A dynamic compensation-based metal pressure sensor calibration system

Abstract

The application belongs to the field of pressure sensor calibration, and particularly relates to a metal pressure sensor calibration system based on dynamic compensation. The system comprises a sealed cavity, a dynamic excitation unit, a reference measurement unit, a to-be-measured sensor interface unit, a synchronous signal acquisition unit, a core algorithm processing unit, a power distribution unit and an environment monitoring unit. The dynamic excitation unit drives the voice coil diaphragm to generate a controlled pressure signal, the reference measurement unit captures displacement as a traceable reference by using a laser interference vibration measuring device, and the synchronous signal acquisition unit performs nanosecond-level alignment sampling. The core algorithm processing unit solves the dynamic characteristic parameters of the sensor through dynamic hysteresis modeling and inverse compensation operation. The application realizes synchronous acquisition and real-time correction of multiple physical fields, can accurately strip nonlinear errors and eliminate resonance interference, and improves the measurement accuracy, traceable accuracy and calibration efficiency of the sensor in a dynamic scene.

Description

Technical Field

[0001] This invention belongs to the field of pressure sensor calibration, specifically relating to a metal pressure sensor calibration system based on dynamic compensation. Background Technology

[0002] With the rapid development of precision measurement and industrial automation technologies, pressure sensors are increasingly widely used in key fields such as aerospace, semiconductor manufacturing, and medical monitoring. The accuracy of their measurement results directly affects the control precision and operational safety of the system. Pressure sensor calibration technology, as a core means of traceability and performance evaluation, aims to establish a highly reliable transfer function through correlation analysis between standard pressure excitation and the sensor's output signal. Especially in the field of micro-pressure measurement, precise calibration processes are crucial for eliminating sensor system errors and improving the sensing capabilities of industrial processes.

[0003] Metal pressure sensors have become the mainstream choice in the field of micro-pressure detection due to their excellent mechanical properties and environmental adaptability. These sensors typically utilize the deformation characteristics of a metal pressure-sensitive diaphragm to convert force into electrical signals. Their calibration process relies on a high-precision pressure generating device and a stable pressure environment to ensure the accurate extraction of key indicators such as sensor sensitivity and linearity. As the demands for dynamic sensing capabilities under complex working conditions continue to increase, calibration systems need to further analyze the sensor's response characteristics under rapidly changing pressure fields, while maintaining static accuracy.

[0004] Existing calibration techniques are mostly based on point-by-point pressure application in quasi-static environments. This leads to the inability to identify dynamic response hysteresis caused by pressure fluctuations and airflow resonance, affecting the sensor's accuracy in dynamic scenarios. Within the micro-pressure range, low-frequency noise signals caused by ambient airflow disturbances and temperature gradients often couple with the sensor output. Traditional calibration schemes struggle to isolate interference in the same frequency band without extending the calibration period. Furthermore, existing systems lack dynamic compensation mechanisms for the nonlinear characteristics of metallic materials, such as hysteresis and creep, making it impossible to establish accurate dynamic response models. This results in unpredictable errors in calibration results under complex and variable real-world conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a calibration system for metal pressure sensors based on dynamic compensation, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a metal pressure sensor calibration system based on dynamic compensation, comprising: a sealed cavity, providing a sealed volume with a predetermined sealing strength, wherein the inner surface of the sealed volume is polished to reduce turbulence noise generated by airflow, and the sealed cavity is provided with a physical interface; A dynamic excitation unit is installed on the physical interface. The dynamic excitation unit includes a high-frequency controlled drive component and an acoustic diaphragm. The high-frequency controlled drive component is used to drive the acoustic diaphragm to generate a dynamic pressure excitation signal with frequency, amplitude and waveform in the sealed cavity. The reference measurement unit includes a laser interferometric vibration measurement device, used to capture the instantaneous displacement of the acoustic diaphragm during its motion and convert the instantaneous displacement into an optical interference signal; The sensor under test interface unit is fixedly connected to the metal pressure sensor to be calibrated and transmits the electrical signal generated by the metal pressure sensor to be calibrated. A synchronous signal acquisition unit is connected to the reference measurement unit and the interface unit of the sensor under test, respectively, and is used to align and sample the optical interference signal and the electrical signal to generate the original dynamic calibration dataset. The core algorithm processing unit is connected to the synchronous signal acquisition unit. It is equipped with a dynamic hysteresis modeling subsystem and an inverse compensation operation subsystem, which are used to calculate the dynamic characteristic response parameters of the metal pressure sensor to be calibrated by constructing a nonlinear mathematical correlation model. Power distribution unit, used to provide regulated current; An environmental monitoring unit is used to acquire environmental status information inside the sealed cavity and feed it back to the core algorithm processing unit for parameter correction.

[0007] Preferably, the sealed cavity is made of an alloy material with high heat capacity and is wrapped with an insulation layer composed of aerogel or multi-layer vacuum insulation board to reduce the impact of external ambient temperature fluctuations on the gas density inside the cavity by increasing the thermal inertia of the system. The inner wall of the sealed cavity is equipped with a sound-absorbing structure, which consists of a geometrically arranged array of micropores. The diameter, spacing between the micropores, and depth of the cavity are set by acoustic impedance matching logic to absorb the reflected waves generated by the dynamic pressure excitation signal at the cavity boundary and to construct a pure traveling wave field within the sealed cavity. The bottom of the sealed cavity is equipped with an active damping bracket, which adopts a combination structure of air spring and damper to isolate the interference of external vibration on the internal sound field and measurement optical path of the cavity.

[0008] Preferably, the high-frequency controlled drive component in the dynamic excitation unit is made of piezoelectric ceramic material with a predetermined piezoelectric constant, and controlled deformation is achieved by voltage control to drive the acoustic diaphragm to vibrate; The edge portion of the acoustic diaphragm is fixed to the sealed cavity by a flexible support structure. The flexible support structure uses a spring sheet with damping characteristics and nonlinear stiffness compensation function to isolate stray mechanical vibrations during the operation of the high-frequency controlled drive component. The driving logic of the dynamic excitation unit includes a hardware-level overload protection function. By monitoring the derivative of the driving current and the feedback signal in real time, the output power is limited when it is determined that the driving excitation exceeds the mechanical limit of the acoustic diaphragm. The high-frequency controlled drive component is also equipped with a closed-loop charge feedback control circuit to compensate for the inherent creep and hysteresis effects of piezoelectric materials.

[0009] Preferably, the laser interferometric vibration measuring device adopts a dual-optical-path interference structure. The first optical path serves as a reference branch, and its optical path is fixed on a low-expansion material reference block. The second optical path serves as a measurement branch and enters the cavity through an optical-grade quartz glass window on the sealed cavity. The optical-grade quartz glass window adopts a multi-static sealing ring design and has a low refractive index temperature sensitivity coefficient. The reference measurement unit obtains the instantaneous displacement of the acoustic diaphragm by calculating the phase difference between the two light waves, and converts the instantaneous displacement into an equivalent dynamic pressure value in the sealed cavity through a preset conversion logic. The core algorithm processing unit integrates benchmark traceability and verification logic, which periodically compares the measured values ​​of the laser interferometric vibration measuring device with the built-in static standard gravity piston data, and realizes online calibration of the calibration system by calculating the deviation between the two at the quasi-static point.

[0010] Preferably, the interface unit of the sensor under test has a multi-dimensional adjustment function, which adjusts its insertion depth in the sealed cavity according to the geometric dimensions of different sensor specifications, so that the pressure sensing center of the metal pressure sensor to be calibrated is in a predetermined uniform sound field area. The interface unit of the sensor under test adopts a fully shielded metal shell and is equipped with a precision differential interface to suppress electromagnetic interference and achieve lossless transmission of electrical signals; The synchronous signal acquisition unit includes a multi-channel isolation sampling circuit, and each sampling channel is equipped with an anti-aliasing filter. The cutoff frequency of the anti-aliasing filter is dynamically adjusted according to the highest effective frequency of the dynamic pressure excitation signal. The sampling frequency of the synchronous signal acquisition unit is set to more than 50 times the main frequency of the dynamic pressure excitation signal in order to characterize the rising and falling edges of the pressure waveform in the time domain.

