A pressure transmitter debugging and checking method

Abstract

The present application relates to the technical field of industrial automation instrument and intelligent sensor detection, in particular to a pressure transmitter debugging and checking method; comprising parameter acquisition, thermal hysteresis evolution, error calculation and pressure reconstruction module; the method collects temperature and pressure data in non-steady state environment; the core is to calculate the internal temperature by using the filling liquid thermal hysteresis model, combine the transient pseudo pressure with the viscoelastic hysteresis error model, calculate the error caused by thermal imbalance and material hysteresis, and reconstruct the real steady state pressure; the present application realizes the change from static waiting to dynamic prediction, eliminates the thermal balance waiting time through prediction, and greatly improves the production efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation instruments and intelligent sensor detection, in particular to a pressure transmitter debugging and verification method. BACKGROUND

[0002] In the production and measurement testing field of pressure transmitters, in order to ensure the measurement accuracy of the sensor, the equipment must be strictly debugged and verified and error corrected; the transmitter internally relies on the filling liquid and the metal diaphragm for pressure transmission, and its physical properties are extremely sensitive to environmental temperature changes and pressure loading rates; the existing verification scheme generally adopts a static verification mode based on thermal equilibrium, that is, the equipment is strictly required to be placed in a constant temperature environment until the internal temperature field is completely stable, and then data collection and calibration can be performed; although this scheme can obtain reference data in the steady state, it highly depends on the long physical standing process, resulting in a long verification cycle of a single device, which seriously restricts the production efficiency; in addition, this static mode cannot effectively identify the dynamic error characteristics in the non-steady state environment, cannot eliminate the transient pseudo pressure caused by the heat conduction lag of the filling liquid, and cannot compensate for the mechanical hysteresis error caused by the viscoelasticity of the metal material, resulting in the decline of the measurement performance of the equipment under actual fast temperature or pressure changes;

[0003] Therefore, how to accurately decouple and compensate the thermal error and mechanical hysteresis error in a non-steady state environment in real time, thereby greatly shortening the verification time and improving the measurement accuracy of the equipment under dynamic working conditions, has become a technical problem to be solved. SUMMARY

[0004] To solve the above technical problems, the present application provides a pressure transmitter debugging and verification method, in particular, the technical scheme of the present application comprises:

[0005] Step one, in a non-steady state verification environment, through the temperature measuring element integrated in the metal shell of the sensitive component of the pressure transmitter and the output port of the pressure transmitter, the physical field parameters are collected in real time, the physical field parameters include the real-time temperature of the shell and the observed pressure value at the current time, and a sampling time variable is defined;

[0006] Step two, a filling liquid thermal lag evolution model is constructed, and the average estimated temperature of the internal filling liquid is calculated by discrete iteration solution using the real-time temperature of the shell;

[0007] Step three, a transient pseudo pressure model and a viscoelastic hysteresis error model are established, the real-time temperature of the shell, the average estimated temperature and the observed pressure value are input into the transient pseudo pressure model and the viscoelastic hysteresis error model, and the transient pseudo pressure value caused by thermal imbalance and the dynamic hysteresis pressure error value caused by material viscoelasticity are respectively calculated;

[0008] Step four, based on the principle of signal superposition to construct the real pressure reconstruction model, using the transient pseudo pressure value and dynamic hysteresis pressure error value to deduct the observed pressure value of multi-physical field error, generate the predicted real steady-state pressure value;

[0009] Step five, get the standard reference true value given by high precision pressure controller, compare the real steady-state pressure value with the standard reference true value, construct the target loss function, and solve the model parameters by nonlinear least squares optimization algorithm, until the real steady-state pressure value converges, output the qualified result.

[0010] Preferably, step one comprises:

[0011] S11, start the non-steady state calibration environment of rapid temperature or pressure change, ensure that the pressure transmitter is in dynamic response state;

[0012] S12, use temperature measuring element to collect real-time temperature of shell, use data interface of pressure transmitter to collect observed pressure value directly output, and record corresponding sampling time variable;

[0013] S13, input the collected real-time temperature of shell and observed pressure value as time series data into the subsequent processing model.

[0014] Preferably, step two comprises:

[0015] S21, based on the lumped parameter method of heat transfer, determine that the rate of heat transfer from the shell to the internal filling liquid is proportional to the current temperature difference;

[0016] S22, introduce the thermal response lag coefficient, which is determined by the specific heat capacity, density and cavity geometry of the filling liquid;

[0017] S23, build filling liquid thermal lag evolution calculation logic: the change rate of internal filling liquid temperature with time is equal to the product of thermal response lag coefficient and temperature difference, wherein the temperature difference is the difference between shell real-time temperature and average estimated temperature;

[0018] S24, discretize the filling liquid thermal lag evolution calculation logic, and use the measurable shell real-time temperature to calculate the average estimated temperature which cannot be directly measured.

