Testing methods and related equipment for ultra-high voltage GIS graphene-copper based composite current-carrying components

Abstract

This application relates to the field of insulation switch testing technology, and provides a testing method and related equipment for ultra-high voltage GIS graphene-copper-based composite current-carrying components. The method involves obtaining performance spatial distribution data by performing performance testing on the target current-carrying component according to the testing method; obtaining a structural defect feature dataset by performing structural defect detection on the target current-carrying component based on the performance spatial distribution data; obtaining a thermal resistance anomaly grid coordinate mapping dataset by performing dynamic current-carrying analysis on the target current-carrying component based on the structural defect feature dataset; obtaining an arc ablation simulation processing of the target current-carrying component based on the thermal resistance anomaly grid coordinate mapping dataset; and obtaining a lifetime prediction dataset by combining a degradation model with multi-physics coupled failure simulation processing. This application achieves a comprehensive evaluation of ultra-high voltage GIS graphene-copper-based composite current-carrying components from performance, structure, current-carrying state to arc ablation through multi-level and multi-angle data acquisition and analysis.

Description

Technical Field

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

[0002] Ultra-high voltage GIS equipment operates in a complex environment, enduring high voltage and high current. As a key component for current transmission, the quality of the graphene-copper composite current-carrying assembly directly affects the operational safety and reliability of the entire system. Any minute defects or performance degradation can trigger localized overheating, arc erosion, or electrical faults, thereby jeopardizing equipment stability. The graphene-copper composite structure, while possessing high electrical conductivity and good thermal conductivity, also requires excellent mechanical properties. However, the composite material may contain interface defects, micropores, and localized performance inhomogeneities.

[0003] Existing testing methods mainly rely on single physical fields (i.e., electrical, thermal, and mechanical properties) and traditional non-destructive testing methods (i.e., X-ray and ultrasonic testing). While these methods can reflect the basic state of components to some extent, they suffer from drawbacks such as isolated data, inaccurate defect identification, insufficient dynamic performance, and inadequate failure prediction. Some methods test material reactions by applying current or heat locally to obtain local performance changes, but they lack real-time analysis of the coupling effect between defects and actual operating conditions, and cannot 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 ultra-high voltage GIS graphene-copper-based composite current-carrying components to solve the problem of insufficient sample detection accuracy caused by testing based on single or local parameters.

[0005] The first aspect of this application provides a method for detecting ultra-high voltage GIS graphene-copper based composite current-carrying components, the method comprising:

[0006] The target flow guiding component is subjected to performance testing according to a preset testing method to obtain performance spatial distribution data;

[0007] The target flow guide component is subjected to structural defect detection processing based on the performance space distribution data to obtain a structural defect feature dataset.

[0008] Based on the structural defect feature dataset, the target flow guiding component is subjected to dynamic current-carrying analysis and detection processing to obtain a thermal resistance anomaly grid coordinate mapping dataset.

[0009] The target flow guiding component is subjected to arc ablation simulation processing based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate.

[0010] A lifetime prediction dataset is obtained by performing multiphysics coupled failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model.

[0011] In an optional implementation, the performance spatial distribution data includes a set of electrical conductivity matrices, a set of thermal conductivity matrices, and a set of mechanical parameters. The step of performing performance testing on the target flow-guiding component according to a preset detection method to obtain the performance spatial distribution data includes:

[0012] The surface region of the acquired target 3D model is divided according to the preset grid format to obtain grid coordinate mapping data;

[0013] The conductivity of the target flow guiding component is measured based on the grid coordinate mapping data to obtain the conductivity matrix set.

[0014] The thermal conductivity of the target flow guide component is measured based on the grid coordinate mapping data to obtain the thermal conductivity matrix set.

[0015] The target flow guide component is subjected to a mechanical property tensile test to obtain the set of mechanical parameters.

[0016] In an optional implementation, the step of performing structural defect detection processing on the target flow guide component based on the performance space distribution data to obtain a structural defect feature dataset includes:

[0017] The target current-carrying component is processed by global conductivity data calculation based on the conductivity matrix set to obtain global average conductivity data, and the conductivity matrix set is filtered based on the global average conductivity data to obtain low conductivity region data.

[0018] X-ray tomography is performed on the target flow guiding component based on the data of the low conductivity region to obtain a three-dimensional voxel model and pore distribution data corresponding to the low conductivity region.

[0019] The target flow guiding component is subjected to ultrasonic phased array scanning processing based on the low conductivity region data to obtain the reflection coefficient distribution data of the low conductivity region;

[0020] The three-dimensional voxel model is subjected to defect classification processing based on the reflection coefficient distribution data and the porosity distribution data to obtain the structural defect feature dataset.

[0021] In an optional implementation, the thermal resistance anomaly grid coordinate mapping dataset includes thermal resistance anomaly coordinate data and corresponding contact resistance data. The step of performing dynamic current-carrying analysis and detection processing on the target current-carrying component based on the structural defect feature dataset to obtain the thermal resistance anomaly grid coordinate mapping dataset includes:

[0022] Based on the structural defect feature dataset, the target flow guiding component is subjected to flow-carrying region localization processing to obtain defect region coordinate data;

[0023] A stepped current loading process is performed on the defect area in the defect area coordinate data to obtain contact voltage drop data and real-time current detection data. Then, dynamic contact resistance calculation is performed based on the contact voltage drop data and the real-time current detection data to obtain the contact resistance data.

[0024] The target flow guiding component is modeled in three dimensions based on the thermal conductivity matrix set to obtain simulated temperature rise data.

[0025] The temperature rise deviation is calculated based on the simulated temperature rise data and the real-time acquired actual temperature rise data to obtain temperature rise deviation data. The thermal resistance anomaly judgment is then performed on the target flow guide component based on the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data.

[0026] The thermal resistance anomaly coordinate data and the contact resistance data are used to perform grid coordinate mapping processing to obtain the thermal resistance anomaly grid coordinate mapping dataset.

[0027] In an optional implementation, the step of performing arc ablation simulation on the target flow guiding component based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate includes:

[0028] The target flow guiding component is subjected to arc target point localization processing based on the thermal resistance anomaly grid coordinate mapping dataset to obtain arc target point coordinate data.

[0029] The target flow guiding component is subjected to arc energy loading processing based on the arc target point coordinate data to obtain arc voltage data and arc current data.

[0030] The arc energy density of the target current guiding component is calculated based on the arc voltage data and the arc current data to obtain arc energy density data.

[0031] The surface morphology of the target flow guide component is scanned based on the arc energy loading region in the arc energy density data to obtain surface roughness data and ablation depth data.

[0032] The ablation rate of the target flow guide component is calculated based on the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

[0033] In an optional implementation, the step of performing multiphysics coupled failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model to obtain a lifetime prediction dataset includes:

[0034] The target flow guiding component is processed by extracting initial performance parameters based on the performance spatial distribution data to obtain initial electrical conductivity data, initial thermal conductivity data, and initial mechanical performance data.

[0035] The local performance degradation rate of the target flow guide component is calculated based on the ablation rate to obtain local degradation parameters.

[0036] Based on the structural defect feature dataset, the target flow guide component is divided into defect regions to obtain performance degradation information for each defect region.

[0037] Based on the thermal resistance anomaly grid coordinate mapping dataset, the target flow guide component is processed to extract local temperature rise anomaly parameters to obtain temperature rise anomaly data.

[0038] The degradation model is used to perform multiphysics coupling simulation on the target flow guide component based on the initial electrical conductivity data, the initial thermal conductivity data, the initial mechanical performance data, the local attenuation parameters, the performance degradation information, and the temperature rise anomaly data, so as to obtain electric field distribution data, temperature field distribution data, and stress field distribution data.

[0039] The target flow guide component is subjected to failure criterion analysis based on the electric field distribution data, the temperature field distribution data, and the stress field distribution data to obtain the lifetime prediction dataset.

[0040] In an optional implementation, the method further includes:

[0041] Based on a preset process-performance relationship database, the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, the ablation rate, and the lifetime prediction dataset are integrated and processed to obtain process-performance correlation data.

[0042] Feature extraction processing is performed on the process-performance correlation data to obtain process parameter data and corresponding key performance index data;

[0043] Based on the process parameter data and the key performance index data, process parameter correlation modeling is performed to obtain a process-performance correlation model.

[0044] The process parameters are optimized using the process-performance correlation model to obtain the optimal combination of process parameters.

[0045] A second aspect of this application provides a detection device for ultra-high voltage GIS graphene-copper-based composite current-carrying components, the device comprising:

[0046] The performance testing module is used to perform performance testing on the target flow guiding component according to a preset testing method in order to obtain performance spatial distribution data.

[0047] Anomaly analysis module is used to perform structural defect detection processing on the target flow guide component based on the performance space distribution data, so as to obtain a structural defect feature dataset;

[0048] The dynamic testing module is used to perform dynamic current-carrying analysis and detection processing on the target flow guiding component based on the structural defect feature dataset, so as to obtain a thermal resistance anomaly grid coordinate mapping dataset.

[0049] The ablation test module is used to perform arc ablation simulation processing on the target flow guiding component based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate.

