A method, system, device, and medium for identifying weaknesses in cable insulation

Abstract

A method, system, device, and medium for identifying weak points in cable insulation layers are disclosed, relating to the field of cable quality inspection technology. In this method, multiple cross-sectional images of the cable sample to be tested are acquired and extracted to obtain an original geometric dimension dataset. The original geometric dimension dataset is then subjected to coarse and local filtering to obtain a final geometric dimension dataset. Based on the angle information corresponding to each preset circumferential point, curve fitting is performed using the final geometric dimension dataset to calculate the eccentricity difference. A standard dimension mean is determined based on the final geometric dimension dataset, and a dynamic tolerance threshold is determined based on the standard dimension mean. The dimensional deviation values ​​corresponding to each measured value in the standard dimension mean and the final geometric dimension dataset are compared with the dynamic tolerance threshold to identify local dimensional weak points. Finally, the overall eccentric weakness defect of the cable cross-section is identified based on the eccentricity difference and the dynamic tolerance threshold. Implementing this method enables accurate identification of defects in cable insulation layers.

Description

Technical Field

[0001] This application relates to the field of cable quality inspection technology, specifically to a method, system, equipment, and medium for identifying weak points and defects in cable insulation. Background Technology

[0002] As a crucial carrier for power transmission and distribution, the quality of the insulation layer of electric wires and cables directly affects the safety and reliability of power grid operation. With the rapid development of ultra-high voltage power transmission and new energy grid integration, the industry has placed higher precision requirements on geometric parameters such as the uniformity of cable insulation thickness, outer diameter, and concentricity. Achieving automated, high-precision detection of cable insulation dimensions and intelligent identification of weak points in the insulation layer are important quality control measures to ensure cable product compliance and the stable operation of the power system.

[0003] Existing automatic inspection technologies for cable insulation dimensions typically employ industrial cameras or line-scan imaging devices with fixed fields of view to acquire images of the cable cross-section. This technology utilizes digital image processing algorithms to perform edge detection and contour extraction on the acquired cross-sectional images. Then, through pixel calibration and geometric calculations, it automatically obtains the thickness and external dimensions of the cable insulation layer, comparing them with preset standard dimensions to output the inspection results.

[0004] However, due to physical interference such as uneven cable sample cut surfaces, surface stains, burrs, and outer sheath reflections, local image distortion and noise are easily generated during optical imaging, resulting in deviations in the extracted edge contours. Existing automatic inspection systems typically perform calculations directly on the extracted raw data, lacking dynamic removal of abnormal data to address imaging distortion and stain interference. This leads to inaccurate calculated dimensional data, and the use of fixed thresholds cannot accurately identify defects in the cable insulation layer. Summary of the Invention

[0005] This application provides a method, system, device, and medium for identifying weak points and defects in cable insulation. This method effectively eliminates discrete data points caused by imaging distortion, and achieves accurate identification of cable insulation defects while obtaining accurate dimensional data.

[0006] In a first aspect, this application provides a method for identifying weak point defects in cable insulation. The method includes: acquiring multiple cross-sectional images of a cable sample under test at multiple preset circumferential points; extracting data from each cross-sectional image to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point; calculating the median absolute deviation based on the original geometric dimension dataset; coarsely filtering the original geometric dimension dataset based on the median absolute deviation; calculating the local variation index of the degree of abrupt change between adjacent data points based on the coarsely filtered dataset for local filtering to obtain a final geometric dimension dataset; performing curve fitting calculation based on the angle information corresponding to each preset circumferential point and the final geometric dimension dataset to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer; determining the standard dimension mean based on the final geometric dimension dataset; determining a dynamic tolerance threshold based on the standard dimension mean; calculating the dimension deviation value corresponding to each measured value in the final geometric dimension dataset based on the standard dimension mean, and comparing each dimension deviation value with the dynamic tolerance threshold to identify local dimensional weak points; and identifying the overall eccentric weak defect of the cable cross section based on the comparison result of the eccentricity difference and the dynamic tolerance threshold.

[0007] By employing the above technical solution, multiple cross-sectional images of the cable sample under test at multiple preset circumferential points are acquired. The original geometric dimension dataset is obtained by extracting the cross-sectional images. The original geometric dimension dataset is coarsely filtered by calculating the median absolute deviation, effectively eliminating outlier data points caused by imaging distortion. Local variability indices are calculated to measure the degree of abrupt changes between adjacent data points in the coarsely filtered dataset, eliminating abnormal abrupt changes caused by local stains and burrs, thus obtaining a high-quality final geometric dimension dataset. Based on the final geometric dimension dataset and angle information, curve fitting calculations are performed to accurately obtain the eccentricity difference between the outer and inner contour centers of the insulation layer. The standard dimension mean is determined based on the final geometric dimension dataset, and a dynamic tolerance threshold is determined based on the standard dimension mean, overcoming the shortcomings of existing technologies that use fixed thresholds and cannot adapt to different cable specifications and process fluctuations. The dimensional deviation values ​​corresponding to each measured value are compared with the dynamic tolerance threshold to identify local dimensional weaknesses. Simultaneously, the eccentricity difference is compared with the dynamic tolerance threshold to identify overall eccentric weakness defects, achieving accurate identification of cable insulation layer defects and improving detection accuracy.

[0008] Optionally, multiple cross-sectional images are extracted separately to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point. Specifically, this includes: preprocessing each cross-sectional image sequentially, and extracting the inner and outer contours of the insulation layer based on edge detection; calculating the geometric center of the inner contour as the first reference point, emitting multiple thickness measurement rays from the first reference point along the angles corresponding to each preset circumferential point, calculating the distance between the intersection points of each thickness measurement ray and the inner and outer contours, obtaining the measured thickness values ​​corresponding to each preset circumferential point, and collecting them to form an original thickness dataset; performing preliminary circle fitting on the outer contour to obtain the preliminary center coordinates of the outer contour as the second reference point; drawing multiple outer diameter measurement lines through the second reference point along the angles corresponding to each preset circumferential point, calculating the distance between the two intersection points of each outer diameter measurement line and the outer contour, obtaining the measured outer diameter values ​​corresponding to each preset circumferential point, and collecting them to form an original outer diameter dataset; and outputting the original outer diameter dataset and the original thickness dataset together as the original geometric dimension dataset.

[0009] By adopting the above technical solution, the cross-sectional image is preprocessed and the inner and outer contours of the insulation layer are extracted based on edge detection. The geometric center of the inner contour is calculated as the first reference point, and thickness measurement rays are emitted from the first reference point along the corresponding angles of each preset circumferential point. This ensures that the thickness measurement always uses the inner contour as a unified reference system, eliminating the influence of cable cross-section position offset on thickness measurement. A preliminary circle fitting is performed on the outer contour to obtain the second reference point, and an outer diameter measurement line is drawn through the second reference point along the corresponding angle, avoiding systematic measurement deviations that may be caused by a single reference point. The original outer diameter dataset and the original thickness dataset are output together as the original geometric dimension dataset, realizing a comprehensive characterization of the geometric parameters of the cable insulation layer.

[0010] Optionally, the median absolute deviation is calculated based on the original geometric dimension dataset. The original geometric dimension dataset is then coarsely filtered based on the median absolute deviation. A local variation index, representing the degree of abrupt change between adjacent data points, is calculated based on the coarsely filtered dataset for local filtering, resulting in the final geometric dimension dataset. Specifically, this includes: using the original thickness dataset and the original outer diameter dataset from the original geometric dimension dataset as the current dataset to be processed; calculating the first median of the current dataset to be processed, and calculating the absolute deviation between each measured value in the current dataset and the first median, obtaining the median of all absolute deviations to obtain the first median absolute deviation; calculating the correction score for each data point in the current dataset to be processed based on the first median and the first median absolute deviation; removing data points from the current dataset whose absolute correction score is greater than a preset global threshold to obtain an intermediate subset; extracting the local variation index between adjacent data points in the intermediate subset, and performing local filtering on the intermediate subset based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset; and combining the final thickness dataset and the final outer diameter dataset to form the final geometric dimension dataset.

[0011] By employing the above technical solutions, the original thickness dataset and the original outer diameter dataset are processed separately. The first median and the first median absolute deviation of the current dataset to be processed are calculated. Compared with the traditional mean and standard deviation methods, the median absolute deviation is not sensitive to outliers and can still obtain robust data distribution characteristic parameters even in the presence of a large number of imaging distortion noise points. Based on the first median and the first median absolute deviation, the correction score of each data point is calculated, and data points with a correction score absolute value greater than a preset global threshold are removed. This coarse filtering process effectively eliminates abnormal data that significantly deviate from the overall distribution due to factors such as uneven cross-sections. After obtaining the intermediate subset dataset, the local variation index between adjacent data points is extracted for local filtering. This local filtering can identify and remove abnormal abrupt changes between adjacent measurement points caused by local stains, etc. Through the synergistic effect of the two-level filtering, both significant outliers in the global range and subtle abrupt changes in the local range can be removed, resulting in a final geometric dimension dataset with higher data quality.

[0012] Optionally, the local variation index between adjacent data points in the intermediate subset is extracted, and the intermediate subset is locally filtered based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset. Specifically, this includes: calculating the local variation index corresponding to each data point in the intermediate subset to obtain the geometric dimension local variation index dataset; calculating the second median of the geometric dimension local variation index dataset, and calculating the local absolute deviation between each local variation index in the geometric dimension local variation index dataset and the second median, and obtaining the median of all local absolute deviations to obtain the second median absolute deviation; calculating the local variation threshold based on the second median, the second median absolute deviation, and a preset sensitivity coefficient; marking data points in the geometric dimension local variation index dataset whose local variation index is greater than the local variation threshold as local outliers, and removing all local outliers from the intermediate subset to obtain the corresponding final thickness dataset or final outer diameter dataset.

