A method and apparatus for surface charge inversion of insulator plates

Abstract

The application provides a surface charge inversion method and device for insulator flat plate, and relates to the field of material electrical performance detection. The method comprises the following steps: constructing a multi-physics field simulation model, and outputting corresponding structured simulation model parameters of a target sample based on the multi-physics field simulation model; calculating the relationship between the measured potential and the charge distribution based on the structured simulation model parameters, and constructing a target transfer matrix; constructing an inversion verification model with a regularization constraint based on the transfer matrix; adaptively selecting the regularization parameter in the inversion verification model, and outputting an optimized inversion verification model; performing consistency checking on the multi-physics field simulation model according to the optimized inversion verification model, and outputting a charge inversion result, wherein the charge inversion result comprises a surface charge density distribution result. The application solves the problem that the existing charge inversion model is unstable due to insufficient noise resistance.

Description

Technical Field

[0001] This application relates to the field of electrical property testing of materials, and in particular to a method and apparatus for surface charge inversion of insulator plates. Background Technology

[0002] With the widespread application of disc insulators in transmission lines, the problem of decreased steep-wave impulse withstand performance has gradually emerged in actual operation, attracting continuous attention within the industry. Multiple disc insulator manufacturers have observed performance fluctuations after impulse voltage tests during type testing and on-site inspections, indicating that this problem is relatively common. Research shows that charge accumulation may occur on the insulator surface after impulse voltage application. The resulting surface charge alters the electric field distribution on the insulating medium surface, causing distortion of the local electric field strength and thus reducing the actual breakdown margin of the insulating medium. In actual engineering operation, this type of electric field distortion may make the insulator more susceptible to flashover or partial discharge during subsequent operation, affecting the long-term reliability and operational safety of transmission lines.

[0003] Due to the difficulty in directly measuring the surface charge distribution of insulators under current technological conditions, electrostatic probes are typically used in engineering practice to measure the surface potential distribution of insulators, and the surface charge distribution is indirectly solved through inversion algorithms. However, in real-world applications, electrostatic probes easily introduce high-frequency noise and environmental interference signals into the measurement link, causing the measured potential data to contain strong random disturbances. These disturbances are then amplified during the inversion calculation process, leading to unstable or distorted charge distribution results. This makes it difficult to accurately reflect the true charge accumulation state of the insulator, thus affecting the analysis and engineering judgment of the steep wave performance degradation mechanism of the insulator. Consequently, after production, disc insulators exhibit the aforementioned problem of reduced breakdown margin during type testing or field operation, resulting in lower actual operational reliability than designed and increasing the operational risks and maintenance costs of the power system.

[0004] Therefore, there is an urgent need for a method and apparatus for surface charge inversion of insulator plates. Summary of the Invention

[0005] This application provides a method and apparatus for surface charge inversion of insulator plates, which solves the problem that the inversion results are unstable due to insufficient noise resistance in existing charge inversion models.

[0006] The first aspect of this application provides a method for surface charge inversion of an insulator plate. The method includes: constructing a multiphysics simulation model and outputting structured simulation model parameters corresponding to a target sample based on the multiphysics simulation model; the target sample is a plate-structured insulator specimen after completing an impulse voltage test; calculating the relationship between the measured potential and charge distribution based on the structured simulation model parameters and constructing a target transfer matrix; the target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample; constructing an inversion verification model with regularization constraints based on the transfer matrix; adaptively selecting the regularization parameters in the inversion verification model and outputting an optimized inversion verification model; performing consistency verification on the multiphysics simulation model based on the optimized inversion verification model and outputting charge inversion results, including surface charge density distribution results.

[0007] Optionally, a multiphysics simulation model is constructed, specifically including: setting the material electrical parameters based on the actual dielectric relative permittivity of the target sample; conducting an impulse voltage test on the target sample and determining the charge measurement sampling points after the test; the charge measurement sampling points are the midpoints of each grid obtained according to a preset grid division method; the preset grid division method is to divide the grid according to the potential measurement sampling points; and constructing a multiphysics simulation model based on the material electrical parameters and the charge measurement sampling points.

[0008] Optionally, an inversion verification model with regularization constraints is constructed based on the transfer matrix, specifically including: introducing the Tikhonov regularization method, combining the transfer matrix, and calculating the areal density estimate of the target sample based on the Tikhonov regularization method; constructing the proportional relationship between the signal variance and the noise variance based on the areal density estimate, and constructing the first regularization parameter of the Tikhonov regularization estimate based on the proportional relationship; and constructing the inversion verification model based on the first regularization parameter.

