Calibration Method and System for Pressure Sensor Measurement Data

Abstract

The present application provides a calibration method and system for pressure sensor measurement data. The method includes: obtaining synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and performing preprocessing to obtain multi-source sensing data and dynamic environmental characteristics; constructing a comprehensive error model with multi-error coupling based on the multi-source sensing data and dynamic environmental characteristics, and generating dynamic compensation coefficients through online parameter identification; performing dynamic compensation calibration on the sensing data according to the dynamic compensation coefficients and a preset confidence evaluation strategy to generate a high-precision pressure value, and the high-precision pressure value is used to represent the real environmental pressure measurement value. Using this method can improve the accuracy and reliability of the tester in the calibration of pressure sensors under the coexistence of multiple errors and complex environments, and improve the accuracy and stability of the calibration of pressure sensor measurement data.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensor calibration, and particularly relates to a calibration method and system for pressure sensor measurement data. Background Art

[0002] Calibration of pressure sensor measurement data, as a core technical field in modern industry and scientific research, is of irreplaceable importance in ensuring the reliability of measurement data and the stability of system performance. With the continuous expansion of high-precision application scenarios such as intelligent manufacturing, aerospace, and medical equipment, the calibration accuracy of pressure sensors directly determines the safety of equipment operation and the credibility of data, and its research value and application requirements are becoming increasingly prominent. However, existing calibration methods often rely on single error correction means or simple traceability methods, and are difficult to comprehensively cope with multi-source error interference in complex environments, resulting in insufficient universality and stability of calibration results. Especially in dynamic measurements or extreme conditions, the error control effect significantly decreases.

[0003] These limitations stem from the fact that several key technical factors in the calibration system have not been effectively solved. First, the accuracy of the tester is limited by the superposition of multiple error sources such as zero drift, linear error, and repeatability error, and the existing methods lack refinement in system compensation. Second, the evaluation of the comprehensive measurement uncertainty lacks a unified statistical framework, making it difficult to quantify the credibility of calibration results. In addition, the implementation of the multi-level traceability chain is often difficult to fully achieve due to equipment condition limitations in actual operation, affecting the rigor of traceability. These unsolved technical factors make it difficult for the calibration system to balance high accuracy and high reliability in the face of complex working conditions, thereby leading to the technical problem of how to improve the performance of the tester in the coexistence of multiple errors.

[0004] However, the current calibration method for pressure sensor measurement data has insufficient accuracy and is difficult to meet the accuracy requirements for calibration under complex working conditions. Summary of the Invention

[0005] Based on this, it is necessary to provide a calibration method and system for pressure sensor measurement data in view of the above technical problems, which can improve the accuracy and reliability of the tester in the coexistence of multiple errors and complex environments during pressure sensor calibration, and enhance the accuracy and stability of pressure sensor measurement data calibration.

[0006] In a first aspect, the present application provides a calibration method for pressure sensor measurement data, which is characterized by including:

[0007] Obtaining synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and performing preprocessing to obtain multi-source sensing data and dynamic environmental characteristics;

[0008] Based on multi-source sensing data and dynamic environmental characteristics, a comprehensive error model with multi-error coupling is constructed, and dynamic compensation coefficients are generated through online parameter identification;

[0009] According to the dynamic compensation coefficients and a preset confidence evaluation strategy, the sensing data is dynamically compensated and calibrated to generate a high-precision pressure value, which is used to represent the real environmental pressure measurement value.

[0010] In a possible embodiment, the synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters is obtained and preprocessed to obtain multi-source sensing data and dynamic environmental characteristics, including:

[0011] The original output signal of the pressure sensor is processed by wavelet threshold denoising to obtain multi-source sensing data, where the threshold parameter is dynamically adjusted according to environmental parameters;

[0012] The first-order difference calculation is performed on the multi-source sensing data to generate a dynamic pressure change rate;

[0013] Based on environmental parameters and the working duration of the sensor, a dynamic environmental characteristic is constructed, and the dynamic environmental characteristic includes temperature, time, and dynamic pressure change rate.

[0014] In a possible embodiment, based on multi-source sensing data and dynamic environmental characteristics, a comprehensive error model with multi-error coupling is constructed, and dynamic compensation coefficients are generated through online parameter identification, including:

[0015] A joint error model is constructed based on multi-source sensing data and dynamic environmental characteristics, and the inputs of the joint error model include a non-linear sensing signal term, a temperature drift term, and a time drift term;

[0016] The recursive least squares method is used to perform online identification of the temperature-dependent parameters and time-dependent parameters in the joint error model to generate dynamic compensation coefficients;

[0017] The non-linear residual compensation term based on spectrum analysis is superimposed on the joint error model to generate an updated comprehensive error model, and the updated comprehensive error model is used to re-output dynamic compensation coefficients.

[0018] In a possible embodiment, according to the dynamic compensation coefficients and a preset confidence evaluation strategy, the sensing data is dynamically compensated and calibrated to generate a high-precision pressure value, including:

[0019] Non-linear compensation is performed on the multi-source sensing data based on the dynamic compensation coefficients to generate an initial calibration value;

[0020] An adaptive Kalman filter is constructed, and the temperature change rate in the dynamic environmental characteristic is input into the state transition matrix to track the dynamic error caused by temperature, and based on the initial calibration value as the observed value, a filtered pressure value is generated;

[0021] Calculate the residual covariance between the filtered pressure value and the initial calibration value, and evaluate the confidence weight of the Kalman filter according to the residual covariance;

[0022] Perform weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate a high-precision pressure value.

