Meta-aramid insulation paper chopped fiber orientation analysis method and device

Abstract

The invention relates to the field of image processing, in particular to a m-aramid insulation paper chopped fiber orientation analysis method and device, and the method comprises the steps: obtaining a sample tomography image of a m-aramid insulation paper sample; performing image preprocessing on the sample tomography image to obtain a standard sample tomography image; extracting a pixel point orientation angle of each pixel point in the tomography image of the standard sample by using a Gaussian algorithm, and constructing an image orientation angle matrix; generating a directivity histogram and a color orientation distribution diagram according to the image orientation angle matrix, and calculating a comprehensive quantitative index according to the image orientation angle matrix; and comparing the comprehensive quantitative index with a fiber orientation standard database to obtain a chopped fiber orientation analysis result, and constructing a chopped fiber orientation analysis report of the meta-aramid insulation paper by utilizing the orientation histogram, the color orientation distribution diagram and the chopped fiber orientation analysis result. According to the method, the accuracy and efficiency of orientation analysis of the chopped fibers of the meta-aramid insulation paper can be improved.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, and particularly relates to a method and apparatus for analyzing the orientation of short-cut fibers in meta-aramid insulating paper. Background Technology

[0002] Fiber orientation is a key microstructural parameter determining the anisotropy of chopped fiber reinforced composites for meta-aramid insulating paper. Its distribution characteristics directly affect the macroscopic mechanical properties, dimensional stability, and service life of the insulating paper. Accurate characterization of fiber orientation is crucial for material design, process optimization, and quality control.

[0003] Currently, image analysis is the mainstream approach to achieving this goal. Advances in high-performance computing and digital image processing algorithms have made it possible to automatically extract quantitative information from microscopic images of materials, laying the foundation for accurate and efficient analysis of fiber orientation. The orientation state of fibers has a direct structure-property relationship with key mechanical properties of composite materials, such as elastic modulus and tensile strength in specific directions. Image processing algorithms based on threshold segmentation and Hough transform can successfully identify and statistically analyze fiber tilt angles from composite material slice images. Numerous domestic and international studies have confirmed the feasibility of using image analysis techniques to quantify fiber orientation; however, current research mainly focuses on the analysis of ideal model systems with long fibers, high fiber volume fractions, or simple backgrounds. Accurate and automated orientation analysis methods for chopped fibers in meta-aramid insulating paper with high background noise and severe fiber overlap and interweaving have not yet been reported.

[0004] Currently, the main methods used are electron microscopy / SEM observation combined with manual statistical methods and semi-automatic analysis based on traditional image processing algorithms. However, the entire analysis process remains cumbersome and highly dependent on operator experience, failing to achieve high-throughput, automated online analysis. Furthermore, both manual and traditional image-based methods typically only provide single-dimensional statistical information such as the average fiber orientation angle, making it difficult to comprehensively and deeply quantify key characteristics such as orientation dispersion, distribution uniformity, and the reliability of the dominant direction. More importantly, the cutting, grinding, and polishing processes during sampling and sample preparation easily introduce human disturbance, altering the original fiber distribution and resulting in lower accuracy of the final analysis results. Summary of the Invention

[0005] This invention provides a method and apparatus for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, which can improve the accuracy and efficiency of the analysis.

[0006] To achieve the above objectives, the present invention provides a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, comprising: Obtain tomographic images of meta-aramid insulating paper samples; Image preprocessing is performed on the tomographic scan images of the sample to obtain tomographic scan images of the standard sample. The Gaussian algorithm was used to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard sample, and an image orientation angle matrix was constructed based on the pixel orientation angle of each pixel. A directional histogram and a color orientation distribution map are generated based on the image orientation angle matrix, and a comprehensive quantitative index is calculated based on the image orientation angle matrix. The results of the short-cut fiber orientation analysis were obtained by comparing the comprehensive quantitative indicators with the fiber orientation standard database. The short-cut fiber orientation analysis report of meta-aramid insulating paper was constructed using the directional histogram, color orientation distribution map and the short-cut fiber orientation analysis results.

