Error analysis driven pressure gauge metering calibration method and system

Abstract

The invention relates to a pressure gauge metering calibration method and system driven by error analysis, and relates to the field of pressure gauge metering calibration, and the method comprises the steps: carrying out the aging analysis of an elastic element according to the accumulated use time and loading and unloading records, and obtaining an aging analysis result; carrying out mechanical error analysis according to an aging analysis result to obtain a mechanical error coefficient, and carrying out lag error analysis to obtain a lag error coefficient; the method comprises the following steps: performing error correction by adopting mechanical and lag error coefficients to obtain a mechanical error value and a lag error value, compensating the lag error value according to a real-time loading and unloading record to obtain a compensated lag error value, and calibrating a metering result in combination with the mechanical error value. According to the invention, the technical problem that the accuracy and reliability of the measurement calibration result of the pressure gauge are insufficient due to the fact that the traditional method cannot accurately quantify the influence of the aging of the elastic element in the pressure gauge on the measurement error can be solved; and the accuracy, the stability and the reliability of the measurement calibration of the pressure gauge can be obviously improved.

Description

Technical Field

[0001] The invention relates to the field of pressure gauge measurement and calibration, and in particular to an error analysis driven pressure gauge measurement and calibration method and system. Background Art

[0002] As a common pressure measurement device, pressure gauge is widely used in industrial production, scientific experiments and engineering monitoring. Its measurement accuracy and reliability are directly related to the operating efficiency and safety of the system. The core component of the pressure gauge is the elastic element (such as spring tube or bellows), whose performance directly determines the accuracy of the pressure response. However, in long-term use, the elastic element will inevitably age due to physical and environmental factors, which will cause significant errors in the measurement results.

[0003] At present, the traditional pressure gauge measurement and calibration method fails to accurately quantify the aging of elastic elements and its impact on measurement errors, resulting in insufficient accuracy and reliability of measurement and calibration results, which needs to be solved urgently. Summary of the invention

[0004] The present invention aims to solve the technical problem that in traditional pressure gauge measurement and calibration methods, the influence of aging of elastic elements in the pressure gauge on the measurement error cannot be accurately quantified, resulting in insufficient accuracy and reliability of the pressure gauge measurement and calibration results. An error analysis driven pressure gauge measurement and calibration method and system are provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides an error analysis-driven pressure gauge measurement and calibration method, comprising: obtaining the cumulative usage time of the pressure gauge, and collecting multiple loading and unloading records of the pressure gauge in use within the historical time, wherein each loading and unloading record includes a loading range and an unloading range; performing an aging analysis of the elastic element in the pressure gauge based on the cumulative usage time and the multiple loading and unloading records to obtain an aging analysis result; performing a mechanical error analysis of the pressure gauge currently performing pressure measurement based on the aging analysis result to obtain a mechanical error coefficient, and performing a hysteresis error analysis of the pressure gauge currently performing pressure measurement to obtain a hysteresis error coefficient; collecting the pressure measurement result currently using the pressure gauge for pressure measurement, performing error correction using the mechanical error coefficient and the hysteresis error coefficient respectively to obtain a mechanical error value and a hysteresis error value, and compensating the hysteresis error value based on the real-time loading and unloading records of the pressure gauge to obtain a compensated hysteresis error value, performing measurement and calibration calculations on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

[0007] In a second aspect, the present invention provides an error analysis-driven pressure gauge measurement and calibration system, comprising: a loading and unloading record acquisition module, used to obtain the cumulative use time of the pressure gauge, and to collect multiple loading and unloading records of the pressure gauge in use within the historical time, wherein each loading and unloading record includes a loading range and an unloading range; an elastic element aging analysis module, used to perform aging analysis of the elastic element in the pressure gauge according to the cumulative use time and multiple loading and unloading records, and obtain an aging analysis result; a measurement error analysis module, used to perform mechanical error analysis of the pressure measurement currently performed by the pressure gauge according to the aging analysis result, and obtain a mechanical error coefficient, and to perform hysteresis error analysis of the pressure measurement currently performed by the pressure gauge, and obtain a hysteresis error coefficient; a measurement calibration calculation module, used to collect the pressure measurement result currently measured by the pressure gauge, respectively use the mechanical error coefficient and the hysteresis error coefficient for error correction, obtain a mechanical error value and a hysteresis error value, and compensate the hysteresis error value according to the real-time loading and unloading records of the pressure gauge to obtain a compensated hysteresis error value, and perform measurement calibration calculation on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

[0008] In a third aspect, the present invention further provides an electronic device, comprising:

[0009] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0010] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method described in any one of the first aspects are implemented.