[0023] In a possible embodiment, the method further includes:

[0024] Calculate the root mean square error of the high-precision pressure value, and the root mean square error is used to evaluate the calibration accuracy;

[0025] When the root mean square error exceeds a preset threshold, trigger an incremental learning mechanism to update the dynamic compensation coefficient;

[0026] Record the updated dynamic compensation coefficient and reset the error counter, and the error counter is initialized and generated through the calibration process.

[0027] In a possible embodiment, superimpose a non-linear residual compensation term based on spectral analysis onto the joint error model to generate an updated comprehensive error model, including:

[0028] Perform Fourier transform on the historical error data to extract the main spectral components, and the historical error data is calculated from the residual between the calibrated pressure value and the reference pressure value;

[0029] Determine the order of the non-linear residual compensation term according to the energy distribution of the main spectral components;

[0030] Generate a sine function compensation term corresponding to the order based on the frequency and amplitude of the main spectral components, and compensate the sine function into the joint error model to generate an updated comprehensive error model.

[0031] In a possible embodiment, perform weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate a high-precision pressure value, including:

[0032] Calculate the difference between the filtered pressure value and the initial calibration value to generate a residual sequence;

[0033] Calculate the covariance matrix of the residual sequence to generate a residual covariance matrix;

[0034] Calculate the confidence weight according to the diagonal elements of the inverse matrix of the residual covariance matrix, and the confidence weight is inversely proportional to the covariance;

[0035] Perform linear weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate a high-precision pressure value.

[0036] Second aspect, the present application also provides a calibration system for pressure sensor measurement data, characterized in that the system includes:

[0037] A data preprocessing module, configured to obtain the original output signal of the pressure sensor and the synchronized acquisition data of environmental parameters and perform preprocessing to obtain multi-source sensing data and dynamic environmental characteristics;

[0038] A dynamic compensation module, configured to construct a comprehensive error model with multi-error coupling based on the multi-source sensing data and dynamic environmental characteristics, and generate dynamic compensation coefficients through online parameter identification;

[0039] A calibrated pressure value generation module, configured to perform dynamic compensation calibration on the sensing data according to the dynamic compensation coefficients and a preset confidence evaluation strategy to generate a high-precision pressure value, where the high-precision pressure value is used to represent the real environmental pressure measurement value.

[0040] Third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned calibration method for pressure sensor measurement data.

[0041] Fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned calibration method for pressure sensor measurement data.

[0042] For the above-mentioned calibration method and system for pressure sensor measurement data, by obtaining the original output signal of the pressure sensor and the synchronized acquisition data of environmental parameters and performing preprocessing, multi-source sensing data and dynamic environmental characteristics are obtained. Based on these data and characteristics, a comprehensive error model with multi-error coupling is constructed, and dynamic compensation coefficients are generated through online parameter identification. According to the dynamic compensation coefficients and a preset confidence evaluation strategy, dynamic compensation calibration is performed on the sensing data to generate a high-precision pressure value, which is used to represent the real environmental pressure measurement value. The above method can effectively solve the problems of accuracy and reliability of pressure sensor calibration in the coexistence of multiple errors and complex environments, and improve the accuracy and stability of pressure sensor measurement data calibration. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1Schematic flow chart of a calibration method for measurement data of a pressure sensor provided by an embodiment of the present invention;

[0045] Figure 2 Schematic structural diagram of a calibration system for measurement data of a pressure sensor provided by an embodiment of the present invention. Detailed implementation manners

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] First, a brief introduction is made to the nouns involved in the embodiments of the present application.

[0048] Wavelet threshold denoising is a signal processing technology based on wavelet transform. By decomposing a signal into wavelet coefficients of different frequency components and applying threshold processing to these coefficients, noise components are removed while useful information of the signal is retained. This method utilizes the multi-resolution analysis characteristics of wavelet transform to effectively distinguish between signal and noise, and is particularly suitable for denoising non-stationary signals.

[0049] The Recursive Least Squares (RLS) method is a real-time parameter estimation method used for online recursive estimation of parameters in dynamic systems. Based on the least squares principle, it continuously updates the estimated value to minimize the sum of squared cumulative errors. This method uses a recursive formula and only requires the current measurement data and the estimated result at the previous moment to efficiently calculate the parameter estimated value at the current moment, without reprocessing all historical data, thus significantly improving the calculation efficiency. It is particularly suitable for dealing with real-time data and parameter estimation problems of dynamically changing systems, and is widely used in fields such as adaptive filtering, system identification, and signal processing.

[0050] The adaptive Kalman filter is an improved filtering algorithm based on Kalman filter theory, which can automatically adjust the filtering parameters in a dynamic environment to adapt to changes in system characteristics. It dynamically adjusts the filtering gain by real-time monitoring the changes in system statistical characteristics (such as the noise covariance matrix), so that it can still effectively estimate the system state when the noise statistical characteristics are unknown or changing. This method combines the optimal estimation characteristics of the Kalman filter and an adaptive mechanism, which can improve the robustness and accuracy of the filter in complex environments, and is widely used in fields such as target tracking, navigation and positioning, signal processing, and robot control, especially suitable for scenarios that require real-time processing and high-precision estimation.