[0007] To address the aforementioned problems, the present invention also provides a device for analyzing the orientation of chopped meta-aramid insulating paper fibers, the device comprising: The sample acquisition module is used to acquire cross-sectional scan images of meta-aramid insulating paper samples; The sample processing module is used to preprocess the tomographic scan images of the samples to obtain tomographic scan images of the standard samples; it uses a Gaussian algorithm to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard samples, and constructs an image orientation angle matrix based on the pixel orientation angle of each pixel; it generates a directional histogram and a color orientation distribution map based on the image orientation angle matrix, and calculates a comprehensive quantitative index based on the image orientation angle matrix. The module for obtaining the chopped fiber orientation analysis results is used to compare the comprehensive quantitative indicators with the fiber orientation standard database to obtain the chopped fiber orientation analysis results, and to construct a chopped fiber orientation analysis report for meta-aramid insulating paper using the directional histogram, color orientation distribution map, and chopped fiber orientation analysis results.

[0008] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for analyzing the orientation of chopped fibers in meta-aramid insulating paper.

[0009] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the above-described method for analyzing the orientation of chopped fibers in meta-aramid insulating paper.

[0010] This invention acquires tomographic scan images of meta-aramid insulating paper samples and performs image preprocessing on these images to improve image quality and further enhance the fiber characteristics of the samples. Furthermore, it utilizes a Gaussian algorithm to extract the pixel orientation angle of each pixel in the tomographic scan image of a standard sample and constructs an image orientation angle matrix based on these pixel orientation angles. This enables pixel-level quantification of fiber orientation, and the comprehensive orientation angle matrix provides richer microstructural information. Moreover, it generates directional histograms and color orientation distribution maps based on the image orientation angle matrix and calculates a comprehensive quantitative index. Through visualization tools and the comprehensive quantitative index, the statistical characteristics of fiber orientation can be comprehensively and multidimensionally characterized, enhancing the depth and interpretability of the analysis. Finally, the comprehensive quantitative index is compared with a fiber orientation standard database to obtain the chopped fiber orientation analysis results, improving the accuracy and efficiency of chopped fiber orientation analysis of meta-aramid insulating paper. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper according to an embodiment of the present invention; Figure 2 This is a structural flowchart of an example of a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the insulating paper scanning principle of the method for analyzing the orientation of chopped meta-aramid insulating paper according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a scanned image of short-cut fibers in meta-aramid paper, provided by an embodiment of the present invention for analyzing the orientation of short-cut fibers in meta-aramid insulating paper. Figure 5 A color orientation distribution map of short-cut fibers in a sample of a meta-aramid insulating paper short-cut fiber orientation analysis method provided in an embodiment of the present invention; Figure 6 A histogram showing the orientation distribution of chopped fibers in meta-aramid insulating paper, provided in an embodiment of the present invention. Figure 7 This is a functional block diagram of a device for analyzing the orientation of chopped meta-aramid insulating paper according to an embodiment of the present invention; Figure 8 This is a schematic diagram of an electronic device for implementing the method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, as provided in an embodiment of the present invention.

[0012] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0014] This application provides a method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0015] Reference Figure 1 The diagram shown is a flowchart illustrating a method for analyzing the orientation of chopped meta-aramid insulating paper according to an embodiment of the present invention. In this embodiment, the method for analyzing the orientation of chopped meta-aramid insulating paper includes: S1. Obtain the sample tomographic scan image of the meta-aramid insulating paper sample.

[0016] Understandably, the meta-aramid insulating paper sample refers to a high-performance insulating material sample made of meta-aramid fibers, in which the aromatic rings and acyl groups are arranged in a "meta" position in the molecular chain.

[0017] It is understood that the sample tomographic images of the meta-aramid insulating paper samples obtained in the embodiments of the present invention can be obtained using X-ray computed tomography (CT) technology.