[0050] The degradation simulation module is used to perform multiphysics coupled failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model, in order to obtain a lifetime prediction dataset.

[0051] A third aspect of this application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the detection method for ultra-high voltage GIS graphene-copper-based composite current-carrying components as described above.

[0052] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the detection method for ultra-high voltage GIS graphene-copper-based composite flow guiding components as described above.

[0053] In summary, this application includes at least the following beneficial technical effects:

[0054] 1. The three-dimensional model of the target flow guiding component is divided into multiple detection areas, and the electrical conductivity, thermal conductivity and mechanical parameters are measured respectively, so as to realize a refined spatial distribution evaluation of the overall performance of the component.

[0055] 2. By using global conductivity data to screen out low conductivity regions, and combining X-ray tomography and ultrasonic phased array scanning, the three-dimensional voxel model, porosity distribution, and reflection coefficient distribution of low conductivity regions can be accurately obtained, realizing comprehensive detection and classification of internal structural defects, thereby effectively identifying potential hazards.

[0056] 3. By applying a stepped current to the defective area and analyzing the temperature rise deviation data, contact resistance and abnormal temperature rise information can be obtained in real time. This provides reliable experimental data for detecting abnormal thermal resistance of the current-conducting component under actual working conditions, ensuring the accuracy of dynamic performance analysis.

[0057] 4. Based on thermal resistance anomaly grid coordinate mapping data, combined with arc energy loading and surface morphology scanning, the arc ablation process can be simulated and the ablation rate calculated. Furthermore, through multiphysics coupled failure simulation, the effects of electric field, temperature field, and stress field are comprehensively considered to achieve accurate prediction of component lifespan, providing a theoretical basis for early fault prevention. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of a detection method for an ultra-high voltage GIS graphene-copper based composite current-carrying component provided in an embodiment of this application;

[0060] Figure 2 This is a functional block diagram of a detection device for an ultra-high voltage GIS graphene-copper-based composite current-carrying component provided in an embodiment of this application;

[0061] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] like Figure 1The diagram shown is a flowchart of the testing method for ultra-high voltage GIS graphene-copper-based composite current-carrying components provided in this application embodiment. The testing method for ultra-high voltage GIS graphene-copper-based composite current-carrying components provided in this application embodiment includes the following steps.

[0064] As special equipment, ultra-high voltage GIS graphene-copper-based composite current-carrying components must withstand high voltage and high current in complex working environments, placing extremely high demands on product quality. Therefore, current-carrying components from the same batch (i.e., automatically manufactured using the same production process parameters) must undergo sampling quality inspection to ensure that the overall product quality meets industry standards. Comprehensive quality inspection and evaluation not only verifies product performance but also identifies potential defects in advance, thereby improving the safety and reliability of the equipment. The ultra-high voltage GIS graphene-copper-based composite current-carrying component testing method provided in this application, through gridded data acquisition, precise structural defect identification, dynamic current carrying capacity and thermal resistance anomaly monitoring, and process-performance correlation modeling, achieves a comprehensive evaluation and accurate prediction of the current-carrying component's condition, providing solid data support for production quality control. The specific operation is as follows:

[0065] Step S1: Perform performance testing on the target flow guide component according to the preset detection method to obtain performance spatial distribution data.

[0066] During the manufacturing process of flow guiding components, uneven material distribution or process differences may lead to significant differences in local performance. By discretizing the sample surface, the electrical and thermal properties of each region can be accurately located and quantified, thereby achieving high-precision quality control.

[0067] Before quality inspection, high-precision 3D data scanning of the target flow guide component sample is required. Specifically, a high-resolution scanner (e.g., a laser scanner or CT scanner) is used to scan the outer surface of the target flow guide component sample to obtain 3D point cloud data containing rich geometric information. This 3D point cloud data is preprocessed to generate a digital 3D model (i.e., the target 3D model) with well-defined boundaries and detailed features. The target 3D model contains the complete geometric contour and surface micro-features of the flow guide component in space.

[0068] After obtaining the target 3D model, the surface of the 3D model is divided according to a preset mesh format. The preset mesh format is usually determined based on quality inspection requirements and material dimensions; for example, the surface of the flow guide component is divided into a 5 mm × 5 mm square mesh. Each mesh cell is assigned a unique coordinate identifier in the model (e.g., G...). 11 G 12...), forming a complete mesh coordinate mapping dataset. This meshing method can discretize the surface of the entire flow guide component, ensuring sufficient spatial resolution for subsequent electrical and thermal conductivity measurements, thereby revealing local performance differences. Specifically, the target 3D model is first imported into dedicated digital processing software. This software automatically meshes the model surface according to preset parameters (such as mesh cell size, starting coordinates, etc.). The meshing operation can be expressed by the following formula:

[0069]

[0070] in, This represents the grid cell located in the i-th column and j-th row. ) represent the initial coordinates of the model surface. and Indicates that the mesh cell is in x shaft and y The dimension in the axial direction, in this embodiment . i and j The value is non-negative and determined according to the model size range. The target surface is discretized so that the measurement results within each grid cell accurately reflect the local performance of that region. The preset mesh generation scheme ensures that the measurement data has good spatial resolution, facilitating subsequent comparison and analysis of local and global performance data. After mesh generation, the output mesh coordinate mapping data includes the unique identifier, geometric position, and size information of each grid cell. This data provides accurate spatial positioning for subsequent local measurements of electrical and thermal conductivity, ensuring that each measurement point accurately corresponds to the specific location of the flow guide component sample.

[0071] During the manufacturing process of the flow guiding component, due to factors such as the dispersion state or microporous structure of graphene, local non-uniformity of electrical conductivity may exist. By measuring within a preset grid cell, the internal electrical conductivity distribution of the material can be accurately revealed, thus providing quantitative data support for quality control and subsequent defect identification. Using the obtained grid coordinate mapping data as a positioning basis, the material conductivity within each grid cell is precisely measured. This embodiment employs a four-probe conductivity measurement method to detect the sample surface. Four microprobes are arranged within each preset grid cell, contacting the sample surface at a fixed interval, and a constant current is applied. Then, the voltage drop between the probes is measured, and finally, the conductivity value of that region is calculated. The specific operation is as follows:

[0072] First, the four-probe tester is combined with an automatic positioning system to locate the center point or predetermined position of each grid cell based on grid coordinate mapping data. A current source applies a constant current (e.g., I=10A) to the probes, while a voltage detector measures the voltage drop between the two probes. The conductivity value within each grid cell is calculated using the following formula:

[0073]

[0074] in, Indicates that it is located at the th Electrical conductivity of grid cells. This indicates the applied constant current, which is set to 10A in this embodiment. This indicates the probe spacing, obtained through probe calibration. This represents the voltage drop between probes obtained during the measurement process. This parameter represents the effective cross-sectional area of ​​the current-conducting component sample within the corresponding grid cell. It is extracted from the reconstructed data of the 3D model. The conductivity of this region is obtained by combining the applied current with the measured voltage drop and normalizing the data using the material's geometric parameters. During the operation, conductivity measurements are performed sequentially for each grid cell, and the results are recorded in a conductivity matrix set. Each element in the matrix corresponds to the conductivity value of a specific grid cell, thus forming the conductivity distribution map of the entire current-conducting component sample.

[0075] Meanwhile, the thermal conductivity of the flow guiding component material directly affects the heat dissipation performance of the equipment under high load. By accurately measuring the thermal conductivity of each region, potential areas of localized overheating or insufficient heat dissipation can be identified, thus providing fundamental 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 realizing the performance spatial distribution data. In this embodiment, microscopic thermal imaging technology 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 sample surface, and then a high-resolution thermal imager is used to acquire the temperature distribution map of the region. The thermal conductivity of the corresponding region can be calculated by combining the temperature gradient data with the material's thermal flux characteristics. The specific operation is as follows:

[0076] First, a preset uniform heat flux density (e.g., q = 100 W / m) is applied to each grid cell. 2 The heat flux density is uniformly transferred to the sample surface via a heating plate or a custom heat flux generator. Next, a steady-state temperature field image is acquired on the sample surface using a thermal microscopy imager. The image data is then digitized to form a temperature matrix. For each grid cell, the temperature gradient is calculated based on its temperature distribution data. The formula for calculating the thermal conductivity value within each grid cell is shown below:

[0077]

[0078] in, Indicates that it is located at the th Thermal conductivity of the grid cells. This represents the heat flux density applied to the sample surface, which is fixed at 100 W / m² in this embodiment. 2 . This indicates the effective thickness along the direction of heat flow, and this value is usually determined by CT scans or other thickness measurement techniques. This represents the steady-state temperature gradient measured within the grid cell, i.e., the difference between the highest and lowest temperatures. Using the temperature field gradient information, combined with the known heat flux density and material geometry, the thermal conductivity of this region is calculated. During the operation, temperature field data is acquired and gradient calculations are performed for each grid cell separately, ultimately forming a thermal conductivity matrix set, where each matrix element corresponds to the thermal conductivity value of a specific grid cell.

