Non-metal armored optical cable surface damage detection and positioning quantification method and medium

Abstract

The invention discloses a non-metal armored optical cable surface damage detection and positioning quantification method and a medium, and relates to the technical field of ocean engineering cable detection.The method comprises the steps that geometric surface three-dimensional point cloud data of a to-be-detected non-metal armored optical cable is obtained, and data preprocessing and cylindrical surface fitting are carried out; obtaining a cylinder model corresponding to the nonmetal armored optical cable to be tested; expanding the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and a two-dimensional thickness map; generating a dynamic reference surface according to the depth map and the pitch of the nonmetal armor layer; and performing defect damage detection and positioning quantification according to the depth map, the thickness map and the dynamic reference surface. The invention provides a damage detection means for the depth map and the thickness map generated based on the geometric surface three-dimensional point cloud data of the to-be-detected nonmetal armored optical cable, the damage is positioned and quantified, and the problem that the positioning and high-precision quantification of the micron-sized damage of the nonmetal armored surface are difficult is solved.

Description

Technical Field

[0001] The present application relates to the technical field of marine engineering cable detection, and in particular to a method and medium for detecting and quantifying surface damage of non-metallic armored optical cables. Background Art

[0002] In the field of marine engineering, cable condition is crucial to ensuring the safe and efficient operation of marine winches and subsea equipment. Marine winch systems play a vital role in ocean exploration and research and are a key component in building marine scientific research capabilities. Full-ocean-depth optical cable winch systems are essential equipment aboard research vessels, used for deploying subsea instruments such as ROVs. As ocean exploration expands from nearshore to offshore, the operating depths of winch systems for retracting and deploying cables are gradually increasing, and the cable lengths used in these winch systems are also increasing.

[0003] Traditional deep-sea research winch systems suffer from issues such as excessive steel cable weight, easy wear and tear, and tangling during the laying of ultra-large cable cables. To address the issue of excessive steel cable weight and the resulting inability to transmit signals, non-metallic armored optical cables are being adopted. Non-metallic armored optical cables are a key medium for information transmission, playing a core role in areas such as internet data transmission and voice communications. In power systems, they are used in power communication networks to ensure the safe operation of the power grid. Once a non-metallic armored optical cable is damaged, such as the sheath peeling off or the armor layer delaminating, external factors may further affect the optical fibers or conductors within the cable. For example, moisture may intrude, increasing optical fiber attenuation and affecting signal transmission quality. In power communication, this can cause faults such as short circuits, threatening the stable operation of the power system.

[0004] The working environment of deep-sea optical cables is often more complex. Environmental factors on the seabed make optical cables susceptible to damage from various external forces. This is especially true in full-sea-depth polar research winch systems with large loads and long cables. Problems such as cable extrusion deformation, pulling elongation, and wear and tear still exist. The performance of the cable varies depending on the degree of damage, and in severe cases, it may cause safety accidents. Therefore, early detection of surface damage allows for timely repair measures. If minor damage is not discovered, it may gradually expand during long-term use due to the continuous effects of environmental factors (such as temperature changes, mechanical stress, etc.). Through damage detection, damage can be treated while it is still in a relatively minor stage to prevent further deterioration of the damage, thereby extending the service life of the optical cable.

[0005] Existing damage detection methods mostly remain at the level of identifying whether there is damage or not. They lack precise calibration of the spatial coordinates of the damage location, cannot achieve millimeter-level positioning accuracy, and it is difficult to quantitatively describe the degree of damage based on point cloud geometry information. Summary of the Invention

[0006] The purpose of this application is to provide a method and medium for detecting and locating surface damage of non-metallic armored optical cables, which can accurately detect and locate surface damage of optical cables, solving the problem of locating and quantifying millimeter-level damage on the surface of non-metallic armor with high precision.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for detecting and quantifying surface damage of a non-metallic armored optical cable, comprising:

[0009] Obtain the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested, and perform data preprocessing and cylindrical fitting to obtain the cylindrical model corresponding to the non-metallic armored optical cable to be tested;

[0010] Unfold the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map;

[0011] generating a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer;

[0012] Defect damage is detected, located and quantified based on depth maps, thickness maps and dynamic reference surfaces.