[0011] The beneficial effects of the present invention are as follows: by acquiring the cumulative usage time and historical loading and unloading records (including loading range and unloading range) of the pressure gauge, an aging analysis is performed on the elastic element in the pressure gauge, the degree of aging thereof is quantified and the aging analysis result is obtained; then based on the aging analysis result, the current mechanical error and hysteresis error of the pressure gauge are further evaluated, and the mechanical error coefficient and the hysteresis error coefficient are calculated; then the current pressure measurement result is collected, and the error correction is performed in combination with the mechanical error coefficient and the hysteresis error coefficient to obtain the mechanical error value and the hysteresis error value; further, the hysteresis error value is dynamically compensated according to the real-time loading and unloading records to obtain the compensated hysteresis error value; finally, according to the mechanical error value and the compensated hysteresis error value, the pressure measurement result is calibrated to obtain the calibrated pressure measurement result; the above method can accurately quantify the aging degree of the elastic element in the pressure gauge, accurately evaluate the measurement error caused by the aging of the elastic element to the pressure gauge, and perform targeted compensation for the measurement result, thereby significantly improving the accuracy, stability and reliability of the pressure gauge measurement calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of a flow chart of a pressure gauge measurement and calibration method driven by error analysis provided by the present invention;

[0013] Figure 2 A schematic diagram of the structure of the pressure gauge measurement and calibration system driven by error analysis provided by the present invention;

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0015] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0016] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0017] Loading and unloading record acquisition module 01, elastic element aging analysis module 02, metrology error analysis module 03, metrology calibration calculation module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0021] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides an error analysis driven pressure gauge measurement and calibration method, which specifically includes the following steps:

[0022] A100: Obtain the accumulated usage time of the pressure gauge, and collect multiple loading and unloading records of the pressure gauge in use during the historical time, wherein each loading and unloading record includes a loading range and an unloading range.

[0023] Furthermore, step A100 of the present invention further includes:

[0024] A110: Obtain the accumulated usage time of the pressure gauge; A120: Collect the loading range and unloading range of each pressure measurement loading and unloading performed on the pressure gauge within the historical time to obtain multiple loading and unloading records.

[0025] Specifically, in a pressure gauge, the elastic element (such as a spring tube or a bellows) is a key component for realizing pressure perception and transmission. Its main function is to convert pressure into mechanical displacement, thereby driving a pointer or sensor to realize pressure measurement and display; obtain the cumulative usage time of the pressure gauge, among which the cumulative usage time of the pressure gauge is an important data source for elastic element aging analysis. The longer the cumulative usage time of the pressure gauge, the greater the aging of the elastic element and the greater the change in the elastic modulus.

[0026] Next, the loading range and unloading range of the pressure gauge during each pressure measurement loading and unloading in the historical use time are collected, wherein the loading range refers to the pressure range covered by the pressure gauge during each pressure measurement, from the loading starting pressure to the loading maximum pressure, and the unloading range refers to the pressure range covered by the pressure gauge during the pressure measurement, from the unloading starting pressure to the unloading zero pressure (or final pressure). There will be a hysteresis effect in the loading and unloading process of the pressure gauge, and the hysteresis effect refers to the phenomenon that the loading curve and the unloading curve do not overlap due to the material properties of the elastic element (such as internal friction, plastic deformation, etc.) during the loading and unloading process of the pressure gauge. The greater the difference between the loading range and the unloading range, the greater the aging of the elastic element and the greater the change in the elastic modulus. Then each loading range and unloading range is taken as a loading and unloading record to obtain multiple loading and unloading records, wherein the loading and unloading records describe the pressure change of the pressure gauge from pressure loading to the maximum value and then unloading to the final value during a complete measurement process. By obtaining multiple loading and unloading records, comprehensive historical data support can be provided for the aging analysis of elastic elements.

[0027] A200: Perform an aging analysis on the elastic element in the pressure gauge according to the accumulated usage time and the multiple loading and unloading records to obtain an aging analysis result.

[0028] Further, step A200 of the present invention further includes:

[0029] A210: Input the accumulated usage time into the time aging classifier and classify it to obtain the time aging parameters, wherein the time aging parameters include the change in the elastic modulus of the elastic element, and the time aging classifier is obtained by collecting a set of sample accumulated usage time and a set of sample time aging parameters and constructing a mapping relationship; A220: Calculate and obtain multiple range differences based on the loading ranges and unloading ranges in the multiple loading and unloading records.

[0030] Specifically, to construct a time aging classifier, first, the cumulative usage time data of multiple similar pressure gauges in the same scenario are collected, and multiple cumulative usage time data are obtained to construct a sample cumulative usage time set, and time aging parameters of corresponding samples under different cumulative usage times are collected, where the time aging parameters include the change in elastic modulus of the elastic element, and multiple time aging parameters are obtained to construct a sample time aging parameter set, where there is a one-to-one mapping relationship between the sample cumulative usage time and the sample time aging parameter.

