Multi-view detection method for distribution and bridging degree of rain ice on suspension insulator and related equipment

Abstract

This application provides a multi-view detection method and related equipment for the distribution and bridging degree of frost on suspension insulators, belonging to the field of intelligent monitoring technology for transmission lines. The method includes: extracting and registering the foreground of the insulator before and after icing from a group of images taken from multiple perspectives; locating the axial position range of each umbrella unit using the axial gradient distribution features of the ice-free reference image; extracting and quantifying the ice edge using the circumferential gradient saliency features of the iced image; defining and calculating multi-dimensional parameters such as the circumferential / axial distribution non-uniformity of the ice, the bridging degree of local umbrella units, and the overall bridging degree of the insulator string; and finally comprehensively evaluating the uniformity of the icing distribution and the bridging risk level, outputting a quantitative report and operation and maintenance decision recommendations. This application effectively overcomes the detection challenges of variable perspectives and complex lighting conditions in natural scenes through bidirectional gradient feature collaboration and multi-view adaptation mechanisms, providing accurate and reliable technical support for power grid icing prevention and disaster reduction.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring and image recognition technology for transmission line equipment, and in particular to a multi-view detection method and related equipment for the distribution of frost accretion and bridging degree of suspension insulators. Background Technology

[0002] In the low-temperature, high-humidity environment of winter or early spring, freezing rain icing easily forms on the surface of transmission line insulators. This type of icing, characterized by its high density and strong adhesion, is the most hazardous. For suspension insulators, where the skirts are stacked vertically, the icicles formed by freezing rain are more likely to grow under gravity, bridging the air gaps between adjacent skirts. This significantly reduces the electrical strength of the insulator, greatly increasing the risk of icing flashover accidents and seriously threatening the safe and stable operation of the power grid.

[0003] Accurately sensing the icing status of insulators, especially quantifying the spatial distribution and bridging degree of ice, is crucial for preventing ice disasters and guiding de-icing maintenance. Traditional detection methods mainly rely on manual inspections or simple image thresholding, which are difficult to adapt to complex and changing natural environments in the field (such as changes in lighting, differences in shooting angles, background interference, etc.), and cannot accurately quantify the distribution characteristics and bridging risks of ice.

[0004] In existing technologies, there are some studies on insulator icing detection based on image processing. For example, some methods attempt to identify icicle outlines using edge detection operators (such as the Canny operator), but their performance is unstable under complex backgrounds and weak edge conditions, and they have difficulty distinguishing between dense or adhered icicles. Other methods identify icing areas by training deep learning models, but the models have poor interpretability, and the outputs are mostly binary classifications (ice-covered / ice-free) or coarse icing area percentages, failing to provide fine-grained parameters crucial for risk assessment, such as icicle length, orientation, distribution uniformity, and specific bridging degree. Furthermore, most methods assume a fixed or near-normal shooting angle, failing to consider the possibility of drones or fixed cameras shooting from different angles during actual inspections, leading to significant deviations in detection results due to changes in viewing angle. Summary of the Invention

[0005] The main objective of this application is to propose a multi-view detection method, electronic device, storage medium, and program product for the distribution and bridging degree of frost accretion on suspension insulators based on gradient dual features. This method can robustly locate insulator umbrella units from images taken in natural scenes, accurately extract ice features, and calculate multi-dimensional quantitative indicators to evaluate the spatial distribution uniformity of frost accretion and the bridging risk level, providing accurate and reliable technical support for power grid icing prevention and disaster reduction decisions.

[0006] To achieve the above objectives, one aspect of this application proposes a multi-view detection method for the distribution of frost accretion and bridging degree of suspension insulators, the method comprising: From a group of images taken by the same terminal, the foreground of the insulator strings before and after frost and ice accumulation is extracted and registered and aligned. By utilizing the axial gradient distribution characteristics of ice-free reference images, the axial position range of each umbrella unit of the insulator string can be located from different viewpoints. By utilizing the circumferential gradient significance features of the rime ice-covered image, the edge of the ice crystals is extracted, and the projected length and direction of the ice crystals are quantified. Based on the above positioning and detection results, the parameters of circumferential distribution non-uniformity, axial distribution non-uniformity, local umbrella unit bridging degree, and overall insulator string bridging degree are calculated. Based on the calculated parameters, the spatial non-uniformity of rime ice distribution and the risk of air gap bridging between umbrellas are quantitatively assessed.

[0007] In some embodiments, extracting the foreground of the insulator strings before and after icing from a group of images captured from the same terminal and performing registration and alignment includes: For both ice-free reference images and ice-covered images to be tested, insulator identification is performed using a target recognition model to obtain their respective vertical rectangular recognition boxes; Using the obtained rectangular recognition box as the region of interest, a semantic segmentation model is used to extract the foreground images of ice-free insulators and ice-covered insulators; Ellipse fitting is performed on the foreground image of the ice-free insulator to obtain the image axis, low-voltage end vertex, and high-voltage end vertex of the insulator; Rotate the foreground image of the rime-covered insulator around the center of the fitted ellipse so that its image axis is parallel to the image axis of the foreground image of the ice-free insulator, and align the low-voltage end vertices of the two to complete the registration.