[0019] Preferably, the process of establishing transient pseudo pressure model in step three comprises:

[0020] S31, based on the thermodynamic state equation of filling liquid, define static thermal expansion difference pressure coefficient and dynamic thermal shock factor;

[0021] S32, calculate the shell temperature change rate, which is the derivative of shell real-time temperature with respect to time;

[0022] S33, combine the shell real-time temperature and the average estimated temperature to construct a transient pseudo-pressure calculation logic: the transient pseudo-pressure value is equal to the sum of the first component and the second component;

[0023] The first component represents the volume mismatch pressure caused by temperature difference, and the value is equal to the static thermal expansion difference-induced pressure coefficient multiplied by the difference between the shell real-time temperature and the average estimated temperature.

[0024] The second component represents the fluid viscous damping effect and thermal shock stress, and the value is equal to the dynamic thermal shock factor multiplied by the shell temperature change rate.

[0025] Preferably, the process of establishing the viscoelastic hysteresis error model in step three includes:

[0026] S34, define the pressure relaxation time constant based on the modified Kelvin-Voigt viscoelastic rheological mechanics model, and the pressure relaxation time constant corresponds to the ratio of the material viscosity coefficient to the elastic modulus;

[0027] S35, extract the first derivative feature of the observed pressure value, and calculate the real-time change rate of the observed pressure;

[0028] S36, construct a viscoelastic hysteresis error calculation logic: the dynamic hysteresis pressure error value is equal to the pressure relaxation time constant multiplied by the real-time change rate of the observed pressure.

[0029] Preferably, step four includes:

[0030] S41, take the observed pressure value as the basic signal;

[0031] S42, take the transient pseudo-pressure value and the dynamic hysteresis pressure error value as error components;

[0032] S43, construct a real pressure reconstruction calculation logic: the predicted real steady-state pressure value is equal to the observed pressure value minus the transient pseudo-pressure value, and then minus the dynamic hysteresis pressure error value;

[0033] S44, substitute the parameters in step three, and expand to obtain a comprehensive prediction logic, so as to predict the steady-state true value in advance when the physical system has not reached equilibrium.

[0034] Preferably, step five includes:

[0035] S51, define the parameter vector to be optimized, and the parameter vector is composed of the thermal response hysteresis coefficient, the static thermal expansion difference-induced pressure coefficient, the dynamic thermal shock factor, and the pressure relaxation time constant, and set the theoretical initial parameter vector based on the physical priori knowledge;

[0036] S52, obtain the standard reference true value corresponding to each sampling time;

[0037] S53, based on the nonlinear least squares method, an optimization objective function is constructed, and the value of the optimization objective function is composed of two parts: the first part is the sum of squares of the differences between the estimated values calculated based on the current parameter vector and the standard reference true values at all sampling times, and the dimensionless normalization processing is performed; the second part is a regularization term, and the value is equal to the regularization coefficient multiplied by the weighted quadratic form of the difference between the current parameter vector and the theoretical initial parameter vector.

[0038] Preferably, step five further comprises:

[0039] S54, the Levenberg-Marquardt algorithm is used to iteratively solve the optimization objective function;

[0040] S55, the parameter vector is updated in each iteration, and the current optimization objective function value is calculated;

[0041] S56, a preset convergence threshold is determined, and whether the optimization objective function value is less than the convergence threshold is determined, if less than, it is determined that the parameter identification is completed, and the final individualized numerical model parameter is locked.

[0042] Preferably, step five further comprises evaluation and output of the verification result:

[0043] S57, the residual error of the real steady-state pressure value at the final time and the standard reference true value is calculated;

[0044] S58, when the residual error is less than the preset qualified precision limit value, it is determined that the verification result label is qualified, and the optimized parameter vector is written into the embedded microprocessor of the pressure transmitter;

[0045] S59, when the residual error is greater than or equal to the qualified precision limit value, it is determined that the verification result label is unqualified, and a re-verification instruction or a hardware fault warning signal is generated.

[0046] Preferably, after step S58, the pressure transmitter uses the written parameter vector and the real pressure reconstruction model in subsequent operation, and only according to the real-time acquisition of the shell real-time temperature and the observed pressure value, the corrected high-precision pressure data is output online in real time.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] 1. The method discards the traditional check must wait for the device to reach the static mode of thermal equilibrium, proposes a debugging and checking method in the non-steady state environment; by constructing the real pressure reconstruction model, the steady state true value can be predicted in advance by using the algorithm in the dynamic process when the physical system has not reached the thermal equilibrium or mechanical equilibrium; this prediction and checking method does not need long time physical static waiting, solves the problem of long time checking of single device caused by relying on thermal equilibrium, greatly improves the production and manufacturing efficiency of pressure transmitter;