[0011] Preferably, the core algorithm processing unit runs a modeling logic based on nonlinear autoregressive external input, using the instantaneous displacement as a standard reference input and the output signal of the metal pressure sensor to be calibrated as the disturbed output, and extracts the sensor's frequency response function, phase hysteresis curve and nonlinear gain characteristics through iterative calculation. The core algorithm processing unit also integrates adaptive cancellation logic, which defines the mechanical vibration characteristics captured by the laser interferometric vibration measurement device as reference noise. By adjusting the compensation coefficient in the time domain, it filters out the non-target pressure components generated by cavity resonance and airflow turbulence from the original output signal of the metal pressure sensor to be calibrated. When performing modeling, the core algorithm processing unit adopts a recursive calculation method with dynamic weight adjustment to automatically allocate error penalty factors for the nonlinear intensity exhibited by the sensor in different pressure ranges.

[0012] Preferably, the core algorithm processing unit integrates a nonlinear feature extraction module based on deep neural networks. It uses convolution operations and attention mechanisms to extract mode vectors representing the nonlinear features of the sensor from the original waveform, and compares them with prior model templates in the feature database to achieve automatic identification of the model features of the metal pressure sensor to be calibrated. The core algorithm processing unit calls the matching template parameters according to the identified model characteristics, and completes the parameter calibration by iteratively fine-tuning the measured data; The core algorithm processing unit also supports remote data communication protocols, which are used to upload the calibrated sensor model parameters to a cloud database to achieve long-term tracking and big data analysis of the performance evolution of different batches of sensors.

[0013] Preferably, the environmental monitoring unit includes a platinum resistance sensor and a static pressure monitoring device integrated into the wall of the sealed cavity. The platinum resistance sensor is used to sense the temperature shift inside the cavity, and the static pressure monitoring device is used to determine the current reference atmospheric pressure level. The core algorithm processing unit calculates the elastic modulus drift of the pressure-sensitive diaphragm based on the temperature offset using a thermodynamic model of the metal material, and calculates the gas compressibility change and sound velocity fluctuation caused by temperature change in combination with the gas state equation. Based on this, the conversion coefficient between displacement and equivalent pressure is corrected in real time. The environmental monitoring unit has a fixed data acquisition frequency and sends environmental status information to the core algorithm processing unit in real time to ensure the robustness of the calibration results under different operating conditions.

[0014] Preferably, the power distribution unit adopts a multi-stage isolation step-down scheme, including a primary switching power supply stage and a secondary linear regulator stage, wherein the secondary linear regulator stage outputs DC voltage using a low-noise reference. The power distribution unit is also equipped with an uninterruptible power supply module, which is used to maintain the system operation for a predetermined time when the external power supply is interrupted, to ensure the complete preservation of calibration data and to guide the safe reset of mechanical moving parts. The power distribution unit also incorporates a real-time online power quality monitoring system to record power supply voltage fluctuations, harmonic content, and grounding resistance status during the calibration period. When the power quality exceeds the preset level requirements, the core algorithm processing unit adds an environmental impact statement to the calibration report.

[0015] Preferably, the inverse compensation operation subsystem performs inverse mapping of the dynamic characteristic response parameters to generate a digital filter or mathematical compensation matrix that is opposite to the original physical hysteresis characteristics of the metal pressure sensor to be calibrated. The digital filter, when the sensor is actually used, cancels out the response hysteresis caused by the mass inertia of the metal pressure-sensitive diaphragm, the internal damping of the material, and stress creep in real time. The core algorithm processing unit also performs time-frequency domain transformation processing on the original dynamic calibration dataset, analyzes the phase hysteresis of the sensor under different pressure frequencies, and constructs a dynamic error compensation matrix using a polynomial fitting method. The dynamic error compensation matrix is ​​stored in the non-volatile memory of the metal pressure sensor to be calibrated. The core algorithm processing unit automatically synthesizes combined pressure waveforms according to the requirements of the calibration task. The combined pressure waveforms include step waves, triangular waves, superimposed sine waves, pseudo-random sequence waves, pressure pulse waves simulating jet engine ignition, and wave waveforms simulating water hammer effect in hydraulic systems. The synchronous signal acquisition unit has a data anomaly detection function. By monitoring the signal amplitude, signal-to-noise ratio and signal slope of each sampling channel in real time, it determines whether the signal is in the normal working range. When it detects signal distortion caused by gas leakage or circuit short circuit, it sends an alarm signal to the central control unit and forcibly stops the current excitation output. The synchronous signal acquisition unit adopts a differential input architecture to suppress common-mode electromagnetic interference at the calibration site; The core algorithm processing unit executes a digital compensation closed-loop verification program, which loads the calculated inverse compensation model onto the acquisition link in real time and applies a set of pseudo-random sequence waveforms as dynamic pressure excitation again. The core algorithm processing unit compares the residual between the sensor output after loading the compensation model and the benchmark output of the benchmark measurement unit. When the root mean square value of the residual is less than a preset accuracy threshold, the dynamic characteristic response parameter and the corresponding compensation model are determined to be effective. The calibration results generated by the core algorithm processing unit include static sensitivity coefficient, linearity deviation, hysteresis error distribution map, and full-band amplitude-frequency response curve, which are displayed in a three-dimensional visualization through a human-computer interaction terminal and automatically generate an electronic calibration report.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention introduces an acoustic microcavity and acoustic diaphragm to construct a dynamic excitation source, thereby realizing the generation of high-frequency and high-precision dynamic pressure signals within a micro-pressure range, which makes up for the lack of traditional static calibration methods in capturing the dynamic response characteristics of sensors; by using a laser interferometric vibrometer as a traceability benchmark, the pressure signal is directly traced from mechanical displacement to optical interference, improving the accuracy of physical quantities in the calibration process.

[0017] 2. The dynamic compensation processing unit used in this invention integrates nonlinear hysteresis modeling and inverse compensation algorithms, which can accurately remove nonlinear errors caused by the physical properties of metal materials from the complex original sensor output, thereby improving the measurement accuracy of the sensor in dynamic pressure scenarios. By actively eliminating environmental interference and cavity resonance through adaptive filtering technology, it achieves efficient suppression of low-frequency noise and interference in the same frequency band without extending the calibration time, thus improving calibration efficiency.

[0018] 3. The system architecture provided by this invention realizes the deep fusion and synchronous acquisition of multiple physical fields such as sound, force, electricity and light. Through precise environmental monitoring and real-time correction mechanisms, it ensures the robustness of calibration results under different working conditions. Its fully automated data flow process and intelligent modeling logic reduce the dependence of the calibration process on human experience, and provide technical support for the large-scale production and high-precision application of metal pressure sensors. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the present invention based on nonlinear hysteresis modeling and inverse compensation operation; Figure 3 This is a flowchart illustrating the main stages of laser interferometric displacement tracing and multi-channel signal synchronous acquisition in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between environmental state perception feedback and core algorithm parameter correction in this invention. Detailed Implementation

[0020] Example 1: Reference Figures 1 to 4 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0021] A calibration system for a metal pressure sensor based on dynamic compensation includes a sealed cavity, a dynamic excitation unit, a reference measurement unit, a sensor under test interface unit, a synchronous signal acquisition unit, a core algorithm processing unit, a power distribution unit, and an environmental monitoring unit.

[0022] The sealed cavity provides controlled physical boundary conditions for the entire calibration process. It is crafted from a high-heat-capacity alloy, such as high-strength aerospace-grade aluminum alloy or stainless steel, and defines a predetermined volume of sealed space. To minimize internal airflow turbulence caused by dynamic pressure changes, the inner surface of the sealed cavity undergoes precision mechanical or electrolytic polishing to achieve a surface roughness at the nanometer level, reducing turbulence noise generated by airflow.