[0013] By adopting the above technical solution, the local variation index corresponding to each data point in the intermediate subset is calculated, and the second median and the absolute deviation of the second median of the local variation index dataset are calculated. The median method, which is robust to outliers, is used to characterize the distribution characteristics of local variation, ensuring that the normal level of local fluctuation can still be accurately assessed even in the presence of a small number of abnormal mutations. Based on the second median, the absolute deviation of the second median, and the preset sensitivity coefficient, the local variation threshold is calculated. Data points with local variation indices greater than the local variation threshold are marked as local outliers and removed. This accurately identifies those abnormal measurement points that have no significant deviation in the global scope but have significant mutations in the local scope, ensuring that the final dataset retains the true geometric change characteristics while eliminating measurement interference.

[0014] Optionally, based on the angle information corresponding to each preset circumferential point, and combined with the final geometric dimension dataset, curve fitting calculation is performed to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer. Specifically, this includes: determining the corresponding preset circumferential points and corresponding angle information to be retained based on the data points retained in the final geometric dimension dataset; emitting rays along each retained angle information relative to the first reference point, and taking the intersection of each ray with the inner contour as the first effective coordinate point set; emitting rays along each retained angle information relative to the second reference point, and taking the intersection of each ray with the outer contour as the second effective coordinate point set; using the least squares circle fitting algorithm, performing circle center fitting on all coordinate points in the first effective coordinate point set and all coordinate points in the second effective coordinate point set, and taking the fitted circle center coordinates as the inner contour center and the outer contour center; calculating the Euclidean distance between the inner contour center and the outer contour center, and taking the Euclidean distance as the eccentricity difference.

[0015] By adopting the above technical solution, the corresponding reserved preset circumferential points and reserved angle information are determined according to the data points retained in the final geometric dimension dataset. This ensures that the subsequent curve fitting calculation uses only high-quality measurement data that has been verified by two-level filtering, avoiding interference from abnormal data on the fitting results. Using the first reference point and the second reference point, rays are emitted along each reserved angle information to obtain the intersection points of the inner and outer contours, forming the first effective coordinate point set and the second effective coordinate point set. The least squares circle fitting algorithm is used to fit the center of the two effective coordinate point sets, and the Euclidean distance between the center of the inner contour and the center of the outer contour is calculated to obtain the eccentricity difference. The eccentricity difference accurately reflects the degree of concentricity deviation of the inner and outer contours of the cable insulation layer.

[0016] Optionally, the standard dimension average includes the standard thickness average and the standard outer diameter average. The dynamic tolerance threshold is determined based on the standard dimension average, specifically including: obtaining the cable sample specification information of the cable sample to be tested; querying the corresponding nominal insulation thickness and standard maximum allowable thinning amount from a preset cable manufacturing standard database based on the specification information; calculating the derating based on the deviation mapping relationship between the standard maximum allowable thinning amount and the standard thickness average, combined with a preset thickness safety margin, to obtain a preset thickness deviation ratio threshold; performing fluctuation correction based on the process extrusion fluctuation characteristics corresponding to the standard outer diameter average, combined with a preset outer diameter safety margin, to obtain a preset outer diameter deviation ratio threshold; calculating the preset eccentricity tolerance threshold by combining the standard thickness average and the preset maximum allowable eccentricity percentage; and outputting the preset thickness deviation ratio threshold, the preset outer diameter deviation ratio threshold, and the preset eccentricity tolerance threshold as dynamic tolerance thresholds.

[0017] By adopting the above technical solution, the cable sample specification information of the cable sample to be tested is obtained, and the corresponding nominal insulation thickness and standard maximum allowable thinning amount are queried from the preset cable manufacturing standard database. The preset thickness deviation ratio threshold can effectively prevent the risk of performance degradation of critical qualified products due to environmental aging and other factors in actual use. The preset outer diameter deviation ratio threshold is a dynamic threshold setting method that takes into account the fluctuation characteristics of actual production process. It avoids misjudging normal process fluctuations by fixed thresholds and can effectively identify abnormal deviations that exceed the reasonable fluctuation range. The preset eccentricity tolerance threshold is calculated by the standard thickness average and the preset maximum allowable eccentricity percentage. The eccentricity judgment standard is adaptively adjusted according to the cable specification. This overcomes the defect of the existing technology that uses fixed thresholds and cannot adapt to different cable specifications and process differences, and significantly improves the accuracy of defect identification.

[0018] Optionally, based on the standard size mean, the dimensional deviation value corresponding to each measured value in the final geometric size dataset is calculated, and each dimensional deviation value is compared with a dynamic tolerance threshold to identify local dimensional weak points; based on the comparison result of the eccentricity difference value and the dynamic tolerance threshold, the overall eccentricity weak defect of the cable cross section is identified, specifically including: based on the final thickness dataset in the final geometric size dataset, the absolute value of the thickness deviation between the measured thickness value corresponding to each reserved preset circumferential point and the standard thickness mean is calculated, and the absolute value of the thickness deviation is divided by the standard thickness mean to obtain the thickness deviation ratio value corresponding to each reserved preset circumferential point; each thickness deviation ratio value is compared with a preset thickness deviation ratio threshold, and if the thickness deviation ratio value corresponding to the first reserved preset circumferential point is greater than the preset thickness deviation ratio threshold, then the first reserved preset circumferential point is marked as a local thickness weak point, and the first reserved preset circumferential point is one of multiple reserved preset points in the final thickness dataset. Any point in the circumferential points; based on the final outer diameter dataset in the final geometric dimension dataset, calculate the absolute value of the outer diameter deviation between the measured outer diameter value and the standard outer diameter mean for each reserved preset circumferential point, and divide the absolute value of the outer diameter deviation by the standard outer diameter mean to obtain the outer diameter deviation ratio value corresponding to each reserved preset circumferential point; compare each outer diameter deviation ratio value with a preset outer diameter deviation ratio threshold. If the outer diameter deviation ratio value corresponding to the second reserved preset circumferential point is greater than the preset outer diameter deviation ratio threshold, then mark the second reserved preset circumferential point as a local outer diameter weak point. The second reserved preset circumferential point is any point among the multiple reserved preset circumferential points in the final outer diameter dataset; output the local thickness weak point and the local outer diameter weak point as local size weak points; compare the eccentricity difference value with a preset eccentricity tolerance threshold. If the eccentricity difference value is greater than the preset eccentricity tolerance threshold, then it is determined that the cable sample under test has an overall eccentricity weak defect.

[0019] By employing the above technical solution, the absolute value of the thickness deviation between the measured thickness value and the standard average thickness at each preset circumferential point in the final thickness dataset is calculated. Dividing this absolute value by the standard average thickness yields the thickness deviation ratio. Comparing each thickness deviation ratio with a preset thickness deviation ratio threshold accurately identifies local thickness weaknesses where the actual thickness significantly deviates from the standard value. Similarly, calculating the outer diameter deviation ratio and comparing it with a preset outer diameter deviation ratio threshold identifies local outer diameter weaknesses, effectively detecting abnormalities in cable dimensions. Comparing the eccentricity difference with a preset eccentricity tolerance threshold determines whether the tested cable sample has an overall eccentric weakness defect, identifying problems with poor concentricity of the inner and outer contours caused by deviations in extrusion process parameters.

[0020] A second aspect of this application provides a system for identifying weak points in cable insulation. The system includes a receiving unit, a processing unit, and an identification unit. The receiving unit acquires multiple cross-sectional images of a cable sample at multiple preset circumferential points. The processing unit extracts data from each of the multiple cross-sectional images to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point. The system calculates the median absolute deviation based on the original geometric dimension dataset, performs coarse filtering on the original geometric dimension dataset based on the median absolute deviation, and calculates the local variation index of the degree of abrupt change between adjacent data points based on the coarsely filtered dataset for local filtering to obtain the most accurate identification result. The final geometric dimension dataset is used to calculate the eccentricity difference between the outer and inner contour centers of the insulation layer by combining the angle information corresponding to each preset circumferential point with the final geometric dimension dataset. The standard dimension mean is determined based on the final geometric dimension dataset, and a dynamic tolerance threshold is determined based on the standard dimension mean. The identification unit calculates the dimension deviation value corresponding to each measured value in the final geometric dimension dataset based on the standard dimension mean, and compares each dimension deviation value with the dynamic tolerance threshold to identify local dimensional weaknesses. Based on the comparison result of the eccentricity difference and the dynamic tolerance threshold, the overall eccentric weakness defect of the cable cross-section is identified.

[0021] In a third aspect, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, causing the electronic device to perform any of the methods described above in this application.

[0022] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform any of the methods described above in this application.

[0023] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Acquire multiple cross-sectional images of the cable sample under test at multiple preset circumferential points. After extracting the cross-sectional images, obtain the original geometric dimension dataset. Coarsely filter the original geometric dimension dataset by calculating the median absolute deviation to effectively remove outlier data points caused by imaging distortion. Calculate the local variation index of the degree of abrupt change between adjacent data points in the coarsely filtered dataset for local filtering to eliminate abnormal abrupt changes caused by local stains and burrs, thus obtaining a high-quality final geometric dimension dataset. Based on the final geometric dimension dataset and angle information, perform curve fitting calculation to accurately obtain the eccentricity difference between the outer and inner contour centers of the insulation layer. Determine the standard dimension mean based on the final geometric dimension dataset, and determine the dynamic tolerance threshold based on the standard dimension mean. This overcomes the shortcomings of existing technologies that use fixed thresholds and cannot adapt to different cable specifications and process fluctuations. Compare the dimensional deviation values ​​corresponding to each measured value with the dynamic tolerance threshold to identify local dimensional weaknesses. At the same time, compare the eccentricity difference with the dynamic tolerance threshold to identify overall eccentric weakness defects, achieving accurate identification of cable insulation layer defects and improving detection accuracy. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for identifying weak points in cable insulation provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a system for identifying weak points in cable insulation provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached drawings: 201, receiving unit; 202, processing unit; 203, identification unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Therefore, accurately identifying local dimensions and overall eccentric weakness defects in cable insulation layers, even under physical interference such as imaging distortion in cable cross-section images, is a pressing problem that needs to be solved. This application provides a method for identifying weak points in cable insulation layers, applied in a server. The server in this application can be a platform providing cable insulation layer inspection services to wire and cable manufacturers. Figure 1 This is a flowchart illustrating a method for identifying weak points in cable insulation provided in an embodiment of this application. (Refer to...) Figure 1 The method includes the following steps S101-S106.