[0049] 2. The method overcomes the difficulty that the internal fluid temperature cannot be directly measured by constructing the filling liquid thermal hysteresis evolution model and the transient pseudo pressure model; the hysteresis internal fluid temperature is calculated by using the measurable shell temperature, and the thermal error is further subdivided into the volume mismatch pressure component caused by temperature difference and the fluid viscous damping and thermal shock component caused by temperature change rate; the multi-physical field decoupling mechanism can effectively identify and eliminate the transient pseudo pressure caused by the filling liquid thermal conduction hysteresis, and significantly improves the measurement accuracy of the device under the condition of the dramatic fluctuation of the environment temperature;

[0050] 3. According to the material characteristics of the metal isolation diaphragm, the viscoelastic hysteresis error model is introduced; by extracting the first derivative characteristics of the observed pressure, the transient influence of the dynamic loading rate on the pressure reading is quantified, so as to accurately calculate the dynamic hysteresis pressure error value caused by the material viscoelasticity; this mechanism makes up for the defect that the existing static checking mode cannot identify the dynamic mechanical error, effectively solves the problem of measurement performance decline caused by mechanical hysteresis under the condition of rapid pressure change, and ensures the high fidelity of the transmitter under dynamic working condition;

[0051] 4. The method uses global optimization algorithm, reversely solves the model parameters based on the standard reference true value, completes the mapping from the general theoretical model to the individual numerical model for each specific transmitter; by solidifying the identified high-precision physical parameters into the embedded microprocessor of the pressure transmitter, the self-correction ability of the device is given; in the subsequent operation, the transmitter does not need to rely on external calibration equipment, and can online real-time offset the environmental interference, greatly improves the intelligent level and long-term running stability of the product. BRIEF DESCRIPTION OF DRAWINGS

[0052] The application will be further explained in conjunction with the drawings and examples:

[0053] Figure 1 is the flow chart of the method of the application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the application more clear and obvious, the application will be further explained in detail in conjunction with specific examples.

[0055] Embodiment 1:

[0056] Please refer to Figure 1 A pressure transmitter debugging and verification method, the specific steps include:

[0057] Step one, in the non-steady state verification environment, through the temperature measuring element integrated in the sensitive component metal shell of the pressure transmitter and the output port of the pressure transmitter, real-time collection of physical field parameters, including the real-time temperature of the shell and the observed pressure value at the current time, and defining the sampling time variable;

[0058] Step two, build a filled liquid thermal hysteresis evolution model, use the real-time temperature of the shell as the boundary condition, and solve by discrete iteration to calculate the average estimated temperature of the internal filled liquid;

[0059] Step three, establish a transient pseudo-pressure model and a viscoelastic hysteresis error model, input the real-time temperature of the shell, the average estimated temperature and the observed pressure value into the transient pseudo-pressure model and the viscoelastic hysteresis error model, respectively, to calculate the transient pseudo-pressure value caused by thermal imbalance and the dynamic hysteresis pressure error value caused by material viscoelasticity;

[0060] Step four, based on the signal superposition principle, build a real pressure reconstruction model, use the transient pseudo-pressure value and the dynamic hysteresis pressure error value to deduct the observed pressure value from the multi-physical field error, and generate the predicted real steady-state pressure value;

[0061] Step five, get the standard reference true value given by the high-precision pressure controller, compare the real steady-state pressure value with the standard reference true value, build a target loss function, and solve the model parameters by a nonlinear least squares optimization algorithm, until the real steady-state pressure value converges, and output the verification qualified result.

[0062] The embodiment provides a pressure transmitter debugging and verification method, which aims to solve the technical problems of long time consumption, low efficiency and difficulty in eliminating dynamic environment error of the existing static verification method; the method realizes high-precision verification of the pressure transmitter in a non-steady state environment by building a multi-physical field coupling model;

[0063] Specifically, the method of the embodiment includes the following core steps:

[0064] Step one, in the non-steady state verification environment, use the temperature measuring element integrated in the metal shell of the pressure transmitter to collect the real-time temperature of the shell, use the output port of the pressure transmitter to collect the observed pressure value at the current time, and define the sampling time variable;

[0065] Step two, build a filled liquid thermal hysteresis evolution model, use the real-time temperature of the shell as the boundary condition, and solve by discrete iteration to calculate the average estimated temperature of the internal filled liquid;

[0066] Step three, establish a transient pseudo-pressure model and a viscoelastic hysteresis error model, take the real-time temperature of the shell, the average estimated temperature and the observed pressure value as input, respectively solve the transient pseudo-pressure value caused by thermal imbalance and the dynamic hysteresis pressure error value caused by material viscoelasticity;

[0067] Step four, based on the principle of signal superposition, construct a real pressure reconstruction model, use the transient pseudo-pressure value and the dynamic hysteresis pressure error value to deduct the observed pressure value from the multi-physical field error, and generate the predicted real steady-state pressure value;

[0068] Step five, obtain the standard reference true value given by the high-precision pressure controller, compare the real steady-state pressure value with the standard reference true value, construct a target loss function, and solve the model parameters by a nonlinear least squares optimization algorithm, until the real steady-state pressure value converges, and output the qualified result;

[0069] The embodiment realizes accurate separation of thermal error and mechanical hysteresis error from observed data by establishing a thermal-fluid-solid multi-physical field coupling model. Compared with the traditional static calibration method of waiting for system thermal equilibrium, the method allows calibration in a non-steady-state environment, predicts the steady-state true value through algorithm compensation, thereby greatly shortening the calibration cycle of a single device and significantly improving production efficiency.

