Cleanliness evaluation method, device and computer equipment for high voltage cable base material

Abstract

The present application relates to a method, device and computer equipment for evaluating the cleanliness of a high-voltage cable base material. The method includes: obtaining a filter sample image and a filter processing image of each filter, and identifying the filter impurity distribution information of each filter; identifying the impurity characteristic information of each filter, and based on the filter impurity distribution information of each filter, identifying the impurity distribution characteristic information and impurity penetration characteristic information of each filter; based on the impurity characteristic information, impurity distribution characteristic information and impurity penetration characteristic information of each filter, through an impurity characteristic evaluation strategy, analyzing the risk information of the filter impurities of each filter on the cable of the high-voltage cable, and based on the risk information of the cable impact of the impurities of each filter on the high-voltage cable and the filter impurity distribution information of each filter, evaluating the cleanliness information of the high-voltage cable base material through a cleanliness evaluation index. The use of this method can improve the accuracy and comprehensiveness of the cleanliness evaluation of the base material.

Description

Technical Field

[0001] The present application relates to the technical field of image detection and image analysis, and in particular to a method, device and computer equipment for evaluating the cleanliness of a high-voltage cable base material. Background Art

[0002] Low-density polyethylene is the base material required for the production of high-voltage cables, and its cleanliness has an important impact on the performance and quality of high-voltage cables. Therefore, how to improve the accuracy of the evaluation of the cleanliness of the base material is the current research focus.

[0003] The traditional method for evaluating the cleanliness of high-voltage cable base materials is to use general methods for resin particle cleanliness testing, such as online particle scanning detection, extruded belt detection, etc. However, the detection object of this detection method is limited to resin particles, and the detection method is single, and the cleanliness detection of the base material is relatively one-sided, resulting in a less comprehensive evaluation of the cleanliness of the base material. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for evaluating the cleanliness of a high-voltage cable base material in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the cleanliness of a high-voltage cable base material, comprising:

[0006] Obtaining filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh, and identifying filter mesh impurity distribution information of each filter mesh based on the filter mesh sample images of each filter mesh;

[0007] Based on the filter processing images of each filter, the impurity characteristic information of each filter is identified through an image analysis network, and based on the filter impurity distribution information of each filter, the impurity distribution characteristic information of each filter and the impurity penetration characteristic information of each filter are identified;

[0008] Based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter, the impurity characteristic evaluation strategy is used to analyze the risk information of the impact of the filter impurities of each filter on the cable of the high-voltage cable; and based on the risk information of the impact of the impurities of each filter on the cable of the high-voltage cable and the filter impurity distribution information of each filter, the cleanliness information of the high-voltage cable base material is evaluated through cleanliness evaluation indicators.

[0009] Optionally, the identifying filter impurity distribution information of each filter based on the filter sample image of each filter includes:

[0010] For each filter screen, image processing is performed on a filter screen sample image of the filter screen to obtain an impurity binary image of the filter screen;

[0011] An impurity boundary range of the filter impurities in the impurity binary image is identified, and filter impurity distribution information of the filter is identified based on a position range of the impurity boundary range of the filter impurities in the impurity binary image.

[0012] Optionally, the filter processing image based on each filter screen, identifying impurity characteristic information of each filter screen through an image analysis network, includes:

[0013] For each filter, image processing is performed on the filter processed image of the filter to obtain the impurity processed binary image of the filter, and based on the impurity binary image of the filter and the impurity processed binary image of the filter, structural change information of the filter impurities of the filter and filter impurity range change information of the filter impurities are identified through an image analysis network;

[0014] Collecting the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method;

[0015] The impurity change characteristics of the impurities on the filter are used as the impurity characteristic information of the filter.

[0016] Optionally, the identifying, based on the filter impurity distribution information of each filter, impurity distribution characteristic information of each filter and impurity penetration characteristic information of each filter includes:

[0017] For each filter screen, based on the impurity distribution information of the filter screen, the surface impurity distribution density of the filter screen impurities of the filter screen is calculated, and based on the filter screen aperture information of the filter screen and the surface impurity distribution density of the filter screen impurities of the filter screen, the impurity penetration coefficient of the filter screen impurities is calculated;

[0018] The surface impurity distribution density of the filter impurities is used as the impurity distribution characteristic information of the filter to which the filter impurities belong, and the impurity penetration coefficient of the filter impurities is used as the penetration characteristic information of the filter to which the filter impurities belong.

[0019] Optionally, the impurity characteristic evaluation strategy includes an impurity attribute analysis strategy and an impurity risk analysis strategy, and the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter are analyzed through the impurity characteristic evaluation strategy to analyze the risk information of the impact of the filter impurities of each filter on the high-voltage cable, including:

[0020] For each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities of the filter is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database;

[0021] Based on the impurity distribution characteristic information of the filter screen and the impurity penetration characteristic information of the filter screen, the risk probability of the impurities in the filter screen to the high-voltage cable is identified through the impurity risk analysis strategy;

[0022] The cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities of the filter on the high-voltage cable.

