Extra-high voltage GIS graphene-copper-based composite diversion assembly detection method and related equipment

Abstract

The invention relates to the technical field of insulation switch detection, and provides an extra-high voltage GIS graphene-copper-based composite diversion assembly detection method and related equipment. Performance detection processing is carried out on a target diversion assembly according to a detection mode to obtain performance space distribution data, and structural defect detection processing is carried out on the target diversion assembly according to the performance space distribution data to obtain a structural defect feature data set; performing dynamic current-carrying analysis and detection processing on the target diversion assembly according to the structural defect feature data set to obtain a thermal resistance anomaly grid coordinate mapping data set, and performing arc ablation simulation processing on the target diversion assembly according to the thermal resistance anomaly grid coordinate mapping data set to obtain an ablation rate. And performing multi-physics field coupling failure simulation processing by combining the degradation model to obtain a life prediction data set. According to the application, through multi-level and multi-angle data acquisition and analysis, comprehensive evaluation of the ultra-high voltage GIS graphene-copper-based composite diversion assembly from performance, structure, current-carrying state to arc ablation and the like is realized.

Description

Technical Field

[0001] This application relates to the technical field of insulation switch detection, and in particular to a detection method and related equipment for a graphene-copper-based composite current-carrying component of ultra-high voltage GIS. Background Art

[0002] The working environment of ultra-high voltage GIS equipment is complex, and it bears high voltage and large current. As a key component for current transmission, the quality of the graphene-copper-based composite current-carrying component is directly related to the operation safety and reliability of the entire system. Any minor defect or performance degradation may cause local overheating, arc ablation or electrical failure, thus endangering the equipment stability. The composite structure of graphene and copper not only has high electrical conductivity and good heat conduction, but also requires excellent mechanical properties. However, there may be problems such as interface defects, microvoids and local performance inhomogeneity inside the composite material.

[0003] The existing detection methods mainly rely on single physical field (i.e., electrical performance, thermal performance and mechanical performance) detection and traditional non-destructive detection means (i.e., X-ray detection and ultrasonic detection). Although they can reflect the basic state of the component to a certain extent, there are disadvantages such as isolated data, inaccurate defect identification, insufficient dynamic performance and lack of failure prediction. Some methods test the material reaction by locally applying current or heat energy to obtain the local performance change, but there is a lack of real-time analysis of the coupling effect between defects and the actual working state during the detection process, and it is impossible to fully simulate dynamic current-carrying conditions such as arc ablation. Summary of the Invention

[0004] In view of this, this application provides a detection method and related equipment for a graphene-copper-based composite current-carrying component of ultra-high voltage GIS to solve the problem of insufficient detection accuracy of samples caused by single or local parameter testing.

[0005] The first aspect of this application provides a detection method for a graphene-copper-based composite current-carrying component of ultra-high voltage GIS, and the method includes: Performing performance detection processing on the target current-carrying component according to a preset detection method to obtain performance spatial distribution data; Performing structural defect detection processing on the target current-carrying component according to the performance spatial distribution data to obtain a structural defect feature data set; Performing dynamic current-carrying analysis detection processing on the target current-carrying component according to the structural defect feature data set to obtain a thermal resistance abnormal grid coordinate mapping data set; Performing arc ablation simulation processing on the target current-carrying component according to the thermal resistance abnormal grid coordinate mapping data set to obtain an ablation rate; Perform multi-physical field coupling failure simulation processing on the performance space distribution data, the structural defect feature dataset, the thermal resistance abnormal grid coordinate mapping dataset, and the ablation rate according to a preset degradation model to obtain a life prediction dataset.

[0006] In an alternative embodiment, the performance space distribution data includes a conductivity matrix set, a thermal conductivity matrix set, and a mechanical parameter set. The performance detection processing of the target diversion component according to a preset detection method to obtain the performance space distribution data includes: Perform surface area division processing on the collected target three-dimensional model according to a preset grid format to obtain grid coordinate mapping data; Perform conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the conductivity matrix set; Perform thermal conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the thermal conductivity matrix set; Perform a mechanical property tensile experiment on the target diversion component to obtain the mechanical parameter set.

[0007] In an alternative embodiment, the structural defect detection processing of the target diversion component according to the performance space distribution data to obtain the structural defect feature dataset includes: Perform global conductivity data calculation processing on the target diversion component according to the conductivity matrix set to obtain global average conductivity data, and perform screening processing on the conductivity matrix set according to the global average conductivity data to obtain low conductivity region data; Perform X-ray tomography processing on the target diversion component according to the low conductivity region data to obtain a three-dimensional voxel model corresponding to the low conductivity region and pore distribution data; Perform ultrasonic phased array scanning processing on the target diversion component according to the low conductivity region data to obtain the reflection coefficient distribution data of the low conductivity region; Perform defect classification processing on the three-dimensional voxel model according to the reflection coefficient distribution data and the pore distribution data to obtain the structural defect feature dataset.

[0008] In an alternative embodiment, the thermal resistance abnormal grid coordinate mapping dataset includes thermal resistance abnormal coordinate data and corresponding contact resistance data. The dynamic current-carrying analysis detection processing of the target diversion component according to the structural defect feature dataset to obtain the thermal resistance abnormal grid coordinate mapping dataset includes: Perform current-carrying region positioning processing on the target diversion component according to the structural defect feature dataset to obtain defect region coordinate data; Perform a stepped current loading process on the defect area in the defect area coordinate data to obtain contact voltage drop data and real-time current detection data, and perform a dynamic contact resistance calculation process based on the contact voltage drop data and the real-time current detection data to obtain the contact resistance data; Perform a three-dimensional heat conduction modeling process on the target current-carrying component according to the thermal conductivity matrix set to obtain simulated temperature rise data; Perform a temperature rise deviation calculation process based on the simulated temperature rise data and the actually obtained real-time temperature rise data to obtain temperature rise deviation data, and perform a thermal resistance anomaly judgment process on the target current-carrying component according to the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data; Perform a grid coordinate mapping process based on the thermal resistance anomaly coordinate data and the contact resistance data to obtain the thermal resistance anomaly grid coordinate mapping data set.

[0009] In an alternative embodiment, the performing an arc ablation simulation process on the target current-carrying component according to the thermal resistance anomaly grid coordinate mapping data set to obtain an ablation rate includes: Perform an arc target positioning process on the target current-carrying component according to the thermal resistance anomaly grid coordinate mapping data set to obtain arc target coordinate data; Perform an arc energy loading process on the target current-carrying component according to the arc target coordinate data to obtain arc voltage data and arc current data; Perform an arc energy density calculation process on the target current-carrying component according to the arc voltage data and the arc current data to obtain arc energy density data; Perform a surface topography scanning process on the target current-carrying component according to the arc energy loading area in the arc energy density data to obtain surface roughness data and ablation depth data; Perform an ablation rate calculation process on the target current-carrying component according to the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

[0010] In an alternative embodiment, the performing a multi-physical field coupling failure simulation process according to the performance space distribution data, the structural defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set includes: Perform an initial performance parameter extraction process on the target current-carrying component according to the performance space distribution data to obtain initial conductivity data, initial thermal conductivity data, and initial mechanical property data; Perform local performance attenuation rate calculation processing on the target diversion component according to the ablation rate to obtain local attenuation parameters; Perform defect area division processing on the target diversion component according to the structural defect feature dataset to obtain performance degradation information for each defect area; Perform local temperature rise anomaly parameter extraction processing on the target diversion component according to the thermal resistance anomaly grid coordinate mapping dataset to obtain temperature rise anomaly data; Perform multi-physical field coupling simulation processing on the target diversion component through the degradation model according to the initial conductivity data, the initial thermal conductivity data, the initial mechanical property data, the local attenuation parameters, the performance degradation information, and the temperature rise anomaly data to obtain electric field distribution data, temperature field distribution data, and stress field distribution data; Perform failure criterion analysis processing on the target diversion component according to the electric field distribution data, the temperature field distribution data, and the stress field distribution data to obtain the life prediction dataset.

[0011] In an optional implementation manner, the method further includes: Perform data integration processing on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, the ablation rate, and the life prediction dataset according to a preset process-performance relationship database to obtain process-performance correlation data; Perform feature extraction processing on the process-performance correlation data to obtain process parameter data and corresponding key performance index data; Perform process parameter correlation modeling processing according to the process parameter data and the key performance index data to obtain a process-performance correlation model; Perform multi-objective optimization processing on process parameters according to the process-performance correlation model to obtain an optimal process parameter combination.

[0012] The second aspect of the present application provides a detection device for a UHV GIS graphene-copper composite diversion component, and the device includes: A performance detection module, configured to perform performance detection processing on a target diversion component according to a preset detection method to obtain performance spatial distribution data; An anomaly analysis module, configured to perform structural defect detection processing on the target diversion component according to the performance spatial distribution data to obtain a structural defect feature dataset; A dynamic test module, configured to perform dynamic current-carrying analysis detection processing on the target diversion component according to the structural defect feature dataset to obtain a thermal resistance anomaly grid coordinate mapping dataset; An ablation test module for performing arc ablation simulation processing on the target current-carrying component according to the thermal resistance abnormal grid coordinate mapping data set to obtain an ablation rate; A degradation simulation module for performing multi-physical field coupling failure simulation processing according to the performance space distribution data, the structural defect feature data set, the thermal resistance abnormal grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set.

