time-domain analysis, time-domain characteristic parameters are obtained, including: peak value, which is the maximum amplitude value of the signal and reflects the maximum change of the signal over time; mean value, which is the average value of the signal and represents the basic level of the signal in the time series; standard deviation, which reflects the volatility and change degree of the signal; time-domain period, which is the information of the periodicity of the signal and is related to the resonant frequency; maximum number of zero crossings, which is the number of zero crossings in the change of the frequency signal and can help to determine the periodicity and stability of the signal.

[0028] Fourier transform is to convert the time-domain signal into a frequency-domain signal. Through Fourier transform, the original time-series signal is decomposed into multiple different frequency components for analyzing the frequency characteristics of the signal. Performing Fourier transform on the collected time sequence of the frequency signal, the result of Fourier transform is presented as a spectrum, that is, the relationship between frequency and amplitude, which reveals the intensity distribution of the signal at each frequency. In practical applications, it can be identified which frequency components have an important impact on the response of the sensor, especially the part related to the resonant frequency.

[0029] Extract frequency-domain characteristic parameters from the frequency signal spectrum, including: the main frequency, which is the frequency component with the largest amplitude in the spectrum and is usually directly related to the resonant frequency of the sensor; the bandwidth, which is the width of the signal frequency range and reflects the frequency distribution range of the signal; the frequency distribution, which is the energy distribution of different frequency components and helps to identify noise or interference frequency bands; harmonic components. In the spectrum, if there are multiple frequency components that are integer multiples of the fundamental frequency, these components are called harmonic components. By analyzing the harmonic components, it is possible to identify whether the signal is affected by non-linear factors. The frequency-domain characteristic parameters are used to accurately identify the resonant frequency and can further analyze the frequency drift caused by temperature changes, material aging or process defects. By continuously monitoring these characteristics, the stability and accuracy of the silicon resonant pressure sensor can be improved.

[0030] Modal decomposition is to decompose the frequency signal into multiple modal components. Each modal represents a specific mode in the signal, such as different vibration modes or resonance modes. This method helps to identify and eliminate noise or irrelevant components in the signal. Modal decomposition methods include empirical modal decomposition, Hilbert-Huang transform, etc. Through these methods, different modal components can be extracted from the frequency signal.

[0031] After modal decomposition, a set of modal components composed of multiple modal components is obtained. Each modal component represents a vibration mode or resonance mode in the signal. In the set of modal components, the component that has the greatest impact on the current sensor performance is selected as the target component. The target component is the modal component most relevant to the resonant frequency of the sensor. By selecting the target component, the key frequency components affecting the sensor performance can be more accurately extracted. The target component can reflect the main response of the sensor under specific conditions, thus providing a basis for subsequent pressure calculation or compensation.

[0032] Extract the component characteristic parameters of the target component. The component characteristic parameters include the amplitude, phase, frequency, and frequency offset of the target component. Combine the time-domain characteristic parameters (such as mean, standard deviation, peak value, etc.) and frequency-domain characteristic parameters (such as main frequency, bandwidth, harmonics, etc.) with the component characteristic parameters (such as amplitude, phase, etc.) of the target component to form a complete set of frequency parameters. These characteristic parameters can reflect different aspects of the sensor response and provide comprehensive information for pressure prediction.

[0033] The pressure processing predictor is a machine learning model used to predict the pressure value of the sensor based on the input set of frequency parameters. This predictor is obtained through supervised learning training, and the specific training process will be detailed in the subsequent steps. Pass the obtained set of frequency parameters as input data to the predictor. Based on the set of frequency parameters, the predictor makes a prediction according to the relationship between the frequency parameters and the pressure values in the training data and outputs the predicted pressure value.

[0034] Verify the real-time pressure value through the predicted pressure value. Specifically, compare the obtained predicted pressure value with the real-time pressure value, calculate the error between the two, set a tolerance range. If the error is less than the predetermined tolerance range, the real-time pressure value is considered accurate; if the error is large, further compensation for the real-time pressure value is required.

