Non-contact electric field sensor design method and system

Abstract

The invention relates to the technical field of electric field measurement, in particular to a non-contact electric field sensor design method and system, and the method comprises the steps: carrying out the electromagnetic analysis and shielding structure topological optimization based on the environment characteristics of a transformer substation, and generating mechanical design parameters; shielding structure parameters are formed through material selection and electrode optimization, a three-dimensional electromagnetic field simulation model is established, and multi-scene interference simulation and sensitivity verification are carried out; and configuring a temperature compensation circuit and optimizing circuit parameters, performing dynamic response analysis and material matching degree evaluation through a high-frequency material test platform, and finally generating sensor specification parameters. According to the method, the anti-interference performance, the measurement precision and the stability of the sensor are remarkably improved, and the method is suitable for high-precision electric field measurement in a complex electromagnetic environment.

Description

Technical Field

[0001] This invention relates to the field of electric field measurement technology, and in particular to a design method and system for a non-contact electric field sensor. Background Technology

[0002] In the field of electric field measurement technology, existing solutions related to the design of non-contact electric field sensors are usually based on experience-driven or single-objective optimization strategies to define electrode structures and shielding effectiveness parameters. These solutions suffer from limitations such as insufficient coupling between electrode shape optimization and high-frequency material layout, lack of dynamic adaptability of temperature drift compensation mechanisms, and limited ability to process multiple parameters collaboratively in complex electromagnetic environments. Secondly, existing methods often rely on static geometric models or independent simulation verification steps. In complex scenarios with strong interference in substations, wide temperature variations, and multi-physics coupling, significant fluctuations in shielding effectiveness, degradation of measurement accuracy with environmental parameters, and sluggish response of compensation circuits can easily occur, making it difficult to achieve stable high-precision spatial electric field measurement. Thirdly, for the joint processing of high-frequency material layout parameters, electrode geometric models, and temperature compensation coefficient matrices, existing technologies generally lack dynamic iteration mechanisms and closed-loop verification links under a multi-objective optimization framework. This makes it difficult to form a coherent process of material layout optimization, electrode shape iteration, drift characteristic testing, and compensation parameter generation in complex electromagnetic environments and temperature variation scenarios, resulting in low sensor design efficiency and insufficient environmental adaptability. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by providing a non-contact electric field sensor design method and system. Through multi-objective optimization using cross-correlation functions and Shannon entropy, combined with dynamic genetic algorithms and finite element simulation verification, iterative optimization of electrode shapes for high-frequency material layout parameters is performed to generate final shielding structure parameters. Temperature-sensitive nodes are extracted from initial compensation circuit parameters, and drift characteristic tests are conducted to construct a temperature compensation coefficient matrix. Under multi-physics coupling conditions, an optimization verification process is established to achieve coherent operations of high-frequency material layout optimization, electrode shape iteration, drift characteristic testing, and compensation parameter generation. This invention solves the problem of how to achieve high-precision spatial electric field measurement in complex substation environments based on the aforementioned high-frequency material layout parameters and the initial electrode geometric model, using multi-objective optimization combining cross-correlation functions and Shannon entropy with dynamic genetic algorithms and finite element simulation verification.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0005] A non-contact electric field sensor design method includes:

[0006] Acquire substation environmental characteristic data, extract the main frequency band of interference based on environmental electromagnetic characteristic parameters and model the spatial electric field intensity, perform electromagnetic interference spectrum analysis and structural topology optimization of preliminary shielding layer requirements, and generate mechanical structure design parameters.

[0007] Based on the mechanical structure design parameters, electromagnetic attenuation indexes are extracted from the shielding layer material combination scheme and high-frequency materials are selected. Interlayer impedance matching calculations are performed and electrode shape iterative optimization operations are carried out on the high-frequency material layout parameters to generate the final shielding structure parameters.

[0008] Obtain the final shielding structure parameters, establish a three-dimensional electromagnetic field simulation model, extract interference coupling features based on the initial field strength distribution data, perform multi-scenario interference simulation and verify electrode sensitivity based on interference suppression effect data, and generate optimized electric field distribution parameters.

[0009] Based on the optimized electric field distribution parameters, the temperature compensation circuit topology is configured, and temperature-sensitive nodes are extracted from the initial compensation circuit parameters for drift characteristic testing and circuit parameter optimization of the temperature compensation coefficient matrix to generate the final compensation circuit configuration.

[0010] The final compensation circuit configuration is obtained, a high-frequency material testing platform is built, and time-domain features are extracted from the original response data to perform microsecond-level response verification and material matching degree analysis on the dynamic response parameters, thereby generating a high-frequency material performance report.

[0011] The high-frequency material performance report and the final shielding structure parameters are integrated to generate an initial specification document. Key performance indicators are extracted from the initial specification document, and threshold compliance verification is performed. The verification pass mark is then converted into a process document to generate the final sensor specification parameters.

[0012] Furthermore, the steps for generating mechanical structure design parameters also include:

[0013] Obtain environmental characteristic data of the substation, perform electromagnetic interference spectrum analysis, and obtain environmental electromagnetic characteristic parameters;

[0014] The interference frequency band is extracted from the electromagnetic characteristic parameters of the environment, the spatial electric field intensity is modeled, and the initial shielding layer requirements are generated.

[0015] Structural topology optimization is performed on the initial shielding layer requirements to generate mechanical structure design parameters.

[0016] Furthermore, the steps for generating the final shielding structure parameters also include:

[0017] Obtain the mechanical structure design parameters, perform interlayer impedance matching calculations, and obtain the shielding layer material combination scheme;

[0018] Electromagnetic attenuation indexes are extracted from shielding layer material combination schemes to select high-frequency materials and generate high-frequency material layout parameters.

[0019] The electrode shape is iteratively optimized by optimizing the high-frequency material layout parameters to generate the final shielding structure parameters.

[0020] Furthermore, the steps for generating the optimized electric field distribution parameters also include:

[0021] Obtain the final shielding structure parameters, establish a three-dimensional electromagnetic field simulation model, and obtain the initial field strength distribution data;

[0022] Interference coupling features are extracted from the initial field strength distribution data, multi-scenario interference simulation is performed, and interference suppression effect data is generated.

[0023] Electrode sensitivity was verified based on the interference suppression effect data, and optimized electric field distribution parameters were generated.

[0024] Furthermore, the step of generating the final compensation circuit configuration also includes:

[0025] Obtain the optimized electric field distribution parameters, configure the temperature compensation circuit topology, and obtain the initial compensation circuit parameters;

[0026] Temperature-sensitive nodes are extracted from the initial compensation circuit parameters, drift characteristics are tested, and a temperature compensation coefficient matrix is ​​generated.

[0027] The circuit parameters of the temperature compensation coefficient matrix are optimized to generate the final compensation circuit configuration.

[0028] Furthermore, the steps for generating high-frequency material performance reports also include:

[0029] Obtain the final compensation circuit configuration, build a high-frequency material testing platform, and obtain the raw response data;

[0030] Extract time-domain features from the raw response data, perform microsecond-level response verification, and generate dynamic response parameters;

[0031] Material matching degree analysis is performed on dynamic response parameters to generate high-frequency material performance reports.

[0032] Furthermore, the steps for generating the final sensor specifications also include:

[0033] Obtain high-frequency material performance reports, integrate final shielding structure parameters, and generate initial specification documents;

[0034] Extract key performance indicators from the initial specification document, perform threshold compliance verification, and generate a verification pass identifier;

[0035] The verification results are used to convert the process documents and generate the final sensor specifications.

[0036] Furthermore, the specific methods for generating the final shielding structure parameters include:

[0037] Analyze the relationship between electrode shape and electric field coupling efficiency:

[0038]

[0039] in, Electrode shape and The similarity index of electric field coupling between them; Let be the shape parameter vector of the initial electrode. This is the vector of shape parameters for the optimized electrode; For the initial electrode shape in the first Electric field strength at each sampling point; For sampling point index; The electric field strength after optimizing the electrode shape; The standard deviation of the electric field for the initial electrode shape. The standard deviation of the electric field after optimizing the electrode shape; The width of the Gaussian kernel; This represents the total number of sampling points; To take the real part of the complex electric field strength; It is the Euclidean norm;

[0040] Construct the shape optimization objective function:

[0041]

[0042] in, It is a measure of shape complexity; For the electrode surface, the first An index of a discrete region or sampling point; This represents the total number of discrete regions of curvature. The probability density of the curvature distribution on the electrode surface; It is a smoothing factor; Here, represents the gradient regularization coefficient. The gradient field represents the shape of the electrode. It is an L2 norm;

[0043] Construct a multi-objective optimization function:

[0044]

[0045] in, The fitness function value; and These are the weighting coefficients; This is the historical best cross-correlation value; The baseline entropy value;

[0046] Perform iterative optimization and define an adaptive mutation probability function:

[0047]

[0048] in, For the first Probability of generational mutation; As the baseline probability; The attenuation coefficient; For the first Average fitness value of the generation, For the first Maximum fitness value;

[0049] Define the electric field coupling efficiency verification function:

[0050]

[0051] in, This serves as a verification indicator for electric field coupling efficiency. This serves as an index for the mesh points in the finite element simulation. To optimize the position of the electrode The electric field strength; The ambient electric field strength; This represents the number of simulation grid points;

[0052] Adjusting electrode edge curvature using gradient descent:

[0053]

[0054] in, These are the final shielding structure parameters; For the first The optimized electrode shape parameters; The learning rate; The gradient field of the electrode shape is used to determine the coupling efficiency.