[0009] Optionally, the regularization parameter in the inversion verification model is adaptively selected, specifically including: performing singular value decomposition on the transfer matrix and reconstructing the spectral domain representation of the Tikhonov regularization estimation solution based on the decomposition results; establishing the regularization parameter square and solution vector norm, and differentiating the regularization parameter square using the solution vector norm; based on the differentiation results, and by analyzing the influence of noise and regularization error terms on the Tikhonov regularization estimation solution, adaptively adjusting the first regularization parameter to determine the second regularization parameter.

[0010] Optionally, based on the derivative results and by analyzing the influence of noise and regularization error terms on the Tikhonov regularized estimation solution, the first regularization parameter is adaptively adjusted to determine the second regularization parameter. Specifically, this includes: plotting an L-curve based on the functional relationship between the square of the regularization parameter and the norm of the solution vector, and using the curvature change trend of the L-curve to represent the influence of noise and regularization error terms on the Tikhonov regularized estimation solution; and selecting the regularization parameter corresponding to the curvature extremum in the L-curve as the second regularization parameter.

[0011] Optionally, the multiphysics simulation model is validated for consistency based on the optimized inversion verification model. Consistency verification includes accuracy verification and cross-validation. Accuracy verification specifically includes: performing inversion calculations on randomly selected surface charge distribution samples based on the optimized inversion verification model and generating an inversion result training set; the inversion result training set includes a noise-free sample set consisting of the original charge distribution and the corresponding calculated potential under noise-free conditions, and a noisy inversion result sample set obtained by inversion after superimposing Gaussian noise on the calculated potential; obtaining target feature values ​​based on the inversion result training set, and inputting the target feature values ​​into a random forest regression model to estimate the error of the inversion result corresponding to the measured potential vector, thereby obtaining a relative error value; determining whether the relative error value meets the preset error threshold condition; if the relative error value does not meet the preset error threshold condition, adjusting the second regularization parameter to the third regularization parameter based on the relative error value.

[0012] Optionally, the second regularization parameter is adjusted according to the relative error value, specifically including: selecting the regularization parameter that minimizes the relative error value from the preset regularization parameter candidate set as the updated third regularization parameter.

[0013] A second aspect of this application provides a surface charge inversion device for insulator plates, the device including an acquisition module and a processing module, wherein, The acquisition module is used to construct a multiphysics simulation model and output the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model. The target sample is a flat plate insulator specimen after completing the impulse voltage test. The relationship between the measured potential and charge distribution is calculated based on the structured simulation model parameters, and the target transfer matrix is ​​constructed. The target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample.

[0014] The processing module is used to construct an inversion verification model with regularization constraints based on the transfer matrix; adaptively select the regularization parameters in the inversion verification model and output the optimized inversion verification model; perform consistency verification on the multiphysics simulation model based on the optimized inversion verification model and output the charge inversion results, which include the surface charge density distribution results.

[0015] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0016] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program, the computer program being executed by a processor using any of the methods described above.

[0017] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Construct a multiphysics simulation model and output the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model; the target sample is a flat plate insulator specimen after completing the impulse voltage test; calculate the relationship between the measured potential and charge distribution based on the structured simulation model parameters, and construct the target transfer matrix; the target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample; construct an inversion verification model with regularization constraints based on the transfer matrix; adaptively select the regularization parameters in the inversion verification model and output the optimized inversion verification model; perform consistency verification on the multiphysics simulation model based on the optimized inversion verification model, and output the charge inversion results, including surface potential coefficients. Based on the established multiphysics simulation model consistent with the actual sample structure, the surface potential coefficient matrix is ​​constructed and an inversion verification model with regularization constraints is introduced. This ensures that the mapping relationship between the measured potential data and the charge distribution has stability constraints during the numerical solution process. By adaptively selecting the regularization parameter, a dynamic balance is maintained between the data fitting error and the regularization error. Furthermore, an error assessment and parameter correction are performed on the inversion results through a consistency verification mechanism to reduce the amplification effect of high-frequency noise introduced by the measurement link during the inversion calculation process. This improves the stability and accuracy of the charge inversion results and obtains surface charge density distribution results that are more consistent with the actual physical state.