[0079] Meanwhile, the flow guiding component not only needs to meet electrical and thermal performance requirements, but its mechanical properties directly affect the stability and safety of the component under mechanical stress. Tensile testing can comprehensively reflect the performance of materials under stress and reveal potential mechanical defects in the material during long-term operation, providing a crucial basis for subsequent life prediction and process optimization. Mechanical performance testing of the target flow guiding component is an essential step. In this embodiment, standardized tensile testing equipment is used to perform stepwise loading tests on the sample until the sample fractures. The specific operation is as follows:

[0080] The flow guide component sample is fixed at both ends of the tensile testing machine, ensuring good alignment. The testing machine gradually applies tensile force at a preset loading rate (e.g., 0.5 mm / min), and sensors record the applied force and the elongation of the sample in real time. The stress-strain curve generated during the test is key data describing the material's mechanical properties, containing information on the material's elastic zone, yield zone, and fracture point. In the tensile test data, the maximum load and elongation at fracture are of particular interest. The tensile strength is calculated using the following formula, which normalizes the maximum load to the initial cross-sectional area of ​​the sample, thus obtaining the material's tensile properties during the tensile process.

[0081]

[0082] in, This indicates the tensile strength of the sample. This indicates the maximum load recorded during the test, which is collected in real time by the built-in sensors of the testing machine. The initial cross-sectional area of ​​the sample is represented by a laser rangefinder or other precision measuring instruments. The calculated tensile strength reflects the material's resistance to fracture. Simultaneously, the elongation at break, an important indicator of material toughness, can be obtained through the stress-strain curve. All mechanical property data, after being recorded by the data acquisition system, constitute a set of mechanical parameters, which includes key performance indicators such as tensile strength and elongation at break.

[0083] By meshing the 3D model of the target flow guide component sample, and then performing electrical conductivity, thermal conductivity, and mechanical properties testing within each mesh, a set of electrical conductivity matrices, a set of thermal conductivity matrices, and a set of mechanical parameters are formed. This spatial distribution data not only comprehensively reflects the local and overall performance of the sample but also provides essential data support for subsequent defect detection, multiphysics coupling simulation, and process parameter optimization.

[0084] Step S2: Perform structural defect detection processing on the target flow guide component based on the performance space distribution data to obtain a structural defect feature dataset.

[0085] During the production of flow guide component samples, due to process control issues or uneven material mixing, some areas may exhibit electrical conductivity lower than the overall average. Identifying these areas provides clear targets for subsequent, more refined internal defect detection (e.g., X-ray computed tomography and ultrasonic testing), ensuring targeted detection methods and more efficient and accurate data acquisition. The global average conductivity of the samples is calculated based on the acquired conductivity matrix set, and this average is used as a benchmark to compare the conductivity of each grid cell, thereby identifying low-conductivity regions.

[0086] During implementation, the first step is to perform statistical calculations on all values ​​in the conductivity matrix set. Each element in the conductivity matrix set... This represents the conductivity value located in the ij-th grid cell. The entire matrix contains the detection data for all grid cells. The calculation process for the global average conductivity data is shown below:

[0087]

[0088] in, This represents the calculated global average conductivity. Indicates the first Line 1 The conductivity detected within a column of grid cells. N represents the total number of grid cells in the conductivity matrix, 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 result.

[0089] Furthermore, during the production of the flow guide component samples, due to process control issues or uneven material mixing, some areas may have electrical conductivity lower than the overall average. Identifying these areas provides clear targets for subsequent, more refined internal defect detection (e.g., X-ray computed tomography and ultrasonic testing), ensuring targeted detection methods and more efficient and accurate data acquisition. After obtaining the global average electrical conductivity data, the data for each grid cell is filtered according to a set screening threshold (e.g., 80% of the global average electrical conductivity). Specifically, for each grid cell, if its electrical conductivity... Less than If so, the grid cell is marked as a low conductivity region.

[0090] X-ray computed tomography (CT) can non-destructively reveal the three-dimensional structure of materials. The generated voxel models not only visually reflect the internal density but also accurately quantify the porosity distribution, providing quantitative data support for detecting potential structural defects in low-conductivity regions. Therefore, after acquiring data on low-conductivity regions, X-ray computed tomography is used to inspect the target flow-guiding component sample to obtain the corresponding three-dimensional voxel model and porosity distribution data for the low-conductivity region. In the implementation process, the selected low-conductivity region data is first used as the basis for locating the scanning target, and a local CT scan is performed on the sample. The CT scanner acquires a series of two-dimensional image data by exposing the sample to X-rays from multiple angles, and then uses computer reconstruction algorithms to generate a three-dimensional voxel model of the region. The specific operation is as follows:

[0091] First, based on the obtained dataset of low conductivity regions, these regions are located in the target 3D model of the sample, and the location information is transmitted to the CT scanner's control system to ensure that the scan is performed only on this local area, saving scanning time and improving resolution. The target region is rotated during scanning, and a 2D X-ray image is acquired at regular intervals (e.g., 1°). During acquisition, X-rays penetrate the sample and are recorded by the detector; different gray values ​​in the image reflect the attenuation characteristics of the material inside the sample. The acquired image data is digitized to form a continuous 2D image sequence. Second, the acquired 2D image sequence is processed using computed tomography (CT) algorithms (e.g., filtered back projection or algebraic reconstruction techniques) to reconstruct a 3D voxel model of the target region. 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 that volume unit. Finally, in the 3D voxel model, voxels with gray values ​​below a predetermined threshold (e.g., 50) are marked, and these voxels are considered to correspond to porous regions inside the sample. The number of low-grayscale voxels and the total number of voxels within the target area were then counted to calculate porosity. The porosity calculation formula is shown below:

[0092]

[0093] in, Porosity is the percentage of volume occupied by pores within a given region. This indicates the number of voxels in the 3D voxel model whose grayscale value is lower than a preset threshold. This represents the total number of voxels within the target region. Porosity is used to quantify the pore distribution within the target region. Higher porosity usually indicates the presence of more voids or structural defects within the material, and this data plays an important guiding role in subsequent defect classification.

[0094] Meanwhile, ultrasonic phased array scanning technology has the advantages of high resolution and rapid detection, enabling precise measurement of the acoustic properties of internal material interfaces under non-destructive testing conditions. Low conductivity regions are often early warning areas for structural defects within materials. By detecting the reflection coefficient of this region, the presence and nature of the defects can be further confirmed, providing complementary information for the overall detection method. Therefore, after acquiring data on the low conductivity region, ultrasonic phased array scanning technology is used to detect the target flow guide component sample to obtain the reflection coefficient distribution data of the low conductivity region. The specific operation is as follows:

[0095] First, based on the obtained data of low conductivity regions, this data is transmitted to the ultrasonic testing system, focusing the detection on these areas and thus improving detection efficiency and resolution. Second, the ultrasonic phased array probe scans the selected area. The probe has multiple built-in transducers that synchronously emit ultrasonic pulses and receive echo signals caused by internal defects. The system controller records the ultrasonic echo intensity and time delay at each detection point based on the echo signals from different directions. Ultrasonic testing ensures that echo information from multiple data points can be obtained within each low conductivity region, forming local reflection coefficient distribution data. The process of calculating the reflection coefficient at each detection point based on the echo signal data can be expressed by the following formula:

[0096]

[0097] in, R This represents the reflection coefficient, used to quantify the intensity of ultrasonic wave reflection at the interface of a medium. The acoustic impedance of the copper substrate in the current guiding component is expressed by the following formula: ,in The density of copper, The speed at which ultrasonic waves propagate in copper. The acoustic impedance of the graphene layer in the flow guiding component is expressed by the following formula: ,in The density of graphene, The ultrasonic wave propagation speed in graphene is represented by the reflection coefficient. The magnitude of the reflection coefficient directly reflects the quality of interfacial bonding within the material. A higher reflection coefficient (e.g., greater than 0.3) usually indicates the presence of delamination defects, while a lower reflection coefficient (e.g., less than 0.1) may indicate the presence of porosity aggregation. After ultrasonic scanning, the reflection coefficient distribution data of each detection point in the low conductivity region is obtained. This data not only reveals the acoustic characteristics between the layers within the material but also helps determine the nature of defects, providing an important basis for subsequent defect classification.

[0098] Finally, by fusing the porosity distribution data obtained from X-ray CT scanning with the reflection coefficient data obtained from ultrasonic scanning, and classifying the data using a defined threshold, the nature and distribution of defects can be systematically revealed. Based on the reflection coefficient distribution data and porosity distribution data, the internal structural defects of the target flow guide component sample are classified using the porosity distribution data in the obtained three-dimensional voxel model and the reflection coefficient distribution data obtained from ultrasonic detection. In the implementation process, the three-dimensional voxel model, porosity distribution data, and reflection coefficient distribution data first need to be fused. 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:

[0099] First, the three-dimensional voxel model obtained from X-ray computed tomography (CT) scans is spatially registered with the ultrasonic testing data to ensure complete alignment of the two datasets in the coordinate system. The registration process employs digital image processing technology, utilizing common feature points to match and correct the two datasets. This process ensures that the reflection coefficient data and porosity distribution data can accurately correspond to the same physical region. Next, each registered region is segmented according to preset defect judgment criteria. Generally, the following judgment conditions can be set:

[0100] When porosity >2%, and When the value is greater than 0.3, it is determined to be a layered defect;

[0101] When porosity >5%, and When the value is less than 0.1, it is determined to be a porosity accumulation defect.