[0055] Furthermore, the specific method for generating the final compensation circuit configuration includes:

[0056] Wavelet transform was used to analyze the characteristics of temperature-sensitive nodes:

[0057]

[0058] in, These are wavelet coefficients; Normalization factor; For the index of the circuit node; For integration time; For nodes In time The voltage; These are Morlet wavelet basis functions; For time sampling points; For scale parameters;

[0059] Construct the temperature-drift response matrix:

[0060]

[0061] in, These are the elements of the temperature-drift response matrix; For the temperature sensor index; An index for the number of temperature scan experiments; This represents the total number of temperature scan experiments. For the first Temperature sensor in this experiment The reading; For nodes The amplitude of the wavelet coefficients; and The mean of the sample;

[0062] Constructing a multivariate regression model:

[0063]

[0064] in, For nodes The amount of voltage drift; This represents the total number of temperature sensors; Temperature sensor The amount of temperature change; These are the regression coefficients; For residual terms;

[0065] L1 regularization is used to optimize coefficient stability:

[0066]

[0067] in, For minimization operation; For nodes The amount of voltage drift; The regularization coefficient is used. These are the model's predicted values; The total number of nodes;

[0068] Optimized Mapping to the compensation coefficient matrix:

[0069]

[0070] in, Temperature sensor For compensation nodes Spatial influence weight; For temperature For compensation elements The gain function; These are the elements of the compensation coefficient matrix; For the index of the compensation node; This is the index of the compensation element.

[0071] Furthermore, the specific process of iteratively optimizing the electrode shape for the high-frequency material layout parameters includes:

[0072] Electric field coupling efficiency analysis of the initial electrode shape;

[0073] During the simulation, input substation environmental characteristic data and electromagnetic interference spectrum to simulate the electric field response of electrodes in the actual operating frequency band;

[0074] The simulation results include key indicators such as electric field distribution, electrode sensitivity, and interference coupling efficiency.

[0075] The present invention also provides a non-contact electric field sensor design system, the system comprising:

[0076] The mechanical structure design parameter generation module is used to acquire substation environmental characteristic data, extract the main interference frequency band based on environmental electromagnetic characteristic parameters and perform spatial electric field intensity modeling, perform electromagnetic interference spectrum analysis and structural topology optimization processing for preliminary shielding layer requirements, and generate mechanical structure design parameters.

[0077] The final shielding structure parameter generation module is used to extract electromagnetic attenuation indexes from the shielding layer material combination scheme and select high-frequency materials based on the mechanical structure design parameters generated by the mechanical structure design parameter generation module, perform interlayer impedance matching calculations and iterative optimization of electrode shape for high-frequency material layout parameters, and generate the final shielding structure parameters.

[0078] The optimized electric field distribution parameter generation module is used to obtain the final shielding structure parameters generated by the final shielding structure parameter generation module, establish a three-dimensional electromagnetic field simulation model, extract interference coupling features based on the initial field strength distribution data, perform multi-scenario interference simulation and verify electrode sensitivity based on interference suppression effect data, and generate optimized electric field distribution parameters.

[0079] The final compensation circuit configuration generation module is used to configure the temperature compensation circuit topology based on the optimized electric field distribution parameters generated by the optimized electric field distribution parameter generation module, extract temperature-sensitive nodes from the initial compensation circuit parameters for drift characteristic testing, and optimize the circuit parameters of the temperature compensation coefficient matrix to generate the final compensation circuit configuration.

[0080] The high-frequency material performance report generation module is used to obtain the final compensation circuit configuration generated by the final compensation circuit configuration generation module, build a high-frequency material testing platform, extract time-domain features from the original response data, perform microsecond-level response verification and material matching degree analysis on dynamic response parameters, and generate a high-frequency material performance report.

[0081] The final sensor specification parameter generation module is used to integrate the high-frequency material performance report and the final shielding structure parameters to generate an initial specification document, extract key performance indicators from the initial specification document, perform threshold compliance verification, and convert the verification pass mark into a process document to generate the final sensor specification parameters.

[0082] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the non-contact electric field sensor design method.

[0083] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the non-contact electric field sensor design method.

[0084] Compared with the prior art, the beneficial effects of the present invention include:

[0085] 1. Through multi-objective optimization of cross-correlation function and Shannon entropy, combined with dynamic genetic algorithm and finite element simulation verification, the electrode shape of high-frequency material layout parameters is iteratively optimized to generate the final shielding structure parameters. By optimizing the high-frequency material layout parameters and electrode shape iteration, the generated final shielding structure parameters can operate stably in complex electromagnetic environment, effectively reduce shielding performance fluctuations, and ensure that the measurement accuracy does not deteriorate with environmental parameters.

[0086] 2. Extract temperature-sensitive nodes from the initial compensation circuit parameters and conduct drift characteristic tests to construct a temperature compensation coefficient matrix. The construction of the temperature compensation coefficient matrix enables the sensor to adapt to temperature changes in a wide temperature variation scenario, reduces the response hysteresis of the compensation circuit, and improves the dynamic adaptability of temperature drift compensation.

[0087] 3. Under multi-physics coupling conditions, an optimized verification process is established to realize the coherent operation of high-frequency material layout optimization, electrode shape iteration, drift characteristic testing and compensation parameter generation. The establishment of the optimized verification process enables high-frequency material layout, electrode shape, drift characteristics and compensation parameters to be processed in a coordinated manner, improving sensor design efficiency and environmental adaptability, and solving the problem of limited multi-parameter collaborative processing capability in the existing technology. Attached Figure Description

[0088] To make the content of this invention easier to understand, the invention will be further described below with reference to the accompanying drawings:

[0089] Figure 1 This is a flowchart illustrating a non-contact electric field sensor design method provided in Embodiment 1 of the present invention.

[0090] Figure 2 This is a structural block diagram of a non-contact electric field sensor design system according to Embodiment 2;

[0091] Figure 3 This is an internal structural diagram of the computer device in Embodiment 3. Detailed Implementation

[0092] The following is in conjunction with the appendix Figures 1-3 The present invention will be further described with reference to specific embodiments, so that those skilled in the art can better understand and implement the present invention, but the embodiments are not intended to limit the present invention.

[0093] Example 1: Refer to Figure 1 This is a flowchart illustrating a non-contact electric field sensor design method provided in an embodiment of the present invention. The flowchart may include at least steps S100-S600:

[0094] S100: Obtain substation environmental characteristic data, extract the main frequency band of interference based on environmental electromagnetic characteristic parameters and perform spatial electric field intensity modeling, perform electromagnetic interference spectrum analysis and structural topology optimization of preliminary shielding layer requirements, and generate mechanical structure design parameters.

[0095] S200: Based on mechanical structure design parameters, extract electromagnetic attenuation index from shielding layer material combination scheme and select high-frequency materials, perform interlayer impedance matching calculation and electrode shape iterative optimization operation on high-frequency material layout parameters, and generate final shielding structure parameters.

[0096] S300: Obtain the final shielding structure parameters, establish a three-dimensional electromagnetic field simulation model, extract interference coupling features based on the initial field strength distribution data, perform multi-scenario interference simulation and verify electrode sensitivity based on interference suppression effect data, and generate optimized electric field distribution parameters.

[0097] S400: Based on the optimized electric field distribution parameters, configure the temperature compensation circuit topology, extract temperature-sensitive nodes from the initial compensation circuit parameters for drift characteristic testing, and optimize the circuit parameters of the temperature compensation coefficient matrix to generate the final compensation circuit configuration.

[0098] S500: Obtain the final compensation circuit configuration, build a high-frequency material testing platform, extract time-domain features from the original response data, perform microsecond-level response verification and material matching degree analysis on dynamic response parameters, and generate a high-frequency material performance report.

[0099] S600 integrates high-frequency material performance reports and final shielding structure parameters to generate an initial specification document. Key performance indicators are extracted from the initial specification document, and threshold compliance verification is performed. Verification pass marks are then converted into process documents to generate the final sensor specification parameters.

[0100] Step S100 includes at least steps S110-S130:

[0101] S110. Obtain environmental characteristic data of the substation, perform electromagnetic interference spectrum analysis, and obtain environmental electromagnetic characteristic parameters.