[0051] According to the above-mentioned glossary, the implementation environment of a calibration method for pressure sensor measurement data provided by an embodiment of the present application is described. Schematically, the implementation environment includes: a sensor, a terminal, a standard pressure generator, and a processor. Among them, the sensor can be connected to the terminal through a signal acquisition module, the terminal communicates with the processor through a data bus, the standard pressure generator can be coupled to the sensor through a mechanical interface to provide a calibration pressure input, and the processor integrates a storage module and an algorithm calculation unit to achieve multi-source data fusion and dynamic compensation calibration; the sensor can include a pressure sensitive element, a piezoresistive pressure sensor, a temperature and humidity sensor, etc.; the processor can be a central processing unit, an artificial intelligence chip, or a multi-core processor, which is not limited here.

[0052] Combined with the above-mentioned glossary and implementation environment, the application scenarios of an embodiment of the present application are described. The calibration method for pressure sensor measurement data provided in an embodiment of the present application can be applied to, but not limited to, the following scenarios:

[0053] In an automotive engine management system, the accuracy of fuel pressure sensors and coolant pressure sensors directly affects the performance and fuel economy of the engine. Through this technical solution, these sensors can be calibrated in real time during vehicle operation to compensate for measurement errors caused by factors such as temperature changes and sensor aging, improve the long-term stability and measurement accuracy of the sensors, and thus optimize the operating efficiency and emission performance of the engine.

[0054] In an industrial automation production line, pressure sensors are often used to monitor hydraulic systems, pneumatic systems, and pipeline pressures. For example, in precision manufacturing processes, it is crucial to measure the coolant pressure of processing equipment and the pressure of hydraulic fixtures with high precision. Through dynamic compensation calibration technology, the measurement errors of sensors can be adjusted in real time to ensure the stability of the production process and the consistency of product quality, while reducing equipment failures and production accidents caused by sensor errors.

[0055] Weather stations require high-precision pressure sensors to measure atmospheric pressure for weather forecasting and climate research. Due to the complexity of the meteorological environment (such as rapid changes in temperature, humidity, and air pressure), traditional calibration methods are difficult to meet the high-precision measurement requirements. Through the above-mentioned technical solution, the pressure sensor can be calibrated dynamically in real time to ensure its measurement accuracy under different meteorological conditions, thereby improving the accuracy and reliability of meteorological data and providing higher-quality data support for weather forecasting and climate research.

[0056] Schematically, the calibration method for pressure sensor measurement data provided by an embodiment of the present application can also be applied to other application scenarios. Only examples are given here, and the specific application scenarios are not limited.

[0057] In an exemplary embodiment, such asFigure 1 As shown, a calibration method for pressure sensor measurement data is provided. In this embodiment, taking the application of this method to the terminal in the foregoing implementation environment as an example, it can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. It includes the following steps 101 to 103:

[0058] Step 101: Obtain the original output signal of the pressure sensor and the synchronized acquisition data of environmental parameters, and perform preprocessing to obtain multi-source sensing data and dynamic environmental characteristics.

[0059] Specifically, the original output signal is obtained through a high-precision pressure sensor, and at the same time, environmental parameter sensors are used to synchronously collect environmental parameter data, such as temperature, humidity, and air pressure. The output signals of these sensors are transmitted to the data acquisition module for preliminary signal conditioning and digital processing. Through preprocessing operations such as filtering, denoising, and data format conversion, high-quality multi-source sensing data and dynamic environmental characteristics are obtained, providing a reliable data basis for subsequent error modeling.

[0060] Step 102: Based on the multi-source sensing data and the dynamic environmental characteristics, construct a comprehensive error model with multi-error coupling, and generate dynamic compensation coefficients through online parameter identification.

[0061] Specifically, based on the preprocessed multi-source sensing data and dynamic environmental characteristics, a comprehensive error model including multiple error sources is constructed. This model can reflect the complex coupling relationship between sensor measurement errors and environmental parameter changes. Through an online parameter identification algorithm, the deviation between the sensor output and the known reference value is analyzed in real time, and the model parameters are dynamically adjusted to generate dynamic compensation coefficients that match the current environment and sensor state. This process ensures the real-time performance and adaptability of the error model, and can effectively cope with environmental changes and sensor performance drift.

[0062] Step 103: According to the dynamic compensation coefficients and the preset confidence evaluation strategy, perform dynamic compensation calibration on the sensing data to generate a high-precision pressure value, and the high-precision pressure value is used to represent the real environmental pressure measurement value.

[0063] Specifically, using the generated dynamic compensation coefficients and combining the preset confidence evaluation strategy, perform dynamic compensation calibration on the sensing data. The confidence evaluation strategy is used to judge the reliability of the compensation coefficients to ensure the accuracy of the calibration process. The data after compensation calibration generates a high-precision pressure value, which can more accurately represent the pressure measurement value in the real environment.

[0064] The above calibration method for the measurement data of the pressure sensor obtains the synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and preprocesses them to obtain multi-source sensing data and dynamic environmental characteristics. Based on these data and characteristics, a comprehensive error model with multi-error coupling is constructed, and dynamic compensation coefficients are generated through online parameter identification. According to the dynamic compensation coefficients and the preset confidence evaluation strategy, the sensing data is dynamically compensated and calibrated to generate a high-precision pressure value for characterizing the true environmental pressure measurement value. The above method can effectively solve the problems of accuracy and reliability of pressure sensor calibration in the coexistence of multiple errors and complex environments, and improve the accuracy and stability of the calibration of the measurement data of the pressure sensor.