[0023] Multiple highly airtight physical interfaces are provided on the wall surface of the sealed cavity. These interfaces house the dynamic excitation unit, the sensor under test interface unit, and the observation window of the laser interferometric vibration measurement device. The exterior of the sealed cavity is wrapped with a uniform insulation layer composed of aerogel or multi-layer vacuum insulation panels. This layer increases the system's thermal inertia and shields the instantaneous influence of external ambient temperature fluctuations on the gas density inside the cavity. The base of the sealed cavity is equipped with an active vibration damping bracket, which employs a combination of air springs and dampers to isolate external ground vibrations from interfering with the precision acoustic field and optical measurement path inside the cavity.

[0024] The sealed cavity is equipped with a sound-absorbing structure. This structure is not a simple covering layer, but a micro-perforated plate structure composed of a geometrically arranged array of micropores. The diameter, spacing, and depth of these micropores are precisely calculated to target and absorb the cavity resonant frequencies that may be induced by dynamic excitation signals. When the dynamic pressure signal is reflected at the cavity boundary, the micropore array uses the principle of acoustic impedance matching to dissipate the reflected wave energy, constructing a pure traveling wave field that approximates a free field within the sealed cavity. This acoustic environment ensures that the pressure excitation signal received by the pressure-sensing surface of the sensor under test has high spectral purity, avoiding calibration deviations caused by standing wave effects.

[0025] The dynamic excitation unit, installed at a dedicated excitation port in the sealed cavity, serves as the system's active excitation source, responsible for generating a dynamic pressure field simulating actual working conditions. The dynamic excitation unit includes a high-frequency controlled drive component and an acoustic diaphragm. The high-frequency controlled drive component employs a piezoelectric ceramic stack material with a predetermined piezoelectric constant, manufactured through a multi-layer lamination process, capable of generating sub-micron level high-frequency controlled deformation under controlled drive voltage. The drive input terminal of the high-frequency controlled drive component is connected to the central control unit, receiving drive logic commands containing frequency, specific amplitude, and specific waveform information.

[0026] The edge portion of the acoustic diaphragm is fixed to the sealed cavity by a complex flexible support structure. This flexible support structure uses corrugated spring sheets with specific damping characteristics and nonlinear stiffness compensation, capable of isolating stray mechanical vibrations generated during the operation of the drive components. The material properties of the acoustic diaphragm itself are pre-selected, typically using beryllium alloys, titanium foil, or multilayer composite carbon fiber materials with high specific stiffness and low damping coefficients to ensure excellent motion linearity across the entire amplitude range, achieving a linear conversion from electrical signals to mechanical displacement and then to dynamic pressure.

[0027] The driving logic of the dynamic excitation unit incorporates hardware-level overload protection. This overload protection function automatically determines whether the current driving excitation exceeds the mechanical limits of the acoustic diaphragm by real-time monitoring of the derivative of the driving current and the feedback signal from the displacement sensor. If an excessively steep slope or peak voltage in the driving signal is detected, potentially causing depolarization of the piezoelectric ceramic, the driving logic automatically limits the output power and issues a safety shutdown command to the system to prevent hardware damage due to physical impact. Furthermore, the high-frequency controlled driving component is equipped with a closed-loop charge feedback control circuit to compensate for the inherent creep and hysteresis effects of the piezoelectric material, ensuring a high degree of consistency between the instantaneous displacement of the acoustic diaphragm and the control command.

[0028] The reference measurement unit, serving as the physical core of the system's measurement traceability, aims to obtain the original physical reference of pressure generation through non-contact measurement. The reference measurement unit includes a high-precision laser interferometric vibration measurement device. The light source of this device employs a frequency-stabilized helium-neon laser or a semiconductor frequency-stabilized laser with a long coherence length, ensuring that the contrast of the interference fringes remains above a preset quality threshold throughout the large-range displacement measurement process.

[0029] The laser interferometric vibration measurement device adopts a dual-optical-path interference structure. The first optical path serves as a reference branch, and its optical path is fixed on a low-expansion material reference block inside the device. The second optical path serves as a measurement branch, entering the cavity through an optical-grade quartz glass window on the sealed cavity, with the probe pointing towards the geometric center of the acoustic diaphragm.

[0030] When the acoustic diaphragm reciprocates under dynamic pressure, the phase of the measurement beam modulates with the displacement. The reference measurement unit calculates the phase difference between the two light waves to obtain the instantaneous velocity and displacement of the acoustic diaphragm in real time. The displacement is converted into an equivalent dynamic standard pressure value within the sealed cavity through a preset conversion logic, combined with the cavity volume and the gas adiabatic compression equation, thus realizing the physical traceability of pressure calibration from mechanical quantities to optical wavelengths.

[0031] The optical-grade quartz glass window employs a multi-ring static seal design, and its material has a low refractive index and temperature sensitivity coefficient, ensuring that the laser beam penetrates without damage while reducing light scattering. The reference measurement unit also integrates reference traceability verification logic, which periodically compares the measurement values ​​from the laser interferometer with the system's built-in static standard gravity piston data. By calculating the deviation between the two at the quasi-static equilibrium point, the system can achieve online self-calibration and automatically compensate for proportionality coefficient errors caused by optical path drift or changes in gas composition.

[0032] The interface unit for the sensor under test is fixedly connected to the metal pressure sensor to be calibrated. This interface unit features multi-dimensional adjustment, allowing operators to adjust its insertion depth within the sealed cavity according to the geometry of sensors of different specifications. This spatial adjustability ensures that the pressure-sensing center of the sensor under test is always located within a predetermined core region where the sound field intensity distribution is most uniform and where standing wave interference is minimal. The interface unit not only provides a physical rigid connection but also integrates a high-speed signal transmission channel for lossless transmission of the weak electrical signals generated by the sensor under pressure to subsequent data acquisition links. To suppress electromagnetic interference, the interface unit employs a fully shielded metal housing and is equipped with a precision differential interface.

[0033] The synchronization signal acquisition unit is electrically connected to both the reference measurement unit and the interface unit of the sensor under test. The core of the synchronization signal acquisition unit is a multi-channel data acquisition subsystem integrating a high-precision clock synchronization circuit. This multi-channel data acquisition subsystem uses a unified nanosecond-level distributed clock signal to trigger sampling of all channels, ensuring perfect alignment of the optical displacement information output by the laser interferometric vibrometer and the electrical signal output by the sensor under test on the time axis, with an alignment accuracy better than 10 nanoseconds. Each sampling channel is equipped with an independent anti-aliasing filter. The cutoff frequency of the anti-aliasing filter is not fixed but dynamically adaptively adjusted according to the highest effective frequency of the dynamic excitation signal, ensuring that the sampling process strictly conforms to the Nyquist sampling theorem.

[0034] The sampling frequency of the synchronous signal acquisition unit is set to more than 50 times the main frequency of the dynamic pressure signal, so as to clearly and completely depict the rising edge, falling edge and any microwave oscillation details caused by nonlinear effects of the pressure waveform in the time domain, and provide a high-resolution basic original dynamic calibration dataset for subsequent dynamic hysteresis modeling.

[0035] The synchronous signal acquisition unit also features a data anomaly detection function. This module automatically determines whether the current signal is within the normal operating range by monitoring the signal amplitude, signal-to-noise ratio, and signal slope of each sampling channel in real time. If the pressure amplitude drops abnormally due to gas leakage, or if the signal is distorted due to a short circuit, the synchronous signal acquisition unit will immediately send an alarm signal to the central control unit and forcibly stop the current excitation output, ensuring the safety and reliability of the calibration process.

[0036] The core algorithm processing unit, connected to the synchronous signal acquisition unit, is the intelligent hub of the entire system. Internally, it includes a dynamic hysteresis modeling subsystem and an inverse compensation calculation subsystem. The core algorithm processing unit receives the raw dynamic calibration dataset from the acquisition unit and calculates the full-band dynamic characteristic response parameters of the sensor to be calibrated by constructing a complex nonlinear mathematical correlation model. This core algorithm processing unit runs a modeling logic based on a nonlinear autoregressive external input, using the displacement representing standard pressure fed back from the laser interferometric vibration measurement device as the standard reference input and the output signal of the sensor under test as the disturbed observation output. Through iterative calculation, the frequency response function, phase hysteresis curve, and nonlinear gain characteristics of the sensor are extracted.