[0102] This step involves acquiring environmental characteristic data of the substation as input. Specifically, this data includes multi-dimensional parameters such as electromagnetic interference spectrum information, temperature variation range, and spatial electric field intensity distribution at the substation site. First, electromagnetic interference monitoring equipment is deployed to continuously sample the electromagnetic spectrum in the substation environment, covering the low-frequency to high-frequency range to ensure comprehensive capture of interference signals that may affect sensor performance. The sampling process utilizes a high-resolution spectrum analyzer combined with time-domain signal acquisition equipment to dynamically capture electromagnetic interference. After acquisition, the sampled data undergoes noise filtering and outlier removal via a preprocessing module. An adaptive filtering algorithm is used to identify and remove occasional pulse interference, ensuring the accuracy and representativeness of the data. Further, regarding the temperature variation range, multiple temperature sensors are deployed, covering key areas of the substation, to record diurnal temperature fluctuations and seasonal trends. The obtained temperature data undergoes time-series analysis to form a temperature variation model. For the spatial electric field intensity distribution, multiple electric field intensity sensor nodes are deployed, combined with 3D positioning technology, to obtain electric field intensity data at different locations within the substation. An electric field distribution map is then generated using a spatial interpolation algorithm. The environmental electromagnetic characteristic parameters are comprehensively composed of the aforementioned electromagnetic interference spectrum analysis results, temperature change model, and spatial electric field distribution map. Specifically, the aforementioned electromagnetic interference spectrum analysis results are used as input, and frequency band division and intensity assessment are performed through a spectrum feature extraction algorithm to obtain the environmental electromagnetic characteristic parameters. These parameters record the main frequency band and amplitude distribution characteristics of electromagnetic interference within the substation, serving as the basic data for subsequent steps. The environmental electromagnetic characteristic parameters are recorded and output, and used as the "environmental electromagnetic characteristic parameters" in the next step S120 for extracting the main interference frequency band and modeling the spatial electric field.

[0103] S120. Extract the main frequency band of interference from the environmental electromagnetic characteristic parameters, perform spatial electric field intensity modeling, and generate preliminary shielding layer requirements.

[0104] Based on the aforementioned environmental electromagnetic characteristic parameters, the dominant interference frequency band is further extracted. Specifically, the main frequency range of electromagnetic interference in the substation is identified using spectral peak detection and energy concentration analysis methods. The frequency band extraction employs a threshold judgment mechanism to filter out frequency bands with dense energy and long durations in the spectrum, eliminating sporadic and low-energy interference frequencies. For the extracted dominant interference frequency band, spatial electric field intensity modeling is performed in conjunction with spatial electric field intensity data. Specifically, using a three-dimensional electromagnetic field modeling tool, the parameters of the dominant interference frequency band and the spatial electric field intensity distribution are input to construct an electromagnetic field distribution model under the substation environment. This model uses the finite element method (FEM) or the boundary element method (BEM) for numerical calculation to simulate the propagation path of electromagnetic waves within the substation structure and their spatial attenuation characteristics. Furthermore, boundary conditions are set for the model based on the electromagnetic characteristic parameters of the metal components and insulators within the substation to ensure the physical rationality of the simulation results. Based on the simulation results, preliminary shielding layer requirements are generated, covering the impedance matching indicators, electromagnetic attenuation targets, and spatial coverage range required for the shielding layer. The preliminary shielding layer requirements are output for use in subsequent step S130. Specifically, step S130 uses the preliminary shielding layer requirements as input to perform structural topology optimization and generate mechanical structure design parameters. The preliminary shielding layer requirements are output as "Preliminary Shielding Layer Requirements" and are called by step S130, forming a continuous data flow link.

[0105] S130. Perform structural topology optimization on the initial shielding layer requirements to generate mechanical structure design parameters;

[0106] Based on the initial shielding layer requirements, structural topology optimization is performed. Specifically, the initial shielding layer requirements are used as input, and a topology optimization algorithm is employed to iteratively adjust the geometry and interlayer layout of the shielding layer, in accordance with multi-layer shielding structure design principles. The algorithm automatically adjusts the thickness, number of layers, and material distribution of the shielding layer based on finite element simulation feedback to meet impedance matching and electromagnetic attenuation design specifications. During optimization, mechanical strength and manufacturing process constraints are considered to ensure the manufacturability and stability of the structural design parameters. Furthermore, the open area ratio, connection method, and grounding scheme of the shielding layer are evaluated to prevent local electromagnetic leakage. The optimization iteration employs a multi-objective optimization method, comprehensively considering electromagnetic and mechanical performance to generate mechanical structure design parameters that meet multiple constraints. These mechanical structure design parameters specifically include detailed information such as the number of shielding layers, material thickness, interlayer spacing, shape and dimensions, and installation interfaces. These design parameters are output as "mechanical structure design parameters" and used as input for the next step, S210, for interlayer impedance matching calculation and material combination scheme formulation. Meanwhile, these design parameters provide basic structural information for subsequent steps such as S200 and S300, forming a closed loop of design and verification.

[0107] Step S200 includes at least steps S210-S230:

[0108] S210. Obtain mechanical structure design parameters, perform interlayer impedance matching calculations, and obtain shielding layer material combination schemes;

[0109] To obtain the mechanical structure design parameters, specifically, these parameters are used as input to first systematically analyze the impedance characteristics of each layer of the shielding structure. This analysis employs electromagnetic field theory combined with a transmission line model to accurately calculate the impedance between different material layers, ensuring the continuity and stability of interlayer impedance matching. Specifically, the physical properties of each layer of material in the mechanical structure design parameters, such as conductivity, dielectric constant, and thickness, are parametrically modeled to construct a corresponding impedance matrix. This impedance matrix is ​​solved numerically to obtain the impedance matching state between each layer. Furthermore, considering frequency response characteristics, multi-band analysis of the impedance matching is performed, covering the main electromagnetic interference frequency bands in the substation environment. During the analysis, an iterative calculation mechanism is used to automatically adjust the material thickness and arrangement order for interlayer structures with impedance mismatch, forming an optimized solution. This process simultaneously records the impedance matching error index and adjustment records for subsequent tracking and optimization. After the impedance matching calculation is completed, the material combination scheme for the multi-layer shielding structure is determined based on the calculation results. Specifically, the impedance matching results are comprehensively evaluated along with the electromagnetic attenuation performance, mechanical strength, and thermal stability of the materials to select material combinations that meet the design specifications. This evaluation employs a multi-parameter weighted algorithm, combining experimental data and a materials database to ensure that the selected material combination achieves a balance between electromagnetic shielding performance and structural stability. The material combination scheme meticulously records information such as the type, thickness, and arrangement order of each material layer. Finally, the shielding layer material combination scheme is output as the "Shielding Layer Material Combination Scheme" for the next step, S220. This scheme also provides fundamental data support for subsequent multi-layer shielding structure parameter generation and electric field distribution simulation verification.

[0110] S220. Extract electromagnetic attenuation indexes from shielding layer material combination schemes, select high-frequency materials, and generate high-frequency material layout parameters.

[0111] Electromagnetic attenuation indices are extracted from the shielding layer material combination scheme. Specifically, the electromagnetic attenuation characteristics of each material in the scheme are first quantitatively analyzed. This analysis is based on the material's conductivity, permeability, and dielectric loss factor, combined with the actual thickness and arrangement, and the electromagnetic wave attenuation effect of the overall shielding layer is calculated using the Transfer Matrix Method (TMM). Furthermore, for high-frequency electromagnetic interference present in the substation environment, a high-frequency response characteristic analysis is performed, covering a frequency range from tens of megahertz to several gigahertz. During the analysis, model parameters are dynamically adjusted based on the material's frequency dependence parameters to simulate the attenuation effect at different frequency bands. Through this process, the electromagnetic attenuation indices of the shielding layer combination are obtained, including key parameters such as attenuation amplitude, phase change, and reflection loss. Based on these electromagnetic attenuation indices, high-frequency materials are selected. Specifically, a multi-objective optimization algorithm is used to consider factors such as attenuation performance, material cost, processing technology, and environmental adaptability to select the optimal high-frequency material combination. The high-frequency material selection process combines laboratory measurement data and material performance databases to ensure that the selected materials have good response speed and durability in practical applications. After selection, high-frequency material layout parameters are generated, specifically including the spatial distribution of the material, interlayer spacing, and integration method with the mechanical structure. These layout parameters describe in detail the specific location and arrangement order of the high-frequency material in the multi-layer shielding structure, facilitating subsequent electrode shape optimization and structural integration. These high-frequency material layout parameters are output as the "high-frequency material layout parameters" for the next step S230, and also provide crucial input for electric field distribution simulation and temperature compensation circuit integration and debugging.