[0065] In a possible embodiment, obtaining the synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and preprocessing them to obtain multi-source sensing data and dynamic environmental characteristics includes:

[0066] Step 201, perform wavelet threshold denoising processing on the original output signal of the pressure sensor to obtain multi-source sensing data, where the threshold parameter is dynamically adjusted according to environmental parameters.

[0067] Specifically, the original output signal of the pressure sensor (such as a voltage or current signal) is obtained in real time through a data acquisition terminal, and environmental parameters (such as temperature and humidity) are collected at the same time. The threshold parameter of the wavelet denoising algorithm is dynamically adjusted according to the current environmental temperature. For example, the threshold is increased in a high-temperature environment to suppress thermal noise, and the threshold is decreased in a low-temperature environment to retain signal details. The original signal is subjected to multi-scale wavelet decomposition, and the high-frequency noise components are suppressed according to the adjusted threshold, and the denoised multi-source sensing data is reconstructed.

[0068] Step 202, perform a first-order difference calculation on the multi-source sensing data to generate a dynamic pressure change rate.

[0069] Exemplarily, for the denoised multi-source sensing data, at intervals of a fixed time window, a first-order difference operation is performed on the pressure signals at adjacent time points to calculate the dynamic pressure change rate. Specifically, the difference between the pressure value at the current moment and the pressure value at the previous moment is divided by the time interval to quantify the instantaneous change trend of the pressure signal.

[0070] Step 203, construct dynamic environmental characteristics based on environmental parameters and the working duration of the sensor. The dynamic environmental characteristics include temperature, time, and the dynamic pressure change rate.

[0071] Exemplarily, the collected ambient temperature, the cumulative working duration of the sensor, and the dynamic pressure change rate are fused to construct a dynamic environmental feature vector. Specifically, with the timestamp as the index, the temperature data, the working duration data, and the dynamic pressure change rate are aligned according to the time series to generate a multi-dimensional feature matrix including temperature-time-pressure change rate, and the dimension difference is eliminated through normalization processing.

[0072] In a possible embodiment, based on the multi-source sensing data and the dynamic environmental features, a comprehensive error model with multi-error coupling is constructed, and dynamic compensation coefficients are generated through online parameter identification, including:

[0073] Step 301, construct a joint error model based on the multi-source sensing data and the dynamic environmental features. The input of the joint error model includes a non-linear sensing signal term, a temperature drift term, and a time drift term.

[0074] Exemplarily, the non-linear pressure signal in the multi-source sensing data, the temperature drift component and the time drift component in the dynamic environmental features can be used as input items to construct a joint error model. Specifically, the non-linear sensing signal term is represented by the non-linear fitting function of the pressure signal, the temperature drift term is described by the coupling polynomial of the temperature and the pressure signal, and the time drift term is expressed by the exponential decay function of the sensor working duration, forming a mathematical model with multi-error coupling to solve the problem of insufficient accuracy of the traditional single error compensation model under complex working conditions and improve the comprehensive suppression ability of the model to multi-source interference.

[0075] Step 302, use the recursive least squares method to perform online identification on the temperature-dependent parameters and time-dependent parameters in the joint error model to generate dynamic compensation coefficients.

[0076] Specifically, obtain the multi-source sensing data and the dynamic environmental features collected in real time during the operation of the sensor and input them into the joint error model. Use the recursive least squares method to iteratively update the temperature-dependent parameters and time-dependent parameters, dynamically adjust the compensation coefficients, ensure that the model parameters are adaptively optimized with the change of the environment, avoid the high computational cost of the traditional batch processing algorithm, realize low-latency real-time parameter identification, make the compensation coefficients dynamically adapt to the error change trend under complex environments, and improve the robustness of the method.

[0077] Step 303, superimpose the non-linear residual compensation term based on spectrum analysis onto the joint error model to generate an updated comprehensive error model, and the updated comprehensive error model is used to re-output the dynamic compensation coefficients.

[0078] Specifically, perform spectral analysis on the residual data of the combined error model to identify the main interference frequency bands, generate a non-linear residual compensation term in the form of a sine function based on the frequency domain characteristics, and superimpose it onto the original model to form an updated comprehensive error model. By introducing the non-linear residual compensation term, further optimize the accuracy of the error model so that it can cover the error sources more comprehensively. The updated model recalculates the dynamic compensation coefficient and outputs it to achieve more accurate error compensation.

[0079] In a possible embodiment, perform dynamic compensation calibration on the sensing data according to the dynamic compensation coefficient and a preset confidence evaluation strategy to generate a high-precision pressure value, including:

[0080] Step 401, perform non-linear compensation on the multi-source sensing data based on the dynamic compensation coefficient to generate an initial calibration value.

[0081] Exemplarily, input the dynamic compensation coefficient into a predefined non-linear error correction model to perform point-by-point correction on the pressure signal in the multi-source sensing data, eliminate the inherent non-linearity error of the sensor, and generate an initial calibration value. Specifically, apply the compensation coefficient in segments according to the amplitude range of the pressure signal to ensure linearity optimization within the full range, so as to improve the reference accuracy of static pressure measurement.

[0082] Step 402, construct an adaptive Kalman filter, input the temperature change rate in the dynamic environmental characteristics into the state transition matrix to track the dynamic error caused by temperature, and generate a filtered pressure value based on the initial calibration value as the observation value.