[0031] The decision tree is a classification model based on feature splitting, and its goal is to generate classification rules by recursively splitting training data; then, based on the decision tree principle, the sample cumulative use time set and the sample time aging parameter set are used as training data, and the decision tree is trained according to the mapping relationship between the sample cumulative use time and the sample time aging parameter to generate a time aging classifier. Through the mapping relationship between the cumulative use time and the time aging parameter, the time aging classifier constructed based on the decision tree principle can accurately predict the time aging parameters of the pressure gauge (i.e., the change in the elastic modulus of the elastic element), and improve the accuracy and efficiency of the time aging parameter analysis. Finally, the cumulative use time is input into the time aging classifier, and the time aging parameters are obtained by classification.

[0032] Next, the range difference is calculated based on the loading range and unloading range in the multiple loading and unloading records. The range difference refers to the absolute value of the difference between the loading range and the unloading range of the pressure gauge during each loading and unloading process. It is used to quantify the asymmetry of loading and unloading, reflect the hysteresis effect of the elastic element, and calculate multiple range differences.

[0033] A230: Training a hysteresis aging analysis channel including multiple hysteresis aging analysis paths.

[0034] Further, step A230 of the present invention further includes:

[0035] A231: Based on the aging test data of the elastic element in the pressure gauge, a sample range difference set is collected, and the change in elastic modulus of the elastic element after running with different sample range differences is collected, and marked as a sample hysteresis aging parameter set; A232: The sample range difference set and the sample hysteresis aging parameter set are divided in equal proportion to obtain multiple groups of hysteresis aging analysis training data; A233: The multiple groups of hysteresis aging analysis training data are respectively used to train the multiple hysteresis aging analysis paths until the training converges, and the multiple hysteresis aging analysis paths that have converged are combined to obtain a hysteresis aging analysis channel.

[0036] Specifically, according to the aging test data of the elastic element in the pressure gauge, a sample range difference set is collected, and the sample range difference set records the difference between the loading range and the unloading range of the pressure gauge under different operating conditions; then the change in the elastic modulus of the elastic element after running with different sample range differences is collected and marked as a sample hysteresis aging parameter to obtain a sample hysteresis aging parameter set, wherein the sample range difference and the sample hysteresis aging parameter correspond one to one. Then, the sample range difference set and the sample hysteresis aging parameter set are divided in equal proportion to obtain multiple groups of hysteresis aging analysis training data, wherein the division ratio can be set according to the expected demand, for example, the sample data is divided into 10 groups of training data in equal proportion.

[0037] BP neural network is a multi-layer feedforward neural network, which adjusts weights and biases through the error back propagation algorithm to achieve the goal of minimizing prediction errors. Multiple hysteresis aging analysis paths are constructed based on the BP neural network. The hysteresis aging analysis path is a BP neural network model that can be iteratively optimized in machine learning, including an input layer, a hidden layer and an output layer, wherein the input data of the input layer is the range difference, and the output data of the output layer is the hysteresis aging parameter. Then, with the sample range difference as input and the sample hysteresis aging parameter as output, multiple groups of hysteresis aging analysis training data are used to supervise the training of multiple hysteresis aging analysis paths. First, the normalized sample range difference is input to the input layer, each hidden layer neuron receives the output of the previous layer, calculates the weighted sum and nonlinearly maps through the activation function, and the output of the hidden layer is passed to the output layer to calculate the predicted value; then, the difference between the true value (target output) and the predicted value of the training sample is used to calculate the loss function value, and the gradient of the loss function to the weight and bias is calculated by the chain rule, and the weight and bias are updated using an optimization algorithm (such as gradient descent or Adam); then, the steps of forward propagation, error calculation and back propagation are repeated to gradually reduce the loss function value until the loss function value converges, and multiple hysteresis aging analysis paths after convergence are obtained, and the hysteresis aging analysis channel is generated according to the combination of the multiple hysteresis aging analysis paths. By constructing the hysteresis aging analysis channel based on the BP neural network, the degree of hysteresis aging can be accurately predicted according to the range difference, while improving the efficiency of hysteresis aging analysis.

[0038] A240: Calculate the ratio of each range difference to the maximum range difference, multiply it by the number of the multiple hysteresis aging analysis paths and round it to obtain multiple hysteresis aging analysis numbers; A250: Input the multiple range differences into the hysteresis aging analysis paths of the multiple hysteresis aging analysis numbers respectively, output and obtain multiple hysteresis aging parameter sets, calculate the average to obtain multiple hysteresis aging parameters, wherein each hysteresis aging parameter includes the change in elastic modulus of the elastic element; A260: Add the time aging parameter and the multiple hysteresis aging parameters to obtain the total aging parameter as the aging analysis result.