[0023] Optionally, the cleanliness evaluation index includes an impurity distribution evaluation index and an impurity risk evaluation index, and the cleanliness information of the high-voltage cable base material is evaluated by the cleanliness evaluation index based on the risk information of the impurities in each filter screen on the cable of the high-voltage cable and the filter impurity distribution information of each filter screen, including:

[0024] Based on the surface impurity distribution density of the filter impurities of each filter, the first cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity distribution evaluation index, and based on the cable impact risk information of the filter impurities of each filter on the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity risk evaluation index;

[0025] Based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

[0026] In a second aspect, the present application also provides a cleanliness evaluation device for a high-voltage cable base material, comprising:

[0027] An acquisition module, used to acquire filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh, and identify filter mesh impurity distribution information of each filter mesh based on the filter mesh sample images of each filter mesh;

[0028] an identification module, for identifying impurity characteristic information of each filter screen based on the filter screen processing image of each filter screen through an image analysis network, and identifying impurity distribution characteristic information of each filter screen and impurity penetration characteristic information of each filter screen based on the filter screen impurity distribution information of each filter screen;

[0029] An evaluation module is used to analyze the risk information of the impact of impurities on the high-voltage cable of each filter based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter through an impurity characteristic evaluation strategy, and based on the risk information of the impact of impurities on the high-voltage cable of each filter and the impurity distribution information of each filter, evaluate the cleanliness information of the high-voltage cable base material through a cleanliness evaluation index.

[0030] Optionally, the acquisition module is specifically used to:

[0031] For each filter screen, image processing is performed on a filter screen sample image of the filter screen to obtain an impurity binary image of the filter screen;

[0032] An impurity boundary range of the filter impurities in the impurity binary image is identified, and filter impurity distribution information of the filter is identified based on a position range of the impurity boundary range of the filter impurities in the impurity binary image.

[0033] Optionally, the identification module is specifically used to:

[0034] For each filter, image processing is performed on the filter processed image of the filter to obtain the impurity processed binary image of the filter, and based on the impurity binary image of the filter and the impurity processed binary image of the filter, structural change information of the filter impurities of the filter and filter impurity range change information of the filter impurities are identified through an image analysis network;

[0035] Collecting the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method;

[0036] The impurity change characteristics of the impurities on the filter are used as the impurity characteristic information of the filter.

[0037] Optionally, the identification module is specifically used to:

[0038] For each filter screen, based on the impurity distribution information of the filter screen, the surface impurity distribution density of the filter screen impurities of the filter screen is calculated, and based on the filter screen aperture information of the filter screen and the surface impurity distribution density of the filter screen impurities of the filter screen, the impurity penetration coefficient of the filter screen impurities is calculated;

[0039] The surface impurity distribution density of the filter impurities is used as the impurity distribution characteristic information of the filter to which the filter impurities belong, and the impurity penetration coefficient of the filter impurities is used as the penetration characteristic information of the filter to which the filter impurities belong.

[0040] Optionally, the evaluation module is specifically used to:

[0041] For each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities of the filter is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database;

[0042] Based on the impurity distribution characteristic information of the filter screen and the impurity penetration characteristic information of the filter screen, the risk probability of the impurities in the filter screen to the high-voltage cable is identified through the impurity risk analysis strategy;

[0043] The cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities of the filter on the high-voltage cable.

[0044] Optionally, the evaluation module is specifically used to:

[0045] Based on the surface impurity distribution density of the filter impurities of each filter, the first cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity distribution evaluation index, and based on the cable impact risk information of the filter impurities of each filter on the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity risk evaluation index;

[0046] Based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

[0047] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0049] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0050] The cleanliness evaluation method, device and computer equipment of the above-mentioned high-voltage cable base material obtain the filter sample images of the filter after filtering the high-voltage cable base material of different production times, and the filter processing images obtained after impurity processing of each filter, and identify the filter impurity distribution information of each filter based on the filter sample images of each filter; based on the filter processing images of each filter, identify the impurity characteristic information of each filter through an image analysis network, and based on the filter impurity distribution information of each filter, identify the impurity distribution feature information of each filter and the impurity penetration feature information of each filter; based on the impurity characteristic information of each filter, the impurity distribution feature information of each filter, and the impurity penetration feature information of each filter, analyze the risk information of the filter impurities of each filter on the cable of the high-voltage cable through the impurity characteristic evaluation strategy, and based on the risk information of the cable impact of the impurities of each filter on the high-voltage cable and the filter impurity distribution information of each filter, evaluate the cleanliness information of the high-voltage cable base material through the cleanliness evaluation index. This solution is different from the existing linear particle scanning detection and extruded belt detection methods in evaluating the cleanliness of the high-voltage cable base material. Through the filter analysis strategy, the cleanliness of the high-voltage cable base material is combined with the risk of impact on the high-voltage cable. A comprehensive analysis is performed from the perspectives of the impurity distribution characteristic information of the filter impurities of the high-voltage cable base material and the impurity penetration characteristic information. The cleanliness of the base material of the high-voltage cable is evaluated from multiple angles and in all aspects. In order to ensure the accuracy of the evaluation, this solution combines the filter sample images of the filter collected at multiple production times to conduct a full-process and multi-angle evaluation, and combines the time characteristics to analyze the cleanliness of the base material in the production process, thereby comprehensively improving the accuracy and comprehensiveness of the base material cleanliness evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 This is an application environment diagram of a cleanliness evaluation method for a high-voltage cable base material in one embodiment;