[0112] S230. Iteratively optimize the electrode shape of the high-frequency material layout parameters to generate the final shielding structure parameters;

[0113] The electrode shape is iteratively optimized based on the high-frequency material layout parameters. Specifically, the high-frequency material layout parameters are first used as input to construct an initial geometric model of the electrode. This model encompasses the electrode's size, shape, and relative position, forming a complete shielding structure parameter framework in conjunction with the high-frequency material layout. Further, an electromagnetic field simulation tool based on the finite element method is used to analyze the electric field coupling efficiency of the initial electrode shape. Specifically, substation environmental characteristic data and electromagnetic interference spectrum are input during the simulation to simulate the electrode's electric field response within the actual operating frequency band. Simulation results include key indicators such as electric field distribution diagrams, electrode sensitivity, and interference coupling efficiency. Based on simulation feedback, iterative optimization of the electrode shape is performed. This optimization employs a hybrid optimization strategy combining a genetic algorithm (GA) and gradient descent to automatically adjust parameters such as the electrode's edge curvature, spacing, and surface texture. After each iteration, the electromagnetic simulation is repeated to evaluate the optimization effect, ensuring that the electrode shape gradually approaches maximum electric field coupling efficiency. During the optimization process, the adjustment range of the electrode shape is constrained by structural strength and manufacturing process limitations to prevent designs that are unmanufacturable or easily damaged. The iterative optimization process continues until the electric field coupling efficiency of the electrode shape reaches a preset threshold or the number of iterations reaches its upper limit. Finally, shielding structure parameters containing the optimized electrode shape parameters are generated, detailing the electrode's geometry, layout, and integration scheme with high-frequency materials. These final shielding structure parameters are output as the "final shielding structure parameters" for the next step, S310. These parameters also provide fundamental data support for subsequent electric field distribution simulation verification and temperature compensation circuit integration and debugging.

[0114] In another embodiment, in S230, the high-frequency material layout parameters are first input, and an initial geometric model of the electrode is constructed. This model is based on the material spatial distribution and interlayer spacing data in the layout parameters. The relationship between the electrode shape and the electric field coupling efficiency is analyzed using a modified cross-correlation function (CCF), defined as:

[0115]

[0116] in:

[0117] Electrode shape and The similarity index of electric field coupling between them;

[0118] , : Vector of shape parameters of the initial and optimized electrodes;

[0119] For the initial electrode shape in the first The electric field intensity at each sampling point is extracted from the electric field distribution data in the high-frequency material layout parameters;

[0120] For sampling point index;

[0121] The electric field strength after optimizing the electrode shape;

[0122] and These represent the standard deviations of the electric field for the two electrode shapes, respectively.

[0123] The width of the Gaussian kernel is 1 / 10 of the minimum interlayer spacing in the material layout parameters;

[0124] The total number of sampling points is determined by the 3D positioning data in the layout parameters;

[0125] Take the real part of the complex electric field strength;

[0126] Euclidean norm: Calculates the distance between shape parameters.

[0127] Data source → Indicator → Variable: Extract electric field distribution data from the high-frequency material layout parameters and record it as... The amplitude of the main frequency band is extracted from the electromagnetic interference spectrum data as... The calculation benchmark. The output of formula ①. As an indicator of electrode shape similarity, it is consumed by the fitness function of the genetic algorithm in formula ③.

[0128] Furthermore, the shape optimization objective function is constructed using the improved Shannon entropy, as shown in Formula ②:

[0129]

[0130] in:

[0131] It is a measure of shape complexity;

[0132] For the electrode surface, the first An index of a discrete region or sampling point;

[0133] This represents the total number of discrete regions of curvature.

[0134] The probability density of the curvature distribution on the electrode surface is calculated by discretizing the geometric model in the layout parameters;

[0135] is the smoothing factor (dimensionless);

[0136] The gradient regularization coefficient is determined by the material's mechanical strength constraints.

[0137] The gradient field represents the shape of the electrode.

[0138] L2 norm, used to calculate the magnitude of the gradient field.

[0139] Data source → Indicators → Variables: Curvature distribution extracted from the electrode geometry model in the high-frequency material layout parameters. Extracted from the manufacturing process constraint database Formula ② This shape complexity metric is output to the next step.

[0140] The coupled evaluation data of the high-frequency material layout parameters and the initial electrode model output in this step is consumed by the "genetic algorithm fitness function" in formula ③.

[0141] Furthermore, based on the above and Construct a multi-objective optimization function, as shown in formula ③:

[0142]

[0143] in:

[0144] Fitness function value;

[0145] and The weighting coefficient is determined by the priority of the electromagnetic attenuation target;

[0146] The initial value is the historical best cross-correlation value, and it is derived from formula ①. ;

[0147] The baseline entropy value is taken from the initial electrode model. .

[0148] Data source → Indicator → Variable: Mapped by the outputs of formulas ① and ② respectively and Extracted from the design specification library and Formula ③ The fitness value is input into the selection operator of the genetic algorithm.

[0149] Iterative optimization is performed using an improved genetic algorithm, and equation ④ defines the adaptive mutation probability function:

[0150]

[0151] in:

[0152] For the first Probability of generational mutation;

[0153] As the baseline probability;

[0154] The attenuation coefficient;

[0155] and The first Average and maximum fitness values.

[0156] Data source → Indicator → Variable: From formula ③ Calculate each generation and Formula ④ outputs the dynamic mutation probability to the genetic algorithm to generate the optimized electrode shape parameters.

[0157] The electrode shape parameters optimized by the genetic algorithm in this step are consumed by the "finite element electromagnetic simulation model" in the next step.

[0158] Furthermore, the optimized electrode shape parameters are input into the finite element electromagnetic simulation model, and formula ⑤ defines the electric field coupling efficiency verification function:

[0159]

[0160] in:

[0161] This serves as a verification indicator for electric field coupling efficiency.

[0162] This serves as an index for the mesh points in the finite element simulation.

[0163] To optimize the position of the electrode The electric field strength;

[0164] The ambient electric field strength is extracted from the spatial electric field distribution map data.

[0165] This represents the number of grid points in the simulation.

[0166] Data source → Indicators → Variables: Generated from optimized electrode shape parameters Extracted from environmental characteristic data Formula ⑤ The coupling efficiency index is input into the final parameter generation module.

[0167] Finally, the curvature of the electrode edge is adjusted using the gradient descent method, as shown in formula ⑥:

[0168]

[0169] in:

[0170] Final shielding structure parameters;

[0171] For the first The optimized electrode shape parameters;

[0172] The learning rate is determined by the simulation step size constraint;

[0173] The gradient field of the electrode shape is used to determine the coupling efficiency.

[0174] Data source → Indicator → Variable: From formula ⑤ Calculate gradient Extracted from the process constraint library Formula ⑥ outputs the final shielding structure parameters, including the optimized electrode size and material layout, which are called by S310's "Final Shielding Structure Parameters".

[0175] This section summarizes the technical effects: Through multi-objective optimization of cross-correlation functions and Shannon entropy, combined with dynamic genetic algorithms and finite element simulation verification, iterative optimization of electrode shapes is achieved, forming shielding structure parameters with high electric field coupling efficiency, ensuring closed-loop verification of sensor measurement accuracy and anti-interference performance.

[0176] Step S300 includes at least steps S310-S330:

[0177] S310. Obtain the final shielding structure parameters, establish a three-dimensional electromagnetic field simulation model, and obtain the initial field strength distribution data;

[0178] After obtaining the final shielding structure parameters, specifically, these parameters are used as input to construct a three-dimensional electromagnetic field simulation model. This model is based on the finite element method and includes precise modeling of the sensor structure's geometry, high-frequency material layout, and electrode shape. Combined with electromagnetic interference spectrum and spatial electric field intensity distribution data from the substation environmental characteristics, complete simulation input conditions are formed. Furthermore, boundary conditions are set in the simulation model, covering the electromagnetic reflection characteristics of metal components within the substation and the dielectric parameters of insulators, ensuring the physical realism of the simulation environment. During the simulation, high-resolution mesh generation technology is used to refine key areas such as electrode edges and shielding layer interfaces to improve the accuracy of field strength distribution calculation. The simulation is executed through a professional electromagnetic simulation software platform, simulating a frequency range covering the main frequency band of substation electromagnetic interference, and dynamically calculating the spatial electric field intensity distribution. The simulation results include a vector distribution map of the spatial electric field, field strength amplitude, and phase information, with a focus on outputting the initial field strength distribution data. The initial field strength distribution data is recorded and output as the "initial field strength distribution data" for the next step S320, which is used for interference coupling feature extraction and multi-scenario interference simulation. At the same time, this data provides a basic simulation basis for the subsequent temperature compensation circuit integration and debugging of S400.

[0179] S320. Extract interference coupling features from the initial field strength distribution data, perform multi-scenario interference simulation, and generate interference suppression effect data;

[0180] Based on the initial field strength distribution data, specifically, interference coupling characteristics are extracted. First, a multi-dimensional analysis of the time-frequency characteristics of the spatial electric field strength is performed. This analysis employs time-frequency transformation algorithms, such as Short-Time Fourier Transform (STFT) and wavelet transform, to identify the coupling modes of different interference sources in space and frequency. Further, a multi-scenario interference simulation model is constructed, incorporating typical interference scenarios in a substation environment. This multi-scenario interference simulation covers various types of electromagnetic interference, including power frequency interference, pulse interference, and high-frequency harmonic interference, simulating their propagation and coupling paths within the sensor structure. During the simulation, the amplitude, frequency, and spatial location of the interference sources are dynamically adjusted to evaluate the suppression effect of the shielding structure on various types of interference. The simulation results are quantitatively described through field strength variation curves and interference energy distribution maps, generating interference suppression effect data. This data includes electromagnetic attenuation rates at different frequency bands, residual amplitude of interference signals, and spatial distribution characteristics, reflecting the comprehensive anti-interference performance of the shielding structure. The interference suppression effect data is output and used as the "interference suppression effect data" for the next step S330 to perform electrode sensitivity verification. At the same time, this data provides an important reference for the subsequent integration and debugging of the S400 temperature compensation circuit and the S500 high-frequency material response test.

