Ammeter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling, and storage medium

Abstract

The invention discloses an electricity meter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling and a storage medium, and relates to the technical field of electric energy metering and detection, and the method comprises the steps: constructing a multi-physical coupling basis vector containing a single variable and a cross variable based on a sensing output signal; hidden variable joint modeling is carried out based on the multi-physical coupling basis vector and parasitic capacitance change, magnetic conductivity drift and shunt resistance offset, and a compensation mapping relation of electromagnetic, thermal and stress coupling is formed; generating a compensation coefficient by adopting the compensation mapping relation, and applying the compensation coefficient in an ammeter metering link to complete an error compensation process; according to the method, voltage, current and phase compensation coefficients are generated through multi-physical coupling basis vector and hidden variable modeling, dynamic description and self-adaptive correction of error sources are achieved, dynamic accurate correction of voltage, current and phase is achieved, and it is ensured that the ammeter keeps high-precision metering for a long time under the complex environment and the device drift condition.

Description

Technical Field

[0001] This invention relates to the field of electricity metering and detection technology, specifically to an adaptive error compensation method, system, device, and storage medium for electricity meters based on multi-physical coupling and latent variable modeling. Background Technology

[0002] With the expansion of power systems and the improvement of their intelligence level, electricity metering devices have transitioned from mechanical structures to electronic meters. Electronic meters, relying on high-precision sampling circuits, digital signal processing units, and data interfaces, can not only complete basic electricity measurement but also gradually undertake functions such as power analysis, data interaction, and operation monitoring. In residential electricity consumption, industrial electricity consumption, and grid dispatching, electronic meters have become core metering equipment. However, with the integration of distributed power sources, the diversification of electricity consumption behaviors, and the frequent occurrence of harmonics and inrush currents, the operating environment of electricity meters is becoming increasingly complex and dynamic, and traditional compensation methods are gradually revealing their limitations.

[0003] Existing research mainly revolves around two paths: hardware compensation and software compensation. Hardware compensation methods focus on the design of component parameters, such as improving the stability of sampling resistors, optimizing the material properties of current transformers, and introducing high-precision reference voltage sources. These methods have certain advantages in reducing single-source errors, but their design complexity and cost are high, limiting their application scope. Software compensation methods rely on algorithms to fit and correct errors. Common techniques include temperature-based function compensation, least squares-based linear regression methods, nonlinear correction based on polynomial expansion, and the neural network fitting methods that have emerged in recent years. Software compensation can improve the performance of meters in dynamic environments to some extent, but its stability and interpretability are still insufficient under long-term operation and complex power signal conditions.

[0004] With the significant increase in the number of deployed electricity meters and their continuous extension of operating time, sampling links and sensing units are gradually showing signs of aging and drift. For example, minute changes in parasitic capacitance can affect voltage measurement accuracy, shifts in permeability can alter transformer characteristics, and deviations in shunt resistance can lead to current measurement errors. These problems often exhibit implicit and cumulative characteristics, making them difficult to solve with a single compensation method. Simultaneously, temperature fluctuations, mechanical stress, and electromagnetic interference in the electricity meter's operating environment frequently interact, forming nonlinear coupling relationships. This multi-factor interaction complicates the sources of error and exacerbates the gap between the compensation model and the real environment. When facing atypical operating conditions or extreme disturbances, the model may produce unreasonable compensation outputs, making it difficult to ensure that the metering results are consistent with the fundamental laws of the power system. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing methods for compensating for electricity meter errors have problems such as the ineffective modeling of the coupling relationship of multiple physical factors, the difficulty in accurately representing the state of hidden variables, low long-term operational stability, and the lack of physical consistency constraints on the compensation results. It also addresses the problem of how to achieve adaptive error compensation based on the interaction of multiple physical quantities and the drift characteristics of hidden devices in complex power environments.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: an adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling, comprising: constructing a multi-physical coupling basis vector containing univariate and cross-variables based on sensor output signals; jointly modeling latent variables based on the multi-physical coupling basis vector and parasitic capacitance changes, permeability drift, and shunt resistance offset to form a compensation mapping relationship of electromagnetic, thermal, and stress coupling; generating compensation coefficients using the compensation mapping relationship, and applying the compensation coefficients within the electricity metering link to complete the error compensation process.

[0008] As a preferred embodiment of the adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling described in this invention, the multi-physical coupling basis vector includes temperature, stress, electromagnetic interference, and cross variables; the cross variables include the product of temperature and stress, the product of temperature and electromagnetic interference, the product of stress and electromagnetic interference, and the product of stress, temperature, and electromagnetic interference.

