High-precision Adaptive Pressure Transmitter Calibration Method and System

Abstract

The present invention relates to the technical field of pressure transmitters, and discloses a calibration method and system for a high-precision adaptive pressure transmitter, including: performing noise filtering processing on pressure signal data to obtain standard signal data, performing joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation; generating a dynamic compensation coefficient matrix for the target pressure transmitter, and calculating the response adaptability of the dynamic compensation coefficient matrix to environmental interference parameters; identifying the non-linear error mode of the pressure transmitter, performing multi-dimensional parameter optimization on the calibration algorithm to obtain optimal parameters, and extracting key calibration node parameters in the compensation function; analyzing the adjustment factor distribution of each order compensation term in the dynamic error correction model to generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model; and generating a multi-mode calibration scheme for the pressure transmitter. The present invention can improve the calibration accuracy of a high-precision adaptive pressure transmitter.

Description

Technical Field

[0001] The present invention relates to a calibration method and system for a high-precision adaptive pressure transmitter, belonging to the technical field of pressure transmitters. Background Art

[0002] A high-precision adaptive pressure transmitter is a key device for accurately measuring pressure in the industrial and scientific research fields. With advanced sensing elements and intelligent algorithms, it can accurately convert pressure into an electrical signal, with extremely high measurement accuracy. At the same time, it has a strong adaptive ability. In chemical production, it can accurately monitor the pressure of the reaction kettle to ensure production safety and product quality.

[0003] Currently, the common calibration methods for high-precision adaptive pressure transmitters are mainly the traditional static calibration method or the calibration method based on temperature compensation. The traditional static calibration method is to connect the pressure transmitter to a high-precision standard pressure source. At different fixed pressure points, compare the measured value of the transmitter with the standard pressure value. By adjusting the relevant parameters of the transmitter, the measurement error is within the allowable range. However, this method cannot consider the influence of environmental factors on the measurement, such as temperature and humidity changes. There is a large deviation in the calibration result under actual dynamic working conditions. The calibration method based on temperature compensation is to first measure the output characteristics of the pressure transmitter at different temperatures, establish a temperature-pressure error model, and then in actual measurement, according to the real-time temperature measured by the environmental temperature sensor, perform compensation calculation on the pressure measurement value. This method makes up for the factor of not considering temperature in the traditional static calibration method. However, this method can only compensate for a single factor of temperature and cannot effectively cope with other environmental factors, making it difficult to accurately fit all working conditions. Therefore, a method that can improve the calibration accuracy of high-precision adaptive pressure transmitters is needed. Summary of the Invention

[0004] The present invention provides a calibration method and system for a high-precision adaptive pressure transmitter, and its main purpose is to improve the calibration accuracy of the high-precision adaptive pressure transmitter.

[0005] To achieve the above object, a calibration method for a high-precision adaptive pressure transmitter provided by the present invention includes:

[0006] Obtain the real-time pressure signal data output by the target pressure transmitter with high-precision adaptability, perform noise filtering processing on the pressure signal data to obtain standard signal data, and perform joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation;

[0007] Input the joint time-frequency characterization of the signal into a preset adaptive compensation model. Based on the adaptive compensation model, generate a dynamic compensation coefficient matrix for the target pressure transmitter, detect the environmental interference parameters of the environment where the target pressure transmitter is located, and calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters;

[0008] Based on the response adaptability, identify the non-linear error pattern of the pressure transmitter, activate the corresponding calibration algorithm from a preset calibration algorithm library according to the non-linear error pattern, perform multi-dimensional parameter optimization on the calibration algorithm to obtain the optimal parameters, determine the optimal compensation function of the optimal parameters, and extract the key calibration node parameters in the compensation function;

[0009] Based on the key calibration node parameters, construct a dynamic error correction model for the pressure transmitter, analyze the distribution of adjustment factors for each order of compensation terms in the dynamic error correction model, and combine a preset accuracy level threshold and the distribution of adjustment factors to generate an adaptive calibration instruction set for each order of compensation terms in the dynamic error correction model;

[0010] Determine the signal reconstruction strategy corresponding to the adaptive calibration instruction set, and based on the signal reconstruction strategy, generate a multi-mode calibration scheme for the pressure transmitter.

[0011] Optionally, the sending of the probe data packet and the data to be authenticated from the party to be authenticated to the destination router includes:

[0012] Send the probe data packet from the party to be authenticated to the destination router;

[0013] Find the destination host corresponding to the probe data packet through the Address Resolution Protocol in the destination router;

[0014] Obtain the destination switch corresponding to the destination host;

[0015] Send the probe data packet to the destination host via the destination switch;

[0016] Judge whether a response data packet from the destination host regarding the probe data packet is received in the destination router;

[0017] When a response data packet from the destination host regarding the probe data packet is received in the destination router, send the data to be authenticated from the party to be authenticated to the destination router.

[0018] Optionally, the performing of the joint time-frequency domain analysis on the standard signal data to obtain the joint time-frequency characterization of the signal includes:

[0019] Perform signal windowing processing on the standard signal data to obtain windowed signal data;

[0020] Extract the time-frequency analysis primitive sequence from the windowed signal data, perform time-frequency fine processing on the time-frequency analysis primitive sequence to obtain the time-frequency distribution energy;

[0021] Perform energy enhancement processing on the time-frequency distribution energy to obtain a refined time-frequency feature map;

[0022] Perform multi-scale quantization processing on the refined time-frequency feature map to obtain a signal time-frequency joint representation.

[0023] Optionally, the performing time-frequency fine processing on the time-frequency analysis primitive sequence to obtain the time-frequency distribution energy includes:

[0024] Perform wavelet transform processing on the time-frequency analysis primitive sequence to obtain sequence wavelet coefficients;

[0025] Calculate the average coefficient corresponding to the sequence wavelet coefficients, and calculate the coefficient standard deviation corresponding to the sequence wavelet coefficients based on the average coefficient;

[0026] Combine the coefficient standard deviation and the average coefficient to perform screening processing on the sequence wavelet coefficients to obtain target wavelet coefficients;

[0027] Calculate the energy density corresponding to the target wavelet coefficients, and perform summation processing on the energy density to obtain the time-frequency distribution energy.

[0028] Optionally, the inputting the signal time-frequency joint representation into a preset adaptive compensation model and generating the dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model includes:

[0029] Perform dimensionality reduction processing on the signal time-frequency joint representation by using the dimensionality reduction algorithm in the adaptive compensation model to obtain a dimensionality-reduced time-frequency joint representation;

[0030] Perform encoding processing on the dimensionality-reduced time-frequency joint representation by using the sparse encoder in the adaptive compensation model to obtain a time-frequency feature vector;

[0031] Collect the environmental time series data corresponding to the target pressure transmitter, and use the environmental feature perception network in the adaptive compensation model to mine the environmental time series features in the environmental time series data;

[0032] Perform splicing processing on the time-frequency feature vector and the environmental time series features by using the time-frequency-environment fusion network in the adaptive compensation model to obtain a time-frequency environment fusion feature;

[0033] According to the preset compensation rules and the time-frequency environment fusion features, use the dynamic compensation generation network in the adaptive compensation model to generate the dynamic compensation coefficient matrix of the target pressure transmitter.