[0083] Specifically, design an adaptive Kalman filter, use the temperature change rate in the dynamic environmental characteristics as the adjustment parameter of the state transition matrix to adjust the dynamic response characteristics of the filter in real time; at the same time, input the initial calibration value as the observation value into the filter, and generate a filtered pressure value through the fusion of state prediction and observation value. The adaptive Kalman filter tracks the environmental dynamic characteristics through the temperature change rate, effectively suppresses the pressure drift caused by temperature transients, and reduces the dynamic pressure tracking error.

[0084] Step 403, calculate the residual covariance between the filtered pressure value and the initial calibration value, and evaluate the confidence weight of the Kalman filter according to the residual covariance.

[0085] Exemplarily, the residual between the filtered pressure value and the initial calibration value can be calculated in real time, the covariance matrix of the residual sequence can be statistically calculated, and the confidence weight of the Kalman filter can be evaluated according to the size of the covariance. Specifically, the smaller the covariance, the higher the consistency between the filtered result and the initial calibration value, and a higher weight is given to the filtered value. Quantify the credibility of the filtered result through the residual covariance, provide an objective basis for dynamic fusion, and avoid the limitations of a single compensation method.

[0086] Step 404: Based on the confidence weights, perform weighted fusion on the filtered pressure value and the initial calibration value to generate a high-precision pressure value.

[0087] Specifically, perform linear weighting on the filtered pressure value and the initial calibration value according to the confidence weights to generate the final high-precision pressure value. When the ambient temperature fluctuates violently, increase the weight of the filtered value to enhance the dynamic tracking ability; when the environment is stable, increase the weight of the initial calibration value to maintain the static accuracy.

[0088] In a possible embodiment, the method may further include:

[0089] Step 501: Calculate the root mean square error of the high-precision pressure value, and the root mean square error is used to evaluate the calibration accuracy.

[0090] Exemplarily, the root mean square error can be calculated by obtaining the high-precision pressure value and the reference pressure value provided by the standard pressure generator, using the data points within a fixed time window as samples. Specifically, after accumulating the squared deviations of each sampling point, take the average and then take the square root to quantify the current calibration accuracy. The root mean square error calculation can objectively reflect the overall deviation level of the calibration result, monitor the calibration performance in real time, and quickly identify the accuracy degradation problems caused by environmental mutations or sensor aging.

[0091] Step 502: When the root mean square error exceeds the preset threshold, trigger the incremental learning mechanism to update the dynamic compensation coefficient.

[0092] Specifically, a root mean square error threshold (e.g., 0.2%FS) can be preset. When the calculated value exceeds the threshold, start the incremental learning mechanism, and adjust the dynamic compensation coefficient based on the error gradient direction. For example, fine-tune the temperature-dependent parameter and the time-dependent parameter through the backpropagation algorithm, so that the compensation coefficient iteratively updates in the direction of reducing the residual. The incremental learning mechanism realizes the self-optimization of the compensation coefficient through dynamic feedback, solves the performance degradation problem of the traditional fixed-parameter model during long-term operation, and ensures that the calibration accuracy continuously meets the requirements.

[0093] Step 503: Record the updated dynamic compensation coefficient and reset the error counter, and the error counter is generated through the initialization of the calibration process.

[0094] Specifically, store the updated dynamic compensation coefficient in the non-volatile memory, and at the same time reset the error counter to zero. Among them, the error counter is set to the default value during system initialization and reset to the initial state each time the incremental learning is triggered. It is used to count the frequency of consecutive over-threshold events. By recording the updated parameters and resetting the counter, it can prevent frequent model oscillations caused by short-term interference, while retaining the long-term learning results and improving the robustness and stability of the system.

[0095] In a possible embodiment, a non-linear residual compensation term based on spectrum analysis is superimposed on the combined error model to generate an updated comprehensive error model, including:

[0096] Step 601: Perform a Fourier transform on the historical error data to extract the main spectral components. The historical error data is generated by calculating the residual between the calibrated pressure value and the reference pressure value.

[0097] Specifically, perform a Fourier transform on the historical error data to extract the main spectral components. The historical error data is generated by calculating the residual between the calibrated pressure value and the reference pressure value. Through the Fourier transform, the frequency band of periodic interference or high-frequency noise can be accurately located, providing data support for the subsequent compensation term design and solving the problem that traditional time-domain analysis methods are insensitive to periodic errors.

[0098] Step 602: Determine the order of the non-linear residual compensation term according to the energy distribution of the main spectral components.

[0099] Specifically, statistically analyze the cumulative energy distribution of the main spectral components. When the cumulative energy ratio reaches a preset threshold, the corresponding number of frequency bands is used as the order of the non-linear residual compensation term. For example, if the energy ratio of the first 3 frequency bands reaches 95%, the compensation term order is set to 3. Dynamically selecting the compensation order based on the energy distribution can avoid overfitting or under-compensation problems caused by a fixed order, minimizing the consumption of computing resources while ensuring accuracy.

[0100] Step 603: Generate a sine function compensation term corresponding to the order based on the frequency and amplitude of the main spectral components, and compensate the sine function into the combined error model to generate an updated comprehensive error model.