[0013] In a second aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned non-metallic armored optical cable surface damage detection and location quantification method.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] The present application provides a method and medium for detecting and locating surface damage of non-metallic armored optical cables, which obtains three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested and constructs a cylindrical model corresponding to the non-metallic armored optical cable to be tested; unfolds the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map; generates a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer; and detects and locates and quantifies defect damage based on the depth map, thickness map, and dynamic reference surface. The present application provides a damage detection method based on depth maps and thickness maps, which not only accurately detects damage, but also accurately locates and quantifies damage, solving the problem of difficult location and high-precision quantification of micron-level damage on the surface of non-metallic armor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a diagram of the application environment of a method for detecting and quantifying surface damage of a non-metallic armored optical cable in one embodiment of the present application;

[0018] Figure 2 A flowchart of a method for detecting and locating surface damage in a non-metallic armored optical cable according to an embodiment of the present application is provided;

[0019] Figure 3 A schematic diagram of the technical concept of a method for detecting and quantifying surface damage of non-metallic armored optical cables provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] Existing damage detection methods mostly remain at the level of identifying whether there is damage or not. They lack precise calibration of the spatial coordinates of the damage location, cannot achieve millimeter-level positioning accuracy, and are difficult to quantitatively describe the degree of damage based on point cloud geometric information. The lack of quantification of parameters such as damage area, depth, and deformation curvature makes it impossible to establish a direct correlation between the distribution of damage locations and structural performance degradation. It is also difficult to form a complete evaluation system that includes the spatial location and geometric characteristics of damage, and is even more unable to provide key data support for the prediction of the remaining life of the cable. Especially in marine engineering scenarios with extremely high safety requirements, this lack of positioning and quantification capabilities may lead to blind maintenance decisions and potential safety hazards. The current three-dimensional point cloud detection lacks positioning and quantification functions in cable damage detection.

[0022] In this regard, the present application provides a method and medium for detecting and quantifying surface damage of non-metallic armored optical cables, and provides a non-contact, purely geometrically driven damage detection method to solve the problems of locating, quantifying, and predicting the life of micron-level damage on the surface of non-metallic armor, thereby reducing hardware costs and improving detection efficiency. The present application generates a dynamic reference surface based on the geometric surface information of the three-dimensional point cloud, eliminates structural interference, combines multi-scale curvature entropy analysis with layered detection, uses thickness, edge, and texture trimodal verification to determine whether the sheath has fallen off, and predicts the remaining life based on the geometric severity index, achieving high-precision damage assessment without dependence on material parameters.

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] The non-metallic armored optical cable surface damage detection and location quantification method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The terminal communicates with the server through the network. The data storage system can store data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested to the server. After the server receives the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested, the server performs data preprocessing and cylindrical fitting to obtain a cylindrical model corresponding to the non-metallic armored optical cable to be tested; unfolds the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map; generates a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer; and performs defect damage detection and location quantification based on the depth map, thickness map and dynamic reference surface. The server can feedback the obtained defect damage detection and location quantification results to the terminal. In addition, in some embodiments, the method for detecting and locating the surface damage of non-metallic armored optical cables can also be implemented independently by a server or a terminal. For example, the terminal can directly perform surface damage detection and positioning quantification on the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested, or the server can obtain the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested from the data storage system and perform surface damage detection and positioning quantification on the non-metallic armored optical cable.

[0025] Terminals include, but are not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices include smart watches, smart bracelets, and head-mounted devices. Servers can be implemented as standalone servers or server clusters consisting of multiple servers, or even cloud servers.

[0026] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for detecting and quantifying surface damage of non-metallic armored optical cables is provided, which is particularly suitable for scenarios where damage detection, positioning and quantification are achieved by relying on contactless external surface geometric data. The method is executed by a computer device, which can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in is used as an example to illustrate, including the following steps 101 to 104.