[0035] Furthermore, perform modal decomposition on the timing of the frequency signal to obtain a set of modal components. Among them, the set of modal components includes target components, including:

[0036] Extract any component from the set of modal components; calculate the ratio of any information quantity between the any component and the timing of the frequency signal; sort the any component in descending order based on the ratio of the any information quantity to obtain a descending list of modal components; take the first component in the descending list of modal components as the target component.

[0037] Extract any component from the set of modal components. The any component refers to any one modal component randomly extracted from the component set and serves as the current analysis object.

[0038] In signal processing, the information quantity is used to measure the contribution degree of a signal or a component to the overall signal. The information quantity can be measured based on factors such as the energy distribution of the spectrum, amplitude change, etc. For any randomly extracted modal component, its information quantity can be calculated based on the following formula: , where represents the signal of the modal component at time t, represents the information quantity of the modal component. Calculate the ratio of the information quantity of the any modal component to the information quantity of the entire frequency signal to evaluate the importance of the modal component in the signal. The larger the ratio of the information quantity, the greater the influence of the modal component on the signal, and the higher its importance in pressure measurement.

[0039] Sort the modal components according to the calculated ratio of any information quantity. The component with a larger ratio of the information quantity indicates that it contributes more to the signal. Therefore, it should be placed in the front during sorting. After sorting, a descending list of modal components is obtained, and each modal component in this list is sorted according to its contribution to the signal.

[0040] In the descending list of modal components, the component ranked first is the component with the largest ratio of the information quantity, which means it is the component that has the most significant influence on the signal. Take the component ranked first as the target component. This target component represents the strongest vibration mode or resonance mode in the signal and is usually the main factor in the sensor response. Therefore, selecting this component as the target component helps to accurately describe the working state of the sensor and provides a reliable basis for subsequent pressure measurement and compensation.

[0041] Furthermore, using the frequency parameter set as the processing data of the pressure processing predictor and obtaining the predicted pressure value through the pressure processing predictor includes:

[0042] Obtaining the historical frequency signal time series of the silicon resonant pressure sensor in a predetermined historical stage, and the end historical moment in the predetermined historical period corresponds to the historical pressure value; constructing the historical frequency parameter set of the historical frequency signal time series and forming a training data set with the historical pressure value; performing supervised learning on the first data set in the training data set to obtain an initial predictor, and verifying the initial predictor through the second data set in the training data set; if the verification passes, using the initial predictor as the pressure processing predictor.

[0043] Collect the historical frequency signal time series data of the silicon resonant pressure sensor within a certain predetermined historical stage. The predetermined historical period is a specified time range, involving long-term sampling data. Selecting this historical period is to ensure that the training of the prediction model has sufficient data support. Record the historical pressure value at the end moment in the predetermined historical period, and pair it with the frequency signal data as label data. This historical pressure value and the corresponding frequency signal time series data together constitute a complete sample data.

[0044] Extract the historical frequency parameter set from the obtained historical frequency signal time series. The historical frequency parameter set corresponds to the frequency parameter set of the current frequency signal time series. Pair the extracted historical frequency parameter set with the corresponding historical pressure value to form a training data set. In this training data set, each sample consists of the frequency parameter set and the corresponding historical pressure value.

[0045] Randomly select a part from the training data set as the first data set, and the remaining part as the second data set. The ratio of the first data set to the second data set is usually 8:2. Use the first data set for supervised learning. Common supervised learning algorithms include linear regression, support vector machine, neural network, etc. Through these algorithms, train the model to enable it to predict the historical pressure value based on the frequency parameter set. After training, obtain the initial predictor; verify the initial predictor through the second data set. The purpose of verification is to evaluate the performance of the predictor on new data and avoid model overfitting. Common evaluation metrics include mean square error, etc. According to the verification results, evaluate the performance of the initial predictor. If the verification error is small, it means the model has good prediction ability. If the verification error is large, it is necessary to adjust the model parameters or select other algorithms.

[0046] If the verification result of the second data set meets the preset performance requirements, for example, the error is less than the preset error threshold, it is considered that the initial predictor already has good generalization ability and can perform accurate pressure prediction on new data. The verified initial predictor is used as the official pressure processing predictor, which can be used to process real-time frequency signal data and output real-time pressure values.