[0037] In one specific embodiment, the nonlinear autoregressive external input modeling logic run by the core algorithm processing unit is specifically a nonlinear autoregressive model with external input. This model is designed to address the dynamic hysteresis nonlinearity problem caused by the mass inertia of the metal pressure-sensing diaphragm, internal material damping, and stress creep of metal pressure sensors. Unlike traditional linear transfer functions, which can only fit the linear response of the sensor, this model can accurately characterize the nonlinear time-varying response characteristics of the sensor under dynamic pressure across the entire frequency band, achieving complete modeling of the dynamic hysteresis characteristics. Specifically, the time series of equivalent dynamic pressure values ​​output by the synchronous signal acquisition unit, converted from the laser interferometric vibration measurement device, is used as the standard reference input vector. The time series of the output electrical signal of the metal pressure sensor to be calibrated is used as the observed output vector. The NARX model predicts the current output using historical outputs and external inputs. Its discrete-time mathematical expression is derived from the discretization of the sensor dynamics differential equations, as follows:

[0038] in, Indicates the model at time... The predicted output; and These represent the maximum delay order of the output and input, respectively, which is determined by the preset mechanical resonant frequency of the sensor under test; the value is 2 to 3 times the number of sampling points in the period corresponding to the resonant frequency. This represents the pure time delay step size of the system; it corresponds to the time delay of the sensor's step response, which can be directly obtained through a pre-calibrated step response test. and These represent the weight matrices of the hidden layer and the output layer, respectively; the parameters within the matrices correspond to the sensor's response gain and phase characteristics at different frequencies. express consecutive moments before The sensor outputs historical values ​​at each moment; express Before the time step, subtract the pure time delay step size After that, continuously The standard pressure input historical value at each moment; and These represent the bias vectors of the hidden layer and the output layer, respectively; This represents a nonlinear activation function. In this embodiment, a Gaussian error linear unit is used to alleviate the gradient vanishing problem, thus adapting to the strong nonlinear fitting requirements under large deformation of metal films. This indicates the matrix transpose.

[0039] The core algorithm processing unit identifies the weight matrix by minimizing the residual between the predicted output and the actual sensor output, and it uses the Levenberg-Marquardt algorithm for iterative solution. This core algorithm combines the convergence speed of the Gauss-Newton method with the global convergence of the gradient descent method, enabling rapid identification of model parameters. Its objective function... Defined as:

[0040] in, Represent the objective loss function to be minimized; Indicates the total number of sampling points; This represents a parameter vector containing all weights and biases; express The measured output signal of the time sensor; express The predicted output signal of the time-matter model; Denotes the squared L2 norm of a vector; This represents the regularization coefficient, used to prevent overfitting. Its value ranges from 1e-6 to 1e-3. When the number of calibration data sets is less than 1000, it is set to 1e-4 to 1e-3; when the number of calibration data sets is greater than 10000, it is set to 1e-6 to 1e-4, balancing the model's fitting accuracy and generalization ability.

[0041] The core algorithm processing unit also integrates adaptive cancellation logic. This adaptive cancellation logic draws on the acoustic echo cancellation principle from advanced signal processing, defining the mechanical vibration characteristics outside the acoustic diaphragm captured by the laser interferometer as reference noise. By adjusting a set of multi-order compensation coefficients in real time in the time domain, background interference caused by weak cavity resonance, airflow turbulence, or other non-target pressure components is precisely filtered out from the sensor's raw output signal. This processing not only improves the signal-to-noise ratio of the calibration curve but also enables the system to achieve high-precision parameter extraction under complex noise backgrounds without increasing the time required for each calibration.

[0042] The complete implementation process of this model is as follows: First, the synchronously acquired reference pressure sequence and sensor output sequence are preprocessed by removing DC, normalizing, and eliminating outliers. The preprocessing rule is to eliminate outlier sampling points exceeding 3 times the standard deviation. Then, the initial value of the weight matrix is ​​set to a random decimal in the range of 0 to 1, and the initial value of the regularization coefficient is determined according to the above rules. The upper limit of the number of iterations is set to 1000. Iterative solution is started, and the iteration is terminated when the residual change rate of 10 consecutive iterations is less than 1e-6, or when the upper limit of the number of iterations is reached. After the iteration converges, the hidden layer weight matrix is ​​extracted and transformed into the frequency response function, phase hysteresis curve, and nonlinear gain characteristics of the sensor, which are then passed to the subsequent inverse compensation operation subsystem. Verified by actual measurements with a metal pressure sensor with a range of 0 to 100 kPa, the root mean square value of the fitting residual of this model for the sensor's dynamic response is less than 0.2%FS, and the fitting error of the frequency response function is less than 0.3 dB. It can accurately extract the dynamic characteristic parameters of the sensor across the entire frequency band. As a preferred implementation, the adaptive cancellation logic employs a normalized minimum mean square error algorithm to construct a transverse finite impulse response filter. This filter addresses the technical problem of coupling between cavity resonance, airflow turbulence, and the effective output signal of the sensor in the same frequency band. Using non-target vibrations captured by the laser interferometric vibration meter as reference noise, it can achieve online filtering of interference in the same frequency band without losing the effective dynamic signal, solving the problem that traditional fixed filtering methods cannot preserve the details of dynamic pressure signals. Specifically, the stray mechanical vibration characteristic sequence captured by the laser interferometric vibration meter, excluding the main vibration of the acoustic diaphragm, is defined as the reference noise input vector. The raw output signal of the sensor under test is used as the desired response. The adaptive filter estimates the interference components in real time based on the reference noise, and its output estimate is... with weight vector The relationship is:

[0043] in, Indicates time The duration is The filter tap coefficient vector; Determined by the longest period of cavity resonance, the value is taken as 1.5 to 2 times the number of sampling points corresponding to the resonant frequency period, ensuring complete coverage of the full-cycle characteristics of cavity resonance.

[0044] By subtracting this estimate from the desired response, the error signal filtered out for non-target pressure components is obtained. The error signal is output as the final high signal-to-noise ratio calibration data to the dynamic hysteresis modeling subsystem, and its calculation logic is as follows:

[0045] Simultaneously, the weight vector is iteratively updated in real time based on the error signal, and its gradient descent update formula is as follows:

[0046] in, The step size factor controls the convergence speed and stability. Its value directly determines the filter's convergence speed and steady-state error. A larger value results in faster convergence but also a larger steady-state error, while a smaller value results in slower convergence but higher stability. The value range is [range missing]. , The maximum eigenvalue of the autocorrelation matrix of the reference noise input can be approximated by the power of the reference noise input signal. In practical applications, for low-frequency micro-voltage calibration scenarios (excitation frequency less than 100Hz), a value of 0.001 to 0.01 is recommended to ensure convergence stability. For high-frequency dynamic calibration scenarios (excitation frequency greater than 1kHz), a value of 0.01 to 0.1 is recommended to improve convergence speed. A variable step size strategy can also be adopted, using a large step size of 0.05 to 0.1 in the initial iteration stage to achieve rapid convergence. When the rate of change of the mean square error is less than 1e... -3 When this happens, switch to a small step size of 0.001 to 0.01 to reduce steady-state error; It is a very small constant term, with a value of 1e. -10 This logic is used to prevent overflow during denominator normalization. Practical testing has verified that this logic suppresses cavity resonance interference by more than 40dB and airflow turbulence noise by more than 30dB, improving the sensor output signal-to-noise ratio by more than 20dB. This logic achieves closed-loop adaptive elimination of cavity resonance and airflow turbulence.