[0053] Figure 2 It is a schematic flow chart of a method for evaluating the cleanliness of a high-voltage cable base material in one embodiment;

[0054] Figure 3 An example diagram of an impurity binary image obtained by performing image processing on a filter sample image in one embodiment;

[0055] Figure 4 A schematic diagram of a process for evaluating the cleanliness of a high-voltage cable base material in one embodiment;

[0056] Figure 5 It is a structural block diagram of a cleanliness evaluation device for a high-voltage cable base material in one embodiment;

[0057] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The cleanliness evaluation method of the high-voltage cable base material provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. In the production process of high-voltage cable materials, the raw materials of the high-voltage cable base material are first filtered. This solution samples the filter screen after the raw materials are filtered, handles impurities, and performs image analysis on the filter screen sample image obtained after image scanning, and the filter screen processing image, so as to obtain the cleanliness information for evaluating the high-voltage cable base material. In this case, the method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this case, the terminal can be, but is not limited to, various personal computers, laptops, mid-size computers, etc. Among them, when evaluating the cleanliness of the base material of the high-voltage cable, the terminal is different from the existing linear particle scanning detection and extruded belt detection methods. Through the filter analysis strategy, the cleanliness of the base material of the high-voltage cable is combined with the risk of impact on the high-voltage cable, and a comprehensive analysis is performed from the perspectives of the impurity distribution characteristic information of the filter impurities of the base material of the high-voltage cable and the impurity penetration characteristic information. Therefore, the cleanliness of the base material of the high-voltage cable is evaluated from multiple angles and in all aspects. In order to ensure the accuracy of the evaluation, this solution combines the filter sample images of the filter collected at multiple production times to conduct a full-process and multi-angle evaluation, and combines the time characteristics to analyze the cleanliness of the base material in the production process, thereby comprehensively improving the accuracy and comprehensiveness of the cleanliness evaluation of the base material.

[0060] In an exemplary embodiment, Figure 2 As shown, a method for evaluating the cleanliness of a high-voltage cable base material is provided, and the method is described by taking the application of the method to a terminal as an example, including the following steps S201 to S203. Among them:

[0061] Step S201, obtain filter sample images of the filter after filtering the high-voltage cable base material produced at different times, and the filter processing image obtained after impurity processing of each filter, and identify the filter impurity distribution information of each filter based on the filter sample images of each filter.

[0062] In this embodiment, the terminal responds to the image upload operation of the staff to obtain the filter sample images of the high-voltage cable base material at different production times. Among them, the filter sample image is the filter of the target area screened by the staff in the filter of the high-voltage cable base material. The specific screening method is that the filter suitable for this method is a stainless steel filter. The service life of the filter is usually 12-36 hours. When the filter is replaced, it is removed while hot and then used for cleanliness analysis. In the multi-layer filter, the filter with the largest mesh number is usually selected for analysis, and the mesh number should be 200 mesh or more. If the filter layer to be analyzed is closely attached to other filter layers, the multi-layer filter can be placed on a hot plate for heating, and the temperature is preferably 160-180°C; after the polymer is softened, the multi-layer filter is carefully separated using tweezers. The obtained filter is then scanned to obtain the filter sample image. Then, in order to ensure the impurity characteristics of the filter impurities of the filter, the staff pre-treats the filter impurities and then scans the image to obtain the filter processing image obtained after each filter is treated with impurities. Among them, the impurity treatment method is to use hot pressing equipment (such as a flat vulcanizer, etc.) to pre-treat the filter sample to make it flat and uniform in thickness. For hot pressing treatment using a flat vulcanizer, the preferred conditions are: hot pressing temperature 120-140°C, hot pressing time 10-20 min, and pressure 10-20MPa. Finally, the terminal identifies the filter impurity distribution information of each filter based on the filter sample image of each filter. The specific identification process will be described in detail later. Among them, in addition to the above-mentioned hot pressing method, the impurity treatment method can also be processed by power supply, vibration, etc.

[0063] Step S202, based on the filter processing image of each filter, through the image analysis network, identify the impurity characteristic information of each filter, and based on the filter impurity distribution information of each filter, identify the impurity distribution characteristic information of each filter and the impurity penetration characteristic information of each filter.

