Insulation pull rod micro-defect local electric field evaluation method based on microscopic computed tomography

Abstract

Due to the lack of effective microcosmic characterization for microdefects, the traditional defect research means are separated from the reality, and the degree of influence caused by the microdefects introduced in actual production and manufacturing is difficult to effectively simulate and analyze. The invention provides an insulation pull rod micro-defect local electric field evaluation method based on micro-CT, and the method comprises the steps: preparing an insulation pull rod micro-defect sample section, obtaining a micro-topography feature image of the sample section through high-precision CT, processing the micro-topography feature image to obtain a surface grid file, and calculating the micro-defect local electric field of the insulation pull rod according to the surface grid file. And carrying out modeling simulation on the surface grid file, and evaluating the distortion condition and severity of an electric field on the surface of the insulating pull rod caused by the microdefect.

Description

Technical Field

[0001] The present application belongs to the technical field of high-voltage equipment, and in particular relates to a method for evaluating the local electric field of micro-defects of insulating pull rods based on micro-computed tomography. Background Art

[0002] The insulating rod is a key component in the gas-insulated metal-enclosed switchgear (GIS), and its performance directly determines the reliability of the GIS equipment. As a transmission component that plays the role of electrical and mechanical connection between the switch body and the operating mechanism, it bears complex electrical, thermal and mechanical loads for a long time. The replacement cost of the insulating rod is high, and the gas-insulated switchgear needs to be shut down and operated, which is not easy to achieve. In recent years, according to the accident statistics of the insulating rods in operation, it is often those micro-defects that can pass the factory inspection that evolve into defects that affect the performance of the insulating rods after long-term operation and frequent opening and closing operations, which in turn cause serious accidents. According to the current industry standards, the size and appearance screening of the insulating rods at the factory are mainly based on visual methods and strong light irradiation methods. The insulating rods used in ultra-high / ultra-high voltage power grids will also be supplemented with X-ray flaw detection as a sampling inspection method. However, in the demolding process after curing and molding in the insulating rod production process, the metal mold is very likely to cause micro-scratches within 1 mm in length on the inner and outer surfaces of the workpiece. Bubble defects are widely found between glass fiber cloth layers and fiber bundles due to manufacturing process problems of winding material layers, and often appear as flat irregular sheets of nearly circular or nearly ellipsoidal air gaps with a diameter of less than 1mm. The results of the three existing detection methods are all two-dimensional images, which are affected by position overlap, low resolution, and human vision differences. They ignore surface micro-defects and inter-layer area defects, and it is difficult to accurately locate the specific location and size of the defects.

[0003] In the research field at home and abroad, the current defect research on insulating pull rods is widely present in the simulation of artificially setting defect size and shape, and simplifying the composite material of the insulating pull rod, which is a 2D plain weave structure of glass fiber cloth layer and epoxy resin system, into an internally uniform object. In order to make the obtained data results more obvious, artificially manufactured regular defects of larger size are used, such as introducing spherical metal particle defects with a diameter of 1-3mm, surface scratches with a width of 1-2mm and a length of about 30mm, or cylindrical holes with a diameter of 1-5mm to represent different types of defects. Based on this, the influence of those tiny defects that can escape factory inspection in actual production on insulation performance is directly summarized, often emphasizing the size and ignoring the influence of the morphology of the micro-defect itself. Due to the lack of effective microscopic representation of micro-defects, the previous defect research methods are divorced from the real reality, and it is difficult to effectively simulate and analyze the degree of influence caused by micro-defects introduced in actual production. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] Based on the fact that the current defect research on insulating pull rods is widely based on the simulation of artificially setting the defect size and shape, and simplifying the composite material of the glass fiber cloth layer and epoxy resin system of the insulating pull rod, which is a 2D plain weave structure, into an internally uniform object. In order to make the obtained data results more obvious, artificially manufactured regular defects of larger size are used, such as introducing spherical metal particle defects with a diameter of 1-3mm, surface scratches with a width of 1-2mm and a length of about 30mm, or cylindrical holes with a diameter of 1-5mm to represent different types of defects. Based on this, the influence of those tiny defects that can escape factory inspection in actual production on the insulation performance is directly summarized, often emphasizing the size and ignoring the influence of the morphology of the micro-defects themselves. Due to the lack of effective microscopic characterization of micro-defects, the previous defect research methods are divorced from the real reality. It is difficult to effectively simulate and analyze the degree of influence caused by micro-defects introduced in actual production and manufacturing. The present application provides a local electric field evaluation method for micro-defects of insulating pull rods based on micro-computed tomography (CT).