[0030] S101: Acquire multiple cross-sectional images of the cable sample under test at multiple preset circumferential points.

[0031] In S101 above, the server of this application, as the executing entity, is located in the intelligent cable insulation layer identification system. This system comprises an optical projection imaging unit, an image processing and analysis unit, a motion control unit, a data processing and storage unit, and a human-machine interaction unit, forming a complete detection system. The optical projection imaging unit includes a high-definition optical projector equipped with a variable magnification module and a matching light source, responsible for acquiring high-definition images of the cable sample cross-section. The image processing and analysis unit integrates a dedicated image processing chip and related algorithm software for processing and analyzing the acquired images. The motion control unit includes a precision rotating platform and an axial movement mechanism, used to drive the cable sample to achieve circumferential positioning and axial movement. The data processing and storage unit includes an industrial computer and a database server, with the server acting as the core controller responsible for the coordinated control and data management of the entire system. The human-machine interaction unit provides an interface for operators to input detection parameters and view detection results. The units work together through electrical and signal connections. The specific collaboration process is as follows: the motion control unit receives control commands from the server to drive the sample into position; the optical projection imaging unit acquires images and transmits them to the image processing and analysis unit; and the data and analysis results processed by the image processing and analysis unit are uploaded to the server for storage and display.

[0032] The cable sample to be tested is first fixed on the precision rotating platform of the motion control unit. The operator inputs the test parameters through the human-machine interface unit. For example, for small and medium-sized cables with an outer diameter between 10mm and 30mm, 8 preset circumferential points can be selected; for medium-sized cables with an outer diameter between 30mm and 50mm, 12 preset circumferential points can be selected; and for large-sized cables with an outer diameter greater than 50mm, 16 preset circumferentially divided points are selected. The server automatically calculates the rotation angle corresponding to each preset circumferential point based on the input parameters and sends rotation commands to the motion control unit through a signal connection.

[0033] After receiving instructions, the motion control unit drives the precision rotating platform to rotate sequentially according to calculated angles, ensuring that each preset circumferential point of the cable sample is accurately aligned with the center of the field of view of the optical projection imaging unit. For example, when 12 preset circumferential points are selected, the angular interval between each point is 360 degrees divided by 12, which equals 30 degrees. After each point reaches the designated position, the server triggers the high-definition optical projector in the optical projection imaging unit to acquire images. The variable magnification module of this projector adaptively adjusts the magnification according to the outer diameter specification of the cable sample under test, ensuring that the insulation layer outline occupies an appropriate proportion in the imaging image.

[0034] After the cross-sectional image of each preset circumferential point is acquired, it is transmitted in real time to the image processing and analysis unit via electrical connection for initial caching. Once the images of all preset circumferential points have been acquired, the server aggregates these cross-sectional images into multiple cross-sectional images.

[0035] S102: Extract from multiple cross-sectional images to obtain the original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point.

[0036] In S102 above, after the image processing and analysis unit acquires multiple cross-sectional images, it needs to convert the cross-sectional images into quantifiable and analyzable geometric dimension data. This is because the original images only contain pixel information and cannot be directly used for dimension calculation and defect determination. The inner and outer contours of the insulation layer are extracted and the measured values ​​are calculated through image processing algorithms.

[0037] Furthermore, multiple cross-sectional images are extracted to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point. Specifically, this includes: preprocessing each cross-sectional image sequentially, and extracting the inner and outer contours of the insulation layer based on edge detection; calculating the geometric center of the inner contour as the first reference point, emitting multiple thickness measurement rays from the first reference point along the angles corresponding to each preset circumferential point, calculating the distance between the intersection points of each thickness measurement ray and the inner and outer contours, obtaining the measured thickness values ​​corresponding to each preset circumferential point, and collecting them to form an original thickness dataset; performing preliminary circle fitting on the outer contour to obtain the preliminary center coordinates of the outer contour as the second reference point; drawing multiple outer diameter measurement lines through the second reference point along the angles corresponding to each preset circumferential point, calculating the distance between the two intersection points of each outer diameter measurement line and the outer contour, obtaining the measured outer diameter values ​​corresponding to each preset circumferential point, and collecting them to form an original outer diameter dataset; and outputting the original outer diameter dataset and the original thickness dataset together as the original geometric dimension dataset.

[0038] Specifically, the image processing and analysis unit performs preprocessing on each cross-sectional image sequentially. The preprocessing includes image grayscale equalization, pixel noise reduction, and sub-pixel edge detection. Image grayscale equalization employs a histogram equalization algorithm, redistributing the image's grayscale values ​​to enhance overall contrast and ensure optimal boundary contrast between the insulation layer and conductor layer, and between the insulation layer and outer sheath. Pixel noise reduction uses a 5×5 window median filtering algorithm. The window size is derived from the system's pre-analysis of noise characteristics in typical cable cross-section images, effectively removing isolated noise points caused by thermal noise from optical sensors or contaminants on the cable cross-section. After preprocessing, the Canny edge detection algorithm is used to extract the inner and outer contours of the insulation layer, and sub-pixel interpolation technology is used for fine-tuning edge positioning, improving edge positioning accuracy to the 0.01 pixel level.

[0039] After extracting the inner contour, the image processing and analysis unit calculates the average abscissa and ordinate of all pixels on the inner contour to obtain the geometric center of the inner contour as the first reference point. Using the geometric center of the inner contour as the reference point ensures that the thickness measurement is always performed in the radial direction, so that the measured thickness value accurately reflects the true insulation layer thickness at each circumferential point. Based on the number and distribution of preset circumferential points, the angle information corresponding to each preset circumferential point is calculated. For example, when the average number of circumferential points is 12, the angles corresponding to each point are 0°, 30°, 60°, 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300°, and 330°, respectively. The server emits a thickness measurement ray from the first reference point at each angle. In the image coordinate system, the ray is represented as a straight line extending from the first reference point at a specific angle. The coordinates of the intersection points of the straight line with the inner contour and the outer contour are calculated. The intersection point with the inner contour is determined by traversing the inner contour pixels and judging the minimum distance between them and the ray. The intersection point with the outer contour is found in the outer contour pixels using the same method.

[0040] After determining the two intersection points, the Euclidean distance between the two intersection points is calculated and converted into the actual physical size based on the pre-calibrated pixel-to-actual-size conversion coefficient of the optical projector, thus obtaining the measured thickness value corresponding to the preset circumferential point. This process is repeated until the measured thickness values ​​for all preset circumferential points have been calculated. These measured thickness values ​​are then arranged in order according to their corresponding circumferential point numbers, forming the original thickness dataset.

[0041] After obtaining the outer contour, a preliminary circle fitting is performed to determine the second reference point. The server extracts a representative set of points from the outer contour pixels at equal angular intervals. The number of extracted points is typically 10% to 20% of the total number of pixels in the outer contour to balance computational efficiency and fitting accuracy. The uniform distribution of these representative points on the circumference avoids excessive influence of local contour deformation on the fitting results. A least-squares circle fitting algorithm is used to minimize the sum of squared radial distances from all representative points to the fitted circle, iteratively solving for the preliminary center coordinates of the outer contour as the second reference point. Determining the second reference point through circle fitting reduces the impact of local deformation of the outer contour on the calculation of the center position.

[0042] After determining the second reference point, multiple outer diameter measurement lines are drawn along the angles corresponding to each preset circumferential point. These measurement lines all pass through the second reference point and extend bidirectionally at specific angles to intersect the outer contour. For each outer diameter measurement line, intersection points with the outer contour are found on both sides. After finding two intersection points, the Euclidean distance between the two intersection points is calculated and converted to the actual physical size according to the pixel-to-actual-size conversion factor, obtaining the measured outer diameter value corresponding to that preset circumferential point. The measured outer diameter values ​​of all preset circumferential points are arranged in order according to the corresponding circumferential point number, forming the original outer diameter dataset. Finally, the original outer diameter dataset and the original thickness dataset are integrated together. Each preset circumferential point is simultaneously associated with the corresponding measured thickness value and measured outer diameter value, forming a complete original geometric dimension dataset, which is then output to the server for further processing.

[0043] S103: Calculate the median absolute deviation based on the original geometric size dataset, perform coarse filtering on the original geometric size dataset based on the median absolute deviation, calculate the local variation index of the degree of abrupt change between adjacent data points based on the coarsely filtered dataset, and perform local filtering to obtain the final geometric size dataset.

[0044] In S103 above, the server performs two-level data filtering on the original geometric size dataset to remove abnormal data caused by imaging distortion and local interference, thereby improving data quality.

[0045] Furthermore, the median absolute deviation is calculated based on the original geometric dimension dataset. The original geometric dimension dataset is then coarsely filtered based on this median absolute deviation. A local variation index, representing the degree of abrupt change between adjacent data points, is calculated based on the coarsely filtered dataset for local filtering, resulting in the final geometric dimension dataset. Specifically, this includes: using the original thickness dataset and the original outer diameter dataset from the original geometric dimension dataset as the current dataset to be processed; calculating the first median of the current dataset to be processed, and calculating the absolute deviation between each measured value in the current dataset and the first median, obtaining the median of all absolute deviations to obtain the first median absolute deviation; calculating the correction score for each data point in the current dataset to be processed based on the first median and the first median absolute deviation; removing data points from the current dataset whose absolute correction score is greater than a preset global threshold to obtain an intermediate subset; extracting the local variation index between adjacent data points in the intermediate subset, and performing local filtering on the intermediate subset based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset; and combining the final thickness dataset and the final outer diameter dataset to form the final geometric dimension dataset.