[0064] In this embodiment, the terminal identifies the impurity characteristic information of each filter through an image analysis network based on the filter processing image of each filter, and identifies the impurity distribution characteristic information of each filter and the impurity penetration characteristic information of each filter based on the filter impurity distribution information of each filter. Among them, the impurity characteristic information is the impurity change characteristic corresponding to the impurity change information of the filter impurities of the filter after impurity processing. The specific identification process will be described in detail later. Among them, the impurity change characteristics include, but are not limited to, impurity thermal conductivity characteristics, impurity electrical conductivity characteristics, and impurity structural change characteristics. Among them, the thermal conductivity characteristics, electrical conductivity characteristics, and structural change characteristics affect the normal progress of production of high-voltage cables during the production process.

[0065] Step S203, based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter, through the impurity characteristic evaluation strategy, analyze the risk information of the impact of the filter impurities of each filter on the high-voltage cable, and based on the risk information of the impact of the impurities of each filter on the high-voltage cable and the filter impurity distribution information of each filter, evaluate the cleanliness information of the high-voltage cable base material through the cleanliness evaluation index.

[0066] In this embodiment, the terminal analyzes the risk information of the filter impurities of each filter on the cable of the high-voltage cable based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter through the impurity characteristic evaluation strategy. The risk information of the filter impurities on the cable of the high-voltage cable is the risk information of the impact of the impurity change characteristics corresponding to the filter impurities on the cable production of the high-voltage cable. The specific analysis process of analyzing the filter impurities through the impurity characteristic evaluation strategy will be described in detail later. Finally, based on the risk information of the cable impact of each filter impurity on the high-voltage cable and the filter impurity distribution information of each filter, the terminal evaluates the cleanliness information of the high-voltage cable base material through the cleanliness evaluation index. Among them, the cleanliness information of the high-voltage cable base material is a cleanliness evaluation summary table of the high-voltage cable base material at different production times, and the generation process of the cleanliness evaluation summary table will be described in detail later.

[0067] Based on the above scheme, when evaluating the cleanliness of the base material of the high-voltage cable, different from the existing linear particle scanning detection and extruded belt detection methods, the cleanliness of the base material of the high-voltage cable is combined with the risk of impact on the high-voltage cable through the filter analysis strategy, and a comprehensive analysis is performed from the perspectives of the impurity distribution characteristic information of the filter impurities of the base material of the high-voltage cable and the impurity penetration characteristic information, so as to evaluate the cleanliness of the base material of the high-voltage cable from multiple angles and in all aspects. In order to ensure the accuracy of the evaluation, this scheme combines the filter sample images of the filter collected at multiple production times to conduct a full-process and multi-angle evaluation, and combines the time characteristics to analyze the cleanliness of the base material in the production process, thereby comprehensively improving the accuracy and comprehensiveness of the cleanliness evaluation of the base material.

[0068] Optionally, based on the filter sample images of each filter, the filter impurity distribution information of each filter is identified, including: for each filter, performing image processing on the filter sample image of the filter to obtain a binary image of impurities of the filter; identifying the impurity boundary range of the filter impurities in the binary impurity image, and identifying the filter impurity distribution information of the filter based on the position range of the impurity boundary range of the filter impurities in the binary impurity image.

[0069] In this embodiment, the terminal processes the sample image of each filter to obtain a binary image of impurities in the filter. Specifically, the terminal processes the sample image of the filter to obtain the following: Figure 3 The filter grayscale image shown in FIG. 1 is then binarized by the terminal to obtain the following: Figure 3 The binary image shown is used as the impurity binary image.

[0070] Then, the terminal identifies the edge information of each impurity in the impurity binary image through an edge recognition algorithm, and uses the range surrounded by each impurity edge information as the impurity boundary range. Finally, the terminal identifies the filter impurity distribution information of the filter based on the position range of the impurity boundary range of the filter impurities in the impurity binary image. The filter impurity distribution information is characterized as the distribution information of the filter impurities in the filter.

[0071] Based on the above scheme, the distribution information of filter impurities in the filter is identified after grayscale processing and binarization processing on the filter sample image, thereby improving the accuracy of identifying the distribution information of filter impurities in the filter.

[0072] Optionally, based on the filter processing images of each filter, the impurity characteristic information of each filter is identified through an image analysis network, including: for each filter, performing image processing on the filter processing image of the filter to obtain an impurity processing binary image of the filter, and based on the impurity binary image of the filter and the impurity processing binary image of the filter, identifying structural change information of filter impurities and filter impurity range change information of filter impurities through an image analysis network; collecting the impurity processing method of the filter, and based on the filter impurity structural change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method; using the impurity change characteristics of the filter impurities as the impurity characteristic information of the filter.