[0006] 2. Technical solution

[0007] In order to achieve the above-mentioned objectives, the present application provides a method for evaluating the local electric field of micro-defects of an insulating rod based on micro-computed tomography, the method comprising: preparing a micro-defect sample segment of an insulating rod, using high-precision CT to obtain a microscopic morphological feature image of the sample segment, processing the microscopic morphological feature image to obtain a surface mesh file, modeling and simulating the surface mesh file, and evaluating the surface electric field distortion and severity of the insulating rod caused by micro-defects.

[0008] Another implementation method provided by the present application is: the microscopic morphology characterization includes cutting the main part of the insulating pull rod into sample segments with the defect area as the center, placing the sample segments on a non-destructive three-dimensional microscopic morphology detection platform for slice scanning, obtaining a slice image set with internal structure, performing three-dimensional reverse reconstruction on the slice image set, obtaining an image grayscale value set with three-dimensional geometric coordinate positions, and using the difference in image grayscale to reflect the geometric morphology.

[0009] Another implementation method provided by the present application is: the non-destructive three-dimensional microscopic morphology detection platform includes a rotating table, one side of the rotating table is a radiation source, and the other side is a high pixel density detector; the slice scanning includes placing the insulating rod sample on the rotating table, performing multi-angle scanning by stepping and rotating the insulating rod sample segment, obtaining a two-dimensional projection image of the internal structure of the insulating rod sample segment, and processing the two-dimensional projection image to obtain a three-dimensional image.

[0010] Another implementation manner provided by the present application is that the non-destructive three-dimensional microscopic morphology detection platform is a non-destructive 3D X-ray micro-computed tomography system.

[0011] Another implementation method provided by the present application is: the image grayscale value set includes cutting and separating the three-dimensional image of the insulating pull rod sample segment to form a new grayscale image set, using the Otsu algorithm to separate the grayscale values ​​of the grayscale image set, and obtaining the optimal threshold through exhaustive search based on the grayscale histogram of the grayscale image set to maximize the inter-class variance between the foreground and background, and converting the grayscale image set into a binary image by calculating the optimal threshold.

[0012] Another implementation method provided by the present application is: the use of image grayscale to distinguish and reflect geometric morphology includes rendering, segmenting, and extracting the screened grayscale image set, extracting characteristic parameters of the defective part, and restoring the geometric morphology of the defective part.

[0013] Another implementation method provided by the present application is: the processing of the microscopic morphological features to obtain a grid file includes processing the geometric morphology, removing pixels that do not converge or do not form a closed geometry and are far smaller than the order of magnitude of the defect size, and refilling a closed and convergent geometric entity surface; simplifying individual pixel points other than the defective part that do not converge in calculation and have unreasonable grid division, retaining pixel units that reflect the defective feature parts, and ensuring that a locally closed grid file with a grid quality that can be used for modeling and simulation calculations can be formed without destroying the actual morphological structure of the micro-defect part.

[0014] Another implementation method provided by the present application is: the modeling and simulation includes identifying defective points of a mesh file, then removing the defective points, filling a plane in the area to form a closed geometric entity without defective points; after the mesh file is repaired, smoothing is performed to further separate the isolated island burr pixel points of the surface mesh file, and determining the optimal number of pixels for smoothing; the processed surface mesh file is imported into the finite element simulation software, and the electric field simulation calculation is realized by adding boundary conditions during operating conditions and Laplace equation restrictions to study the field strength distortion of defective parts of the object.