[0008] In some embodiments, the method of locating the axial position range of each umbrella unit of the insulator string from different viewpoints by utilizing the axial gradient distribution characteristics of the ice-free reference image includes: The Sobel vertical operator is used to calculate the axial gradient map of the registered ice-free insulator foreground image. Multi-scale Gaussian filtering is used to smooth the axial gradient map to obtain a multi-scale gradient map; The multi-scale gradient map is summed row by row to generate an initial gradient curve, which is then smoothed. Based on the insulator material, local maxima or minima are detected on the smoothed gradient curve. The row index of the extreme points is used to determine the axial position range of each umbrella unit. Based on the axial ratio between ice-free insulators and frost-covered insulators, the position range of umbrella unit of ice-free insulators is mapped onto the image of frost-covered insulators.

[0009] In some embodiments, detecting local maxima or local minima on the smoothed gradient curve based on the insulator material includes: If it is a composite insulator, then detect the local maximum point on the smooth gradient curve, and its corresponding eave position; If it is a glass or porcelain insulator, then detect the local minimum point on the smooth gradient curve, and the corresponding umbrella unit center position. The insulator material is determined by the percentage of pixels in the foreground image of the ice-free insulator that meet the preset red threshold range.

[0010] In some embodiments, the step of extracting the ice edge and quantifying the projected length and direction of the ice using the circumferential gradient saliency features of the image of the ice-covered rime includes: For the registered foreground image of the rime-covered insulator, the Sobel horizontal operator is used to calculate its circumferential gradient map; Adaptive binarization and morphological restoration are performed on the circumferential gradient map to obtain the ice crystal edge map; Connected components are extracted from the ice crystal edge map, and the contour of each connected component is simplified and fitted with the minimum bounding rectangle. Valid icicle rectangles are selected based on a preset aspect ratio threshold, and the projection length of each valid icicle rectangle on the insulator image axis is calculated as the icicle projection length. Based on the coordinates of the center point of the icicle, determine its corresponding umbrella unit index and whether it is located on the left or right side of the insulator image axis.

[0011] In some embodiments, the threshold for adaptive binarization is dynamically determined based on the gradient extrema of the circumferential gradient map; the morphological repair employs a dilation-then-erosion operation, with the structural element being "+" shaped.

[0012] In some embodiments, the calculation of the circumferential distribution non-uniformity of the ice crystals includes: for each umbrella unit, calculating the sum of the projected lengths of all ice crystals on the left and right sides of its axis, and calculating the circumferential distribution parameters of the umbrella unit based on the difference in lengths on both sides; The calculation of the axial distribution non-uniformity of the ice crystals includes: determining the icing state of each umbrella unit based on whether there are ice crystals, and calculating the axial distribution parameters based on the number or distribution characteristics of continuously icy umbrella units. The calculation of the bridging degree of the local umbrella unit includes: for each umbrella unit, taking the ratio of the projected length of the longest icicle inside it to the distance between the umbrella units; The calculation of the overall bridging degree of the insulator string includes: scanning along the insulator axis, identifying "ice strips" with continuously distributed ice pixels, and calculating the ratio of the total height of all ice strips to the total length of the insulator string image.

[0013] In some embodiments, the step of quantitatively assessing the spatial non-uniformity of rime ice distribution and the risk of inter-umbrella air gap bridging based on calculated parameters includes: Based on the circumferential and axial unevenness of the ice distribution, a comprehensive distribution index is calculated, and the uniformity level of the spatial distribution of rime ice is evaluated accordingly. Risk classification is performed based on the local bridging degree value of each umbrella unit, and the bridging risk index of a single umbrella unit is calculated. The overall bridging risk level of the entire insulator string is assessed based on the overall bridging degree value of the insulator string. Calculate the spatial clustering index of medium and high bridging risk umbrella units; Based on the overall bridging risk level, umbrella unit bridging risk distribution, and spatial clustering index, a comprehensive bridging risk index is calculated, and corresponding operation and maintenance decision recommendations are output.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0017] Compared with the prior art, the present invention has the following significant advantages: 1) Robust and adaptable to multiple perspectives: By using the registration mechanism between the ice-free reference image and the image under test, and the umbrella unit mapping method based on axial proportion, the problem of inconsistent detection benchmarks caused by changes in shooting angle and distance is effectively overcome, and the applicability of the method in real inspection scenarios is improved.

[0018] 2) High detection accuracy: It creatively combines axial and circumferential gradient features. The axial gradient feature is sensitive to the periodic structure of the insulator skirt, enabling precise positioning of the umbrella unit; the circumferential gradient feature strongly responds to the edge of the icicle perpendicular to the axis, effectively extracting icicles with different orientations and transparency. The combination of the two overcomes the limitations of single features in complex scenarios.

[0019] 3) Comprehensive and precise assessment dimensions: It not only detects the presence or absence of icing, but also defines multi-dimensional quantitative indicators such as circumferential / axial distribution non-uniformity, local / overall bridging degree, and risk clustering index. These indicators provide a refined and structured description of the icing state from both spatial distribution and electrical risk perspectives, far exceeding traditional binary classification or area proportion assessments.