[0073] In this embodiment, the terminal performs image processing on the filter processing image of each filter to obtain the impurity processing binary image of the filter. Based on the impurity binary image of the filter and the impurity processing binary image of the filter, the image analysis network is used to identify the structural change information of the filter impurities of the filter and the filter impurity range change information of the filter impurities. The generation process of the impurity processing binary image is the same as the generation process of the impurity binary image, and no redundant description is given here. The image analysis network is a convolutional neural network based on the self-attention mechanism. The convolutional neural network identifies the change information of the impurity range of the filter impurities of the filter and the change information of the impurity structure by comparing and analyzing the impurity processing binary image and the impurity binary image, wherein the change information of the impurity range is the degree of change of the impurity distribution range of the filter impurities, for example, the impurity distribution range becomes larger, the impurity distribution density becomes larger, and the impurity structure change information is the structural change, shape change, and state change (granular matter becomes powder, powder becomes granular, etc.) of the filter impurities.

[0074] Then, the terminal collects the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifies the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method. Among them, the database stores multiple impurity change characteristics corresponding to different impurity processing types, and in the impurity change characteristics corresponding to each impurity processing type, each impurity change characteristic corresponds to a filter impurity range change range and a filter impurity structure change range. The terminal identifies the impurity change characteristics corresponding to the filter impurity by identifying the filter impurity structure change range to which the filter impurity structure change information of the filter impurities belongs, and the filter impurity range change range to which the filter impurity range change information of the filter impurities belongs. Among them, the impurity change characteristics include but are not limited to impurity change characteristics such as high-temperature cracking characteristics, high-temperature melting characteristics, electrical conductivity characteristics, and thermal conductivity characteristics.

[0075] Based on the above scheme, by comparing the impurity binary image and the impurity processing binary image, the impurity change characteristics of the filter magazine are analyzed, which improves the accuracy of identifying the impact of the impurities on the high-voltage cable production process.

[0076] Optionally, based on the impurity distribution information of each filter, the impurity distribution characteristic information of each filter and the impurity penetration characteristic information of each filter are identified, including: for each filter, based on the impurity distribution information of the filter, calculating the surface impurity distribution density of the filter impurities of the filter, and based on the filter aperture information of the filter and the surface impurity distribution density of the filter impurities of the filter, calculating the impurity penetration coefficient of the filter impurities; using the surface impurity distribution density of the filter impurities as the impurity distribution characteristic information of the filter to which the filter impurities belong, and using the impurity penetration coefficient of the filter impurities as the penetration characteristic information of the filter to which the filter impurities belong.

[0077] In this embodiment, the terminal calculates the surface impurity distribution density of the filter impurities of each filter based on the impurity distribution information of the filter, and calculates the impurity penetration coefficient of the filter impurities based on the filter aperture information of the filter and the surface impurity distribution density of the filter impurities of the filter. The impurity distribution density includes the front impurity distribution density and the back impurity distribution density, and the calculation formula of the impurity distribution density is: impurity distribution density = impurity range size / filter surface range size; the impurity size (micrometer) is defined as the equivalent circle diameter of the pixel area (square micrometer), and the conversion relationship is: impurity size = 2*(pixel area / pi) 0.5 .

[0078] The calculation formula of the impurity penetration coefficient of the filter impurities is: (back impurity distribution density / front impurity distribution density) * parameter value corresponding to the filter aperture value. Among them, the filter aperture values ​​of different filter aperture information correspond to different parameter values, which are used to adjust the calculated value of the impurity penetration coefficient, thereby ensuring the accuracy of the calculated penetration coefficient.

[0079] Finally, the terminal uses the surface impurity distribution density of the filter impurities as the impurity distribution characteristic information of the filter to which the filter impurities belong, and uses the impurity penetration coefficient of the filter impurities as the penetration characteristic information of the filter to which the filter impurities belong.

[0080] Based on the above scheme, by calculating the impurity penetration coefficient and the surface impurity distribution density, the analysis accuracy of the situation where impurities still remain in the high-voltage cable base material after filtration is improved, thereby improving the analysis accuracy of the cleanliness of the high-voltage cable base material.

[0081] Optionally, the impurity characteristic evaluation strategy includes an impurity attribute analysis strategy and an impurity risk analysis strategy. Based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter, the impurity characteristic evaluation strategy is used to analyze the cable impact risk information of the filter impurities of each filter on the high-voltage cable, including: for each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities of the filter is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database; based on the impurity distribution characteristic information of the filter and the impurity penetration characteristic information of the filter, the impurity risk analysis strategy is used to identify the risk probability of filter impurities on the high-voltage cable; the cable impact risk type corresponding to the filter impurities and the risk probability of filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities of the filter on the high-voltage cable.

[0082] In this embodiment, the terminal evaluates the impurity attribute information of the filter impurities of each filter based on the impurity characteristic information of the filter through the impurity attribute analysis strategy, and queries the cable impact risk type corresponding to the impurity attribute information of the filter impurities in the database. Among them, the impurity attribute analysis strategy includes a first correspondence between different impurity characteristic information and impurity attribute information. The terminal identifies the impurity attribute information corresponding to the impurity characteristic information of the filter impurities by querying the first correspondence. The database stores a plurality of second correspondences between impurity attribute information and cable impact risk types. The terminal identifies the cable impact risk type corresponding to the impurity attribute information in the database based on the second correspondence.