[0015] The present application also provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computer program.

[0016] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0017] 3. Beneficial effects

[0018] Compared with the prior art, the method for evaluating the local electric field of micro-defects of insulating rods based on micro-CT provided in the present application has the following beneficial effects:

[0019] The present application provides a method for evaluating the local electric field of micro-defects of insulating rods based on micro-CT, which relates to the fields of defect detection of high-voltage electrical equipment and insulation performance evaluation.

[0020] The present application provides a local electric field evaluation method for micro-defects of insulating tie rods based on micro-CT, explores effective characterization methods for micro-defects and the degree of influence of micro-defects on the insulation performance of the tie rods, and performs electric field simulation based on the actual morphology of micro-defects, providing new ideas for future insulating tie rod production, factory inspection and standard formulation.

[0021] The present application provides a method for evaluating the local electric field of micro-defects of insulating rods based on micro-CT, which is a technology for detecting micro-defects of insulating rods and analyzing the effects of electric field distortion. Through microscopic morphology characterization, an electric field simulation analysis of defective insulating rods based on real morphology is established to study the effects of micro-defects that have been ignored in factory inspections in the past on insulation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a basic structural schematic diagram of the insulating pull rod cross section of the present application;

[0023] Figure 2 It is a schematic diagram of the principle of the micro-CT technology of the present application;

[0024] Figure 3 is a schematic diagram of the micro-CT scanning results of the present application;

[0025] Figure 4 It is a schematic diagram of the internal structure of the insulating pull rod of the present application;

[0026] Figure 5 It is a schematic diagram of the insulating pull rod after surface micro-defect treatment of the present application;

[0027] Figure 6 It is a schematic diagram of the electric field simulation results of the surface micro scratch defects of the present application;

[0028] Figure 7 It is a schematic diagram of the terminal device structure of this application. DETAILED DESCRIPTION

[0029] Hereinafter, the specific embodiments of the present application will be described in detail with reference to the accompanying drawings. According to these detailed descriptions, those skilled in the art can clearly understand the present application and can implement the present application. Without violating the principles of the present application, the features in the various embodiments can be combined to obtain new implementations, or certain features in certain embodiments can be replaced to obtain other preferred implementations.

[0030] Micro-CT (micro computed tomography) is a high-resolution three-dimensional imaging technology widely used in biomedicine, materials science and other fields. It is based on the principle of micro-focus X-ray imaging and can clearly display the microscopic structure inside the sample without destroying the sample.

[0031] The specific steps of Otsu's algorithm are as follows:

[0032] Calculate histogram: First, you need to calculate the grayscale histogram of the image to understand the distribution of pixels at each grayscale level in the image.

[0033] Initialization threshold: Select an initial threshold and divide the image into foreground and background categories based on this threshold.

[0034] Calculate intra-class variance and inter-class variance: For each class, calculate its intra-class variance, that is, the variance of pixel intensities within the same class; and inter-class variance, that is, the variance of pixel intensities between different classes.

[0035] Solving the maximum inter-class variance: By adjusting the threshold, calculating the intra-class variance and inter-class variance under different thresholds, and finding the threshold that maximizes the inter-class variance.

[0036] Determine the optimal threshold: When the threshold that maximizes the between-class variance is found, that threshold is the optimal threshold.

[0037] An important feature of the Otsu algorithm is that it is not affected by image brightness and contrast, which makes the algorithm effective in image binary segmentation under different lighting conditions. In addition, the Otsu algorithm is suitable for images with a large difference between foreground and background pixel values.

[0038] Otsu's algorithm is an adaptive threshold determination method that automatically calculates the optimal global threshold by maximizing the inter-class variance. It is widely used in image processing and computer vision.

[0039] See also Figures 1 to 7The present application provides a method for evaluating the local electric field of micro-defects of an insulating rod based on micro-CT, the method comprising: preparing a micro-defect sample of an insulating rod, using high-precision CT to obtain a microscopic morphological feature image of the sample, processing the microscopic morphological feature image to obtain a surface mesh file, modeling and simulating the surface mesh file, and evaluating the surface electric field distortion and severity of the insulating rod caused by the micro-defects.