[0039] Specifically, the maximum range difference among the multiple range differences is obtained, and the ratio of the other range differences to the maximum range difference among the multiple range differences is calculated to obtain multiple range difference ratios; then the multiple range difference ratios are multiplied by the number of the multiple hysteresis aging analysis paths and rounded to obtain multiple hysteresis aging analysis numbers. For example, assuming that the selected range difference is 5, the maximum range difference is 10, and the number of hysteresis aging analysis paths is 20, the range difference ratio is 0.5, and the corresponding number of hysteresis aging analyses is 10. By setting the corresponding number of hysteresis aging analyses according to the range difference ratio, more computing resources can be invested in the large range difference and less computing resources can be invested in the small range difference, so as to achieve a reasonable allocation of computing resources and improve the utilization efficiency of computing resources.

[0040] Then, according to the plurality of hysteresis aging analysis numbers, a corresponding number of hysteresis aging analysis paths in the hysteresis aging analysis channel are selected, and the plurality of range differences are respectively input into the hysteresis aging analysis paths of the plurality of hysteresis aging analysis numbers for analysis, and a plurality of hysteresis aging parameter sets are output, and the mean value of each hysteresis aging parameter set is calculated to obtain a plurality of hysteresis aging parameters, wherein each hysteresis aging parameter includes the elastic modulus change of the elastic element. Finally, the time aging parameter and the plurality of hysteresis aging parameters are added, and the sum result is used as the total aging parameter (total elastic modulus change) as the aging analysis result.

[0041] A300: Based on the aging analysis result, a mechanical error analysis of the pressure gauge currently measuring pressure is performed to obtain a mechanical error coefficient, and a hysteresis error analysis of the pressure gauge currently measuring pressure is performed to obtain a hysteresis error coefficient.

[0042] Furthermore, step A300 of the present invention further includes:

[0043] A310: Based on the pressure gauge measurement test data in the historical time, the total elastic modulus change of the elastic element in the pressure gauge is collected, marked as the sample aging analysis result set, and the mechanical error ratio and hysteresis error ratio of the pressure gauge under different sample aging analysis results are tested, marked as the sample mechanical error coefficient set and the sample hysteresis error coefficient set; A320: Using the sample aging analysis result set as input training data, and using the sample mechanical error coefficient set and the sample hysteresis error coefficient set as output training data respectively, supervised training is performed to obtain the mechanical error analysis branch and the hysteresis error analysis branch; A330: Input the aging analysis results into the mechanical error analysis branch and the hysteresis error analysis branch respectively, and output the obtained mechanical error coefficient and hysteresis error coefficient.

[0044] Specifically, according to the pressure gauge measurement test data in the historical time, the total elastic modulus change of the elastic element in the pressure gauge is collected and set as the sample aging analysis result to obtain the sample aging analysis result set; then the mechanical error ratio and hysteresis error ratio of the pressure gauge under different sample aging analysis results are tested, where the mechanical error ratio is the percentage of the deviation between the current measurement value and the actual value of the pressure gauge to the actual value. For example, if the error between the pressure value of the pressure gauge loading test and the actual pressure value under the sample aging analysis result is -5%, then the mechanical error ratio is -5%; the hysteresis error ratio refers to the hysteresis error percentage between the test pressure value and the actual pressure value during the loading and unloading process of the pressure gauge under the sample aging analysis result. For example, if the error between the pressure value of the pressure gauge loading and unloading test and the actual pressure value under the sample aging analysis result is -5%, then the hysteresis error ratio is -5%. The mechanical error ratio is marked as the sample mechanical error coefficient, and the hysteresis error ratio is marked as the sample hysteresis error coefficient, to obtain the sample mechanical error coefficient set and the sample hysteresis error coefficient set.

[0045] Based on the BP neural network, the mechanical error analysis branch and the hysteresis error analysis branch are constructed respectively. The input data of the input layer of the mechanical error analysis branch is the aging analysis result, and the output data of the output layer is the mechanical error coefficient; the input data of the input layer of the hysteresis error analysis branch is the aging analysis result, and the output data of the output layer is the hysteresis error coefficient. Next, the sample aging analysis result set is used as input training data, the sample mechanical error coefficient set is used as output training data of the mechanical error analysis branch, and the sample lag error coefficient set is used as output training data of the lag error analysis branch. The mechanical error analysis branch and the lag error analysis branch are supervised and trained to obtain the trained mechanical error analysis branch and the lag error analysis branch. Taking the training of the mechanical error analysis branch as an example, first, the normalized sample aging analysis result is input into the input layer of the neural network, the input features are weighted and summed and mapped through the activation function, the hidden layer output is passed to the output layer, and the predicted value of the mechanical error coefficient is calculated; then, the error between the predicted value and the true value is calculated, and the weight and bias of the model are updated according to the gradient information of the error. The forward propagation and back propagation processes are repeated to gradually optimize the model parameters until the loss function converges to obtain the trained mechanical error analysis branch.