[0013] A third aspect of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned detection method for the UHV GIS graphene-copper-based composite current-carrying component are implemented.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned detection method for the UHV GIS graphene-copper-based composite current-carrying component are implemented.

[0015] In summary, the present application at least includes the following beneficial technical effects: 1. Divide the three-dimensional model of the target current-carrying component into multiple detection regions, and measure the conductivity, thermal conductivity, and mechanical parameters respectively, realizing a refined spatial distribution evaluation of the overall performance of the component.

[0016] 2. Use the global conductivity data to screen out the low-conductivity regions, and combine X-ray tomography and ultrasonic phased array scanning to accurately obtain the three-dimensional voxel model, pore distribution, and reflection coefficient distribution of the low-conductivity regions, realizing a comprehensive detection and classification of internal structure defects, thereby effectively identifying potential hidden dangers.

[0017] 3. Apply a stepped current load to the defect region and analyze the temperature rise deviation data to obtain the contact resistance and temperature rise abnormal information in real time. This provides reliable experimental data for detecting the thermal resistance abnormality of the current-carrying component under the actual working condition and ensures the accuracy of the dynamic performance analysis.

[0018] 4. Based on the thermal resistance abnormal grid coordinate mapping data, combined with means such as arc energy loading and surface topography scanning, the arc ablation process can be simulated and the ablation rate can be calculated. Further, through multi-physical field coupling failure simulation, considering the effects of the electric field, temperature field, and stress field comprehensively, the accurate prediction of the service life of the component is realized, providing a theoretical basis for preventing faults in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 is a flowchart of a detection method for a UHV GIS graphene - copper - based composite current - conducting component provided by an embodiment of the present application; Figure 2 is a functional module diagram of a detection device for a UHV GIS graphene - copper - based composite current - conducting component provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0022] As Figure 1 shown, it is a flowchart of a detection method for a UHV GIS graphene - copper - based composite current - conducting component provided by an embodiment of the present application. The detection method for a UHV GIS graphene - copper - based composite current - conducting component provided by an embodiment of the present application includes the following steps.

[0023] As a special equipment, the UHV GIS graphene - copper - based composite current - conducting component needs to withstand high voltage and large current in a complex working environment, and has extremely high requirements for product quality. Therefore, the current - conducting components of the same batch (i.e., automatically manufactured using the same production process parameters) must pass sampling quality inspection to ensure that the overall product quality meets industry standards. A comprehensive quality inspection and evaluation can not only verify product performance but also detect potential defects in advance, thereby improving the safety and reliability of the equipment. The detection method for a UHV GIS graphene - copper - based composite current - conducting component provided by an embodiment of the present application realizes a comprehensive evaluation and accurate prediction of the state of the current - conducting component through grid - based data acquisition, accurate identification of structural defects, dynamic current - carrying and thermal resistance anomaly monitoring, and process - performance correlation modeling, providing solid data support for production quality control. The specific operations are as follows: Step S1: Perform performance detection processing on the target current - conducting component according to a preset detection method to obtain performance spatial distribution data.

[0024] During the manufacturing process of the flow guiding component, significant differences in local performance may occur due to uneven material distribution or process variations. By discretizing the sample surface, it is possible to accurately locate and quantify the electrical and thermal properties of each region, thereby achieving high-precision quality inspection control.

[0025] Before conducting the quality inspection operation, high-precision three-dimensional data scanning and acquisition of the target flow guiding component samples participating in the quality inspection are required. Specifically, a high-resolution scanner (e.g., a laser scanner or a CT scanner) is used to scan the outer surface of the target flow guiding component sample to obtain three-dimensional point cloud data containing rich geometric information. This three-dimensional point cloud data undergoes data preprocessing to generate a digital three-dimensional model (i.e., the target three-dimensional model) with clear boundaries and detailed features. The target three-dimensional model contains the complete geometric contour and surface micro-features of the flow guiding component in space.

[0026] After obtaining the target three-dimensional model, the surface of the three-dimensional model is divided according to a preset grid format. The preset grid format is usually determined according to the quality inspection requirements and the material size. For example, the surface of the flow guiding component is divided into 5 mm × 5 mm square grids. Each grid cell is assigned a unique coordinate identifier (e.g., G 11 、G 12 、……) in the model, and a complete grid coordinate mapping data set is formed. This grid division method can discretize the surface of the entire flow guiding component, ensuring sufficient spatial resolution for subsequent conductivity and thermal conductivity measurements, thereby revealing local performance differences. Specifically, first, the target three-dimensional model is imported into a dedicated digital processing software. The software automatically divides the model surface according to preset parameters (such as grid cell size, starting coordinates, etc.). The grid division operation can be represented by the following formula: Where represents the grid cell located in the i-th column and j-th row. ([[]] ) represent the starting coordinates of the model surface respectively. And represent the sizes of the grid cell in the x axis and y axis directions. In this embodiment . i And jis a non - negative number, determined according to the model size range. The target surface is discretized so that the measurement results within each grid cell can accurately reflect the local performance of the area. The preset grid division scheme can ensure that the measurement data has good spatial resolution, facilitating subsequent comparison and analysis of local performance data and global performance data. After the grid division is completed, the output grid coordinate mapping data contains the unique identifier, geometric position, and size information of each grid cell. These data provide an accurate spatial positioning basis for subsequent local measurements of conductivity and thermal conductivity, ensuring that each measurement point can accurately correspond to the specific position of the flow - guiding component sample.

[0027] During the manufacturing process of the flow - guiding component, due to factors such as the dispersion state of graphene or the microscopic pore structure, there may be local non - uniformity in conductivity. By measuring separately within the preset grid cells, the internal conductivity distribution of the material can be accurately revealed, thus providing quantitative data support for quality control and subsequent defect determination. Using the obtained grid coordinate mapping data as the positioning basis, the conductivity of the material within each grid cell is precisely measured. In the embodiment of this application, the four - probe conductivity measurement method is used to detect the sample surface. By arranging four tiny probes within each preset grid cell, contacting the sample surface at a fixed spacing, applying a constant current, and then measuring the voltage drop between the probes, the conductivity value of this area is finally calculated. The specific operation is as follows: First, combine the four - probe tester with an automatic positioning system, and position the center point or predetermined position of each grid cell according to the grid coordinate mapping data. The current source applies a constant current to the probes (for example, I = 10A), while the voltage detector measures the voltage drop between two probes. The conductivity value calculation formula within each grid cell is as follows: Among them, represents the conductivity of the th grid cell. represents the applied constant current, which is set to 10A in this embodiment. represents the probe spacing, obtained through probe calibration. represents the voltage drop between the probes obtained during the measurement. represents the effective cross - sectional area corresponding to the flow - guiding component sample in this grid cell, and this parameter is obtained by extracting the reconstruction data of the three - dimensional model. By combining the applied current and the measured voltage drop, and performing normalization processing using the geometric parameters of the material, the conductivity of this area is obtained. During the operation process, the conductivity of each grid cell is measured in sequence, and the measurement results are recorded in the conductivity matrix set. Each element in the matrix corresponds to the conductivity value of a specific grid, thus forming the conductivity distribution map of the entire flow - guiding component sample.

[0028] Meanwhile, since the thermal conductivity of the material of the flow guiding component directly affects the heat dissipation performance of the device under high load. By accurately measuring the thermal conductivity of each region, problem areas with possible local overheating or insufficient heat dissipation can be identified, thus providing basic data for subsequent thermal resistance anomaly detection and arc ablation simulation. After obtaining the grid coordinate mapping data, detecting the thermal conductivity within each grid cell is one of the keys to achieving performance spatial distribution data. In the embodiment of the present application, a microscopic thermal imaging technique is used to measure the thermal conductivity of the sample. By applying a uniform heat flux density, a stable temperature field is generated on the surface of the sample, and then a high-resolution thermal imaging camera is used to obtain the temperature distribution map of this region. Based on the temperature gradient data and the heat flow characteristics of the material, the thermal conductivity of the corresponding region can be calculated. The specific operation is as follows: First, a preset uniform heat flux density (for example, q = 100 W / m 2 ) is applied to each grid cell, and this heat flux density is uniformly transferred to the surface of the sample through a heating plate or a customized heat flux generator. Next, a microscopic thermal imaging camera is used to collect the image of the steady-state temperature field on the surface of the sample, and the image data is digitally processed to form a temperature matrix. For each grid cell, the temperature gradient is calculated based on its temperature distribution data. The calculation formula for the thermal conductivity value within each grid cell is as follows: Where, represents the thermal conductivity of the th grid cell. represents the heat flux density applied to the surface of the sample, which is fixed at 100 W / m 2 in this embodiment. represents the effective thickness of the heat flow along the conduction direction, and this value is usually determined by CT scanning or other thickness measurement techniques. represents the steady-state temperature gradient measured within this grid cell, that is, the difference between the highest temperature and the lowest temperature. Using the gradient information of the temperature field, combined with the known heat flux density and the geometric dimensions of the material, the thermal conductivity of this region is calculated. During the operation process, the temperature field data collection and gradient calculation are respectively carried out for each grid cell, and finally a set of thermal conductivity matrices is formed, where each matrix element corresponds to the thermal conductivity value of a specific grid cell.