[0009] As a preferred embodiment of the meter adaptive error compensation method based on multi-physical coupling and latent variable modeling described in this invention, the latent variable joint modeling includes: using observations constructed based on active power residuals, reactive power residuals, and harmonic energy residuals; and using an extended Kalman filter update step to identify the state of parasitic capacitance change parameters, permeability drift parameters, and shunt resistance offset parameters.

[0010] As a preferred embodiment of the adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling described in this invention, the update step includes: iteratively adjusting the weight parameters of the explicit coupling basis using a recursive least squares method with a forgetting factor, and adjusting the parameters of the nonlinear term using a one-step fine-tuning method of lightweight networks.

[0011] As a preferred embodiment of the meter adaptive error compensation method based on multi-physics coupling and latent variable modeling described in this invention, the compensation mapping relationship includes a compensation coefficient generated by superimposing three parts: an explicit coupled linear term, a linear term based on latent variables, and a nonlinear term based on voltage, current, phase, and total harmonic distortion rate.

[0012] As a preferred embodiment of the adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling described in this invention, the compensation coefficients include: voltage compensation coefficient, current compensation coefficient, and phase compensation coefficient; the voltage compensation coefficient includes a scaling factor for correcting the voltage amplitude and an offset for correcting the DC bias of the voltage signal; the current compensation coefficient includes a scaling factor for correcting the current amplitude and an offset for correcting the amplitude-frequency response of the corresponding channel of the current signal; the phase compensation coefficient includes an angle correction for correcting the fundamental phase difference between voltage and current and an angle correction for correcting the harmonic phase difference.

[0013] As a preferred embodiment of the adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling described in this invention, the error compensation process includes a projection correction step based on energy conservation constraints; the projection correction linearizes the energy balance equation, calculates the disturbance under quadratic programming, and writes it back to the voltage compensation coefficient, current compensation coefficient, and phase compensation coefficient.

[0014] Another objective of this invention is to provide an adaptive error compensation system for electricity meters based on multi-physical coupling and latent variable modeling. This system can construct a compensation mapping relationship of electromagnetic, thermal, and stress coupling by fusing temperature, stress, and electromagnetic interference signals with parasitic capacitance change parameters, permeability drift parameters, and shunt resistance offset parameters. This solves the problem that current electricity meter compensation technology relies on modeling with only a single physical quantity and lacks effective identification of latent drift of devices.

[0015] As a preferred embodiment of the adaptive error compensation system for electricity meters based on multi-physics coupling and latent variable modeling described in this invention, the system includes: a coupling basis vector construction module, a latent variable joint modeling module, and a compensation application module. The coupling basis vector construction module is used to form a feature expression set containing univariate and cross-variables based on the output information of temperature, stress, and electromagnetic interference, providing a unified latent variable modeling input framework. The latent variable joint modeling module is used to fuse the coupling basis vectors with parasitic capacitance change parameters, permeability drift parameters, and shunt resistance offset parameters. Through observational inputs based on active power residuals, reactive power residuals, and harmonic residuals, combined with the extended Kalman filter calculation process, the latent variable state is updated in real time, forming a compensation mapping relationship of electromagnetic, thermal, and stress coupling. The compensation application module is used to generate voltage compensation coefficients, current compensation coefficients, and phase compensation coefficients based on the compensation mapping relationship. Under the constraint of energy conservation, the compensation coefficients are corrected through projection operations. The corrected compensation coefficients are then applied to the voltage channel, current channel, and phase calculation channel respectively to correct errors in the electricity metering link.

[0016] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for adaptive error compensation of electricity meters based on multi-physical coupling and latent variable modeling.

[0017] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling.

[0018] The beneficial effects of this invention are as follows: The meter adaptive error compensation method based on multi-physical coupling and latent variable modeling provided by this invention achieves a comprehensive characterization of environmental factors and cross-effects by constructing multi-physical coupling basis vectors, and achieves dynamic characterization of parasitic capacitance changes, permeability drift and shunt resistance offset by latent variable joint modeling. By applying compensation coefficients in the metering link, real-time correction of voltage, current and phase is achieved. This invention achieves better results in terms of input feature coverage, model adaptability and metering reliability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart shows the overall process of the adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As one embodiment of the present invention, an adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling is provided, comprising:

[0023] S1: Based on the sensor output signal, construct a multi-physical coupling basis vector containing single variables and cross variables.

[0024] Furthermore, the single variable 101 includes temperature 10, stress 11, and electromagnetic interference 12.