[0040] The raw data obtained by high-precision CT is a slice-type image of the sample segment containing the internal microscopic structure. By accumulating and compounding several two-dimensional images of an object into a three-dimensional entity through three-dimensional processing software, the true geometric appearance of the object can be reproduced. Then the geometric entity is processed into a surface mesh file, which can be imported into the finite element simulation software as an object, and the electric field distortion can be analyzed by applying the corresponding required boundary values ​​and formulas.

[0041] Figure 1 1 is the outer resin layer, 2 is the alkali-free glass fiber cloth winding layer, and 3 is the inner resin layer.

[0042] Furthermore, the microscopic morphology characterization includes cutting the main part of the insulating pull rod into sample segments with the defect area as the center, placing the sample segments on a non-destructive three-dimensional microscopic morphology detection platform for slice scanning, obtaining a slice image set with internal structure, performing three-dimensional reverse reconstruction on the slice image set, obtaining an image grayscale value set with three-dimensional geometric coordinate positions, and using the difference in image grayscale to reflect the geometric morphology.

[0043] The scanning and imaging method of CT on objects determines that only slice-type two-dimensional images of objects including internal structures can be obtained. The higher the precision of CT, the more slices of objects can be sliced ​​in a fixed direction, and the thinner the slices. Then the three-dimensional geometric entity reconstructed by compounding these two-dimensional image sets will have higher precision and clearer and more complete internal structures.

[0044] Furthermore, the non-destructive three-dimensional microscopic morphology detection platform includes a rotating table, one side of the rotating table is a ray source, and the other side is a high pixel density detector; the slice scanning includes placing the insulating rod sample on the rotating table, performing multi-angle scanning by stepping and rotating the insulating rod sample segment, obtaining a two-dimensional projection of the internal structure of the insulating rod sample segment, and processing the two-dimensional projection to obtain a three-dimensional image. The scanning is a slice scanning of a three-dimensional geometric object, and the obtained data is a two-dimensional image grayscale projection.

[0045] Furthermore, the non-destructive three-dimensional microscopic morphology detection platform is a non-destructive 3D X-ray micro-computed tomography system.

[0046] Furthermore, the image grayscale value set includes cutting and separating the three-dimensional image of the insulating pull rod sample segment to form a new grayscale image set, using the Otsu algorithm to separate the grayscale image set, and obtaining the optimal threshold through exhaustive search based on the grayscale histogram of the grayscale image set, so as to maximize the inter-class variance between the foreground and the background, and converting the grayscale image set into a binary image by calculating the optimal threshold. This makes the separation of objects and background more complete and reduces the situation where the edges of objects are unclear due to the close density grayscale values.

[0047] Furthermore, the use of image grayscale distinction to reflect geometrical morphology includes rendering, segmenting, and extracting the screened grayscale image set, extracting characteristic parameters of defective parts, and restoring the geometrical morphology of the defective parts.

[0048] Furthermore, the processing of the microscopic morphological features to obtain a grid file includes processing the geometric morphology, removing pixels that do not converge or do not form a closed geometry and are far smaller than the order of magnitude of the defect size, and refilling a closed and convergent geometric entity surface; simplifying individual pixel points other than the defective part that do not converge in calculation and have unreasonable grid division, retaining pixel units that reflect the defective feature parts, and ensuring that a locally closed grid file with a grid quality that can be used for modeling and simulation calculations can be formed without destroying the actual morphological structure of the micro-defect part.

