High-precision pressure sensing and self-calibration system

Abstract

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.

Description

Technical Field

[0001] This invention relates to the field of pressure sensing technology, and in particular to a high-precision pressure sensing and self-calibration system. Background Technology

[0002] Pressure sensors, as key components for sensing the physical world, have been widely used in high-precision measurement fields such as industrial automation, environmental monitoring, medical equipment, and aerospace. However, with the increasing demands for measurement accuracy, stability, and reliability from various applications, existing pressure sensing technologies face the following two prominent challenges: First, traditional calibration methods are outdated and cannot be performed online. Current high-precision pressure sensor calibration generally relies on periodic calibration using a high-precision standard pressure source in a specific laboratory environment. This method is not only time-consuming and labor-intensive, disrupting normal system operation, but more importantly, it cannot cope with real-time drift caused by factors such as temperature fluctuations, mechanical stress relaxation, and material aging during actual field operation. This disconnect between measurement and calibration makes it difficult to guarantee the actual accuracy of the sensor during long-term service, introducing unpredictable risks to the entire measurement and control system.

[0003] Second, the ability to compensate for environmental interference and long-term drift is insufficient. Although existing technologies commonly employ simple linear or static compensation algorithms (such as temperature compensation) to suppress some environmental interference, these methods fall short for complex, nonlinear, time-varying drift phenomena. In particular, the performance degradation of sensors under the coupled effects of multiple environmental stresses (such as temperature and humidity cycles and vibration) is complex, and traditional methods struggle to establish accurate error models and effectively compensate for it. This leads to a slow deterioration in the sensor's output accuracy over its lifespan, a process that cannot be monitored or predicted in real time, ultimately forcing replacement due to exceeding tolerance limits, increasing maintenance costs and operational risks.

[0004] Therefore, there is an urgent need for a high-precision pressure calibration system that can achieve real-time online self-calibration and intelligently compensate for complex nonlinear drift, in order to break through the bottleneck of existing technologies and meet the needs of modern industry for high reliability, high stability and predictive maintenance. Summary of the Invention

[0005] To address the above issues, this invention provides a high-precision pressure sensing and self-calibration system. This system can achieve high-precision acquisition and real-time correction of pressure data through online collaboration between a digital twin model and machine learning algorithms. It also has sensor drift compensation and health status monitoring capabilities to ensure long-term stable operation.

[0006] This invention provides a high-precision pressure sensing and self-calibration system, comprising the following modules: The pressure sensing module is used to acquire pressure signals through a pressure sensor and convert the pressure signals into initial electrical signals. The environmental monitoring module is used to collect environmental parameters in real time. The data processing module is used to preprocess the initial electrical signal; The digital twin module is used to construct a virtual model corresponding to the pressure sensor, and outputs the ideal pressure prediction value of the virtual model under the corresponding conditions based on the preprocessed pressure data and environmental parameters. The self-calibration module is used to generate the final pressure output value by comparing and analyzing the ideal pressure prediction value with the preprocessed pressure data.

[0007] Furthermore, the pressure sensing module includes a high-precision piezoresistive or capacitive pressure sensor, which has a bridge circuit composed of four resistors inside and converts pressure changes into electrical signals through an elastic body. The sensor surface is provided with micro-strain gauges or thin film structures to enhance the response sensitivity to minute pressure changes.

[0008] By employing high-precision piezoresistive or capacitive sensors combined with an internal bridge circuit, high sensitivity and linearity in pressure measurement are ensured. The miniature strain gauges or thin-film structures on the sensor surface significantly enhance the ability to capture minute pressure changes, enabling the system to accurately respond to weak signals and improving performance in low-pressure or high-precision applications.