[0027] Step 101: Acquire the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested, perform data preprocessing and cylindrical fitting, and obtain a cylindrical model corresponding to the non-metallic armored optical cable to be tested.

[0028] Step 102 : unfold the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map.

[0029] Step 103: Generate a dynamic reference surface according to the depth map and the pitch of the non-metallic armor layer.

[0030] Step 104 : Detect, locate and quantify defects and damage based on the depth map, thickness map and dynamic reference surface.

[0031] This application defines defect damage and divides the damage into three categories, namely, dents / indentations, outer surface delamination, and sheath detachment.

[0032] Implement the above steps 101 to 104 to obtain the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested and construct a cylindrical model corresponding to the non-metallic armored optical cable to be tested; unfold the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map; generate a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer; and detect and locate and quantify defect damage based on the depth map, thickness map, and dynamic reference surface. The present application provides a damage detection method based on depth maps and thickness maps, which can not only accurately detect damage, but also accurately locate and quantify damage, solving the problem of difficult location and high-precision quantification of micron-level damage on the surface of non-metallic armor.

[0033] In another exemplary embodiment of the present application, in step 101, a three-dimensional surround space is formed by multiple high-precision line laser scanners to scan the non-metallic armored optical cable to be tested, and the initial geometric surface three-dimensional point cloud data of the non-metallic armored optical cable to be tested is collected, and the data is preprocessed, and the preprocessed point cloud data is approximated and fitted.

[0034] In specific implementation, as a preferred embodiment of the present application, multiple high-precision line laser scanners using the Gocator2130 model are evenly arranged around the non-metallic armored optical cable to construct a three-dimensional surround scanning space. For example, three scanners are arranged at 120° intervals from each other to ensure a three-dimensional full-surround, all-round coverage scanning area of ​​the optical cable surface.

[0035] Data preprocessing of the collected initial geometric surface 3D point cloud data includes removing outliers using the Statistical Outlier Removal (SOR) algorithm. For each point in the point cloud, the average distance between it and its k neighboring points (k is 30, for example) is calculated. The mean μ and standard deviation σ of these distances are then calculated. A threshold T = μ + nσ (n is 3, for example) is set. Points with distances exceeding this threshold are identified as outliers and removed.

[0036] The data preprocessing of the collected initial geometric surface three-dimensional point cloud data also includes: smoothing; using the Gaussian filtering method to reduce noise interference by performing weighted averaging on each point and its neighboring points, so as to make the point cloud surface smoother.

[0037] The pre-processed point cloud data is approximated by fitting the point cloud data with a random sample consensus algorithm (RANSAC). The cylinder equation is defined as (xa) 2 +(yb) 2 =r 2 , where (a, b) are the coordinates of the center of the cylinder cross section, and r is the cylinder radius. A number of points (e.g., 1000 points) are randomly selected from the point cloud data to fit a cylindrical model. The distances from the remaining points to the cylindrical surface are calculated. Points with distances less than a set threshold of 0.5 mm are considered inliers. This process is repeated until every remaining point has been traversed. The cylindrical model with the largest number of inliers is selected as the final fitting result.

[0038] This application uses multiple high-precision line laser scanners to form a three-dimensional surround space for scanning, capable of collecting 3D point cloud data of the initial geometric surface of the optical cable in all directions and with high precision. Compared with using only a single or a small number of scanning devices, more complete and accurate 3D information can be obtained, laying a solid foundation for subsequent inspections. The application uses point cloud statistical outlier removal (SOR) and approximate cylindrical fitting, combined with the random sampling consensus algorithm (RANSAC) for preprocessing, to effectively remove noise and outliers and accurately fit the shape of the optical cable. This simpler preprocessing method can improve data quality and reduce subsequent analysis errors.