[0046] Finally, the aging analysis results are input into the mechanical error analysis branch and the hysteresis error analysis branch respectively to obtain the mechanical error coefficient and the hysteresis error coefficient.

[0047] A400: Collect the pressure measurement result currently measured by the pressure gauge, use the mechanical error coefficient and the hysteresis error coefficient to perform error correction, obtain the mechanical error value and the hysteresis error value, and compensate the hysteresis error value according to the real-time loading and unloading record of the pressure gauge to obtain the compensated hysteresis error value, perform measurement calibration calculation on the pressure measurement result in combination with the mechanical error value, and obtain the calibrated pressure measurement result.

[0048] Further, step A400 of the present invention further includes:

[0049] A410: collecting the pressure measurement result currently measured by the pressure gauge; A420: respectively multiplying the pressure measurement result by the mechanical error coefficient and the hysteresis error coefficient to obtain the mechanical error value and the hysteresis error value.

[0050] Specifically, the pressure measurement result currently measured by the pressure gauge is collected, and the mechanical error coefficient is multiplied by the pressure measurement result, and the product of the two is used as the mechanical error value; the hysteresis error coefficient is multiplied by the pressure measurement result, and the product of the two is used as the hysteresis error value.

[0051] Further, step A400 of the present invention further includes:

[0052] A430: Obtain the real-time loading and unloading record of the pressure gauge for the most recent loading and unloading, and calculate the real-time range difference based on the real-time loading range and the real-time unloading range in the real-time loading and unloading record; A440: Calculate the ratio of the real-time range difference and the average of the multiple range differences, and use the ratio to perform product compensation calculation on the lag error value to obtain the compensated lag error value; A450: Use the compensated lag error value and the mechanical error value to perform measurement calibration calculation on the pressure measurement result to obtain a calibrated pressure measurement result.

[0053] Specifically, the real-time loading and unloading record (including real-time loading range and real-time unloading range) of the pressure gauge for the most recent loading and unloading is obtained, and then the range difference is calculated based on the real-time loading range and the real-time unloading range to obtain the real-time range difference, wherein the real-time range difference refers to the absolute value of the difference between the real-time loading range and the real-time unloading range.

[0054] The average of multiple range differences obtained by analyzing multiple loading and unloading records is calculated to obtain the range difference average, and the ratio of the real-time range difference and the range difference average is calculated. The hysteresis error value is multiplied by the ratio, and the product of the two is used as the compensated hysteresis error value, wherein the size of the hysteresis error is related to the size of the current loading and unloading range difference. The larger the real-time range difference, the greater the error impact caused by the hysteresis effect, and the larger the hysteresis error.

[0055] Finally, the pressure measurement result is calibrated and calculated using the compensated hysteresis error value and the mechanical error value to obtain a calibrated pressure measurement result. For example, the calibrated pressure measurement result is the pressure value after the pressure measurement result is added with the compensated hysteresis error value (negative) and the mechanical error value (negative). By calibrating the pressure measurement result using the compensated hysteresis error value and the mechanical error value, the influence of the mechanical error and the hysteresis error can be effectively eliminated, a more accurate calibrated pressure measurement result can be obtained, and the pressure measurement error can be minimized.

[0056] The error analysis driven pressure gauge calibration method provided by the embodiment of the present invention has at least the following technical effects:

[0057] By obtaining the cumulative usage time and historical loading and unloading records (including loading range and unloading range) of the pressure gauge, an aging analysis is performed on the elastic element in the pressure gauge, the degree of aging is quantified and the aging analysis results are obtained; then based on the aging analysis results, the current mechanical error and hysteresis error of the pressure gauge are further evaluated, and the mechanical error coefficient and the hysteresis error coefficient are calculated; then the current pressure measurement result is collected, and the error correction is performed in combination with the mechanical error coefficient and the hysteresis error coefficient to obtain the mechanical error value and the hysteresis error value; further, the hysteresis error value is dynamically compensated according to the real-time loading and unloading records to obtain the compensated hysteresis error value; finally, the pressure measurement result is calibrated according to the mechanical error value and the compensated hysteresis error value to obtain the calibrated pressure measurement result; the above method can accurately quantify the aging degree of the elastic element in the pressure gauge, accurately evaluate the measurement error caused by the aging of the elastic element to the pressure gauge, and perform targeted compensation for the measurement result, thereby significantly improving the accuracy, stability and reliability of the pressure gauge measurement calibration.