[0034] Optionally, calculating the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters includes:

[0035] Perform time synchronization processing on the environmental interference parameters to obtain synchronized environmental interference parameters;

[0036] Extract the interference parameter indicators in the synchronized environmental interference parameters, and perform coupling processing on the interference parameter indicators to obtain coupled parameter indicators;

[0037] Obtain the operating environment scenario corresponding to the target pressure transmitter, and based on the operating environment scenario, screen out the associated parameter indicators from the interference parameter indicators;

[0038] Combine the associated parameter indicators, the coupled parameter indicators, and the dynamic compensation coefficient matrix to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters.

[0039] Optionally, combining the associated parameter indicators, the coupled parameter indicators, and the dynamic compensation coefficient matrix to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters includes:

[0040] Based on the coupled parameter indicators, calculate the indicator standard deviation corresponding to the environmental interference parameters;

[0041] Query the indicator sensitivity corresponding to the associated parameter indicators, and combine the indicator sensitivity, the indicator standard deviation, and the coupled parameter indicators to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters through the following formula:

[0042] ;

[0043] Wherein, represents the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters, represents the dynamic compensation coefficient matrix corresponding to the a-th moment, represents the coupled parameter indicators corresponding to the a-th moment, represents the indicator standard deviation corresponding to the a-th moment, a represents the starting moment, N represents the ending moment, represents the indicator sensitivity, represents the indicator change rate of the associated parameter indicators at the a-th moment, is a constant term, which is .

[0044] Optionally, identifying the non - linear error mode of the pressure transmitter based on the response fitness includes:

[0045] Query the adaptation feature interval corresponding to the response fitness;

[0046] Based on the adaptation feature interval, determine the error feature weight corresponding to the pressure transmitter;

[0047] According to the error feature weight, extract the historical error features corresponding to the pressure transmitter;

[0048] Analyze the typical error modes covered in the historical error features;

[0049] Based on the typical error modes, determine the non - linear error mode of the pressure transmitter for the current working state.

[0050] Optionally, extracting the key calibration node parameters in the compensation function includes:

[0051] Analyze the parameter space range corresponding to the compensation function;

[0052] Based on the parameter space range, determine the key parameter region in the compensation function;

[0053] Query the parameter importance labels associated with the key parameter region;

[0054] According to the parameter importance labels, extract the parameter subset in the compensation function;

[0055] Based on the parameter subset, determine the key calibration node parameters in the compensation function.

[0056] Optionally, combining the preset accuracy level threshold and the adjustment factor distribution to generate the adaptive calibration instruction set for each order compensation term in the dynamic error correction model includes:

[0057] Analyze the threshold execution rules corresponding to the accuracy level threshold;

[0058] Based on the threshold execution rules, determine the calibration execution level corresponding to the pressure transmitter;

[0059] According to the calibration execution level, extract the calibration execution process corresponding to the pressure transmitter;

[0060] Based on the calibration execution process, construct the adaptive calibration framework corresponding to the pressure transmitter;

[0061] According to the adaptive calibration framework, generate the adaptive calibration instruction set for each order compensation term in the dynamic error correction model.

[0062] To solve the above problems, the present invention also provides a high-precision adaptive pressure transmitter calibration system, which includes:

[0063] A signal characterization analysis module, configured to obtain real-time pressure signal data output by a target pressure transmitter with high precision and adaptability, perform noise filtering processing on the pressure signal data to obtain standard signal data, and perform joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint characterization;

[0064] A response adaptability calculation module, configured to input the signal time-frequency joint characterization into a preset adaptive compensation model, generate a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model, detect environmental interference parameters of the environment where the target pressure transmitter is located, and calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters;

[0065] A node parameter extraction module, configured to identify a non-linear error pattern of the pressure transmitter based on the response adaptability, activate a corresponding calibration algorithm from a preset calibration algorithm library according to the non-linear error pattern, perform multi-dimensional parameter optimization on the calibration algorithm to obtain optimal parameters, determine an optimal compensation function of the optimal parameters, and extract key calibration node parameters in the compensation function;

[0066] A calibration instruction set generation module, configured to construct a dynamic error correction model of the pressure transmitter based on the key calibration node parameters, analyze the adjustment factor distribution of each order compensation term in the dynamic error correction model, and generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model in combination with a preset accuracy level threshold and the adjustment factor distribution;

[0067] A calibration scheme generation module, configured to determine a signal reconstruction strategy corresponding to the adaptive calibration instruction set, and generate a multi-mode calibration scheme for the pressure transmitter based on the signal reconstruction strategy.

[0068] Compared with the problems described in the background art, the present invention can effectively eliminate interference clutter and improve signal quality by performing noise filtering processing on the pressure signal data, laying a solid foundation for subsequent precise analysis; by performing joint time-frequency domain analysis on the standard signal data, it can comprehensively analyze the characteristics of the signal in the time and frequency dimensions, accurately insight into the pressure change law, and provide a key basis for the precise calibration and performance optimization of the pressure transmitter. Further, the present invention can generate a dynamic compensation coefficient matrix for the target pressure transmitter based on the adaptive compensation model, which can accurately fit the time-frequency characteristics of the pressure signal and complex environmental working conditions, effectively correct measurement errors, and greatly improve the measurement accuracy of the pressure transmitter in different scenarios, ensuring the stability and reliability of measurement data. The present invention can identify the non-linear error mode of the pressure transmitter based on the response adaptability, accurately judge the error characteristics of the pressure transmitter under different working conditions, and provide a reliable basis for subsequent calibration. Further, the present invention constructs a dynamic error correction model for the pressure transmitter based on the key correction node parameters, which can effectively integrate key parameter information and lay a foundation for accurately correcting the dynamic error of the pressure transmitter. Among them, the dynamic error correction model refers to a mathematical model constructed according to the working principle, error characteristics, and key correction node parameters of the pressure transmitter for real-time correction of the dynamic error generated during the operation of the pressure transmitter. Further, the present invention can optimize the signal collected by the pressure transmitter in a targeted manner by determining the signal reconstruction strategy corresponding to the adaptive calibration instruction set, providing a high-quality data basis for subsequent calibration, helping to improve the accuracy and reliability of calibration, and ensuring the measurement accuracy of the pressure transmitter under complex working conditions. Therefore, the high-precision adaptive pressure transmitter calibration method and system provided by the embodiments of the present invention can improve the calibration accuracy of high-precision adaptive pressure transmitters. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 FIG. is a schematic flowchart of a high-precision adaptive pressure transmitter calibration method provided by an embodiment of the present invention;

[0070] Figure 2 FIG. is a schematic diagram of a module for implementing the high-precision adaptive pressure transmitter calibration method provided by an embodiment of the present invention.