[0102] Defects within the target area are initially classified based on quantitative parameters, allowing the detection data to intuitively reflect the distribution and nature of different defects.

[0103] Finally, for the registered and classified regions, the spatial coordinates, defect type, and defect size (calculated from the volume or area of ​​the defect region in the voxel model) of each defect are recorded. This data constitutes the structural defect feature dataset. This dataset is stored in a structured table format, with each record corresponding to a detected defect, including the specific location at the time of detection, the classification result, and the quantitative indicators of the defect.

[0104] Step 3: Perform dynamic current-carrying analysis and detection processing on the target flow guiding component based on the structural defect feature dataset to obtain a thermal resistance anomaly grid coordinate mapping dataset.

[0105] Current-carrying components often exhibit localized electrical performance anomalies in areas with structural defects. These abnormal areas produce different temperature rises and contact resistance responses compared to normal areas during dynamic current carrying, thus providing a quantitative basis for defect identification. During dynamic current carrying tests, it is first necessary to identify areas with structural defects in the target current-carrying component sample so that the electrical and thermal responses of these areas can be monitored 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 areas on the target three-dimensional model of the sample. In practice, the system uses digital image processing technology to match and correct the target three-dimensional model with the defect coordinate data in the structural defect feature dataset, and uses interpolation algorithms to smooth the edge areas to ensure accurate and continuous boundaries of the defect areas. After data extraction, the defect area coordinate data is output and stored in the detection system. The defect area coordinate dataset provides a clear positioning basis for applying current to the sample, ensuring that the testing instrument can automatically align with the defective areas during operation.

[0106] In areas with structural defects, the conductive interface of the current-conducting component may exhibit localized poor contact, leading to abnormal changes in contact resistance with varying current. After accurately locating the defective areas, a stepped current loading test is performed on these areas to measure the voltage drop response of each defective area under progressively increasing current, and the dynamic contact resistance data is calculated accordingly. This process is automatically controlled by precision instruments to ensure stable voltage drop data acquisition at each preset current level, while simultaneously using real-time current detection data to achieve accurate calculation of the dynamic contact resistance. Specifically, the automatic positioning system inputs the obtained coordinate data of the defective areas into the current loading device. This device employs a stepped current loading method, starting from 0A and gradually increasing the current in fixed steps (e.g., 1 kA) until a preset upper limit (e.g., 10 kA) is reached. At each current level, a miniature voltage probe mounted on the surface of the current-conducting component accurately measures the contact voltage drop data of the corresponding area, while the sensor records real-time current detection data. This application uses Ohm's law to calculate the contact resistance data at each loading current level based on contact voltage drop data and real-time current detection data, thereby quantitatively reflecting the electrical response characteristics of the defect area of ​​the current-carrying component during dynamic current carrying.

[0107] Simultaneously, to accurately predict the thermal effects caused by local resistance anomalies, a three-dimensional thermal conduction model of the target flow-guiding component is required. Based on the thermal conductivity matrix set, combined with the geometric parameters and current-carrying conditions of the graphene-copper composite material obtained experimentally, the thermal conduction behavior of the sample is simulated to obtain simulated temperature rise data for each region during current carrying. Specifically, the thermal conductivity matrix data of the flow-guiding component sample is input into finite element analysis software. This software establishes a three-dimensional model of the flow-guiding component sample and assigns corresponding thermal conductivity parameters to each mesh element according to the thermal conductivity matrix. Then, by applying boundary conditions consistent with those in dynamic current-carrying tests (e.g., preset heat flux density, Joule heat sources generated by contact resistance, etc.), the software automatically solves the steady-state or transient heat conduction equations. By establishing a three-dimensional thermal conduction model of the flow-guiding component, a heat flux density q and a Joule heat source are applied to the model. Q Joule The temperature field distribution equation can be expressed by the following formula:

[0108]

[0109] in, This indicates the density of the material. This indicates the specific heat capacity of the material. Indicates temperature. This represents the rate of change of temperature with respect to time. The divergence of the temperature gradient reflects the internal thermal conductivity of the material. is the thermal conductivity. This represents the Joule heat generated by contact resistance and current, and its calculation can be based on... It is concluded that, among them The contact resistance is represented by a temperature field distribution equation, which describes the temperature evolution of the material under dynamic current-carrying conditions caused by internal heat sources and external heat flow. By numerically solving this equation, simulated temperature rise data for each region under specific current-carrying conditions can be obtained. During the solution process, the finite element software divides the entire sample into numerous elements, and the aforementioned equation is used to solve for the local temperature of each element. The final output simulated temperature rise data is the temperature rise value under the loading conditions within each mesh element. This dataset intuitively reflects the temperature rise caused by Joule heating in different regions. This simulated data will serve as an important basis for subsequent temperature rise deviation calculations and thermal resistance anomaly judgments.

[0110] During dynamic current carrying, uneven Joule heat release caused by abnormal local contact resistance can lead to a significant deviation between the actual temperature rise and the simulated expected temperature rise. After obtaining the simulated temperature rise data, it is compared with the temperature rise data acquired in real time during actual testing to identify areas in the sample with abnormal thermal resistance. To this end, the actual temperature rise of the defective area is first monitored in real time using an infrared thermal imager during dynamic current carrying tests, and the actual temperature rise data is recorded. Then, the simulated temperature rise data is compared with the actual temperature rise data, and the deviation value between the two (i.e., the temperature rise deviation data) is calculated. The temperature rise deviation data is used to quantify the difference between the actual temperature rise and the expected temperature rise in each region; areas with large temperature rise deviations usually indicate abnormal thermal resistance problems.

[0111] After calculating the temperature rise deviation for all grid cells, a temperature rise deviation dataset is obtained. Next, each grid cell is judged for thermal resistance anomalies based on preset criteria (e.g., if the temperature rise deviation exceeds 15%). Specifically, if the temperature rise deviation of a certain grid cell in the temperature rise deviation dataset exceeds a set threshold, that grid cell is marked as a thermal resistance anomaly region. The spatial coordinates of these anomaly regions are recorded, thus obtaining the thermal resistance anomaly coordinate data.

[0112] Finally, to generate a complete dynamic current-carrying test output, the contact resistance data and thermal resistance anomaly coordinate data of the defective areas need to be integrated according to a preset grid format to generate a thermal resistance anomaly grid coordinate mapping dataset. This dataset not only contains the spatial coordinates of each anomaly region but also records the corresponding contact resistance values, facilitating subsequent multiphysics coupling analysis and further lifetime prediction. Specifically, based on the preset grid coordinate mapping data, the contact resistance data and thermal resistance anomaly coordinate data are matched. The processing employs a data registration algorithm to compare the coordinate information in the two datasets, ensuring that the coordinates of each thermal resistance anomaly region accurately correspond to the corresponding contact resistance value. After registration, a structured data table is generated according to the preset grid format, with each record containing the following information: grid number, corresponding thermal resistance anomaly coordinates, and the dynamic contact resistance value within that grid.

[0113] Step S4: Perform arc ablation simulation on the target flow guiding component based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate.

[0114] Due to the non-uniformity of the internal structure of the flow guiding component, localized areas of abnormal thermal resistance are often associated with structural defects. By locating these areas, arc energy can be precisely applied, thus concentrating the arc ablation effect on critical parts and providing more representative ablation data. Specifically, firstly, all data in the thermal resistance anomaly grid coordinate mapping dataset is traversed, and the data for each grid cell is analyzed. For each grid cell, if its recorded contact resistance value exceeds a predetermined safety threshold (e.g., if this threshold is set to...), then... R th If a grid cell meets the criteria (e.g., a region of electric arc influence), it is considered to possess the necessary conditions to function as an electric arc influence area. The detection system uses digital signal processing algorithms to mark and summarize the grid data that meet these conditions, thus forming a preliminary set of electric arc influence area data. Subsequently, through data registration technology, these selected regions are visualized on the 3D model of the target guide component, and their boundaries are smoothed using geometric interpolation methods to ensure the continuity and accuracy of the positioning data.

[0115] For example, during the localization process, the thermal resistance anomaly grid coordinate mapping dataset is... M Each grid cell in G ij Screening is performed if the contact resistance of the grid cell is... satisfy If the condition is met, then record the center coordinates of that grid cell. The coordinates of the electric arc target can be represented by the following formula:

[0116]

[0117] in, This represents the obtained coordinate data of the electric arc target point. Indicates the first The center coordinates of the grid cell. This represents the dynamic contact resistance data of the i-th and j-th grid cells. This represents a preset contact resistance threshold; areas exceeding this threshold are considered abnormal. The arc target point coordinate data is used to systematically filter out grid regions with abnormal thermal resistance characteristics and generate a set of coordinate data for the arc-acting target points, providing accurate positioning information for subsequent arc energy loading processing.