[0029] Meanwhile, the flow guiding component not only needs to meet the electrical and thermal performance requirements, but its mechanical properties directly affect the stability and safety of the component under mechanical stress. Tensile tests can comprehensively reflect the performance of the material under the stressed state and reveal possible mechanical defects in the long-term operation process of the material, providing key basis for subsequent life prediction and process optimization. Conducting mechanical property tests on the target flow guiding component is an essential step. In the embodiment of the present application, a standardized tensile test device is used to gradually load the sample until the sample breaks. The specific operation is as follows: Fix the sample of the flow guiding component at both ends of the tensile testing machine, and keep the sample in good alignment. The testing machine gradually applies a tensile force at a preset loading rate (e.g., 0.5 mm / min), and the applied force and the elongation of the sample are recorded in real time by sensors. The stress-strain curve generated during the test is the key data for describing the mechanical properties of the material, and this curve contains information about the elastic region, yield region, and fracture point of the material. In the tensile test data, the maximum load and the elongation at fracture are focused on. Calculate the tensile strength through the following formula, which normalizes the maximum load and the initial cross-sectional area of the sample, so as to obtain the tensile properties of the material during the tensile process.

[0030] Among them, represents the tensile strength of the sample. represents the maximum load recorded during the test, and this data is collected in real time by the built-in sensor of the testing machine. represents the initial cross-sectional area of the sample, and this parameter is usually measured by a laser rangefinder or other precision measuring instruments. The calculated tensile strength can reflect the ability of the material to resist fracture. At the same time, through the stress-strain curve, the elongation at fracture of the material can also be obtained, which is an important index of the toughness of the material. After all the mechanical property data are recorded by the data acquisition system, they constitute a set of mechanical parameters, and this data set includes key performance indexes such as tensile strength and elongation at fracture.

[0031] By meshing the 3D model of the target flow guiding component sample, and then performing detection and processing of conductivity, thermal conductivity, and mechanical properties in each grid respectively, a conductivity matrix set, a thermal conductivity matrix set, and a set of mechanical parameters are formed. These performance spatial distribution data not only comprehensively reflect the local and overall performance of the sample, but also provide essential data support for subsequent defect detection, multi-physical field coupling simulation, and process parameter optimization.

[0032] Step S2, perform structural defect detection and processing on the target flow guiding component according to the performance spatial distribution data to obtain a data set of structural defect characteristics.

[0033] During the production process of the flow guiding component sample, due to process control or uneven material mixing, the conductivity of some regions will be lower than the overall average level. Screening out these regions can provide a clear detection target for subsequent more refined internal defect detection (e.g., X-ray tomography and ultrasonic testing), ensuring that the detection method is highly targeted and the data acquisition is more efficient and accurate. Calculate the global average conductivity of the sample according to the obtained conductivity matrix set, and use this average value as a benchmark to compare the conductivity of each grid unit, so as to screen out the data of the low-conductivity regions.

[0034] During the implementation process, it is first necessary to perform statistical calculations on all the values in the conductivity matrix set. Each element in the conductivity matrix set represents the conductivity value located in the ij grid cell, and the entire matrix contains the detection data of all grid cells. The calculation process of the global average conductivity data is as follows: where, represents the calculated global average conductivity. represents at the row and the column grid cell, the detected conductivity. N represents the total number of grid cells in the conductivity matrix, which is equal to n×m, where n is the number of rows and m is the number of columns. N is used to average the accumulated results.

[0035] Furthermore, during the production process of the flow guide component sample, due to process control or uneven material mixing, the conductivity of some areas will be lower than the overall average level. Screening out these areas can provide clear detection targets for subsequent more refined internal defect detection (e.g., X-ray tomography and ultrasonic testing), ensuring that the detection method is highly targeted and the data acquisition is more efficient and accurate. After obtaining the global average conductivity data, the data of each grid cell is screened according to the set screening threshold (e.g., 80% of the global average conductivity). Specifically, for each grid cell, if its conductivity is less than , then this grid cell is marked as a low-conductivity area. X-ray tomography can non-destructively reveal the three-dimensional structure inside the material. The generated voxel model not only intuitively reflects the internal density but also can accurately quantify the pore distribution, providing quantitative data support for detecting potential structural defects in the low-conductivity area. Therefore, after obtaining the data of the low-conductivity area, X-ray tomography technology is used to detect the target flow guide component sample to obtain the three-dimensional voxel model and pore distribution data of the corresponding low-conductivity area. During the implementation process, first, the data of the screened low-conductivity area is used as the positioning basis for the scanning target, and local CT scanning is performed on the sample. The CT scanner exposes the sample to X-rays from multiple angles, collects a series of two-dimensional image data, and then uses a computer reconstruction algorithm to generate the three-dimensional voxel model of this area. The specific operations are as follows: First, based on the obtained low-conductivity region dataset, locate these regions in the target three-dimensional model of the sample and transfer the location information to the control system of the CT scanner to ensure that the scanning is only performed on this local area, so as to save scanning time and improve resolution. Rotate the target area for scanning, and acquire a two-dimensional X-ray image every certain angle (for example, 1°). During the acquisition process, the X-ray penetrates the sample and is recorded by the detector. Different gray values in the image reflect the attenuation characteristics of the materials inside the sample. Among them, the acquired image data is digitally processed to form a continuous two-dimensional image sequence. Secondly, use a computerized tomography reconstruction algorithm (such as the filtered back-projection method or the algebraic reconstruction technique) to process the acquired two-dimensional image sequence and reconstruct the three-dimensional voxel model of the target area. Each voxel represents a small volume unit inside the sample, and the gray value of the voxel is related to the material density and composition within this volume unit. Finally, in the three-dimensional voxel model, by setting a gray value threshold, mark the voxels with gray values lower than the predetermined gray value (for example, 50), and consider that these voxels correspond to the pore regions inside the sample. Subsequently, count the number of low-gray voxels and the total number of voxels in the target area to calculate the porosity. The porosity calculation formula is as follows: where, represents the porosity, that is, the volume percentage of pores in the region. represents the number of voxels with gray values lower than the preset threshold in the three-dimensional voxel model. represents the total number of voxels in the target area. The porosity is used to quantify the pore distribution in the target area. A higher porosity usually means that there are more voids or structural defects inside the material. This data is of great guiding significance for subsequent defect classification.

[0036] At the same time, the ultrasonic phased array scanning technology has the advantages of high resolution and fast detection, and can accurately measure the acoustic characteristics of the internal interfaces of materials under non-destructive testing conditions. The low-conductivity region is often a warning region for the existence of structural defects inside the material. By detecting the reflection coefficient of this region, the existence and nature of the defects can be further confirmed, providing complementary information for the overall detection method. Therefore, after obtaining the low-conductivity region data, the ultrasonic phased array scanning technology is used to detect the target flow guiding component sample to obtain the reflection coefficient distribution data of the low-conductivity region. The specific operation is as follows: First, according to the obtained data of the low-conductivity regions, this data is passed to the ultrasonic detection system to focus the detection on these regions, thereby improving the detection efficiency and resolution. Secondly, the ultrasonic phased array probe scans the selected regions. The probe has multiple built-in transducers to synchronously emit ultrasonic pulses and receive the echo signals caused by internal defects. The system controller records the ultrasonic echo intensity and time delay of each detection point according to the echo signals in different directions. Through ultrasonic detection, echo information of multiple data points can be obtained within each low-conductivity region to form local reflection coefficient distribution data. The process of calculating the reflection coefficient of each detection point based on the echo signal data can be expressed by the following formula: where, R represents the reflection coefficient, which is used to quantify the reflection intensity of ultrasonic waves at the medium interface. represents the acoustic impedance of the copper matrix in the flow guiding component, and its calculation formula is , where is the density of copper, is the ultrasonic propagation speed in copper. represents the acoustic impedance of the graphene layer in the flow guiding component, and its calculation formula is , where is the density of graphene, is the ultrasonic propagation speed in graphene. The magnitude of the reflection coefficient can directly reflect the bonding quality of the internal interfaces of the material. A higher reflection coefficient (for example, greater than 0.3) usually indicates the existence of delamination defects, while a lower reflection coefficient (for example, lower than 0.1) may indicate the existence of pore aggregation phenomena. After ultrasonic scanning and processing, the reflection coefficient distribution data of each detection point within the low-conductivity region will be obtained. This data not only reveals the acoustic characteristics between the internal layers of the material but also can assist in determining the nature of the defects and provide an important basis for subsequent defect classification.

[0037] Finally, by fusing the pore distribution data obtained from X-ray CT scanning with the reflection coefficient data obtained from ultrasonic scanning and classifying the data using a clear threshold, the nature and distribution of the defects can be systematically revealed. Based on the reflection coefficient distribution data and the pore distribution data, using the pore distribution data in the obtained three-dimensional voxel model and the reflection coefficient distribution data obtained from ultrasonic detection, the internal structure defects of the target flow guiding component sample are classified. During the implementation process, first, the three-dimensional voxel model, the pore distribution data, and the reflection coefficient distribution data need to be subjected to data fusion processing. During the data fusion process, the porosity of each region in the voxel model is correlated with the reflection coefficient of the corresponding region. The specific operation is as follows: First, perform spatial registration on the three-dimensional voxel model obtained from X-ray tomography and the ultrasonic detection data to ensure that the two types of data are completely aligned in the coordinate system. The registration process uses digital image processing techniques to match and correct the two types of data using common feature points. This process ensures that the reflectivity data and the pore distribution data can be accurately corresponding to the same physical region. Furthermore, according to the preset defect determination criteria, perform segmentation processing on the data of each registered region. Generally, the following determination conditions can be set: When the porosity > 2%, and > 0.3, it is determined as a delamination defect; When the porosity > 5%, and < 0.1, it is determined as a pore aggregation defect.