[0071] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] An embodiment of the present application provides a calibration method for a high-precision adaptive pressure transmitter. The execution subject of the high-precision adaptive pressure transmitter calibration method includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the high-precision adaptive pressure transmitter calibration method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0074] Embodiment 1:

[0075] Referring to Figure 1 As shown, it is a schematic flowchart of a calibration method for a high-precision adaptive pressure transmitter provided by an embodiment of the present invention. In this embodiment, the high-precision adaptive pressure transmitter calibration method includes:

[0076] S1. Obtain the real-time pressure signal data output by the target pressure transmitter with high precision and adaptability, perform noise filtering processing on the pressure signal data to obtain standard signal data, and perform joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation.

[0077] By performing noise filtering processing on the pressure signal data, the present invention can effectively remove interference clutter and improve signal quality, laying a solid foundation for subsequent accurate analysis; by performing joint time-frequency domain analysis on the standard signal data, the characteristics of the signal in the time and frequency dimensions can be comprehensively analyzed, and the pressure change law can be accurately insight, providing a key basis for the accurate calibration and performance optimization of the pressure transmitter. Among them, the target pressure transmitter is a device applied to various scenarios that require accurate pressure measurement, the real-time pressure signal data is a numerical sequence reflecting the dynamic pressure change output by the target pressure transmitter, the standard signal data is a pure signal obtained by removing interference after noise filtering processing of the pressure signal data, and the signal time-frequency joint representation is a comprehensive result obtained by performing joint time-frequency domain analysis on the standard signal data, which comprehensively presents the time and frequency characteristics of the signal. Further, the acquisition of the real-time pressure signal data output by the target pressure transmitter can be realized through a signal collector; the wavelet transform algorithm can be used to perform noise filtering processing on the pressure signal data.

[0078] As an embodiment of the present invention, the performing joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation includes:

[0079] Perform signal windowing processing on the standard signal data to obtain windowed signal data;

[0080] Extract the time-frequency analysis primitive sequence from the windowed signal data, perform time-frequency fine processing on the time-frequency analysis primitive sequence to obtain the time-frequency distribution energy;

[0081] Perform energy enhancement processing on the time-frequency distribution energy to obtain a refined time-frequency feature map;

[0082] Perform multi-scale quantization processing on the refined time-frequency feature map to obtain a signal time-frequency joint representation.

[0083] Wherein, the windowed signal data is a segmented signal obtained by performing signal windowing on the standard signal data and with the boundaries of each segment smoothed by a window function; the time-frequency analysis primitive sequence is a basic unit sequence in the windowed signal data for subsequent time-frequency analysis; the time-frequency distribution energy is data representing the energy distribution of the signal at different times and frequencies obtained by performing time-frequency fine processing on the time-frequency analysis primitive sequence; the refined time-frequency feature map is a visualization map that more clearly highlights the key time-frequency features of the signal obtained by performing energy enhancement processing on the time-frequency distribution energy.

[0084] Furthermore, the standard signal data can be subjected to signal windowing processing to obtain windowed signal data by selecting a suitable window function (such as a Hanning window, a Hamming window, etc.) and setting corresponding parameters; the time-frequency analysis primitive sequence in the windowed signal data can be extracted by an algorithm based on preset rules (such as time segment division, specific frequency range screening, etc.); the time-frequency analysis primitive sequence can be subjected to time-frequency fine processing by using a time-frequency analysis algorithm such as the Wigner-Ville distribution to obtain the time-frequency distribution energy; the time-frequency distribution energy can be subjected to energy enhancement processing by means of a ridge line tracking enhancement algorithm, adaptive threshold adjustment, etc. to obtain a refined time-frequency feature map; the refined time-frequency feature map can be subjected to multi-scale quantization processing by constructing a multi-scale quantization model (such as histogram statistics, eigenvalue decomposition, etc. at different scales) to obtain a signal time-frequency joint representation.

[0085] Furthermore, as an alternative embodiment of the present invention, the performing time-frequency fine processing on the time-frequency analysis primitive sequence to obtain the time-frequency distribution energy includes:

[0086] Perform wavelet transform processing on the time-frequency analysis primitive sequence to obtain sequence wavelet coefficients;

[0087] Calculate the average coefficient corresponding to the sequence wavelet coefficients, and based on the average coefficient, calculate the coefficient standard deviation corresponding to the sequence wavelet coefficients;

[0088] Combine the coefficient standard deviation and the average coefficient to perform screening processing on the sequence wavelet coefficients to obtain target wavelet coefficients;

[0089] Calculate the energy density corresponding to the target wavelet coefficients, and perform a summation process on the energy density to obtain the time-frequency distribution energy.

[0090] Among them, the sequence wavelet coefficients are the coefficients obtained by performing wavelet transform on the time-frequency analysis primitive sequence, and are used to characterize the characteristics of the signal at different time scales and frequencies; the target wavelet coefficients are the specific coefficients obtained by screening the sequence wavelet coefficients, and are the part selected from the original coefficients that meet certain conditions; the energy density is the energy distribution corresponding to the target wavelet coefficients, and reflects the energy concentration degree of the target wavelet coefficients in the time-frequency plane.

[0091] Further, the time-frequency analysis primitive sequence can be wavelet-transformed by selecting an appropriate wavelet function (such as Haar, Daubechies, etc.) and setting parameters such as the decomposition level to obtain sequence wavelet coefficients; the average coefficient corresponding to the sequence wavelet coefficients can be calculated by summing all the sequence wavelet coefficients and then dividing by the total number of coefficients; based on the average coefficient, the coefficient standard deviation corresponding to the sequence wavelet coefficients can be calculated by calculating the sum of the squares of the differences between each sequence wavelet coefficient and the average coefficient, then dividing by the total number of coefficients and taking the square root; combining the coefficient standard deviation and the average coefficient, the sequence wavelet coefficients can be screened by setting a reasonable threshold (such as the average coefficient plus several times the coefficient standard deviation) to obtain the target wavelet coefficients; the energy density can be obtained by calculating the square of the modulus value corresponding to the target wavelet coefficients; the energy density at each time-frequency position is summed to obtain the time-frequency distribution energy.

[0092] S2. Input the signal time-frequency joint representation into a preset adaptive compensation model. Based on the adaptive compensation model, generate the dynamic compensation coefficient matrix of the target pressure transmitter, detect the environmental interference parameters of the environment where the target pressure transmitter is located, and calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters.

[0093] In the present invention, by generating the dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model, it can accurately fit the time-frequency characteristics of the pressure signal and complex environmental conditions, effectively correct the measurement error, greatly improve the measurement accuracy of the pressure transmitter in different scenarios, and ensure the stability and reliability of the measurement data. Among them, the adaptive compensation model is a mathematical model that can automatically adjust and generate a dynamic compensation coefficient matrix suitable for the target pressure transmitter according to the input signal time-frequency joint representation to adapt to different working states and environmental changes.