[0020] 4) High level of intelligence and practicality: Intelligent image processing technologies such as material adaptive judgment, gradient adaptive thresholding, and morphological restoration are introduced to improve the stability of the algorithm under different operating conditions. The final output is a comprehensive report containing specific parameters, risk levels, and clear operation and maintenance recommendations, which can be directly used to guide power grid production and operation, demonstrating high practical value.

[0021] 5) Provide support for preventive maintenance: By quantifying bridging risks and identifying risk clusters, early warnings can be issued before severe bridging or even flashover occurs in insulators, realizing the transformation from "post-event maintenance" to "pre-event prevention" and improving the power grid's proactive defense capabilities against freezing disasters. Attached Figure Description

[0022] Figure 1 This is a flowchart of a multi-view detection method for the distribution of frost accretion and bridging degree of suspension insulators provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of the steps and modules of the multi-view detection method for the distribution of frost accretion and bridging degree of suspension insulators provided in the embodiments of this application.

[0024] Figure 3 This is a schematic diagram comparing the circumferential gradient and axial significance of insulator umbrella units of different materials provided in the embodiments of the present invention.

[0025] Figure 4 This is a flowchart of the insulator image umbrella unit localization and mapping based on axial gradient distribution provided in an embodiment of the present invention.

[0026] Figure 5 This is a schematic diagram of the ice crystal edge extraction and fitting process based on circumferential gradient significance provided in an embodiment of the present invention.

[0027] Figure 6This is a schematic diagram of the image icicle distribution of the insulator string and its umbrella unit provided in an embodiment of the present invention, wherein (a) is an example of circumferential distribution and (b) is an example of axial distribution.

[0028] Figure 7 This is a schematic diagram of the calculation of the overall bridging degree of insulator string frost accretion provided by the multi-view shooting of the embodiment of the present invention, wherein (a) is the front view and (b) is the top view.

[0029] Figure 8 This is a block diagram of a detection system provided in an embodiment of this application.

[0030] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the scope of this application.

[0033] The distribution and length of icicles are related to the electrical performance of insulators. However, the difficulty in image detection of insulator icing distribution and bridging degree lies in the segmentation of the skirts and the quantitative representation of icicles. Although some methods can identify insulators from cluttered backgrounds and provide their degree of icing, it is difficult to quantitatively assess the icing state of insulators. In recent years, some experts and scholars have made beneficial attempts to address these issues. For example, one existing technical solution proposes to identify the edges of icicles on the windward side of the insulator through image enhancement and iterative Canny edge detection algorithm. By comparing the number of pixels of the icicles in the image with the length of the insulator, the maximum length of icicles between the insulator skirts is calculated. However, this technology cannot be directly applied to natural backgrounds, and the iterative Canny edge detection algorithm has difficulty detecting multiple icicles hanging from the edges of the skirts, thus its practicality needs to be improved. Therefore, how to accurately represent and efficiently identify the skirts and icicles hanging from the edges of icy insulators, and quantitatively assess the distribution and degree of icing on the insulator surface, has become a key technical problem that urgently needs to be solved in the field of current power transmission line equipment condition sensing technology.

[0034] In view of this, this application provides a multi-view detection method, electronic device, storage medium, and program product for frost distribution and bridging degree of suspension insulators, to solve the problems of poor view adaptability, inaccurate icing detection, and unsystematic risk assessment in the prior art, and to improve the accuracy and reliability of icing state perception.

[0035] The multi-view detection method for frost distribution and bridging degree of suspension insulators provided in this application relates to the field of intelligent monitoring technology for transmission line equipment. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the multi-view detection method for frost distribution and bridging degree of suspension insulators, but is not limited to the above forms.

[0036] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0037] like Figure 1 and Figure 2 As shown, this embodiment provides a multi-view detection method for the distribution of frost accretion and bridging degree of suspension insulators, including the following steps: Step S1: Image Preprocessing and Registration. From a group of images captured by the same terminal (e.g., a drone or a fixed camera), the foreground of the insulator string before and after icing is extracted, and precise registration and spatial alignment are performed through geometric transformation. Specifically, this includes: using object recognition models (e.g., YOLO, Faster R-CNN, etc.) to locate the insulators in both the ice-free reference image and the icing-covered image to obtain their bounding boxes; using semantic segmentation models (e.g., U-Net, DeepLab, etc.) to extract the insulator foreground within the bounding boxes; performing ellipse fitting on the ice-free insulator foreground to determine its image axis and high / low voltage endpoints; and performing rotation and translation transformations on the icing-covered insulator foreground according to the fitting parameters to align it with the ice-free reference foreground on the axis and at the low voltage end, eliminating the influence of perspective and position differences.

[0038] In one embodiment, step S1 specifically includes the following steps S11-S13: S11. Insulator image classification and recognition with and without ice and rime ice from the same viewing angle: Refer to the images without ice respectively. Images of rime ice to be tested The pre-trained target recognition model is used for processing, and the vertical rectangular recognition box of the insulator in both un-iced and icy conditions is output. , .in For identifying ice-free insulators, This is a frame for identifying insulators covered in frost and ice.