[0038] Preliminarily classify the defects in the target area according to the quantitative parameters, so that the detection data can intuitively reflect the distribution and nature of different defects.

[0039] Finally, for the registered and classified regions, record the spatial coordinates, defect types, and defect sizes (calculated from the volume or area of the defect region in the voxel model) of each defect. These data constitute the structural defect feature data set. This data set is saved in a structured table form, and each record corresponds to a detected defect, including the specific location during detection, the classification result, and the quantitative indicators of the defect.

[0040] Step 3: Perform dynamic current-carrying analysis and detection processing on the target diversion component according to the structural defect feature data set to obtain a thermal resistance anomaly grid coordinate mapping data set.

[0041] In regions with structural defects, the current-carrying components often exhibit abnormal local electrical properties. These abnormal regions will generate different temperature rises and contact resistance responses compared to normal regions during the dynamic current-carrying process, thus providing a quantitative basis for defect determination. During the dynamic current-carrying test, it is first necessary to identify the regions with structural defects in the target current-carrying component sample, so as to focus on monitoring the electrical and thermal responses of these regions in subsequent tests. The structural defect feature dataset is input into the positioning system. The positioning system analyzes the input data, extracts the coordinate information of each defect in three-dimensional space, and marks these regions on the target three-dimensional model of the sample. In the specific implementation process, the system uses digital image processing technology to match and correct the defect coordinate data in the target three-dimensional model with the structural defect feature dataset, and uses interpolation algorithms to smooth the edge regions to ensure that the boundaries of the defect regions are accurate and continuous. After the data extraction is completed, the defect region coordinate data is immediately output and stored in the detection system. The defect region coordinate dataset provides a clear positioning basis for subsequent current loading on the sample, ensuring that the testing instrument can automatically align with the defective regions during operation.

[0042] In the regions of the current-carrying component with structural defects, local poor contact may occur at the conductive interface, resulting in an abnormal change trend of the contact resistance with the current. After accurately positioning the defect regions, step current loading tests are performed on these regions to measure the voltage drop responses of each defect region under the action of gradually increasing current, and the dynamic contact resistance data is calculated accordingly. This process automatically controls the current loading process through precision instruments to ensure that voltage drop data can be stably collected at each preset current level, and at the same time, combined with real-time current detection data, the precise calculation of the dynamic contact resistance is realized. Specifically, for the obtained defect region coordinate data, the automatic positioning system inputs these coordinate data into the current loading device. The device adopts a step current loading method, that is, starting from 0 A, the current is gradually increased in fixed steps (for example, 1 kA) until the preset upper limit (for example, 10 kA) is reached. At each current level, the micro voltage probes installed on the surface of the current-carrying component will accurately measure the contact voltage drop data of the corresponding regions, while the sensor records the real-time current detection data. This application uses Ohm's law to calculate the contact resistance data at each loading current level based on the contact voltage drop data and the real-time current detection data, so as to quantitatively reflect the electrical response characteristics of the defect regions of the current-carrying component during the dynamic current-carrying process.

[0043] Meanwhile, in order to accurately predict the thermal effects caused by local resistance anomalies, it is necessary to perform three-dimensional heat conduction modeling on the target current-carrying component. According to the set of thermal conductivity matrices, combined with the geometric parameters and current-carrying conditions of the graphene-copper composite material obtained from experiments, the heat conduction behavior of the sample is simulated to obtain the simulated temperature rise data generated in each region during the current-carrying process. Specifically, the thermal conductivity matrix data of the current-carrying component sample is input into the finite element analysis software. The software establishes a three-dimensional model of the current-carrying component sample and assigns corresponding thermal conductivity parameters to each grid cell according to the thermal conductivity matrix. Then, by applying boundary conditions consistent with those in the dynamic current-carrying test (e.g., preset heat flux density, Joule heat source generated by contact resistance, etc.), the software automatically solves the steady-state or transient heat conduction equation. By establishing a three-dimensional heat conduction model of the current-carrying component, applying the heat flux density q and the Joule heat source to the model Q Joule , the temperature field distribution equation can be expressed by the following formula: where represents the material density. represents the specific heat capacity of the material. represents the temperature. represents the rate of change of temperature with respect to time. represents the divergence of the temperature gradient, reflecting the internal heat conduction effect of the material, where is the thermal conductivity. represents the Joule heat generated by contact resistance and current, and its calculation can be based on obtained, where is the contact resistance. The temperature field distribution equation describes the temperature evolution of the material under the action of internal heat sources and external heat fluxes during the dynamic current-carrying process. By numerically solving this equation, the simulated temperature rise data in each region under specific current-carrying conditions can be obtained. During the solution process, the finite element software divides the entire sample into a large number of elements, and each element uses the above equation to solve the local temperature. The finally output simulated temperature rise data is the temperature rise value in each grid cell under the loading conditions, and this dataset intuitively reflects the temperature rise situation caused by Joule heat in different regions. This simulated data will serve as an important basis for subsequent temperature rise deviation calculation and thermal resistance anomaly judgment.

[0044] During the dynamic current-carrying process, uneven release of Joule heat caused by abnormal local contact resistance will lead to an obvious deviation between the actual temperature rise and the simulated expected temperature rise. After obtaining the simulated temperature rise data, compare this simulated data with the temperature rise data obtained in real-time during actual testing to determine the area with abnormal thermal resistance in the sample. To this end, first, during the dynamic current-carrying test, use an infrared thermal imager to monitor the actual temperature rise of the defective area in real-time and record the actual temperature rise data. Subsequently, compare the simulated temperature rise data with the actual temperature rise data and calculate the deviation value between the two (i.e., the temperature rise deviation data). The temperature rise deviation data is used to quantify the difference between the actual temperature rise and the expected temperature rise in each area. Areas with a large temperature rise deviation usually indicate problems with abnormal thermal resistance.

[0045] After calculating the temperature rise deviation for all grid cells, a temperature rise deviation data set will be obtained. Next, perform an abnormal thermal resistance judgment on each grid cell according to a preset judgment criterion (for example, if the temperature rise deviation exceeds 15%). The specific operation is as follows: in the temperature rise deviation data set, if the temperature rise deviation data of a certain grid cell exceeds the set threshold, then mark this grid cell as an area with abnormal thermal resistance. Record the spatial coordinates of these abnormal areas to obtain the abnormal thermal resistance coordinate data.

[0046] Finally, in order to form a complete output of the dynamic current-carrying test, it is necessary to integrate the contact resistance data of the defective area and the abnormal thermal resistance coordinate data according to a preset grid format to generate an abnormal thermal resistance grid coordinate mapping data set. This data set not only contains the spatial coordinates of each abnormal area but also records the contact resistance value of the corresponding area, which is convenient for subsequent multi-physics field coupling analysis and further life prediction. Specifically, according to the preset grid coordinate mapping data, perform a matching process on the contact resistance data and the abnormal thermal resistance coordinate data. The processing process uses a data registration algorithm to compare the coordinate information in the two data sets to ensure that the coordinates of each abnormal thermal resistance area can accurately correspond to the corresponding contact resistance value. After registration is completed, generate a structured data table according to the preset grid format. Each record entry contains the following information: grid number, corresponding abnormal thermal resistance coordinates, and the dynamic contact resistance value within this grid.

[0047] Step S4: Perform an arc ablation simulation process on the target current-carrying component according to the abnormal thermal resistance grid coordinate mapping data set to obtain the ablation rate.

[0048] Due to the non-uniformity of the internal structure of the flow guiding component, local regions with abnormal thermal resistance are often associated with structural defects. By locating these regions, arc energy can be precisely applied, enabling the arc ablation effect to be concentrated on key parts and providing more representative ablation data. Specifically, first, all the data in the thermal resistance abnormal grid coordinate mapping dataset are traversed, and the data of each grid cell are analyzed. For each grid cell, if the recorded contact resistance value exceeds a predetermined safety threshold (for example, if the threshold is set as R th ), then it is considered that this grid cell meets the conditions for the arc action region. The detection system uses digital signal processing algorithms to mark and summarize the grid data that meet the conditions, thus forming a preliminary set of arc action region data. Subsequently, through data registration technology, these selected regions are visually displayed on the three-dimensional model of the target flow guiding component, and geometric interpolation methods are used to smooth their boundaries to ensure the continuity and accuracy of the positioning data.

[0049] Exemplarily, during the positioning process, for each grid cell M in the thermal resistance abnormal grid coordinate mapping dataset G ij is screened. If the contact resistance of this grid cell meets the conditions, the central coordinates of this grid cell are recorded. The arc target point coordinate data can be expressed by the following formula: where represents the obtained arc target point coordinate data. represents the central coordinates of the th grid cell. represents the dynamic contact resistance data of the i,j grid cell. represents the preset contact resistance threshold, and the regions exceeding this threshold are regarded as abnormal. The arc target point coordinate data are used to systematically screen out the grid regions with abnormal thermal resistance characteristics and generate a set of coordinate data of the arc action target points, providing accurate positioning information for subsequent arc energy loading processing.