[0070] Embodiment 2:

[0071] Step one includes:

[0072] S11, start a non-steady-state calibration environment with rapid temperature or pressure change, to ensure that the pressure transmitter is in a dynamic response state;

[0073] S12, use the temperature measuring element to collect the real-time temperature of the shell, use the data interface of the pressure transmitter to collect the observed pressure value directly output, and record the corresponding sampling time variable;

[0074] S13, input the collected real-time temperature of the shell and observed pressure value as time series data into the subsequent processing model.

[0075] This embodiment is a specific embodiment of step one in embodiment 1;

[0076] In step one, the non-steady-state calibration environment refers to an environment in which the pressure or temperature changes at a preset high rate, such as a rapid temperature changing box or a rapid pressure rising and falling platform. In this environment, the pressure transmitter is in a dynamic response state, and its output signal contains significant dynamic error components;

[0077] The data collection process is as follows:

[0078] S11, start a non-steady state calibration environment with rapid temperature or pressure change, ensure that the pressure transmitter is in a dynamic response state; wherein the calibration environment is equipped with a high-frequency dynamic standard pressure sensor with a dynamic response frequency much higher than the measured pressure transmitter, to ensure that the standard reference true value obtained in the non-steady state environment has no lag;

[0079] S12, collect the real-time temperature of the shell using a temperature measuring element , at the same time, collect the observed pressure value directly output by the pressure transmitter using the data interface of the pressure transmitter , and record the corresponding sampling time variable ;

[0080] S13, input the collected real-time temperature of the shell and observed pressure value as time series data into the subsequent processing model to form a discretized data set ;

[0081] By collecting data in a dynamic response state, transient error characteristics that the pressure transmitter may encounter in actual working conditions can be captured, providing rich excitation signals for subsequent model parameter identification and ensuring the applicability of the calibration result in dynamic working conditions.

[0082] Embodiment 3:

[0083] Step two includes:

[0084] S21, based on the lumped parameter method of heat transfer, determine that the rate of heat transfer from the shell to the internal filling liquid is proportional to the current temperature difference;

[0085] S22, introduce a thermal response lag coefficient, which is determined by the specific heat capacity, density and cavity geometry of the filling liquid;

[0086] S23, build a filling liquid thermal lag evolution calculation logic: the rate of change of the internal filling liquid temperature with time is equal to the product of the thermal response lag coefficient and the temperature difference, wherein the temperature difference is the difference between the real-time temperature of the shell and the average estimated temperature;

[0087] S24, discretize the filling liquid thermal lag evolution calculation logic, and use the measurable real-time temperature of the shell to calculate the average estimated temperature which cannot be directly measured.

[0088] This embodiment is a specific implementation of step two in embodiment 1, and focuses on solving the problem that the internal fluid temperature cannot be directly measured;

[0089] In this embodiment, the filling liquid thermal lag evolution model is built based on the lumped parameter method of heat transfer; the core is to use the measurable shell temperature to calculate the lagged internal fluid temperature;

[0090] S21, based on the heat transfer lumped parameter method, determine the rate of heat transfer from the shell to the internal filling liquid is proportional to the current temperature difference;

[0091] S22, introduce the thermal response lag coefficient ; thermal response lag coefficient refers to the physical quantity representing the rate of the internal system of the sensor to reach thermal equilibrium, the unit is ; in this embodiment, its value is determined by the specific heat capacity, density of the filling liquid and the geometric structure of the sensor cavity, which reflects the size of thermal inertia;

[0092] S23, build filling liquid thermal hysteresis evolution calculation logic, its mathematical expression is as follows:

[0093] ;

[0094] Among them:

[0095] : the average estimated temperature of the internal filling liquid, the unit , its value is obtained by model iteration calculation;

[0096] : real-time temperature of the shell, the unit , its value is obtained from the real-time acquisition of the temperature measuring element;

[0097] : thermal response lag coefficient, the unit , its value is preset in the initial stage, and is optimized by algorithm subsequently;

[0098] : sampling time variable, the unit ;

[0099] S24, discretize the above differential equation, specifically using explicit Euler difference method, set the sampling period as , discretize the continuous time variable to sequence , the iteration formula is as follows:

[0100] ;

[0101] Among them, is the average estimated temperature of the internal filling liquid at the current time, is the estimated value at the last time, the initial time is set to be equal to the ambient temperature; is the real-time temperature of the shell at time;

[0102] use the measurable real-time temperature of the shell at the current time and the fluid temperature at the previous time, to estimate the average estimated temperature at the current time which is not directly measurable ;

[0103] This step establishes the dynamic correlation between the shell temperature and the internal fluid temperature; due to the fact that the heat capacity of the insulating silicone oil inside the sensor is much larger than the metal diaphragm and the thermal conductivity is poor, the fluid temperature lags significantly behind the shell temperature; this model accurately quantifies this hysteresis effect, providing an accurate temperature field benchmark for subsequent calculations of thermal expansion errors caused by temperature differences.