[0058] Embodiment 2, as Figure 2As shown, based on the same inventive concept as the error analysis driven pressure gauge measurement and calibration method provided in Example 1, an embodiment of the present invention also provides an error analysis driven pressure gauge measurement and calibration system, including: a loading and unloading record acquisition module 01, used to obtain the cumulative use time of the pressure gauge, and collect multiple loading and unloading records used in the pressure gauge in the historical time, wherein each loading and unloading record includes a loading range and an unloading range; an elastic element aging analysis module 02, used to perform aging analysis of the elastic element in the pressure gauge according to the cumulative use time and multiple loading and unloading records, and obtain an aging analysis result; a measurement error analysis module 03, used to analyze the aging of the elastic element in the pressure gauge according to the aging analysis result. The analysis result is used to perform a mechanical error analysis of the pressure measurement currently performed by the pressure gauge to obtain a mechanical error coefficient, and to perform a hysteresis error analysis of the pressure measurement currently performed by the pressure gauge to obtain a hysteresis error coefficient; a measurement calibration calculation module 04 is used to collect the pressure measurement result currently measured by the pressure gauge, respectively use the mechanical error coefficient and the hysteresis error coefficient to perform error correction, obtain a mechanical error value and a hysteresis error value, and compensate the hysteresis error value according to the real-time loading and unloading record of the pressure gauge to obtain a compensated hysteresis error value, and perform a measurement calibration calculation on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

[0059] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: obtaining the accumulated usage time of the pressure gauge; collecting the loading range and unloading range of each pressure measurement loading and unloading of the pressure gauge in the historical time, and obtaining multiple loading and unloading records.

[0060] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: inputting the accumulated usage time into a time aging classifier, classifying and obtaining time aging parameters, wherein the time aging parameters include the change in elastic modulus of the elastic element, and the time aging classifier obtains the time aging parameters by collecting a sample accumulated usage time set and a sample time aging parameter set, and constructing a mapping relationship; calculating and obtaining multiple range differences according to the loading ranges and unloading ranges in the multiple loading and unloading records; training a hysteresis aging analysis channel including multiple hysteresis aging analysis paths; calculating the ratio of each range difference to the maximum range difference, multiplying it by the number of the multiple hysteresis aging analysis paths and rounding it to obtain multiple hysteresis aging analysis numbers; inputting the multiple range differences into the hysteresis aging analysis paths of the multiple hysteresis aging analysis numbers respectively, outputting multiple hysteresis aging parameter sets, and calculating the mean to obtain multiple hysteresis aging parameters, wherein each hysteresis aging parameter includes the change in elastic modulus of the elastic element; adding the time aging parameter and the multiple hysteresis aging parameters to obtain a total aging parameter as an aging analysis result.

[0061] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: collecting a sample range difference set based on the aging test data of the elastic element in the pressure gauge, and collecting the change in elastic modulus of the elastic element after running with different sample range differences, marked as a sample hysteresis aging parameter set; dividing the sample range difference set and the sample hysteresis aging parameter set in equal proportion to obtain multiple groups of hysteresis aging analysis training data; using the multiple groups of hysteresis aging analysis training data respectively to train the multiple hysteresis aging analysis paths until the training converges, and combining the multiple converged hysteresis aging analysis paths to obtain a hysteresis aging analysis channel.

[0062] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: based on the pressure gauge measurement test data in the historical time, collecting the total elastic modulus change of the elastic element in the pressure gauge, marking it as a sample aging analysis result set, and testing the mechanical error ratio and hysteresis error ratio of the pressure gauge under different sample aging analysis results, marking them as a sample mechanical error coefficient set and a sample hysteresis error coefficient set; using the sample aging analysis result set as input training data, and using the sample mechanical error coefficient set and the sample hysteresis error coefficient set as output training data, respectively, to perform supervised training to obtain a mechanical error analysis branch and a hysteresis error analysis branch; inputting the aging analysis results into the mechanical error analysis branch and the hysteresis error analysis branch, respectively, and outputting the obtained mechanical error coefficient and hysteresis error coefficient.

[0063] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: collecting the pressure measurement result of the pressure gauge currently used for pressure measurement; multiplying the mechanical error coefficient and the hysteresis error coefficient by the pressure measurement result respectively to calculate the mechanical error value and the hysteresis error value.

[0064] Furthermore, the error analysis driven pressure gauge measurement and calibration system also includes: obtaining the real-time loading and unloading record of the pressure gauge's most recent loading and unloading, and calculating the real-time range difference based on the real-time loading range and the real-time unloading range in the real-time loading and unloading record; calculating the ratio of the real-time range difference to the average of the multiple range differences, and using the ratio to perform a product compensation calculation on the lag error value to obtain a compensated lag error value; using the compensated lag error value and the mechanical error value to perform a measurement calibration calculation on the pressure measurement result to obtain a calibrated pressure measurement result.