[0047] Furthermore, the predetermined compensation items include design rationality compensation, material property compensation, process adaptability compensation, and environmental suitability compensation.

[0048] The predetermined compensation items are set according to the factors affecting the monitoring accuracy of the silicon resonant pressure sensor, including design rationality compensation, material property compensation, process adaptability compensation, and environmental suitability compensation. Among them, to compensate for the possible unreasonable factors in the sensor design process, such as asymmetric structures, design defects, etc., these factors will affect the measurement accuracy of the pressure sensor. Therefore, the design rationality compensation aims to make corresponding adjustments according to the design defects; due to the non-linear characteristics, temperature sensitivity, elastic modulus changes, etc. of the silicon material, the material itself may cause performance deviations of the sensor. The material property compensation aims to correct the sensor output according to these changes; the small errors in the manufacturing process (such as silicon bonding, surface treatment, etc.) may cause performance deviations of the sensor. The process adaptability compensation adjusts for these manufacturing errors to ensure the stability and accuracy of the sensor; the performance of the sensor may be affected by the external environment, such as temperature, humidity, electromagnetic interference, etc. The environmental suitability compensation aims to cope with these environmental changes and maintain the accuracy and stability of the sensor under different environments.

[0049] These compensation items are common influencing factors in the design and manufacture of the sensor and can significantly affect the performance of the silicon resonant pressure sensor. By introducing these predetermined compensation items, the output of the sensor can be corrected more accurately, enabling it to maintain high precision under different working conditions.

[0050] Furthermore, traversing and screening the historical compensation database with the multi-dimensional compensation information as the screening constraint to obtain the target sensor includes:

[0051] Extracting the first historical data group from the historical compensation database, where the first historical data group includes the first multi-dimensional compensation information of the first sensor; calculating the first similarity between the multi-dimensional compensation information and the first multi-dimensional compensation information; if the first similarity reaches the predetermined similarity limit value, then taking the first sensor as the target sensor.

[0052] The historical compensation database contains historical compensation records of multiple sensors. Each record includes multi-dimensional compensation information of the sensor, and this information involves different types of compensation items, including design rationality compensation, process adaptability compensation, material property compensation, etc. Randomly extract the first historical data group from the historical compensation database as the current analysis object. The first historical data group includes the first multi-dimensional compensation information of the first sensor.

[0053] By comparing the multi-dimensional compensation information of the current sensor with the first multi-dimensional compensation information in the first historical data group, calculate the similarity value. The specific calculation process will be elaborated in the subsequent steps. The calculation result is the first similarity value, which represents the similarity degree between the multi-dimensional compensation information of the current sensor and the historical sensor. The higher the similarity value, the more similar the compensation characteristics of these two sensors are.

[0054] Set a predetermined similarity limit value. If the similarity value exceeds this limit, it is considered that the compensation information of the current sensor and the historical sensor is very similar. The selection of the similarity limit is based on experience or set through an optimization method (such as cross-validation). For example, it is set to 0.8. A similarity exceeding 0.8 indicates that the two are very similar.

[0055] If the calculated first similarity exceeds the predetermined similarity limit, then regard this historical sensor as the target sensor, and the compensation information of this target sensor can be used for the compensation of the current sensor. By selecting the target sensor that best matches the characteristics of the current sensor, the compensation characteristics in the historical data can be borrowed for the pressure compensation and error correction of the current sensor.

[0056] Furthermore, calculating the first similarity between the multi-dimensional compensation information and the first multi-dimensional compensation information includes:

[0057] Extract any item from the predetermined compensation items; sequentially match the any item in the multi-dimensional compensation information and the first multi-dimensional compensation information to obtain the any compensation coefficient and the first any compensation coefficient respectively; calculate the any coefficient difference between the any compensation coefficient and the first any compensation coefficient; read the similarity decision plan and obtain the target coefficient difference in combination with the any coefficient difference; record the target coefficient difference as the first similarity.

[0058] The predetermined compensation items include multiple compensation factors, including design rationality compensation, material property compensation, process adaptability compensation, and environmental suitability compensation. Randomly extract a specific compensation item from these predetermined compensation items as the item to be processed.