[0009] Furthermore, the data processing module specifically includes: The signal conditioning unit is used to amplify and filter the initial electrical signal to improve the signal-to-noise ratio; The analog-to-digital converter unit is used to convert the conditioned analog signal into a digital signal; The time-frequency domain analysis unit is used to perform time-domain and frequency-domain analysis on digital signals to extract time-domain and frequency-domain features; The dynamic compensation unit is used to generate real-time compensation coefficients based on environmental parameters, time-domain characteristics, and frequency-domain characteristics, to perform preliminary correction on digital signals, and to output pre-processed pressure data.

[0010] By setting up data processing modules that include signal conditioning, analog-to-digital conversion, and time-frequency domain analysis, comprehensive optimization of the original electrical signal is achieved. This design effectively improves the signal-to-noise ratio and can deeply mine signal characteristics from both time and frequency domains, providing a rich and accurate data foundation for subsequent dynamic compensation and ensuring data quality from the source.

[0011] Furthermore, the dynamic compensation unit specifically includes: The feature fusion subunit is used to normalize the time-domain features, frequency-domain features and environmental parameters, and combine them into a multi-dimensional feature vector. The coefficient mapping subunit establishes a polynomial fitting model to describe the nonlinear mapping relationship between environmental parameters, signal characteristics and ideal compensation coefficients. The multidimensional feature vector is input into the polynomial fitting model for calculation, and the real-time compensation coefficients are output. The signal correction subunit is used to apply the real-time compensation coefficient to the digital signal, perform offset and gain correction on the signal through arithmetic operations, and output the preprocessed pressure data.

[0012] By employing feature fusion and a polynomial fitting model, this unit can accurately characterize the complex nonlinear relationship between environmental parameters, signal characteristics, and compensation coefficients. This method enables refined and proactive compensation for environmental interference (such as temperature drift), significantly improving the initial correction accuracy of the signal and reducing the burden on the back-end self-calibration module.

[0013] Furthermore, the self-calibration module specifically includes: The deviation calculation unit is used to calculate the real-time deviation between the preprocessed pressure data and the ideal pressure prediction value. The iterative learning unit, with a built-in machine learning model, takes the environmental parameters, preprocessed pressure data, and real-time deviation as inputs to learn and predict the drift characteristics of the pressure sensor and outputs dynamic compensation values. The data fusion unit is used to fuse the dynamic compensation value with the preprocessed pressure data to generate the final pressure output value.

[0014] By calculating deviations in real time and using machine learning models to predict drift characteristics, dynamic and adaptive compensation for sensor nonlinear drift and aging effects is achieved. Finally, high-precision results are output through data fusion, ensuring that the system maintains extremely high measurement accuracy and stability throughout its entire lifespan.

[0015] Furthermore, the self-calibration module also includes a health management unit, which calculates the health index of the pressure sensor based on the historical sequence data of the real-time deviation, and generates a predictive maintenance warning signal when the health index is lower than a preset adaptive threshold.

[0016] The introduction of a health management unit represents a leap from passive calibration to proactive prediction. By analyzing historical deviation data to calculate a health index, it can issue early warnings before sensor performance degrades to the point of affecting accuracy, supporting predictive maintenance, greatly improving system reliability and availability, and reducing the risk of unexpected downtime and maintenance costs.

[0017] Furthermore, the deviation calculation unit specifically includes: The difference calculation subunit is used to calculate the absolute deviation between the preprocessed pressure data and the ideal pressure prediction value in real time. The normalization processing subunit is used to normalize the absolute deviation according to the range to obtain the relative deviation value; The moving average filter subunit is used to perform moving average processing on the relative deviation values ​​of multiple consecutive sampling periods to obtain the real-time deviation.

[0018] By normalizing and applying moving average filtering to the deviation, the impact of measurement noise and transient interference on deviation judgment can be effectively suppressed. This makes the calculated real-time deviation more realistically and stably reflect the systematic drift of the sensor, providing reliable and clean data input for subsequent iterative learning and health prediction.