[0049] Further, the modeling simulation includes identifying the defective points of the grid file (points that do not form a closed plane with the surrounding points, singular points with an angle greater than the convergence of the coupled calculation), and then removing these pixel points, filling the plane in the area to form a closed geometric entity without defective points. Not all defective points can be removed and filled by this method. It is generally believed that the defective points of the grid file itself are much smaller than the defects of the object morphology under study, and only then can this method be optimized (less than 100 times or more). If the size of the defective points of the grid file and the defect of the object morphology under study is in the same order of magnitude, it is considered that the geometric segmentation in the previous step is not thorough, and the background pixels are mistakenly processed as the pixels of the object itself, so the previous step is returned to re-separate the scan data based on the grayscale histogram. Step 2: After the grid file is repaired in the previous step, a smoothing process that does not affect the simulation calculation results is performed in this step. The purpose is to speed up the simulation without affecting the accuracy of the calculation results. Smoothing requires a lot of attempts to determine the degree of smoothing. Appropriate smoothing can further separate the island burr pixels of the surface grid file, making the simulation more accurate and fast. Excessive smoothing will result in the loss of simulation accuracy due to real morphological defects, which will lead to the situation where simulation accuracy is sacrificed for simulation rate. By trying and comparing, determine how many pixels are optimal for smoothing. Step 3: The surface mesh file obtained in the previous two steps can be directly imported into the finite element simulation software. By adding boundary conditions during operation and Laplace equation restrictions, electric field simulation calculations can be realized to study the field strength distortion of defective parts of the object.

[0050] The present application also provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computer program.

[0051] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0052] Example

[0053] 1. Prepare micro-defect samples of insulating pull rods and characterize their microscopic morphology

[0054] The glass fiber reinforced insulating rod for 126kV GIS circuit breaker produced by Taikai Group Co., Ltd. was selected as the research object. The basic structure of the insulating rod cross section is as follows Figure 1As shown, it is composed of glass fiber cloth wrapped with epoxy resin impregnation layer. The insulation body is a GFRP hollow tube with an outer diameter of 40mm, an inner diameter of 30mm and a length of 300mm. To facilitate laboratory research, the insulation body part is cut into 100mm long sample sections with the defect area as the center and placed on a non-destructive three-dimensional micro-morphology detection platform.

[0055] The structural principle of Micro-CT is as follows Figure 2 The insulating rod sample is placed on a rotating table, with a radiation source on one side and a high pixel density detector on the other. The insulating rod sample is rotated step by step to perform multi-angle transillumination, and the sample is sliced ​​and scanned. The image set with internal structure is reconstructed in three-dimensional reverse mode in the image processing software to obtain a three-dimensional point grayscale set with geometric coordinates, such as Figure 3 The device used is Zeiss Xradia Context, which is equipped with a high pixel density detector that enables non-destructive 3D imaging with a resolution of microns.

[0056] 2. Simulation of electric field distortion based on micro-defect micromorphology

[0057] Take surface defects as an example. A micro scratch on the outer surface is partially magnified, and the three-dimensional image of the area is cut and separated to form a new grayscale image set. The grayscale image of the area is separated by the Otsu algorithm. Based on the grayscale histogram of the image, an optimal threshold is found through exhaustive search to maximize the inter-class variance between the foreground and the background. This is because when the inter-class variance is the largest, it means that the difference in grayscale values ​​between the foreground and the background is the largest, which can achieve a better binarization effect. It is converted into a binary image by calculating the optimal threshold. The grayscale threshold can reflect the surface height, that is, the scratch depth and unevenness, such as Figure 4 .

[0058] The selected grayscale image set is rendered, segmented, and extracted to extract the characteristic parameters of the defective part and restore the geometric morphology of the defective part. Voxel meshing technology is a commonly used three-dimensional pixel grid discretization technology, which is suitable for any complex structure. It can convert three-dimensional image data into a grid model composed of a series of small cubes (voxels). According to specific needs, different grid resolutions and grid types (such as tetrahedron, hexahedron, etc.) can be selected. Figure 5The three-dimensional pixel data of the defective part shown (CT mainly reflects the density difference inside the sample. Different densities have different grayscale values, which leads to imaging. CT scanning obtains a set of (often thousands of) two-dimensional grayscale images, which are compounded and accumulated into three-dimensional geometric entities according to the tangential coordinate positions. The original three-dimensional morphology reproduced contains the pixel data of the defective part.). The mesh quality can be tested by importing the mesh file into the finite element simulation software. Remove some pixels that do not converge or do not form a closed geometry and are far smaller than the order of magnitude of the defect size and refill a closed and convergent geometric entity surface. Simplify individual pixels that do not converge and unreasonable mesh division except for the defective part, retain the pixel units that reflect the defective feature parts, and ensure that a locally closed mesh file with mesh quality that can be used for modeling and simulation calculations can be formed without destroying the real morphological structure of the micro-defective part.