[0025] Furthermore, the multi-physics coupling basis vector 103 includes temperature 10, stress 11, electromagnetic interference 12, and cross-variable 102; the cross-variable 102 includes the product of temperature and stress, the product of temperature and electromagnetic interference, the product of stress and electromagnetic interference, and the product of stress, temperature, and electromagnetic interference.

[0026] It should be noted that the product term is completed through a digital arithmetic logic unit, for example, by setting up a corresponding multiplication instruction pipeline in the signal processor, inputting the temperature quantity 10, stress quantity 11 and electromagnetic interference quantity 12 one by one and performing combined operations. The generated coupling basis vector is stored in the form of a column vector and used as input data for subsequent joint modeling of latent variables. This ensures the integrity and consistency from the sensor output signal to the construction of multi-physical coupling features, and provides a high-dimensional, fully covered input feature space for the establishment of compensation mapping relationships.

[0027] It should also be noted that the sensor output signal 100 includes, by setting the operating baseline and symbol convention, a unified sampling period index is established by setting the sampling rate, analysis window length, and harmonic upper limit, represented as: , Harmonic index, represented as: , in, This represents the index of a discrete sampling point within a window. This indicates the total number of sampling points within the current sampling period. Indicates the harmonic index. This indicates the maximum order of the current harmonic. The effective values ​​of the voltage and current signals sampled from the meter are calculated and expressed as follows: , , in, Indicates the first Within the first time window Voltage sampling value at point, Indicates the first Within the first time window Current sampling value at the point, This represents the effective value of the voltage sampled by the meter. This represents the effective value of the current obtained from the meter sampling. Fundamental component features are extracted from the acquired effective values ​​of the voltage and current signals, and the total harmonic distortion rate of the voltage and current is calculated, expressed as: , , , , in, Indicates the current number The fundamental complex component of the voltage within a time window Indicates the current number The fundamental complex component of the current within a time window. This represents the discrete Fourier basis functions, used to project the meter's sampled signal into the frequency domain to extract the fundamental and harmonic components. This indicates the highest order in harmonic analysis, i.e., the upper limit of the harmonic order considered when calculating the total harmonic distortion rate. Indicates the first Total harmonic distortion of voltage within a time window Indicates the first Total harmonic distortion of current within a time window Indicates the current number The voltage in the first time window Second harmonic amplitude Indicates the current number The current in the first time window The amplitude of the second harmonic, through normalized harmonic content, measures the degree of distortion of voltage and current waveforms, providing support for harmonic characteristics. This is achieved by normalizing temperature, stress, and electromagnetic interference to obtain a unified-dimensional multi-physics coupling basis vector, expressed as: , , in, This represents the normalized dimensionless temperature variable. This indicates the current temperature (°C). This represents the dimensionless stress variable after normalization. This represents the dimensionless variable of electromagnetic interference after normalization. Indicates the current profit ( ), This indicates the current electromagnetic interference level (V / m). Indicates the reference value for electromagnetic interference. This represents the normalized parameter of electromagnetic interference. Indicates the first Multi-physical coupling basis vectors for windows; perform cross-variable coupling.

[0028] It should also be noted that, as a reference Figure 1 In this study, by constructing coupled basis vectors of single variable 101 and cross variable 102, the nonlinear relationship between temperature 10, stress 11 and electromagnetic interference 12 can be fully characterized, providing high-dimensional input features for latent variable modeling and error compensation.

[0029] S2: Based on the multi-physics coupling basis vector and the changes in parasitic capacitance, permeability drift, and shunt resistance offset, latent variables are jointly modeled to form a compensation mapping relationship of electromagnetic, thermal, and stress coupling.

[0030] Furthermore, the joint modeling of latent variables includes using observations constructed based on active power residuals, reactive power residuals, and harmonic energy residuals, expressed as: , in, This represents the residual vector, which is the observation input used for estimating latent variables. This represents the current reference active power (W), which, along with reactive power, serves as the benchmark for residual comparison. This indicates the current reference reactive power (W). This represents the effective value of the compensated voltage (V). This represents the effective value of the compensated current (A). This represents the dimensionless harmonic energy calibration function, used to calculate the impact of harmonics on power based on THD. express The product of these is the fundamental power reference (W). This indicates a voltage compensation coefficient of 23. This indicates a current compensation coefficient of 24. This indicates a phase compensation coefficient of 25. Indicates the current harmonic voltage, Indicates the current harmonic current, Indicates the current harmonic phase, used to characterize the harmonic energy distribution. Indicates the total harmonic distortion (THD) of the voltage. The total harmonic distortion rate of the current is represented by an extended Kalman filter update step, which identifies the status of parasitic capacitance change parameters, permeability drift parameters, and shunt resistance offset parameters.