[0018] It is understood that obtaining a meta-aramid insulating paper sample in this embodiment of the invention includes: taking a piece of meta-aramid insulating paper with uniform specifications, using a precision cutting tool to prepare a standard-sized sample, and polishing it to make its observation surface flat and smooth in order to meet the requirements of high-quality image acquisition.

[0019] S2. Perform image preprocessing on the sample tomographic scan image to obtain the standard sample tomographic scan image.

[0020] Specifically, the image preprocessing of the sample tomographic image to obtain a standard sample tomographic image includes: A filtering algorithm was used to denoise the sample tomographic image to obtain a denoised sample tomographic image. The standard sample tomographic image is obtained by performing contrast enhancement on the noise-reduced sample tomographic image.

[0021] Furthermore, after obtaining the tomographic images of the standard samples, representative regions of interest with uniform fiber distribution can be selected manually or automatically for focused analysis. This not only improves the efficiency of subsequent calculation processes but also enhances the relevance and representativeness of the analysis.

[0022] S3. Use the Gaussian algorithm to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard sample, and construct the image orientation angle matrix based on the pixel orientation angle of each pixel.

[0023] Understandably, the Gaussian algorithm refers to an image processing method based on the Gaussian function.

[0024] Specifically, the Gaussian algorithm is used to extract the pixel orientation angle of each pixel in the tomographic image of the standard sample, including: The horizontal and vertical gradient components of the standard sample tomographic image are obtained by convolving the preprocessed standard sample tomographic image with the first-order partial derivative of the Gaussian function. The fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample is calculated according to the preset fiber orientation angle calculation formula, thus obtaining the pixel orientation angle of each pixel.

[0025] Understandably, the horizontal gradient component refers to the rate of change of gray level in the horizontal direction (x-axis direction) of an image.

[0026] Understandably, the vertical gradient component refers to the rate of change of gray level in the vertical direction (y-axis direction) of an image.

[0027] It is understandable that the fiber orientation angle perpendicular to the gradient direction, which is the pixel orientation angle of each pixel, means that the fiber's own arrangement direction is usually consistent with the direction of the image edge, and the direction of the edge is perpendicular to the gradient direction. The gradient direction refers to the direction in which the gray level changes the fastest in the image.

[0028] Furthermore, the fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample is calculated according to the preset fiber orientation angle calculation formula, resulting in the pixel orientation angle of each pixel, including: The fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample is calculated using the following formula, thus obtaining the pixel orientation angle of each pixel. : in, for Oriented gradient components, for Oriented gradient components, It is a two-parameter arctangent function.

[0029] S4. Generate a directional histogram and a color orientation distribution map based on the image orientation angle matrix, and calculate a comprehensive quantitative index based on the image orientation angle matrix.

[0030] Understandably, a directional histogram is a frequency distribution map of angles obtained based on the statistical analysis of the image orientation angle matrix, which can show the overall distribution trend of fiber orientation.

[0031] Understandably, a color orientation distribution map refers to a two-dimensional image formed by mapping the orientation angle of each pixel to a color code.

[0032] Specifically, a directional histogram and a color orientation distribution map are generated based on the image orientation angle matrix, including: Extract the angle values ​​of all pixels in the image orientation angle matrix, perform binning frequency statistics on the angle values ​​of all pixels, and construct a directional histogram based on the results of the binning frequency statistics; By using a linear mapping function, the orientation angle of each pixel in the image orientation angle matrix is ​​converted into a specific hue, and the specific hue is converted into the RGB color space to obtain a color orientation distribution map.

[0033] Understandably, binning frequency statistics refers to dividing the orientation angle of all pixels into several angle intervals and then counting the number of pixels in each interval.

[0034] It is understandable that the orientation angle refers to the angle of the fiber arrangement direction at a certain pixel in the image.

[0035] Understandably, a linear mapping function is a function that maps one range of values ​​proportionally to another range of values.