[0019] Furthermore, the iterative learning unit specifically includes: The feature extraction subunit is used to extract drift feature vectors related to sensor drift from environmental parameters, pressure data, and real-time deviations. The model training subunit employs an online learning algorithm to continuously update the support vector machine model based on the drift feature vectors in order to fit the nonlinear drift behavior of the sensor. The compensation prediction subunit is used to predict the drift amount based on the current environmental parameters and pressure data as input to the support vector machine model, and output the corresponding dynamic compensation value.

[0020] Leveraging the powerful ability of support vector machines (SVMs) to handle nonlinear problems, this unit can accurately learn and predict complex drift patterns from historical data. Online learning algorithms ensure the model continuously adapts to changes in sensor conditions, maintaining an optimal match between the compensation strategy and the sensor's current characteristics, thus achieving intelligent calibration.

[0021] Furthermore, calculating the health index of the pressure sensor based on the historical sequence data of the real-time deviation specifically includes: Perform statistical analysis on historical sequence data of real-time deviations to calculate their mean, variance, and trend slope; A health assessment function is constructed based on the mean, variance, and trend slope, and the function form is as follows: ; Where μ is the mean deviation; σ represents the variance of the deviation. k is the slope of the deviation trend; ω1, ω2, and ω3 are weighting coefficients; H represents the health index.

[0022] This health index function integrates the static distribution (mean, variance) and dynamic trend (slope) of the deviation, enabling a comprehensive and quantitative assessment of the sensor's health status from multiple dimensions. This assessment method is scientifically sound and more reliable than a single threshold judgment, allowing for earlier and more accurate detection of signs of performance degradation.

[0023] Furthermore, the digital twin module specifically includes: The modeling unit is used to construct a virtual model based on the structural parameters, material properties, and working mechanism of the pressure sensor; A real-time simulation engine is used to receive the preprocessed pressure data and environmental parameters, and use them as boundary conditions to drive the virtual model to solve in real time, simulate the response of an ideal sensor under corresponding physical conditions, and output the ideal pressure prediction value. The model update unit is used to periodically optimize and update the parameters of the virtual model in reverse according to the calibration results of the self-calibration module, so as to keep it synchronized with the real state of the physical sensor.

[0024] The digital twin module provides an ideal reference value unaffected by real-world interference through a high-fidelity virtual model. Its real-time simulation and reverse update capabilities enable this virtual model to evolve synchronously with the physical entity, providing a continuously reliable truth benchmark for self-calibration—a key factor in achieving high-precision, long-term self-calibration.

[0025] Compared with existing technologies, the advantages of this invention are as follows: This system significantly improves the accuracy, stability, and long-term reliability of pressure sensing measurements through the collaborative work of multiple modules. The system employs high-sensitivity piezoresistive or capacitive sensors with a bridge circuit structure, combined with micro-strain gauges or thin-film designs, effectively enhancing the response capability to minute pressure changes. The data processing module incorporates signal conditioning, analog-to-digital conversion, and dynamic compensation mechanisms. By fusing and analyzing environmental parameters and signal time-frequency domain characteristics, and using a polynomial fitting model to generate real-time compensation coefficients, it effectively suppresses signal drift and distortion caused by environmental interference such as temperature and vibration. The digital twin module constructs a virtual model based on the sensor's physical characteristics, outputting ideal pressure prediction values ​​through real-time simulation, providing a highly reliable reference benchmark for system calibration. The self-calibration module utilizes machine learning algorithms to continuously analyze the deviation between measured and ideal values, dynamically predicting and compensating for sensor drift, achieving online optimization and correction of the output values. Furthermore, the system integrates health management functions, calculating a health index through statistical analysis of historical deviation data, enabling predictive maintenance and fault warnings. The overall system has good self-adaptability and can be widely used in high-precision pressure monitoring scenarios such as industrial control, medical equipment, and aerospace, significantly reducing measurement errors and maintenance costs caused by sensor performance degradation. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this drawing or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this drawing. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0027] Figure 1 This is a flowchart of the overall system of the present invention.