[0018] 2. The Tikhonov regularization method is introduced, and the areal density estimate of the target sample is calculated based on the transfer matrix and the Tikhonov regularization method. The proportional relationship between the signal variance and the noise variance is constructed based on the areal density estimate, and the first regularization parameter of the Tikhonov regularization estimate solution is constructed based on the proportional relationship. An inversion verification model is constructed based on the first regularization parameter, thereby improving the stability and physical rationality of the charge density inversion results and providing a reliable foundation for the subsequent adaptive optimization and consistency verification of the regularization parameter.

[0019] 3. Perform singular value decomposition on the transfer matrix, and reconstruct the spectral domain representation of the Tikhonov regularized estimation solution based on the decomposition results; establish the regularization parameter square and solution vector norm, and differentiate the regularization parameter square using the solution vector norm; based on the differentiation results, and by analyzing the influence of noise and regularization error terms on the Tikhonov regularized estimation solution, adaptively adjust the first regularization parameter to determine the second regularization parameter. Thus, by establishing the functional relationship between the regularization parameter square and the solution vector norm and performing differentiation analysis, clarify the trend of the influence of regularization parameter changes on solution stability and energy distribution. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a surface charge inversion method for an insulator plate provided in an embodiment of this application; Figure 2 This is a schematic diagram of an actual sample of a flat plate insulator provided in an embodiment of this application; Figure 3 This is a schematic diagram of a potential measurement area provided in an embodiment of this application; Figure 4 This is a schematic diagram of a sample model with completed mesh generation, provided in an embodiment of this application. Figure 5 This is a schematic diagram of a surface charge inversion device for an insulator plate provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0021] Explanation of reference numerals in the attached figures: 51, acquisition module; 52, processing module; 601, processor; 602, communication bus; 603, user interface; 604, network interface; 605, memory. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0023] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0024] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0025] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0026] Please refer to Figure 1 The diagram illustrates a flow chart of a surface charge inversion method for an insulator plate provided in an embodiment of this application. The flow chart mainly includes the following steps: S101 to S105.

[0027] Step S101: Construct a multiphysics simulation model and output the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model.

[0028] Specifically, during the measurement process using an electrostatic probe, the probe easily introduces high-frequency noise, which amplifies the noise during the inversion process. To address this issue, this application first constructs a multiphysics simulation model and then outputs the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model. The target sample is a flat plate insulator specimen after completing the impulse voltage test; please refer to [reference needed]. Figure 2 , Figure 2 A schematic diagram of an actual sample of a flat plate insulator provided in an embodiment of this application is shown, wherein the transparent part is glass and the red part is silicone rubber coating.

[0029] In one possible implementation, step S101 further includes: setting material electrical parameters based on the actual dielectric relative permittivity of the target sample; conducting an impulse voltage test on the target sample and determining the charge measurement sampling points after the test; the charge measurement sampling points are the midpoints of each grid obtained according to a preset grid division method; the preset grid division method is to divide the grid according to the potential measurement sampling points; and constructing a multiphysics simulation model based on the material electrical parameters and the charge measurement sampling points.

[0030] Specifically, a model is built in multiphysics simulation software based on the actual sample, and material parameters are set. In an electrostatic field, the relationship between charge and potential depends only on the relative electrostatic constant of the medium. To achieve charge inversion for the actual sample, the material electrical parameters need to be set in the simulation software according to the relative permittivity of the actual medium. Please refer to Table 1 for the relative permittivity of different materials.

[0031] [Table 1] Appendix 1 Material Parameters Based on the charge measurement sampling points after the impulse voltage test, the following steps are included: First, after completing the impulse voltage test, keep the spatial position and orientation of the target sample unchanged. Within the potential measurement area, lay out the electrostatic probe measurement trajectory according to the preset measurement path, and determine the spacing between measurement points based on the spatial resolution of the potential measurement equipment. Please refer to [reference needed]. Figure 3 , Figure 3 A schematic diagram of a potential measurement region provided in an embodiment of this application is presented. Secondly, based on the actual sampling point location for potential measurement, the surface region of the target sample is divided into grids according to a spatial distribution rule consistent with the potential measurement sampling points, establishing a one-to-one correspondence between each grid unit and its corresponding potential measurement sampling point. Thirdly, the geometric center point of each grid unit is determined as the charge measurement sampling point to ensure a clear spatial mapping relationship between the potential observation point and the charge discrete unit during charge inversion. Finally, the spatial coordinate information of each charge measurement sampling point is recorded, and the spatial coordinate information, along with the material's electrical parameters, is input into a multiphysics simulation software. A discrete calculation model consistent with the actual sample structure is established under the electrostatic field physics interface, thereby completing the construction of the multiphysics simulation model. Please refer to [reference needed]. Figure 4 , Figure 4 A schematic diagram of a sample model with completed mesh generation, provided in an embodiment of this application, is shown.