[0039] In another exemplary embodiment of the present application, in step 102, the cylindrical surface of the cylindrical model is unfolded to generate a two-dimensional depth map and thickness map, which specifically includes:

[0040] (2-1) The cylindrical coordinates corresponding to the cylindrical model are meshed, and the inverse distance weighted interpolation method is used to interpolate and calculate the height value of each grid node to obtain the depth map corresponding to the non-metallic armored optical cable to be tested.

[0041] (2-2) A thickness map corresponding to the non-metallic armored optical cable to be tested is obtained based on the depth map and the nominal thickness information corresponding to the non-metallic armored optical cable to be tested.

[0042] In specific implementation, the cylindrical expansion must first undergo coordinate transformation, according to the conversion formula The Cartesian coordinates of the three-dimensional point cloud are converted into cylindrical coordinates, where (a, b) are the coordinates of the center of the cylinder section and r is the radius of the cylinder, which are obtained by the cylinder fitting in step 101.

[0043] For the generation of depth and thickness maps, the cylindrical coordinates are divided into regular M×N grids. Since the number of point cloud points obtained by segmented scanning is maintained at around 130,000 on average, M×N=100×100 is set. For each grid node (θ i ,z j ), use the inverse distance weighted interpolation (IDW) method to interpolate and calculate the height value of the node in For nodes (θ i ,z j ) to the neighboring point (θ k ,z k ), p is set to 2, and the depth map H(θ,z) of the regular grid is obtained; according to the nominal thickness information of the known non-metallic armored optical cable specifications, the thickness map T(θ,z) is calculated in combination with the depth map. i is the angular coordinate of the grid node in cylindrical coordinates, z j is the height coordinate of the grid node in cylindrical coordinates, (θ i ,z j ) is the coordinate of a grid node divided in the cylindrical coordinate system.

[0044] This application implements coordinate transformation when performing cylindrical expansion, and uses the inverse distance weighted interpolation (IDW) method to interpolate the depth map to output a regular grid depth map. This processing method can convert three-dimensional point cloud data into a two-dimensional form that is easier to analyze, and the interpolation method can make the depth map smoother and more accurate.

[0045] In another exemplary embodiment of the present application, in step 103, generating a dynamic reference surface according to the depth map and the pitch of the non-metallic armor layer specifically includes:

[0046] (3-1) Perform Fourier transform on the depth map to extract the dominant frequency and determine the pitch of the non-metallic armor layer.

[0047] The pitch setting of the non-metallic armor layer is as follows: Since the non-metallic armor layer usually has a periodic internal spiral winding structure, the pitch P is defined as the axial distance of the same armor wire around the optical cable. By performing Fourier transform on the depth map H(θ,z), its axial periodicity is analyzed and the dominant frequency is extracted. The actual pitch P is thus determined.

[0048] (3-2) Along the axial direction of the non-metallic armored optical cable to be tested, with the pitch as the window length, the depth map is subjected to moving average filtering, the average value of the depth value in the window is calculated, and the height value of the reference plane is obtained.

[0049] The adaptive reference plane is constructed by performing a moving average filter on the depth map H(θ,z) along the axial direction with a pitch P as the window length, and calculating the average value of the depth value in the window to obtain the height value of the reference plane. z′ represents a variable used for integration along the axial direction when calculating the reference plane height value. It is a local representation of the axial coordinate z in the depth map H(θ,z). It is used to integrate the depth values ​​within the range with the pitch P as the window length, so as to obtain the average value of the depth values ​​in the window, that is, the reference plane height value.

[0050] (3-3) The dynamic reference surface is obtained based on the height value of the reference surface.

[0051] The final reference surface H base (θ,z) is the dynamic reference surface used for subsequent defect detection, which can accurately separate the normal geometry and damaged areas.

[0052] This application constructs an adaptive reference plane based on the moving average filtering of the armor layer pitch to generate a dynamic reference plane, which can accurately separate the normal geometric shape and the damaged area according to the actual armor structure characteristics of the optical cable. Compared with the fixed reference plane method, it can better adapt to optical cables of different specifications and states, and improve the accuracy of damage detection.