[0094] As an embodiment of the present invention, inputting the signal time-frequency joint representation into a preset adaptive compensation model, and generating a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model includes:

[0095] Performing dimensionality reduction processing on the signal time-frequency joint representation by using the dimensionality reduction algorithm in the adaptive compensation model to obtain a dimensionality-reduced time-frequency joint representation;

[0096] Performing encoding processing on the dimensionality-reduced time-frequency joint representation by using the sparse encoder in the adaptive compensation model to obtain a time-frequency feature vector;

[0097] Collecting the environmental time-series data corresponding to the target pressure transmitter, and mining the environmental time-series features in the environmental time-series data by using the environmental feature perception network in the adaptive compensation model;

[0098] Performing splicing processing on the time-frequency feature vector and the environmental time-series features by using the time-frequency-environment fusion network in the adaptive compensation model to obtain time-frequency environment fusion features;

[0099] Generating the dynamic compensation coefficient matrix of the target pressure transmitter by using the dynamic compensation generation network in the adaptive compensation model according to a preset compensation rule and the time-frequency environment fusion features.

[0100] Among them, the dimension-reduced time-frequency joint representation is the key representation retained after reducing the dimension of the signal time-frequency joint representation by using the dimension reduction algorithm in the adaptive compensation model; the dimension reduction algorithm includes the principal component analysis (PCA) algorithm; the time-frequency feature vector is an expression vector obtained by encoding the dimension-reduced time-frequency joint representation using the sparse encoder in the adaptive compensation model, and the sparse encoder is composed of hidden layer neurons arranged as 128→64→32; the environmental time-series data are data such as temperature, vibration acceleration, and electromagnetic intensity that change with time collected by the environmental sensors corresponding to the target pressure transmitter; the environmental time-series feature is the time-series feature mined from the environmental time-series data by using the environmental feature perception network in the adaptive compensation model; the environmental feature perception network is composed of a bidirectional LSTM layer (64 units); the time-frequency environment fusion feature is a feature obtained by splicing after reassigning weights to the time-frequency feature vector and the environmental time-series feature by using the time-frequency-environment fusion network in the adaptive compensation model; the time-frequency-environment fusion network is composed of fully connected layers; the preset compensation rule is a compensation relationship criterion for different frequency bands (0.1 - 10 kHz) and pressure range segments (10% FS step); according to the time-frequency environment fusion feature, the dynamic compensation generation network is the network layer in the adaptive compensation model for precisely dynamically compensating the target pressure transmitter, and is composed of a specific matrix generation mechanism. For example, when the preset compensation rule stipulates that specific proportional compensation is required in the frequency band of 0.5 - 1 kHz and the pressure range is 30% - 40% FS, combined with the information reflected by the time-frequency environment fusion feature that the current pressure transmitter is in this frequency band and range, the dynamic compensation generation network will generate the compensation factor at the corresponding position in the matrix according to this condition, and then generate a complete 6×6 dynamic compensation coefficient matrix.

[0101] The present invention can accurately evaluate the fit degree of the compensation strategy with the current environment by calculating the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters, and timely discover and correct the deviation of the compensation scheme. Among them, the environmental interference parameters are information on environmental factors such as humidity, vibration, and electromagnetic interference in the environment where the target pressure transmitter is located. Further, the detection of the environmental interference parameters in the environment where the target pressure transmitter is located can be realized by sensors, such as humidity sensors, vibration sensors, and Hall sensors.

[0102] As an embodiment of the present invention, the calculation of the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters includes:

[0103] Perform time synchronization processing on the environmental interference parameters to obtain synchronized environmental interference parameters;

[0104] Extract the interference parameter index from the synchronous environmental interference parameters, perform coupling processing on the interference parameter index to obtain a coupled parameter index;

[0105] Obtain the operating environment scenario corresponding to the target pressure transmitter, and based on the operating environment scenario, screen out the associated parameter index from the interference parameter index;

[0106] Combine the associated parameter index, the coupled parameter index, and the dynamic compensation coefficient matrix to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters.

[0107] Among them, the synchronous environmental interference parameter is a specific manifestation of the environmental interference parameter in the same time dimension; the interference parameter index is a specific value or characteristic that can be extracted from the synchronous environmental interference parameter and used to measure the interference situation; the coupled parameter index is the result of the interaction and integration of the interference parameter index; the operating environment scenario is the specific environmental condition where the target pressure transmitter actually operates; the associated parameter index is the parameter index with the highest degree of association and the most representative among the interference parameter indexes.

[0108] Further, perform time synchronization processing on the environmental interference parameter through a specific time calibration algorithm and timestamp comparison mechanism to obtain the synchronous environmental interference parameter; the interference parameter index in the synchronous environmental interference parameter can be extracted through a feature extraction algorithm and a signal analysis tool; the interference parameter index can be coupled through a multi-parameter fusion model and a coupling calculation method to obtain a coupled parameter index; the operating environment scenario corresponding to the target pressure transmitter can be obtained through on-site monitoring, historical data query, and information interaction with related devices; based on the operating environment scenario, use the association analysis algorithm and threshold judgment rule to screen out the associated parameter index from the interference parameter index.

[0109] Further, as an optional embodiment of the present invention, the combining the associated parameter index, the coupled parameter index, and the dynamic compensation coefficient matrix to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters includes:

[0110] Based on the coupled parameter index, calculate the index standard deviation corresponding to the environmental interference parameter;

[0111] Query the index sensitivity corresponding to the associated parameter index, and combine the index sensitivity, the index standard deviation, and the coupled parameter index to calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters through the following formula:

[0112] ;

[0113] Among them, represents the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters, represents the dynamic compensation coefficient matrix corresponding to the a-th moment, represents the coupling parameter index corresponding to the a-th moment, represents the standard deviation of the index corresponding to the a-th moment, a represents the starting moment, and N represents the ending moment, represents the index sensitivity, represents the index change rate of the correlation parameter index corresponding to the a-th moment, is a constant term, which is .

[0114] Among them, the index sensitivity represents the degree of influence or change response of the correlation parameter index to the system output or target result, and can be calculated by collecting historical data and using the regression analysis method to calculate the corresponding sensitivity.

[0115] S3. Based on the response adaptability, identify the nonlinear error mode of the pressure transmitter, activate the corresponding calibration algorithm from the preset calibration algorithm library according to the nonlinear error mode, perform multi-dimensional parameter optimization on the calibration algorithm to obtain the optimal parameters, determine the optimal compensation function of the optimal parameters, and extract the key calibration node parameters in the compensation function.

[0116] Through the present invention, by identifying the nonlinear error mode of the pressure transmitter based on the response adaptability, the error characteristics of the pressure transmitter under different working conditions can be accurately judged, providing a reliable basis for subsequent calibration. Among them, the nonlinear error mode refers to the classification and definition of the current error manifestation form of the pressure transmitter after comprehensively considering multi-faceted information such as the response adaptability, historical error data of the pressure transmitter, working environment parameters, and measurement signal characteristics, such as polynomial nonlinear error mode, piecewise linear error mode, periodic nonlinear error mode, etc.

[0117] As an embodiment of the present invention, the identifying the nonlinear error mode of the pressure transmitter based on the response adaptability includes:

[0118] Query the adaptation feature interval corresponding to the response adaptability;

[0119] Based on the adaptation feature interval, determine the error feature weight corresponding to the pressure transmitter;

[0120] According to the error feature weight, extract the historical error features corresponding to the pressure transmitter;

[0121] Analyze the typical error modes covered in the historical error features;

[0122] Based on the described typical error patterns, determine the non-linear error pattern of the pressure transmitter for the current working state.