[0046] Specifically, the server first processes the original thickness dataset and the original outer diameter dataset from the original geometric dimension dataset independently as the current datasets to be processed, setting appropriate filtering parameters based on their respective numerical characteristics. Taking the original thickness dataset as an example, the server sorts the measured thickness values ​​corresponding to all preset circumferential points in the original thickness dataset according to their numerical values. After sorting, if the total number of data is odd, the value in the middle position is taken as the first median; if the total number of data is even, the arithmetic mean of the two middle values ​​is taken as the first median. Compared with the arithmetic mean, the median has a stronger ability to resist interference from outliers.

[0047] After determining the first median, the server iterates through each measured thickness value in the current dataset, calculates the absolute deviation between each measured thickness value and the first median, sorts all absolute deviation values, and calculates the median to obtain the first median absolute deviation. The first median absolute deviation robustly reflects the overall dispersion of the current dataset. Based on the first median and the first median absolute deviation, the server calculates a corrected score for each data point: Corrected score = (Measured value - First median) / (First median absolute deviation × k), where k = 1.4826 is used to convert the median absolute deviation into an estimate of the standard deviation, giving the corrected score the properties of a standardized statistic.

[0048] The server sets a preset global threshold of 3, following the 3σ criterion in statistics. The server iterates through the current dataset; if the absolute value of the corrected score for a data point exceeds the preset global threshold of 3, the data point is marked as a global outlier and removed from the current dataset, resulting in an intermediate subset. The original outer diameter dataset undergoes the same global coarse filtering process independently.

[0049] After obtaining the intermediate subset, the server performs local variation analysis to identify anomalous mutations between adjacent data points. Since the data points in the intermediate subset are arranged in angular order according to a preset circumferential position, the server sequentially calculates the local variation index between each pair of adjacent data points.

[0050] Furthermore, local variation indices between adjacent data points in the intermediate subset are extracted. Based on these local variation indices, the intermediate subset is locally filtered to obtain the corresponding final thickness dataset or final outer diameter dataset. Specifically, this includes: calculating the local variation index corresponding to each data point in the intermediate subset to obtain a geometric dimension local variation index dataset; calculating the second median of the geometric dimension local variation index dataset and calculating the local absolute deviation between each local variation index in the geometric dimension local variation index dataset and the second median, and obtaining the median of all local absolute deviations to obtain the second median absolute deviation; calculating the local variation threshold based on the second median, the second median absolute deviation, and a preset sensitivity coefficient; marking data points in the geometric dimension local variation index dataset with local variation indices greater than the local variation threshold as local outliers, and removing all local outliers from the intermediate subset to obtain the corresponding final thickness dataset or final outer diameter dataset.

[0051] Specifically, the server calculates the corresponding local variation index for each data point in the intermediate subset. The local variation index is used to quantify the degree of size abrupt change between the current data point and its adjacent data points in the circumferential direction. It can effectively identify anomalous data points that, although not reaching the global anomaly threshold, exhibit significant jumps in local areas. Since the data points in the intermediate subset are arranged according to the angular order of the preset circumferential points, each data point has a corresponding circumferential index number. The server retrieves the two adjacent data points in the circumferential direction of the current data point based on the circumferential index order.

[0052] For any current data point in the intermediate subset, if the circumference index number is i, then the circumference index number of the previous adjacent data point is i-1, and the circumference index number of the next adjacent data point is i+1. Since the circumference of the cable cross-section has a periodic characteristic, when the current data point is the first data point (circumference index number 1), the previous adjacent data point is the last data point (circumference index number n), where n is the total number of data points in the intermediate subset; when the current data point is the last data point (circumference index number n), the next adjacent data point is the first data point (circumference index number 1). The local variation index is calculated in the following format: Local variation index = |current data point value - (previous data point value + next data point value) / 2|, where if the current data point is the first data point, then the previous data point is the last data point; if the current data point is the last data point, then the next data point is the first data point.

[0053] The server retrieves the two adjacent data points along the circumference of the current data point, calculates the arithmetic mean of their values ​​as a local reference value, and then subtracts the current data point's value from this arithmetic mean, taking the absolute value to obtain the local variation index corresponding to the current data point. The server repeats the above calculation for all data points in the intermediate subset, compiling all local variation indices to form a geometrically sized local variation index dataset.

[0054] The server sorts all local variability indices in the geometric local variability index dataset according to their numerical values ​​and calculates the second median using the same method as the first median. The server then calculates the local absolute deviation between each local variability index and the second median, sorts all local absolute deviation values, and calculates the median to obtain the absolute deviation of the second median.

[0055] The server calculates the local variation threshold based on the second median, the absolute deviation of the second median, and a preset sensitivity coefficient. The formula for this threshold is: Second Median + Absolute Deviation of Second Median × Preset Sensitivity Coefficient × k. Here, k = 1.4826. The preset sensitivity coefficient is pre-set according to the quality control requirements of cable testing and can be set to 3. The preset sensitivity coefficient is derived from statistical analysis experience on different cable product types and quality standards.

[0056] After determining the local variation threshold, the server compares each local variation index in the local variation index dataset with the threshold. If a local variation index is greater than the threshold, it indicates a significant abrupt change in the corresponding data point relative to its local neighbors, and this data point is marked as a local outlier. When the local variation index is less than or equal to the threshold, the data point is considered normal and does not require marking. The server records the circumferential index numbers of all data points marked as local outliers in the intermediate subset and removes the original data corresponding to these outliers from the intermediate subset. After the removal operation, if the current intermediate subset originates from the original thickness dataset, the final thickness dataset is obtained; if it originates from the original outer diameter dataset, the final outer diameter dataset is obtained. The server combines the final thickness dataset and the final outer diameter dataset to form the final geometric dimension dataset and outputs it. The final geometric dimension dataset undergoes dual processing of global coarse filtering and local fine filtering, accurately reflecting the actual dimensional distribution characteristics of the cable insulation layer.

[0057] S104: Based on the angle information corresponding to each preset circumferential point, and combined with the final geometric dimension dataset, curve fitting calculation is performed to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer.

[0058] In step S104 above, after obtaining the final geometric dimension dataset, the server needs to calculate the eccentricity difference of the insulation layer. This eccentricity difference is used to evaluate the concentricity quality of the cable insulation layer, as eccentricity can lead to insufficient insulation strength on one side of the cable during actual use, posing a risk of localized breakdown. It is a crucial indicator for cable quality control. Accurate calculation of the eccentricity difference requires determining the spatial positions of the outer and inner contour centers of the insulation layer.

[0059] Furthermore, based on the angle information corresponding to each preset circumferential point, and combined with the final geometric dimension dataset, curve fitting calculation is performed to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer. Specifically, this includes: determining the corresponding preset circumferential points and corresponding angle information to be retained based on the data points retained in the final geometric dimension dataset; emitting rays along each retained angle information relative to the first reference point, and taking the intersection of each ray with the inner contour as the first effective coordinate point set; emitting rays along each retained angle information relative to the second reference point, and taking the intersection of each ray with the outer contour as the second effective coordinate point set; using the least squares circle fitting algorithm, performing circle center fitting on all coordinate points in the first effective coordinate point set and all coordinate points in the second effective coordinate point set, and taking the fitted circle center coordinates as the inner contour center and the outer contour center; calculating the Euclidean distance between the inner contour center and the outer contour center, and taking the Euclidean distance as the eccentricity difference.

[0060] Specifically, after obtaining the final geometric dimension dataset, the server needs to first determine the valid data points to be retained in the dataset. Because of the aforementioned global coarse filtering and local filtering, some abnormal data points in the original geometric dimension dataset have been removed, the number of data points retained in the final geometric dimension dataset may be less than the total number of preset circumferential points. The server iterates through the final thickness dataset and the final outer diameter dataset in the final geometric dimension dataset, extracting the circumferential index number corresponding to each retained data point. This circumferential index number has a one-to-one correspondence with the preset circumferential points. The server determines the corresponding preset circumferential points to be retained based on the circumferential index number and obtains the retention angle information corresponding to these preset circumferential points from the preset circumferential point configuration parameters. The retention angle information represents the circumferential angle of each preset circumferential point relative to the reference starting position of the cable cross-section, expressed in degrees or radians. This reference starting position is typically set to 0 degrees or 0 radians directly above the cable cross-section. For example, if the total number of preset circumference points is 12, that is, the circumference is divided into 12 equal parts, then the angle between adjacent points is 30 degrees. If 10 data points are retained after data filtering, the server extracts the angle information corresponding to these 10 retained preset circumference points, such as 0 degrees, 30 degrees, 60 degrees, 120 degrees, 150 degrees, 180 degrees, 210 degrees, 240 degrees, 270 degrees, and 300 degrees. Among them, the data points corresponding to 90 degrees and 330 degrees are removed due to anomalies.

[0061] After determining the retained angle information, the server needs to establish a spatial coordinate system for curve fitting calculations. Since the inner and outer contours of the insulation layer correspond to the outer surface of the conductor core and the outer surface of the insulation layer, respectively, and each has its own independent center position, the origin of the coordinate system needs to be determined separately for ray projection and intersection calculation. The server uses the theoretical center position of the conductor core in the cable cross-section image as the first reference point. This first reference point is selected based on the ideal condition that the conductor core should be located at the geometric center of the cable cross-section, and the inner contour should be distributed around the center of the conductor core. The server establishes a first Cartesian coordinate system with the first reference point as the origin and emits rays along each retained angle. The direction of each ray is determined by the corresponding retained angle information. The server calculates the intersection coordinates of each ray with the inner contour from the pixel coordinate data of the inner contour edge of the insulation layer extracted by the image processing and analysis unit. The server iterates through the inner contour edge pixels, and for each ray with an angle of θ, it selects edge pixels that satisfy a polar coordinate angle equal to or close to θ; these pixels are the intersection points of the ray and the inner contour. Since actual edge pixels may not fall exactly on the theoretical rays, the server uses an angle tolerance range for filtering. The angle tolerance is set to one-quarter of the angle between adjacent preset circumferential points. This tolerance parameter is derived from a comprehensive consideration of edge pixel dispersion and imaging accuracy. The server aggregates the coordinates of all intersections between rays and the inner contour to form a first effective coordinate point set. This first effective coordinate point set contains key spatial location information describing the shape of the inner contour of the insulating layer.