[0053] In another exemplary embodiment of the present application, in step 104, detecting and quantifying defects and damage based on the depth map, thickness map, and dynamic reference surface specifically includes:

[0054] (4-1) Perform dent / indentation detection based on the depth map and the dynamic reference surface, perform morphological processing on the detected dent / indentation damage area, and calculate the loss area and maximum damage depth of the dent / indentation damage area.

[0055] (4-2) Perform layered damage detection based on the depth map.

[0056] (4-3) The sheath detachment detection is performed based on the thickness map, and the sheath detachment area is morphologically processed to calculate the loss area and damage depth of the sheath detachment area.

[0057] In another exemplary embodiment of the present application, in step (4-1), performing depression / indentation detection based on the depth map and the dynamic reference surface specifically includes:

[0058] A height deviation map is calculated based on the depth map and the dynamic reference surface; the height deviation value in the height deviation map is compared with a preset depth threshold to determine the dent / indentation damage area.

[0059] Depression / indentation detection by calculating the height deviation map ΔH = HH base , set the depth threshold to T h =-0.6mm, negative value indicates concavity, when ΔH≤T h Then the damaged area is initially located. Then the damaged area is morphologically processed to optimize the outline of the damaged area and finally the damaged area S is calculated. dent and the maximum depth D max .

[0060] In another exemplary embodiment of the present application, in step (4-2), layered damage detection is performed based on the depth map, specifically including:

[0061] (1) Applying Gaussian kernels with different standard deviations to the depth map to perform filtering processing, and obtain the filtered depth maps corresponding to different standard deviations.

[0062] Based on the outer surface of the point cloud, a multi-scale Gaussian filter is used to calculate the curvature. The depth map H(θ,z) is filtered by applying Gaussian kernels with different standard deviations σ=1mm, σ=3mm, and σ=5mm. The Gaussian kernel expression is: In the two-dimensional depth map, for each point (θ i ,z j ) filter calculation to perform convolution operation: H σ (θ i ,z j )=∑ m ∑ n H(θ m ,z n )G σ (θ i -θ m ,z j -z n ), calculate the filtered depth map. (θ m ,z n ) is the depth map except (θ i ,z j ) other than the point.

[0063] (2) Calculate the second-order derivative of each filtered depth map to obtain the surface curvature at each scale.

[0064] Calculate the second-order derivative of the filtered depth map to obtain the curvature at each scale. According to the surface curvature formula Get the surface curvature K at each scale σ .

[0065] (3) Discretize the surface curvature at each scale.

[0066] For each scale σ, the surface curvature distribution K σ , first discretize it into a series of values ​​K σ,i (i=1,2,…,N), N is the number of discrete points.

[0067] (4) Calculate the information entropy of the curvature distribution at each scale based on the discretized surface curvature at each scale.

[0068] Calculate the information entropy of curvature distribution at each scale

[0069] (5) Layered damage detection is performed based on the information entropy of the curvature distribution at each scale and the preset entropy value threshold.

[0070] Setting E th is the entropy threshold. If an area is at least 2 scales E σ >E th , it is determined to be delamination damage.

[0071] In another exemplary embodiment of the present application, the sheath detachment detection is performed in combination with three modes. The thickness difference is mode 1, and the thickness difference threshold is set to ΔT = 0.5 mm by comparing the difference between the nominal thickness and the actual thickness. When the thickness difference is greater than the threshold, the sheath may be detached in this area; the edge sharpness index (ESI) is mode 2, and the gradient G of the thickness map is calculated by the Sobel operator. T , defining the edge sharpness index S is the area of ​​the thickness map used to calculate the edge sharpness index. Its function is to ensure that the value of the edge sharpness index (ESI) can reflect the sharpness characteristics of the edge within a unit area, thereby facilitating threshold comparison and damage detection. The edge sharpness index threshold is set to 3mm / mm. When the ESI exceeds this threshold, there may be sheath shedding in this area. The texture period analysis is mode 3. A two-dimensional fast Fourier transform (FFT) is performed on the texture of the sheath and braid layer to calculate the energy ratio of low frequency (sheath) to high frequency (braid layer). Set the energy ratio threshold to 0.5, when R EWhen the value is less than 0.5, the sheath may be detached in this area. Only when the detection results of the three modes meet the corresponding conditions can it be determined as sheath detachment, and the damage area and depth can be quantified using morphological processing.