[0047] In the distributed calibration architecture of Example 2, the recursive calculation method for dynamic weight adjustment is implemented by constructing a piecewise weighted loss function. This function is designed to address the differences in nonlinear intensity exhibited by the sensor in different pressure ranges, balancing the nonlinear fitting accuracy across the entire range and solving the problem of excessive errors in the low-pressure range of traditional fitting methods. Specifically, the dynamic pressure range is set according to the calibration task. Define a pressure amplitude Dynamic error penalty factor weighting function :

[0048] in, and These are preset hyperparameters. The maximum gain controlling the error penalty intensity in the low-voltage range ranges from 2 to 10. The decay rate of the control weight with pressure change should be controlled, with a value ranging from 3 to 8; for micro-pressure sensors (range less than 1 kPa), the recommended value is... Take 5 to 10. A value of 6 to 8 is recommended, focusing on improving the fitting accuracy in the low-pressure range. This is particularly important for large-range sensors (range greater than 100 kPa). Take 2 to 5. A value of 3 to 5 is used to ensure the consistency of fitting accuracy across the entire range. The above parameter range was determined through orthogonal experimental optimization, which minimizes the fitting error across the entire range. In the low-pressure range, Approaching 1+ Assign higher error penalty weights; in the high-pressure range, Approaching 1. Actual measurements have verified that the fitting model using this weighting function has a fitting error of less than 0.1%FS in the low-pressure range and less than 0.15%FS in the high-pressure range. The overall error balance is improved by more than 80% compared to traditional equal-weighted fitting.

[0049] The inverse compensation operation performed by the dynamic compensation processing unit involves mathematically reversing the extracted dynamic characteristic response parameters. The process generates a digital filter or mathematical compensation matrix that is the complete opposite of the sensor's original physical hysteresis characteristics. When the sensor is subsequently deployed in actual industrial conditions, this digital filter can receive the sensor's original readings in real time and compensate for the response hysteresis caused by the mass inertia of the metal pressure-sensitive diaphragm, internal material damping, and stress creep, ensuring that the sensor's real-time output accurately reflects transient pressure changes.

[0050] The power distribution unit provides ultra-low ripple regulated current to all the aforementioned electronic components. This power distribution unit employs a multi-stage isolated step-down scheme, including a primary industrial-grade switching power supply stage and a secondary precision linear regulator stage. The secondary linear regulator stage utilizes an ultra-low noise reference, enabling it to output a DC voltage with a peak-to-peak value of less than 10 microvolts, suppressing crosstalk from industrial power grid fluctuations to weak signal acquisition links. Furthermore, the power distribution unit is equipped with an uninterruptible power supply (UPS) module, which maintains continuous system operation in the event of a sudden external power outage, ensuring the complete preservation of ongoing calibration data and guiding the safe reset of mechanical moving parts.

[0051] The environmental monitoring unit acquires the real-time environmental status inside the sealed cavity. The environmental monitoring unit includes a high-precision platinum resistance temperature sensor and a static pressure monitoring device integrated into the cavity wall. The platinum resistance sensor has a resolution of one-thousandth of a degree Celsius, used to sense minute temperature shifts in the gas inside the cavity in real time; the static pressure monitoring device uses a highly stable absolute pressure sensor to determine the current reference atmospheric pressure. The collected environmental data is sent to the core algorithm processing unit at a fixed frequency. Based on the ideal gas law and its modified model, the algorithm processing unit calculates in real-time the changes in gas compressibility and sound velocity caused by temperature changes, and dynamically corrects the conversion coefficient between displacement and equivalent pressure accordingly.

[0052] Example 2: As an extension and architectural variant of Example 1, Example 2 describes a distributed dynamic compensation system scheme suitable for large-scale rapid calibration in industrial applications.

[0053] In Embodiment 2, the sealed cavity is designed as a ring or matrix structure with multiple independent calibration stations. Each station is equipped with an independent interface unit for the sensor under test, but shares the same high-performance dynamic excitation unit. In Embodiment 2, the dynamic excitation unit employs a larger-sized electrodynamically driven acoustic diaphragm, and the dynamic pressure waves generated are uniformly guided to each calibration station through a set of damped and optimized acoustic conduits.

[0054] In Example 2, the core algorithm processing unit employs a recursive calculation method with dynamically adjusted weights. To address the different nonlinear intensity characteristics exhibited by the sensor in the low-pressure and high-pressure ranges, the processing unit automatically assigns different error penalty factors during the modeling process. In the low-pressure range, the algorithm focuses more on suppressing background noise and fitting linearity within small ranges; in the high-pressure range, it focuses on correcting geometric nonlinearities and elastic modulus changes caused by large deformations of the metal diaphragm. Through this piecewise weighted optimization strategy, the system can achieve balanced calibration accuracy across the entire range.

[0055] In the distributed calibration architecture of Example 2, the recursive calculation method for dynamic weight adjustment is implemented by constructing a piecewise weighted loss function. Specifically, this is based on the dynamic pressure interval set by the calibration task. Define a pressure amplitude Dynamic error penalty factor weighting function :

[0056] in, and These are preset hyperparameters. The maximum gain that controls the intensity of the penalty. Control the rate of decay of the weight as pressure changes. In the low-pressure range, Approaching Assign higher error penalty weights; in the high-pressure range, Approaching .

[0057] Substituting this dynamic weight function into the recursive calculation process of model training, the corrected weighted total cost function is obtained. Expressed as:

[0058] In each iteration of parameter update, the core algorithm processing unit, based on this... Calculate relative to the parameter vector The gradient is calculated, and backpropagation is performed using an adaptive moment estimator optimizer. This mechanism of automatically allocating error penalty factors across different pressure ranges ensures the consistency of nonlinear fitting accuracy across the entire model range.

[0059] The core algorithm processing unit also integrates a nonlinear feature extraction module based on deep neural networks. This nonlinear feature extraction module, through pre-learning from tens of thousands of historical calibration samples, can automatically identify the characteristic signal patterns of sensors with different metal materials and diaphragm thicknesses. When a new, unknown sensor model is inserted into the interface, the processing unit can automatically identify its model characteristics and retrieve a matching prior model template from the built-in feature database. Fine-tuning of the template parameters can be completed through rapid iteration with only a few sets of measured data, shortening the calibration time for a single sensor.

[0060] In one specific embodiment, the nonlinear feature extraction module based on a deep neural network specifically includes a sequentially connected one-dimensional convolutional layer, a pooling layer, and a multi-head self-attention mechanism layer. The original output waveform of the sensor under test is preprocessed into a tensor. ,in The length of the sampling sequence. The feature dimension is 1 (initially). The one-dimensional convolutional layer extracts local temporal features through a sliding convolutional kernel; its operational logic is as follows:

[0061] in, This represents the feature map output by the convolutional layer; This represents a one-dimensional effective convolution operation; $ Represents the convolution kernel tensor. Indicates the kernel size. This indicates the number of convolution kernels, which is equivalent to the number of output channels. For convolution bias; It is a linear rectification activation function.

[0062] Will Input is fed into a multi-head self-attention mechanism layer to capture global nonlinear correlation features, for the Each attention head, whose query, key, and value matrices are obtained through linear mapping:

[0063] in, This is the corresponding projection weight matrix. Reduce the dimensionality of the features for each head. The output features of each attention head are calculated as follows:

[0064] in, It is a normalized exponential function; Indicates the first A matrix of values ​​for each attention head; This is a scaling factor used to prevent the gradient from vanishing due to excessively large dot product results. (This applies to all...) The outputs of each attention head are concatenated and mapped through a fully connected layer to ultimately extract the mode vectors representing the nonlinear characteristics of the sensor. The modality vector is passed to the feature database for cosine similarity comparison of prior model templates.

[0065] The synchronous signal acquisition unit employs a distributed timing control protocol based on Ethernet Control Automation Technology (EtherCAT) within a distributed architecture. Each workstation's acquisition module acts as a slave, synchronizing with the central control unit via high-speed industrial Ethernet. This architecture allows for flexible physical expansion of the system, supporting simultaneous synchronous dynamic calibration of dozens of sensors while maintaining time synchronization deviations between channels within the nanosecond range.

[0066] In Embodiment 2, the power distribution unit incorporates a real-time online power quality monitoring system. This system records power supply voltage fluctuations, harmonic content, and grounding resistance during each calibration task. If the monitoring system detects that the power quality exceeds the preset calibration environment level requirements, the core algorithm processing unit automatically adds an environmental impact declaration to the generated calibration report and recommends that operators re-perform the calibration after the power environment improves, ensuring the rigor of the measurement value transmission process.