[0062] Using the same method, the server uses the theoretical center position of the insulation layer in the cable cross-section image as the second reference point. The selection of the second reference point is based on the principle that the outer contour of the insulation layer should be distributed around the geometric center of the entire cable. The server establishes a second Cartesian coordinate system with the second reference point as the origin, emits rays along each preserved angle, and calculates the intersection coordinates of each ray with the outer contour from the pixel coordinate data of the outer contour edge extracted by the image processing and analysis unit. The server uses the same angle tolerance filtering method as the first effective coordinate point set to collect the intersection coordinates of all rays with the outer contour to form a second effective coordinate point set. This point set contains key spatial location information describing the shape of the outer contour of the insulation layer. By emitting rays with the first and second reference points as origins respectively, the accuracy of the inner contour intersection points and the outer contour intersection points in their respective coordinate systems can be ensured, avoiding the influence of ray direction deviations that may exist in a single coordinate system on the intersection point calculation. It should be noted that the first and second reference points are in different positions in the same image coordinate system; the first reference point is the geometric center of the inner contour, and the second reference point is the center of the fitted circle of the outer contour. When performing circle fitting, the server first transforms each valid coordinate point from its local coordinate system to a unified image coordinate system. The specific transformation formula is as follows: For the first set of valid coordinate points, the image coordinates are (x1, y1) = (x... 基准1 +rcosθ,y 基准1 +rsinθ), for the second effective set of coordinate points, the image coordinates are (x2, y2) = (x... 基准2 +Rcosθ,y 基准2 +Rsinθ), where (x) 基准1 y 基准1 ) and (x 基准2 y 基准2 ) are the coordinates of the first reference point and the second reference point in the image coordinate system, respectively, and r and R are the radial distances of the inner and outer contours at the corresponding angles.

[0063] After obtaining the first and second sets of valid coordinate points, the server uses a least-squares circle fitting algorithm to calculate the center of the circle for each set. This algorithm can calculate the center coordinates and radius of the optimal fitted circle based on multiple discrete coordinate points, minimizing the sum of squared radial distances from all data points to the fitted circle. The server first executes the least-squares circle fitting algorithm on all coordinate points in the first set of valid coordinate points. Specifically, the calculation process involves setting the center coordinates of the fitted circle to (a, b) and the radius to r, establishing the objective function as the sum of squared differences between the distances from all coordinate points to the center and the radius, and obtaining a system of linear equations about a and b by taking the partial derivatives of the objective function with respect to the center coordinates a and b and setting the partial derivatives to zero. The server solves this system of linear equations to obtain the optimal center coordinates (a, b), which are used as the spatial location of the inner contour center of the insulation layer. Using the same least-squares circle fitting algorithm, the server calculates the center of the circle for all coordinate points in the second set of valid coordinate points, and the obtained optimal center coordinates are used as the spatial location of the outer contour center of the insulation layer. The least squares circle fitting algorithm is more robust than the simple geometric center calculation method. It can effectively reduce the impact of measurement errors of individual coordinate points on the calculation of the circle center position, especially when the contour shape has slight irregularities or measurement noise, it can still obtain a stable and reliable circle center position.

[0064] After determining the spatial coordinates of the inner and outer contour centers, the server calculates the Euclidean distance between the two centers as the eccentricity difference. This Euclidean distance directly reflects the degree of offset of the insulation layer relative to the conductor core. Specifically, if the coordinates of the inner contour center are (a1, b1) and the coordinates of the outer contour center are (a2, b2), the Euclidean distance calculation formula is √[(a2-a1)²+(b2-b1)²]. The server stores the calculated Euclidean distance as the eccentricity difference in the detection result data. The eccentricity difference is measured in millimeters or micrometers and can be directly compared with the allowable eccentricity value specified in cable industry standards. Compared to traditional single-point diameter measurement or finite-point averaging methods, the eccentricity difference calculation method based on curve fitting algorithms can more comprehensively and accurately reflect the true eccentricity of the insulation layer, effectively eliminating the influence of randomness in measurement point selection and individual outliers on eccentricity assessment.

[0065] S105: Determine the standard size mean based on the final geometric size dataset, and determine the dynamic tolerance threshold based on the standard size mean.

[0066] In step S105 above, the server calculates the arithmetic mean of the final thickness dataset in the final geometric dimension dataset. This involves summing the thickness values ​​of all retained data points and dividing by the total number of retained data points to obtain the standard thickness mean. Using the same method, the server processes the final outer diameter dataset to obtain the standard outer diameter mean. The standard thickness mean and standard outer diameter mean represent the average dimensional level of the cable insulation layer along the entire circumference, serving as benchmark reference values ​​for subsequent weak point identification.

[0067] The server encapsulates the standard thickness mean and standard outer diameter mean into a standard geometric mean for output. This standard geometric mean is not only used for subsequent weak point identification and judgment but also serves as a key quality indicator in the test report, facilitating comparison and verification with the nominal values ​​in cable manufacturing standards. The dynamic tolerance threshold is determined based on the standard dimensional mean, specifically including: obtaining the cable sample specification information of the cable to be tested; querying the corresponding nominal insulation thickness and standard maximum allowable thinning amount from a preset cable manufacturing standard database based on the specification information; calculating the derating based on the deviation mapping relationship between the standard maximum allowable thinning amount and the standard thickness mean, combined with a preset thickness safety margin, to obtain a preset thickness deviation ratio threshold; correcting for fluctuations based on the process extrusion fluctuation characteristics corresponding to the standard outer diameter mean, combined with a preset outer diameter safety margin, to obtain a preset outer diameter deviation ratio threshold; calculating the preset eccentricity tolerance threshold by combining the standard thickness mean and the preset maximum allowable eccentricity percentage; and outputting the preset thickness deviation ratio threshold, preset outer diameter deviation ratio threshold, and preset eccentricity tolerance threshold as dynamic tolerance thresholds.

[0068] Specifically, after determining the average standard dimensions, the server needs to calculate a dynamic tolerance threshold based on the average standard dimensions. This dynamic tolerance threshold is used to determine whether there are abnormal deviations in the measured data of each circumferential point during subsequent weak point identification. The tolerance threshold is dynamically determined based on the actual situation of the cable under test. The server obtains the cable sample specification information of the cable sample under test from the human-computer interaction unit or database. This specification information includes key parameters such as cable model, voltage rating, conductor cross-sectional area, and insulation material type. The server accesses a preset cable manufacturing standard database based on the specification information. This database pre-stores the nominal insulation thickness and standard maximum allowable thinning amount corresponding to different specifications of cables specified in different standards (such as national standard GB / T 5023.3, industry standards, or enterprise standards). The server queries the database to obtain the corresponding nominal insulation thickness and standard maximum allowable thinning amount through a specification matching algorithm.

[0069] For the thickness deviation threshold, the server divides the standard maximum allowable thinning amount by the standard average thickness to obtain the thickness deviation baseline ratio. To improve the safety margin of detection, the server introduces a preset thickness safety margin coefficient to derating the thickness deviation baseline ratio. The preset thickness safety margin coefficient is pre-set according to the safety requirements of the cable application. For ordinary civilian cables, it can be set to 0.8 to 0.9, and for high-voltage cables or applications with strict safety requirements, it can be set to 0.6 to 0.7. The server multiplies the thickness deviation baseline ratio by the preset thickness safety margin coefficient to obtain the preset thickness deviation ratio threshold. The preset thickness deviation ratio threshold is more stringent than the standard maximum allowable thinning ratio, enabling early warning and identification of potential weak points before the cable insulation thickness reaches the standard lower limit.

[0070] For example, for a PVC insulated power cable with a rated voltage of 0.6 / 1kV, if the standard maximum allowable thinning is 0.17 mm and the standard average thickness is 0.72 mm, then the thickness deviation benchmark ratio is 23.6%. If the preset thickness safety margin factor is set to 0.76, then the preset thickness deviation ratio threshold is 23.6% × 0.76 ≈ 18%.

[0071] For the outer diameter deviation threshold, the server queries a preset process fluctuation characteristic database based on the standard outer diameter average or calculates the corresponding process extrusion fluctuation characteristic value through interpolation. This database is established based on process statistics of cable manufacturers and stores typical process fluctuation values ​​corresponding to different outer diameter ranges. The server divides the process extrusion fluctuation characteristic value by the standard outer diameter average to obtain the outer diameter deviation benchmark ratio. To ensure the sensitivity of weak point identification while avoiding misjudging normal process fluctuations as abnormalities, the server introduces a preset outer diameter safety margin coefficient (usually set to 1.2 to 1.5) to correct the outer diameter deviation benchmark ratio, thus obtaining the preset outer diameter deviation ratio threshold.

[0072] For the eccentricity tolerance threshold, the server multiplies the average standard thickness by the preset maximum permissible eccentricity percentage to obtain the absolute value of the preset eccentricity tolerance threshold. The preset maximum permissible eccentricity percentage is determined according to the applicable cable manufacturing standards and is preset during system configuration or selected by the operator according to the testing requirements. For example, if the average standard thickness is 0.72 mm and the preset maximum permissible eccentricity percentage is 15%, then the preset eccentricity tolerance threshold is 0.108 mm.

[0073] For the eccentricity tolerance threshold, the server calculates the preset eccentricity tolerance threshold by comparing the standard average thickness with the preset maximum permissible eccentricity percentage. The preset maximum permissible eccentricity percentage is determined according to the eccentricity requirements in the cable manufacturing standards and is preset during system configuration or selected by the operator based on testing requirements. For example, if the standard average thickness is 0.72 mm and the preset maximum permissible eccentricity percentage is 15%, then the preset eccentricity tolerance threshold is 0.108 mm.

[0074] The server encapsulates the preset thickness deviation ratio threshold, preset outer diameter deviation ratio threshold, and preset eccentricity tolerance threshold into a dynamic tolerance threshold for output. The dynamic tolerance threshold comprehensively considers standard requirements, process characteristics, and safety margins, and can adapt to the testing needs of cables of different specifications, providing a scientific and reasonable basis for subsequent weak point identification.