[0083] Finally, based on the impurity distribution characteristic information of the filter and the impurity penetration characteristic information of the filter, the terminal identifies the risk probability of the filter impurities to the high-voltage cable through the impurity risk analysis strategy. The impurity risk analysis strategy includes the impurity distribution density range and the penetration coefficient range corresponding to different risk probabilities, and the terminal identifies the impurity distribution density range to which the impurity distribution density of the filter impurities belongs, and identifies the penetration coefficient range to which the penetration coefficient of the filter impurities belongs, thereby identifying the risk probability corresponding to the filter impurities. The risk probability is used to characterize the penetration probability of the filter impurities penetrating into the high-voltage cable base material after filtration.

[0084] Finally, the terminal uses the cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable as the cable impact risk information of the filter impurities on the high-voltage cable.

[0085] Based on the above scheme, by combining the cable impact risk type and risk probability of filter impurities, the cable impact risk information of filter impurities on high-voltage cables is identified, thereby improving the accuracy of identifying cable impact risk information.

[0086] Optionally, the cleanliness evaluation index includes an impurity distribution evaluation index and an impurity risk evaluation index. Based on the risk information of the cable impact of impurities on each filter screen and the filter impurity distribution information of each filter screen, the cleanliness information of the high-voltage cable base material is evaluated through the cleanliness evaluation index, including: based on the surface impurity distribution density of the filter impurities of each filter screen, the first cleanliness information of the high-voltage cable base material corresponding to each filter screen is identified through the impurity distribution evaluation index, and based on the risk information of the cable impact of the filter impurities of each filter screen on the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter screen is identified through the impurity risk evaluation index; based on the first cleanliness information of the high-voltage cable base material corresponding to each filter screen and the second cleanliness information of the high-voltage cable base material corresponding to each filter screen, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

[0087] In this embodiment, the terminal identifies the first cleanliness information of the high-voltage cable base material corresponding to each filter screen through the impurity distribution evaluation index based on the surface impurity distribution density of the filter impurities of each filter screen, and identifies the second cleanliness information of the high-voltage cable base material corresponding to each filter screen through the impurity risk evaluation index based on the cable impact risk information of the filter impurities of each filter screen on the high-voltage cable. Among them, the impurity distribution evaluation index includes the range of each surface impurity distribution density (including the front impurity distribution density and the back impurity distribution density) and the third correspondence between the cleanliness information. The terminal identifies the first cleanliness information of the high-voltage cable base material by querying the third correspondence, and the impurity risk evaluation index includes the different risk probability ranges of each cable impact risk type and the fourth correspondence between the cleanliness information. The terminal identifies the second cleanliness information of the high-voltage cable aggregate by querying the fourth correspondence.

[0088] Then, the terminal generates a cleanliness evaluation summary table of the high-voltage cable base material based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, and uses the cleanliness evaluation summary table as the cleanliness information of the high-voltage cable base material.

[0089] Based on the above scheme, the cleanliness information of the high-voltage cable base material is evaluated from two perspectives: surface impurity distribution density and cable impact risk information, thereby improving the accuracy and comprehensiveness of the analysis.

[0090] This application also provides an example of cleanliness evaluation of a high-voltage cable base material, such as Figure 4 As shown, the specific processing process includes the following steps:

[0091] Step S401, obtaining filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh.

[0092] Step S402: for each filter screen, image processing is performed on the filter screen sample image of the filter screen to obtain an impurity binary image of the filter screen.

[0093] Step S403, identifying the impurity boundary range of the filter impurities in the impurity binary image, and identifying the filter impurity distribution information of the filter based on the position range of the impurity boundary range of the filter impurities in the impurity binary image.

[0094] Step S404, for each filter, perform image processing on the filter processed image of the filter to obtain the impurity processed binary image of the filter, and based on the impurity binary image of the filter and the impurity processed binary image of the filter, identify the structural change information of the filter impurities and the filter impurity range change information of the filter impurities through an image analysis network.

[0095] Step S405, collecting the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method.

[0096] Step S406: Using the impurity change characteristics of the filter impurities as the impurity characteristic information of the filter.

[0097] Step S407, for each filter, based on the impurity distribution information of the filter, calculate the surface impurity distribution density of the filter impurities of the filter, and based on the filter aperture information of the filter and the surface impurity distribution density of the filter impurities of the filter, calculate the impurity penetration coefficient of the filter impurities.

[0098] Step S408, using the surface impurity distribution density of the filter impurities as impurity distribution characteristic information of the filter to which the filter impurities belong, and using the impurity penetration coefficient of the filter impurities as penetration characteristic information of the filter to which the filter impurities belong.

[0099] Step S409, for each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database.

[0100] Step S410, based on the impurity distribution characteristic information of the filter and the impurity penetration characteristic information of the filter, the risk probability of the filter impurities to the high-voltage cable is identified through an impurity risk analysis strategy.