[0028] Figure 2 This is a schematic diagram of the system principle of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments provided by this invention without inventive effort are within the scope of protection of this invention.

[0030] This invention provides a high-precision pressure sensing and self-calibration system, such as... Figure 1 , Figure 2 As shown, it specifically includes the following modules: The pressure sensing module is used to acquire pressure signals through a pressure sensor and convert the pressure signals into initial electrical signals.

[0031] Specifically, the pressure sensing module includes a high-precision piezoresistive or capacitive pressure sensor, which has a bridge circuit consisting of four resistors inside and converts pressure changes into electrical signals through an elastic body. The sensor surface is provided with micro-strain gauges or thin film structures to enhance the response sensitivity to minute pressure changes.

[0032] The environmental monitoring module is used to collect environmental parameters in real time, including but not limited to temperature, humidity, and mechanical vibration.

[0033] The data processing module is used to preprocess the initial electrical signal.

[0034] Furthermore, the data processing module specifically includes the following units: The signal conditioning unit is used to amplify and filter the initial electrical signal to improve the signal-to-noise ratio.

[0035] The signal conditioning unit is responsible for the initial processing of the weak initial electrical signal output by the pressure sensor, specifically including: Signal amplification: A high-precision instrumentation amplifier is used to amplify the signal with programmable gain to adapt to pressure measurement requirements of different ranges and improve the dynamic range of the signal.

[0036] Filtering: High-frequency noise, such as electromagnetic interference and vibration noise, is filtered out by a low-pass filter, and power frequency interference is suppressed by a band-stop filter, which significantly improves the signal-to-noise ratio (SNR).

[0037] The output is a conditioned analog signal with good linearity and anti-interference capability.

[0038] An analog-to-digital converter is used to convert conditioned analog signals into digital signals.

[0039] The time-frequency domain analysis unit is used to perform time-domain and frequency-domain analysis on digital signals to extract time-domain and frequency-domain features.

[0040] Time-domain analysis: Calculates the statistical characteristics of a signal, such as mean, variance, peak value, and waveform factor, to reflect the amplitude variation and stability of the signal.

[0041] Frequency domain analysis: By converting the signal to the frequency domain through fast Fourier transform or wavelet transform, features such as the dominant frequency component, spectral energy distribution, and harmonic components are extracted to identify periodic interference or resonance phenomena.

[0042] The dynamic compensation unit is used to generate real-time compensation coefficients based on environmental parameters, time-domain characteristics, and frequency-domain characteristics, to perform preliminary correction on digital signals, and to output pre-processed pressure data.

[0043] Furthermore, the dynamic compensation unit specifically includes the following sub-units: The feature fusion subunit is used to normalize the time-domain features, frequency-domain features, and environmental parameters, and combine them into a multi-dimensional feature vector to eliminate dimensional differences and facilitate model processing.

[0044] The coefficient mapping subunit establishes a polynomial fitting model to describe the nonlinear mapping relationship between environmental parameters, signal characteristics, and ideal compensation coefficients. The multidimensional feature vector is input into the polynomial fitting model for calculation, and the real-time compensation coefficients are output.

[0045] Specifically, a third-order polynomial fitting model is established to describe the nonlinear relationship between the multidimensional feature vectors and the compensation coefficients. The specific formula is as follows: ; Where a0 is a constant term; a i The coefficient of the linear term; b ij The coefficient of the quadratic term; c ijk The coefficient of the cubic term; n is the dimension of the multidimensional feature vector; C is the real-time compensation coefficient.

[0046] Multiple sets of different pressures and temperatures were applied using a standard pressure generator to collect sample data. The least squares method was used to fit the model parameters. The multidimensional feature vectors were input into the polynomial fitting model to calculate the real-time compensation coefficients.

[0047] The signal correction subunit is used to apply the real-time compensation coefficient to the digital signal, perform offset and gain correction on the signal through arithmetic operations, and output the preprocessed pressure data.