[0031] It should be noted that the update steps include iteratively adjusting the weight parameters of the explicit coupled basis using a recursive least squares method with a forgetting factor, and adjusting the parameters of the nonlinear term using a one-step fine-tuning method of lightweight networks.

[0032] It should be noted that the compensation mapping relationship 201 consists of three parts: an explicit coupled linear term, a linear term based on hidden variables, and a nonlinear term based on voltage, current, phase, and total harmonic distortion rate. These three parts are superimposed to generate the compensation coefficient 202.

[0033] It should also be noted that, as a reference Figure 1As shown, by introducing parasitic capacitance change 20, permeability drift 21 and shunt resistance offset 22 on the basis of multi-physical coupling basis vector 103, a joint modeling of latent variables driven by residual observation is constructed. The voltage compensation coefficient 23, current compensation coefficient 24 and phase compensation coefficient 25 are output by using compensation mapping relationship, so as to realize the dynamic characterization and accurate compensation of electromagnetic, thermal and stress coupling mismatch, and enable the meter to maintain the consistency and stability of the metering link under complex operating conditions.

[0034] S3: Use the compensation mapping relationship to generate compensation coefficients, and apply the compensation coefficients within the meter metering link to complete the error compensation process.

[0035] Furthermore, the compensation coefficient 202 includes a voltage compensation coefficient 23, a current compensation coefficient 24, and a phase compensation coefficient 25; the voltage compensation coefficient includes a scaling factor for correcting the voltage amplitude and an offset for correcting the DC bias of the voltage signal; the current compensation coefficient includes a scaling factor for correcting the current amplitude and an offset for correcting the amplitude-frequency response of the corresponding channel of the current signal; the phase compensation coefficient includes an angle correction for correcting the fundamental phase difference between the voltage and current and an angle correction for correcting the harmonic phase difference.

[0036] The compensation coefficient is calculated as follows: , , in, Represents the compensation factor vector The compensation factor is used to correct for voltage, current, and phase. This represents the explicit coupling coefficient matrix, corresponding to the cross-variables of temperature, stress, and electromagnetic interference. This represents the latent variable mapping coefficient matrix, which transforms the influence of latent variables into compensation amounts. This represents the normalized latent variable estimate after the update step based on the extended Kalman filter. This represents a lightweight neural network used for compensating for nonlinear corrections. express That is, the feature input vector. This indicates the change in parasitic capacitance relative to a reference value, reflecting the degree of capacitor aging. This indicates the permeability drift relative to a reference value, reflecting the shift in magnetic properties. This indicates that the shunt resistor deviates from the reference value, reflecting the degradation of the current sensing path.

[0037] It should be noted that the error compensation process includes the projection correction 300 step based on the energy conservation constraint; the projection correction 300 linearizes the energy balance equation, calculates the disturbance under quadratic programming, and writes it back to the voltage compensation coefficient 23, current compensation coefficient 24 and phase compensation coefficient 25.

[0038] It should also be noted that a projection correction of 300 is expressed as: , , , in, Indicates voltage compensation amount, Indicates current compensation amount, This represents the phase correction amount, used for secondary adjustments to the existing compensation coefficients. Indicates voltage weighting, Indicates current weight, The phase weight represents the sensitivity analysis of the meter to metering errors under factors such as changes in parasitic capacitance, permeability drift, and shunt resistance deviation. It is used to balance the contribution of the compensation component to the energy conservation constraint under multi-physical coupling.

[0039] It should also be noted that by applying a compensation coefficient within the metering link and combining it with the projection correction of 300 based on energy conservation constraints, dynamic and accurate correction of voltage, current, and phase can be achieved, effectively eliminating deviations caused by parasitic environmental interference.

[0040] Example 2, an embodiment of the present invention, provides an adaptive error compensation system for electricity meters based on multi-physical coupling and latent variable modeling, including a coupling basis vector construction module, a latent variable joint modeling module, and a compensation application module.

[0041] The coupled basis vector construction module is used to form a set of feature expressions containing univariate and cross-variables based on the output information of temperature, stress and electromagnetic interference, providing a unified input framework for latent variable modeling.

[0042] The latent variable joint modeling module is used to fuse the coupling basis vector with parasitic capacitance variation parameters, permeability drift parameters, and shunt resistance offset parameters. By using the observation inputs based on active power residuals, reactive power residuals, and harmonic residuals, combined with the extended Kalman filter calculation process, the state of latent variables is updated in real time to form a compensation mapping relationship of electromagnetic, thermal, and stress coupling.