[0059] The geometric entity formed by the repaired mesh file is used as a model to perform electric field simulation analysis. The electric field conditions of the insulating pull rod in operation are added to both sides of the defect area in proportion to the actual size, and the electric field intensity distribution when there is no defect and the electric field intensity distribution when there is a defect are simulated. The ratio of the field intensity amplitude when there is a defect to the field intensity amplitude when there is no defect is called the field intensity distortion rate f. The field intensity distortion rate f and the uniformity of the electric field distribution are used to describe the severity of the electric field distortion. Figure 6 As shown in the figure, the electric field distortion and severity of the insulating rod surface caused by micro defects are discussed. For the types of defects with serious impact, the requirements should be raised during factory inspection and prevented during the manufacturing process. For example, the impregnation time should be appropriately extended during the production process, and the resin viscosity should be adjusted to reduce the probability of bubble micro defects. The possibility of insulation accidents caused by insulating rods with micro defects after long-term operation in the power grid is reduced to ensure the safety, stability and reliability of GIS equipment.

[0060] The proposed grayscale image set obtained by extracting micro-defect features of insulating rods based on Micro-CT technology can effectively reflect the true morphology of defects ignored by conventional detection methods, and reflect the integrity of the glass fiber cloth structure inside the insulating rod. The three-dimensional morphology data is used to form a grid file, and the geometric entity formed by the grid file is imported into the finite element electric field simulation model. It can effectively analyze the electric field distortion under the combined action of micro-defect morphology and internal glass fiber woven cloth structure, and obtain the electric field distortion caused by micro-defects that were ignored in previous studies. It provides a new and effective means for the detection and characterization of micro-defects in GIS insulating rods in the future, and provides a new direction for the future production and manufacturing of insulating rods, inspection standards, and prevention of insulation hazards caused by defects.

[0061] Turn on the Zeiss Xradia Context device and the radiation source. The CT device generates a beam of X-rays through the X-ray tube. These X-rays have the ability to penetrate objects. When X-rays pass through the scanned object, tissues of different densities absorb the X-rays to different degrees, that is, the attenuation of the radiation intensity is different. The detector array located on the other side of the scanned object receives the X-rays that pass through the object. The detector measures the intensity of the X-rays and converts them into electrical signals. This process is performed at multiple angles as the X-ray tube and detector rotate. As the X-ray tube and detector rotate, X-ray scans at multiple angles are performed. The data collected by the detector at each angle is converted into digital signals and transmitted to the computer. In this process, each angle scan provides a two-dimensional projection of the internal structure of the object. After the computer receives the data from the detector, it uses complex reconstruction algorithms to process the data. The reconstruction algorithm converts the data into pixels through mathematical calculations (such as Fourier transform, filtering, back projection, etc.) to form a two-dimensional or three-dimensional image. These images reflect the distribution of different densities and structures inside the object. The reconstructed images can be displayed on a computer screen, showing views of the object from multiple angles, such as the cross-section, coronal, and sagittal planes, providing rich internal structure information.

[0062] The terminal device of this embodiment includes: at least one processor ( Figure 7 Only one is shown in the figure) a processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein when the processor executes the computer program, the steps in any of the following metabolic pathway prediction method embodiments are implemented.

[0063] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that this is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the figure, or may combine certain components, or different components, and may also include, for example, input and output devices, network access devices, etc.

[0064] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0065] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. In other embodiments, the memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (MC), a secure digital (SD) card, a flash card, etc., equipped on the terminal device.