[0101] Specifically, generate a sine function compensation term corresponding to the order based on the frequency and amplitude of the main spectral components, and superimpose these sine function compensation terms on the combined error model to generate an updated comprehensive error model. By introducing the sine function compensation term, the periodic components in the error signal can be compensated specifically, further optimizing the performance of the error model and enabling it to more comprehensively reflect the actual error characteristics of the sensor.

[0102] In a possible embodiment, a high-precision pressure value is generated by weighted fusion of the filtered pressure value and the initial calibration value based on the confidence weight, which may include:

[0103] Step 701: Calculate the difference between the filtered pressure value and the initial calibration value to generate a residual sequence.

[0104] Exemplarily, by obtaining the filtered pressure value and the initial calibration value in real time, the difference between the two is calculated at each time point to generate a residual sequence. Specifically, the filtered pressure value at each sampling moment is subtracted from the initial calibration value at the corresponding moment to obtain the residual value and form a time series. The difference calculation intuitively reflects the deviation distribution between the filtering result and the initial calibration value, providing basic data for subsequent reliability evaluation and quickly identifying abnormal fluctuations or model mismatch problems.

[0105] Step 702: Calculate the covariance matrix for the residual sequence to generate a residual covariance matrix.

[0106] Specifically, using the residual data within a fixed time window as samples, calculate the covariance relationship of the residual values at each moment to quantify the characteristics of residual fluctuations. The covariance matrix depicts the statistical distribution characteristics of the residual sequence, reveals the correlation between the filtering result and the initial calibration value, and provides a mathematical basis for dynamic weight allocation.

[0107] Step 703: Calculate the confidence weight based on the diagonal elements of the inverse matrix of the residual covariance matrix. The confidence weight is inversely proportional to the covariance.

[0108] Specifically, extract the diagonal elements of the inverse matrix of the residual covariance matrix and calculate the confidence weight for each time point. The smaller the covariance value, the larger the corresponding weight, and vice versa. For example, when the residual covariance at a certain moment is the minimum value, the highest weight is assigned to the filtered pressure value, and the weight of the initial calibration value is correspondingly reduced. The covariance-based weight allocation mechanism dynamically balances the credibility of the filtering result and the initial calibration value, suppresses the influence of single-path error on the final output, and improves the robustness of the fusion result.

[0109] Step 704: Perform linear weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate a high-precision pressure value.

[0110] Specifically, perform linear weighted summation on the filtered pressure value and the initial calibration value according to the weight value to generate the final high-precision pressure value. The adaptive weighted fusion strategy takes into account both the dynamic tracking ability and the static calibration accuracy, reduces the comprehensive error of the output pressure value under complex working conditions, and meets the requirements of high-stability industrial measurement.

[0111] In summary, for the calibration method of pressure sensor measurement data provided by the embodiments of the present application, by acquiring the original signals of the pressure sensor and environmental parameters collected synchronously, wavelet threshold denoising and dynamic pressure change rate calculation are used to generate multi-source sensing data and dynamic environmental feature vectors, and the coupling relationship of temperature, time drift, and nonlinear error is quantified; a comprehensive error model including nonlinear terms, temperature drift terms, and time drift terms is constructed, the recursive least squares method is used to online identify dynamic compensation coefficients, and based on spectral analysis, periodic residual compensation terms are superimposed to achieve collaborative modeling and precise correction of multiple error sources; an adaptive Kalman filter is used to track dynamic environmental disturbances, and the confidence weight is combined to fuse the filtering results and the initial calibration value to balance static accuracy and dynamic response characteristics; a closed-loop verification mechanism is introduced to calculate the root mean square error of the output, trigger incremental learning to optimize the compensation coefficient and reset the error counter to form long-term stability control. Through the synergistic effect of multi-source data fusion, dynamic parameter identification, residual spectrum compensation, and closed-loop self-optimization technologies, the above technical solutions improve the accuracy and reliability of pressure sensor calibration under the coexistence of multiple errors and complex working conditions, effectively suppress the coupling effects of temperature drift, time drift, and high-frequency interference, improve the accuracy and stability of pressure sensor calibration, and enhance the adaptability and reliability of the calibration process.

[0112] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0113] Based on the same inventive concept, the embodiments of the present application also provide a calibration system for pressure sensor measurement data for implementing the above-mentioned calibration method of pressure sensor measurement data. The solution provided by this system to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the calibration system for pressure sensor measurement data provided below can refer to the limitations on the calibration method of pressure sensor measurement data in the above text, and will not be repeated here.

[0114] In an exemplary embodiment, as Figure 2 shown, a calibration system 10 for pressure sensor measurement data is provided, including:

[0115] The data preprocessing module 11 is configured to obtain the original output signal of the pressure sensor and the synchronous acquisition data of the environmental parameters and perform preprocessing to obtain multi-source sensing data and dynamic environmental features.

[0116] The dynamic compensation module 12 is configured to construct a comprehensive error model with multi-error coupling based on the multi-source sensing data and the dynamic environmental features, and generate dynamic compensation coefficients through online parameter identification.

[0117] The calibrated pressure value generation module 13 is configured to perform dynamic compensation calibration on the sensing data according to the dynamic compensation coefficients and a preset confidence evaluation strategy, and generate high-precision pressure values, which are used to represent the real environmental pressure measurement values.

[0118] In a possible embodiment, the data preprocessing module 11 may include:

[0119] The data denoising unit is configured to perform wavelet threshold denoising processing on the original output signal of the pressure sensor to obtain multi-source sensing data, where the threshold parameter is dynamically adjusted according to the environmental parameters.

