Intelligent three-dimensional modeling method and system for transformer substation cable hiding project

Abstract

The invention relates to the technical field of power systems, and discloses an intelligent three-dimensional modeling method and system for a transformer substation cable hiding project, and the method comprises the steps: obtaining image data of a cable in a laying process; based on the image data, a three-dimensional space coordinate sequence of the cable center line is recovered through a computer vision algorithm, and a space path is formed; based on the image data, the redundant coiling length of the cable at the wall penetrating hole or the shaft entrance blocking point is recognized and quantified; constructing a mechanical model to simulate the natural sag form of the cable based on the material physical properties of the cable and the suspension point spacing; according to the space path, the redundant coiling length and the natural sag form, a cable three-dimensional model is generated through parametric modeling, and digital management and control are carried out on the construction quality of the transformer substation hidden cable project through the cable three-dimensional model. According to the invention, the digital management and control of the construction quality of the hidden cable engineering of the transformer substation can be realized, and the accuracy, efficiency and engineering traceability of the construction quality management and control are improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, specifically to an intelligent 3D modeling method and system for concealed cable engineering in substations. Background Technology

[0002] Concealed works in substations (such as cable laying) are a key component of power grid infrastructure. After construction, they are difficult to inspect directly due to soil covering or sealing. Therefore, quality control during the construction phase is directly related to the long-term safe operation of the power grid.

[0003] Currently, quality control of concealed cable projects in substations mainly relies on manual measurements (such as with measuring tapes), visual inspections, and records made from scattered photos and paper reports. On the one hand, manual operations are easily affected by environmental factors such as lighting and space, making it impossible to accurately quantify complex morphological parameters such as cable sag and redundant coiling length at sealing points, resulting in low quality assessment accuracy and poor consistency. On the other hand, scattered records cannot form a complete digital archive; if a fault occurs after the project is concealed, it is difficult to quickly locate the problem or trace the construction responsibility, severely restricting operation and maintenance efficiency. Therefore, existing technologies suffer from insufficient accuracy in quality assessment and weak full-cycle traceability capabilities, failing to meet the high-quality management requirements of concealed cable projects in substations.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] This application provides an intelligent 3D modeling method and system for concealed cable engineering in substations, which can realize digital control of the construction quality of concealed cable engineering in substations, and improve the accuracy, efficiency and traceability of construction quality control.

[0006] In a first aspect, embodiments of this application provide an intelligent 3D modeling method for concealed cable works in substations, including: Acquire image data during the cable laying process; Based on the image data, the three-dimensional spatial coordinate sequence of the cable centerline is recovered by computer vision algorithms to form a spatial path; Based on the image data, the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point is identified and quantified. Based on the material physical properties of the cable and the spacing between suspension points, a mechanical model is constructed to simulate the natural sag shape of the cable. Based on the spatial path, the redundant winding length, and the natural sag shape, a three-dimensional model of the cable is generated through parametric modeling, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable.

[0007] Secondly, embodiments of this application provide an intelligent 3D modeling system for concealed cable works in substations, comprising: The image acquisition module is used to acquire image data of the cable during the laying process; The spatial path module is used to recover the three-dimensional spatial coordinate sequence of the cable centerline based on the image data using computer vision algorithms, thereby forming a spatial path. The length calculation module is used to identify and quantify the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point based on the image data. The sag simulation module is used to construct a mechanical model based on the material physical properties of the cable and the spacing between suspension points to simulate the natural sag shape of the cable. The model building module is used to generate a three-dimensional model of the cable through parametric modeling based on the spatial path, the redundant winding length, and the natural sag shape, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable.

[0008] This application provides an intelligent 3D modeling method and system for concealed cable engineering in substations. First, acquiring image data during cable laying provides a reliable and complete source of information for subsequent analysis of the actual cable laying status, avoiding the problems of traditional management methods that rely on manual observation, fragmented data, and strong subjectivity. Second, based on the image data, computer vision algorithms are used to reconstruct the three-dimensional spatial coordinate sequence of the cable centerline to form a spatial path, which can accurately capture the actual laying trajectory of the cable, solving the pain points of traditional methods that are difficult to accurately grasp the spatial position of the cable and cannot objectively judge whether the path meets the design requirements. At the same time, identifying and quantifying the redundant coiling length of the cable at the sealing point can accurately control whether the reserved length is compliant, thus avoiding insufficient redundancy from affecting subsequent maintenance and adjustments. This also prevents resource waste or spatial layout violations caused by excessive redundancy. Furthermore, by constructing a mechanical model based on the physical properties of cable materials and the spacing between suspension points to simulate the natural sag shape, the model can closely match the actual bending state of the cable under gravity, avoiding quality hazards such as cable wear and abnormal tension caused by improper sag control. Finally, the spatial path, redundant winding length, and natural sag shape are integrated through parametric modeling to generate a three-dimensional cable model. This allows for an intuitive and accurate presentation of the actual cable laying situation, enabling digital control of the construction quality of concealed cable projects in substations, thereby improving the accuracy, efficiency, and traceability of construction quality control. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is an application environment diagram of the intelligent 3D modeling method for concealed cable engineering in substations provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the intelligent 3D modeling method for concealed cable engineering in substations provided in this application embodiment; Figure 3 This is a schematic diagram of the intelligent 3D modeling system for concealed cable engineering in substations provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0012] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