[0075] S106: Based on the standard size mean, calculate the size deviation value corresponding to each measured value in the final geometric size dataset, and compare each size deviation value with the dynamic tolerance threshold to identify local size weak points; based on the comparison result of the eccentricity difference value and the dynamic tolerance threshold, identify the overall eccentricity weak defect of the cable cross section.

[0076] In the above S106, after the server completes the calculation of the standard size mean and dynamic tolerance threshold, it needs to perform point-by-point analysis on the measured data of each preset circumferential point in the final geometric size dataset to identify possible local dimensional weaknesses in the cable insulation layer in the circumferential direction. This is because even if the average thickness and average outer diameter of the cable insulation layer meet the standard requirements, the thickness of some local points being too thin or the outer diameter being abnormal may still lead to insufficient insulation strength in that area, becoming a weak link in electrical breakdown.

[0077] Furthermore, based on the standard size mean, the dimensional deviation value corresponding to each measured value in the final geometric size dataset is calculated, and each dimensional deviation value is compared with a dynamic tolerance threshold to identify local dimensional weaknesses. Specifically, this includes: calculating the absolute value of the thickness deviation between the measured thickness value corresponding to each reserved preset circumferential point and the standard thickness mean, based on the final thickness dataset in the final geometric size dataset; dividing the absolute value of the thickness deviation by the standard thickness mean to obtain the thickness deviation ratio value corresponding to each reserved preset circumferential point; comparing each thickness deviation ratio value with a preset thickness deviation ratio threshold; if the thickness deviation ratio value corresponding to the first reserved preset circumferential point is greater than the preset thickness deviation ratio threshold, then the first reserved preset circumferential point is marked as a local thickness weakness, and the first reserved preset circumferential point is the final thickness. The dataset contains multiple reserved preset circumferential points. Based on the final outer diameter dataset in the final geometric dimension dataset, the absolute value of the outer diameter deviation between the measured outer diameter value corresponding to each reserved preset circumferential point and the standard average outer diameter is calculated. The absolute value of the outer diameter deviation is then divided by the standard average outer diameter to obtain the outer diameter deviation ratio value corresponding to each reserved preset circumferential point. Each outer diameter deviation ratio value is compared with a preset outer diameter deviation ratio threshold. If the outer diameter deviation ratio value corresponding to the second reserved preset circumferential point is greater than the preset outer diameter deviation ratio threshold, the second reserved preset circumferential point is marked as a local outer diameter weak point. The second reserved preset circumferential point is any one of the multiple reserved preset circumferential points in the final outer diameter dataset. The local thickness weak point and the local outer diameter weak point are output as local dimensional weak points.

[0078] Specifically, the server extracts the final thickness dataset from the final geometric dimension dataset. This dataset contains the measured thickness values ​​corresponding to all reserved preset circumferential points, each with a corresponding relationship to a specific circumferential point and its angle information. The server calculates the absolute value of the thickness deviation between the measured thickness value for each reserved preset circumferential point and the previously calculated standard thickness average. This absolute deviation is obtained by subtracting the standard thickness average from the measured thickness value and taking the absolute value, reflecting the degree of deviation of the thickness at that point relative to the overall average level. To eliminate the influence of differences in nominal thickness between different cable specifications on deviation determination, the server divides the absolute value of the thickness deviation at each point by the standard thickness average to obtain the thickness deviation ratio for each reserved preset circumferential point. This ratio is expressed as a percentage, ensuring specification independence and comparability.

[0079] The server compares the calculated thickness deviation ratios with the previously determined preset thickness deviation ratio thresholds point by point. If the thickness deviation ratio corresponding to a preset circumferential point is greater than the preset thickness deviation ratio threshold, it indicates that the measured thickness at that point deviates from the average thickness beyond the safety margin, posing a risk of localized thinning of the insulation layer. The server marks this point as a localized thinning point and records detailed information such as the circumferential angle, measured thickness value, absolute thickness deviation, and thickness deviation ratio at that point.

[0080] For example, when inspecting a 0.6 / 1kV PVC insulated power cable, the circumference is divided into 12 preset circumferential points, with one measurement point set every 30 degrees. After data filtering, 10 valid points are retained. The standard average thickness is calculated to be 0.72 mm, and the preset thickness deviation ratio threshold is determined to be 18% after derating. During point-by-point comparison, the server finds that the measured thickness of the first preset circumferential point, located at a 150-degree angle, is 0.58 mm. The calculated absolute thickness deviation is 0.72 minus 0.58, which equals 0.14 mm. The thickness deviation ratio is 0.14 divided by 0.72, which is approximately 19.4%. This ratio is greater than the preset thickness deviation ratio threshold of 18%. Therefore, the server marks this 150-degree angle point as a local thinness point and marks it in red in the cable cross-section diagram of the inspection report.

[0081] Furthermore, if the thickness deviation ratio corresponding to a certain preset circumferential point is less than or equal to a preset thickness deviation ratio threshold, it indicates that the measured thickness at that point does not pose a risk of local thinning, i.e., it is in a normal state. The process then continues to compare the thickness deviation ratios corresponding to each preset circumferential point in the final thickness dataset with the preset thickness deviation ratio threshold until all preset circumferential points in the final thickness dataset have been compared, at which point the iteration operation stops. Using the same analysis and judgment logic, the server extracts the final outer diameter dataset from the final geometric dimension dataset. This dataset contains the measured outer diameter values ​​corresponding to all reserved preset circumferential points. The server calculates the absolute value of the outer diameter deviation between the measured outer diameter value corresponding to each reserved preset circumferential point and the standard average outer diameter, and divides the absolute value of the outer diameter deviation by the standard average outer diameter to obtain the outer diameter deviation ratio value corresponding to each reserved preset circumferential point. The server compares each outer diameter deviation ratio value with a preset outer diameter deviation ratio threshold point by point. If the outer diameter deviation ratio value corresponding to a certain reserved preset circumferential point is greater than the preset outer diameter deviation ratio threshold, it indicates that the fluctuation of the outer diameter dimension at that point relative to the average outer diameter has exceeded the normal process fluctuation range, and there may be abnormalities such as uneven extrusion of the insulation layer, material accumulation, or depressions. The server marks this point as a local outer diameter weak point and records the corresponding detailed information.

[0082] Continuing with the aforementioned cable inspection example, assuming the standard average outer diameter is calculated to be 6.8 mm, and the preset outer diameter deviation ratio threshold is determined to be 4.5% after process fluctuation correction, the server, during point-by-point comparison, found that the measured outer diameter of the second preset circumference point (located at a 270-degree angle) was 7.15 mm. The calculated absolute value of the outer diameter deviation was 7.15 minus 6.8, equaling 0.35 mm. The outer diameter deviation ratio was 0.35 divided by 6.8, approximately 5.1%, which is greater than the preset outer diameter deviation ratio threshold of 4.5%. Therefore, the server marked this 270-degree angle point as a local weak point in the outer diameter.

[0083] Furthermore, when the outer diameter deviation ratio corresponding to a certain reserved preset circumferential point is less than or equal to a preset outer diameter deviation ratio threshold, it is confirmed that there is no local outer diameter weak point. Then, the outer diameter deviation ratio corresponding to each reserved preset circumferential point in the final outer diameter dataset is compared with the preset outer diameter deviation ratio threshold until all reserved preset circumferential points in the final outer diameter dataset have been compared, at which point the traversal operation stops. The server aggregates all points marked as local thickness weak points and points marked as local outer diameter weak points, outputting them as local dimensional weak points. The local dimensional weak point output data structure includes complete information for each weak point, such as the circumferential angle, weak point type identifier, measured value, standard mean, absolute deviation value, deviation ratio, and whether it exceeds the standard, facilitating quality inspectors to quickly locate defects and assess their severity.

[0084] In the above example, the 150-degree angle point was identified as a local thickness weakness point and the 270-degree angle point was identified as a local outer diameter weakness point. The existence of these two weakness points indicates that the insulation layer covering quality of the cable in the circumferential direction is not uniform enough. This needs to be clearly pointed out in the test report, and the manufacturer should be advised to check the wear of the corresponding extrusion mold or adjust the extrusion process parameters.

[0085] After identifying local dimensional weak points, the server also needs to determine the overall eccentricity of the cable cross-section. Eccentricity of the insulation layer relative to the conductor core leads to uneven circumferential distribution of the insulation thickness. In severe cases of eccentricity, the insulation thickness at the thinnest point may not meet standard requirements. Furthermore, eccentricity defects are typically systematic and persistent; failure to detect and correct them promptly can result in safety hazards in a large number of cable products. Based on the comparison between the eccentricity difference and the dynamic tolerance threshold, the overall eccentricity weakness defect of the cable cross-section is identified. Specifically, this involves comparing the eccentricity difference with a preset eccentricity tolerance threshold. If the eccentricity difference exceeds the preset eccentricity tolerance threshold, the cable sample under test is determined to have an overall eccentricity weakness defect.

[0086] Specifically, the server compares the calculated eccentricity difference with a preset eccentricity tolerance threshold. The eccentricity difference is obtained by calculating the Euclidean distance between the inner and outer contour centers of the insulation layer using a least-squares circle fitting algorithm, which objectively reflects the overall eccentricity of the insulation layer. If the eccentricity difference is greater than the preset eccentricity tolerance threshold, it indicates that the offset distance between the centers of the inner and outer contours of the insulation layer has exceeded the standard allowable eccentricity range, and the server determines that the cable sample under test has an overall eccentricity weakness defect. Continuing with the above example, suppose the eccentricity difference calculated through curve fitting is 0.115 mm, while the preset eccentricity tolerance threshold calculated based on the standard average thickness of 0.72 mm and the preset maximum allowable eccentricity percentage of 15% is 0.108 mm. Since the eccentricity difference of 0.115 mm is greater than the preset eccentricity tolerance threshold of 0.108 mm, the server determines that the cable sample has an overall eccentric weakness defect, and clearly marks the eccentricity defect level as unqualified in the test report. At the same time, it marks the eccentricity direction as the offset angle of the inner contour center relative to the outer contour center. The offset angle is obtained by calculating the angle between the line connecting the centers of the inner and outer contours and the reference starting position of the cable section.