[0059] Each compensation item corresponds to a specific compensation coefficient. Based on the extracted any item, match the multi-dimensional compensation information of the current sensor and the first multi-dimensional compensation information of the historical sensor item by item. After matching, obtain the any compensation coefficient and the first any compensation coefficient.

[0060] Calculate the difference between any compensation coefficient and the first arbitrary compensation coefficient to obtain an arbitrary coefficient difference, which represents the difference between the two sensors in this compensation item. The larger the compensation coefficient difference, the greater the difference between the two sensors in this compensation item, which may be due to differences in sensor design, materials, processes, etc.

[0061] The similarity decision plan is a preset table that contains rules for taking different decisions based on different coefficient differences. It helps to determine how to adjust the compensation coefficients of the sensors according to the compensation coefficient differences. By reading the similarity decision plan and combining the calculated arbitrary coefficient difference with the rules in the plan, a target coefficient difference is obtained, which reflects the difference between the current sensor and the historical sensor in the compensation item.

[0062] Take the calculated target coefficient difference as the first similarity. This similarity value reflects the similarity degree between the current sensor and the historical sensor in terms of compensation. The higher the similarity, the closer the compensation characteristics of the two are.

[0063] Furthermore, reading the similarity decision plan and combining it with the arbitrary coefficient difference to obtain the target coefficient difference includes:

[0064] Obtain a predetermined table, where the predetermined table includes 16 cells in 4 rows and 4 columns; according to the similarity decision plan, fill the arbitrary coefficient difference into the first area of the predetermined table; extract any cell in the second area of the predetermined table and calculate the arbitrary initial filling value of the cell; form an arbitrary reference cell set of the cell and compare to obtain the target cell in the arbitrary reference cell set; take the sum of the target filling value corresponding to the target cell and the arbitrary initial filling value and update it to the cell to form a table filling result; take the filling value of the predetermined cell in the table filling result as the target coefficient difference.

[0065] Obtain a predetermined table. The predetermined table is a two-dimensional table structure that contains 16 cells in 4 rows and 4 columns. This table is used to store the adjustment values of the compensation coefficient differences. Each cell represents a specific compensation adjustment value, and these adjustment values are filled according to the similarity decision plan and the compensation coefficient differences. The size of the table is 4x4, which is suitable for handling the mutual relationship between multiple compensation items.

[0066] The similarity decision plan provides a guiding principle for determining how to adjust the compensation coefficient of the current sensor based on the difference in compensation coefficients. The plan contains different rules, such as "if the coefficient difference is a certain value, fill a certain adjustment value at a specific position in the table", which is used to map the actually calculated compensation adjustment coefficient to the table for subsequent adjustments. The first area of the predefined table refers to a sub-area of the table, such as the first row and the first column of the table. According to the rules in the decision plan, any coefficient difference is filled into the cell in this first area.

[0067] The second area refers to another area in the table that is different from the first area. This area corresponds to other compensation items related to the compensation coefficient difference or further refined decision rules. Select an arbitrary cell from the second area for calculation. This cell corresponds to the adjustment value of a specific compensation item between the current sensor and the historical sensor.

[0068] According to the specific decision rules of the second area, calculate the initial filling value of this cell. The initial filling value is usually determined based on the compensation coefficient difference, the similarity value, and the rules specified in the decision plan. For example, assume that the decision plan stipulates that if a certain compensation coefficient difference is large, the filling value of this cell will be high, indicating that a larger compensation adjustment is required. This initial filling value represents the compensation amplitude that needs to be made to the current sensor under a specific compensation item, and this value will be adjusted according to the working state and compensation requirements of the sensor.

[0069] The reference cell set consists of a group of cells associated with the current cell. These cells represent similar compensation items or areas in the table that have similar characteristics to the current compensation item, including the left adjacent cell, the upper adjacent cell, and the upper left adjacent cell of the arbitrary cell. By comparing the cells in the reference cell set, determine the target cell that best matches the current cell. This target cell is the compensation value that is most similar to the current compensation item. The target cell provides a reference filling value for determining the final adjustment value of the current cell.