[0118] Furthermore, after obtaining the coordinate data of the arc target point, the target guiding component sample undergoes arc energy loading processing to simulate the actual arc action environment. A high-current arc is applied to the predetermined arc target point, and arc voltage and arc current data are collected during the loading process, thus providing basic data for subsequent energy density calculations. During operation, the arc energy loading device utilizes a precision control system based on the arc target point coordinate data... The system precisely aligns with the target area and applies current to each target point according to a pre-defined arc loading scheme. During loading, a stepped current increase is employed, gradually increasing the current from 0 to a predetermined maximum value (e.g., 20 kA). Simultaneously, a high-speed data acquisition system records the arc voltage and arc current in real time at each loading stage. Specifically, firstly, the detection system transmits the arc target point coordinate data to the positioning module of the arc loading device, which precisely positions the arc application point on the surface of the current-conducting component. Subsequently, the device loads each target point according to a pre-defined current curve, simultaneously acquiring arc voltage data through a 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 throughout the entire arc energy loading process, the device operates strictly according to the pre-defined loading scheme, ensuring high-precision voltage and current data are obtained at each arc target point.

[0119] Arc ablation is directly related to the energy density of the arc input; high energy density regions typically exhibit more significant ablation on the sample surface. Accurate calculation of the arc energy density provides crucial physical quantity information for subsequent surface morphology scanning and ablation rate calculation. After obtaining the arc voltage and arc current data recorded during arc loading, the next task is to calculate the arc energy density. Arc energy density describes the energy released by the arc in a localized region of the sample per unit time. This data is critical for evaluating the arc ablation effect, as higher energy density usually indicates more pronounced localized ablation. Specifically, the data acquisition system inputs the high-speed recorded arc voltage and current data into a numerical integration module. Numerical integration algorithms (e.g., trapezoidal integration or Simpson's method) are used to accumulate the instantaneous power over the time interval to calculate the total energy. Subsequently, the total energy is divided by the area of ​​the loaded region to obtain the arc energy density. The arc energy density can be calculated using the following formula:

[0120]

[0121] in, This represents the energy density of the electric arc. Indicates at time The measured arc voltage. Indicates at time The measured arc current. This represents the area of ​​the arc-loaded region, which is determined by the geometric dimensions of the preset loading region. This indicates the total duration of the electric arc loading. Used to normalize total energy to energy per unit area.

[0122] After arc loading, the surface of the flow guiding component sample will undergo physical changes, such as localized material ablation and the formation of surface microcracks. These changes lead to significant fluctuations in the surface morphology. By measuring surface roughness and ablation depth, the degree of arc ablation can be directly reflected, providing a geometric basis for subsequent ablation rate calculation. This application's embodiment uses a non-contact scanning instrument, which has the advantages of high precision and non-destructive testing, ensuring data accuracy and stability, thus providing solid data support for the quantitative analysis of the arc ablation effect. After obtaining the arc energy density data, a detailed scan of the surface morphology of the target flow guiding component sample in the arc loading area is performed to determine the surface changes caused by arc ablation. Through high-precision surface morphology scanning, surface roughness data and ablation depth data are obtained, providing direct geometric parameters for the final ablation rate calculation. Specifically, for the arc energy loading area, a non-contact surface morphology detection instrument such as a white light interferometer or a laser scanning microscope will be used to perform a fine scan of the sample surface. During the scanning process, the instrument can acquire high-resolution surface contour data, which is expressed as discrete height points y... i The data is recorded in the form of [data missing]. Subsequently, the collected height data is processed to calculate two key parameters: surface roughness Ra and ablation depth h. Surface roughness can be calculated using the following formula:

[0123]

[0124] in, This indicates surface roughness. This represents the surface height value of the i-th discrete point. This represents the average of all discrete height values. n represents the total number of discrete points. Surface roughness is used to quantify the undulations of the surface microstructure; the higher the roughness value, the more significant the morphological changes caused by the electric arc.

[0125] The ablation depth h is measured by comparing the three-dimensional surface data obtained from instrument scanning with the original surface data, directly reading or calculating the material thickness reduced due to arc ablation. The scanner provides accurate depth measurements through high-precision imaging; ablation depth data is typically expressed in micrometers. Throughout the surface morphology scanning process, the detection system first determines the scanning area on the sample based on the loading area indicated by the arc energy density data; then, a white light interferometer is used to meticulously scan this area, acquiring surface height distribution data; next, digital image processing algorithms are used to calculate the surface roughness and ablation depth, and finally, these data are stored as structured data records.

[0126] Arc ablation directly reflects the degree of material degradation under the high energy of an electric arc. The ablation rate, as a quantitative indicator, directly reflects the relationship between the volume lost by the material due to ablation and the energy input. A higher ablation rate indicates lower ablation resistance and a greater safety hazard; conversely, a lower ablation rate indicates 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 ablation rate calculation. The ablation rate is typically used to quantify the ratio between the volume of material ablated by an electric arc and the arc energy input. The ablation rate calculation process can be expressed by the following formula:

[0127]

[0128] in, The ablation rate is used to quantify the volume of material ablated per unit energy input. The ablation depth is the thickness of the material surface removed after the electric arc is applied. This indicates the ablation area of ​​the arc-loaded region, a parameter obtained from a scanner or pre-determined geometry of the loaded region. This represents the energy density of the electric arc. The ablation rate is used to quantitatively correlate the actual ablation results with the input energy, calculate the volume of material ablated under unit energy input, and thus quantitatively reflect the ablation resistance of the material under electric arc loading.

[0129] Step S5: Using a preset degradation model, perform multiphysics coupling failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate to obtain a lifetime prediction dataset.

[0130] It should be understood that the performance spatial distribution data includes a set of electrical conductivity matrices, a set of thermal conductivity matrices, and a set of mechanical parameters. Key parameters representing the initial state of the sample are extracted from the performance spatial distribution data to provide initial conditions for subsequent degradation simulations. First, the detection system selects the electrical conductivity value within each preset grid cell from the performance spatial distribution data and obtains the initial electrical conductivity data for the entire sample by statistically calculating the data from all grid cells. 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 performance data.

[0131] Following dynamic current-carrying and arc ablation testing, the sample undergoes physical and chemical changes in localized regions due to high energy input, leading to a degradation in electrical conductivity, thermal conductivity, and mechanical properties. The calculation of local performance degradation rates aims to quantitatively describe the degradation rate of each key performance indicator over time under specific arc ablation conditions, providing local degradation parameters for the degradation model. These local degradation parameters include the degradation rate of electrical conductivity, thermal conductivity, and mechanical properties. In practice, the detection system uses ablation rate data obtained from arc ablation simulations as input, combined with previously obtained initial performance parameters, and calculates parameters such as the local performance degradation rate through statistical analysis. Similarly, the detection system calculates the degradation of initial thermal conductivity and thermal conductivity measured over time, as well as the degradation of initial tensile strength and tensile strength measured over corresponding time periods, to obtain the local thermal conductivity degradation rate and mechanical property degradation rate. These local degradation parameters provide input indicators for subsequent multiphysics coupled simulations, reflecting the performance change trends of materials under arc ablation and other degradation effects. In practice, the detection system correlates the ablation rate with the initial conductivity, calculating the percentage decrease in conductivity per unit time. The conductivity decay rate can be expressed by the following formula:

[0132]

[0133] in, This indicates the rate of decay of local conductivity. This represents the local conductivity measured after time t. The initial electrical conductivity is represented by t, and the elapsed time is represented by t. The conductivity decay rate is used to quantify the degree of conductivity decay per unit time, providing a quantitative indicator of local performance degradation. A similar approach can be used to calculate the decay rate of thermal conductivity and mechanical property parameters. The detection system performs decay calculations on the initial thermal conductivity and the thermal conductivity measured after time, as well as on the initial tensile strength and the tensile strength after the corresponding time, to obtain the local thermal conductivity decay rate and mechanical property decay rate. These local decay parameters provide input indicators for subsequent multiphysics coupled simulations, reflecting the performance change trend of materials under arc ablation and other degradation effects.

[0134] In actual production, the defect distribution of flow guide components exhibits spatial non-uniformity, with different defect regions contributing differently to overall performance degradation. By dividing the components into defect regions and evaluating each region individually, the local performance degradation can be accurately reflected, providing more refined spatial distribution parameters for degradation simulation. The structural defect feature dataset has been obtained using multimodal imaging technology, which records in detail the coordinates, dimensions, and defect types of each defect region in the samples. The goal of the defect region segmentation process is to divide the flow guide component samples into multiple defect regions and evaluate the performance degradation information within each region, providing spatial distribution information for subsequent degradation simulation.

[0135] Meanwhile, it should be understood that temperature rise anomalies are important thermal characteristics of the current-conducting components caused by local structural defects and arc erosion. By quantifying the temperature rise anomaly parameters, the local thermal resistance anomaly can be objectively reflected. Through dynamic current-carrying tests and heat conduction modeling, simulated and actual temperature rise data have been obtained, and the temperature rise deviation has been calculated, thus obtaining the thermal resistance anomaly coordinate data. Local temperature rise anomaly parameters are extracted using the thermal resistance anomaly grid coordinate mapping dataset, and the temperature rise deviation is quantified into temperature rise anomaly data, providing thermal anomaly input for degradation simulation.