[0050] Furthermore, after obtaining the arc target point coordinate data, arc energy loading processing is performed on the target flow guiding component sample to simulate the actual arc action environment. A high-current arc is applied at the predetermined arc target points, and arc voltage data and arc current data during the loading process are collected, thus providing basic data for subsequent energy density calculation. During the operation process, the arc energy loading device uses a precision control system according to the arc target point coordinate data Precisely align the target area and apply current to each target point according to the preset arc loading scheme. During the loading process, a stepped current increase method is adopted, and the current gradually increases from 0 to a predetermined maximum value (e.g., 20 kA). At the same time, the arc voltage and arc current at each loading stage are recorded in real time through a high-speed data acquisition system. Specifically, first, the detection system transfers the arc target point coordinate data to the positioning module of the arc loading device, and the device precisely locates the arc action point on the surface of the diversion component. Subsequently, the device loads each target point according to the preset current curve. While applying the arc current, the arc voltage data is collected through the built-in voltage sensor. All data is recorded at a high sampling frequency to ensure the integrity of the dynamic response data. It should be understood that during the entire arc energy loading process, the device operates strictly in accordance with the preset loading scheme to ensure high-precision voltage and current data can be obtained at each arc target point.

[0051] The arc ablation effect is directly related to the energy density input by the arc. In the sample surface, more significant ablation phenomena usually occur in the high energy density regions. By accurately calculating the arc energy density, it can provide an important physical quantity basis for subsequent surface topography scanning and ablation rate calculation. After obtaining the arc voltage data and arc current data recorded during the arc loading process, the next task is to calculate the arc energy density data. The arc energy density describes the energy released by the arc in a local area of the sample per unit time. This data is crucial for evaluating the arc ablation effect because the higher the energy density, usually the more obvious the local ablation effect. Specifically, the data acquisition system inputs the high-speed recorded arc voltage and current data into the numerical integration module, and uses a numerical integration algorithm (e.g., trapezoidal integration method or Simpson's method) to accumulate the instantaneous power within the time interval to calculate the total energy. Subsequently, the total energy is divided by the area of the loading region to obtain the arc energy density. The arc energy density can be calculated by the following formula: where, represents the arc energy density. represents the arc voltage measured at time represents the arc current measured at time represents the area of the arc loading region, and this area is determined by the geometric dimensions of the preset loading region. represents the total duration of the arc loading. is used to normalize the total energy to the energy per unit area.

[0052] ​​After the arc is loaded, physical changes will occur on the surface of the current-carrying component sample, such as local ablation of the material, formation of surface microcracks, etc. These changes will lead to obvious undulations in the surface topography. By measuring the surface roughness and ablation depth, the degree of the arc ablation effect can be intuitively reflected, providing a geometric basis for the subsequent calculation of the ablation rate. The non-contact scanning instrument adopted in the embodiment of the present application has the advantages of high precision and non-destructive detection, which can ensure the accuracy and stability of the data, thus providing solid data support for the quantitative analysis of the arc ablation effect. After obtaining the arc energy density data, the surface topography of the target current-carrying component sample in the arc loading area is scanned in detail to determine the surface changes caused by the arc ablation. Through high-precision surface topography scanning, surface roughness data and ablation depth data are obtained, providing direct geometric parameters for the calculation of the final ablation rate. Specifically, for the arc energy loading area, a non-contact surface topography detection instrument such as a white light interferometer or a laser scanning microscope will be used to finely scan the sample surface. During the scanning process, the instrument can collect high-resolution surface profile data, which is recorded in the form of discrete height points y i The form is recorded. Subsequently, the collected height data is processed to calculate two key parameters: the surface roughness Ra and the ablation depth h. The surface roughness can be calculated by the following formula: Among them, Indicates the surface roughness. Represents the surface height value of the i-th discrete point. Represents the average value of all discrete height values. n represents the total number of discrete points. The surface roughness is used to quantify the undulation of the surface microstructure. The higher the roughness value, the more significant the topographic change of the surface due to the arc effect.

[0053] The measurement of the ablation depth h is obtained by comparing the three-dimensional surface data obtained by instrument scanning with the original surface data, and directly reading or calculating the thickness of the material cut due to arc ablation. The scanner can provide accurate depth measurement values through high-precision imaging. The ablation depth data is usually expressed in micrometers. During the entire surface topography scanning and processing process, the detection system first determines the scanning area on the sample according to the loading area indicated in the arc energy density data; then uses a white light interferometer to finely scan this area and collect surface height distribution data; then uses digital image processing algorithms to calculate the surface roughness and ablation depth, and finally stores these data as structured data records.

[0054] The arc ablation effect directly reflects the degradation degree of materials under the action of high-energy arcs. As a quantitative index, the ablation rate can intuitively reflect the relationship between the volume of materials lost due to ablation and the energy input. A higher ablation rate indicates a lower ablation resistance of the material and a greater potential safety hazard; on the contrary, it indicates that the material has better ablation resistance. After obtaining key parameters such as arc energy density data, surface roughness data, and ablation depth data, the final ablation rate is obtained through calculation and processing of the ablation rate. The ablation rate is usually used to quantify the ratio between the volume of materials ablated under the action of arcs and the arc energy input. The ablation rate calculation process can be expressed by the following formula: where, represents the ablation rate and is used to quantify the volume of ablated materials under unit energy input. represents the ablation depth, that is, the thickness reduced on the material surface after the action of the arc. represents the ablation area of the arc loading region, and this parameter is obtained from the scanner or the geometric dimensions of the pre-determined loading region. represents the arc energy density. The ablation rate is used to quantitatively correlate the actual ablation results with the input energy, calculate the volume of ablated materials under unit energy input, and thus quantitatively reflect the ablation resistance of the material under arc loading.

[0055] Step S5: Perform multi-physical field coupling failure simulation processing according to the performance space distribution data, the structural defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set.

[0056] It should be understood that the performance space distribution data includes a conductivity matrix set, a thermal conductivity matrix set, and a mechanical parameter set. By extracting key parameters representing the initial state of the sample from the performance space distribution data, initial conditions are provided for subsequent degradation simulation. First, the detection system will select the conductivity values within each preset grid cell from the performance space distribution data and obtain the initial conductivity data of the overall sample through statistical calculation of all grid cell data. Similarly, the detection system performs statistical calculations on the thermal conductivity matrix set and the mechanical parameter set respectively to obtain the initial thermal conductivity data and the initial mechanical property data.

[0057] After dynamic current-carrying and arc ablation detection, physical and chemical changes occur in the local area of the sample due to high-energy input, resulting in attenuation of electrical conductivity, thermal conductivity, and mechanical properties. The calculation and processing of the local property attenuation rate aim to quantitatively describe the attenuation rate of each key performance index over time under specific arc ablation conditions, providing local attenuation parameters for the degradation model. Among them, the local attenuation parameters include the electrical conductivity attenuation rate, the thermal conductivity attenuation rate, and the mechanical property attenuation rate. In the specific implementation process, the detection system uses the ablation rate data obtained in the arc ablation simulation as input, combines the initial performance parameters obtained previously, and calculates parameters such as the local property attenuation rate through statistical analysis methods. Similarly, the detection system calculates the attenuation of the initial thermal conductivity and the thermal conductivity measured after a certain time, as well as the initial tensile strength and the tensile strength after the corresponding time, to obtain the local thermal conductivity attenuation rate and the mechanical property attenuation rate. These local attenuation parameters provide input indicators for subsequent multi-physics field coupling simulations and can reflect the performance change trend of materials under arc ablation and other degradation effects. In the specific operation, the detection system correlates the ablation rate with the initial electrical conductivity and calculates the percentage of the decrease in electrical conductivity per unit time. The electrical conductivity attenuation rate can be expressed by the following formula: Where, represents the local electrical conductivity attenuation rate. represents the local electrical conductivity measured after time t. represents the initial electrical conductivity. t represents the elapsed time. The electrical conductivity attenuation rate is used to quantify the attenuation degree of electrical conductivity per unit time and provides a quantitative index for local property degradation. A similar processing method can be used for the calculation of the attenuation rates of thermal conductivity and mechanical property parameters. The detection system calculates the attenuation of the initial thermal conductivity and the thermal conductivity measured after a certain time, as well as the initial tensile strength and the tensile strength after the corresponding time, to obtain the local thermal conductivity attenuation rate and the mechanical property attenuation rate. These local attenuation parameters provide input indicators for subsequent multi-physics field coupling simulations and can reflect the performance change trend of materials under arc ablation and other degradation effects.

[0058] In actual production, the defect distribution of the diversion component has spatial inhomogeneity, and different defect regions contribute differently to the overall performance degradation. By dividing the defect regions and evaluating each region separately, the local performance degradation can be accurately reflected, providing more refined spatial distribution parameters for the degradation simulation. The structural defect feature dataset has been obtained through multi-modal imaging technology, which details the coordinates, sizes, and defect types of each defect region in the sample. The goal of the defect region division process is to divide the diversion component sample into multiple defect regions and evaluate the performance degradation information within each region, providing spatial distribution information for subsequent degradation simulations.