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

[0014] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0015] To address the aforementioned technical problems and overcome the shortcomings of existing technologies, this application provides an intelligent 3D modeling method and system for concealed cable engineering in substations. This system enables digital control over the construction quality of concealed cable engineering in substations, improving the accuracy, efficiency, and traceability of construction quality control.

[0016] Figure 1 This is an application environment diagram of an intelligent 3D modeling method for concealed cable works in a substation, as shown in one embodiment. (Refer to...) Figure 1 The intelligent 3D modeling method for substation cable concealment engineering is applied to an intelligent 3D modeling system for substation cable concealment engineering. This intelligent 3D modeling system for substation cable concealment engineering includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; the mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. Server 120 is configured to execute the aforementioned intelligent 3D modeling method for concealed cable engineering in substations. Specifically, this method may include: acquiring image data of the cable during the laying process; recovering the 3D spatial coordinate sequence of the cable centerline using computer vision algorithms based on the image data to form a spatial path; identifying and quantifying the redundant coiling length of the cable at wall penetration holes or shaft entrance sealing points based on the image data; constructing a mechanical model to simulate the natural sag shape of the cable based on the material physical properties and suspension point spacing of the cable; and generating a 3D model of the cable through parametric modeling based on the spatial path, redundant coiling length, and natural sag shape, so as to digitally control the construction quality of concealed cable engineering in substations through the 3D model of the cable.

[0017] Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent 3D modeling method for concealed cable works in substations according to an embodiment of this application. This embodiment primarily uses the application of this intelligent 3D modeling method for concealed cable works in substations to computer equipment as an example. Specifically, the intelligent 3D modeling method for concealed cable works in substations according to an embodiment of this application may include the following steps: S1. Acquire image data during the cable laying process; Specifically, for step S1, collect image information of the entire process of cable laying from the start of cable laying to key nodes (such as wall penetration, entry into shaft, suspension, etc.) during the construction phase of the substation cable concealment project. These images need to clearly reflect the real-time laying status of the cable, the key locations of the environment (such as wall penetration holes, shaft entrances, suspension points), and the relative positional relationship between the cable and the surrounding structure, so as to provide real and continuous basic data support for subsequent analysis of the actual cable laying situation.

[0018] S2. Based on image data, the three-dimensional spatial coordinate sequence of the cable centerline is recovered using computer vision algorithms to form a spatial path; Specifically, for step S2, computer vision algorithms are used to perform in-depth analysis on the acquired image data. First, the feature information of the cable is extracted from the image, such as the cable edge, key points of the outline, and relative marker points with the surrounding fixed structures. Then, the algorithm calculates the specific coordinates of these feature points in three-dimensional space, including position parameters in the three dimensions of X, Y, and Z. These coordinates are arranged in sequence according to the cable laying order to form a continuous path that accurately reflects the direction of the cable centerline in space, i.e., the spatial path.

[0019] S3. Based on image data, identify and quantify the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point; Specifically, for step S3, for special locations where cables need to be sealed, such as through-wall holes and shaft entrances, the specific location of the sealing point and the area where the cable is coiled are first accurately located by analyzing the acquired images. Then, the key parameters of the coiled cable (such as the number of coils and the outline size of each coil) are extracted from the images, and the actual length of the coiled part of the cable, i.e. the redundant coiling length, is calculated, which provides a quantitative basis for judging whether the reserved length of the cable at this location meets the engineering requirements.

[0020] S4. Based on the material physical properties of the cable and the spacing between suspension points, a mechanical model is constructed to simulate the natural sag shape of the cable; Specifically, for step S4, first obtain the material physical properties of the cable itself (such as cable density, elastic modulus, weight per unit length, etc.), and the distance between two adjacent suspension points during cable laying (such as horizontal spacing, vertical height difference). Then, based on these parameters, construct a mechanical model that can reflect the cable's stress condition (mainly gravity). Calculate the sag shape of the cable under gravity in its natural state (without additional external force pulling), including sag height, sag curve shape, etc., i.e., natural sag shape, to ensure that the model can fit the actual physical bending characteristics of the cable.