[0136] In actual operation, the flow guiding component is subjected to the combined effects of multiple electric, thermal, and mechanical fields, resulting in a complex and interconnected degradation process. A pre-defined degradation model can comprehensively simulate the evolution of various material properties during degradation, thus providing a scientific basis for lifetime prediction. The degradation model performs multi-physics coupled simulations based on obtained initial electrical conductivity data, initial thermal conductivity data, initial mechanical property data, local attenuation parameters, performance degradation information, and temperature rise anomaly data. It simulates the evolution of various fields in the material under arc ablation and degradation, providing quantitative physical field distribution data for lifetime prediction. The degradation model uses the following set of coupled equations to describe the interaction relationships of the various physical fields:

[0137]

[0138] in, It represents the electric potential distribution. This represents the electrical conductivity of the material; here, it is the initial electrical conductivity. The local conductivity is obtained after local attenuation correction. This indicates the density of the material. This indicates specific heat capacity. It represents the temperature field. This represents the thermal conductivity of the material, specifically the initial thermal conductivity. Corrected for local attenuation and abnormal temperature rise. It represents the square of the electric field strength, reflecting the generation of Joule heat. This represents the stress tensor. This represents volume forces. The coupled equations describe the evolution of temperature and stress distribution within the material under the influence of an electric field, comprehensively considering electrical, thermal, and mechanical interactions to provide global physics data for degradation simulation.

[0139] During the simulation, the detection system inputs the initial parameters and local correction parameters into the FEA software and solves the above coupling equations within a preset time step to obtain electric field distribution data. V(x,y,z) gradient and temperature field distribution data T(x, y,z) and stress field distribution data σ(x,y,z) Among them, the temperature field distribution data, combined with the aforementioned temperature rise anomaly data, reflects the actual temperature distribution in the local thermal anomaly region of the material; the electric field distribution data reflects the decrease in electrical conductivity due to local degradation; and the stress field data reveals the mechanical changes caused by thermal expansion and material deterioration.

[0140] After multiphysics coupling simulation, the obtained electric field, temperature field, and stress field distribution data provide an intuitive description of the state of the flow guide component during the degradation process. The purpose of failure criterion analysis is to judge the data of each region according to the preset failure criteria, determine which regions have reached or exceeded the failure limit, and thus deduce the remaining life of the entire sample. In specific implementation, the detection system will use the failure criterion formula to analyze the simulation data point by point to determine whether each region has reached the electrical failure, thermal failure, or mechanical failure criteria.

[0141] For example, for electrical failure, a local conductivity drop to 80% of its initial value is defined as electrical failure; for thermal failure, a local temperature rise exceeding 150 K is defined as the criterion; and for mechanical failure, a stress concentration factor greater than 3 is defined as the failure criterion. For each grid cell, the detection system calculates the current local conductivity σ(x,y,z) and the initial conductivity. The ratio is used to determine whether it is lower than the preset failure threshold of 0.8.

[0142] Similarly, criterion analysis is performed on the temperature field and stress field data separately. The detection system integrates various criteria to determine whether there is a failure risk for each grid cell and statistically analyzes the overall distribution of failure areas. Through failure criterion analysis of all grid cells, a lifetime prediction dataset is obtained, which records the failure state and predicted lifetime of each key area during the simulation time.

[0143] In an optional implementation, the method further includes adjusting the production process based on the obtained performance spatial distribution data, structural defect feature dataset, thermal resistance anomaly grid coordinate mapping dataset, ablation rate, and lifetime prediction dataset.

[0144] Through a pre-defined process-performance relationship database, the data obtained from the aforementioned tests are systematically integrated to form a comprehensive dataset reflecting the relationship between various performance characteristics of the flow guide component and manufacturing process parameters—that is, process-performance correlation data. To ensure the scientific rigor and accuracy of subsequent process parameter optimization, data from different sources and dimensions must be integrated and normalized in a unified format. First, the electrical conductivity matrix set, thermal conductivity matrix set, and mechanical parameter set obtained from the performance spatial distribution data all reflect the initial performance state of the flow guide component sample in terms of electrical, thermal, and mechanical aspects. Simultaneously, the structural defect feature dataset provides information on the spatial location, defect type, and defect size of defect regions within the sample. Furthermore, the thermal resistance anomaly grid coordinate mapping dataset records local thermal resistance anomaly regions and their corresponding dynamic contact resistance data that appear during dynamic current-carrying tests. Next, the ablation rate obtained through arc ablation simulation quantitatively reflects the degree of material ablation under the action of an electric arc. Finally, the lifetime prediction dataset is the prediction data of the overall degradation and failure time of the sample obtained through multi-physics coupled failure simulation. These data come from different detection modules, and their data units, dimensions, and numerical ranges are different. They must be preprocessed and normalized before they can be jointly analyzed on the same platform.

[0145] After data preprocessing and normalization, all datasets were converted to a standard format. The next step is to integrate all the data. A pre-defined process-performance relationship database contains manufacturing process parameters (such as sintering temperature, graphene doping ratio, pressure, etc.). This step establishes a correlation between these manufacturing parameters and various test data. Specifically, for each batch or sample, performance data, defect data, thermal resistance anomaly data, ablation rate, and lifetime prediction data are matched with the corresponding process parameters based on the collection time and batch number, forming a structured record. This process uses database join operations to horizontally merge the data tables through key fields (such as batch number, sample number, etc.) to generate a comprehensive data record. Each record contains process parameters and all corresponding test indicators, forming a process-performance correlation data record.

[0146] After obtaining the process-performance correlation data, feature extraction is performed on the data to extract feature data reflecting manufacturing process parameters and their corresponding key performance indicators. The detection system employs data mining and statistical analysis techniques to extract features from the process-performance correlation data. First, descriptive statistics are performed on all data records to calculate the mean, variance, correlation coefficient, and other indicators of each process parameter and performance indicator, assessing the interrelationships between variables. Dimensionality reduction methods such as principal component analysis (PCA) are used to extract the principal components that best explain the data variance from the original high-dimensional data, and the correlation between these components and manufacturing process parameters is determined. For example, process-performance correlation data... The first half consists of process parameters, and the second half consists of key performance indicators. Principal component analysis can be used to obtain several principal components. Each principal component is a linear combination of the original data. The feature extraction process is shown below:

[0147] Assume the original data matrix is Each column represents a variable. After converting to zero-mean data, calculate its covariance matrix. Then solve for the eigenvalues ​​of the covariance matrix. With feature vectors , principal component Defined as .in, This represents the normalized data matrix. This represents the data covariance matrix. Let i represent the i-th eigenvalue. Indicates and The corresponding feature vector activity. Let represent the i-th principal component. Let n represent the total number of data records. By extracting the main variables from high-dimensional data, the data dimensionality is reduced, and the dominant relationships between various process parameters and performance indicators are determined.

[0148] After the extracted principal components are analyzed, variables closely related to the manufacturing process are identified 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 by detection (e.g., initial electrical conductivity, thermal conductivity, tensile strength, ablation rate, and lifetime prediction).

[0149] After obtaining the process parameter data and corresponding key performance index data, the next goal is to establish a mathematical correlation model between process parameters and product performance, namely, a process-performance correlation model. This model aims to quantitatively describe the impact of various manufacturing process parameters on the performance of the flow guide component samples (including electrical, thermal, and mechanical indicators, as well as ablation and lifespan prediction data), providing a theoretical basis for subsequent multi-objective optimization. Specifically, the detection system uses multivariate nonlinear regression analysis and machine learning methods to model the extracted feature data. Let the process parameter vector be denoted as... Key performance indicators are marked as Using regression modeling, the following mathematical model is constructed:

[0150]

[0151] in, This represents a vector of key performance indicators. This indicates the sintering temperature. This indicates the graphene doping ratio. It indicates pressure. is the regression coefficient. The term representing the square of the sintering temperature reflects a nonlinear effect. This represents the interaction terms between process parameters. This represents the error term. This mathematical model is used to quantitatively describe the impact of various process parameters and their interactions on product performance indicators. Regression coefficients reflect the weight and direction of these impacts, providing a basis for optimizing process parameters.

[0152] The detection system uses historical or experimental data to estimate the parameters of the above model, and employs the least squares method or other optimization algorithms to determine the best-fit parameters, thereby establishing a process-performance correlation model. Once established, the model can predict the changing trends of product performance indicators under different combinations of process parameters, providing a quantitative basis for subsequent process parameter optimization.