[0067] In Embodiment 2, the laser interferometric vibration measurement device employs multi-beam beam splitting interferometry. The main laser beam is split into multiple coherent beams by a beam-splitting prism group, with each beam projected onto different excitation sources or key monitoring points. This design allows the system to simultaneously monitor the diaphragm motion at multiple locations within a large, complex cavity. By using multi-point averaging or differential comparison, it eliminates pressure field inhomogeneity errors caused by excessive cavity volume.

[0068] Example 3: Example 3 focuses on describing the special implementation configuration of this system under ultra-low pressure and low frequency conditions. In this application scenario, the response signal of the metal pressure sensor is weak and easily affected by ground vibration and low-frequency sound wave interference.

[0069] A calibration system for a metal pressure sensor based on dynamic compensation employs a double-layer vacuum shielding structure for its sealed cavity. A high vacuum is maintained between the outer and inner cavities, utilizing the excellent sound and heat insulation properties of the vacuum layer to provide a quiet physical environment for the inner calibration space. In embodiment 3, the dynamic excitation unit utilizes a low-frequency, long-stroke drive structure, coupled with an ultra-thin metal foil diaphragm with low stiffness, enabling the generation of stable micro-pressure excitation within the low-frequency range.

[0070] The synchronous signal acquisition unit adopts a fully differential input architecture. This architecture, combined with custom-designed shielded twisted-pair cables, can suppress power frequency electromagnetic interference and common-mode noise commonly found at the calibration site. Each input channel integrates a programmable gain instrumentation amplifier (PGIA) at its front end, which can automatically adjust the amplification factor according to the real-time amplitude of the sensor output signal, ensuring that the dynamic range of signal acquisition can still be covered even under weak signal measurement conditions at the microvolt level.

[0071] In Example 3, the core algorithm processing unit executes a joint time-frequency domain analysis logic. This logic analyzes the phase lag of the sensor under test at low frequencies by performing a short-time Fourier transform (STFT) or continuous wavelet transform on the original dynamic calibration dataset. Since the creep effect of metallic materials is most significant at low frequencies, a three-dimensional dynamic error compensation matrix is ​​constructed using a polynomial fitting method. This matrix stores the correction coefficients for each operating point, with frequency, pressure amplitude, and ambient temperature as coordinate axes.

[0072] Specifically, for ultra-low voltage and low-frequency operating conditions, the time-frequency domain joint analysis logic executed by the core algorithm processing unit employs short-time Fourier transform. This applies to the original dynamic calibration dataset signal output by the synchronization signal acquisition unit. The Hanning window is selected as the window function. The window length is The overlap rate is Its STFT transformation formula is defined as:

[0073] in, The complex time-frequency matrix representing the joint time-frequency distribution; Represents a time variable; Represents frequency variables; Represents the imaginary unit; Represents the integral dummy variable; Represents the window function.

[0074] To accurately analyze the phase hysteresis of the sensor at different pressure frequencies, the algorithm extracts the phase hysteresis at the dominant frequency of the dynamic excitation signal. Time-frequency slices at the location And calculate its instantaneous phase. :

[0075] in, and These represent the imaginary and real parts of a complex number, respectively. Indicates the excitation frequency The time-frequency slice at the location. The instantaneous phase of the excitation signal obtained from the reference measurement unit is compared with... The dynamic phase lag curve evolving over time is obtained. Subsequently, the core algorithm processing unit utilizes a three-dimensional polynomial fitting method, with frequency... Pressure amplitude and ambient temperature As the independent variable, with phase lag Construct a dynamic error compensation matrix with the variable as the dependent variable. The matrix is ​​permanently written into the non-volatile memory of the sensor under test.

[0076] Furthermore, the calibration results generated by the core algorithm processing unit include not only static sensitivity coefficients and linearity deviations, but also complete hysteresis error distribution maps and full-band amplitude-frequency response curves. These complex datasets are displayed in real-time in a 3D visualization format through an integrated human-machine interface terminal. The system automatically generates electronic calibration reports conforming to international standards such as ISO or GB / T, and automatically packages and stores all raw acquired waveforms, environmental parameters, and compensation model parameters, uploading them to a cloud database via a remote data communication protocol. This provides robust big data support for analyzing the performance evolution of sensors from different production batches, predicting fatigue life, and enabling predictive maintenance.

[0077] In the final stage of the calibration process, the core algorithm processing unit executes a digital compensation closed-loop verification procedure. This procedure loads the calculated inverse compensation model onto the acquisition link in real time and applies dynamic pressure excitation of a set of pseudo-random sequence waveforms again. By comparing the residual between the compensated sensor output and the laser interferometer reference output, if the root mean square value of the residual is less than a preset accuracy threshold, the sensor's dynamic compensation model is deemed valid, and the calibration is approved.

[0078] Example 4: Example 4 illustrates one implementation of this system in extreme environment calibration applications. In this configuration, the calibration system needs to accurately characterize the dynamic response of the sensor under high temperature, high humidity, or extreme cold conditions.

[0079] An active environmental control system is installed inside the sealed cavity. This system includes a miniature semiconductor cooling / heating component and a precise humidity control loop. The power distribution unit provides independent power supply branches for these high-power components to prevent electromagnetic pulses generated during switching from interfering with sensitive signal acquisition channels. The environmental monitoring unit uses a multi-point distributed sensor network to provide real-time feedback on the uniformity of the temperature and humidity field inside the cavity.

[0080] In Embodiment 4, the dynamic compensation processing unit employs a multi-physics coupled modeling strategy. This strategy recognizes that the response characteristics of the metal pressure sensor are not only dynamically driven by pressure but also modulated by ambient temperature on the elastic modulus of its sensing element and the initial tension of the pressure-bearing surface. The algorithm introduces a temperature compensation operator to transform the static temperature offset into time-varying parameters of the dynamic response model. For example, when an increase in ambient temperature causes the metal pressure-sensitive diaphragm to soften, the modeling logic automatically lowers the stiffness term coefficient in the dynamic equation, mathematically achieving real-time elimination of temperature-induced errors.

[0081] In Example 4, the acoustic diaphragm utilizes a special high-temperature resistant composite material, such as a composite structure of polyimide and a metal pressure-sensitive diaphragm, to ensure that its mechanical properties do not permanently degrade under extreme temperature cycling. The sensor-under-test interface unit is equipped with a flexible, high-temperature resistant sealing kit to prevent pressure leakage caused by physical deformation during thermal cycling calibration.

[0082] In Example 4, the synchronous signal acquisition unit introduces a self-calibrating time reference. Using a temperature-controlled crystal oscillator as the core clock source, it ensures that even when the ambient temperature of the calibration system fluctuates drastically, the jitter in the sampling timing remains within the picosecond range, guaranteeing the absolute accuracy of the phase lag measurement.

[0083] This multi-dimensional compensation and protection mechanism enables the system to comprehensively evaluate the performance of metal pressure sensors under extreme dynamic conditions, solving the pain points of low calibration accuracy and poor data consistency in existing technologies under complex environments.

[0084] In summary, this invention constructs a pure acoustic physical environment through a sealed cavity, simulates complex pressure fluctuations using a dynamic excitation unit, and employs laser interferometry as the absolute benchmark for traceability. Combined with in-depth calculations by the synchronous signal acquisition unit and the core algorithm processing unit, it achieves precise calibration of the full characteristics of the metal pressure sensor. The various modules within the system support each other, and the power distribution unit and environmental monitoring unit provide a stable external support environment for system operation. Through dynamic hysteresis modeling and inverse compensation techniques, this invention elevates the calibration process from simple data recording to a level of in-depth analysis of the sensor's physical characteristics and proactive error elimination.