[0059] Meanwhile, it should be understood that the abnormal temperature rise is an important thermal characteristic of the current-carrying component due to local structural defects and arc ablation. By quantifying the abnormal temperature rise parameters, the abnormal local thermal resistance can be objectively reflected. Through dynamic current-carrying tests and heat conduction modeling, simulated temperature rise data and actual temperature rise data have been obtained, and the temperature rise deviation has been calculated, thereby obtaining the abnormal thermal resistance coordinate data. By extracting the local abnormal temperature rise parameters through the abnormal thermal resistance grid coordinate mapping data set, the temperature rise deviation is quantified into abnormal temperature rise data, providing thermal abnormal input for degradation simulation.

[0060] During the actual operation of the current-carrying component, it is affected by multiple fields of electricity, heat, and force, and its degradation process is complex and mutually influential. Through the preset degradation model, the evolution of various material properties during the degradation process can be comprehensively simulated, thereby providing a scientific basis for life prediction. The degradation model performs multi-physics field coupling simulation based on the obtained initial conductivity data, initial thermal conductivity data, initial mechanical property data, local attenuation parameters, performance degradation information, and abnormal temperature rise data, simulating the evolution of each field of the material under arc ablation and degradation, and providing quantitative physical field distribution data for life prediction. The degradation model uses the following coupled equations to describe the interaction relationship between each physical field: where, represents the electric potential distribution. represents the conductivity of the material, and the initial conductivity is corrected by local attenuation to obtain the local conductivity. represents the material density. represents the specific heat capacity. represents the temperature field. represents the thermal conductivity of the material, and the initial thermal conductivity is corrected by local attenuation and abnormal temperature rise. represents the square of the electric field strength, reflecting the generation of Joule heat. represents the stress tensor. represents the body force. The coupled equations are used to describe the distribution evolution of temperature and stress inside the material under the action of the electric field, comprehensively considering the electrical, thermal, and mechanical interactions, and providing global physical field data for degradation simulation.

[0061] During the simulation process, the detection system inputs each initial parameter and local correction parameter into the FEA software, and solves the above coupled equations within the preset time step to obtain the gradient of the electric field distribution data V(x,y,z) the temperature field distribution data T(x, y,z) and the stress field distribution data σ(x,y,z)Among them, the temperature field distribution data combined with the aforementioned abnormal temperature rise data reflect the actual temperature distribution in the local thermal anomaly region of the material; the electric field distribution data reflect the decrease in electrical conductivity due to local degradation; and the stress field data reveal the mechanical changes caused by thermal expansion and material deterioration.

[0062] After multi-physical field coupling simulation processing, the obtained electric field, temperature field, and stress field distribution data provide an intuitive description of the state of the diversion component during the degradation process. The purpose of the failure criterion analysis and processing is to judge the data of each region according to the preset failure criteria, determine which regions have reached or exceeded the failure limit values, and thus deduce the remaining life of the overall sample. In the specific implementation process, the detection system will use the failure criterion formula to analyze the simulation data point by point to determine whether each region meets the electrical failure, thermal failure, or mechanical failure criteria.

[0063] Exemplarily, for electrical failure, it is set that when the local electrical conductivity drops to 80% of the initial value, it is determined as electrical failure; for thermal failure, it is set that the local temperature rise exceeds 150 K as the judgment criterion; for mechanical failure, it is set that the stress concentration factor is greater than 3 as the failure criterion. For each grid cell, the detection system calculates the ratio of the current local electrical conductivity σ(x, y, z) to the initial electrical conductivity and determines whether it is lower than the preset failure threshold of 0.8.

[0064] Similarly, criterion analysis is performed on the temperature field and stress field data respectively. The detection system synthesizes various criteria to determine whether there is a failure risk for each grid cell and counts the distribution of the overall failure regions. Through the failure criterion analysis of all grid cells, a life prediction data set will be obtained, which records the failure states and predicted lives of each key region during the simulation time.

[0065] In an optional implementation manner, the method further includes adjusting the production process according to the information such as the obtained performance space distribution data, structural defect feature data set, thermal resistance abnormal grid coordinate mapping data set, ablation rate, and life prediction data set.

[0066] Through a pre-set process-performance relationship database, the various data obtained from the aforementioned detections are systematically integrated to form a comprehensive dataset that reflects the relationship between the various performances of the flow guiding component and the manufacturing process parameters, namely, the process-performance correlation data. To ensure the scientific nature and accuracy of subsequent process parameter optimization, it is necessary to integrate and normalize the data from different sources and different dimensions. First, the conductivity matrix set, thermal conductivity matrix set, and mechanical parameter set obtained from the performance space distribution data all reflect the initial performance states of the flow guiding component samples in terms of electricity, heat, and mechanics. At the same time, the structural defect feature dataset provides the spatial positions, defect types, and defect size information of the defect areas existing inside the samples. In addition, the thermal resistance anomaly grid coordinate mapping dataset records the local thermal resistance anomaly areas and their corresponding dynamic contact resistance data that appear in the dynamic current-carrying test. Furthermore, the ablation rate obtained through arc ablation simulation processing quantitatively reflects the degree of material ablation of the samples under the action of the arc. Finally, the life prediction dataset is the prediction data of the overall degradation and failure time of the samples obtained through multi-physical field coupling failure simulation processing. These data come from different detection modules, and their data units, dimensions, and numerical ranges are all different, and they must be pre-processed and normalized before they can be jointly analyzed on the same platform.

[0067] After data pre-processing and normalization, each dataset is converted into a standard format. Next, all the data are integrated. The pre-set process-performance relationship database contains manufacturing process parameters (such as sintering temperature, graphene doping ratio, pressure, etc.). In this step, these manufacturing parameters are associated with the various detection data. The specific method is to match the performance data, defect data, thermal resistance anomaly data, ablation rate, and life prediction data with the corresponding process parameters according to the acquisition time and batch number for each batch or sample to form a structured record. This process uses database join operations to horizontally merge each data table through key fields (such as batch number, sample number, etc.) to generate comprehensive data records. Each record contains the process parameters and all the corresponding detection indicators, forming a process-performance correlation data record.

[0068] After obtaining the process-performance correlation data, feature extraction processing is performed on the process-performance correlation data to extract the feature data that reflects the manufacturing process parameters and their corresponding key performance indicators. The detection system uses data mining and statistical analysis techniques to perform feature extraction on the process-performance correlation data. First, descriptive statistics are performed on all the data records to calculate indicators such as the mean, variance, and correlation coefficient of each process parameter and performance indicator, and to evaluate the mutual relationship between the various variables. Through dimensionality reduction methods such as principal component analysis (PCA), the main components that can best explain the data variance are extracted from the original high-dimensional data, and the correlation between these components and the manufacturing process parameters is determined. Exemplarily, the process-performance correlation data , where the first half are process parameters and the second half are key performance indicators. By using the principal component analysis method, several principal components can be obtained. , where each principal component is a linear combination of the original data. The feature extraction process is as follows: Assume the original data matrix is , where each column represents a variable. After is converted into zero-mean data, its covariance matrix is calculated . Then, the eigenvalues and eigenvectors of the covariance matrix are solved. The principal component is defined as . Among them, represents the normalized data matrix. represents the data covariance matrix. represents the i-th eigenvalue. represents the eigenvector activity corresponding to . represents the i-th principal component. n represents the total number of data records. By extracting the main variables from the high-dimensional data, the data dimension is reduced, and the dominant relationship between each process parameter and the performance index is determined.

[0069] After analyzing the extracted principal components, the variables closely related to the manufacturing process are determined and recorded as process parameter data; at the same time, the corresponding key performance index data are retained. These characteristic data reflect both the core parameters in the manufacturing process (e.g., sintering temperature, doping ratio, pressure, etc.) and the key performance indicators obtained from the detection (e.g., initial conductivity, thermal conductivity, tensile strength, ablation rate, and life prediction value).

[0070] After obtaining the process parameter data and the corresponding key performance index data, the next goal is to establish a mathematical correlation model between the process parameters and the product performance, that is, the process-performance correlation model. This model aims to quantitatively describe the influence of each manufacturing process parameter on the performance of the flow guide component samples (including electrical, thermal, mechanical indicators, and ablation and life prediction data), providing a theoretical basis for subsequent multi-objective optimization. Specifically, the detection system uses multiple nonlinear regression analysis and machine learning methods to model the extracted characteristic data. Assume the process parameter vector is denoted as , and the key performance indicator is denoted as . Using the regression modeling method, the following mathematical model is constructed: Among them, represents the key performance indicator vector. represents the sintering temperature. represents the graphene doping ratio. represents the pressure. is the regression coefficient. represents the square term of the sintering temperature, reflecting the non-linear effect. represents the interaction term between process parameters. represents the error term. This mathematical model is used to quantitatively describe the influence of each process parameter and its interaction on the product performance index, and reflects the weight and direction of the influence through the regression coefficient, providing a basis for process parameter optimization.

[0071] The detection system uses historical data or experimental data to estimate the parameters of the above model, and adopts the least squares method or other optimization algorithms to determine the best fitting parameters, thereby establishing a process-performance correlation model. After the model is established, it can predict the change trend of the product performance index under different process parameter combinations, providing a quantitative basis for subsequent process parameter optimization.