[0036] Understandably, a specific hue refers to the basic attribute of a color, indicating its position on the color wheel.

[0037] For example, generating a directional histogram and a color orientation distribution map based on an image orientation angle matrix can be achieved using the following implementation steps: Both the directional histogram and the color orientation distribution map are generated based on the previously calculated image orientation angle matrix. The directional histogram visually displays the overall distribution characteristics of the chopped fiber orientation (such as concentration direction and dispersion) by performing binning frequency statistics on the angle values ​​of all pixels in the image orientation angle matrix. The color orientation distribution map, on the other hand, uses a linear mapping function (such as H=θ+) to represent the orientation of the chopped fibers. The orientation angle of each pixel is converted into a specific hue, and then converted from the HSV color space to the RGB color space to generate a pseudo-color image that visually reflects the spatial distribution of fiber orientation, resulting in a color orientation distribution map. Both methods comprehensively represent the fiber orientation information from two dimensions: statistical distribution and spatial visualization.

[0038] Specifically, a comprehensive quantitative index is calculated based on the image orientation angle matrix, including: Based on the image orientation angle matrix, core quantitative indicators are calculated using circular statistics and tensor analysis methods. Then, an indicator feature evaluation system is constructed using the core quantitative indicators, which include dominant direction, dispersion, concentration intensity, and confidence level.

[0039] Furthermore, core quantitative indicators are calculated using circular statistics and tensor analysis methods, including: The angles in the image orientation angle matrix are converted into unit vectors, and the dominant direction is obtained by calculating the circular average of the unit vectors. The dispersion is obtained by calculating the circular standard deviation of the unit vectors. The probability distribution of the orientation angle matrix of the image in each direction is statistically analyzed, and an orientation tensor is constructed based on the probability distribution. The average composite vector length is calculated based on the orientation tensor to obtain the concentration strength, and the eigenvalue difference is calculated based on the orientation tensor to obtain the confidence level.

[0040] Understandably, the dominant direction refers to the overall fiber orientation direction calculated using the average value of the angle circle.

[0041] Understandably, dispersion refers to a parameter quantified by the circular standard deviation, which describes the consistency and dispersion of fiber orientation.

[0042] Understandably, concentration intensity refers to the average composite vector length, which measures the degree of concentration of fibers aligning in the dominant direction.

[0043] Understandably, confidence level refers to the degree of reliability of the analysis results, calculated based on the differences in the eigenvalues ​​of the orientation tensor.

[0044] Understandably, eigenvalue difference calculation refers to comparing the differences between the principal eigenvalues ​​of a tensor and other eigenvalues.

[0045] S5. Compare the comprehensive quantitative indicators with the fiber orientation standard database to obtain the short fiber orientation analysis results, and use the directional histogram, color orientation distribution map and short fiber orientation analysis results to construct a short fiber orientation analysis report for meta-aramid insulating paper.

[0046] This invention acquires tomographic scan images of meta-aramid insulating paper samples and performs image preprocessing on these images to improve image quality and further enhance the fiber characteristics of the samples. Furthermore, it utilizes a Gaussian algorithm to extract the pixel orientation angle of each pixel in the tomographic scan image of a standard sample and constructs an image orientation angle matrix based on these pixel orientation angles. This enables pixel-level quantification of fiber orientation, and the comprehensive orientation angle matrix provides richer microstructural information. Moreover, it generates directional histograms and color orientation distribution maps based on the image orientation angle matrix and calculates a comprehensive quantitative index. Through visualization tools and the comprehensive quantitative index, the statistical characteristics of fiber orientation can be comprehensively and multidimensionally characterized, enhancing the depth and interpretability of the analysis. Finally, the comprehensive quantitative index is compared with a fiber orientation standard database to obtain the chopped fiber orientation analysis results, improving the accuracy and efficiency of chopped fiber orientation analysis of meta-aramid insulating paper.