[0032] Step S102: Calculate the relationship between the measured potential and charge distribution based on the parameters of the structured simulation model, and construct the target transfer matrix.

[0033] Specifically, the target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample. The relationship between measured potential and charge distribution is as follows:

[0034] in, , , for the first Surface potential of each grid; , , for the first Surface charge density of each grid; , , , indicating when the When the first grid cell has a unit charge density and the charge density of the remaining grid cells is zero, in the th... The potential response coefficient induced at each grid location. Let H represent the target transfer matrix. Based on the meaning of the matrix above, the calculation method for H can be obtained, and the calculation steps are as follows: At the... One grid cell is set to a unit charge density, and the charge density of the remaining grid cells is set to zero, i.e., let , and when From time to time Numerical calculations of the electrostatic field under these boundary conditions yield the corresponding potential vector. Because only the first Given a unit charge density in each grid cell, according to the principle of linear superposition, the calculated potential values ​​at each measurement point are numerically equal to the transfer matrix H at the [missing value]. The elements of the column, i.e. ,in Following the above method, a unit charge density was applied individually to each grid cell, and the electric field numerical calculation was repeated for a total of [number missing]. By performing one calculation, all elements of the transfer matrix H can be obtained column by column, thus constructing a complete surface potential coefficient matrix.

[0035] Step S103: Construct an inversion verification model with regularization constraints based on the transfer matrix.

[0036] Specifically, after obtaining the transfer matrix, a linear mapping relationship is established between the measured potential vector and the transfer matrix, and a solution model for the inverse problem of the charge density vector is constructed. To address the ill-conditioned nature and sensitivity to high-frequency noise in the numerical inversion process of the transfer matrix, a Tikhonov regularization constraint term is introduced to add stability constraints to the original data fitting terms, thereby constructing an inversion verification model with regularization constraints. By setting regularization parameters to adjust the weight ratio between the data fitting terms and the regularization term, the inversion verification model can suppress noise amplification effects while ensuring fitting accuracy, thus providing a basis for the subsequent adaptive selection of regularization parameters.

[0037] In one possible implementation, step S103 further includes: introducing the Tikhonov regularization method, combining the transfer matrix and calculating the areal density estimate of the target sample based on the Tikhonov regularization method; constructing a proportional relationship between the signal variance and the noise variance based on the areal density estimate, and constructing a first regularization parameter of the Tikhonov regularization estimate based on the proportional relationship; and constructing an inversion verification model based on the first regularization parameter.

[0038] Specifically, after obtaining the transfer matrix H, if the linear equation is directly inverted, the estimated value of the charge density can be expressed as: in, H is the measured potential vector, and H is the transfer matrix. Let be the surface charge density vector. Since the transfer matrix is ​​usually ill-conditioned numerically, direct inversion will amplify the high-frequency noise in the measured potential, causing the inverted charge distribution results to oscillate violently, making it difficult to obtain effective physical information.

[0039] To suppress noise amplification, the Tikhonov regularization method is introduced, adding a stability constraint term to the original data fitting relationship to obtain the areal density estimate. The expression is: in, This is the transpose of the transit matrix; It is the identity matrix; Represents the variance of the signal; Represents the variance of the noise; Let be the first regularization parameter of the Tikhonov regularization estimate solution; This represents the equivalent inversion operator after regularization. The first regularization parameter adjusts the weight ratio between the data fitting term and the regularization constraint term, and its value directly affects the stability and accuracy of the inversion result. Then, under the influence of the first regularization parameter, an inversion verification model with regularization constraints is constructed.

[0040] Step S104: Adaptively select the regularization parameters in the inversion verification model and output the optimized inversion verification model.