[0101] Step S411, the cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities on the high-voltage cable.

[0102] Step S412, based on the surface impurity distribution density of the filter impurities of each filter, the first cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity distribution evaluation index, and based on the risk information of the filter impurities of each filter on the cable of the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity risk evaluation index.

[0103] Step S413, based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

[0104] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0105] Based on the same inventive concept, the embodiment of the present application also provides a high-voltage cable base material cleanliness evaluation device for implementing the above-mentioned high-voltage cable base material cleanliness evaluation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more high-voltage cable base material cleanliness evaluation devices provided below can refer to the limitations of the high-voltage cable base material cleanliness evaluation method above, and will not be repeated here.

[0106] In an exemplary embodiment, Figure 5 As shown, a cleanliness evaluation device for a high-voltage cable base material is provided, comprising: an acquisition module 510, an identification module 520 and an evaluation module 530, wherein:

[0107] The acquisition module 510 is used to acquire filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh, and identify filter mesh impurity distribution information of each filter mesh based on the filter mesh sample images of each filter mesh;

[0108] an identification module 520, configured to identify impurity characteristic information of each filter screen based on the filter screen processing image of each filter screen through an image analysis network, and to identify impurity distribution characteristic information of each filter screen and impurity penetration characteristic information of each filter screen based on the filter screen impurity distribution information of each filter screen;

[0109] The evaluation module 530 is used to analyze the risk information of the impact of the impurities of each filter on the high-voltage cable based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter through an impurity characteristic evaluation strategy, and evaluate the cleanliness information of the high-voltage cable base material through a cleanliness evaluation index based on the risk information of the impact of the impurities of each filter on the high-voltage cable and the impurity distribution information of each filter.

[0110] Optionally, the acquisition module 510 is specifically configured to:

[0111] For each filter screen, image processing is performed on a filter screen sample image of the filter screen to obtain an impurity binary image of the filter screen;

[0112] An impurity boundary range of the filter impurities in the impurity binary image is identified, and filter impurity distribution information of the filter is identified based on a position range of the impurity boundary range of the filter impurities in the impurity binary image.

[0113] Optionally, the identification module 520 is specifically configured to:

[0114] For each filter, image processing is performed on the filter processed image of the filter to obtain the impurity processed binary image of the filter, and based on the impurity binary image of the filter and the impurity processed binary image of the filter, structural change information of the filter impurities of the filter and filter impurity range change information of the filter impurities are identified through an image analysis network;

[0115] Collecting the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method;

[0116] The impurity change characteristics of the impurities on the filter are used as the impurity characteristic information of the filter.

[0117] Optionally, the identification module 520 is specifically configured to:

[0118] For each filter screen, based on the impurity distribution information of the filter screen, the surface impurity distribution density of the filter screen impurities of the filter screen is calculated, and based on the filter screen aperture information of the filter screen and the surface impurity distribution density of the filter screen impurities of the filter screen, the impurity penetration coefficient of the filter screen impurities is calculated;

[0119] The surface impurity distribution density of the filter impurities is used as the impurity distribution characteristic information of the filter to which the filter impurities belong, and the impurity penetration coefficient of the filter impurities is used as the penetration characteristic information of the filter to which the filter impurities belong.

[0120] Optionally, the evaluation module 530 is specifically used to:

[0121] For each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities of the filter is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database;

[0122] Based on the impurity distribution characteristic information of the filter screen and the impurity penetration characteristic information of the filter screen, the risk probability of the impurities in the filter screen to the high-voltage cable is identified through the impurity risk analysis strategy;

[0123] The cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities of the filter on the high-voltage cable.

[0124] Optionally, the evaluation module 530 is specifically used to:

[0125] Based on the surface impurity distribution density of the filter impurities of each filter, the first cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity distribution evaluation index, and based on the cable impact risk information of the filter impurities of each filter on the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity risk evaluation index;

[0126] Based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

[0127] Each module in the above-mentioned cleanliness evaluation device for high-voltage cable base material can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0128] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for evaluating the cleanliness of a high-voltage cable base material is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0129] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0130] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0132] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0133] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0135] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the cleanliness of a high-voltage cable base material, characterized in that: The method comprises: Obtaining filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh, and identifying filter mesh impurity distribution information of each filter mesh based on the filter mesh sample images of each filter mesh; Based on the filter processing images of each filter, the impurity characteristic information of each filter is identified through an image analysis network, and based on the filter impurity distribution information of each filter, the impurity distribution characteristic information of each filter and the impurity penetration characteristic information of each filter are identified; Based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter, the impurity characteristic evaluation strategy is used to analyze the risk information of the impact of the filter impurities of each filter on the cable of the high-voltage cable; and based on the risk information of the impact of the impurities of each filter on the cable of the high-voltage cable and the filter impurity distribution information of each filter, the cleanliness information of the high-voltage cable base material is evaluated through cleanliness evaluation indicators.