[0087] After identifying local dimensional weak points and determining overall eccentricity weak defects, the server classifies the identified weak defects according to a preset weak point classification rule. This classification rule comprehensively considers factors such as defect type, degree of exceeding standards, and safety margin. For local thickness weak points, if the thickness deviation ratio exceeds the preset thickness deviation ratio threshold but does not exceed the ratio corresponding to the standard maximum allowable thinning amount, it is marked as a general risk level; if it exceeds the ratio corresponding to the standard maximum allowable thinning amount, it is marked as a severe risk level. For local outer diameter weak points, if the outer diameter deviation ratio exceeds the preset outer diameter deviation ratio threshold by less than 1.5 times, it is marked as a general risk level; if it exceeds 1.5 times, it is marked as a severe risk level. For overall eccentricity weak defects, if the eccentricity difference exceeds the preset eccentricity tolerance threshold but does not exceed the absolute value corresponding to the standard maximum allowable eccentricity, it is marked as a general risk level; if it exceeds the standard maximum allowable eccentricity, it is marked as a severe risk level. In the aforementioned example, the thickness deviation ratio of 19.4% at the 150-degree corner point exceeded the preset threshold of 18%, but did not exceed the standard maximum allowable thinning amount of 23.6%, and was marked as a general risk level.

[0088] The server automatically integrates all weak point identification results and grading assessment information to generate a test report data package. This data package includes basic information about the cable sample, original measurement data, average standard dimensions, a detailed list of local dimensional weak points, overall eccentricity weak defect judgment results, weak point grading assessment results, and a schematic diagram of the cable cross-section. The cross-section diagram uses a visual method to show the distribution of weak points; local thickness weak points are marked with red triangles, and local outer diameter weak points are marked with yellow dots. The overall eccentricity is represented by arrows indicating the offset vectors of the inner and outer contour centers. The server binds the test report data package with the cable sample's unique identification code, test timestamp, testing equipment number, and operator information, storing it on the database server to establish a complete test data traceability chain, meeting the requirements of third-party quality inspection agencies for the traceability and compliance of test data.

[0089] This application also provides a system for identifying weak points in cable insulation. Figure 2 This is a schematic diagram of a system for identifying weak points in cable insulation provided in an embodiment of this application. (Refer to...) Figure 2 The system includes a receiving unit 201, a processing unit 202, and an identification unit 203. The receiving unit 201 acquires multiple cross-sectional images of the cable sample under test at multiple preset circumferential points; Processing unit 202 extracts data from multiple cross-sectional images to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point; calculates the median absolute deviation based on the original geometric dimension dataset; performs coarse filtering on the original geometric dimension dataset based on the median absolute deviation; calculates the local variation index of the degree of abrupt change between adjacent data points based on the coarsely filtered dataset for local filtering to obtain the final geometric dimension dataset; performs curve fitting calculation based on the angle information corresponding to each preset circumferential point and the final geometric dimension dataset to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer; determines the standard dimension mean based on the final geometric dimension dataset; and determines the dynamic tolerance threshold based on the standard dimension mean. The identification unit 203 calculates the size deviation value corresponding to each measured value in the final geometric size dataset based on the standard size mean, and compares each size deviation value with the dynamic tolerance threshold to identify local size weak points; based on the comparison result of the eccentricity difference value and the dynamic tolerance threshold, it identifies the overall eccentricity weak defect of the cable cross section.

[0090] In one possible implementation, the processing unit 202 is used to preprocess each cross-sectional image sequentially, and extract the inner and outer contours of the insulating layer based on edge detection; calculate the geometric center of the inner contour as the first reference point, emit multiple thickness measurement rays from the first reference point along the angles corresponding to each preset circumferential point, calculate the distance between the intersection points of each thickness measurement ray and the inner and outer contours, obtain the measured thickness values ​​corresponding to each preset circumferential point, and collect them to form an original thickness dataset; perform preliminary circle fitting on the outer contour to obtain the preliminary center coordinates of the outer contour as the second reference point; draw multiple outer diameter measurement lines through the second reference point along the angles corresponding to each preset circumferential point, calculate the distance between the two intersection points of each outer diameter measurement line and the outer contour, obtain the measured outer diameter values ​​corresponding to each preset circumferential point, and collect them to form an original outer diameter dataset; output the original outer diameter dataset and the original thickness dataset together as the original geometric dimension dataset.

[0091] In one possible implementation, the processing unit 202 is used to take the original thickness dataset and the original outer diameter dataset from the original geometric dimension dataset as the current dataset to be processed; calculate the first median of the current dataset to be processed, and calculate the absolute deviation between each measured value in the current dataset to be processed and the first median, and obtain the median of all absolute deviations to obtain the first median absolute deviation; calculate the correction score of each data point in the current dataset to be processed based on the first median and the first median absolute deviation; remove data points from the current dataset to be processed whose absolute value of the correction score is greater than a preset global threshold to obtain an intermediate subset; extract the local variation index between adjacent data points in the intermediate subset, and perform local filtering on the intermediate subset based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset, and combine the final thickness dataset and the final outer diameter dataset to form the final geometric dimension dataset.

[0092] In one possible implementation, the processing unit 202 is used to calculate the local variation index corresponding to each data point in the intermediate subset to obtain a geometric size local variation index dataset, wherein the local variation index represents the degree of size mutation between the current data point and its adjacent data points; calculate the second median of the geometric size local variation index dataset, and calculate the local absolute deviation between each local variation index in the geometric size local variation index dataset and the second median, and obtain the median of all local absolute deviations to obtain the second median absolute deviation; calculate the local variation threshold based on the second median, the second median absolute deviation, and a preset sensitivity coefficient; mark the data points in the geometric size local variation index dataset whose local variation index is greater than the local variation threshold as local outliers, and remove all local outliers from the intermediate subset to obtain the corresponding final thickness dataset or final outer diameter dataset.

[0093] In one possible implementation, the processing unit 202 is used to determine the corresponding reserved preset circumferential points and corresponding reserved angle information based on the data points retained in the final geometric dimension dataset; emitting rays along each reserved angle information relative to the first reference point, and taking the intersection of each ray with the inner contour as the first effective coordinate point set; emitting rays along each reserved angle information relative to the second reference point, and taking the intersection of each ray with the outer contour as the second effective coordinate point set; using the least squares circle fitting algorithm, performing circle center fitting on all coordinate points in the first effective coordinate point set and all coordinate points in the second effective coordinate point set respectively, and taking the fitted circle center coordinates as the inner contour center and the outer contour center; calculating the Euclidean distance between the inner contour center and the outer contour center, and taking the Euclidean distance as the eccentricity difference.

[0094] In one possible implementation, the receiving unit 201 is used to acquire the cable sample specification information of the cable sample to be tested, and query the corresponding nominal insulation thickness and standard maximum allowable thinning amount from a preset cable manufacturing standard database according to the specification information; the processing unit 202 is used to perform derating calculation based on the deviation mapping relationship between the standard maximum allowable thinning amount and the standard thickness average value, combined with the preset thickness safety margin, to obtain a preset thickness deviation ratio threshold; based on the process extrusion fluctuation characteristics corresponding to the standard outer diameter average value, combined with the preset outer diameter safety margin, fluctuation correction is performed to obtain a preset outer diameter deviation ratio threshold; the standard thickness average value and the preset maximum allowable eccentricity percentage are calculated to obtain a preset eccentricity tolerance threshold; and the preset thickness deviation ratio threshold, the preset outer diameter deviation ratio threshold, and the preset eccentricity tolerance threshold are output as dynamic tolerance thresholds.

[0095] In one possible implementation, the identification unit 203 is configured to calculate, based on the final thickness dataset in the final geometric dimension dataset, the absolute value of the thickness deviation between the measured thickness value corresponding to each reserved preset circumferential point and the standard average thickness, and divide the absolute value of the thickness deviation by the standard average thickness to obtain the thickness deviation ratio value corresponding to each reserved preset circumferential point; compare each thickness deviation ratio value with a preset thickness deviation ratio threshold; if the thickness deviation ratio value corresponding to the first reserved preset circumferential point is greater than the preset thickness deviation ratio threshold, then mark the first reserved preset circumferential point as a local thickness weak point, where the first reserved preset circumferential point is any one of the multiple reserved preset circumferential points in the final thickness dataset; and calculate the thickness deviation ratio between each reserved preset circumferential point based on the final outer diameter dataset in the final geometric dimension dataset. The absolute value of the outer diameter deviation between the measured outer diameter value and the standard average outer diameter is calculated, and the absolute value of the outer diameter deviation is divided by the standard average outer diameter to obtain the outer diameter deviation ratio value corresponding to each reserved preset circumferential point. Each outer diameter deviation ratio value is compared with a preset outer diameter deviation ratio threshold. If the outer diameter deviation ratio value corresponding to the second reserved preset circumferential point is greater than the preset outer diameter deviation ratio threshold, the second reserved preset circumferential point is marked as a local outer diameter weak point. The second reserved preset circumferential point is any one of the multiple reserved preset circumferential points in the final outer diameter dataset. The local thickness weak points and local outer diameter weak points are output as local size weak points. The eccentricity difference value is compared with a preset eccentricity tolerance threshold. If the eccentricity difference value is greater than the preset eccentricity tolerance threshold, it is determined that the cable sample under test has an overall eccentricity weak defect.

[0096] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0097] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This application provides a schematic diagram of the structure of an electronic device. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0098] The communication bus 305 is used to enable communication between these components.

[0099] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0100] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0101] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 302, and by calling data stored in memory 302. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and application requests; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0102] The memory 302 may include random access memory (RAM) or read-only memory. Optionally, the memory 302 may include a non-transitory computer-readable storage medium. The memory 302 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 302 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may store data involved in the various method embodiments described above. Optionally, the memory 302 may also be at least one storage device located remotely from the aforementioned processor 301.