[0043] The compensation application module is used to generate voltage compensation coefficient, current compensation coefficient and phase compensation coefficient based on the compensation mapping relationship. Under the constraint of energy conservation, the compensation coefficient is corrected by projection operation. The corrected compensation coefficient is then applied to the voltage channel, current channel and phase calculation channel respectively to correct the error in the metering link.

[0044] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0046] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0047] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0048] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling, characterized in that, include: Based on the sensor output signal, a multi-physical coupling basis vector containing univariate and cross-variable variables is constructed. Based on the multi-physics coupling basis vector and the parasitic capacitance change, permeability drift, and shunt resistance offset, a joint model of latent variables is formed to establish a compensation mapping relationship of electromagnetic, thermal, and stress coupling. Compensation coefficients are generated using a compensation mapping relationship, and these coefficients are applied within the metering link to complete the error compensation process.

2. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in claim 1, characterized in that: The multi-physics coupling basis vectors include, Temperature, stress, electromagnetic interference, and cross-variables; The cross-variables include the product of temperature and stress, the product of temperature and electromagnetic interference, the product of stress and electromagnetic interference, and the product of stress, temperature, and electromagnetic interference.

3. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in claim 1 or 2, characterized in that: The joint modeling of latent variables includes, The observations are constructed based on the residuals of active power, reactive power, and harmonic energy. An extended Kalman filter update step is used to identify the state of parasitic capacitance variation parameters, permeability drift parameters, and shunt resistance offset parameters.

4. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in claim 3, characterized in that: The update steps include, A recursive least squares method with a forgetting factor is used to iteratively adjust the weight parameters of the explicit coupled basis, and a one-step fine-tuning method using lightweight networks is used to adjust the parameters of the nonlinear terms.

5. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in any one of claims 1, 2, or 4, characterized in that: The compensation mapping relationship includes, The compensation coefficient is generated by superimposing three parts: an explicitly coupled linear term, a linear term based on hidden variables, and a nonlinear term based on voltage, current, phase, and total harmonic distortion rate.

6. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in claim 5, characterized in that: The compensation coefficient includes, Voltage compensation coefficient, current compensation coefficient, and phase compensation coefficient; The voltage compensation coefficient includes a scaling factor for correcting the voltage amplitude and an offset for correcting the DC bias of the voltage signal. The current compensation coefficient includes a scaling factor for correcting the current amplitude and an amplitude-frequency response offset for correcting the corresponding channel of the current signal. The phase compensation coefficient includes an angle correction amount for correcting the fundamental phase difference between voltage and current, and an angle correction amount for correcting the harmonic phase difference.

7. The adaptive error compensation method for electricity meters based on multi-physics coupling and latent variable modeling as described in any one of claims 1, 2, 4, or 6, characterized in that: The error compensation process includes, Projection correction steps based on energy conservation constraints; The projection correction linearizes the energy balance equation, calculates the disturbance under quadratic programming, and writes it back to the voltage compensation coefficient, current compensation coefficient, and phase compensation coefficient.

8. An adaptive error compensation system for electricity meters based on multi-physical coupling and latent variable modeling, employing the adaptive error compensation method for electricity meters based on multi-physical coupling and latent variable modeling as described in any one of claims 1 to 7, characterized in that: It includes a coupled basis vector construction module, a latent variable joint modeling module, and a compensation application module; The coupled basis vector construction module is used to form a feature expression set containing univariate and cross-variables based on the output information of temperature, stress and electromagnetic interference, and to provide a unified latent variable modeling input framework. The latent variable joint modeling module is used to fuse the coupling basis vector with the parasitic capacitance change parameter, permeability drift parameter and shunt resistance offset parameter for modeling. By using the observation input based on the active power residual, reactive power residual and harmonic residual, combined with the extended Kalman filter calculation process, the state of the latent variables is updated in real time to form a compensation mapping relationship of electromagnetic, thermal and stress coupling. The compensation application module is used to generate voltage compensation coefficient, current compensation coefficient and phase compensation coefficient based on the compensation mapping relationship. Under the constraint of energy conservation, the compensation coefficient is corrected by projection operation. The corrected compensation coefficient is applied to the voltage channel, current channel and phase calculation channel respectively to correct the error in the meter metering link.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the meter adaptive error compensation method based on multi-physical coupling and latent variable modeling as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the meter adaptive error compensation method based on multi-physical coupling and latent variable modeling as described in any one of claims 1 to 7.

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