[0123] Among them, the adaptation feature interval refers to different range intervals obtained by dividing the response adaptation degree according to certain rules. These intervals represent different levels of the adaptation degree between the working state of the pressure transmitter and the ideal state. For example, they can be divided into intervals such as high adaptation, medium adaptation, and low adaptation; the error feature weight is determined based on the adaptation feature interval, reflecting the importance of different error features in the current working state. If the adaptation degree is in the high adaptation interval, it means that the error feature has a relatively small impact on the current state and the weight is low; on the contrary, if the adaptation degree is in the low adaptation interval, it indicates that the error feature has a greater impact and the weight is high; the historical error features refer to the set of various error characteristics shown by the pressure transmitter during past operations, including the magnitude, direction, change trend, occurrence frequency, etc. of the error.

[0124] Furthermore, the query of the adaptation feature interval corresponding to the response adaptation degree can be implemented through an interval division algorithm. For example, the response adaptation degree is divided into corresponding intervals by the quantile method or the K-means clustering algorithm to obtain the adaptation feature interval; the determination of the error feature weight corresponding to the pressure transmitter can be implemented through a weight assignment algorithm. For example, the weight of different error features is assigned by the analytic hierarchy process (AHP) or the entropy weight method to obtain the error feature weight; the extraction of the historical error features corresponding to the pressure transmitter can be implemented through a feature extraction method. For example, key features are extracted from the historical error data of the pressure transmitter by the principal component analysis (PCA) or the singular value decomposition (SVD) to obtain the historical error features; the analysis of the typical error patterns covered in the historical error features can be implemented through a pattern recognition algorithm. For example, the historical error features are classified by the decision tree algorithm or the support vector machine (SVM) to obtain the typical error patterns; the determination of the non-linear error pattern of the pressure transmitter for the current working state can be implemented through a classification algorithm. For example, the working state of the pressure transmitter is classified by the naive Bayes algorithm or the K-nearest neighbor algorithm (KNN) to obtain the non-linear error pattern.

[0125] The present invention activates the corresponding calibration algorithm from a preset calibration algorithm library according to the non - linear error pattern, which helps to accurately select a suitable calibration method, improve the calibration efficiency, perform multi - dimensional parameter optimization on the calibration algorithm to obtain the optimal parameters, and assist in quickly finding the parameter combination that enables the calibration algorithm to achieve the best effect, avoiding the blindness and inefficiency of parameter selection. Among them, the preset calibration algorithm library refers to a pre - constructed and stored set containing various calibration algorithms for different non - linear error patterns, which includes polynomial fitting calibration algorithms, piece - wise linear calibration algorithms, neural - network - based calibration algorithms, etc. The calibration algorithm is an error correction algorithm corresponding to the non - linear error pattern, and the optimal parameters refer to the set of parameters selected from the multi - dimensional parameter space that can enable the calibration algorithm to achieve the optimal performance, which covers various adjustment parameters in the calibration algorithm, such as the coefficients of polynomial fitting, the weights of neural networks, etc. Further, the activation of the corresponding calibration algorithm according to the non - linear error pattern can be achieved through pattern - algorithm mapping technology. For example, a mapping relationship between the non - linear error pattern and the calibration algorithm is established using a hash table or a database table to quickly activate the corresponding calibration algorithm; the multi - dimensional parameter optimization of the calibration algorithm can be achieved through optimization algorithms. For example, genetic algorithms or particle swarm optimization algorithms are used to search for the optimal parameters in the multi - dimensional parameter space to obtain the optimal parameters.

[0126] The present invention determines the optimal compensation function of the optimal parameters, which helps to construct an accurate error compensation model, provide precise correction for the measurement results of the pressure transmitter, improve the measurement accuracy, and at the same time, can provide a reliable mathematical basis for subsequent error correction, enhancing the stability and reliability of the pressure transmitter. Among them, the optimal compensation function refers to a mathematical function constructed based on the optimal parameters that can effectively compensate for the non - linear error of the pressure transmitter. For example, for a polynomial non - linear error pattern, the optimal compensation function can be a high - degree polynomial function. Optionally, the determination of the optimal compensation function of the optimal parameters can be achieved through a function fitting algorithm. For example, the least - squares method or the maximum - likelihood estimation method is used to fit the optimal compensation function according to the optimal parameters.

[0127] The present invention extracts the key calibration node parameters in the compensation function, which helps to accurately locate the key parameters in the compensation function, provide core data support for error compensation, avoid the interference of invalid parameters, and improve the efficiency and accuracy of error compensation. Among them, the key calibration node parameters refer to the core parameters further refined based on the compensation function that can directly affect the error compensation effect of the pressure transmitter. For example, in a polynomial compensation function, the key calibration node parameters can be the coefficients of the polynomial; in a neural - network - based compensation function, the key calibration node parameters can be the key weights of the neural network.

[0128] As an embodiment of the present invention, extracting the key calibration node parameters in the compensation function includes:

[0129] Analyze the parameter space range corresponding to the compensation function;

[0130] Based on the parameter space range, determine the key parameter region in the compensation function;

[0131] Query the parameter importance tags associated with the key parameter region;

[0132] According to the parameter importance tags, extract the parameter subset in the compensation function;

[0133] Based on the parameter subset, determine the key calibration node parameters in the compensation function.

[0134] Wherein, the parameter space range refers to the data boundary containing potential effective parameters determined after constructing the compensation function. For example, if the compensation function is a polynomial function, the parameter space range can cover all coefficients of the polynomial; the key parameter region refers to the set of parameters with relatively high importance or relevance within the parameter space range. In the context of the compensation function, it can be the part of parameters that have a greater impact on the error compensation effect. For example, in the neural network compensation function, the weight parameters of the hidden layer can be used as the key parameter region; the parameter importance tag refers to the identifier for classifying and generalizing the parameters associated with the key parameter region. For example, for the key parameter region related to "error-sensitive parameters", its parameter importance tags can include "high-sensitivity parameters", "medium-sensitivity parameters", etc.; the parameter subset refers to the specific set of parameters extracted from the compensation function according to the parameter importance tags, which is the specific presentation of the parameters related to the key parameter region and contains valuable key parameters.

[0135] S4. Based on the key calibration node parameters, construct the dynamic error correction model of the pressure transmitter, analyze the distribution of adjustment factors of each order compensation term in the dynamic error correction model, and combine the preset accuracy level threshold and the adjustment factor distribution to generate the adaptive calibration instruction set for each order compensation term in the dynamic error correction model.