[0048] Specifically, the following formulas are used to perform offset and gain correction on the digital signal: ; Among them, P pre This is the preprocessed pressure data; P digit It is a digital signal; P offset This is the zero-point offset value.

[0049] The digital twin module is used to construct a virtual model corresponding to the pressure sensor, and outputs the ideal pressure prediction value of the virtual model under the corresponding conditions based on the preprocessed pressure data and environmental parameters.

[0050] The digital twin module creates a virtual model that works exactly like a physical sensor under ideal conditions. When this virtual model receives the exact same inputs as the physical sensor (i.e., measured pressure and environmental conditions), it simulates the theoretical value that a "perfect sensor" should output—the ideal pressure prediction. This theoretical value is used to measure whether there are any deviations in the output of the real physical sensor.

[0051] Furthermore, the digital twin module specifically includes the following units: The modeling unit is used to construct a virtual model based on the structural parameters, material properties, and working mechanism of the pressure sensor.

[0052] Specifically, import the physical parameters of the pressure sensor: structural dimensions, material properties, and working mechanism; create a 3D model in ANSYS Mechanical, using tetrahedral meshing to ensure that the model is consistent with the structure of the pressure sensor.

[0053] Define the pressure loading surface and the fixed constraint surface, and import environmental parameters as thermal and force boundaries; apply standard pressure, simulate and output the electrical signal of the virtual sensor, and compare it with the measured value of the pressure sensor. If the error is less than the error threshold, the model fidelity is ensured.

[0054] A real-time simulation engine is used to receive the preprocessed pressure data and environmental parameters, and use them as boundary conditions to drive the virtual model to solve in real time, simulating the response of an ideal sensor under corresponding physical conditions, and outputting the ideal pressure prediction value.

[0055] Specifically, the ANSYS explicit dynamics solver is used, and the simulation step size is set to be synchronized with the physical sampling. The electrical signal output of the virtual model under the current boundary conditions is solved, and the virtual electrical signal is converted into the ideal pressure prediction value.

[0056] The model update unit is used to periodically optimize and update the parameters of the virtual model in reverse according to the calibration results of the self-calibration module, so as to keep it synchronized with the real state of the physical sensor.

[0057] Specifically, the system receives real-time deviations from the calibration module, uses gradient descent to adjust the parameters of the virtual model, and ensures that the average deviation between the optimized ideal pressure prediction and the preprocessed pressure data is less than the deviation threshold. The updated model parameters are then stored in the industrial computer to ensure that the virtual model is synchronized with the aging and drift status of the pressure sensor.

[0058] The self-calibration module is used to generate the final pressure output value by comparing and analyzing the ideal pressure prediction value with the preprocessed pressure data.

[0059] Furthermore, the self-calibration module specifically includes the following units: The deviation calculation unit is used to calculate the real-time deviation between the preprocessed pressure data and the ideal pressure prediction value.

[0060] Specifically, the deviation calculation unit includes the following sub-units: The difference calculation subunit is used to calculate the preprocessed pressure data in real time. Compared with ideal pressure prediction value absolute deviation : .

[0061] The normalization processing subunit is used to normalize the absolute deviation according to the measurement range to obtain the relative deviation value. .

[0062] The moving average filter subunit is used to perform moving average processing on the relative deviation values ​​of multiple consecutive sampling periods to obtain the real-time deviation.

[0063] Specifically, the filter window size is set to 5 sampling periods, and the moving average deviation (i.e., real-time deviation) is calculated. : .

[0064] The iterative learning unit, with a built-in machine learning model, takes the environmental parameters, preprocessed pressure data, and real-time deviation as inputs to learn and predict the drift characteristics of the pressure sensor and outputs dynamic compensation values.

[0065] Specifically, the iterative learning unit includes the following sub-units: The feature extraction subunit is used to extract drift feature vectors related to sensor drift from environmental parameters, pressure data, and real-time deviations.