[0039] S12. Insulator Foreground Segmentation: Using a pre-trained semantic segmentation model, the rectangular bounding boxes obtained in the previous steps are segmented... For the region of interest, the image content within the region is segmented pixel-wise. This is done within the vertical rectangular bounding box. , The inner image region classifies the foreground and background, outputting an ice-free reference image. binary foreground mask Images of rime ice to be tested binary foreground mask In a binary foreground mask, pixels with a mask value of 1 are foreground pixels of the insulator, while pixels with a mask value of 0 belong to the background. (Image of an ice-free insulator foreground) Foreground image of rime ice-covered insulators The extraction calculation formula is:

[0040] In the formula, Images representing insulators without ice or insulators covered with frost.

[0041] S13. Insulator ellipse fitting, including: S131, Foreground image of ice-free insulators Extract its outer contour point set The equation of the elliptic curve is fitted using the least squares method:

[0042] In the formula, A , B , C , D , E , F All of these are coefficients of the elliptic algebraic equation.

[0043] S132. The coordinates of the ellipse center can be calculated from the parameters of the ellipse equation. Major axis tilt angle Major axis length and minor axis length The calculation formula is:

[0044]

[0045] , S133. Take the principal axis of the fitted ellipse of the ice-free insulator as the image axis of the insulator, and its two endpoints are respectively... and Compare two points Coordinates, take The upper vertex with the smaller value is the low-pressure end, denoted by the coordinates. ,Pick The lower vertex with the larger value is the high-voltage end, denoted by the coordinates. . and The calculation formula is:

[0046] S134. Insulator Foreground Tilting Correction: Correct the foreground image of ice-free insulators. Foreground image of rime ice-covered insulators Around the center of the fitted ellipse respectively Rotation angle Obtain a rotating foreground image of an ice-free insulator. Rotating foreground image of rime ice-covered insulators For any point in the image, let the coordinates before and after rotation be... , The corresponding relationship is as follows:

[0047] S135, Correction of insulator foreground tilt before and after frost accretion, including: Low-voltage end translation and alignment: Translate the low-voltage end vertex of the rotated icing-covered insulator to a position coinciding with the low-voltage end vertex of the rotated ice-free insulator. Translate the rotated icing-covered insulator foreground so that its low-voltage end vertex... With the rotated ice-free insulator foreground low-voltage end vertex By overlapping, a translated foreground image of the rime-covered insulator is obtained. Translation offset , And the expression for translation transformation is:

[0048]

[0049] Step S2: Umbrella Unit Localization Based on Axial Gradient. Utilizing the axial gradient distribution characteristics of the registered ice-free reference image, the axial position intervals of each umbrella unit in the insulator string are located and mapped onto the icy image. Specifically, this includes: calculating the axial gradient map of the ice-free foreground image (using the Sobel vertical operator); smoothing the gradient map using multi-scale Gaussian filtering to suppress noise; generating and smoothing the gradient curve accumulated along the axial direction; detecting local maxima (corresponding to the eaves of composite insulators) or local minima (corresponding to the center of the umbrella disc of glass / ceramic insulators) on the gradient curve based on the insulator material (determined by the proportion of red pixels); the positions of these extreme points define the axial intervals of each umbrella unit; finally, mapping the umbrella unit intervals onto the icy image based on the axial length ratio of the insulators in the two images.

[0050] In one embodiment, see Figure 3 and Figure 4 Step S2 specifically includes the following steps S21-S26: S21. Axial gradient map extraction: Rotating the foreground image of ice-free insulators. Using Sobel vertical operator Calculate its axial gradient Highlighting the brightness variation along the insulator axis, an axial gradient map of the rotating foreground image of the ice-free insulator is obtained. :

[0051] In the formula, * represents the convolution operation.

[0052] S22. Multi-scale noise suppression: To suppress curve noise fluctuations caused by uneven illumination, umbrella surface reflection, etc., multi-scale Gaussian filtering is used to smooth the ROI, including: S221. Select Gaussian kernels at three scales: large, medium, and small. Calculate smoothed images at various scales :

[0053] In the formula, , respectively corresponding to small-scale Gaussian kernels =3×3, mesoscale Gaussian kernel =7×7, Large-scale Gaussian kernel =11×11.

[0054] S222. Calculate the axial gradient map for the smoothed image at each scale. And merged into a multi-scale gradient map. :

[0055] S23. Gradient Curve Generation: To reflect the cumulative distribution of the gradient along the axial direction, a multi-scale gradient map is generated. The initial gradient curve is obtained by summing the gradient values ​​corresponding to each pixel in the gradient map row by row. :

[0056] In the formula, The length of the longer side of the vertical rectangular identification frame for ice-free insulators. This is the length of the shorter side of the vertical rectangular identification frame for ice-free insulators.

[0057] S24. Gradient curve smoothing: using a Savitzky-Golay filter. Smoothing is performed to obtain the smoothed gradient curve. Filter window width and smooth gradient curve The calculation formulas are as follows:

[0058]

[0059] In the formula, This represents the number of umbrella units for ice-free insulators. These are the Savitzky-Golay filter coefficients.