[0120] The dynamic pressure change calculation unit is configured to perform first-order difference calculation on the multi-source sensing data to generate a dynamic pressure change rate.

[0121] The dynamic environmental feature construction unit is configured to construct dynamic environmental features based on the environmental parameters and the working duration of the sensor. The dynamic environmental features include temperature, time, and the dynamic pressure change rate.

[0122] In a possible embodiment, the dynamic compensation module 12 may include:

[0123] The joint error model unit is configured to construct a joint error model based on the multi-source sensing data and the dynamic environmental features. The inputs of the joint error model include a non-linear sensing signal term, a temperature drift term, and a time drift term.

[0124] The dynamic compensation unit is configured to perform online identification of the temperature-dependent parameters and the time-dependent parameters in the joint error model by using the recursive least squares method to generate dynamic compensation coefficients.

[0125] The comprehensive error model unit is configured to superimpose a non-linear residual compensation term based on spectrum analysis onto the joint error model to generate an updated comprehensive error model, and the updated comprehensive error model is used to re-output dynamic compensation coefficients.

[0126] In a possible embodiment, the calibrated pressure value generation module 13 may include:

[0127] The initial calibration unit is configured to perform non-linear compensation on the multi-source sensing data based on the dynamic compensation coefficients to generate an initial calibration value.

[0128] An adaptive Kalman filter unit, which is used to construct an adaptive Kalman filter, input the temperature change rate in the dynamic environment features into the state transition matrix to track the dynamic error caused by temperature, and generate a filtered pressure value based on the initial calibration value as the observed value.

[0129] A confidence weight calculation unit, which is used to calculate the residual covariance between the filtered pressure value and the initial calibration value, and evaluate the confidence weight of the Kalman filter according to the residual covariance.

[0130] A high-precision pressure value generation unit, which is used to perform weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate a high-precision pressure value.

[0131] In a possible embodiment, the calibration system 10 for the pressure sensor measurement data may further include:

[0132] A calibration accuracy evaluation module, which is used to calculate the root mean square error of the high-precision pressure value, and the root mean square error is used to evaluate the calibration accuracy.

[0133] An incremental learning module, which is used to trigger an incremental learning mechanism to update the dynamic compensation coefficient when the root mean square error exceeds a preset threshold.

[0134] An error counting module, which is used to record the updated dynamic compensation coefficient and reset the error counter, and the error counter is initialized and generated through the calibration process.

[0135] In a possible embodiment, the comprehensive error model unit may include:

[0136] A main spectrum extraction sub-unit, which is used to perform Fourier transform on the historical error data to extract the main spectrum components, and the historical error data is generated by calculating the residual between the calibrated pressure value and the reference pressure value.

[0137] An order determination sub-unit, which is used to determine the order of the non-linear residual compensation term according to the energy distribution of the main spectrum components.

[0138] A model update sub-unit, which is used to generate a sine function compensation term corresponding to the order based on the frequency and amplitude of the main spectrum components, and compensate the sine function to the joint error model to generate an updated comprehensive error model.

[0139] In a possible embodiment, the high-precision pressure value generation unit may include:

[0140] A difference calculation sub-unit, which is used to calculate the difference between the filtered pressure value and the initial calibration value to generate a residual sequence.

[0141] A covariance matrix calculation sub-unit, which is used to calculate the covariance matrix of the residual sequence to generate a residual covariance matrix.

[0142] A confidence weight subunit, configured to calculate a confidence weight according to diagonal elements of an inverse matrix of a residual covariance matrix, where the confidence weight is inversely proportional to the covariance.

[0143] A weighted fusion subunit, configured to perform linear weighted fusion on a filtered pressure value and an initial calibration value based on the confidence weight to generate a high-precision pressure value.

[0144] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of a calibration method for pressure sensor measurement data as described above are implemented.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0146] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. A person of ordinary skill in the art can understand and implement it without creative work.

[0147] The above embodiments only represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A calibration method for the measurement data of a pressure sensor, characterized in that The method includes: Obtaining the synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and performing preprocessing to obtain multi-source sensing data and dynamic environmental characteristics; Based on the multi-source sensing data and the dynamic environmental characteristics, constructing a comprehensive error model with multi-error coupling and generating dynamic compensation coefficients through online parameter identification; According to the dynamic compensation coefficients and a preset confidence evaluation strategy, performing dynamic compensation calibration on the sensing data to generate a high-precision pressure value, where the high-precision pressure value is used to represent the real environmental pressure measurement value; Among them, the obtaining the synchronous acquisition data of the original output signal of the pressure sensor and environmental parameters and performing preprocessing to obtain multi-source sensing data and dynamic environmental characteristics includes: Performing wavelet threshold denoising processing on the original output signal of the pressure sensor to obtain the multi-source sensing data, where the threshold parameter is dynamically adjusted according to the environmental parameters; Performing first-order difference calculation on the multi-source sensing data to generate a dynamic pressure change rate; Based on the environmental parameters and the working duration of the sensor, constructing the dynamic environmental characteristics, where the dynamic environmental characteristics include temperature, time, and the dynamic pressure change rate; The constructing a comprehensive error model with multi-error coupling based on the multi-source sensing data and the dynamic environmental characteristics and generating dynamic compensation coefficients through online parameter identification includes: Constructing a joint error model based on the multi-source sensing data and the dynamic environmental characteristics, where the inputs of the joint error model include a non-linear sensing signal term, a temperature drift term, and a time drift term; Using the recursive least squares method to perform online identification on the temperature-dependent parameters and time-dependent parameters in the joint error model to generate the dynamic compensation coefficients; Superimposing a non-linear residual compensation term based on spectrum analysis onto the joint error model to generate an updated comprehensive error model, where the updated comprehensive error model is used to re-output the dynamic compensation coefficients; Among them, the performing dynamic compensation calibration on the sensing data according to the dynamic compensation coefficients and a preset confidence evaluation strategy to generate a high-precision pressure value includes: Performing non-linear compensation on the multi-source sensing data based on the dynamic compensation coefficients to generate an initial calibration value; Constructing an adaptive Kalman filter, inputting the temperature change rate in the dynamic environmental characteristics into the state transition matrix to track the dynamic error caused by temperature, and generating a filtered pressure value based on the initial calibration value as the observation value; Calculating the residual covariance between the filtered pressure value and the initial calibration value, and evaluating the confidence weight of the Kalman filter according to the residual covariance; Performing weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate the high-precision pressure value.