[0136] Based on the key calibration node parameters, the present invention constructs a dynamic error correction model for the pressure transmitter, which can effectively integrate key parameter information and lay a foundation for accurately correcting the dynamic error of the pressure transmitter. Among them, the dynamic error correction model refers to a mathematical model constructed according to the working principle, error characteristics and key calibration node parameters of the pressure transmitter, and is used to correct the dynamic error generated during the operation of the pressure transmitter in real time. For example, for a pressure transmitter with polynomial nonlinear error, the dynamic error correction model can be a high-order polynomial function, and the actual error curve can be fitted by adjusting the key calibration node parameters. Further, based on the key calibration node parameters, the dynamic error correction model of the pressure transmitter can be constructed by mathematical modeling methods, such as neural network algorithms. Using the powerful fitting ability of the neural network, the key calibration node parameters are used as inputs and the error of the pressure transmitter is used as the output, and the neural network is trained to establish the dynamic error correction model.

[0137] The present invention generates an adaptive calibration instruction set for each order compensation term in the dynamic error correction model by combining a preset accuracy level threshold and the adjustment factor distribution, which can make the error correction process more intelligent and accurate, and ensure that the pressure transmitter can automatically adjust each order compensation term under various working conditions to achieve the optimal error correction effect and improve the measurement accuracy. Among them, the adjustment factor distribution refers to the numerical change rules of each adjustment factor under different working conditions or measurement ranges and their mutual relationships in the dynamic error correction model of the pressure transmitter, which reflects the influence degree and action mode of the adjustment factor on the model output result. The adaptive calibration instruction set refers to a series of instruction sets generated according to the accuracy level threshold and the adjustment factor distribution, and is used to guide the automatic adjustment of each order compensation term in the dynamic error correction model. For example, when the adjustment factor distribution shows that the adjustment of a certain order compensation term is insufficient and the current measurement accuracy does not meet the requirements of the accuracy level threshold, the adaptive calibration instruction set may instruct to increase the adjustment factor value of this order compensation term. Further, statistical methods such as variance analysis, regression analysis or sensitivity analysis can be used to analyze the adjustment factor distribution of each order compensation term in the dynamic error correction model.

[0138] As an embodiment of the present invention, the generating the adaptive calibration instruction set for each order compensation term in the dynamic error correction model by combining a preset accuracy level threshold and the adjustment factor distribution includes:

[0139] Analyze the threshold execution rule corresponding to the accuracy level threshold;

[0140] Based on the threshold execution rule, determine the calibration execution level corresponding to the pressure transmitter;

[0141] According to the calibration execution level, extract the calibration execution process corresponding to the pressure transmitter;

[0142] Based on the calibration execution process, construct an adaptive calibration framework corresponding to the pressure transmitter;

[0143] According to the adaptive calibration framework, generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model.

[0144] Among them, the threshold execution rule refers to the specific operation specifications and guiding principles followed by the accuracy level threshold in the actual application process. For example, in a high-precision measurement scenario, the threshold execution rule will clearly require that when the measurement error exceeds a certain range, the dynamic error correction model must be adjusted immediately; the calibration execution level refers to a classification of the depth and complexity of the calibration operation according to the threshold execution rule and the specific working conditions of the pressure transmitter. For example, for a pressure transmitter used in ordinary industrial applications, the calibration execution level can be relatively low, mainly performing simple parameter adjustments; while for a pressure transmitter used in high-precision scientific research experiments, the calibration execution level will be higher, requiring more complex model optimization and parameter calibration; the calibration execution process refers to a series of ordered operation steps and work processes formulated for a specific calibration execution level, which covers all links from the start to the end of the calibration, and clarifies the tasks and work order to be completed in each stage. For example, in the calibration execution process, it is possible to first measure the current error of the pressure transmitter, then determine the compensation terms to be adjusted according to the error situation and the accuracy level threshold, and then adjust the corresponding adjustment factors; the adaptive calibration framework refers to the overall structure and mode based on the calibration execution process for guiding the adaptive calibration of the pressure transmitter. For example, in the adaptive calibration framework, it will be clear which high-precision measurement equipment should be used in the error measurement stage; which algorithm should be used to determine the adjustment amount in the adjustment factor adjustment stage.

[0145] Further, the threshold execution rule corresponding to the accuracy level threshold can be implemented through a rule extraction tool. For example, regular expressions or a rule engine (such as Drools) can be used to parse and extract the accuracy level threshold to obtain the threshold execution rule. The calibration execution level corresponding to the pressure transmitter can be determined through a hierarchical evaluation model. For example, models such as analytic hierarchy process or fuzzy evaluation can be used to hierarchically evaluate the working conditions and error conditions of the pressure transmitter to obtain the calibration execution level. The calibration execution process corresponding to the pressure transmitter can be extracted through a process modeling tool. For example, BPMN (Business Process Modeling and Notation) or UML activity diagrams can be used to model and extract the calibration execution process to obtain the calibration execution process. The adaptive calibration framework corresponding to the pressure transmitter can be constructed through a framework design tool. For example, Axure or Sketch can be used to visually design the adaptive calibration framework to construct the adaptive calibration framework. The adaptive calibration instruction set for each order compensation term in the dynamic error correction model can be generated through an instruction generation algorithm. For example, a rule-based system or a machine learning algorithm can be used to generate the adaptive calibration instruction set according to the adaptive calibration framework and the calibration execution process.

[0146] S5. Determine the signal reconstruction strategy corresponding to the adaptive calibration instruction set, and based on the signal reconstruction strategy, generate a multi-mode calibration plan for the pressure transmitter.

[0147] By determining the signal reconstruction strategy corresponding to the adaptive calibration instruction set, the present invention can specifically optimize the signals collected by the pressure transmitter, provide a high-quality data basis for subsequent calibration, help improve the accuracy and reliability of calibration, and ensure the measurement accuracy of the pressure transmitter under complex working conditions.

[0148] Among them, the signal reconstruction strategy refers to a set of methods and steps for reconstructing and processing the original measurement signal of the pressure transmitter to achieve the calibration target of the pressure transmitter. For example, for the noise interference existing in the pressure signal, a filtering algorithm is used to remove the noise; for the signal loss caused by sensor failure, an interpolation algorithm is used to fill in the data. Optionally, the signal reconstruction strategy corresponding to the adaptive calibration instruction set can be implemented through a strategy matching algorithm. For example, in a rule matching-based manner, the calibration instruction is associated with the signal reconstruction strategy, and the appropriate signal reconstruction strategy can be quickly determined according to the type and target of the calibration instruction.

[0149] Further, generating a multi-mode calibration plan for the pressure transmitter based on the signal reconstruction strategy can formulate a flexible and accurate calibration plan according to different working scenarios and error characteristics of the pressure transmitter, effectively improve the adaptability of the calibration work, and ensure that the pressure transmitter can maintain good measurement performance in various situations.

[0150] Among them, the multi-mode calibration scheme refers to a set of scheme collections containing multiple calibration modes formulated according to factors such as the operating environment, working state, and error characteristics of the pressure transmitter. Each calibration mode corresponds to a specific working scenario. For example, when there is strong electromagnetic interference in the industrial production environment, an anti-interference calibration mode is adopted; when the pressure transmitter drifts due to long-term continuous operation, a drift compensation calibration mode is enabled.