[0060] S25, Extreme point detection and umbrella unit positioning, including: S251. Insulator Material Judgment: Based on the number of red pixels on ice-free insulators. r The material is differentiated based on its ROI ratio, and the color component is taken to meet the requirements. R ∈[90, 255]、 G ∈[0, 50]、 B Pixels in the range [0, 50] are red pixels, and the material is determined based on the following criteria:

[0061] In the formula, It is a proportionality constant.

[0062] S252. Extreme Point Type Selection: For composite insulators, select the position corresponding to the eaves where the gradient maximum value is maximized. The local maximum point is detected; for glass / ceramic insulators, the center position of the umbrella unit corresponding to the gradient minimum is taken. The local minimum point is detected.

[0063] S253. Peak Detection: Employs the Automatic Peak Detection (AMPD) algorithm, traversing from the low-voltage side to the high-voltage side. Determine the set of row indices for extreme points. ,get There are extreme points; take This is the starting row and represents the low-voltage end of the ice-free insulator. The terminator is the high-voltage end of the line and is indicated as the ice-free insulator.

[0064] S254, Determination of Ice-Free Insulator Shelter Units: Using Extreme Point Row Indexing Determine the positioning row of the umbrella unit and take the axial interval. This is defined as a single ice-free insulator shed unit section, with the height of this unit section representing the shed spacing of the ice-free insulator. . The calculation formula is:

[0065] S26. Mapping of Ice-Covered Insulator Umbrella Units: Positioning umbrella units of ice-free insulators using axial scaling mapping. Positioning line converted to frost insulator ;Pick This is a single unit section for frost-covered insulators, in which... This represents the low-voltage end of an insulator clad in ice and frost. This represents the high-voltage end of the rime ice-covered insulator, where... This represents the length of the longer side of the vertical rectangular identification frame for frost-covered insulators. k Location row index of each frost-covered insulator umbrella unit The calculation formula is:

[0066] In the formula, This is the floor function.

[0067] Step S3: Icing Detection and Quantization Based on Circumferential Gradient. Utilizing the saliency of the circumferential gradient in the rime ice image, icing edges are extracted and their geometric properties are quantified. Specifically, this includes: calculating the circumferential gradient map of the rime ice foreground image (using the Sobel level operator); binarizing the gradient map using an adaptive threshold based on gradient extrema, then repairing broken edges and removing noise through morphological dilation and erosion (using "+" shaped structuring elements) to obtain a binary map of icing edges; searching for connected components in the binary map, simplifying the contour of each connected component using the Douglas-Peucker algorithm, and fitting its minimum bounding rectangle using the rotating caliper algorithm; filtering valid icings based on the aspect ratio of the rectangles; calculating the projection length of the long side of each valid icing rectangle onto the insulator image axis, as the icing projection length; and determining the umbrella unit to which the icing rectangle belongs and whether it is located to the left or right of the axis based on the center coordinates of the icing rectangle.

[0068] In one embodiment, see Figure 5 and Figure 6 Step S3 specifically includes the following steps S31-S39: S31. Circumferential gradient map extraction: Translation of the foreground image of the rime-covered insulator. Using Sobel level operators Calculate its circumferential gradient map To enhance the gradient response of the icicle edge in the horizontal direction perpendicular to the insulator axis, the calculation formula is as follows:

[0069] In the formula, This represents the convolution operation.

[0070] S32. Gradient Adaptive Binarization: To extract the ice crystal edges, the circumferential gradient map is binarized. Perform adaptive binarization to generate a binary ice gradient map. :

[0071]

[0072] In the formula, It is an adaptive threshold based on gradient extrema, which can adapt to gradient changes under different lighting and ice transparency.

[0073] S33. Gradient Edge Morphological Repair: Based on the "+" Shape Operator k T First, the broken edges are connected using dilation, and then noise is removed using erosion to obtain the repaired ice crystal edge image. The calculation formula is:

[0074] In the formula, This represents the expansion operation; This represents the erosion operation.

[0075] S34, Iceberg Connectivity Search: Traverse the Iceberg Edge Map And search for connected components to form a set of connected components. For each connected component Extract its contour point set Each set of contour points corresponds to one candidate icicle edge.

[0076] S35, ice crystal fitting, including: S351, Icicle outline simplification: Take distance threshold The Douglas-Peucker algorithm was used to simplify the set of contour points. A simplified contour point set is obtained. .

[0077] S352. Minimum bounding rectangle fitting: Calculate the simplified contour point set using the rotating caliper algorithm. minimum bounding rectangle Its four vertices are arranged clockwise, as shown below:

[0078] S352, Extraction of parameters for fitted rectangle: from Extract the center point coordinates of the fitted rectangle Length of the longer side of the fitted rectangle and the length of the shorter side The angle between the longer side of the fitted rectangle and the y-axis of the image. .

[0079] S353. Icicle Validity Determination: If the fitted rectangle meets the following conditions, it is determined to be a valid icicle, and valid icicle rectangles are selected. :

[0080] In the formula, The aspect ratio threshold is used to fit the rectangle.