[0072] Therefore, in step (4-3), the sheath detachment detection is performed according to the thickness map, specifically including:

[0073] (1) Compare the difference between the nominal thickness of the non-metallic armored optical cable to be tested and the actual thickness in the corresponding thickness chart.

[0074] (2) Compare the thickness difference with a preset thickness difference threshold to determine the first sheath detachment detection result.

[0075] (3) Calculate the gradient of the thickness map using the Sobel operator.

[0076] (4) Calculate the edge sharpness index based on the gradient of the thickness map.

[0077] (5) Compare the edge sharpness index with a preset edge sharpness index threshold (set according to the optical cable specifications) to determine the second sheath detachment detection result.

[0078] (6) Perform two-dimensional fast Fourier transform on the texture of the sheath and braid of the non-metallic armored optical cable to be tested, and calculate the energy ratio of the sheath to the braid.

[0079] (7) Compare the energy ratio with a preset energy ratio threshold (set according to the optical cable specifications) to determine the third sheath detachment detection result.

[0080] (8) Determine a final sheath detachment area according to the first sheath detachment detection result, the second sheath detachment detection result, and the third sheath detachment detection result.

[0081] The defect detection of this application carries out qualitative and quantitative defect classification, preliminarily locates the damaged area through threshold segmentation, and then uses morphological processing to optimize the outline of the damaged area. This combination can more accurately identify and depict the dent / indentation damage, which is more advantageous than a single detection method; multi-scale Gaussian filtering is used to calculate the curvature, combined with information entropy and multi-scale curvature entropy (MSCE) methods, it can capture the abnormal changes in the curvature of delamination damage from different scales, achieve high-sensitivity detection, and not miss tiny delamination damage; using a three-modal verification method, detection is carried out from three different angles: thickness mutation, edge sharpness index (ESI) and texture period (FFT energy ratio), and comprehensive consideration of various information. Only when all three modal results meet the conditions can it be determined that the sheath has fallen off, which greatly improves the accuracy and reliability of detection. Compared with the single-dimensional detection method, it can effectively reduce misjudgment.

[0082] In another exemplary embodiment of the present application, the method for detecting and quantifying surface damage of non-metallic armored optical cables further includes:

[0083] (1) Determine the similar historical optical cable samples of the non-metallic armored optical cable to be tested based on the geometric severity index GSI, damage type, pitch and operating years.

[0084] (2) Estimate the remaining life of the non-metallic armored optical cable to be tested based on the remaining life of similar historical optical cable samples.

[0085] A damage feature vector f = [GSI, damage type, pitch, years of operation] is constructed. The k-nearest neighbor (k-NN) regression method is used to calculate the similarity between the current optical cable and historical optical cable samples using Euclidean distance in the historical database. The k most similar historical optical cable samples are found (as an example, k is set to 8, which can be adjusted based on the number of samples). The average of the remaining life of these k historical optical cable samples is taken as the conservative remaining life prediction result for the current optical cable.

[0086] This application scans and judges the three types of damage of optical cables respectively, and performs matching verification in the historical database according to the degree of damage, which can roughly estimate the conservative remaining life prediction results.

[0087] In another exemplary embodiment of the present application, the geometric severity index comprehensively considers multiple damage parameters to more comprehensively assess the severity of damage to the optical cable. The larger the GSI value, the more severe the damage to the optical cable and the shorter its remaining life may be. By calculating the GSI, a quantitative damage indicator can be provided for subsequent life prediction. The calculation formula of the geometric severity index is:

[0088]

[0089] Where GSI represents the geometric severity index; α, β, γ, and δ are weight coefficients that can be adjusted according to actual conditions. The default values ​​are α = 0.4, β = 0.3, γ = 0.2, and δ = 0.1. max is the maximum depth of the depression / indentation; r is the radius of the cylinder; S dent is the damage area of ​​the dent / indentation; S total is the total surface area of ​​the optical cable; ∑E σ is the sum of the information entropy of the curvature distribution at each scale; ΔT is the thickness difference in the sheath sheathing detection.