[0103] like Figure 3 As shown, the memory 302, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for identifying weak points and defects in cable insulation.

[0104] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program for identifying weak point defects in cable insulation stored in the memory 302. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0111] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practical application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for identifying weak points in cable insulation, characterized in that, The method includes: Acquire multiple cross-sectional images of the cable sample under test at multiple preset circumferential points; Extract from multiple cross-sectional images to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each of the preset circumferential points; The median absolute deviation is calculated based on the original geometric size dataset. The original geometric size dataset is then coarsely filtered based on the median absolute deviation. The local variation index of the degree of abrupt change between adjacent data points is calculated based on the coarsely filtered dataset to perform local filtering, thereby obtaining the final geometric size dataset. Based on the angle information corresponding to each preset circumferential point, and combined with the final geometric size dataset, curve fitting calculation is performed to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulating layer. The standard size mean is determined based on the final geometric size dataset, and the dynamic tolerance threshold is determined based on the standard size mean. Based on the standard size mean, the size deviation value corresponding to each of the measured values ​​in the final geometric size dataset is calculated, and each of the size deviation values ​​is compared with the dynamic tolerance threshold to identify local size weak points; Based on the comparison between the eccentricity difference and the dynamic tolerance threshold, the overall eccentricity weakness defect of the cable cross section is identified.

2. The method according to claim 1, characterized in that, The step of extracting data from multiple cross-sectional images to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each of the preset circumferential points specifically includes: Each of the cross-sectional images is preprocessed sequentially, and the inner and outer contours of the insulating layer are extracted based on edge detection; The geometric center of the inner contour is calculated as the first reference point. Multiple thickness measurement rays are emitted from the first reference point along the angles corresponding to each of the preset circumferential points. The distance between the intersection points of each thickness measurement ray and the inner contour and the outer contour is calculated to obtain the measured thickness value corresponding to each preset circumferential point and to collect them to form the original thickness dataset. A preliminary circle fit is performed on the outer contour to obtain the preliminary center coordinates of the outer contour as the second reference point; Multiple outer diameter measurement lines are drawn along the angles corresponding to each of the preset circumferential points through the second reference point. The distance between the two intersection points of each outer diameter measurement line and the outer contour is calculated to obtain the measured outer diameter value corresponding to each preset circumferential point and to collect them to form the original outer diameter dataset. The original outer diameter dataset and the original thickness dataset are combined and output as the original geometric dimension dataset.

3. The method according to claim 2, characterized in that, The process of calculating the median absolute deviation based on the original geometric size dataset, coarsely filtering the original geometric size dataset based on the median absolute deviation, and calculating the local variation index of the degree of abrupt change between adjacent data points based on the coarsely filtered dataset for local filtering to obtain the final geometric size dataset specifically includes: The original thickness dataset and the original outer diameter dataset in the original geometric dimension dataset are respectively used as the current dataset to be processed; Calculate the first median of the current dataset to be processed, and calculate the absolute deviation between each measured value in the current dataset to be processed and the first median. Find the median of all the absolute deviations to obtain the absolute deviation of the first median. Based on the first median and the absolute deviation of the first median, calculate the corrected score for each data point in the current dataset to be processed; Remove data points from the current dataset to be processed whose absolute value of the corrected score is greater than a preset global threshold to obtain an intermediate subset dataset; Extract the local variation index between adjacent data points in the intermediate subset, and perform local filtering on the intermediate subset based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset. Combine the final thickness dataset and the final outer diameter dataset to form the final geometric dimension dataset.

4. The method according to claim 3, characterized in that, The step of extracting the local variation index between adjacent data points in the intermediate subset, and performing local filtering on the intermediate subset based on the local variation index to obtain the corresponding final thickness dataset or final outer diameter dataset, specifically includes: Calculate the local variation index corresponding to each data point in the intermediate subset to obtain the geometric size local variation index dataset. Calculate the second median of the local variation index dataset of geometric dimensions, and calculate the local absolute deviation between each local variation index in the local variation index dataset of geometric dimensions and the second median. Find the median of all the local absolute deviations to obtain the second median absolute deviation. The local variation threshold is calculated based on the second median, the absolute deviation of the second median, and the preset sensitivity coefficient. Data points in the geometric dimension local variation index dataset whose local variation index is greater than the local variation threshold are marked as local outliers, and all local outliers are removed from the intermediate subset dataset to obtain the corresponding final thickness dataset or the final outer diameter dataset.

5. The method according to claim 3, characterized in that, The step of calculating the eccentricity difference between the outer contour center and the inner contour center of the insulating layer by combining the angle information corresponding to each of the preset circumferential points with the final geometric dimension dataset includes: Based on the data points retained in the final geometric dimension dataset, determine the corresponding preset circumferential points to be retained and the corresponding angle information to be retained; Relative to the first reference point, rays are emitted along each of the preserved angle information, and the intersections of each ray with the inner contour are taken as the first effective coordinate point set; Relative to the second reference point, rays are emitted along each of the preserved angle information, and the intersections of each ray with the outer contour are taken as the second effective coordinate point set; Using the least squares circle fitting algorithm, circle centers are fitted to all coordinate points in the first set of effective coordinate points and all coordinate points in the second set of effective coordinate points, respectively. The fitted circle center coordinates are used as the inner contour center and the outer contour center. Calculate the Euclidean distance between the center of the inner contour and the center of the outer contour, and use the Euclidean distance as the eccentricity difference.

6. The method according to claim 1, characterized in that, The average standard dimensions include the average standard thickness and the average standard outer diameter. A dynamic tolerance threshold is determined based on the average standard dimensions, specifically including: Obtain the cable sample specification information of the cable sample to be tested, and query the corresponding nominal insulation thickness and standard maximum allowable thinning amount from the preset cable manufacturing standard database according to the specification information; Based on the deviation mapping relationship between the standard maximum allowable thinning amount and the standard average thickness, and combined with the preset thickness safety margin, the derating calculation is performed to obtain the preset thickness deviation ratio threshold. Based on the process extrusion fluctuation characteristics corresponding to the standard outer diameter average value, fluctuation correction is performed in combination with the preset outer diameter safety margin to obtain the preset outer diameter deviation ratio threshold. The preset eccentricity tolerance threshold is obtained by calculating the average standard thickness and the preset maximum allowable eccentricity percentage. The preset thickness deviation ratio threshold, the preset outer diameter deviation ratio threshold, and the preset eccentricity tolerance threshold are output as the dynamic tolerance threshold.

7. The method according to claim 6, characterized in that, Based on the standard size mean, the dimensional deviation value corresponding to each of the measured values ​​in the final geometric size dataset is calculated, and each of the dimensional deviation values ​​is compared with the dynamic tolerance threshold to identify local dimensional weaknesses. Based on the comparison between the eccentricity difference and the dynamic tolerance threshold, the overall eccentricity weakness defect of the cable cross-section is identified, specifically including: Based on the final thickness dataset in the final geometric dimension dataset, calculate the absolute value of the thickness deviation between the measured thickness value corresponding to each reserved preset circumferential point and the standard average thickness value, and divide the absolute value of the thickness deviation by the standard average thickness value to obtain the thickness deviation ratio value corresponding to each reserved preset circumferential point. Each of the thickness deviation ratio values ​​is compared with the preset thickness deviation ratio threshold. If the thickness deviation ratio value corresponding to the first reserved preset circumferential point is greater than the preset thickness deviation ratio threshold, then the first reserved preset circumferential point is marked as a local thickness weak point. The first reserved preset circumferential point is any one of the multiple reserved preset circumferential points in the final thickness dataset. Based on the final outer diameter dataset in the final geometric dimension dataset, calculate the absolute value of the outer diameter deviation between the measured outer diameter value corresponding to each reserved preset circumference point and the average standard outer diameter, and divide the absolute value of the outer diameter deviation by the average standard outer diameter to obtain the outer diameter deviation ratio value corresponding to each reserved preset circumference point. The outer diameter deviation ratio values ​​are compared with the preset outer diameter deviation ratio threshold. If the outer diameter deviation ratio value corresponding to the second reserved preset circumferential point is greater than the preset outer diameter deviation ratio threshold, the second reserved preset circumferential point is marked as a local outer diameter weak point. The second reserved preset circumferential point is any one of the multiple reserved preset circumferential points in the final outer diameter dataset. The local thickness weak points and the local outer diameter weak points are output as the local size weak points; The eccentricity difference is compared with the preset eccentricity tolerance threshold. If the eccentricity difference is greater than the preset eccentricity tolerance threshold, it is determined that the cable sample under test has the overall eccentricity weakness defect.

8. A system for identifying weak points in cable insulation, characterized in that, The system includes a receiving unit, a processing unit, and an identification unit. The receiving unit acquires multiple cross-sectional images of the cable sample under test at multiple preset circumferential points. The processing unit extracts data from multiple cross-sectional images to obtain an original geometric dimension dataset containing the measured values ​​corresponding to each preset circumferential point. The median absolute deviation is calculated based on the original geometric size dataset. The original geometric size dataset is then coarsely filtered based on the median absolute deviation. The local variation index of the degree of abrupt change between adjacent data points is calculated based on the coarsely filtered dataset for local filtering to obtain the final geometric size dataset. Based on the angle information corresponding to each of the preset circumferential points, curve fitting is performed in conjunction with the final geometric size dataset to obtain the eccentricity difference between the outer contour center and the inner contour center of the insulation layer. The standard size mean is determined based on the final geometric size dataset, and the dynamic tolerance threshold is determined based on the standard size mean. The identification unit calculates the size deviation value corresponding to each measured value in the final geometric size dataset based on the standard size mean, and compares each size deviation value with the dynamic tolerance threshold to identify local size weak points; based on the comparison result of the eccentricity difference value and the dynamic tolerance threshold, it identifies the overall eccentricity weak defect of the cable cross section.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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