[0041] Specifically, after constructing the inversion verification model with regularization constraints, a functional relationship between parameter changes and the norm of the solution vector is established around the first regularization parameter. By analyzing this functional relationship, the influence of noise terms and regularization error terms on the Tikhonov regularized estimation solution is evaluated. Based on the degree of influence, the optimal value range of the regularization parameter is determined, and a second regularization parameter that satisfies the balance between stability and fitting accuracy is selected within the range. The second regularization parameter is substituted into the inversion verification model to update the model parameters, thereby outputting the optimized inversion verification model.

[0042] In one possible implementation, step S104 further includes: performing singular value decomposition on the transfer matrix and reconstructing the spectral domain representation of the Tikhonov regularization estimation solution based on the decomposition results; establishing the regularization parameter square and solution vector norm, and differentiating the regularization parameter square using the solution vector norm; and adaptively adjusting the first regularization parameter based on the differentiation result and by analyzing the influence of the noise term and the regularization error term on the Tikhonov regularization estimation solution to determine the second regularization parameter.

[0043] Specifically, in the inversion verification model, to achieve adaptive selection of the regularization parameter, the transfer matrix H is first subjected to singular value decomposition, which is represented as a decomposition consisting of a left singular vector, singular values, and a right singular vector, where the left singular vector is denoted as... The right singular vector is denoted as Singular values ​​are denoted as Based on this, the Tikhonov regularized estimate can be rewritten in spectral domain form:

[0044] in, For the filter factor, This is the regularization parameter.

[0045] To determine a suitable regularization parameter, a regularization parameter squared is established. The functional relationship between the norm of the solution vector and the norm of the solution vector is defined as follows: in, Let represent the squared norm of the solution vector of the Tikhonov regularized estimate. Taking the derivative, we get:

[0046] Further, by taking the second derivative, we can obtain... Therefore, about It is a decreasing convex function. Subsequently, the measured potential vector is decomposed:

[0047] in, For the true value component, Let be the noise vector. The corresponding regularization can be expressed as:

[0048] in, and They are represented as follows: The solution obtained by regularization of the true value. This represents the effect of noise on the solution. The norm of both parts is analyzed as... The trend of change can be appropriate. When the solution vector norm is mainly dominated by noise; when When a larger value is taken, the norm of the solution vector is mainly dominated by the true value. Therefore, it can be concluded that... An L-shaped curve is formed in the coordinate system.

[0049] In the L-curve, the portion of the curve that is close to the vertical corresponds to the noise-dominated region, and the portion that is close to the horizontal corresponds to the true value-dominated region. Choosing a regularization parameter at the corner can achieve a balance between suppressing noise and maintaining the true structure, thus determining the second regularization parameter as the value of the regularization parameter corresponding to the corner of the L-curve.

[0050] Step S105: Perform consistency verification on the multiphysics simulation model based on the optimized inversion verification model, and output the charge inversion results, which include the surface charge density distribution results.

[0051] Specifically, after obtaining the optimized inversion verification model, the model is used to perform consistency verification on the measured potential data corresponding to the multiphysics simulation model. By substituting the measured potential vector into the optimized inversion verification model for inversion calculation, the corresponding charge density estimation results are obtained. At the same time, the error evaluation results obtained from accuracy verification and cross-validation are combined to determine whether the inversion results and the simulation model meet the preset consistency conditions. When the consistency conditions are met, the current regularization parameters and the inversion model are determined to be valid models, and the charge inversion results are output. The charge inversion results include surface charge density distribution data corresponding to each grid cell and surface charge density distribution maps generated based on the data.

[0052] In one possible implementation, step S105 further includes: consistency verification includes accuracy verification and cross-validation. Accuracy verification specifically includes: performing inversion calculations on randomly set surface charge distribution samples based on the optimized inversion verification model, and generating an inversion result training set; the inversion result training set includes a noise-free sample set consisting of the original charge distribution and the corresponding calculated potential under noise-free conditions, and a noisy inversion result sample set obtained by inversion after superimposing Gaussian noise on the calculated potential; obtaining target feature values ​​based on the inversion result training set, and inputting the target feature values ​​into a random forest regression model to estimate the error of the inversion result corresponding to the measured potential vector, and obtaining a relative error value; determining whether the relative error value meets the preset error threshold condition; if the relative error value does not meet the preset error threshold condition, adjusting the second regularization parameter to the third regularization parameter according to the relative error value, specifically including: selecting the regularization parameter that minimizes the relative error value from the preset regularization parameter candidate set as the updated third regularization parameter.