[0104] Example 4:

[0105] The process of establishing a transient pseudo-pressure model in step three includes:

[0106] S31, based on the filling liquid thermodynamic state equation, define the static thermal expansion difference pressure coefficient and the dynamic thermal shock factor;

[0107] S32, calculate the shell temperature change rate, which is the derivative of the real-time shell temperature with respect to time;

[0108] S33, combine the real-time shell temperature and the average estimated temperature to construct the transient pseudo-pressure calculation logic: the transient pseudo-pressure value is equal to the sum of the first component and the second component;

[0109] Wherein, the first component represents the volume mismatch pressure caused by temperature difference, the value is equal to the static thermal expansion difference pressure coefficient multiplied by the difference between the real-time shell temperature and the average estimated temperature;

[0110] The second component represents the fluid viscous damping effect and thermal shock stress, the value is equal to the dynamic thermal shock factor multiplied by the shell temperature change rate.

[0111] This embodiment is a specific embodiment of the process of establishing a transient pseudo-pressure model in step three in example 1;

[0112] S31, based on the fluid thermodynamic state equation, define the following key parameters:

[0113] Static thermal expansion difference pressure coefficient : refers to the physical quantity representing the pressure drift caused by the difference in expansion coefficient between the shell and the liquid under unit temperature difference, unit ;

[0114] Dynamic thermal shock factor : refers to the physical quantity representing the influence of fluid viscous damping effect and thermal shock stress on pressure reading at the moment of rapid temperature change, unit ;

[0115] S32, calculate the shell temperature change rate , which is the derivative of the real-time temperature of the shell with respect to time, in order to suppress the interference of high-frequency noise of the sensor on the differential calculation, a five-point moving average filter is used to smooth the original temperature sequence , and then the central difference method is used to calculate the temperature change rate:

[0116] ;

[0117] , wherein is the smoothed shell temperature at time t, is the smoothed shell temperature at time t, is the smoothed shell temperature at time t;

[0118] S33, combining the real-time temperature of the shell and the average estimated temperature , the transient pseudo-pressure calculation logic is constructed:

[0119] ;

[0120] , wherein:

[0121] : the transient pseudo-pressure value caused by thermal imbalance, unit ;

[0122] The first component : represents the volume mismatch pressure caused by temperature difference; when , the volume change of the metal cavity does not match the volume change of the silicone oil, resulting in additional internal pressure;

[0123] The second component : represents the viscous damping effect and thermal shock stress of the filling liquid;

[0124] This model successfully decouples the causes of dynamic thermal error; it not only considers the traditional static temperature drift, but also creatively introduces a dynamic thermal shock component related to the temperature change rate, so that it can accurately restore the real physical field disturbance pressure when the environmental temperature fluctuates dramatically.

[0125] Example 5:

[0126] The process of establishing the viscoelastic hysteresis error model in step three includes:

[0127] S34, based on the modified Kelvin-Voigt viscoelastic rheological mechanics model, the pressure relaxation time constant is defined, which corresponds to the ratio of the material viscosity coefficient to the elastic modulus;

[0128] ​​​S35, extract the first derivative feature of the observed pressure value, calculate the real-time change rate of the observed pressure;

[0129] S36, construct a viscoelastic hysteresis error calculation logic: the dynamic hysteresis pressure error value is equal to the pressure relaxation time constant multiplied by the real-time change rate of the observed pressure.

[0130] This embodiment is a specific embodiment of the process of establishing a viscoelastic hysteresis error model in step three of embodiment 1;

[0131] S34, based on the modified Kelvin-Voigt viscoelastic rheological mechanics model, define the pressure relaxation time constant ; The pressure relaxation time constant refers to the characteristic time required for the internal lattice of the sensor diaphragm to recover from the transient state to the steady state after being subjected to a step pressure impact, with the unit ; Physically, it corresponds to the ratio of the material viscosity coefficient to the elastic modulus;

[0132] S35, extract the first derivative feature of the observed pressure value, calculate the real-time change rate of the observed pressure ;

[0133] S36, construct a viscoelastic hysteresis error calculation logic, whose mathematical expression is as follows:

[0134] ;

[0135] Wherein:

[0136] : Dynamic hysteresis pressure error value caused by material viscoelasticity, unit ;

[0137] : Real-time change rate of the observed pressure, unit , derived from the difference calculation of the collected data;

[0138] In the high-frequency verification of rapid pressure rise and fall, the metal isolation diaphragm shows stress relaxation characteristics, resulting in return error; this model introduces the first derivative term of the pressure change rate, quantifies the transient influence of the dynamic loading rate on the pressure reading, and effectively eliminates the inherent mechanical hysteresis error of the sensor.