[0181] S330. Verify the electrode sensitivity of the interference suppression effect data and generate optimized electric field distribution parameters.

[0182] Based on the received interference suppression effect data, specifically, electrode sensitivity is verified. The verification first calculates the electrode's response sensitivity to changes in electric field frequency and amplitude based on the spatial gradient information of the electric field distribution in the interference suppression effect data. Specifically, an equivalent circuit model of the electrode is used to map the interference suppression effect data to the voltage response at the electrode input, and the electrode sensitivity curve is analyzed through numerical simulation. Further, combined with the optimized electrode shape parameters and high-frequency material layout, multi-parameter sensitivity analysis is performed to evaluate the electrode's response consistency and linearity under different environmental conditions. During the sensitivity verification process, a sensitivity threshold is set, and electrode parameter combinations that do not meet design requirements are automatically filtered and fed back to the optimization algorithm for adjustment. The verification results are output in the form of optimized electric field distribution parameters, detailing the electrode sensitivity index, response time, and spatial distribution characteristics. These optimized electric field distribution parameters are output as the "optimized electric field distribution parameters" for the next step, S410, for temperature compensation circuit configuration and debugging. Simultaneously, these parameters provide key performance data support for subsequent S500 high-frequency material response testing and S600 final sensor specification output.

[0183] Step S400 includes at least steps S410-S430:

[0184] S410. Obtain the optimized electric field distribution parameters, configure the temperature compensation circuit topology, and obtain the initial compensation circuit parameters.

[0185] The optimized electric field distribution parameters are obtained. Specifically, these parameters are used as input for configuring the temperature compensation circuit topology. Specifically, based on the electrode sensitivity and spatial electric field variation characteristics in the optimized electric field distribution parameters, an initial topology for the temperature compensation circuit is constructed. This topology covers the identification of temperature-sensitive nodes, the selection of circuit components, and the design of connection methods. The temperature-sensitive nodes are determined by analyzing the dependence of electrode response on temperature changes and combining circuit simulation results, focusing on changes in electrode spacing, changes in electrode material resistivity with temperature, and the temperature drift characteristics of the signal amplifier. Furthermore, a modular design method is adopted, dividing the compensation circuit into a temperature detection module, a signal adjustment module, and a feedback control module to ensure the flexibility and scalability of the circuit structure. The temperature detection module integrates multiple high-precision temperature sensors, arranged near the electrodes and key circuit nodes, to achieve real-time acquisition of local temperatures. The signal adjustment module uses adjustable resistors, capacitors, and operational amplifiers to construct a temperature compensation network, adjusting the amplitude and phase of the electrode signal according to temperature changes. The feedback control module uses a digital signal processing unit to dynamically adjust and correct the compensation coefficients. The temperature compensation circuit topology configuration process, combined with circuit simulation software, involves multiple rounds of simulation verification to ensure the circuit's response stability within the expected temperature range. During configuration, a mathematical model of temperature and electrode response is established to map the impact of temperature changes on electrode sensitivity, guiding the initial setting of compensation circuit parameters. These initial compensation circuit parameters are recorded as "Initial Compensation Circuit Parameters" and output for use in the next step, S420, and also provide basic data support for subsequent temperature compensation coefficient matrix generation and final compensation circuit configuration.

[0186] S420. Extract temperature-sensitive nodes from the initial compensation circuit parameters, perform drift characteristic tests, and generate a temperature compensation coefficient matrix.

[0187] Temperature-sensitive nodes are extracted from the initial compensation circuit parameters. Specifically, a temperature sweep experiment is first conducted to test the temperature drift characteristics of key nodes in the initial compensation circuit parameters. The test uses a high-precision temperature-controlled environmental chamber to simulate the actual temperature variation range of a substation, covering the entire temperature range from low to high. During the test, the electrode output signals and the voltage and current changes of each node in the compensation circuit are collected in real time to form temperature-electrical response curves. Further, the test data is preprocessed using a data acquisition system, including filtering, noise reduction, and outlier removal, to ensure the accuracy and representativeness of the data. For the processed data, a multivariate regression analysis method is applied to establish a temperature drift model, quantifying the impact of temperature on the electrode response and the performance of the compensation circuit. The temperature drift model reveals the drift amplitude and trend of each temperature-sensitive node, serving as the basis for subsequent compensation coefficient calculations. Based on the temperature drift model, a temperature compensation coefficient matrix is ​​generated. Specifically, the drift characteristics of the temperature-sensitive nodes are mapped to the compensation circuit parameter space to construct a multidimensional coefficient matrix, covering the compensation resistance, capacitance, and amplification gain adjustment values ​​corresponding to different temperature points. This matrix supports dynamic compensation strategies, enabling real-time parameter adjustment during temperature changes. During the generation of the compensation coefficient matrix, an optimization algorithm is used to select the optimal combination of compensation parameters, balancing the compensation effect with circuit complexity. The temperature compensation coefficient matrix is ​​output as the "temperature compensation coefficient matrix" for the next step of S430, and also provides key input for the subsequent final compensation circuit configuration.

[0188] In another embodiment, in S420, initial compensation circuit parameters are input, and node drift data is obtained through a temperature scanning experiment. Wavelet transform is used to analyze the characteristics of temperature-sensitive nodes, and equation ⑦ is:

[0189]

[0190] in:

[0191] These are wavelet coefficients;

[0192] Normalization factor;

[0193] For the index of the circuit node;

[0194] For integration time;

[0195] For nodes In time The voltage;

[0196] These are Morlet wavelet basis functions;

[0197] For time sampling points;

[0198] It is a scale parameter, determined by the rate of temperature change.

[0199] Data Source → Indicators → Variables: Extracting node voltage data from initial compensation circuit parameters Recorded by the temperature-controlled environment chamber Formula ⑦ Output to formula ⑧.

[0200] Furthermore, the temperature-drift response matrix is ​​constructed, as shown in Equation ⑧:

[0201]

[0202] in:

[0203] These are the elements of the temperature-drift response matrix, i.e., the correlation coefficients;

[0204] For the temperature sensor index;

[0205] An index for the number of temperature scan experiments;

[0206] This represents the total number of temperature scan experiments.

[0207] For the first Temperature sensor in this experiment The reading;

[0208] For nodes The amplitude of the wavelet coefficients;

[0209] and This is the sample mean.

[0210] Data source → Indicator → Variable: From formula ① extract Extracted from temperature sensor data Formula ⑧ As a sensitive node identification indicator, it is output to the next step formula ⑨.

[0211] The wavelet coefficients and correlation matrix of the temperature-sensitive nodes output in this step are consumed by the "multivariate regression model" in formula ⑨ in the next step.

[0212] Furthermore, based on formula ⑧ Construct a multivariate regression model, formula ⑨ is:

[0213]

[0214] in:

[0215] For nodes The amount of voltage drift;

[0216] This represents the total number of temperature sensors;

[0217] Temperature sensor The amount of temperature change;

[0218] These are the regression coefficients;

[0219] This is the residual term.

[0220] Data source → Indicators → Variables: Filtered using formula ⑧ node pairs As input, extracted from temperature scan experimental data and Formula 9 Output the compensation coefficient matrix for the next step.

[0221] L1 regularization is used to optimize the stability of the coefficients, and formula 10 is:

[0222]

[0223] in:

[0224] For minimization operation;

[0225] For nodes The amount of voltage drift;

[0226] is the regularization coefficient, determined by overfitting risk analysis;

[0227] V represents the model's predicted value.

[0228] Total number of nodes.

[0229] Data source → Indicator → Variable: From formula ⑨ Calculate the loss function using experimental data. Formula 10 outputs the optimized loss function. To the compensation coefficient matrix.

[0230] The regression coefficients and optimization weights of the temperature-sensitive nodes output in this step are consumed by the "dynamic compensation strategy" in the next step.

[0231] Furthermore, the optimized Mapping to the compensation coefficient matrix, formula (11) is:

[0232]

[0233] in:

[0234] Temperature sensor For compensation nodes Spatial influence weight;

[0235] For temperature For compensation elements The gain function;

[0236] These are the elements of the compensation coefficient matrix;

[0237] For the index of the compensation node;

[0238] This is the index of the compensation element.

[0239] Data source → Indicators → Variables: From formula ⑩ Circuit topology parameter extraction Extracted from component manual Formula (11) outputs the temperature compensation coefficient matrix, which is called by the "Temperature Compensation Coefficient Matrix" of S430.

[0240] This section summarizes the technical effects: By using wavelet transform and multivariate regression analysis, temperature-sensitive nodes are accurately identified and a dynamic compensation coefficient matrix is ​​generated, ensuring the output stability of the sensor over a wide temperature range and forming a closed-loop control link to suppress temperature drift.

[0241] S430. Optimize the circuit parameters of the temperature compensation coefficient matrix to generate the final compensation circuit configuration.