[0066] The model training subunit employs an online learning algorithm to continuously update the support vector machine (SVM) model based on the drift feature vectors, in order to fit the nonlinear drift behavior of the sensor.

[0067] Specifically, a support vector machine regression model is adopted, with radial basis function (RBF) as the kernel function. The model is updated once every preset number of sampling points: the drift feature vector and the corresponding actual drift amount (obtained by calibration through a standard pressure generator) are used as samples and added to the training set. The SVM model parameters are updated using an online learning algorithm to fit the nonlinear drift behavior of the sensor.

[0068] The compensation prediction subunit is used to predict the drift amount based on the current environmental parameters and pressure data as input to the support vector machine model, and output the corresponding dynamic compensation value.

[0069] Specifically, the current feature vector is input into the SVM model to predict the drift amount. The drift amount is essentially the difference between the actual pressure value and the pressure value after sensor preprocessing. The dynamic compensation value is equal to the predicted drift amount.

[0070] The data fusion unit is used to fuse the dynamic compensation value with the preprocessed pressure data to generate the final pressure output value.

[0071] Specifically, the dynamic compensation value is directly superimposed on the preprocessed pressure data to eliminate drift error and generate the final pressure output value.

[0072] Furthermore, the system also includes a health management unit, which calculates the health index of the pressure sensor based on the historical sequence data of the real-time deviation, and generates a predictive maintenance warning signal when the health index is lower than a preset adaptive threshold.

[0073] Specifically, historical sequence data of real-time deviations are obtained, and statistical analysis is performed on the historical sequence data of real-time deviations to calculate their mean, variance, and trend slope.

[0074] A health assessment function is constructed based on the mean, variance, and trend slope, and the function form is as follows: ; Where μ is the mean deviation; σ represents the variance of the deviation. k is the slope of the deviation trend; ω1, ω2, and ω3 are weighting coefficients; H represents the health index.

[0075] A preset adaptive threshold is set. When the health index H is less than the adaptive threshold, an early warning signal is output through the display terminal, and the early warning information is uploaded to the cloud management platform.

[0076] It should be noted that the present invention is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments that have the same structure and perform the same effects as the technical concept within the scope of the present invention are included within the scope of the present invention. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of the present invention, are also included within the scope of the present invention.

Claims

1. A high-precision pressure sensing and self-calibration system, characterized in that, include: The pressure sensing module is used to acquire pressure signals through a pressure sensor and convert the pressure signals into initial electrical signals. The environmental monitoring module is used to collect environmental parameters in real time. The data processing module is used to preprocess the initial electrical signal; The digital twin module is used to construct a virtual model corresponding to the pressure sensor, and outputs the ideal pressure prediction value of the virtual model under the corresponding conditions based on the preprocessed pressure data and environmental parameters. The self-calibration module is used to generate the final pressure output value by comparing and analyzing the ideal pressure prediction value with the preprocessed pressure data.

2. The high-precision pressure sensing and self-calibration system as described in claim 1, characterized in that, The pressure sensing module includes a high-precision piezoresistive or capacitive pressure sensor, which has a bridge circuit composed of four resistors inside and converts pressure changes into electrical signals through an elastic body. The sensor surface is provided with micro-strain gauges or thin film structures to enhance the response sensitivity to minute pressure changes.

3. The high-precision pressure sensing and self-calibration system as described in claim 1, characterized in that, The data processing module specifically includes: The signal conditioning unit is used to amplify and filter the initial electrical signal to improve the signal-to-noise ratio; The analog-to-digital converter unit is used to convert the conditioned analog signal into a digital signal; The time-frequency domain analysis unit is used to perform time-domain and frequency-domain analysis on digital signals to extract time-domain and frequency-domain features; The dynamic compensation unit is used to generate real-time compensation coefficients based on environmental parameters, time-domain characteristics, and frequency-domain characteristics, to perform preliminary correction on digital signals, and to output pre-processed pressure data.