[0065] For example 3, please refer to Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: obtaining the cumulative use time of the pressure gauge, and collecting multiple loading and unloading records of the pressure gauge in use during the historical time, wherein each loading and unloading record includes a loading range and an unloading range; performing an aging analysis of the elastic element in the pressure gauge according to the cumulative use time and the multiple loading and unloading records, and obtaining an aging analysis result; and obtaining an aging analysis result according to the aging Analyze the results, perform mechanical error analysis on the pressure gauge currently measuring pressure to obtain the mechanical error coefficient, and perform hysteresis error analysis on the pressure gauge currently measuring pressure to obtain the hysteresis error coefficient; collect the pressure measurement result currently measured by the pressure gauge, perform error correction using the mechanical error coefficient and the hysteresis error coefficient respectively to obtain the mechanical error value and the hysteresis error value, and compensate the hysteresis error value according to the real-time loading and unloading records of the pressure gauge to obtain the compensated hysteresis error value, perform measurement calibration calculation on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

[0066] Example 4, please refer to Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by the processor, the following steps are implemented: obtaining the cumulative use time of the pressure gauge, and collecting multiple loading and unloading records of the pressure gauge in use during the historical time, wherein each loading and unloading record includes a loading range and an unloading range; performing an aging analysis of the elastic element in the pressure gauge according to the cumulative use time and the multiple loading and unloading records, and obtaining an aging analysis result; performing a mechanical error analysis of the pressure measurement currently performed by the pressure gauge according to the aging analysis result, and obtaining a mechanical error coefficient, and performing a hysteresis error analysis of the pressure measurement currently performed by the pressure gauge, and obtaining a hysteresis error coefficient; collecting the pressure measurement result currently measured by the pressure gauge, respectively using the mechanical error coefficient and the hysteresis error coefficient for error correction, and obtaining a mechanical error value and a hysteresis error value, and compensating the hysteresis error value according to the real-time loading and unloading records of the pressure gauge to obtain a compensated hysteresis error value, and performing a measurement calibration calculation on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

[0067] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0068] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. Error analysis driven pressure gauge calibration method, characterized in that: Methods include: Obtaining the accumulated usage time of the pressure gauge, and collecting a plurality of loading and unloading records of the pressure gauge in use during the historical time, wherein each loading and unloading record includes a loading range and an unloading range; Performing an aging analysis on the elastic element in the pressure gauge according to the accumulated usage time and the multiple loading and unloading records to obtain an aging analysis result; According to the aging analysis result, a mechanical error analysis of the pressure gauge currently measuring pressure is performed to obtain a mechanical error coefficient, and a hysteresis error analysis of the pressure gauge currently measuring pressure is performed to obtain a hysteresis error coefficient; The pressure measurement result currently measured by the pressure gauge is collected, and the mechanical error coefficient and the hysteresis error coefficient are respectively used to perform error correction to obtain a mechanical error value and a hysteresis error value, and the hysteresis error value is compensated according to the real-time loading and unloading record of the pressure gauge to obtain a compensated hysteresis error value, and the pressure measurement result is subjected to measurement calibration calculation in combination with the mechanical error value to obtain a calibrated pressure measurement result.

2. The error analysis driven pressure gauge calibration method according to claim 1, characterized in that: Obtain the accumulated usage time of the pressure gauge, and collect multiple loading and unloading records of the pressure gauge in use during the historical time, including: Get the cumulative usage time of the pressure gauge; The loading range and unloading range of the pressure gauge are collected each time the pressure measurement is loaded and unloaded in the historical time, and a plurality of loading and unloading records are obtained.

3. The error analysis driven pressure gauge calibration method according to claim 1, characterized in that: According to the accumulated use time and the multiple loading and unloading records, an aging analysis of the elastic element in the pressure gauge is performed to obtain an aging analysis result, including: Input the accumulated usage time into a time aging classifier, and classify to obtain a time aging parameter, wherein the time aging parameter includes a change in elastic modulus of the elastic element, and the time aging classifier is obtained by collecting a set of accumulated usage time of samples and a set of sample time aging parameters, and constructing a mapping relationship; Calculating and obtaining a plurality of range differences according to the loading ranges and unloading ranges in the plurality of loading and unloading records; training a hysteresis aging analysis channel including a plurality of hysteresis aging analysis paths; Calculating a ratio of each range difference to the maximum range difference, multiplying the ratio by the number of the plurality of hysteresis aging analysis paths, and rounding the ratio to obtain a plurality of hysteresis aging analysis numbers; Inputting the multiple range differences into the hysteresis aging analysis paths of the multiple hysteresis aging analysis quantities respectively, outputting multiple hysteresis aging parameter sets, and calculating the average to obtain multiple hysteresis aging parameters, wherein each hysteresis aging parameter includes a change in elastic modulus of the elastic element; The time aging parameter and a plurality of hysteresis aging parameters are added together to obtain a total aging parameter as an aging analysis result.