[0242] The circuit parameters of the temperature compensation coefficient matrix are optimized. Specifically, based on the temperature compensation coefficient matrix, parameter optimization is performed using a circuit simulation platform. During simulation, each compensation parameter in the temperature compensation coefficient matrix is ​​input, and the dynamic response of the compensation circuit under different temperature conditions is simulated in conjunction with electrode sensitivity and circuit topology. Furthermore, through iterative simulation and error analysis, the impact of compensation parameters on circuit output stability is identified, with a focus on indicators such as signal distortion, noise amplification, and response delay. The optimization algorithm employs a multi-objective optimization method, comprehensively considering temperature compensation effect, circuit power consumption, and component cost, automatically adjusting the compensation resistor, capacitor, and amplifier gain parameters. During optimization, parameter boundaries and constraints are set to prevent parameters from exceeding the actual operating range of components and manufacturing process limitations. Based on the optimization results, a final compensation circuit configuration is generated, detailing the specifications, connection methods, and parameter settings of each component. The final compensation circuit configuration supports digital storage and transmission, facilitating subsequent manufacturing and debugging. This configuration also includes calibration data and temperature compensation strategy descriptions, forming a complete circuit design document. The final compensation circuit configuration is output as a call to the "final compensation circuit configuration" in the next step S510, while also providing important support for high-frequency material response testing and final sensor specification output.

[0243] Step 500 includes at least steps S510-S530:

[0244] S510. Obtain the final compensation circuit configuration, build a high-frequency material testing platform, and obtain the raw response data;

[0245] The final compensation circuit configuration is obtained; specifically, this configuration is used as input to build a high-frequency material testing platform. The testing platform includes a high-frequency signal generator, a Time-Domain Response Acquisition System (TDRAS), and a temperature control environment simulation device, capable of simulating the electromagnetic interference frequency range and temperature variation conditions in the actual working environment of a substation. Specifically, the high-frequency signal generator outputs a test signal covering tens of megahertz to several gigahertz, which, after adjustment by the compensation circuit, is applied to the high-frequency material sample. The TDRAS records the material's response characteristics to the input signal in real time. Further, during the setup process, the signal amplifier gain and filter characteristics in the testing platform are configured according to the parameters of the final compensation circuit configuration to ensure a high signal-to-noise ratio and dynamic range of the acquired data. The testing process employs an automated control system, gradually adjusting the input signal frequency and amplitude according to a preset test plan, while simultaneously monitoring the material's response delay, amplitude changes, and phase shift. The test data undergoes real-time preprocessing, including baseline correction, noise filtering, and outlier removal, to form clear raw response data. The raw response data is recorded and output as the "raw response data" to be called in the next step S520, and at the same time provides basic data support for subsequent dynamic response parameter extraction and material matching degree analysis.

[0246] S520. Extract time-domain features from the raw response data, perform microsecond-level response verification, and generate dynamic response parameters.

[0247] Temporal features are extracted from the raw response data. Specifically, the raw response data is first processed by signal alignment to eliminate time base drift and trigger delay during the test. Signal alignment employs cross-correlation analysis to determine and correct the time offset between the response signal and the input signal. Further, microsecond-level response verification is performed on the corrected signal. Specifically, high-resolution time-domain analysis is used to extract key time-domain parameters such as rise time, half-peak width, and attenuation characteristics. This time-domain feature extraction, combined with wavelet transform and envelope detection algorithms, accurately captures transient changes and subtle oscillations in the material response. Furthermore, a dynamic response parameter dataset is constructed for different frequency test points, covering response speed, signal stability, and frequency-related time-domain indicators. The dynamic response parameters are normalized using multidimensional data analysis methods to eliminate the influence of test environment fluctuations and system noise. The extraction process of dynamic response parameters includes outlier detection and removal mechanisms to ensure the accuracy and representativeness of the parameters. The dynamic response parameters are output as the "Dynamic Response Parameters" call in the next step of S530, and also provide important input for the generation of high-frequency material performance reports.

[0248] S530: Perform material matching degree analysis on dynamic response parameters and generate a high-frequency material performance report;

[0249] Material matching degree analysis is performed on the dynamic response parameters. Specifically, the dynamic response parameters are first compared with a preset high-frequency material performance benchmark library. The benchmark library contains the response characteristics of various candidate materials under different frequencies and temperatures, forming a standard dynamic response curve. The matching degree of the materials is quantified by calculating similarity indices between the dynamic response parameters and the benchmark curves, such as correlation coefficient and root mean square error. Furthermore, a comprehensive performance evaluation model is constructed by combining the mechanical strength, thermal stability, and electromagnetic compatibility data of the materials. The evaluation model uses a multi-attribute decision analysis method to weigh various performance indicators and generate a material performance score. Based on the score results, a combination of high-frequency materials with excellent performance and suitable for non-contact electric field sensor applications is selected. During the material matching degree analysis, key parameters and abnormal situations are recorded to form a complete performance tracking record. Finally, a high-frequency material performance report is generated, which describes in detail the dynamic response characteristics, matching degree score, and comprehensive performance evaluation results of the materials. The high-frequency material performance report is output as the "High-Frequency Material Performance Report" to be called in the next step S610, and at the same time provides key performance basis for the final sensor specification output.

[0250] Step S600 includes at least steps S610-S630:

[0251] S610. Obtain high-frequency material performance reports, integrate final shielding structure parameters, and generate initial specification documents;

[0252] The high-frequency material performance report is obtained. Specifically, the high-frequency material performance report and the final shielding structure parameters are used as input and integrated into an initial specification document. Specifically, the dynamic response characteristics, material matching score, and comprehensive performance evaluation results in the high-frequency material performance report are first systematically summarized. Combined with the electrode shape, material layout, and shielding layer structure details in the final shielding structure parameters, a preliminary description of the overall sensor performance is formed. This integration process is executed through a professional document management system, using a templated document format to ensure accurate correspondence and information integrity of various technical parameters. Furthermore, key fields in the initial specification document are formatted, including unified parameter units, standardized electrical performance indicators, and material specification coding, improving document readability and subsequent processing compatibility. The document clearly records the core data from the high-frequency material performance report, such as response time, frequency response range, and temperature adaptability, which are correlated with the electromagnetic attenuation index, electrode sensitivity, and mechanical dimension parameters of the final shielding structure parameters, forming a closed-loop data link. The initial specification document is output as the "initial specification document" for the next step S620, and also provides basic data support for subsequent sensor performance threshold verification and manufacturing process document generation.

[0253] S620. Extract key performance indicators from the initial specification document, perform threshold compliance verification, and generate a verification pass mark.

[0254] Key performance indicators (KPIs) are extracted from the initial specification document. Specifically, an automated data extraction tool is used to identify and extract key performance indicators such as electromagnetic attenuation rate, electrode coupling efficiency, temperature compensation range, and high-frequency response speed. This extraction process combines regular expressions and semantic analysis algorithms to ensure accurate positioning and numerical integrity of the KPIs. Further, threshold compliance verification is performed on the extracted KPIs. Specifically, a performance threshold database is established based on preset design standards and industry specifications, covering the minimum and maximum allowable values ​​for each KPI. The verification uses a rule engine to judge each KPI against its corresponding threshold, identifying parameters that exceed or fail to meet the standards. For any abnormal indicators found, the system automatically generates an anomaly report, recording the parameter name, actual value, and degree of deviation for subsequent design adjustments. During the verification process, correlation analysis between indicators is used to determine the consistency and coordination of overall performance, preventing individual KPI anomalies from affecting the overall evaluation results. If all threshold conditions are met, a verification pass indicator is generated, clearly indicating that the initial specification document meets the design requirements. The verification pass identifier is output as the "verification pass identifier" to be called in the next step S630, and at the same time provides an authoritative basis for the generation of the final sensor specification parameters.

[0255] S630. Convert the verification pass mark into process documents to generate the final sensor specification parameters;

[0256] The verification pass identifier is used to convert the process document. Specifically, the verification pass identifier is first used as a trigger signal to start the process document generation system. This system automatically matches the corresponding manufacturing process flow and process parameters based on the design parameters and performance indicators in the initial specification document and in conjunction with the process standard library. Specifically, the process document conversion process includes the automatic arrangement of process step definitions, material handling instructions, assembly sequence, and quality control points. During the conversion process, the system verifies the parameters of key process nodes to ensure that the parameters in the process document are consistent with the design specifications, avoiding process deviations. Furthermore, considering the manufacturing equipment capabilities and process environment conditions, the system adjusts the control parameters such as process time, temperature, and pressure in the process document to form a personalized manufacturing guidance document. The process document is stored in a digital format, supporting version management and traceability functions, facilitating real-time updates and feedback during the manufacturing process. Finally, a final sensor specification parameter document containing a complete manufacturing process flow, parameter settings, and quality inspection standards is generated. The final sensor specification parameters are output and used as the "substation environmental characteristic data" for the next step S100, forming a closed-loop feedback mechanism between design and manufacturing.