[0139] Embodiment 6:

[0140] Step four includes:

[0141] S41, take the observed pressure value as the basic signal;

[0142] S42, take the transient pseudo-pressure value and the dynamic hysteresis pressure error value as error components;

[0143] S43, construct real pressure reconstruction calculation logic: the predicted real steady-state pressure value is equal to the observed pressure value minus the transient pseudo-pressure value, minus the dynamic hysteresis pressure error value;

[0144] S44, substitute the parameters in step three, expand to get the comprehensive prediction logic, so as to predict the steady-state true value in advance when the physical system does not reach equilibrium.

[0145] This embodiment is a specific implementation of step four in embodiment 1, which realizes the reconstruction of real pressure;

[0146] S41-S42, substitute the observed pressure value As a basic signal, the calculated transient pseudo-pressure value And the dynamic hysteresis pressure error value As an error component;

[0147] S43, construct real pressure reconstruction calculation logic:

[0148] ;

[0149] S44, substitute the parameters in step three, expand to get the comprehensive prediction logic:

[0150] ;

[0151] Wherein:

[0152] : predicted real steady-state pressure value, unit ;

[0153] This step uses the signal superposition and error compensation theory, and when the physical system has not reached thermal equilibrium or mechanical equilibrium, it eliminates thermal error and mechanical hysteresis error through mathematical means, predicts the steady-state true value of the system in advance, and realizes the efficient process of prediction and verification.

[0154] Embodiment 7:

[0155] Step five includes:

[0156] S51, define the parameter vector to be optimized, the parameter vector is composed of thermal response hysteresis coefficient, static thermal expansion difference pressure coefficient, dynamic thermal impact factor and pressure relaxation time constant, and set the theoretical initial parameter vector based on physical prior knowledge;

[0157] S52, obtain the standard reference true value corresponding to each sampling time;

[0158] S53, based on the nonlinear least squares method, construct the optimization objective function, the numerical value of the optimization objective function consists of two parts: the first part is the sum of squares of the difference between the estimated value calculated based on the current parameter vector and the standard reference true value at all sampling times, and dimensionless normalization processing is carried out; the second part is the regularization term, the numerical value is equal to the regularization coefficient multiplied by the weighted quadratic form of the difference between the current parameter vector and the theoretical initial parameter vector.

[0159] This embodiment is a specific implementation of the optimization objective constructed in step five of embodiment 1;

[0160] S51, define the parameter vector to be optimized ; set the theoretical initial parameter vector based on physical prior knowledge ; specifically, the theoretical initial value According to the sensor design parameters, the initial thermal response coefficient ; wherein, is the natural convection heat transfer coefficient, is the contact area, is the specific heat capacity of silicone oil, is the mass of silicone oil; the initial thermal expansion coefficient is the product of the volume expansion coefficient of silicone oil and the volume modulus of the diaphragm;

[0161] The regularization coefficient is selected by the L-curve method, by calculating the logarithmic curve of the residual norm and the solution norm under different , select the value of corresponding to the maximum curvature point of the curve, to achieve the best balance between fitting accuracy and parameter physical constraints;

[0162] S52, obtain the standard reference true value corresponding to each sampling time ;

[0163] S53, based on the nonlinear least squares method, construct the optimization objective function :

[0164] Considering the dimension and huge value difference of each physical quantity in the parameter vector , in order to prevent large numerical value parameters from dominating the optimization process, a diagonal weighting matrix is introduced; the modified objective function is:

[0165] ;

[0166] Wherein, is the total number of sampling times, is the parameter normalization weight matrix, , the square reciprocal of the initial value is used to eliminate the dimensional influence, and ensure that each parameter has the same sensitivity weight in the optimization space; is a characteristic pressure normalization constant, used to eliminate the physical dimension of the first part of the residual term, so that it can be added with the second part of the dimensionless parameter term; is a regularization coefficient; represents the sum of squares of the difference between the model estimate and the standard reference true value at all sampling times, used to ensure the fitting accuracy;

[0167] Due to the slight difference in oil filling amount of each transmitter and the processing tolerance, the physical parameters have discreteness; the objective function not only pursues the minimization of fitting error, but also constrains the optimization parameters not to deviate from the range allowed by physical meaning through the regularization term, ensuring the physical authenticity and robustness of the model parameters.

[0168] Embodiment 8:

[0169] Step five further comprises:

[0170] S54, the Levenberg-Marquardt algorithm is used to iteratively solve the optimization objective function;

[0171] S55, the parameter vector is updated in each iteration, and the current optimization objective function value is calculated;

[0172] S56, a preset convergence threshold is set, and it is judged whether the optimization objective function value is less than the convergence threshold; if it is less than the convergence threshold, it is determined that the parameter identification is completed, and the final individualized numerical model parameter is locked.