[0047] Reference Figure 2 The diagram shown is a structural flowchart of an example of a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper provided in an embodiment of the present invention.

[0048] Reference Figure 3 The diagram shown is a schematic diagram of the insulating paper scanning principle of the method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper according to an embodiment of the present invention.

[0049] Understandably, in this embodiment of the invention, high-resolution micro-X-ray computed tomography is used to scan the sample to obtain its two-dimensional cross-sectional image. The image resolution is set to 0.5 micrometers per pixel to ensure that the morphology of individual fibers is clearly distinguishable. The sample position is kept fixed during the scanning process to avoid artifacts introduced by vibration.

[0050] Reference Figure 4 The image shown is a schematic diagram of a scanned image of short-cut fibers in meta-aramid paper, provided by an embodiment of the present invention for analyzing the orientation of short-cut fibers in meta-aramid insulating paper.

[0051] Understandably, such as Figure 4 As shown, the fibers exhibit a distinct orientation distribution within the material. In some areas, the fibers are densely packed and oriented in a consistent direction, while in other areas they display an interlaced and random arrangement. A clear contrast exists between the fibers and the surrounding polymer matrix, allowing for clear identification of the morphology and orientation of individual fibers. Based on the stability of the image contrast and resolution, it can be confirmed that the fiber structure features presented in the image are not caused by artifacts or noise from X-ray computed tomography, but truly reflect the intrinsic microstructure of the material.

[0052] like Figure 5The image shown is a color orientation distribution diagram of the short-cut fibers in a sample obtained by the method for analyzing the orientation of meta-aramid insulating paper short-cut fibers according to an embodiment of the present invention.

[0053] Understandably, such as Figure 5 As shown, a series of results of orientation analysis of chopped fibers are presented, including three sub-figures. (a) is the original grayscale image of the chopped fibers, showing the initial distribution pattern of the fibers; (b) is a color orientation distribution map generated by the Gaussian algorithm, in which different colors are used to distinguish fiber angles, thus visually visualizing the orientation distribution; (c) is a color-angle correspondence color wheel, which marks the correspondence between scale values ​​such as 0.0°, 90.0° and -90.0° and colors, establishing a mapping between colors and fiber angles, which facilitates the interpretation of the specific orientations represented by the colors in figure (b).

[0054] like Figure 6 The image shows a histogram of the orientation distribution of chopped fibers in meta-aramid insulating paper, provided by an embodiment of the present invention.

[0055] Understandably, Figure 6 The histogram illustrates the angular orientation distribution of the chopped fibers. The horizontal axis represents the fiber angular direction, ranging from -90° to 90°; the vertical axis may represent the frequency, probability density, or number of pixels of fibers within the corresponding angular range. The distribution curve shows a significant and sharp peak near 80°, indicating that the orientation of the vast majority of fibers is highly concentrated around 80°. Simultaneously, the overall shape of the curve is approximately symmetrical, suggesting that while there is some dispersion in fiber orientation, there is still a clear dominant direction. This distribution characteristic indicates that the chopped fibers in the sample exhibit strong anisotropy, good orientation consistency, and a strong preferential orientation trend dominated by a near-vertical direction.

[0056] like Figure 7 The diagram shown is a functional block diagram of a meta-aramid insulating paper chopped fiber orientation analysis device provided in an embodiment of the present invention.

[0057] The meta-aramid insulating paper chopped fiber orientation analysis device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the meta-aramid insulating paper chopped fiber orientation analysis device 100 may include a sample acquisition module 101, a sample processing module 102, and a chopped fiber orientation analysis result acquisition module 103.

[0058] The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0059] In this embodiment, the functions of each module / unit are as follows: The sample acquisition module 101 is used to acquire sample tomographic scan images of meta-aramid insulating paper samples.