[0072] Finally, after establishing the process-performance correlation model, through multi-objective optimization processing, the optimal process parameter combination is obtained from the model to achieve the comprehensive goal of optimal product performance and lowest manufacturing cost. The multi-objective optimization processing incorporates process parameters and each performance index into a unified objective function, and determines the most suitable process parameter configuration under the satisfaction of each performance requirement by solving the Pareto optimal solution set. Specifically, the detection system takes the process-performance correlation model as the input and sets the optimization objective function. The optimization objectives usually include maximizing the life prediction value and other performance indicators, while minimizing indicators such as manufacturing cost and resource consumption. The multi-objective optimization objective function in the following form is used for modeling: where, represents the life prediction data, and the larger the value, the longer the product life. represents the manufacturing cost corresponding to the process parameters, and the lower the cost, the better. are the weight coefficients respectively, used to reflect the relative importance of life and cost in optimization. The multi-objective optimization objective function is used to comprehensively consider the two key indicators of product life and manufacturing cost, and obtain the optimal process parameter combination by solving this multi-objective optimization problem, so that the product performance reaches the best and the manufacturing cost is optimal.

[0073] The detection system uses a genetic algorithm (e.g., NSGA-II) or other multi-objective optimization algorithms to solve the objective function, and searches for the Pareto front solution set under the constraint conditions of given process parameters (e.g., the range of sintering temperature, the fluctuation range of graphene doping ratio, the upper limit of pressure, etc.). During the optimization process, each candidate solution corresponds to a set of process parameter combinations, and the system calculates its corresponding model output and evaluates its fitness under the objective function. After multiple iterations, the algorithm finally converges to a set of optimal solutions, and one or more optimal process parameter combinations are selected as the output.

[0074] This application is applied to the field of insulation switch detection technology. By performing performance detection processing on the target current-carrying component according to the detection method, performance spatial distribution data is obtained. Based on the performance spatial distribution data, structural defect detection processing is carried out on the target current-carrying component to obtain a structural defect feature data set. According to the structural defect feature data set, dynamic current-carrying analysis detection processing is carried out on the target current-carrying component to obtain a thermal resistance anomaly grid coordinate mapping data set. According to the thermal resistance anomaly grid coordinate mapping data set, arc ablation simulation processing is carried out on the target current-carrying component to obtain an ablation rate, and multi-physical field coupling failure simulation processing is carried out in combination with a degradation model to obtain a life prediction data set. Through multi-level and multi-angle data collection and analysis, this application realizes a comprehensive evaluation of the UHV GIS graphene-copper-based composite current-carrying component from performance, structure, current-carrying state to arc ablation and so on.

[0075] As Figure 2 shown, it is a functional module diagram of a detection device for a UHV GIS graphene-copper-based composite current-carrying component provided by an embodiment of this application.

[0076] In some embodiments, the detection device 2 for the UHV GIS graphene-copper-based composite current-carrying component may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the detection device 2 for the UHV GIS graphene-copper-based composite current-carrying component can be stored in the memory of the server and executed by at least one processor to execute (see in detail Figure 1 the description) the functions of the detection method for the UHV GIS graphene-copper-based composite current-carrying component.

[0077] In this embodiment, according to the functions it executes, the detection device 2 for the UHV GIS graphene-copper-based composite current-carrying component can be divided into multiple functional modules. The functional modules may include: a performance detection module 21, an anomaly analysis module 22, a dynamic test module 23, an ablation test module 24, a degradation simulation module 25, and a process optimization module 26. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0078] The performance detection module 21 is used to perform performance detection processing on the target current-carrying component according to a preset detection method to obtain performance spatial distribution data.

[0079] In an optional implementation manner, the performance detection module 21 is specifically used for: Performing surface area division processing on the collected target three-dimensional model according to a preset grid format to obtain grid coordinate mapping data; Perform conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the conductivity matrix set; Perform thermal conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the thermal conductivity matrix set; Perform a mechanical property tensile experiment on the target diversion component to obtain the mechanical parameter set.

[0080] The anomaly analysis module 22 is used to perform structural defect detection processing on the target diversion component according to the performance space distribution data to obtain a structural defect feature data set.

[0081] In an optional implementation manner, the anomaly analysis module 22 is specifically used for: Perform global conductivity data calculation processing on the target diversion component according to the conductivity matrix set to obtain global average conductivity data, and perform screening processing on the conductivity matrix set according to the global average conductivity data to obtain low conductivity region data; Perform X-ray tomography processing on the target diversion component according to the low conductivity region data to obtain a three-dimensional voxel model corresponding to the low conductivity region and pore distribution data; Perform ultrasonic phased array scanning processing on the target diversion component according to the low conductivity region data to obtain reflection coefficient distribution data of the low conductivity region; Perform defect classification processing on the three-dimensional voxel model according to the reflection coefficient distribution data and the pore distribution data to obtain the structural defect feature data set.

[0082] The dynamic test module 23 is used to perform dynamic current-carrying analysis and detection processing on the target diversion component according to the structural defect feature data set to obtain a thermal resistance anomaly grid coordinate mapping data set.

[0083] In an optional implementation manner, the dynamic test module 23 is specifically used for: Perform current-carrying area positioning processing on the target diversion component according to the structural defect feature data set to obtain defect area coordinate data; Perform stepped current loading on the defect areas in the defect area coordinate data to obtain contact voltage drop data and real-time current detection data, and perform dynamic contact resistance calculation processing according to the contact voltage drop data and the real-time current detection data to obtain the contact resistance data; Perform three-dimensional heat conduction modeling on the target diversion component according to the thermal conductivity matrix set to obtain simulated temperature rise data; Perform temperature rise deviation calculation and processing based on the simulated temperature rise data and the actually obtained real-time temperature rise data to obtain temperature rise deviation data, and perform thermal resistance anomaly judgment and processing on the target diversion component according to the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data; Perform grid coordinate mapping processing based on the thermal resistance anomaly coordinate data and the contact resistance data to obtain the thermal resistance anomaly grid coordinate mapping data set.

[0084] The ablation test module 24 is used to perform arc ablation simulation processing on the target diversion component according to the thermal resistance anomaly grid coordinate mapping data set to obtain an ablation rate.

[0085] In an optional implementation manner, the ablation test module 24 is specifically used for: Perform arc target point positioning processing on the target diversion component according to the thermal resistance anomaly grid coordinate mapping data set to obtain arc target point coordinate data; Perform arc energy loading processing on the target diversion component according to the arc target point coordinate data to obtain arc voltage data and arc current data; Perform arc energy density calculation processing on the target diversion component according to the arc voltage data and the arc current data to obtain arc energy density data; Perform surface topography scanning processing on the target diversion component according to the arc energy loading area in the arc energy density data to obtain surface roughness data and ablation depth data; Perform ablation rate calculation processing on the target diversion component according to the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

[0086] The degradation simulation module 25 is used to perform multi-physical field coupling failure simulation processing according to the performance space distribution data, the structural defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set.

[0087] In an optional implementation manner, the degradation simulation module 25 is specifically used for: Perform initial performance parameter extraction processing on the target diversion component according to the performance space distribution data to obtain initial conductivity data, initial thermal conductivity data, and initial mechanical property data; Perform local performance attenuation rate calculation processing on the target diversion component according to the ablation rate to obtain local attenuation parameters; Perform defect area division processing on the target diversion component according to the structural defect feature data set to obtain performance degradation information for each defect area; Performing local temperature rise anomaly parameter extraction processing on the target diversion component according to the thermal resistance anomaly grid coordinate mapping data set to obtain temperature rise anomaly data; Performing multi-physical field coupling simulation processing on the target diversion component through the degradation model according to the initial conductivity data, the initial thermal conductivity data, the initial mechanical property data, the local attenuation parameter, the performance degradation information, and the temperature rise anomaly data to obtain electric field distribution data, temperature field distribution data, and stress field distribution data; Performing failure criterion analysis processing on the target diversion component according to the electric field distribution data, the temperature field distribution data, and the stress field distribution data to obtain the life prediction data set.

[0088] In an optional embodiment, the UHV GIS graphene-copper-based composite diversion component detection device 2 further includes a process optimization module 26, and the process optimization module 26 is used for: Performing data integration processing on the performance space distribution data, the structure defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, the ablation rate, and the life prediction data set according to a preset process-performance relationship database to obtain process-performance correlation data; Performing feature extraction processing on the process-performance correlation data to obtain process parameter data and corresponding key performance index data; Performing process parameter correlation modeling processing according to the process parameter data and the key performance index data to obtain a process-performance correlation model; Performing multi-objective optimization processing on process parameters according to the process-performance correlation model to obtain an optimal process parameter combination.

[0089] It should be understood that the various change modes and specific embodiments in the methods provided in the above embodiments are equally applicable to the UHV GIS graphene-copper-based composite diversion component detection device in this embodiment. Through the foregoing detailed description of the UHV GIS graphene-copper-based composite diversion component detection method, those skilled in the art can clearly know the implementation method of the UHV GIS graphene-copper-based composite diversion component detection device in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0090] As Figure 3 shown, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0091] In a preferred embodiment of the present invention, the electronic device 3 may include, but is not limited to: a memory 31, at least one processor 32, and at least one communication bus 33.

[0092] Those skilled in the art should understand that Figure 3 The structure of the illustrated electronic device 3 does not constitute a limitation on the embodiments of the present invention. The electronic device 3 may further include more or fewer other hardware or software than shown in the figure, or different component arrangements.