[0173] This embodiment is a specific embodiment of the solving algorithm in step five in embodiment 1;

[0174] S54, the Levenberg-Marquardt (L-M) algorithm is used to iteratively solve the optimization objective function ; the L-M algorithm combines the advantages of gradient descent method and Gauss-Newton method, and has the characteristics of fast convergence speed and strong robustness;

[0175] S55-S56, the parameter vector is updated in each iteration , and the current optimization objective function value is calculated; a preset convergence threshold is set, and it is judged whether it is true; if it is less than the convergence threshold, it is determined that the parameter identification is completed, and the final individualized numerical model parameter is locked;

[0176] By using the efficient optimization capability of the L-M algorithm, the mapping from the general theoretical physical model to the individualized numerical model for each specific transmitter is completed, and the accurate compensation for individual differences is realized.

[0177] Embodiment 9:

[0178] Step five further comprises evaluation and output of the verification result:

[0179] S57, calculate the residual of the real steady-state pressure value at the final time and the standard reference true value;

[0180] S58, when the residual is less than the preset qualified precision limit value, determine that the verification result label is qualified, and write the optimized parameter vector into the embedded microprocessor of the pressure transmitter;

[0181] S59, when the residual is greater than or equal to the qualified precision limit value, determine that the verification result label is unqualified, and generate a re-verification instruction or a hardware failure warning signal.

[0182] After step S58, in subsequent operation, the pressure transmitter uses the written parameter vector and the real pressure reconstruction model to output corrected high-precision pressure data online in real time only according to the real-time collected shell real-time temperature and observed pressure value.

[0183] The embodiment describes the output and subsequent application of the verification result;

[0184] S57-S59, calculate the residual of the real steady-state pressure value at the final time and the standard reference true value; when the residual is less than the preset qualified precision limit value, determine that the result is qualified, and write the optimized parameter vector into the EEPROM of the embedded microprocessor of the pressure transmitter; if the residual is out of standard, generate a re-verification instruction or a hardware failure warning;

[0185] In subsequent operation, the pressure transmitter no longer relies on external calibration equipment, but uses the written parameter vector and the built-in real pressure reconstruction model to output corrected high-precision pressure data online in real time only according to the real-time collected and ;

[0186] By solidifying the identified high-precision parameters into hardware, the pressure transmitter is given self-correction capability; regardless of how the subsequent working environment changes, the transmitter can use the embedded model to offset environmental interference in real time, ensure continuous output of high-precision pressure data, and greatly improve the intelligent level and environmental adaptability of the product.

[0187] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for commissioning and verifying a pressure transmitter, characterized in that, The specific steps include: Step 1: Under unsteady-state calibration environment, physical field parameters are collected in real time through the temperature sensing element integrated in the metal housing of the pressure transmitter and the output port of the pressure transmitter. The physical field parameters include the real-time temperature of the housing and the observed pressure value at the current moment, and the sampling time variable is defined. Step 2: Construct a thermal hysteresis evolution model of the filling liquid. Using the real-time temperature of the shell, calculate the average estimated temperature of the internal filling liquid through discretization and iterative solution. Step 3: Establish the transient pseudo-pressure model and the viscoelastic hysteresis error model. Input the real-time shell temperature, average estimated temperature and observed pressure value into the transient pseudo-pressure model and the viscoelastic hysteresis error model, respectively, and calculate the transient pseudo-pressure value caused by thermal imbalance and the dynamic hysteresis pressure error value caused by material viscoelasticity. Step 4: Construct a real pressure reconstruction model based on the signal superposition principle, and use transient pseudo-pressure values ​​and dynamic hysteresis pressure error values ​​to perform multi-physics field error subtraction on the observed pressure values ​​to generate the predicted real steady-state pressure values; Step 5: Obtain the standard reference true value given by the high-precision pressure controller, compare the actual steady-state pressure value with the standard reference true value, construct the target loss function, and solve the model parameters in reverse using a nonlinear least squares optimization algorithm until the actual steady-state pressure value converges, and output the verification result. Step three involves establishing a transient pseudo-pressure model, which includes: S31. Based on the thermodynamic equation of state of the filling liquid, define the static thermal expansion difference pressure coefficient and the dynamic thermal shock factor. S32. Calculate the shell temperature change rate, which is the derivative of the real-time shell temperature with respect to time. S33. Combining the real-time shell temperature and the average estimated temperature, construct the transient pseudo-pressure calculation logic: the transient pseudo-pressure value is equal to the sum of the first component and the second component; The first component represents the volume mismatch pressure caused by the temperature difference, and its value is equal to the static thermal expansion difference pressure coefficient multiplied by the difference between the real-time shell temperature and the average estimated temperature. The second component characterizes the fluid viscosity damping effect and thermal shock stress, and its value is equal to the dynamic thermal shock factor multiplied by the shell temperature change rate.