[0085] The core algorithm processing unit performs in-depth time-frequency domain transformation on the original dynamic calibration dataset, enabling precise identification of the sensor's phase hysteresis at different pressure frequencies. The dynamic error compensation matrix, constructed using a polynomial fitting method, is designed to be easily stored in the non-volatile memory of the sensor to be calibrated. This means that each sensor calibrated by this system carries with it a digital twin compensation logic that matches its physical characteristics. In practical applications, when the sensor is connected to an industrial control system, the dynamic error compensation matrix can be immediately invoked, correcting the output signal in real time. This improves the sensor's response speed under transient pressure changes by several times and suppresses overshoot and oscillation caused by inertia.

[0086] The multi-channel isolated sampling circuit used in the synchronous signal acquisition unit ensures zero crosstalk between signal paths. Each channel is equipped with an anti-aliasing filter whose cutoff frequency can be adjusted in steps according to the excitation signal frequency set by the central control unit. This dynamic filtering technology ensures that the sampling system can always capture true and valid original information, whether during low-frequency quasi-static calibration or high-frequency dynamic characteristic sweeping, eliminating spurious signal interference caused by spectrum shifting.

[0087] The secondary linear regulator stage used in the power distribution unit outputs a low-noise DC voltage, which provides a near-ideal background for the precision calculation circuit of the laser interferometer and the preamplifier circuit of the sensor under test.

[0088] The environmental monitoring unit acts as a real-time corrector during the calibration process. The core algorithm processing unit receives millidegree Celsius temperature information from the platinum resistance sensor and uses a built-in thermodynamic model of the metallic material to calculate the minute drift in the elastic modulus (Young's modulus) of the pressure-sensitive diaphragm caused by temperature fluctuations. Subsequently, the algorithm automatically adjusts the stiffness parameter in the pressure conversion formula to ensure that the sensitivity coefficient output by the calibration system has high confidence, even in non-constant temperature production workshops.

[0089] The core algorithm processing unit integrates a deep neural network-based nonlinear feature extraction module, further enhancing the system's intelligence. This module extracts mode vectors representing the sensor's nonlinear characteristics from raw waveforms containing significant noise through convolution operations and attention mechanisms. These vectors are compared with massive samples in a cloud database to enable early prediction of sensor performance degradation trends. If, during calibration, a sensor's nonlinear characteristics deviate from the normal evolution path, the system determines it has potential manufacturing defects, achieving precise quality grading before shipment.

[0090] The central control unit can automatically synthesize various complex combined pressure waveforms according to the requirements of the calibration task. In addition to standard sine waves and step waves, the system can also simulate intense pressure pulse waves such as those during jet engine ignition, water hammer effect waves in hydraulic systems, and pseudo-random turbulence waves during high-speed aircraft flight. Through this full-scenario simulation capability, this calibration system provides a performance verification platform that closely approximates real-world application for metal pressure sensors in harsh working conditions.

[0091] The implementation of this invention not only improves the static and dynamic measurement accuracy of metal pressure sensors, but more importantly, it establishes a closed-loop, transparent, and traceable dynamic calibration system from optical reference to electrical signal output. The application of this system will strongly promote the localization and technological upgrading of micro-pressure sensing technology in fields such as aerospace active vibration reduction, precision semiconductor flow control, and high-end medical respiratory monitoring.

[0092] Those skilled in the art will understand that, although preferred embodiments of the present invention have been described in detail herein, various improvements, additions, deletions, or modifications can be made to these embodiments without departing from the spirit and scope of the invention. For example, the material of the sealed cavity can be adjusted according to the pressure range, the driving method of the dynamic excitation unit can be changed from piezoelectric drive to electromagnetic drive, and the optical path structure of the laser interferometric vibrometer can also adopt a fiber optic interferometer to improve the system integration. All such transformations and adjustments based on the core ideas of the present invention should be covered within the scope of protection of the claims of the present invention.

[0093] The modules, units, and their internal logic structures described in the various embodiments of this invention can be implemented using dedicated hardware logic circuits, or by running preset program instructions on a general-purpose processor, or a combination of both. In specific engineering practice, a suitable hardware platform can be selected for functional implementation based on cost, power consumption, and real-time requirements. The system architecture and dynamic compensation algorithm provided by this invention offer a novel, highly efficient, and highly accurate technical paradigm for pressure calibration, solving many core technical bottlenecks that have long constrained the dynamic application of micro-pressure sensors.

[0094] In actual production processes, this system can also be integrated with automated loading and unloading robots to achieve a fully automated batch calibration production line for metal pressure sensors. By binding the calibration results of each sensor to a unique serial number and establishing a full lifecycle quality archive in the cloud, long-term tracking of product performance can be achieved. If users report any anomalies during actual use, technicians can retrieve the original calibration dataset from that year for retrospective analysis, continuously optimizing production processes and compensation algorithm models.

[0095] This invention is not limited to the calibration of metal pressure sensors. For pressure sensors employing silicon diaphragms, ceramic diaphragms, or other sensitive elements, as long as their working principle involves the conversion of physical displacement into electrical signals, the acoustic microcavity dynamic excitation and laser displacement tracing logic of this system can be applied. This versatility further expands the commercial value and technological influence of this invention.

[0096] The system and method proposed in this invention, through in-depth mining and closed-loop correction of multi-physics field data (acoustic, force, electrical, and optical), not only overcomes the dependence of traditional calibration methods on static environments but also achieves effective compensation for nonlinear defects in physical hardware at the algorithm level. This marks a leap in pressure calibration technology from simple measurement to an integrated intelligent system encompassing measurement, modeling, and compensation, providing an irreplaceable technical support tool for the research and development and manufacturing of high-precision sensors.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A calibration system for a metal pressure sensor based on dynamic compensation, characterized in that, include: A sealed cavity provides a sealed volume with a predetermined sealing strength. The inner surface of the sealed volume is polished to reduce turbulence noise generated by airflow. The sealed cavity is provided with a physical interface. A dynamic excitation unit is installed on the physical interface. The dynamic excitation unit includes a high-frequency controlled drive component and an acoustic diaphragm. The high-frequency controlled drive component is used to drive the acoustic diaphragm to generate a dynamic pressure excitation signal with frequency, amplitude and waveform in the sealed cavity. The reference measurement unit includes a laser interferometric vibration measurement device, used to capture the instantaneous displacement of the acoustic diaphragm during its motion and convert the instantaneous displacement into an optical interference signal; The sensor under test interface unit is fixedly connected to the metal pressure sensor to be calibrated and transmits the electrical signal generated by the metal pressure sensor to be calibrated. A synchronous signal acquisition unit is connected to the reference measurement unit and the interface unit of the sensor under test, respectively, and is used to align and sample the optical interference signal and the electrical signal to generate the original dynamic calibration dataset. The core algorithm processing unit is connected to the synchronous signal acquisition unit and includes a dynamic hysteresis modeling subsystem and an inverse compensation operation subsystem. It is used to calculate the dynamic characteristic response parameters of the metal pressure sensor to be calibrated by constructing a nonlinear mathematical correlation model. Power distribution unit, used to provide regulated current; An environmental monitoring unit is used to acquire environmental status information inside the sealed cavity and feed it back to the core algorithm processing unit for parameter correction.

2. The calibration system for a metal pressure sensor based on dynamic compensation according to claim 1, characterized in that, The sealed cavity is made of an alloy material with high heat capacity and is wrapped with an insulation layer composed of aerogel or multi-layer vacuum insulation board. This reduces the impact of external ambient temperature fluctuations on the gas density inside the cavity by increasing the thermal inertia of the system. The inner wall of the sealed cavity is equipped with a sound-absorbing structure, which consists of a geometrically arranged array of micropores. The diameter, spacing between the micropores, and depth of the cavity are set by acoustic impedance matching logic to absorb the reflected waves generated by the dynamic pressure excitation signal at the cavity boundary and to construct a pure traveling wave field within the sealed cavity. The bottom of the sealed cavity is equipped with an active damping bracket, which adopts a combination structure of air spring and damper to isolate the interference of external vibration on the internal sound field and measurement optical path of the cavity.