[0151] As an embodiment of the present invention, to generate a multi-mode calibration scheme based on the signal reconstruction strategy, it is first necessary to analyze the technical characteristics corresponding to the signal reconstruction strategy. Based on the technical characteristics, clarify the specific requirements for signal reconstruction, such as the degree of noise removal and the requirements for signal integrity. According to these requirements, extract the key operating parameters of the pressure transmitter in different working scenarios, such as the pressure fluctuation range, ambient temperature change, measurement frequency, etc. Through cluster analysis, similar working scenarios are divided into the same category, and corresponding calibration modes are designed for each category to determine the calibration process, method, and equipment used. Finally, each calibration mode is integrated to form the multi-mode calibration scheme of the pressure transmitter.

[0152] Compared with the problems described in the background art, the present invention can effectively eliminate interference clutter and improve signal quality by performing noise filtering processing on the pressure signal data, laying a solid foundation for subsequent accurate analysis; by performing joint time-frequency domain analysis on the standard signal data, it can comprehensively analyze the characteristics of the signal in the time and frequency dimensions, accurately insight into the pressure change law, and provide a key basis for the accurate calibration and performance optimization of the pressure transmitter. Further, the present invention can generate a dynamic compensation coefficient matrix for the target pressure transmitter based on the adaptive compensation model, which can accurately fit the time-frequency characteristics of the pressure signal and complex environmental conditions, effectively correct measurement errors, and greatly improve the measurement accuracy of the pressure transmitter in different scenarios, ensuring the stability and reliability of measurement data. The present invention can identify the non-linear error mode of the pressure transmitter based on the response adaptability, accurately judge the error characteristics of the pressure transmitter under different working conditions, and provide a reliable basis for subsequent calibration. Further, the present invention constructs a dynamic error correction model for the pressure transmitter based on the key correction node parameters, which can effectively integrate key parameter information and lay a foundation for accurately correcting the dynamic error of the pressure transmitter. Among them, the dynamic error correction model refers to a mathematical model constructed according to the working principle, error characteristics and key correction node parameters of the pressure transmitter, which is used to correct the dynamic error generated during the operation of the pressure transmitter in real time. Further, the present invention can optimize the signal collected by the pressure transmitter in a targeted manner by determining the signal reconstruction strategy corresponding to the adaptive calibration instruction set, provide a high-quality data basis for subsequent calibration, help improve the accuracy and reliability of calibration, and ensure the measurement accuracy of the pressure transmitter under complex working conditions. Therefore, the high-precision adaptive pressure transmitter calibration method and system provided by the embodiments of the present invention can improve the calibration accuracy of the high-precision adaptive pressure transmitter.

[0153] Embodiment 2:

[0154] As Figure 2 shown, it is a functional module diagram of a high-precision adaptive pressure transmitter calibration system of the present invention.

[0155] The high-precision adaptive pressure transmitter calibration system 200 of the present invention can be installed in an electronic device. According to the functions achieved, the high-precision adaptive pressure transmitter calibration system can include a signal characterization analysis module 201, a response adaptability calculation module 202, a node parameter extraction module 203, a calibration instruction set generation module 204, and a calibration scheme generation module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0156] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0157] The signal characterization analysis module 201 is configured to obtain real-time pressure signal data output by a high-precision adaptive target pressure transmitter, perform noise filtering processing on the pressure signal data to obtain standard signal data, and perform joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint characterization;

[0158] The response adaptability calculation module 202 is configured to input the signal time-frequency joint characterization into a preset adaptive compensation model, generate a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model, detect environmental interference parameters of the environment where the target pressure transmitter is located, and calculate the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters;

[0159] The node parameter extraction module 203 is configured to identify a non-linear error pattern of the pressure transmitter based on the response adaptability, activate a corresponding calibration algorithm from a preset calibration algorithm library according to the non-linear error pattern, perform multi-dimensional parameter optimization on the calibration algorithm to obtain optimal parameters, determine an optimal compensation function of the optimal parameters, and extract key calibration node parameters in the compensation function;

[0160] The calibration instruction set generation module 204 is configured to construct a dynamic error correction model of the pressure transmitter based on the key calibration node parameters, analyze the distribution of adjustment factors of each order compensation term in the dynamic error correction model, and generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model in combination with a preset accuracy level threshold and the distribution of adjustment factors;

[0161] The calibration scheme generation module 205 is configured to determine a signal reconstruction strategy corresponding to the adaptive calibration instruction set, and generate a multi-mode calibration scheme for the pressure transmitter based on the signal reconstruction strategy.

[0162] Specifically, each module in the high-precision adaptive pressure transmitter calibration system 200 in the embodiments of the present invention adopts the same technical means as those in the above-mentioned Figure 1 high-precision adaptive pressure transmitter calibration method described, and can produce the same technical effects, which will not be elaborated here.

[0163] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A calibration method for a high-precision adaptive pressure transmitter, characterized in that The method includes: Obtaining real-time pressure signal data output by a high-precision adaptive target pressure transmitter, performing noise filtering processing on the pressure signal data to obtain standard signal data, and performing joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation; Inputting the signal time-frequency joint representation into a preset adaptive compensation model, generating a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model, detecting environmental interference parameters of the environment where the target pressure transmitter is located, and calculating the response adaptation degree of the dynamic compensation coefficient matrix to the environmental interference parameters; Based on the response adaptation degree, identifying the non-linear error mode of the pressure transmitter, activating a corresponding calibration algorithm from a preset calibration algorithm library according to the non-linear error mode, performing multi-dimensional parameter optimization on the calibration algorithm to obtain optimal parameters, determining an optimal compensation function of the optimal parameters, and extracting key calibration node parameters in the compensation function; Based on the key calibration node parameters, constructing a dynamic error correction model of the pressure transmitter, analyzing the adjustment factor distribution of each order compensation term in the dynamic error correction model, and generating an adaptive calibration instruction set for each order compensation term in the dynamic error correction model in combination with a preset accuracy level threshold and the adjustment factor distribution; Determining a signal reconstruction strategy corresponding to the adaptive calibration instruction set, and generating a multi-mode calibration scheme for the pressure transmitter based on the signal reconstruction strategy, The step of inputting the signal time-frequency joint representation into a preset adaptive compensation model and generating a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model includes: Performing dimensionality reduction processing on the signal time-frequency joint representation by using a dimensionality reduction algorithm in the adaptive compensation model to obtain a dimensionality-reduced time-frequency joint representation; Performing encoding processing on the dimensionality-reduced time-frequency joint representation by using a sparse encoder in the adaptive compensation model to obtain a time-frequency feature vector; Collecting environmental time-series data corresponding to the target pressure transmitter, and mining environmental time-series features in the environmental time-series data by using an environmental feature perception network in the adaptive compensation model; Performing splicing processing on the time-frequency feature vector and the environmental time-series features by using a time-frequency-environment fusion network in the adaptive compensation model to obtain a time-frequency environment fusion feature; Generating a dynamic compensation coefficient matrix of the target pressure transmitter by using a dynamic compensation generation network in the adaptive compensation model according to a preset compensation rule and the time-frequency environment fusion feature.