2. The method for debugging and verifying a pressure transmitter according to claim 1, characterized in that: Step one includes: S11. Start the unsteady-state calibration environment with rapid temperature or pressure changes to ensure that the pressure transmitter is in a dynamic response state. S12. Use the temperature sensing element to collect the real-time temperature of the shell, use the data interface of the pressure transmitter to collect the directly output observed pressure value, and record the corresponding sampling time variable. S13. The collected real-time shell temperature and observed pressure values ​​are input as time series data into the subsequent processing model.

3. The method for debugging and verifying a pressure transmitter according to claim 2, characterized in that: Step two includes: S21. Based on the lumped parameter method of heat transfer, determine the proportional relationship between the rate of heat transfer from the shell to the internal filling liquid and the current temperature difference. S22. Introduce the thermal response hysteresis coefficient, which is determined by the physical properties of the filling liquid, such as its specific heat capacity, density, and cavity geometry. S23. Construct the calculation logic for the thermal hysteresis evolution of the filling liquid: The rate of change of the internal filling liquid temperature over time is equal to the product of the thermal response hysteresis coefficient and the temperature difference, where the temperature difference is the difference between the real-time temperature of the shell and the average estimated temperature. S24. Discretize the calculation logic of thermal hysteresis evolution of the filling liquid, and use the measurable real-time shell temperature to calculate the average estimated temperature that cannot be directly measured.

4. The method for debugging and verifying a pressure transmitter according to claim 3, characterized in that: Step three involves establishing the viscoelastic hysteresis error model, which includes: S34. Based on the modified Kelvin-Voyt viscoelastic rheological model, define the pressure relaxation time constant, which corresponds to the ratio of the material's viscosity coefficient to its elastic modulus. S35. Extract the first derivative features of the observed pressure values ​​and calculate the real-time rate of change of the observed pressure. S36. Construct the logic for calculating viscoelastic hysteresis error: The dynamic hysteresis pressure error value is equal to the pressure relaxation time constant multiplied by the real-time rate of change of the observed pressure.

5. The method for debugging and verifying a pressure transmitter according to claim 4, characterized in that: Step four includes: S41. Use the observed pressure value as the basic signal; S42. Take the transient pseudo-pressure value and the dynamic hysteresis pressure error value as error components; S43. Construct the calculation logic for real pressure reconstruction: The predicted real steady-state pressure value is equal to the observed pressure value minus the transient pseudo-pressure value, and then minus the dynamic hysteresis pressure error value. S44. Substitute the parameters from step three and expand to obtain the comprehensive prediction logic, thereby predicting the steady-state true value in advance when the physical system has not reached equilibrium.

6. The method for debugging and verifying a pressure transmitter according to claim 5, characterized in that: Step five includes: S51. Define the parameter vector to be optimized. The parameter vector consists of the thermal response hysteresis coefficient, the static thermal expansion difference pressure coefficient, the dynamic thermal shock factor, and the pressure relaxation time constant. Set the theoretical initial parameter vector based on physical prior knowledge. S52. Obtain the standard reference true value corresponding to each sampling time; S53. Based on the nonlinear least squares method, construct an optimization objective function. The value of the optimization objective function consists of two parts: the first part is the sum of squares of the differences between the estimated value calculated based on the current parameter vector and the true value of the standard reference at all sampling times, and is normalized without dimension; the second part is the regularization term, which is equal to the regularization coefficient multiplied by the weighted quadratic form of the difference between the current parameter vector and the theoretical initial parameter vector.

7. The method for debugging and verifying a pressure transmitter according to claim 6, characterized in that: Step five also includes: S54. The Levenberg-Marquardt algorithm is used to iteratively solve the objective function. S55. Update the parameter vector in each iteration and calculate the current value of the optimization objective function; S56. Preset convergence threshold, determine whether the value of the optimization objective function is less than the convergence threshold. If it is less than the threshold, determine that the parameter identification is complete and lock the final individualized numerical model parameters.

8. The method for debugging and verifying a pressure transmitter according to claim 7, characterized in that: Step five also includes the evaluation and output of the verification results: S57. Calculate the residual between the true steady-state pressure value and the true standard reference value at the final moment; S58. When the residual is less than the preset acceptable accuracy limit, the verification result label is determined to be qualified, and the optimized parameter vector is written into the embedded microprocessor of the pressure transmitter. S59. When the residual is greater than or equal to the acceptable accuracy limit, the verification result label is determined to be unacceptable, and a re-verification instruction or hardware fault warning signal is generated.

9. A method for debugging and verifying a pressure transmitter according to claim 8, characterized in that: After step S58, in subsequent operation, the pressure transmitter uses the written parameter vector and the real pressure reconstruction model to output corrected high-precision pressure data online in real time, based solely on the real-time collected housing temperature and observed pressure values.

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