[0066] Furthermore, the memory may include both an internal storage unit of the terminal device and an external storage device. The memory is used to store an operating system, an application, a boot loader, data, and other programs, such as program codes of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0067] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0068] The embodiment of the present application provides a computer program product, when the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present application implements all or part of the process in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, under legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunications signals.

[0069] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0070] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0071] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application is described in detail with reference to the above-described embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above-described embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. In the above-described embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Claims

1. A method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography, characterized in that: The method comprises: preparing a micro-defect sample section of an insulating pull rod, using high-precision CT to obtain a microscopic morphological feature image of the sample section, processing the microscopic morphological feature image to obtain a surface mesh file, modeling and simulating the surface mesh file, and evaluating the surface electric field distortion and severity of the insulating pull rod caused by micro-defects.

2. The method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 1, characterized in that: The microscopic morphology characterization includes cutting the main part of the insulating pull rod into sample segments with the defect area as the center, placing the sample segments on a non-destructive three-dimensional microscopic morphology detection platform for slice scanning, obtaining a slice image set with internal structure, performing three-dimensional reverse reconstruction on the slice image set, obtaining an image grayscale value set with three-dimensional geometric coordinate positions, and using the difference in image grayscale to reflect the geometric morphology.

3. The method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 2, characterized in that: The non-destructive three-dimensional microscopic morphology detection platform includes a rotating table, one side of the rotating table is a radiation source, and the other side is a high pixel density detector; the slice scanning includes placing the insulating rod sample on the rotating table, performing multi-angle scanning by stepping and rotating the insulating rod sample segment, obtaining a two-dimensional projection image of the internal structure of the insulating rod sample segment, and processing the two-dimensional projection image to obtain a three-dimensional image.

4. The method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 2, characterized in that: The non-destructive three-dimensional microscopic morphology detection platform is a non-destructive 3D X-ray micro-computed tomography system.

5. The method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 4, characterized in that: The image grayscale value set includes cutting and separating the three-dimensional image of the insulating pull rod sample segment to form a new grayscale image set, using the Otsu algorithm to separate the grayscale values ​​of the grayscale image set, and obtaining the optimal threshold through exhaustive search based on the grayscale histogram of the grayscale image set to maximize the inter-class variance between the foreground and the background, and converting the grayscale image set into a binary image by calculating the optimal threshold.

6. The method for evaluating the local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 2, characterized in that: The method of using image grayscale to distinguish and reflect geometrical morphology includes rendering, segmenting and extracting the selected grayscale image set, extracting characteristic parameters of defective parts and restoring the geometrical morphology of defective parts.

7. The method for evaluating the local electric field of micro defects of insulating rods based on micro-computed tomography according to any one of claims 1 to 5, characterized in that: The processing of the microscopic morphological features to obtain a grid file includes processing the geometric morphology, removing pixel points that do not converge or do not form a closed geometry and are far smaller than the order of magnitude of the defect size, and refilling a closed and convergent geometric entity surface; simplifying individual pixel points that do not converge in calculation and have unreasonable grid division except for the defect site, and retaining the pixel units that reflect the defect feature site.

8. The method for evaluating local electric field of micro-defects of insulating rods based on micro-computed tomography according to claim 7, characterized in that: The modeling and simulation includes identifying defective points of a mesh file, then removing the defective points, filling a plane in the area to form a closed geometric entity without defective points; after the mesh file is repaired, smoothing is performed to further separate the isolated island burr pixel points of the surface mesh file, and determining the optimal number of pixels for smoothing; importing the processed surface mesh file into finite element simulation software, and realizing electric field simulation calculation by adding boundary conditions during operating conditions and Laplace equation restrictions to study the field strength distortion of defective parts of the object.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for evaluating the local electric field of micro-defects of an insulating rod based on micro-computed tomography as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating the local electric field of micro-defects of an insulating rod based on micro-computed tomography as described in any one of claims 1 to 8 is implemented.

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