[0060] The sample processing module 102 is used to perform image preprocessing on the sample tomographic scan image to obtain a standard sample tomographic scan image; to extract the pixel orientation angle of each pixel in the standard sample tomographic scan image using a Gaussian algorithm, and to construct an image orientation angle matrix based on the pixel orientation angle of each pixel; to generate a directional histogram and a color orientation distribution map based on the image orientation angle matrix, and to calculate a comprehensive quantitative index based on the image orientation angle matrix.

[0061] The chopped fiber orientation analysis result acquisition module 103 is used to compare the comprehensive quantitative index with the fiber orientation standard database to obtain the chopped fiber orientation analysis result, and to construct a chopped fiber orientation analysis report for meta-aramid insulating paper using the directional histogram, color orientation distribution map and chopped fiber orientation analysis result.

[0062] like Figure 8 The diagram shown is a schematic representation of an electronic device that implements a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, according to an embodiment of the present invention.

[0063] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a method program for analyzing the orientation of chopped fibers in meta-aramid insulating paper.

[0064] In some embodiments, the processor 10 may 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. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a program for analyzing the orientation of short-cut fibers in meta-aramid insulating paper), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0065] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, but also to temporarily store data that has been output or will be output.

[0066] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0067] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0068] Figure 8 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 8The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0069] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, 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 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0070] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0071] The program for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, stored in the memory 11 of the electronic device, is a combination of multiple instructions. When run in the processor 10, it can achieve the following: Obtain tomographic images of meta-aramid insulating paper samples; Image preprocessing is performed on the tomographic scan images of the sample to obtain tomographic scan images of the standard sample. The Gaussian algorithm was used to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard sample, and an image orientation angle matrix was constructed based on the pixel orientation angle of each pixel. A directional histogram and a color orientation distribution map are generated based on the image orientation angle matrix, and a comprehensive quantitative index is calculated based on the image orientation angle matrix. The results of the short-cut fiber orientation analysis were obtained by comparing the comprehensive quantitative indicators with the fiber orientation standard database. The short-cut fiber orientation analysis report of meta-aramid insulating paper was constructed using the directional histogram, color orientation distribution map and the short-cut fiber orientation analysis results.

[0072] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0073] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0074] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: Obtain tomographic images of meta-aramid insulating paper samples; Image preprocessing is performed on the tomographic scan images of the sample to obtain tomographic scan images of the standard sample. The Gaussian algorithm was used to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard sample, and an image orientation angle matrix was constructed based on the pixel orientation angle of each pixel. A directional histogram and a color orientation distribution map are generated based on the image orientation angle matrix, and a comprehensive quantitative index is calculated based on the image orientation angle matrix. The results of the short-cut fiber orientation analysis were obtained by comparing the comprehensive quantitative indicators with the fiber orientation standard database. The short-cut fiber orientation analysis report of meta-aramid insulating paper was constructed using the directional histogram, color orientation distribution map and the short-cut fiber orientation analysis results.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, 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.

[0076] 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; that is, 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.

[0077] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0078] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0079] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0080] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0081] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0082] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper, characterized in that, The method includes: Obtain tomographic images of meta-aramid insulating paper samples; Image preprocessing is performed on the tomographic scan images of the sample to obtain tomographic scan images of the standard sample. The Gaussian algorithm was used to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard sample, and an image orientation angle matrix was constructed based on the pixel orientation angle of each pixel. A directional histogram and a color orientation distribution map are generated based on the image orientation angle matrix, and a comprehensive quantitative index is calculated based on the image orientation angle matrix. The results of the short-cut fiber orientation analysis were obtained by comparing the comprehensive quantitative indicators with the fiber orientation standard database. The short-cut fiber orientation analysis report of meta-aramid insulating paper was constructed using the directional histogram, color orientation distribution map and the short-cut fiber orientation analysis results.

2. The method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper as described in claim 1, characterized in that, The step of extracting the pixel orientation angle of each pixel in the standard sample tomographic image using the Gaussian algorithm includes: By convolving the standard sample tomographic image with the first-order partial derivative of the Gaussian function, the horizontal and vertical gradient components of the standard sample tomographic image are obtained. The fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample is calculated according to the preset fiber orientation angle calculation formula, thus obtaining the pixel orientation angle of each pixel.