[0081] S36. Quantitative Analysis of Icicle Projection Length: For each valid icicle rectangle The projection length of the icicle is taken as the projection length of the longer side of the rectangle along the axis of the insulator image. The calculation formula is:

[0082] S37. Determining the umbrella unit to which an icicle belongs: For each valid icicle rectangle... Based on the ordinate of the center point of the icicle According to the interval of each umbrella unit of the frost insulator Determine the index of the umbrella unit where the valid icicle rectangle is located. The calculation formula is:

[0083] S38. Determining the location of an icicle: For each valid icicle rectangle... Based on the x-coordinate of the center point of the icicle x-axis of the insulator axis with frost and ice accumulation Based on the relationship, determine whether the icicle is located on the left (L) or right (R) side of the axis:

[0084] S39. Output of ice crystal detection results: Output the detection result set of each valid ice crystal. ,in The effective number of ice crystals.

[0085] Step S4: Multi-dimensional parameter calculation. Based on the above localization and detection results, a series of quantitative parameters are defined and calculated: 1) Unevenness of circumferential distribution of icicles: For each umbrella unit, the ratio of the difference and the sum of the total lengths of the icicles on its left and right sides is calculated to characterize the degree of skewness in the circumferential distribution of icicles.

[0086] 2) Axial distribution non-uniformity of ice: Based on the characteristics of umbrella unit segments with continuous ice, quantify whether the icing is continuously distributed or segmented along the axis.

[0087] 3) Local umbrella unit bridging degree: For each umbrella unit, calculate the ratio of the longest internal icicle projection length to the distance between umbrellas in that unit, reflecting the risk of bridging of the air gap between individual umbrellas.

[0088] 4) Overall bridging degree of insulator string: Identify continuous "ice strips" (i.e. dense areas of ice pixels) along the insulator axis, calculate the ratio of the total height of all ice strips to the total length of the insulator string image, and reflect the overall bridging trend.

[0089] In one embodiment, see Figure 7 Step S4 specifically includes the following steps S41-S44: S41. Calculation of circumferential distribution non-uniformity of ice crystals, including: S411, Statistics on the length of icicles on both sides of the umbrella unit: For the first For each umbrella unit, the total length of the icicles on its left (L) and right (R) sides is calculated using the following formula:

[0090] in, and They belong to the first An umbrella unit consisting of icicles located on the left and right sides of the axis.

[0091] S412, Uniformity parameter of umbrella unit circumferential distribution : No. Circumferential distribution non-uniformity of each umbrella unit:

[0092] In the formula, A Let L, R, and E be constants, representing non-uniformity on the left, non-uniformity on the right, uniformity on both sides, or no ice crystals, respectively.

[0093] S413, Circumferential distribution parameters of insulator strings Based on all umbrella units Define the circumferential distribution characteristics of the entire string of insulators:

[0094] S42. Calculation of axial distribution non-uniformity of ice formations, including: S421, Determination of Icing Status of Umbrella Unit: For the first k Each umbrella unit defines the icing state:

[0095] S422, Axial Distribution Parameters Calculation: Quantify the axial distribution of frost on the surface of the insulator string based on the continuity of the frost-covered umbrella unit:

[0096] in, Indicates continuous icing. This indicates segmented icing or melting.

[0097] S43. Calculation of bridging degree of rime ice on local umbrella units, including: S431, Detection of the longest icicle in the umbrella unit: For the first For each umbrella unit, iterate through the projected lengths of all icicles, and select the icicle with the largest projected length as the longest icicle in the umbrella unit. The calculation formula is:

[0098] In the formula, For the first k The set of all icicles in each umbrella unit.

[0099] S432, Local Bridging Degree Calculation: For the first... Given a given umbrella unit, determine the length of the longest icicle within that unit and the distance between the umbrella units. The ratio of the glaze bridging degree of the umbrella unit is obtained. . No. individual umbrella unit bridging degree The calculation formula is:

[0100] S44. Calculation of overall bridging degree of insulator string frost and ice accumulation, including: S441, Icy Strip Identification and Height Counting: (Taking...) For row indexing, along the insulator axis from the low-voltage end ( ) to the high-voltage end ( Scan line by line and count the number of lines. Number of pixels of effective ice crystals And based on the ice zone, a threshold is determined. Generate Boolean sequence :

[0101] S442, Icy Strip Extraction: From Boolean Sequence Extract all consecutive row intervals with a value of 1, each interval representing an ice floe. Let the total number of extracted intervals be... The first ice belt, the first The row interval corresponding to each ice floe is: Its height Let be the row number of this interval. The ice belt begins in the row. And the ice belt terminates the journey The following criteria must be met: and ; and ; For all ,have .

[0102] S443. Overall Bridging Degree Calculation: Calculate the total height of the ice strip and the length of the insulator string covered with frost. The ratio of the two values ​​is used to obtain the bridging degree of the insulator string under frost and ice accretion. The formula for its calculation is:

[0103] Step S5: Comprehensive Risk Assessment and Output. Integrate the parameters from Step S4 to conduct a systemic risk assessment: 1) Distribution uniformity assessment: Combine circumferential and axial distribution non-uniformity to calculate the comprehensive distribution index and classify the uniformity level (e.g., low, medium, and severe non-uniformity).