[0153] Finally, after establishing the process-performance correlation model, multi-objective optimization is used to obtain the optimal combination of process parameters from the model, achieving the comprehensive goal of optimal product performance and lowest manufacturing cost. Multi-objective optimization incorporates process parameters and various performance indicators into a unified objective function. By solving the Pareto optimal solution set, the most suitable process parameter configuration is determined to meet all performance requirements. Specifically, the detection system uses the process-performance correlation model as input and sets the optimization objective function. Optimization objectives typically include maximizing the predicted lifetime value and other performance indicators, while minimizing manufacturing costs, resource consumption, and other indicators. The following form of multi-objective optimization objective function is used for modeling:

[0154]

[0155] in, This represents lifespan prediction data; the higher the value, the longer the product's lifespan. This indicates the manufacturing cost corresponding to the process parameters; the lower the cost, the better. These are weighting coefficients, used to reflect the relative importance of product lifespan and cost in the optimization process. The multi-objective optimization objective function comprehensively considers two key indicators: product lifespan and manufacturing cost. By solving this multi-objective optimization problem, the optimal combination of process parameters is obtained, resulting in optimal product performance and lowest manufacturing cost.

[0156] The detection system utilizes genetic algorithms (e.g., NSGA-II) or other multi-objective optimization algorithms to solve the objective function. Under constraints of given process parameters (e.g., the range of sintering temperature, the fluctuation range of graphene doping ratio, the upper limit of pressure, etc.), it searches for the Pareto front solution set. During optimization, each candidate solution corresponds to a set of process parameter combinations. The system calculates its corresponding model output and evaluates its fitness under the objective function. After multiple iterations, the algorithm eventually converges to a set of optimal solutions, from which one or more optimal process parameter combinations are selected as output.

[0157] This application applies to the field of insulation switch testing technology. It obtains performance spatial distribution data by performing performance testing on the target current-carrying component according to the testing method. Based on the performance spatial distribution data, it obtains a structural defect feature dataset by performing structural defect detection on the target current-carrying component. Based on the structural defect feature dataset, it obtains a thermal resistance anomaly grid coordinate mapping dataset by performing dynamic current-carrying analysis on the target current-carrying component. Based on the thermal resistance anomaly grid coordinate mapping dataset, it obtains the ablation rate by performing arc ablation simulation on the target current-carrying component. Finally, it obtains a lifetime prediction dataset by combining a degradation model with multi-physics coupled failure simulation. This application achieves a comprehensive evaluation of ultra-high voltage GIS graphene-copper-based composite current-carrying components from performance, structure, current-carrying state to arc ablation through multi-level and multi-angle data acquisition and analysis.

[0158] like Figure 2 The diagram shown is a functional block diagram of a detection device for an ultra-high voltage GIS graphene-copper-based composite current-carrying component provided in an embodiment of this application.

[0159] In some embodiments, the UHV GIS graphene-copper-based composite current-carrying component testing device 2 may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the UHV GIS graphene-copper-based composite current-carrying component testing device 2 may be stored in the server's memory and executed by at least one processor to perform (see details). Figure 1 (Description) Function of the detection method for ultra-high voltage GIS graphene-copper-based composite flow guiding components.

[0160] In this embodiment, the UHV GIS graphene-copper-based composite current-carrying component testing device 2 can be divided into multiple functional modules according to its functions. These functional modules may include: a performance testing module 21, an anomaly analysis module 22, a dynamic testing module 23, an ablation testing module 24, a degradation simulation module 25, and a process optimization module 26. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0161] The performance detection module 21 is used to perform performance detection processing on the target flow guide component according to a preset detection method to obtain performance spatial distribution data.

[0162] In an optional implementation, the performance detection module 21 is specifically used for:

[0163] The surface region of the acquired target 3D model is divided according to the preset grid format to obtain grid coordinate mapping data;

[0164] The conductivity of the target flow guiding component is measured based on the grid coordinate mapping data to obtain the conductivity matrix set.

[0165] The thermal conductivity of the target flow guide component is measured based on the grid coordinate mapping data to obtain the thermal conductivity matrix set.

[0166] The target flow guide component is subjected to a mechanical property tensile test to obtain the set of mechanical parameters.

[0167] The anomaly analysis module 22 is used to perform structural defect detection processing on the target flow guide component based on the performance space distribution data, so as to obtain a structural defect feature dataset.

[0168] In an optional implementation, the anomaly analysis module 22 is specifically used for:

[0169] The target current-carrying component is processed by global conductivity data calculation based on the conductivity matrix set to obtain global average conductivity data, and the conductivity matrix set is filtered based on the global average conductivity data to obtain low conductivity region data.

[0170] X-ray tomography is performed on the target flow guiding component based on the data of the low conductivity region to obtain a three-dimensional voxel model and pore distribution data corresponding to the low conductivity region.

[0171] The target flow guiding component is subjected to ultrasonic phased array scanning processing based on the low conductivity region data to obtain the reflection coefficient distribution data of the low conductivity region;

[0172] The three-dimensional voxel model is subjected to defect classification processing based on the reflection coefficient distribution data and the porosity distribution data to obtain the structural defect feature dataset.

[0173] The dynamic testing module 23 is used to perform dynamic current-carrying analysis and detection processing on the target flow guiding component based on the structural defect feature dataset, so as to obtain a thermal resistance anomaly grid coordinate mapping dataset.

[0174] In an optional implementation, the dynamic testing module 23 is specifically used for:

[0175] Based on the structural defect feature dataset, the target flow guiding component is subjected to flow-carrying region localization processing to obtain defect region coordinate data;

[0176] A stepped current loading process is performed on the defect area in the defect area coordinate data to obtain contact voltage drop data and real-time current detection data. Then, dynamic contact resistance calculation is performed based on the contact voltage drop data and the real-time current detection data to obtain the contact resistance data.

[0177] The target flow guiding component is modeled in three dimensions based on the thermal conductivity matrix set to obtain simulated temperature rise data.

[0178] The temperature rise deviation is calculated based on the simulated temperature rise data and the real-time acquired actual temperature rise data to obtain temperature rise deviation data. The thermal resistance anomaly judgment is then performed on the target flow guide component based on the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data.

[0179] The thermal resistance anomaly coordinate data and the contact resistance data are used to perform grid coordinate mapping processing to obtain the thermal resistance anomaly grid coordinate mapping dataset.

[0180] The ablation test module 24 is used to perform arc ablation simulation processing on the target flow guide component according to the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate.

[0181] In an optional implementation, the ablation test module 24 is specifically used for:

[0182] The target flow guiding component is subjected to arc target point localization processing based on the thermal resistance anomaly grid coordinate mapping dataset to obtain arc target point coordinate data.

[0183] The target flow guiding component is subjected to arc energy loading processing based on the arc target point coordinate data to obtain arc voltage data and arc current data.

[0184] The arc energy density of the target current guiding component is calculated based on the arc voltage data and the arc current data to obtain arc energy density data.

[0185] The surface morphology of the target flow guide component is scanned based on the arc energy loading region in the arc energy density data to obtain surface roughness data and ablation depth data.

[0186] The ablation rate of the target flow guide component is calculated based on the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

[0187] The degradation simulation module 25 is used to perform multiphysics coupling failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model, so as to obtain a lifetime prediction dataset.

[0188] In an optional implementation, the degradation simulation module 25 is specifically used for:

[0189] The target flow guiding component is processed by extracting initial performance parameters based on the performance spatial distribution data to obtain initial electrical conductivity data, initial thermal conductivity data, and initial mechanical performance data.

[0190] The local performance degradation rate of the target flow guide component is calculated based on the ablation rate to obtain local degradation parameters.

[0191] Based on the structural defect feature dataset, the target flow guide component is divided into defect regions to obtain performance degradation information for each defect region.

[0192] Based on the thermal resistance anomaly grid coordinate mapping dataset, the target flow guide component is processed to extract local temperature rise anomaly parameters to obtain temperature rise anomaly data.

[0193] The degradation model is used to perform multiphysics coupling simulation on the target flow guide component based on the initial electrical conductivity data, the initial thermal conductivity data, the initial mechanical performance data, the local attenuation parameters, the performance degradation information, and the temperature rise anomaly data, so as to obtain electric field distribution data, temperature field distribution data, and stress field distribution data.

[0194] The target flow guide component is subjected to failure criterion analysis based on the electric field distribution data, the temperature field distribution data, and the stress field distribution data to obtain the lifetime prediction dataset.

[0195] In an optional embodiment, the UHV GIS graphene-copper-based composite current-carrying component testing device 2 further includes a process optimization module 26, which is used for:

[0196] Based on a preset process-performance relationship database, the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, the ablation rate, and the lifetime prediction dataset are integrated and processed to obtain process-performance correlation data.

[0197] Feature extraction processing is performed on the process-performance correlation data to obtain process parameter data and corresponding key performance index data;

[0198] Based on the process parameter data and the key performance index data, process parameter correlation modeling is performed to obtain a process-performance correlation model.

[0199] The process parameters are optimized using the process-performance correlation model to obtain the optimal combination of process parameters.

[0200] It should be understood that the various variations and specific embodiments of the methods provided in the above embodiments are also applicable to the UHV GIS graphene-copper-based composite current-carrying component testing device of this embodiment. Through the foregoing detailed description of the UHV GIS graphene-copper-based composite current-carrying component testing method, those skilled in the art can clearly understand the implementation method of the UHV GIS graphene-copper-based composite current-carrying component testing device of this embodiment. For the sake of brevity, it will not be described in detail here.