[0093] In some embodiments, the electronic device 3 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital signal processors, and embedded devices, etc.

[0094] It should be noted that the electronic device 3 is only an example. Other existing or future electronic products that can be adapted to this application should also be included within the protection scope of this application and are hereby incorporated by reference.

[0095] In some embodiments, a computer program is stored in the memory 31. When the computer program is executed by the at least one processor 32, all or part of the steps in the detection method of the UHV GIS graphene-copper composite current-carrying component as described are implemented. The memory 31 includes read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.

[0096] In some embodiments, the at least one processor 32 is the control core (Control Unit) of the electronic device 3, connecting various components of the entire electronic device 3 through various interfaces and lines. By running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31, it performs various functions of the electronic device 3 and processes data. For example, when the at least one processor 32 executes the computer program stored in the memory 31, all or part of the steps of the method for detecting the ultra-high voltage GIS graphene-copper composite current-carrying component described in the embodiments of the present application are implemented; or all or part of the functions of the device for detecting the ultra-high voltage GIS graphene-copper composite current-carrying component are implemented. The at least one processor 32 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions packaged, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc.

[0097] In some embodiments, the at least one communication bus 33 is configured to implement connection communication between the memory 31 and the at least one processor 32, etc. Although not shown, the electronic device 3 may further include a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the at least one processor 32 through a power management device, thereby implementing functions such as management of charging, discharging, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 3 may further include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0098] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module stored in a storage medium includes several instructions for causing an electronic device (which may be a personal computer, an electronic device, or a network device, etc.) or a processor to execute part of the methods described in the various embodiments of the present application.

[0099] In several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0100] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] The above are all preferred embodiments of this application. The protection scope of this application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. A detection method for a UHV GIS graphene - copper - based composite current - guiding component, characterized in that, The method includes: Performing performance detection processing on the target diversion component according to a preset detection method to obtain performance spatial distribution data; Performing structural defect detection processing on the target diversion component according to the performance spatial distribution data to obtain a structural defect feature data set; Performing dynamic current-carrying analysis detection processing on the target diversion component according to the structural defect feature data set to obtain a thermal resistance anomaly grid coordinate mapping data set; Performing arc ablation simulation processing on the target diversion component according to the thermal resistance anomaly grid coordinate mapping data set to obtain an ablation rate; Performing multi-physical-field coupling failure simulation processing according to the performance spatial distribution data, the structural defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set.

2. The detection method of the UHV GIS graphene-copper-based composite current-carrying component according to claim 1, characterized in that The performance spatial distribution data includes a conductivity matrix set, a thermal conductivity matrix set, and a mechanical parameter set. The performing performance detection processing on the target diversion component according to a preset detection method to obtain performance spatial distribution data includes: Performing surface area division processing on the collected target three-dimensional model according to a preset grid format to obtain grid coordinate mapping data; Performing conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the conductivity matrix set; Performing thermal conductivity measurement processing on the target diversion component according to the grid coordinate mapping data to obtain the thermal conductivity matrix set; Performing a mechanical property tensile experiment processing on the target diversion component to obtain the mechanical parameter set.

3. The detection method of the UHV GIS graphene-copper composite current-carrying component according to claim 2, wherein The performing structural defect detection processing on the target diversion component according to the performance spatial distribution data to obtain a structural defect feature data set includes: Performing global conductivity data calculation processing on the target diversion component according to the conductivity matrix set to obtain global average conductivity data, and performing screening processing on the conductivity matrix set according to the global average conductivity data to obtain low-conductivity region data; Performing X-ray tomography processing on the target diversion component according to the low-conductivity region data to obtain a three-dimensional voxel model corresponding to the low-conductivity region and pore distribution data; Performing ultrasonic phased array scanning processing on the target diversion component according to the low-conductivity region data to obtain reflection coefficient distribution data of the low-conductivity region; Performing defect classification processing on the three-dimensional voxel model according to the reflection coefficient distribution data and the pore distribution data to obtain the structural defect feature data set.

4. The detection method of the UHV GIS graphene - copper - based composite current - conducting component according to claim 2, characterized in that, The thermal resistance anomaly grid coordinate mapping data set includes thermal resistance anomaly coordinate data and corresponding contact resistance data. The performing dynamic current-carrying analysis detection processing on the target diversion component according to the structural defect feature data set to obtain a thermal resistance anomaly grid coordinate mapping data set includes: Performing current-carrying area positioning processing on the target diversion component according to the structural defect feature data set to obtain defect area coordinate data; Perform a stepped current loading process on the defect regions in the defect region coordinate data to obtain contact voltage drop data and real-time current detection data, and perform a dynamic contact resistance calculation process based on the contact voltage drop data and the real-time current detection data to obtain the contact resistance data; Perform a three-dimensional heat conduction modeling process on the target current-carrying component according to the thermal conductivity matrix set to obtain simulated temperature rise data; Perform a temperature rise deviation calculation process based on the simulated temperature rise data and the actually obtained real-time temperature rise data to obtain temperature rise deviation data, and perform a thermal resistance anomaly judgment process on the target current-carrying component according to the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data; Perform a grid coordinate mapping process based on the thermal resistance anomaly coordinate data and the contact resistance data to obtain the thermal resistance anomaly grid coordinate mapping data set.

5. The detection method of the UHV GIS graphene-copper-based composite current-carrying component according to claim 1, characterized in that, The arc ablation simulation process performed on the target current-carrying component according to the thermal resistance anomaly grid coordinate mapping data set to obtain the ablation rate includes: Perform an arc target positioning process on the target current-carrying component according to the thermal resistance anomaly grid coordinate mapping data set to obtain arc target coordinate data; Perform an arc energy loading process on the target current-carrying component according to the arc target coordinate data to obtain arc voltage data and arc current data; Perform an arc energy density calculation process on the target current-carrying component according to the arc voltage data and the arc current data to obtain arc energy density data; Perform a surface topography scanning process on the target current-carrying component according to the arc energy loading region in the arc energy density data to obtain surface roughness data and ablation depth data; Perform an ablation rate calculation process on the target current-carrying component according to the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

6. The detection method of the UHV GIS graphene - copper - based composite current - conducting component according to claim 1, wherein, The multi-physical field coupling failure simulation process performed through a preset degradation model according to the performance space distribution data, the structural defect feature data set, the thermal resistance anomaly grid coordinate mapping data set, and the ablation rate to obtain the life prediction data set includes: Perform an initial performance parameter extraction process on the target current-carrying component according to the performance space distribution data to obtain initial conductivity data, initial thermal conductivity data, and initial mechanical property data; Perform a local performance attenuation rate calculation process on the target current-carrying component according to the ablation rate to obtain local attenuation parameters; Perform a defect region division process on the target current-carrying component according to the structural defect feature data set to obtain performance degradation information for each defect region; Perform a local temperature rise anomaly parameter extraction process on the target current-carrying component according to the thermal resistance anomaly grid coordinate mapping data set to obtain temperature rise anomaly data; Performing multi - physical - field coupling simulation processing on the target current - guiding component according to the initial conductivity data, the initial thermal conductivity data, the initial mechanical property data, the local attenuation parameter, the performance degradation information, and the abnormal temperature rise data through the degradation model to obtain electric - field distribution data, temperature - field distribution data, and stress - field distribution data; Performing failure criterion analysis processing on the target current - guiding component according to the electric - field distribution data, the temperature - field distribution data, and the stress - field distribution data to obtain the life prediction data set; 7. The detection method of the UHV GIS graphene - copper - based composite current - conducting component according to claim 1, characterized in that, The method further includes: Performing data integration processing on the performance space distribution data, the structural defect feature data set, the thermal - resistance abnormal grid coordinate mapping data set, the ablation rate, and the life prediction data set according to a preset process - performance relationship database to obtain process - performance correlation data; Performing feature extraction processing on the process - performance correlation data to obtain process parameter data and corresponding key performance index data; Performing process parameter correlation modeling processing according to the process parameter data and the key performance index data to obtain a process - performance correlation model; Performing multi - objective optimization processing on the process parameters according to the process - performance correlation model to obtain an optimal process parameter combination; 8. A detection device for a UHV GIS graphene - copper - based composite current - guiding component, characterized in that, The device includes: A performance detection module for performing performance detection processing on the target current - guiding component according to a preset detection method to obtain performance space distribution data; An abnormal analysis module for performing structural defect detection processing on the target current - guiding component according to the performance space distribution data to obtain a structural defect feature data set; A dynamic test module for performing dynamic current - carrying analysis detection processing on the target current - guiding component according to the structural defect feature data set to obtain a thermal - resistance abnormal grid coordinate mapping data set; An ablation test module for performing arc ablation simulation processing on the target current - guiding component according to the thermal - resistance abnormal grid coordinate mapping data set to obtain an ablation rate; A degradation simulation module for performing multi - physical - field coupling failure simulation processing according to the performance space distribution data, the structural defect feature data set, the thermal - resistance abnormal grid coordinate mapping data set, and the ablation rate through a preset degradation model to obtain a life prediction data set; 9. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for detecting a UHV GIS graphene - copper - based composite current - guiding component according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting a UHV GIS graphene - copper - based composite current - guiding component according to any one of claims 1 to 7.

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