[0090] This application adopts the k-nearest neighbor (k-NN) regression method and performs matching verification based on historical damage extension data. It predicts the remaining life of the current optical cable by finding the k historical optical cable samples that are most similar to the current optical cable, making full use of the empirical information of historical data. It is more in line with the actual situation than some existing life prediction methods based on theoretical models and can provide more accurate life prediction results.

[0091] This application provides a non-contact, purely geometry-driven solution to the problems of low positioning accuracy and insufficient quantification capability of existing three-dimensional point cloud detection for non-metallic structural damage. This method obtains three-dimensional point cloud data of the outer surface through contactless three-dimensional scanning of optical cables. After data preprocessing, the point cloud is fitted into a cylinder and coordinate conversion is performed. The corresponding depth map and thickness map are obtained by grid division, the armor layer pitch is extracted based on Fourier transform, and an adaptive reference plane is constructed through moving average filtering to separate normal and damaged areas. Defect detection is divided into three categories: dent / indentation damage and morphological processing to quantify area and depth; layered damage fusion multi-scale feature detection; sheath detachment damage is jointly verified by thickness difference, edge sharpness index, and texture energy ratio, and is judged only when all conditions are met. A geometric severity index is established, and the nearest neighbor regression method is combined with historical data to predict conservative remaining life. This method improves data integrity through all-round scanning with multiple scanners, accurately separates damage with an adaptive reference surface, effectively reduces misjudgment with three-modal verification, and reduces life prediction errors with the nearest neighbor regression method. It achieves millimeter-level positioning, multi-parameter quantification, and life prediction of surface damage of non-metallic armored optical cables. It is suitable for scenarios such as deep-sea winch systems, and provides an efficient digital solution for cable operation and maintenance.

[0092] Based on the existing three-dimensional point cloud scanning technology, this application improves the problem of deep-sea non-metallic optical cable damage detection and damage quantification in practical applications. It adopts three-dimensional point cloud technology and multimodal data analysis to achieve high-precision detection and damage quantification of non-metallic armored optical cable surface damage and rough assessment of remaining service life, providing an efficient digital solution for the operation and maintenance of non-metallic armored optical cables, and can be widely promoted in the field of submarine non-metallic armored cable damage detection.

[0093] The present application also provides an application scenario, which applies the above-mentioned non-metallic armored optical cable surface damage detection and positioning quantification method. Specifically: the non-metallic armored optical cable surface damage detection and positioning quantification method provided in this embodiment can be applied in the optical cable damage detection and quantification scenario. It includes a data acquisition link, which is used to collect three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested; a damage detection and quantification link, which is used to perform damage detection and positioning quantification based on the collected point cloud data. The non-metallic armored optical cable surface damage detection and positioning quantification method provided in this embodiment belongs to the damage detection and quantification link.

[0094] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0095] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for detecting and quantifying surface damage of non-metallic armored optical cables, characterized in that: include: Obtain the three-dimensional point cloud data of the geometric surface of the non-metallic armored optical cable to be tested, and perform data preprocessing and cylindrical fitting to obtain the cylindrical model corresponding to the non-metallic armored optical cable to be tested; Unfold the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map; generating a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer; Defect damage is detected, located and quantified based on depth maps, thickness maps and dynamic reference surfaces.

2. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 1, characterized in that: Unfold the cylindrical surface of the cylindrical model to generate a two-dimensional depth map and thickness map, including: The cylindrical coordinates corresponding to the cylindrical model are meshed, and the height value of each mesh node is calculated by interpolation using the inverse distance weighted interpolation method to obtain the depth map corresponding to the non-metallic armored optical cable to be tested; A thickness map corresponding to the non-metallic armored optical cable to be tested is obtained according to the depth map and the nominal thickness information corresponding to the non-metallic armored optical cable to be tested.

3. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 1, characterized in that: Generate a dynamic reference surface based on the depth map and the pitch of the non-metallic armor layer, including: Perform Fourier transform on the depth map to extract the dominant frequency and determine the pitch of the non-metallic armor layer; Along the axial direction of the non-metallic armored optical cable to be tested, with the pitch as the window length, the depth map is subjected to moving average filtering, the average value of the depth value in the window is calculated, and the height value of the reference plane is obtained; The dynamic reference surface is derived from the height value of the datum surface.

4. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 1, characterized in that: Detection, location, and quantification of defects and damage based on depth maps, thickness maps, and dynamic reference surfaces. Specifically, this includes: Perform dent / indentation detection based on the depth map and dynamic reference surface, perform morphological processing on the detected dent / indentation damage area, and calculate the loss area and maximum damage depth of the dent / indentation damage area; Layered damage detection based on depth maps; The sheath detachment detection is performed based on the thickness map, and the sheath detachment area is morphologically processed to calculate the loss area and damage depth of the sheath detachment area.

5. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 4, characterized in that: Perform dent / indentation detection based on depth maps and dynamic reference surfaces, including: Calculate the height deviation map based on the depth map and the dynamic reference surface; The height deviation values ​​in the height deviation map are compared with the preset depth threshold to determine the dent / indentation damage area.

6. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 5, characterized in that: Perform layered damage detection based on the depth map, including: Applying Gaussian kernels with different standard deviations to the depth map to perform filtering, and obtaining filtered depth maps corresponding to different standard deviations; Calculate the second-order derivative of each filtered depth map to obtain the surface curvature at each scale; Discretize the surface curvature at each scale; The information entropy of the curvature distribution at each scale is calculated based on the discretized surface curvature at each scale; Layered damage detection is performed based on the information entropy of the curvature distribution at each scale and the preset entropy threshold.

7. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 6, characterized in that: Sheath detachment detection is performed based on the thickness map, including: Compare the difference between the nominal thickness of the non-metallic armored optical cable to be tested and the actual thickness in the corresponding thickness chart; comparing the thickness difference with a preset thickness difference threshold to determine a first sheath detachment detection result; Calculate the gradient of the thickness map using the Sobel operator; Calculate the edge sharpness index based on the gradient of the thickness map; comparing the edge sharpness index with a preset edge sharpness index threshold to determine a second sheath detachment detection result; Perform two-dimensional fast Fourier transform on the texture of the sheath and braid of the non-metallic armored optical cable to be tested, and calculate the energy ratio of the sheath to the braid; comparing the energy ratio with a preset energy ratio threshold to determine a third sheath detachment detection result; A final sheath detachment area is determined according to the first sheath detachment detection result, the second sheath detachment detection result, and the third sheath detachment detection result.

8. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 7, characterized in that: The non-metallic armored optical cable surface damage detection and location quantification method also includes: Determine similar historical optical cable samples of the non-metallic armored optical cable to be tested based on geometric severity index, damage type, pitch and service years; The remaining life of the non-metallic armored optical cable to be tested is estimated based on the remaining life of similar historical optical cable samples.

9. The method for detecting and quantifying surface damage of non-metallic armored optical cables according to claim 8, characterized in that: The calculation formula of the geometric severity index is: Where GSI represents the geometric severity index; α, β, γ, and δ are weight coefficients; D max is the maximum depth of the depression / indentation; r is the radius of the cylinder; S dent is the damage area of ​​the dent / indentation; S total is the total surface area of ​​the optical cable; ∑E σ is the sum of the information entropy of the curvature distribution at each scale; ΔT is the thickness difference in the sheath sheathing detection.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting and quantifying surface damage of a non-metallic armored optical cable according to any one of claims 1 to 9 is implemented.

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