[0104] 2) Bridging Risk Assessment: Each umbrella unit is classified into low, medium, and high risk levels based on its bridging degree value, and its risk index is calculated. The overall risk level of the entire insulator string is assessed based on the overall bridging degree. Furthermore, the spatial clustering index of medium- and high-risk umbrella units is calculated to identify areas of concentrated risk.

[0105] 3) Comprehensive decision-making: Based on the overall bridging risk level, risk distribution and clustering, calculate the comprehensive bridging risk index, and generate a quantitative assessment report that includes distribution characteristics, risk levels at all levels and specific operation and maintenance recommendations (such as routine monitoring, enhanced monitoring or immediate handling).

[0106] In one embodiment, step S5 specifically includes the following steps S51-S56: S51. Assessment of the spatial uniformity of rime ice distribution, including: S511. Quantitative Assessment of Circumferential Icing: Defining the Circumferential Icing Index The circumferential ice-covering threshold is taken as ,when The system was determined to have significant circumferential icing throughout the insulator string. Circumferential icing index. The calculation formula is:

[0107] In the formula, Avoid dividing by zero for small constants.

[0108] S512. Quantitative Assessment of Axial Segmented Icing: Defining the Axial Segmented Icing Index The axial segmented icing threshold is taken as ,when The system was used to determine if the insulator string exhibited significant segmented icing or melting phenomena. Axial segmented icing index. The calculation formula is:

[0109] In the formula, This represents the number of ice-free gaps between adjacent ice-covered umbrella units.

[0110] S513. Quantitative assessment of comprehensive distribution uniformity: Calculate the comprehensive distribution index of rime ice by combining circumferential and axial icing indices. ;according to Values ​​are divided into uniformity levels. For low degree of unevenness, Moderately uneven It is severely uneven.

[0111] Comprehensive distribution index The calculation formula is:

[0112] In the formula, and These are the weighting coefficients.

[0113] S52. Risk assessment of inter-umbrella air gap bridging, including: S521, Umbrella Unit Bridging Risk Classification: According to the... individual umbrella unit bridging degree The value categorizes the bridging risk of a single umbrella unit into three levels and calculates risk weights, with low bridging risk: Weight Bridge risks: Weight High bridging risk: Weight .

[0114] S522, Calculate the first Individual umbrella unit bridging risk index :

[0115] S53. Overall bridging risk assessment: based on the degree of icing bridging of the insulator string. The value is used to assess the bridging risk level of the entire insulator string. To minimize bridging risk, To bridge the risk, This poses a high risk of bridging.

[0116] S54. Spatial Bridging Risk Clustering Assessment: Calculate the clustering index of medium and high bridging risk umbrella units. ,when At this point, it indicates that high-bridging-risk umbrella units exhibit spatial clustering, further increasing the bridging risk. (Medium and high bridging-risk clustering index) The calculation formula is:

[0117] In the formula, The number of umbrella units for medium and high bridging risks. This represents the number of adjacent medium- and high-bridging risk umbrella units.

[0118] S55, Comprehensive Bridging Risk Index Calculate the comprehensive bridging risk index by combining multiple dimensions. ,according to Values ​​are used to classify overall risk levels, with low risk (L_risk): Medium risk (M_risk): High risk (H_risk): Comprehensive bridging risk index The calculation formula is:

[0119] in, , , These are the weighting coefficients.

[0120] S56. Quantitative Assessment Report on Local-Global Ice Accretion of Output Insulators, including: 1) Distribution characteristics of rime ice: 1.1) Circumferential distribution: Partial icing index and the judgment result; 1.2) Axial distribution: Segmented icing index and the judgment result; 1.3) Comprehensive Evenness Index and level; 2) Risk of bridging due to rime ice: 2.1) Section individual umbrella unit bridging degree and risk level distribution; 2.2) Overall bridging degree and risk level; 2.3) High-risk clustering index and determination of aggregation; 2.4) Comprehensive Bridging Risk Index and risk level.

[0121] 3) Operation and maintenance decision-making recommendations: 3.1) Low risk: Routine monitoring, paying close attention to weather changes; 3.2) Medium risk: Increase monitoring frequency and prepare de-icing contingency plans; 3.3) High risk: Immediate on-site verification, and power outage for de-icing if necessary.

[0122] Figure 8 A system block diagram for implementing the above method is shown. The system includes corresponding functional modules that can be integrated into a server or edge computing device to achieve automated processing, and the beneficial effects achieved are the same as those achieved in the above method embodiments.

[0123] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0124] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0126] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0127] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0128] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0130] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0131] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0132] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0135] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0136] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0138] The units described above 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.

[0139] 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.

[0140] 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 medium. 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 storage medium and includes multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A multi-view detection method for frost distribution and bridging degree of suspension insulators, characterized in that, The method includes the following steps: From a group of images taken by the same terminal, the foreground of the insulator strings before and after frost and ice accumulation is extracted and registered and aligned. By utilizing the axial gradient distribution characteristics of ice-free reference images, the axial position range of each umbrella unit of the insulator string can be located from different viewpoints. By utilizing the circumferential gradient significance features of the rime ice-covered image, the edge of the ice crystals is extracted, and the projected length and direction of the ice crystals are quantified. Calculate the parameters of circumferential non-uniformity of ice distribution, axial non-uniformity of distribution, local umbrella unit bridging degree, and overall bridging degree of insulator string; Based on the calculated parameters, the spatial non-uniformity of rime ice distribution and the risk of air gap bridging between umbrellas are quantitatively assessed.