4. The error analysis driven pressure gauge calibration method according to claim 3, characterized in that: Training of a Hysteresis Aging Analysis Channel that includes multiple Hysteresis Aging Analysis paths, including: According to the aging test data of the elastic element in the pressure gauge, a sample range difference set is collected, and the change in elastic modulus of the elastic element after running with different sample range differences is collected, which is marked as a sample hysteresis aging parameter set; Dividing the sample range difference set and the sample hysteresis aging parameter set in equal proportion to obtain multiple groups of hysteresis aging analysis training data; The multiple groups of hysteresis aging analysis training data are respectively used to train the multiple hysteresis aging analysis paths until the training converges, and the converged multiple hysteresis aging analysis paths are combined to obtain the hysteresis aging analysis channel.

5. The error analysis driven pressure gauge calibration method according to claim 1, characterized in that: According to the aging analysis result, a mechanical error analysis of the pressure gauge currently measuring pressure is performed to obtain a mechanical error coefficient, and a hysteresis error analysis of the pressure gauge currently measuring pressure is performed to obtain a hysteresis error coefficient, including: According to the pressure gauge measurement test data in the historical time, the total elastic modulus change of the elastic element in the pressure gauge is collected and marked as a sample aging analysis result set, and the mechanical error ratio and hysteresis error ratio of the pressure gauge under different sample aging analysis results are tested and marked as a sample mechanical error coefficient set and a sample hysteresis error coefficient set; The sample aging analysis result set is used as input training data, and the sample mechanical error coefficient set and the sample hysteresis error coefficient set are used as output training data respectively, and supervised training is performed to obtain a mechanical error analysis branch and a hysteresis error analysis branch; The aging analysis results are input into the mechanical error analysis branch and the hysteresis error analysis branch respectively, and a mechanical error coefficient and a hysteresis error coefficient are obtained as output.

6. The error analysis driven pressure gauge calibration method according to claim 1, characterized in that: The pressure measurement result currently measured by the pressure gauge is collected, and the mechanical error coefficient and the hysteresis error coefficient are respectively used for error correction to obtain the mechanical error value and the hysteresis error value, including: Collecting the pressure measurement result currently measured by the pressure gauge; The mechanical error coefficient and the hysteresis error coefficient are respectively multiplied by the pressure measurement result to calculate and obtain the mechanical error value and the hysteresis error value.

7. The error analysis driven pressure gauge calibration method according to claim 3, characterized in that: According to the real-time loading and unloading records of the pressure gauge, the hysteresis error value is compensated to obtain the compensated hysteresis error value, and the pressure measurement result is calibrated and calculated in combination with the mechanical error value to obtain the calibrated pressure measurement result, including: Obtaining the most recent real-time loading and unloading record of the pressure gauge, and calculating the real-time range difference according to the real-time loading range and the real-time unloading range in the real-time loading and unloading record; Calculating a ratio of the real-time range difference to an average of the plurality of range differences, and using the ratio to perform product compensation calculation on a hysteresis error value to obtain a compensated hysteresis error value; The compensation hysteresis error value and the mechanical error value are used to perform a measurement calibration calculation on the pressure measurement result to obtain a calibrated pressure measurement result.

8. Error analysis driven pressure gauge calibration system, characterized in that: The steps for implementing the error analysis driven pressure gauge calibration method according to any one of claims 1 to 7 include: A loading and unloading record collection module is used to obtain the cumulative use time of the pressure gauge and collect multiple loading and unloading records of the pressure gauge in use during the historical time, wherein each loading and unloading record includes a loading range and an unloading range; An elastic element aging analysis module, used to perform aging analysis on the elastic element in the pressure gauge according to the accumulated usage time and a plurality of loading and unloading records, and obtain an aging analysis result; A measurement error analysis module, used to perform a mechanical error analysis of the pressure gauge currently measuring pressure according to the aging analysis result to obtain a mechanical error coefficient, and to perform a hysteresis error analysis of the pressure gauge currently measuring pressure to obtain a hysteresis error coefficient; The metrology calibration calculation module is used to collect the pressure measurement results currently measured by the pressure gauge, respectively use the mechanical error coefficient and the hysteresis error coefficient to perform error correction, obtain the mechanical error value and the hysteresis error value, and compensate the hysteresis error value according to the real-time loading and unloading records of the pressure gauge to obtain the compensated hysteresis error value, and perform metrology calibration calculation on the pressure measurement result in combination with the mechanical error value to obtain a calibrated pressure measurement result.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of the error analysis driven pressure gauge measurement and calibration method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements the steps of the error analysis driven pressure gauge calibration method as described in any one of claims 1 to 7.

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