[0053] Specifically, in the accuracy verification phase, mesh elements were randomly selected multiple times and surface charge distributions were set in the COMSOL simulation environment. The corresponding surface potentials were obtained through electrostatic field numerical calculations, and a set of potential vectors under noise-free conditions was constructed. With charge distribution set The potential vectors correspond one-to-one with the charge distributions, forming a noise-free sample set. To simulate the noise introduced during actual measurements, Gaussian noise is superimposed on the potential vectors. The mean of the Gaussian noise is 0, and the standard deviation is selected as 0.5% of the maximum absolute value of the potential vector, resulting in a noisy potential vector. The noisy potential vector is then inverted using the selected regularization parameters and the Tikhonov regularization method to obtain the corresponding inverted charge density results. This constitutes a noisy inversion result sample set, thereby generating the inversion result training set.

[0054] After obtaining the training set of inversion results, eight eigenvalues ​​are generated based on the training set data. The eigenvalues ​​include the solution vector, the L2 norm of the solution vector, the mean of the solution vector, the standard deviation of the solution vector, the L2 norm of the residual vector, the mean of the residual vector, the standard deviation of the residual vector, and the standard deviation of the estimated noise. The eigenvalues ​​and the reasons for their selection are shown in Appendix 2.

[0055] [Table 2] The target feature values ​​are input into the random forest regression model for training, establishing a mapping relationship between the feature values ​​and the inversion error. Subsequently, the measured potential vector is inverted to obtain the corresponding charge density result. Features are extracted from the inversion result and input into the random forest regression model for error estimation, obtaining a relative error value to evaluate the accuracy of the inversion result. The random forest regression model is further cross-validated to evaluate its generalization ability. It is then determined whether the relative error value meets the preset error threshold condition. If the relative error value does not meet the preset error threshold condition, the second regularization parameter is adjusted based on the relative error value, and updated to the third regularization parameter, thereby achieving further optimization of the regularization parameter. The third regularization parameter is selected by redetermining the regularization parameter corresponding to the curvature extremum in the L-curve and modifying it according to the principle of minimizing the relative error value.

[0056] The steps for cross-validation are as follows: First, divide the inversion result training set into multiple non-overlapping data subsets according to a preset ratio. In each training round, select one data subset as the validation subset and the remaining data subsets as the training subsets. Second, train the random forest regression model based on the training subsets to obtain the model parameters for the current round. Calculate the error of the trained model using the validation subsets to obtain the corresponding validation error value. Then, repeat the above division and training process, ensuring that each data subset serves as a validation subset for model evaluation, and record the validation error value for each round. Next, perform statistical analysis on the validation error values ​​for each round, calculate the average validation error and the error fluctuation range to evaluate the stability and generalization ability of the model under different data partitioning conditions. Finally, when the average validation error meets the preset generalization ability requirements, determine the random forest regression model as the target model that passes cross-validation. If the preset requirements are not met, adjust the model parameters or re-divide the training and validation subsets, and repeat the above steps.

[0057] Please refer to Figure 5 This illustration shows a schematic diagram of a surface charge inversion device for an insulator plate according to an embodiment of this application. The device includes an acquisition module 51 and a processing module 52, wherein... The acquisition module 51 is used to construct a multiphysics simulation model and output the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model. The target sample is a flat plate insulator sample after completing the impulse voltage test. The relationship between the measured potential and charge distribution is calculated based on the structured simulation model parameters, and the target transfer matrix is ​​constructed. The target transfer matrix is ​​the surface point coefficient matrix corresponding to the target sample.

[0058] Processing module 52 is used to construct an inversion verification model with regularization constraints based on the transfer matrix; adaptively select the regularization parameters in the inversion verification model and output the optimized inversion verification model; perform consistency verification on the multiphysics simulation model according to the optimized inversion verification model and output the charge inversion results, which include the surface charge density distribution results.

[0059] In one possible implementation, the acquisition module 51 is used to construct a multiphysics simulation model, specifically including: setting material electrical parameters based on the actual dielectric relative permittivity of the target sample; conducting an impact voltage test on the target sample and determining the charge measurement sampling points after the test; the charge measurement sampling points are the midpoints of each grid obtained according to a preset grid division method; the preset grid division method is to divide the grid according to the potential measurement sampling points; and constructing a multiphysics simulation model based on the material electrical parameters and the charge measurement sampling points.