2. The method according to claim 1, characterized in that, The process of extracting the foreground of the insulator strings before and after icing from a group of images taken from the same terminal and registering and aligning them includes: For both ice-free reference images and ice-covered images to be tested, insulator identification is performed using a target recognition model to obtain their respective vertical rectangular recognition boxes; Using the obtained rectangular recognition box as the region of interest, a semantic segmentation model is used to extract the foreground images of ice-free insulators and ice-covered insulators; Ellipse fitting is performed on the foreground image of the ice-free insulator to obtain the image axis, low-voltage end vertex, and high-voltage end vertex of the insulator; Rotate the foreground image of the rime-covered insulator around the center of the fitted ellipse so that its image axis is parallel to the image axis of the foreground image of the ice-free insulator, and align the low-voltage end vertices of the two to complete the registration.

3. The method according to claim 1, characterized in that, The method of locating the axial position range of each shed unit of the insulator string from different viewpoints by utilizing the axial gradient distribution characteristics of ice-free reference images includes: The Sobel vertical operator is used to calculate the axial gradient map of the registered ice-free insulator foreground image. Multi-scale Gaussian filtering is used to smooth the axial gradient map to obtain a multi-scale gradient map; The multi-scale gradient map is summed row by row to generate an initial gradient curve, which is then smoothed. Based on the insulator material, local maxima or minima are detected on the smoothed gradient curve. The row index of the extreme points is used to determine the axial position range of each umbrella unit. Based on the axial ratio between ice-free insulators and frost-covered insulators, the position range of umbrella unit of ice-free insulators is mapped onto the image of frost-covered insulators.

4. The method according to claim 3, characterized in that, The step of detecting local maxima or local minima on the smoothed gradient curve based on the insulator material includes: If it is a composite insulator, then detect the local maximum point on the smooth gradient curve, and its corresponding eave position; If it is a glass or porcelain insulator, then detect the local minimum point on the smooth gradient curve, and the corresponding umbrella unit center position. The insulator material is determined by the percentage of pixels in the foreground image of the ice-free insulator that meet the preset red threshold range.

5. The method according to claim 1, characterized in that, The process of extracting ice edges and quantifying the projected length and orientation of ice floes using the circumferential gradient saliency features of the image of rime ice includes: For the registered foreground image of the rime-covered insulator, the Sobel horizontal operator is used to calculate its circumferential gradient map; Adaptive binarization and morphological restoration are performed on the circumferential gradient map to obtain the ice crystal edge map; Connected components are extracted from the ice crystal edge map, and the contour of each connected component is simplified and fitted with the minimum bounding rectangle. Valid icicle rectangles are selected based on a preset aspect ratio threshold, and the projection length of each valid icicle rectangle on the insulator image axis is calculated as the icicle projection length. Based on the coordinates of the center point of the icicle, determine its corresponding umbrella unit index and whether it is located on the left or right side of the insulator image axis.

6. The method according to claim 5, characterized in that, The threshold for adaptive binarization is dynamically determined based on the gradient extrema of the circumferential gradient map; the morphological repair employs a dilation-then-erosion operation, with the structural element being a "+" shape.

7. The method according to claim 1, characterized in that, The calculation of the circumferential distribution non-uniformity of the ice crystals includes: for each umbrella unit, calculating the sum of the projected lengths of all ice crystals on the left and right sides of its axis, and calculating the circumferential distribution parameters of the umbrella unit based on the difference in lengths on both sides; The calculation of the axial distribution non-uniformity of the ice crystals includes: determining the icing state of each umbrella unit based on whether there are ice crystals, and calculating the axial distribution parameters based on the number or distribution characteristics of continuously icy umbrella units. The calculation of the bridging degree of the local umbrella unit includes: for each umbrella unit, taking the ratio of the projected length of the longest icicle inside it to the distance between the umbrella units; The calculation of the overall bridging degree of the insulator string includes: scanning along the insulator axis, identifying "ice strips" with continuously distributed ice pixels, and calculating the ratio of the total height of all ice strips to the total length of the insulator string image.

8. The method according to claim 1 or 7, characterized in that, The quantitative assessment of the spatial non-uniformity of rime ice distribution and the risk of inter-umbrella air gap bridging based on the calculated parameters includes: Based on the circumferential and axial unevenness of the ice distribution, a comprehensive distribution index is calculated, and the uniformity level of the spatial distribution of rime ice is evaluated accordingly. Risk classification is performed based on the local bridging degree value of each umbrella unit, and the bridging risk index of a single umbrella unit is calculated. The overall bridging risk level of the entire insulator string is assessed based on the overall bridging degree value of the insulator string. Calculate the spatial clustering index of medium and high bridging risk umbrella units; Based on the overall bridging risk level, umbrella unit bridging risk distribution, and spatial clustering index, a comprehensive bridging risk index is calculated, and corresponding operation and maintenance decision recommendations are output.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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