[0070] Extract the target filling value corresponding to the target cell. This filling value represents the compensation adjustment value of the historical sensor or the ideal state; any initial filling value reflects the preliminary adjustment amount of the current sensor under a specific compensation item. Add the target filling value to any initial filling value to obtain the updated filling value. This process reflects that the current sensor needs to make certain adjustments according to the reference value of the target cell. Through the addition process, update the arbitrary cell in the table to form a new filling value. This step makes the compensation coefficient adjustment of the current sensor closer to the compensation effect of the historical sensor.

[0071] The predetermined cell is a specific cell in a table, and the filled value of this cell will ultimately determine the target coefficient difference. It can be a cell related to the main compensation term of the current sensor or a cell that needs to be focused on during the adjustment process. Extract the filled value of the predetermined cell from the table filling result. This value represents the final adjustment amount required by the current sensor under a specific compensation term after adjustment. Use the extracted filled value as the target coefficient difference, which reflects the difference between the current sensor and the historical sensor in terms of the compensation term. Through this difference, the pressure measurement of the sensor can be adjusted.

[0072] Furthermore, the set of arbitrary reference cells includes the left adjacent cell, the upper adjacent cell, and the upper left adjacent cell of the arbitrary cell.

[0073] The set of arbitrary reference cells includes the left adjacent cell, the upper adjacent cell, and the upper left adjacent cell of the arbitrary cell. Among them, the left adjacent cell is the adjacent cell on the left side of the current arbitrary cell, the upper adjacent cell is the adjacent cell above the current arbitrary cell, and the upper left adjacent cell is the cell in the upper left corner of the current arbitrary cell. By constructing this set of reference cells, the information of the surrounding cells can be used to improve the accuracy of the adjustment of the current cell. The surrounding cells may have considered other similar compensation factors, so as to better provide a reference value for the compensation adjustment of the current cell.

[0074] In summary, the compensation method for a silicon resonant pressure sensor provided by the embodiments of the present application has the following technical effects:

[0075] By collecting the timing of frequency signals in real time and analyzing them, the pressure value of the current sensor can be accurately obtained, which provides the basic data for subsequent compensation and enables the system to quickly respond to pressure changes. By collecting multi-dimensional compensation characteristics of the sensor based on predetermined compensation items, various factors affecting the performance of the sensor can be comprehensively considered, which effectively integrates the performance of the sensor under different conditions, helps to compensate for errors caused by various environments or manufacturing processes, and provides more accurate compensation data. By screening the target sensor in the historical compensation database, the most matching compensation reference can be found based on the existing data. This process ensures that the known data of the historical sensor can be used to provide a compensation reference for the current sensor, avoiding complex modeling from scratch and improving the efficiency of sensor compensation. By comparing the detected pressure value of the target sensor with the preset pressure value, the pressure deviation can be accurately calculated and normalized to obtain a multi-dimensional compensation coefficient, eliminating the deviation caused by sensor errors, further improving the reliability of compensation, and ensuring the accuracy of the measurement result. By using the multi-dimensional compensation coefficient as a weight to adjust the real-time pressure value, the effective pressure value is finally obtained. This compensation adjustment can dynamically correct the sensor during the real-time measurement process, effectively eliminating various factors affecting the measurement result of the sensor, making the finally obtained effective pressure value more accurate and adapting to changes in different environments.