[0060] In one possible implementation, the processing module 52 is used to construct an inversion verification model with regularization constraints based on the transfer matrix, specifically including: introducing the Tikhonov regularization method, combining the transfer matrix and calculating the areal density estimate corresponding to the target sample based on the Tikhonov regularization method; constructing the proportional relationship between the signal variance and the noise variance based on the areal density estimate, and constructing the first regularization parameter of the Tikhonov regularization estimate solution based on the proportional relationship; and constructing the inversion verification model based on the first regularization parameter.

[0061] In one possible implementation, the processing module 52 is used to adaptively select the regularization parameter in the inversion verification model, specifically including: performing singular value decomposition on the transfer matrix and reconstructing the spectral domain representation of the Tikhonov regularization estimation solution based on the decomposition results; establishing the regularization parameter square and solution vector norm, and differentiating the regularization parameter square using the solution vector norm; and adaptively adjusting the first regularization parameter based on the differentiation result and by analyzing the influence of the noise term and the regularization error term on the Tikhonov regularization estimation solution to determine the second regularization parameter.

[0062] In one possible implementation, the processing module 52 is used to adaptively adjust the first regularization parameter based on the derivative result and by analyzing the influence of the noise term and the regularization error term on the Tikhonov regularized estimation solution, in order to determine the second regularization parameter. Specifically, this includes: plotting an L-curve based on the functional relationship between the square of the regularization parameter and the norm of the solution vector, and using the curvature change trend of the L-curve to represent the influence of the noise term and the regularization error term on the Tikhonov regularized estimation solution; and selecting the regularization parameter corresponding to the curvature extremum in the L-curve as the second regularization parameter.

[0063] In one possible implementation, the processing module 52 is used to perform consistency verification on the multiphysics simulation model based on the optimized inversion verification model. The consistency verification includes accuracy verification and cross-validation. Specifically, the accuracy verification includes: performing inversion calculation on randomly set surface charge distribution samples based on the optimized inversion verification model and generating an inversion result training set; the inversion result training set includes a noise-free sample set consisting of the original charge distribution and the corresponding calculated potential under noise-free conditions, and a noisy inversion result sample set obtained by inversion after superimposing Gaussian noise on the calculated potential; obtaining target feature values ​​based on the inversion result training set, and inputting the target feature values ​​into a random forest regression model to estimate the error of the inversion result corresponding to the measured potential vector, thereby obtaining a relative error value; determining whether the relative error value meets the preset error threshold condition; if the relative error value does not meet the preset error threshold condition, adjusting the second regularization parameter to the third regularization parameter according to the relative error value.

[0064] In one possible implementation, the processing module 52 is used to adjust the second regularization parameter according to the relative error value, specifically including: selecting the regularization parameter that minimizes the relative error value from a preset set of regularization parameter candidates as the updated third regularization parameter.

[0065] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0066] This application also provides an electronic device. (See reference...) Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 601, at least one communication bus 602, a user interface 603, at least one network interface 604, and a memory 605.

[0067] The communication bus 602 is used to enable communication between these components.

[0068] The user interface 603 may include a display screen and a camera. Optionally, the user interface 603 may also include a standard wired interface and a wireless interface.

[0069] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0070] The processor 601 may include one or more processing cores. The processor 601 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and by calling data stored in the memory 605. Optionally, the processor 601 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 601 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 601 and may be implemented as a separate chip.

[0071] The memory 605 may include random access memory (RAM) or read-only memory. Optionally, the memory 605 may include a non-transitory computer-readable storage medium. The memory 605 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 605 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 605 may also be at least one storage device located remotely from the aforementioned processor 601. (Refer to...) Figure 6 The memory 605, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for surface charge inversion of insulator plates.

[0072] exist Figure 6In the illustrated electronic device, the user interface 603 is primarily used to provide an input interface for the user and acquire user input data; while the processor 601 can be used to call the surface charge inversion application for insulator plates stored in the memory 605. When executed by one or more processors 601, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0073] This application also provides a non-transitory computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0074] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0075] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 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 application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0079] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0080] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for surface charge inversion of insulator plates, characterized in that, The method includes: A multiphysics simulation model is constructed, and the structured simulation model parameters corresponding to the target sample are output based on the multiphysics simulation model; the target sample is a flat plate insulator specimen after completing the impulse voltage test. The relationship between the measured potential and charge distribution is calculated based on the parameters of the structured simulation model, and a target transfer matrix is ​​constructed; the target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample. An inversion verification model with regularization constraints is constructed based on the transfer matrix; The regularization parameters in the inversion verification model are adaptively selected, and the optimized inversion verification model is output. The consistency of the multiphysics simulation model is verified based on the optimized inversion verification model, and the charge inversion results are output, including the surface charge density distribution results.