4. The high-precision pressure sensing and self-calibration system as described in claim 3, characterized in that, The dynamic compensation unit specifically includes: The feature fusion subunit is used to normalize the time-domain features, frequency-domain features and environmental parameters, and combine them into a multi-dimensional feature vector. The coefficient mapping subunit establishes a polynomial fitting model to describe the nonlinear mapping relationship between environmental parameters, signal characteristics and ideal compensation coefficients. The multidimensional feature vector is input into the polynomial fitting model for calculation, and the real-time compensation coefficients are output. The signal correction subunit is used to apply the real-time compensation coefficient to the digital signal, perform offset and gain correction on the signal through arithmetic operations, and output the preprocessed pressure data.

5. The high-precision pressure sensing and self-calibration system as described in claim 1, characterized in that, The self-calibration module specifically includes: The deviation calculation unit is used to calculate the real-time deviation between the preprocessed pressure data and the ideal pressure prediction value. The iterative learning unit, with a built-in machine learning model, takes the environmental parameters, preprocessed pressure data, and real-time deviation as inputs to learn and predict the drift characteristics of the pressure sensor and outputs dynamic compensation values. The data fusion unit is used to fuse the dynamic compensation value with the preprocessed pressure data to generate the final pressure output value.

6. The high-precision pressure sensing and self-calibration system as described in claim 5, characterized in that, The self-calibration module also includes a health management unit, which calculates the health index of the pressure sensor based on the historical sequence data of the real-time deviation, and generates a predictive maintenance warning signal when the health index is lower than a preset adaptive threshold.

7. The high-precision pressure sensing and self-calibration system as described in claim 5, characterized in that, The deviation calculation unit specifically includes: The difference calculation subunit is used to calculate the absolute deviation between the preprocessed pressure data and the ideal pressure prediction value in real time. The normalization processing subunit is used to normalize the absolute deviation according to the range to obtain the relative deviation value; The moving average filter subunit is used to perform moving average processing on the relative deviation values ​​of multiple consecutive sampling periods to obtain the real-time deviation.

8. The high-precision pressure sensing and self-calibration system as described in claim 5, characterized in that, The iterative learning unit specifically includes: The feature extraction subunit is used to extract drift feature vectors related to sensor drift from environmental parameters, pressure data, and real-time deviations. The model training subunit employs an online learning algorithm to continuously update the support vector machine model based on the drift feature vectors in order to fit the nonlinear drift behavior of the sensor. The compensation prediction subunit is used to predict the drift amount based on the current environmental parameters and pressure data as input to the support vector machine model, and output the corresponding dynamic compensation value.

9. A high-precision pressure sensing and self-calibration system as described in claim 6, characterized in that, The calculation of the health index of the pressure sensor based on the historical sequence data of the real-time deviation specifically includes: Perform statistical analysis on historical sequence data of real-time deviations to calculate their mean, variance, and trend slope; A health assessment function is constructed based on the mean, variance, and trend slope, and the function form is as follows: ； Where μ is the mean deviation; σ represents the variance of the deviation. k is the slope of the deviation trend; ω1, ω2, and ω3 are weighting coefficients; H represents the health index.

10. The high-precision pressure sensing and self-calibration system as described in claim 1, characterized in that, The digital twin module specifically includes: The modeling unit is used to construct a virtual model based on the structural parameters, material properties, and working mechanism of the pressure sensor; A real-time simulation engine is used to receive the preprocessed pressure data and environmental parameters, and use them as boundary conditions to drive the virtual model to solve in real time, simulate the response of an ideal sensor under corresponding physical conditions, and output the ideal pressure prediction value. The model update unit is used to periodically optimize and update the parameters of the virtual model in reverse according to the calibration results of the self-calibration module, so as to keep it synchronized with the real state of the physical sensor.

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