2. The method according to claim 1, characterized in that, It further includes: Calculating the root mean square error of the high-precision pressure value, where the root mean square error is used to evaluate the calibration accuracy; When the root mean square error exceeds a preset threshold, triggering an incremental learning mechanism to update the dynamic compensation coefficients; Recording the updated dynamic compensation coefficients and resetting the error counter, where the error counter is initialized and generated through the calibration process.

3. The method according to claim 1, characterized in that, Superimposing the non - linear residual compensation term based on spectrum analysis onto the joint error model to generate an updated comprehensive error model includes: Performing Fourier transform on historical error data to extract main spectral components, where the historical error data is generated by calculating the residual between the calibrated pressure value and the reference pressure value; Determining the order of the non - linear residual compensation term according to the energy distribution of the main spectral components; Generating a sine - function compensation term corresponding to the order based on the frequency and amplitude of the main spectral components, and compensating the sine - function into the joint error model to generate the updated comprehensive error model.

4. The method according to claim 1, wherein Weighted - fusing the filtered pressure value and the initial calibration value based on the confidence weight to generate the high - precision pressure value, including: Calculating the difference between the filtered pressure value and the initial calibration value to generate a residual sequence; Calculating the covariance matrix of the residual sequence to generate a residual covariance matrix; Calculating the confidence weight according to the diagonal elements of the inverse matrix of the residual covariance matrix, where the confidence weight is inversely proportional to the covariance; Linearly weighted - fusing the filtered pressure value and the initial calibration value based on the confidence weight to generate the high - precision pressure value.

5. A calibration system for pressure sensor measurement data, characterized in that, The system includes: A data pre - processing module, configured to obtain the original output signal of the pressure sensor and the synchronized acquisition data of environmental parameters and perform pre - processing to obtain multi - source sensing data and dynamic environmental characteristics; A dynamic compensation module, configured to construct a comprehensive error model with multi - error coupling based on the multi - source sensing data and the dynamic environmental characteristics, and generate dynamic compensation coefficients through online parameter identification; A calibrated pressure value generation module, configured to perform dynamic compensation calibration on the sensing data according to the dynamic compensation coefficients and a preset confidence evaluation strategy to generate a high - precision pressure value, where the high - precision pressure value is used to represent the real - environment pressure measurement value; Among them, the data pre - processing module includes: A data denoising unit, configured to perform wavelet threshold denoising on the original output signal of the pressure sensor to obtain the multi - source sensing data, where the threshold parameter is dynamically adjusted according to the environmental parameters; A dynamic pressure change calculation unit, configured to perform first - order difference calculation on the multi - source sensing data to generate a dynamic pressure change rate; A dynamic environmental characteristic construction unit, configured to construct the dynamic environmental characteristics based on the environmental parameters and the sensor working duration, where the dynamic environmental characteristics include temperature, time, and the dynamic pressure change rate; The dynamic compensation module includes: A joint error model unit, configured to construct a joint error model based on the multi - source sensing data and the dynamic environmental characteristics, where the inputs of the joint error model include non - linear sensing signal terms, temperature drift terms, and time - drift terms; A dynamic compensation unit, configured to perform online identification of the temperature - dependent parameters and time - dependent parameters in the joint error model by using the recursive least - squares method to generate the dynamic compensation coefficients; A comprehensive error model unit is configured to superimpose a non - linear residual compensation term based on spectrum analysis onto the combined error model to generate an updated comprehensive error model, and the updated comprehensive error model is used to re - output the dynamic compensation coefficient; The calibration pressure value generation module includes: An initial calibration unit is configured to perform non - linear compensation on the multi - source sensing data based on the dynamic compensation coefficient to generate an initial calibration value; An adaptive Kalman filter unit is configured to construct an adaptive Kalman filter, input the temperature change rate in the dynamic environment characteristics into the state transition matrix to track the dynamic error caused by temperature, and generate a filtered pressure value based on the initial calibration value as the observed value; A confidence weight calculation unit is configured to calculate the residual covariance between the filtered pressure value and the initial calibration value, and evaluate the confidence weight of the Kalman filter according to the residual covariance; A high - precision pressure value generation unit is configured to perform weighted fusion on the filtered pressure value and the initial calibration value based on the confidence weight to generate the high - precision pressure value.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 4.

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