[0257] Example 2:

[0258] Reference Figure 2 This embodiment provides a non-contact electric field sensor design system, which includes:

[0259] The mechanical structure design parameter generation module is used to acquire substation environmental characteristic data, extract the main interference frequency band based on environmental electromagnetic characteristic parameters and perform spatial electric field intensity modeling, perform electromagnetic interference spectrum analysis and structural topology optimization processing for preliminary shielding layer requirements, and generate mechanical structure design parameters.

[0260] The final shielding structure parameter generation module is used to extract electromagnetic attenuation indexes from the shielding layer material combination scheme and select high-frequency materials based on the mechanical structure design parameters generated by the mechanical structure design parameter generation module, perform interlayer impedance matching calculations and iterative optimization of electrode shape for high-frequency material layout parameters, and generate the final shielding structure parameters.

[0261] The optimized electric field distribution parameter generation module is used to obtain the final shielding structure parameters generated by the final shielding structure parameter generation module, establish a three-dimensional electromagnetic field simulation model, extract interference coupling features based on the initial field strength distribution data, perform multi-scenario interference simulation and verify electrode sensitivity based on interference suppression effect data, and generate optimized electric field distribution parameters.

[0262] The final compensation circuit configuration generation module is used to configure the temperature compensation circuit topology based on the optimized electric field distribution parameters generated by the optimized electric field distribution parameter generation module, extract temperature-sensitive nodes from the initial compensation circuit parameters for drift characteristic testing, and optimize the circuit parameters of the temperature compensation coefficient matrix to generate the final compensation circuit configuration.

[0263] The high-frequency material performance report generation module is used to obtain the final compensation circuit configuration generated by the final compensation circuit configuration generation module, build a high-frequency material testing platform, extract time-domain features from the original response data, perform microsecond-level response verification and material matching degree analysis on dynamic response parameters, and generate a high-frequency material performance report.

[0264] The final sensor specification parameter generation module is used to integrate the high-frequency material performance report and the final shielding structure parameters to generate an initial specification document, extract key performance indicators from the initial specification document, perform threshold compliance verification, and convert the verification pass mark into a process document to generate the final sensor specification parameters.

[0265] Furthermore, the steps for generating mechanical structure design parameters by the mechanical structure design parameter generation module include:

[0266] Obtain environmental characteristic data of the substation, perform electromagnetic interference spectrum analysis, and obtain environmental electromagnetic characteristic parameters;

[0267] The interference frequency band is extracted from the electromagnetic characteristic parameters of the environment, the spatial electric field intensity is modeled, and the initial shielding layer requirements are generated.

[0268] Structural topology optimization is performed on the initial shielding layer requirements to generate mechanical structure design parameters.

[0269] Furthermore, the steps for the final shielding structure parameter generation module to generate the final shielding structure parameters include:

[0270] Mechanical structure design parameters are obtained from the mechanical structure design parameter generation module, interlayer impedance matching calculation is performed, and shielding layer material combination scheme is obtained;

[0271] Electromagnetic attenuation indexes are extracted from shielding layer material combination schemes to select high-frequency materials and generate high-frequency material layout parameters.

[0272] The electrode shape is iteratively optimized by optimizing the high-frequency material layout parameters to generate the final shielding structure parameters.

[0273] Furthermore, the steps of the optimized electric field distribution parameter generation module in generating the optimized electric field distribution parameters also include:

[0274] The final shielding structure parameters are obtained from the final shielding structure parameter generation module, a three-dimensional electromagnetic field simulation model is established, and the initial field strength distribution data is obtained.

[0275] Interference coupling features are extracted from the initial field strength distribution data, multi-scenario interference simulation is performed, and interference suppression effect data is generated.

[0276] Electrode sensitivity was verified based on the interference suppression effect data, and optimized electric field distribution parameters were generated.

[0277] Furthermore, the steps of the final compensation circuit configuration generation module in generating the final compensation circuit configuration include:

[0278] The optimized electric field distribution parameters are obtained from the optimized electric field distribution parameter generation module, the temperature compensation circuit topology is configured, and the initial compensation circuit parameters are obtained.

[0279] Temperature-sensitive nodes are extracted from the initial compensation circuit parameters, drift characteristics are tested, and a temperature compensation coefficient matrix is ​​generated.

[0280] The circuit parameters of the temperature compensation coefficient matrix are optimized to generate the final compensation circuit configuration.

[0281] Furthermore, the final sensor specification parameter generation module generates a high-frequency material performance report, including the following steps:

[0282] The final compensation circuit configuration is obtained from the final compensation circuit configuration generation module, a high-frequency material testing platform is built, and the raw response data is obtained.

[0283] Extract time-domain features from the raw response data, perform microsecond-level response verification, and generate dynamic response parameters;

[0284] Material matching degree analysis is performed on dynamic response parameters to generate high-frequency material performance reports.

[0285] Furthermore, the final sensor specification parameter generation module generates the final sensor specification parameters through the following steps:

[0286] Obtain high-frequency material performance reports, integrate final shielding structure parameters, and generate initial specification documents;

[0287] Extract key performance indicators from the initial specification document, perform threshold compliance verification, and generate a verification pass identifier;

[0288] The verification results are used to convert the process documents and generate the final sensor specifications.

[0289] Further specific processing methods for the system of this invention can be found in the design method of non-contact electric field sensor.

[0290] Example 3:

[0291] This embodiment provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores news data and data such as time decay factors. The network interface is used for communication with external terminals via a network connection.

[0292] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0293] Example 4:

[0294] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a non-contact electric field sensor design method.

[0295] Example 5:

[0296] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a non-contact electric field sensor design method.

[0297] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0298] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A design method for a non-contact electric field sensor, characterized in that, include: Acquire substation environmental characteristic data, extract the main frequency band of interference based on environmental electromagnetic characteristic parameters and model the spatial electric field intensity, perform electromagnetic interference spectrum analysis and structural topology optimization of preliminary shielding layer requirements, and generate mechanical structure design parameters. Based on mechanical structure design parameters, electromagnetic attenuation index is extracted from shielding layer material combination scheme and high-frequency material selection is performed. Interlayer impedance matching calculation is performed and electrode shape iterative optimization operation is carried out on high-frequency material layout parameters to generate final shielding structure parameters. The final shielding structure parameters are obtained, a three-dimensional electromagnetic field simulation model is established, and interference coupling features are extracted based on the initial field strength distribution data. Multi-scenario interference simulation is performed, and electrode sensitivity is verified based on the interference suppression effect data. Optimized electric field distribution parameters are generated. Based on the optimized electric field distribution parameters, the temperature compensation circuit topology is configured, and temperature-sensitive nodes are extracted from the initial compensation circuit parameters for drift characteristic testing and circuit parameter optimization of the temperature compensation coefficient matrix to generate the final compensation circuit configuration. Obtain the final compensation circuit configuration, build a high-frequency material testing platform, extract time-domain features from the original response data to perform microsecond-level response verification and material matching degree analysis on dynamic response parameters, and generate a high-frequency material performance report. The initial specification document is generated by integrating the high-frequency material performance report and the final shielding structure parameters. Key performance indicators are extracted from the initial specification document, and threshold compliance verification is performed. The verification pass mark is then converted into a process document to generate the final sensor specification parameters.

2. The method according to claim 1, characterized in that, The steps for generating mechanical structure design parameters also include: Obtain environmental characteristic data of the substation, perform electromagnetic interference spectrum analysis, and obtain environmental electromagnetic characteristic parameters; The interference frequency band is extracted from the electromagnetic characteristic parameters of the environment, the spatial electric field intensity is modeled, and the initial shielding layer requirements are generated. Structural topology optimization is performed on the initial shielding layer requirements to generate mechanical structure design parameters.

3. The method according to claim 1, characterized in that, The steps for generating the final shielding structure parameters also include: Obtain the mechanical structure design parameters, perform interlayer impedance matching calculations, and obtain the shielding layer material combination scheme; Electromagnetic attenuation indexes are extracted from shielding layer material combination schemes to select high-frequency materials and generate high-frequency material layout parameters. The electrode shape is iteratively optimized by optimizing the high-frequency material layout parameters to generate the final shielding structure parameters.

4. The method according to claim 1, characterized in that, The steps for generating the optimized electric field distribution parameters also include: Obtain the final shielding structure parameters, establish a three-dimensional electromagnetic field simulation model, and obtain the initial field strength distribution data; Interference coupling features are extracted from the initial field strength distribution data, multi-scenario interference simulation is performed, and interference suppression effect data is generated. Electrode sensitivity was verified based on the interference suppression effect data, and optimized electric field distribution parameters were generated.

5. The method according to claim 1, characterized in that, The steps for generating the final compensation circuit configuration also include: Obtain the optimized electric field distribution parameters, configure the temperature compensation circuit topology, and obtain the initial compensation circuit parameters; Temperature-sensitive nodes are extracted from the initial compensation circuit parameters, drift characteristics are tested, and a temperature compensation coefficient matrix is ​​generated. The circuit parameters of the temperature compensation coefficient matrix are optimized to generate the final compensation circuit configuration.

6. The method according to claim 1, characterized in that, The steps for generating high-frequency material performance reports also include: Obtain the final compensation circuit configuration, build a high-frequency material testing platform, and obtain the raw response data; Extract time-domain features from the raw response data, perform microsecond-level response verification, and generate dynamic response parameters; Material matching degree analysis is performed on dynamic response parameters to generate high-frequency material performance reports.