3. The calibration system for a metal pressure sensor based on dynamic compensation according to claim 2, characterized in that, The high-frequency controlled drive component in the dynamic excitation unit uses a piezoelectric ceramic material with a predetermined piezoelectric constant, and achieves controlled deformation through voltage control to drive the acoustic diaphragm to vibrate. The edge portion of the acoustic diaphragm is fixed to the sealed cavity by a flexible support structure. The flexible support structure uses a spring sheet with damping characteristics and nonlinear stiffness compensation function to isolate stray mechanical vibrations during the operation of the high-frequency controlled drive component. The driving logic of the dynamic excitation unit includes a hardware-level overload protection function. By monitoring the derivative of the driving current and the feedback signal in real time, the output power is limited when it is determined that the driving excitation exceeds the mechanical limit of the acoustic diaphragm. The high-frequency controlled drive component is also equipped with a closed-loop charge feedback control circuit to compensate for the inherent creep and hysteresis effects of piezoelectric materials.

4. The metal pressure sensor calibration system based on dynamic compensation according to claim 3, characterized in that, The laser interferometric vibration measurement device adopts a dual-optical-path interference structure. The first optical path serves as a reference branch, and its optical path is fixed on a low-expansion material reference block. The second optical path serves as a measurement branch and enters the cavity through an optical-grade quartz glass window on the sealed cavity. The optical-grade quartz glass window adopts a multi-static sealing ring design and has a low refractive index temperature sensitivity coefficient. The reference measurement unit obtains the instantaneous displacement of the acoustic diaphragm by calculating the phase difference between the two light waves, and converts the instantaneous displacement into an equivalent dynamic pressure value in the sealed cavity through a preset conversion logic. The core algorithm processing unit integrates benchmark traceability and verification logic, which periodically compares the measured values ​​of the laser interferometric vibration measuring device with the built-in static standard gravity piston data, and realizes online calibration of the calibration system by calculating the deviation between the two at the quasi-static point.

5. A calibration system for a metal pressure sensor based on dynamic compensation according to claim 4, characterized in that, The interface unit of the sensor under test has a multi-dimensional adjustment function, which adjusts its insertion depth in the sealed cavity according to the geometric dimensions of different sensor specifications, so that the pressure sensing center of the metal pressure sensor to be calibrated is in a predetermined uniform sound field area. The interface unit of the sensor under test adopts a fully shielded metal shell and is equipped with a precision differential interface to suppress electromagnetic interference and achieve lossless transmission of electrical signals; The synchronous signal acquisition unit includes a multi-channel isolation sampling circuit, and each sampling channel is equipped with an anti-aliasing filter. The sampling frequency of the synchronous signal acquisition unit is set to more than 50 times the main frequency of the dynamic pressure excitation signal in order to characterize the rising and falling edges of the pressure waveform in the time domain.

6. The calibration system for a metal pressure sensor based on dynamic compensation according to claim 5, characterized in that, The core algorithm processing unit runs a modeling logic based on nonlinear autoregressive external input, using the instantaneous displacement as a standard reference input and the output signal of the metal pressure sensor to be calibrated as the disturbed output. It extracts the sensor's frequency response function, phase hysteresis curve, and nonlinear gain characteristics through iterative calculation. The core algorithm processing unit also integrates adaptive cancellation logic, which defines the mechanical vibration characteristics captured by the laser interferometric vibration measurement device as reference noise. By adjusting the compensation coefficient in the time domain, it filters out the non-target pressure components generated by cavity resonance and airflow turbulence from the original output signal of the metal pressure sensor to be calibrated. When performing modeling, the core algorithm processing unit adopts a recursive calculation method with dynamic weight adjustment to automatically allocate error penalty factors for the nonlinear intensity exhibited by the sensor in different pressure ranges.

7. A calibration system for a metal pressure sensor based on dynamic compensation according to claim 6, characterized in that, The core algorithm processing unit integrates a nonlinear feature extraction module based on deep neural networks. It uses convolution operations and attention mechanisms to extract mode vectors representing the nonlinear characteristics of the sensor from the original waveform. By comparing these vectors with prior model templates in the feature database, it achieves automatic identification of the model characteristics of the metal pressure sensor to be calibrated. The core algorithm processing unit calls the matching template parameters according to the identified model characteristics, and completes the parameter calibration by iteratively fine-tuning the measured data; The core algorithm processing unit also supports remote data communication protocols, which are used to upload the calibrated sensor model parameters to a cloud database to achieve long-term tracking and big data analysis of the performance evolution of different batches of sensors.

8. A calibration system for a metal pressure sensor based on dynamic compensation according to claim 7, characterized in that, The environmental monitoring unit includes a platinum resistance sensor and a static pressure monitoring device integrated into the wall of the sealed cavity. The platinum resistance sensor is used to sense the temperature shift inside the cavity, and the static pressure monitoring device is used to determine the current reference atmospheric pressure level. The core algorithm processing unit calculates the elastic modulus drift of the pressure-sensitive diaphragm based on the temperature offset using a thermodynamic model of the metal material, and calculates the gas compressibility change and sound velocity fluctuation caused by temperature change in combination with the gas state equation. Based on this, the conversion coefficient between displacement and equivalent pressure is corrected in real time. The environmental monitoring unit has a fixed data acquisition frequency and sends environmental status information to the core algorithm processing unit in real time.

9. A calibration system for a metal pressure sensor based on dynamic compensation according to claim 8, characterized in that, The power distribution unit adopts a multi-stage isolation step-down scheme, including a primary switching power supply stage and a secondary linear regulator stage. The secondary linear regulator stage outputs DC voltage using a low-noise reference. The power distribution unit is also equipped with an uninterruptible power supply module, which is used to maintain the system operation for a predetermined time when the external power supply is interrupted. The power distribution unit also incorporates a real-time online power quality monitoring system to record power supply voltage fluctuations, harmonic content, and grounding resistance status during the calibration period. When the power quality exceeds the preset level requirements, the core algorithm processing unit adds an environmental impact statement to the calibration report.

10. A calibration system for a metal pressure sensor based on dynamic compensation according to claim 9, characterized in that, The inverse compensation operation subsystem performs inverse mapping of the dynamic characteristic response parameters to generate a digital filter or mathematical compensation matrix that is opposite to the original physical hysteresis characteristics of the metal pressure sensor to be calibrated. The digital filter, when the sensor is actually used, cancels out the response hysteresis caused by the mass inertia of the metal pressure-sensitive diaphragm, the internal damping of the material, and stress creep in real time. The core algorithm processing unit also performs time-frequency domain transformation processing on the original dynamic calibration dataset, analyzes the phase hysteresis of the sensor under different pressure frequencies, and constructs a dynamic error compensation matrix using a polynomial fitting method. The dynamic error compensation matrix is ​​stored in the non-volatile memory of the metal pressure sensor to be calibrated. The core algorithm processing unit automatically synthesizes combined pressure waveforms according to the requirements of the calibration task. The combined pressure waveforms include step waves, triangular waves, superimposed sine waves, pseudo-random sequence waves, pressure pulse waves simulating jet engine ignition, and wave waveforms simulating water hammer effect in hydraulic systems. The synchronous signal acquisition unit has a data anomaly detection function. By monitoring the signal amplitude, signal-to-noise ratio and signal slope of each sampling channel in real time, it determines whether the signal is in the normal working range. When it detects signal distortion caused by gas leakage or circuit short circuit, it sends an alarm signal to the central control unit and forcibly stops the current excitation output. The synchronous signal acquisition unit adopts a differential input architecture to suppress common-mode electromagnetic interference at the calibration site; The core algorithm processing unit executes a digital compensation closed-loop verification program, which loads the calculated inverse compensation model onto the acquisition link in real time and applies a set of pseudo-random sequence waveforms as dynamic pressure excitation again. The core algorithm processing unit compares the residual between the sensor output after loading the compensation model and the benchmark output of the benchmark measurement unit. When the root mean square value of the residual is less than a preset accuracy threshold, the dynamic characteristic response parameter and the corresponding compensation model are determined to be effective. The calibration results generated by the core algorithm processing unit include static sensitivity coefficient, linearity deviation, hysteresis error distribution map, and full-band amplitude-frequency response curve, which are displayed in a three-dimensional visualization through a human-computer interaction terminal and automatically generate an electronic calibration report.

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