2. The method according to claim 1, characterized in that The identifying the filter impurity distribution information of each filter based on the filter sample image of each filter includes: For each filter screen, image processing is performed on a filter screen sample image of the filter screen to obtain an impurity binary image of the filter screen; An impurity boundary range of the filter impurities in the impurity binary image is identified, and filter impurity distribution information of the filter is identified based on a position range of the impurity boundary range of the filter impurities in the impurity binary image.

3. The method according to claim 2, characterized in that The filter processing image based on each filter screen, identifying the impurity characteristic information of each filter screen through an image analysis network, includes: For each filter, image processing is performed on the filter processed image of the filter to obtain the impurity processed binary image of the filter, and based on the impurity binary image of the filter and the impurity processed binary image of the filter, structural change information of the filter impurities of the filter and filter impurity range change information of the filter impurities are identified through an image analysis network; Collecting the impurity processing method of the filter, and based on the filter impurity structure change information of the filter impurities and the filter impurity range change information of the filter impurities, identifying the impurity change characteristics of the filter impurities through the impurity processing type corresponding to the impurity processing method; The impurity change characteristics of the impurities on the filter are used as the impurity characteristic information of the filter.

4. The method according to claim 1, characterized in that: The identifying of impurity distribution characteristic information of each filter screen and impurity penetration characteristic information of each filter screen based on the filter impurity distribution information of each filter screen includes: For each filter screen, based on the impurity distribution information of the filter screen, the surface impurity distribution density of the filter screen impurities of the filter screen is calculated, and based on the filter screen aperture information of the filter screen and the surface impurity distribution density of the filter screen impurities of the filter screen, the impurity penetration coefficient of the filter screen impurities is calculated; The surface impurity distribution density of the filter impurities is used as the impurity distribution characteristic information of the filter to which the filter impurities belong, and the impurity penetration coefficient of the filter impurities is used as the penetration characteristic information of the filter to which the filter impurities belong.

5. The method according to claim 4, characterized in that The impurity characteristic evaluation strategy includes an impurity attribute analysis strategy and an impurity risk analysis strategy. Based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter, the impurity characteristic evaluation strategy is used to analyze the risk information of the impact of the filter impurities of each filter on the cable of the high-voltage cable, including: For each filter, based on the impurity characteristic information of the filter, the impurity attribute information of the filter impurities of the filter is evaluated through the impurity attribute analysis strategy, and the cable impact risk type corresponding to the impurity attribute information of the filter impurities is queried in the database; Based on the impurity distribution characteristic information of the filter screen and the impurity penetration characteristic information of the filter screen, the risk probability of the impurities in the filter screen to the high-voltage cable is identified through the impurity risk analysis strategy; The cable impact risk type corresponding to the filter impurities and the risk probability of the filter impurities on the high-voltage cable are used as the cable impact risk information of the filter impurities of the filter on the high-voltage cable.

6. The method according to claim 5, characterized in that The cleanliness evaluation index includes an impurity distribution evaluation index and an impurity risk evaluation index. Based on the risk information of the impurities in each filter screen affecting the high-voltage cable and the filter impurity distribution information of each filter screen, the cleanliness information of the high-voltage cable base material is evaluated through the cleanliness evaluation index, including: Based on the surface impurity distribution density of the filter impurities of each filter, the first cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity distribution evaluation index, and based on the cable impact risk information of the filter impurities of each filter on the high-voltage cable, the second cleanliness information of the high-voltage cable base material corresponding to each filter is identified through the impurity risk evaluation index; Based on the first cleanliness information of the high-voltage cable base material corresponding to each filter and the second cleanliness information of the high-voltage cable base material corresponding to each filter, a cleanliness evaluation summary table of the high-voltage cable base material is generated, and the cleanliness evaluation summary table is used as the cleanliness information of the high-voltage cable base material.

7. A cleanliness evaluation device for a high voltage cable base material, characterized in that: The device comprises: An acquisition module, used to acquire filter mesh sample images of filter meshes after filtering high-voltage cable base materials produced at different times, and filter mesh processing images obtained after impurity processing of each filter mesh, and identify filter mesh impurity distribution information of each filter mesh based on the filter mesh sample images of each filter mesh; an identification module, for identifying impurity characteristic information of each filter screen based on the filter screen processing image of each filter screen through an image analysis network, and identifying impurity distribution characteristic information of each filter screen and impurity penetration characteristic information of each filter screen based on the filter screen impurity distribution information of each filter screen; An evaluation module is used to analyze the risk information of the impact of impurities on the high-voltage cable of each filter based on the impurity characteristic information of each filter, the impurity distribution characteristic information of each filter, and the impurity penetration characteristic information of each filter through an impurity characteristic evaluation strategy, and based on the risk information of the impact of impurities on the high-voltage cable of each filter and the impurity distribution information of each filter, evaluate the cleanliness information of the high-voltage cable base material through a cleanliness evaluation index.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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