2. The method according to claim 1, characterized in that, The construction of the multiphysics simulation model specifically includes: The material electrical parameters are set based on the actual dielectric relative permittivity of the target sample. An impulse voltage test is performed on the target sample, and the charge measurement sampling points are determined after the test. The charge measurement sampling points are the midpoints of each grid obtained according to a preset grid division method. The preset grid division method is to divide the grid according to the potential measurement sampling points. The multiphysics simulation model is constructed based on the material's electrical parameters and the charge measurement sampling points.

3. The method according to claim 1, characterized in that, Based on the transfer matrix, an inversion verification model with regularization constraints is constructed, specifically including: The Tikhonov regularization method is introduced, and the estimated areal density of the target sample is calculated based on the transfer matrix and the Tikhonov regularization method. Based on the areal density estimate, a proportional relationship between the signal variance and the noise variance is constructed, and based on the proportional relationship, the first regularization parameter of the Tikhonov regularized estimation solution is constructed. The inversion verification model is constructed based on the first regularization parameter.

4. The method according to claim 3, characterized in that, The adaptive selection of regularization parameters in the inversion verification model specifically includes: The transfer matrix is ​​subjected to singular value decomposition, and the Tikhonov regularized estimation solution is reconstructed in the spectral domain based on the decomposition results. Establish the regularization parameter square and solution vector norm, and differentiate the regularization parameter square using the solution vector norm; Based on the derivative results, and by analyzing the influence of the noise term and the regularization error term on the Tikhonov regularization estimation solution, the first regularization parameter is adaptively adjusted to determine the second regularization parameter.

5. The method according to claim 4, characterized in that, Based on the derivative results, and by analyzing the influence of noise and regularization error terms on the Tikhonov regularization estimation solution, the first regularization parameter is adaptively adjusted to determine the second regularization parameter. Specifically, this includes: Based on the functional relationship between the square of the regularization parameter and the norm of the solution vector, an L-curve is plotted, and the curvature change trend of the L-curve indicates the degree of influence of the noise term and the regularization error term on the Tikhonov regularization estimation solution. The regularization parameter corresponding to the curvature extremum in the L-curve is selected as the second regularization parameter.

6. The method according to claim 4, characterized in that, The multiphysics simulation model is then subjected to consistency verification based on the optimized inversion verification model. This consistency verification includes accuracy verification and cross-validation. Specifically, the accuracy verification includes: Based on the optimized inversion verification model, inversion calculations are performed on randomly selected surface charge distribution samples, and an inversion result training set is generated. The inversion result training set includes a noise-free sample set consisting of the original charge distribution and the corresponding calculated potential under noise-free conditions, and a noisy inversion result sample set obtained by inversion after superimposing Gaussian noise on the calculated potential. Based on the training set of the inversion results, target feature values ​​are obtained, and the target feature values ​​are input into the random forest regression model to estimate the error of the inversion results corresponding to the measured potential vector, thereby obtaining the relative error value. Determine whether the relative error value meets the preset error threshold condition; If the relative error value does not meet the preset error threshold condition, then the second regularization parameter is adjusted to the third regularization parameter according to the relative error value.

7. The method according to claim 6, characterized in that, The step of adjusting the second regularization parameter based on the relative error value specifically includes: The regularization parameter that minimizes the relative error value is selected from the preset set of regularization parameter candidates as the updated third regularization parameter.

8. A surface charge inversion device for insulator plates, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to construct a multiphysics simulation model and output the structured simulation model parameters corresponding to the target sample based on the multiphysics simulation model; the target sample is a flat plate insulator specimen after completing the impulse voltage test; the relationship between the measured potential and charge distribution is calculated based on the structured simulation model parameters, and a target transfer matrix is ​​constructed; the target transfer matrix is ​​the surface potential coefficient matrix corresponding to the target sample; The processing module is used to construct an inversion verification model with regularization constraints based on the transfer matrix; adaptively select the regularization parameters in the inversion verification model and output the optimized inversion verification model; perform consistency verification on the multiphysics simulation model according to the optimized inversion verification model and output the charge inversion results, which include the surface charge density distribution results.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to enable communication between the components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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