[0076] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A compensation method for a silicon resonant pressure sensor, characterized in that: The method comprises: Collecting a frequency signal time sequence output by the silicon resonant pressure sensor, and analyzing the frequency signal time sequence to obtain a real-time pressure value at a real time moment; Obtaining a predetermined compensation item, and collecting multi-dimensional compensation features of the silicon resonant pressure sensor based on the predetermined compensation item to obtain multi-dimensional compensation information; Using the multi-dimensional compensation information as a screening constraint, a historical compensation database is traversed and screened to obtain a target sensor; Normalizing and comparing the pressure value deviation obtained by the pressure value detected by the target sensor and the preset pressure value to obtain a multi-dimensional compensation coefficient; Performing compensation adjustment on the real-time pressure value using the multi-dimensional compensation coefficient as a weight to obtain an effective pressure value of the silicon resonant pressure sensor at the real-time moment; The predetermined compensation items include design rationality compensation, material property compensation, process adaptability compensation and environmental suitability compensation; The historical compensation database is traversed and screened using the multi-dimensional compensation information as a screening constraint to obtain the target sensor, including: Extracting a first historical data group from the historical compensation database, wherein the first historical data group includes first multi-dimensional compensation information of a first sensor; Calculating a first similarity between the multi-dimensional compensation information and the first multi-dimensional compensation information; If the first similarity reaches a predetermined similarity limit, using the first sensor as the target sensor; The calculating of the first similarity between the multi-dimensional compensation information and the first multi-dimensional compensation information includes: extracting any item from the predetermined compensation items; sequentially matching the arbitrary items in the multidimensional compensation information and the first multidimensional compensation information to obtain an arbitrary compensation coefficient and a first arbitrary compensation coefficient respectively; Calculating an arbitrary coefficient difference between the arbitrary compensation coefficient and the first arbitrary compensation coefficient; Read similar decision plans and combine the arbitrary coefficient differences to obtain a target coefficient difference; Recording the target coefficient difference as the first similarity; The process of reading similar decision plans and combining the arbitrary coefficient differences to obtain the target coefficient difference includes: Obtaining a predetermined table, wherein the predetermined table includes 4 rows and 4 columns, with a total of 16 cells; According to the similarity decision plan, the arbitrary coefficient difference is filled into the first area of the predetermined table; Extracting any cell in the second area of the predetermined table, and calculating any initial filling value of the any cell; forming an arbitrary reference cell set of the arbitrary cell, and obtaining a target cell in the arbitrary reference cell set by comparison; Taking the sum of the target filling value corresponding to the target cell and the arbitrary initial filling value, and updating it to the arbitrary cell to form a table filling result; Taking the filling value of a predetermined cell in the table filling result as the target coefficient difference; The arbitrary reference cell set includes the left neighboring cell, the upper neighboring cell and the upper left neighboring cell of the arbitrary cell.

2. The method according to claim 1, characterized in that After acquiring the frequency signal time sequence output by the silicon resonant pressure sensor and analyzing the frequency signal time sequence to obtain the real-time pressure value at the real time, the method further includes: Acquiring time domain characteristic parameters of the frequency signal time series; Performing Fourier transform on the frequency signal time series to obtain a frequency signal spectrum; Acquiring frequency domain characteristic parameters of the frequency signal spectrum; Performing modal decomposition on the frequency signal time series to obtain a modal component set, wherein the modal component set includes a target component; Acquire component characteristic parameters of the target component, and form a frequency parameter set in coordination with the time domain characteristic parameters and the frequency domain characteristic parameters; Using the frequency parameter set as processing data of a pressure processing predictor, and obtaining a predicted pressure value through the pressure processing predictor; The real-time pressure value is verified by the predicted pressure value.

3. The method according to claim 2, characterized in that Performing modal decomposition on the frequency signal time series to obtain a modal component set, wherein the modal component set includes a target component, including: extracting any component from the modal component set; Calculating and obtaining an arbitrary information quantity ratio of the arbitrary component to the frequency signal time sequence; Arranging the arbitrary components in descending order based on the arbitrary information ratio to obtain a descending list of modal components; The first component in the descending list of modal components is taken as the target component.

4. The method according to claim 2, characterized in that Using the frequency parameter set as processing data of a pressure processing predictor, and obtaining a predicted pressure value through the pressure processing predictor, includes: Acquire a historical frequency signal time sequence of the silicon resonant pressure sensor in a predetermined historical period, wherein the end historical moment in the predetermined historical period corresponds to a historical pressure value; assembling a historical frequency parameter set of the historical frequency signal time series and forming a training data set with the historical pressure values; Performing supervised learning on a first data set in the training data set to obtain an initial predictor, and validating the initial predictor using a second data set in the training data set; If the verification is successful, the initial predictor is used as the pressure treatment predictor.

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