7. The method according to claim 1, characterized in that, The steps for generating the final sensor specifications also include: Obtain high-frequency material performance reports, integrate final shielding structure parameters, and generate initial specification documents; Extract key performance indicators from the initial specification document, perform threshold compliance verification, and generate a verification pass identifier; The verification results are used to convert the process documents and generate the final sensor specifications.

8. The method according to claim 1, characterized in that, The expressions that generate the final shielding structure parameters include: Analyze the relationship between electrode shape and electric field coupling efficiency: in, Electrode shape and The similarity index of electric field coupling between them; Let be the shape parameter vector of the initial electrode. This is the vector of shape parameters for the optimized electrode; For the initial electrode shape in the first Electric field strength at each sampling point; For sampling point index; The electric field strength after optimizing the electrode shape; The standard deviation of the electric field for the initial electrode shape. The standard deviation of the electric field after optimizing the electrode shape; The width of the Gaussian kernel; This represents the total number of sampling points; To take the real part of the complex electric field strength; It is the Euclidean norm; Construct the shape optimization objective function: in, It is a measure of shape complexity; For the electrode surface, the first An index of a discrete region or sampling point; This represents the total number of discrete regions of curvature. The probability density of the curvature distribution on the electrode surface; It is a smoothing factor; Here, represents the gradient regularization coefficient. The gradient field represents the shape of the electrode. It is an L2 norm; Construct a multi-objective optimization function: in, The fitness function value; and These are the weighting coefficients; This is the historical best cross-correlation value; The baseline entropy value; Perform iterative optimization and define an adaptive mutation probability function: in, For the first Probability of generational mutation; As the baseline probability; The attenuation coefficient; For the first Average fitness value of the generation, For the first Maximum fitness value; Define the electric field coupling efficiency verification function: in, This serves as a verification indicator for electric field coupling efficiency. This serves as an index for the mesh points in the finite element simulation. To optimize the position of the electrode The electric field strength; The ambient electric field strength; This represents the number of simulation grid points; Adjusting electrode edge curvature using gradient descent: in, These are the final shielding structure parameters; For the first The optimized electrode shape parameters; The learning rate; The gradient field of the electrode shape is used to determine the coupling efficiency.

9. The method according to claim 1, characterized in that, The expressions that generate the final compensation circuit configuration include: Wavelet transform was used to analyze the characteristics of temperature-sensitive nodes: in, These are wavelet coefficients; Normalization factor; For the index of the circuit node; For integration time; For nodes In time The voltage; These are Morlet wavelet basis functions; For time sampling points; For scale parameters; Construct the temperature-drift response matrix: in, These are the elements of the temperature-drift response matrix; For the temperature sensor index; An index for the number of temperature scan experiments; This represents the total number of temperature scan experiments. For the first Temperature sensor in this experiment The reading; For nodes The amplitude of the wavelet coefficients; and The mean of the sample; Furthermore, a multivariate regression model is constructed: in, For nodes The amount of voltage drift; This represents the total number of temperature sensors; Temperature sensor The amount of temperature change; These are the regression coefficients; For residual terms; L1 regularization is used to optimize coefficient stability: in, For minimization operation; For nodes The amount of voltage drift; The regularization coefficient is used. These are the model's predicted values; The total number of nodes; Optimized Mapping to the compensation coefficient matrix: in, Temperature sensor For compensation nodes Spatial influence weight; For temperature For compensation elements The gain function; These are the elements of the compensation coefficient matrix; For the index of the compensation node; This is the index of the compensation element.

10. The method according to claim 1, characterized in that, Iterative optimization of electrode shape for high-frequency material layout parameters includes: Electric field coupling efficiency analysis of the initial electrode shape; During the simulation, input substation environmental characteristic data and electromagnetic interference spectrum to simulate the electric field response of electrodes in the actual operating frequency band; The simulation results include key indicators such as electric field distribution, electrode sensitivity, and interference coupling efficiency.

11. A non-contact electric field sensor design system, characterized in that, The system includes: The mechanical structure design parameter generation module is used to acquire substation environmental characteristic data, extract the main interference frequency band based on environmental electromagnetic characteristic parameters and perform spatial electric field intensity modeling, perform electromagnetic interference spectrum analysis and structural topology optimization processing for preliminary shielding layer requirements, and generate mechanical structure design parameters. The final shielding structure parameter generation module is used to extract electromagnetic attenuation indexes from the shielding layer material combination scheme and select high-frequency materials based on the mechanical structure design parameters generated by the mechanical structure design parameter generation module, perform interlayer impedance matching calculations and iterative optimization of electrode shape for high-frequency material layout parameters, and generate the final shielding structure parameters. The optimized electric field distribution parameter generation module is used to obtain the final shielding structure parameters generated by the final shielding structure parameter generation module, establish a three-dimensional electromagnetic field simulation model, extract interference coupling features based on the initial field strength distribution data, perform multi-scenario interference simulation and verify electrode sensitivity based on interference suppression effect data, and generate optimized electric field distribution parameters. The final compensation circuit configuration generation module is used to configure the temperature compensation circuit topology based on the optimized electric field distribution parameters generated by the optimized electric field distribution parameter generation module, extract temperature-sensitive nodes from the initial compensation circuit parameters for drift characteristic testing, and optimize the circuit parameters of the temperature compensation coefficient matrix to generate the final compensation circuit configuration. The high-frequency material performance report generation module is used to obtain the final compensation circuit configuration generated by the final compensation circuit configuration generation module, build a high-frequency material testing platform, extract time-domain features from the original response data, perform microsecond-level response verification and material matching degree analysis on dynamic response parameters, and generate a high-frequency material performance report. The final sensor specification parameter generation module is used to integrate the high-frequency material performance report and the final shielding structure parameters to generate an initial specification document, extract key performance indicators from the initial specification document, perform threshold compliance verification, and convert the verification pass mark into a process document to generate the final sensor specification parameters.

12. The system according to claim 11, characterized in that, The steps for generating mechanical structure design parameters by the mechanical structure design parameter generation module include: Obtain environmental characteristic data of the substation, perform electromagnetic interference spectrum analysis, and obtain environmental electromagnetic characteristic parameters; The interference frequency band is extracted from the electromagnetic characteristic parameters of the environment, the spatial electric field intensity is modeled, and the initial shielding layer requirements are generated. Structural topology optimization is performed on the initial shielding layer requirements to generate mechanical structure design parameters.

13. The system according to claim 11, characterized in that, The final shielding structure parameter generation module generates the final shielding structure parameters through the following steps: Mechanical structure design parameters are obtained from the mechanical structure design parameter generation module, interlayer impedance matching calculation is performed, and shielding layer material combination scheme is obtained; Electromagnetic attenuation indexes are extracted from shielding layer material combination schemes to select high-frequency materials and generate high-frequency material layout parameters. The electrode shape is iteratively optimized by optimizing the high-frequency material layout parameters to generate the final shielding structure parameters.

14. The system according to claim 11, characterized in that, The steps in generating optimized electric field distribution parameters by the optimized electric field distribution parameter generation module also include: The final shielding structure parameters are obtained from the final shielding structure parameter generation module, a three-dimensional electromagnetic field simulation model is established, and the initial field strength distribution data is obtained. Interference coupling features are extracted from the initial field strength distribution data, multi-scenario interference simulation is performed, and interference suppression effect data is generated. Electrode sensitivity was verified based on the interference suppression effect data, and optimized electric field distribution parameters were generated.

15. The system according to claim 11, characterized in that, The final compensation circuit configuration generation module generates the final compensation circuit configuration through the following steps: The optimized electric field distribution parameters are obtained from the optimized electric field distribution parameter generation module, the temperature compensation circuit topology is configured, and the initial compensation circuit parameters are obtained. Temperature-sensitive nodes are extracted from the initial compensation circuit parameters, drift characteristics are tested, and a temperature compensation coefficient matrix is ​​generated. The circuit parameters of the temperature compensation coefficient matrix are optimized to generate the final compensation circuit configuration.

16. The system according to claim 11, characterized in that, The final sensor specification parameter generation module generates a high-frequency material performance report through the following steps: The final compensation circuit configuration is obtained from the final compensation circuit configuration generation module, a high-frequency material testing platform is built, and the raw response data is obtained. Extract time-domain features from the raw response data, perform microsecond-level response verification, and generate dynamic response parameters; Material matching degree analysis is performed on dynamic response parameters to generate high-frequency material performance reports.

17. The system according to claim 11, characterized in that, The final sensor specification parameter generation module generates the final sensor specification parameters through the following steps: Obtain high-frequency material performance reports, integrate final shielding structure parameters, and generate initial specification documents; Extract key performance indicators from the initial specification document, perform threshold compliance verification, and generate a verification pass identifier; The verification results are used to convert the process documents and generate the final sensor specifications.

18. 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 non-contact electric field sensor design method according to any one of claims 1 to 10.

19. 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 non-contact electric field sensor design method according to any one of claims 1 to 10.

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