[0201] like Figure 3 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application.

[0202] 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.

[0203] Those skilled in the art should understand that Figure 3 The structure of the electronic device 3 shown does not constitute a limitation of the embodiments of the present invention. The electronic device 3 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0204] 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, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices.

[0205] It should be noted that the electronic device 3 is merely an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0206] In some embodiments, the memory 31 stores a computer program that, when executed by the at least one processor 32, implements all or part of the steps in the UHV GIS graphene-copper-based composite current-carrying component detection method described above. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for a function, etc.

[0207] In some embodiments, the at least one processor 32 is the control unit of the electronic device 3, connecting various components of the electronic device 3 via various interfaces and lines. It executes programs or modules stored in the memory 31 and calls data stored in the memory 31 to perform various functions and process data. For example, when the at least one processor 32 executes the computer program stored in the memory 31, it implements all or part of the steps of the ultra-high voltage GIS graphene-copper-based composite current-carrying component detection method described in this application embodiment; or it implements all or part of the functions of the ultra-high voltage GIS graphene-copper-based composite current-carrying component detection device. The at least one processor 32 can be composed of integrated circuits, such as a single-packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0208] In some embodiments, the at least one communication bus 33 is configured to enable communication between the memory 31 and the at least one processor 32, etc. Although not shown, the electronic device 3 may also include a power supply (e.g., a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 32 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0209] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause an electronic device (which may be a personal computer, electronic device, or network device, etc.) or processor to execute portions of the methods described in the various embodiments of this application.

[0210] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0211] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0212] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for detecting ultra-high voltage GIS graphene-copper based composite current-carrying components, characterized in that, The method includes: The target flow guiding component is subjected to performance testing according to a preset testing method to obtain performance spatial distribution data, wherein the performance spatial distribution data includes a set of electrical conductivity matrices, a set of thermal conductivity matrices, and a set of mechanical parameters. The surface region of the acquired target 3D model is divided according to the preset grid format to obtain grid coordinate mapping data; The conductivity of the target flow guiding component is measured based on the grid coordinate mapping data to obtain the conductivity matrix set. The thermal conductivity of the target flow guide component is measured based on the grid coordinate mapping data to obtain the thermal conductivity matrix set. The target flow guide component is subjected to a mechanical property tensile test to obtain the set of mechanical parameters. The target flow guide component is subjected to structural defect detection processing based on the performance space distribution data to obtain a structural defect feature dataset. Based on the structural defect feature dataset, the target flow guiding component is subjected to dynamic current-carrying analysis and detection processing to obtain a thermal resistance anomaly grid coordinate mapping dataset. The target flow guiding component is subjected to arc ablation simulation processing based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate. A lifetime prediction dataset is obtained by performing multiphysics coupled failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model.

2. The detection method for ultra-high voltage GIS graphene-copper based composite current-carrying components according to claim 1, characterized in that, The step of performing structural defect detection processing on the target flow guide component based on the performance space distribution data to obtain a structural defect feature dataset includes: The target current-carrying component is processed by global conductivity data calculation based on the conductivity matrix set to obtain global average conductivity data, and the conductivity matrix set is filtered based on the global average conductivity data to obtain low conductivity region data. X-ray tomography is performed on the target flow guiding component based on the data of the low conductivity region to obtain a three-dimensional voxel model and pore distribution data corresponding to the low conductivity region. The target flow guiding component is subjected to ultrasonic phased array scanning processing based on the low conductivity region data to obtain the reflection coefficient distribution data of the low conductivity region; The three-dimensional voxel model is subjected to defect classification processing based on the reflection coefficient distribution data and the porosity distribution data to obtain the structural defect feature dataset.

3. The detection method for ultra-high voltage GIS graphene-copper based composite current-carrying components according to claim 1, characterized in that, The thermal resistance anomaly grid coordinate mapping dataset includes thermal resistance anomaly coordinate data and corresponding contact resistance data. The step of performing dynamic current-carrying analysis and detection processing on the target current-carrying component based on the structural defect feature dataset to obtain the thermal resistance anomaly grid coordinate mapping dataset includes: Based on the structural defect feature dataset, the target flow guiding component is subjected to flow-carrying region localization processing to obtain defect region coordinate data; A stepped current loading process is performed on the defect area in the defect area coordinate data to obtain contact voltage drop data and real-time current detection data. Then, dynamic contact resistance calculation is performed based on the contact voltage drop data and the real-time current detection data to obtain the contact resistance data. The target flow guiding component is modeled in three dimensions based on the thermal conductivity matrix set to obtain simulated temperature rise data. The temperature rise deviation is calculated based on the simulated temperature rise data and the real-time acquired actual temperature rise data to obtain temperature rise deviation data. The thermal resistance anomaly judgment is then performed on the target flow guide component based on the temperature rise deviation data to obtain the thermal resistance anomaly coordinate data. The thermal resistance anomaly coordinate data and the contact resistance data are used to perform grid coordinate mapping processing to obtain the thermal resistance anomaly grid coordinate mapping dataset.

4. The detection method for ultra-high voltage GIS graphene-copper based composite current-carrying components according to claim 1, characterized in that, The step of performing arc ablation simulation on the target flow guide component based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate includes: The target flow guiding component is subjected to arc target point localization processing based on the thermal resistance anomaly grid coordinate mapping dataset to obtain arc target point coordinate data. The target flow guiding component is subjected to arc energy loading processing based on the arc target point coordinate data to obtain arc voltage data and arc current data. The arc energy density of the target current guiding component is calculated based on the arc voltage data and the arc current data to obtain arc energy density data. The surface morphology of the target flow guide component is scanned based on the arc energy loading region in the arc energy density data to obtain surface roughness data and ablation depth data. The ablation rate of the target flow guide component is calculated based on the arc energy density data, the surface roughness data, and the ablation depth data to obtain the ablation rate.

5. The detection method for ultra-high voltage GIS graphene-copper based composite current-carrying components according to claim 1, characterized in that, The process of performing multiphysics coupled failure simulation based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model to obtain a lifetime prediction dataset includes: The target flow guiding component is processed by extracting initial performance parameters based on the performance spatial distribution data to obtain initial electrical conductivity data, initial thermal conductivity data, and initial mechanical performance data. The local performance degradation rate of the target flow guide component is calculated based on the ablation rate to obtain local degradation parameters. Based on the structural defect feature dataset, the target flow guide component is divided into defect regions to obtain performance degradation information for each defect region. Based on the thermal resistance anomaly grid coordinate mapping dataset, the target flow guide component is processed to extract local temperature rise anomaly parameters to obtain temperature rise anomaly data. The degradation model is used to perform multiphysics coupling simulation on the target flow guide component based on the initial electrical conductivity data, the initial thermal conductivity data, the initial mechanical performance data, the local attenuation parameters, the performance degradation information, and the temperature rise anomaly data, so as to obtain electric field distribution data, temperature field distribution data, and stress field distribution data. The target flow guide component is subjected to failure criterion analysis based on the electric field distribution data, the temperature field distribution data, and the stress field distribution data to obtain the lifetime prediction dataset.

6. The detection method for ultra-high voltage GIS graphene-copper based composite current-carrying components according to claim 1, characterized in that, The method further includes: Based on a preset process-performance relationship database, the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, the ablation rate, and the lifetime prediction dataset are integrated and processed to obtain process-performance correlation data. Feature extraction processing is performed on the process-performance correlation data to obtain process parameter data and corresponding key performance index data; Based on the process parameter data and the key performance index data, process parameter correlation modeling is performed to obtain a process-performance correlation model. The process parameters are optimized using the process-performance correlation model to obtain the optimal combination of process parameters.

7. A testing device for ultra-high voltage GIS graphene-copper-based composite current-carrying components, applied to the testing method for ultra-high voltage GIS graphene-copper-based composite current-carrying components according to any one of claims 1 to 6, characterized in that, The device includes: The performance testing module is used to perform performance testing on the target flow guiding component according to a preset testing method in order to obtain performance spatial distribution data. Anomaly analysis module is used to perform structural defect detection processing on the target flow guide component based on the performance space distribution data, so as to obtain a structural defect feature dataset; The dynamic testing module is used to perform dynamic current-carrying analysis and detection processing on the target flow guiding component based on the structural defect feature dataset, so as to obtain a thermal resistance anomaly grid coordinate mapping dataset. The ablation test module is used to perform arc ablation simulation processing on the target flow guide component based on the thermal resistance anomaly grid coordinate mapping dataset to obtain the ablation rate. The degradation simulation module is used to perform multiphysics coupled failure simulation processing based on the performance spatial distribution data, the structural defect feature dataset, the thermal resistance anomaly grid coordinate mapping dataset, and the ablation rate using a preset degradation model, in order to obtain a lifetime prediction dataset.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the detection method for ultra-high voltage GIS graphene-copper-based composite flow guiding components according to any one of claims 1 to 6.

9. 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 detection method for ultra-high voltage GIS graphene-copper-based composite flow guiding components according to any one of claims 1 to 6.

Images