2. The high-precision adaptive pressure transmitter calibration method according to claim 1, characterized in that, The step of performing joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint representation includes: Performing signal windowing processing on the standard signal data to obtain windowed signal data; Extracting a time-frequency analysis primitive sequence from the windowed signal data, and performing time-frequency fine processing on the time-frequency analysis primitive sequence to obtain time-frequency distribution energy; Performing energy enhancement processing on the time-frequency distribution energy to obtain a refined time-frequency feature map; Performing multi-scale quantization processing on the refined time-frequency feature map to obtain a signal time-frequency joint representation.

3. The high-precision adaptive pressure transmitter calibration method according to claim 2, characterized in that, Performing time-frequency fine processing on the time-frequency analysis primitive sequence to obtain time-frequency distribution energy, including: Performing wavelet transform processing on the time-frequency analysis primitive sequence to obtain sequence wavelet coefficients; Calculating the average coefficient corresponding to the sequence wavelet coefficients, and based on the average coefficient, calculating the coefficient standard deviation corresponding to the sequence wavelet coefficients; Combining the coefficient standard deviation and the average coefficient to perform screening processing on the sequence wavelet coefficients to obtain target wavelet coefficients; Calculating the energy density corresponding to the target wavelet coefficients, and performing summation processing on the energy density to obtain time-frequency distribution energy.

4. The high-precision adaptive pressure transmitter calibration method according to claim 1, characterized in that The calculating the response fitness of the dynamic compensation coefficient matrix to the environmental interference parameters includes: Performing time synchronization processing on the environmental interference parameters to obtain synchronized environmental interference parameters; Extracting the interference parameter indicators in the synchronized environmental interference parameters, and performing coupling processing on the interference parameter indicators to obtain coupled parameter indicators; Obtaining the operating environment scenario corresponding to the target pressure transmitter, and based on the operating environment scenario, screening out associated parameter indicators from the interference parameter indicators; Combining the associated parameter indicators, the coupled parameter indicators and the dynamic compensation coefficient matrix to calculate the response fitness of the dynamic compensation coefficient matrix to the environmental interference parameters.

5. The high-precision adaptive pressure transmitter calibration method according to claim 4, wherein The combining the associated parameter indicators, the coupled parameter indicators and the dynamic compensation coefficient matrix to calculate the response fitness of the dynamic compensation coefficient matrix to the environmental interference parameters includes: Based on the coupled parameter indicators, calculating the indicator standard deviation corresponding to the environmental interference parameters; Querying the indicator sensitivity corresponding to the associated parameter indicators, and combining the indicator sensitivity, the indicator standard deviation and the coupled parameter indicators, and calculating the response fitness of the dynamic compensation coefficient matrix to the environmental interference parameters through the following formula: ； Among them, represents the response adaptability of the dynamic compensation coefficient matrix to the environmental interference parameters, represents the dynamic compensation coefficient matrix corresponding to the a-th moment, represents the coupling parameter index corresponding to the a-th moment, represents the standard deviation of the index corresponding to the a-th moment, a represents the starting moment, and N represents the ending moment, represents the index sensitivity, represents the index change rate of the correlation parameter index corresponding to the a-th moment, is a constant term, which is .

6. The high-precision adaptive pressure transmitter calibration method according to claim 1, characterized in that, Based on the response fitness, identifying the non-linear error mode of the pressure transmitter includes: Querying the adaptation characteristic interval corresponding to the response fitness; Based on the adaptation characteristic interval, determining the error characteristic weight corresponding to the pressure transmitter; According to the error characteristic weight, extracting the historical error characteristics corresponding to the pressure transmitter; Analyzing the typical error modes covered in the historical error characteristics; Based on the typical error modes, determining the non-linear error mode of the pressure transmitter for the current working state.

7. The high-precision adaptive pressure transmitter calibration method according to claim 1, characterized in that The extracting the key calibration node parameters in the compensation function includes: Analyzing the parameter space range corresponding to the compensation function; Based on the parameter space range, determining the key parameter region in the compensation function; Querying the parameter importance label associated with the key parameter region; According to the parameter importance label, extracting the parameter subset in the compensation function; Based on the parameter subset, determining the key calibration node parameters in the compensation function.

8. The high-precision adaptive pressure transmitter calibration method according to claim 1, characterized in that, The combining the preset accuracy level threshold and the adjustment factor distribution to generate the adaptive calibration instruction set for each order compensation term in the dynamic error correction model includes: Analyzing the threshold execution rule corresponding to the accuracy level threshold; Execute rules based on the threshold to determine the calibration execution level corresponding to the pressure transmitter; Extract the calibration execution process corresponding to the pressure transmitter according to the calibration execution level; Construct an adaptive calibration framework corresponding to the pressure transmitter based on the calibration execution process; Generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model according to the adaptive calibration framework; 9. A high-precision adaptive pressure transmitter calibration system, characterized in that, The system includes: A signal characterization analysis module, configured to obtain real-time pressure signal data output by a high-precision adaptive target pressure transmitter, perform noise filtering processing on the pressure signal data to obtain standard signal data, and perform joint time-frequency domain analysis on the standard signal data to obtain a signal time-frequency joint characterization; A response fitness calculation module, configured to input the signal time-frequency joint characterization into a preset adaptive compensation model, and generate a dynamic compensation coefficient matrix of the target pressure transmitter based on the adaptive compensation model, including performing dimensionality reduction processing on the signal time-frequency joint characterization using a dimensionality reduction algorithm in the adaptive compensation model to obtain a reduced-dimensional time-frequency joint characterization; performing encoding processing on the reduced-dimensional time-frequency joint characterization using a sparse encoder in the adaptive compensation model to obtain a time-frequency feature vector; collecting environmental timing data corresponding to the target pressure transmitter, and using an environmental feature perception network in the adaptive compensation model to mine environmental timing features in the environmental timing data; performing splicing processing on the time-frequency feature vector and the environmental timing features using a time-frequency-environment fusion network in the adaptive compensation model to obtain a time-frequency environment fusion feature; generating a dynamic compensation coefficient matrix of the target pressure transmitter using a dynamic compensation generation network in the adaptive compensation model according to a preset compensation rule and the time-frequency environment fusion feature; And detect environmental interference parameters of the environment where the target pressure transmitter is located, and calculate the response fitness of the dynamic compensation coefficient matrix to the environmental interference parameters; A node parameter extraction module, configured to identify a non-linear error pattern of the pressure transmitter based on the response fitness, activate a corresponding calibration algorithm from a preset calibration algorithm library according to the non-linear error pattern, perform multi-dimensional parameter optimization on the calibration algorithm to obtain optimal parameters, determine an optimal compensation function of the optimal parameters, and extract key calibration node parameters in the compensation function; A calibration instruction set generation module, configured to construct a dynamic error correction model of the pressure transmitter based on the key calibration node parameters, analyze the adjustment factor distribution of each order compensation term in the dynamic error correction model, and generate an adaptive calibration instruction set for each order compensation term in the dynamic error correction model in combination with a preset precision level threshold and the adjustment factor distribution; A calibration scheme generation module, configured to determine a signal reconstruction strategy corresponding to the adaptive calibration instruction set, and generate a multi-mode calibration scheme for the pressure transmitter based on the signal reconstruction strategy.

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