3. The method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper as described in claim 2, characterized in that, The step of calculating the fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample according to the preset fiber orientation angle calculation formula, to obtain the pixel orientation angle of each pixel, includes: The fiber orientation angle perpendicular to the gradient direction of each pixel in the tomographic image of the standard sample is calculated using the following formula, thus obtaining the pixel orientation angle of each pixel. : in, for Oriented gradient components, for Oriented gradient components, It is a two-parameter arctangent function.

4. The method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper as described in claim 1, characterized in that, The step of generating a directional histogram and a color orientation distribution map based on the image orientation angle matrix includes: Extract the angle values ​​of all pixels in the image orientation angle matrix, perform binning frequency statistics on the angle values ​​of all pixels, and construct a directional histogram based on the results of the binning frequency statistics; By using a linear mapping function, the orientation angle of each pixel in the image orientation angle matrix is ​​converted into a specific hue, and the specific hue is converted into the RGB color space to obtain a color orientation distribution map.

5. The method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper as described in claim 1, characterized in that, The calculation of the comprehensive quantitative index based on the image orientation angle matrix includes: Based on the image orientation angle matrix, core quantitative indicators are calculated using circular statistics and tensor analysis methods. Then, an indicator feature evaluation system is constructed using the core quantitative indicators, which include dominant direction, dispersion, concentration intensity, and confidence level.

6. The method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper as described in claim 5, characterized in that, The calculation of core quantitative indicators using circular statistics and tensor analysis methods includes: The angles in the image orientation angle matrix are converted into unit vectors, and the dominant direction is obtained by calculating the circular average of the unit vectors. The dispersion is obtained by calculating the circular standard deviation of the unit vectors. The probability distribution of the orientation angle matrix of the image in each direction is statistically analyzed, and an orientation tensor is constructed based on the probability distribution. The average composite vector length is calculated based on the orientation tensor to obtain the concentration strength, and the eigenvalue difference is calculated based on the orientation tensor to obtain the confidence level.

7. The method for analyzing the orientation of chopped short fibers in meta-aramid insulating paper as described in claim 1, characterized in that, The image preprocessing of the sample tomographic scan image to obtain the standard sample tomographic scan image includes: A filtering algorithm was used to denoise the sample tomographic image to obtain a denoised sample tomographic image. The standard sample tomographic image is obtained by performing contrast enhancement on the noise-reduced sample tomographic image.

8. A device for analyzing the orientation of chopped short fibers in meta-aramid insulating paper, characterized in that, The apparatus is used to implement the method for analyzing the orientation of chopped fibers in meta-aramid insulating paper as described in any one of claims 1 to 7, the apparatus comprising: The sample acquisition module is used to acquire cross-sectional scan images of meta-aramid insulating paper samples; The sample processing module is used to preprocess the tomographic scan images of the samples to obtain tomographic scan images of the standard samples; it uses a Gaussian algorithm to extract the pixel orientation angle of each pixel in the tomographic scan image of the standard samples, and constructs an image orientation angle matrix based on the pixel orientation angle of each pixel; it generates a directional histogram and a color orientation distribution map based on the image orientation angle matrix, and calculates a comprehensive quantitative index based on the image orientation angle matrix. The module for obtaining the chopped fiber orientation analysis results is used to compare the comprehensive quantitative indicators with the fiber orientation standard database to obtain the chopped fiber orientation analysis results, and to construct a chopped fiber orientation analysis report for meta-aramid insulating paper using the directional histogram, color orientation distribution map, and chopped fiber orientation analysis results.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for analyzing the orientation of chopped fibers in meta-aramid insulating paper as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for analyzing the orientation of short-cut fibers in meta-aramid insulating paper as described in any one of claims 1 to 7.

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