[0021] S5. Based on the spatial path, redundant winding length and natural sag shape, a three-dimensional model of the cable is generated through parametric modeling, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable. Specifically, for step S5, the obtained spatial path is used as the basic skeleton of the cable 3D model, the obtained redundant coiling length is used as the basis for adjusting the local shape of the sealing point, and the obtained natural sag shape is used as the basis for correcting the shape of the suspended cable section. These three are integrated, and parametric modeling is used to transform these key parameters into the specific structure of the 3D model, generating a 3D model that can completely and realistically reflect the actual laying state of the cable. Then, by comparing this 3D model with the engineering design requirements (such as design path, design redundant length, and design sag range), it is determined whether the construction is compliant, thus realizing digital supervision and control of the construction quality of concealed cable engineering.

[0022] This embodiment uses a 3D model of cables to accurately recreate the actual laying status of concealed cables in substations, effectively realizing digital control of construction quality. It not only solves the problem of difficulty in intuitively and accurately grasping the spatial position, redundant length and sag of cables in traditional control, but also improves the efficiency and objectivity of construction quality control, providing reliable support for quality traceability of concealed works.

[0023] Furthermore, in some embodiments, step S1, "acquiring image data of the cable during the laying process," may specifically include: S11. Install multiple image acquisition devices at key locations in the concealed works of the substation to form a preliminary coverage monitoring network; Specifically, for step S11, fixed image acquisition equipment is deployed at the core nodes of cable laying to prioritize coverage of key areas crucial to subsequent analysis during construction, thus establishing a basic monitoring framework for acquiring cable laying image data. Key locations refer to the core areas of cable laying within the concealed works of the substation, such as the entrances and exits of cable trenches, bends, both sides of wall penetrations, the perimeter of shaft entrances, and the vicinity of cable suspension points. These areas are where deviations in cable laying status (such as direction, connections, and coiling) are most likely to occur or require focused recording, necessitating continuous image capture using fixed equipment.

[0024] S12. Configure mobile image acquisition equipment to supplement blind spots in the initial coverage monitoring network and form a full coverage monitoring network; Specifically, for step S12, in order to solve the problem of blind spots in the preliminary fixed monitoring network, due to the complex environment of the substation concealed project site (such as obstacles in the trench, temporary adjustment of the cable laying path, and limited viewing angle of the fixed equipment), the fixed equipment alone cannot cover all the laying areas. Therefore, it is necessary to fill these blind spots with flexible and movable equipment to ensure that every step of the cable laying can be recorded by images.

[0025] S13. Real-time transmission of image data based on wireless network; Specifically, for step S13, it is ensured that the collected image data can be transmitted to the back-end processing stage in a timely and efficient manner to avoid the lag caused by data storage in local devices. If the image data needs to be exported uniformly after the construction is completed, it may affect subsequent analysis due to equipment failure or data loss. Real-time transmission can ensure that the data is stored and available immediately, and at the same time, it is convenient for back-end personnel to monitor the progress and status of cable laying in real time.

[0026] This embodiment constructs a fully covered image acquisition network without blind spots by using fixed equipment to cover key areas and mobile equipment to fill blind spots, ensuring that the image data is complete and without loss throughout the entire cable laying process. At the same time, it relies on the wireless network to realize real-time transmission of image data, ensuring that the data is available immediately. This provides comprehensive, timely and reliable basic data support for subsequent work such as restoring the cable spatial path and quantifying redundant length based on image data, avoiding the impact of incomplete or delayed data on the accuracy of subsequent analysis.

[0027] Furthermore, in some embodiments, step S2, "based on image data, recovering the three-dimensional spatial coordinate sequence of the cable centerline using a computer vision algorithm to form a spatial path," may specifically include: S21. Preprocess the image data, including denoising, contrast enhancement, and feature point extraction; Specifically, for step S21, interference information in the image data is eliminated, the distinction between the cable and the background is enhanced, and key reference points on the cable are extracted to provide a clear and reliable image foundation for subsequent 3D coordinate calculation. Noise reduction removes useless interference signals caused by the environment (such as dust or light fluctuations in substation trenches) or equipment (such as camera sensor noise) to prevent noise from affecting the identification of cable features. Contrast enhancement uses technical means to amplify the brightness or color difference between the cable and the surrounding background (such as trench walls or supports), solving the problem of blurred cable outlines caused by dim lighting or cluttered backgrounds. Feature point extraction involves selecting stable and easily identifiable key points on the cable (such as cable joints, printed markings, and edge inflection points) from the processed image. These points are the core references for establishing image relationships and calculating 3D coordinates.

[0028] S22. Estimate the camera pose and 3D coordinates of cable feature points from image data using the structure-of-motion algorithm; Specifically, in step S22, the spatial position and orientation of the camera during shooting are deduced by utilizing the correlation between multiple images, and the specific positions of the cable feature points in three-dimensional space are calculated. The core logic of the structure-of-motion motion (SOP) algorithm is that it does not require prior knowledge of the camera's installation position. By analyzing the pixel position differences of the same feature point in different images and combining the image imaging principle, the pose of the camera when shooting each image (including the camera's spatial coordinates X / Y / Z and the camera's rotation angle) and the three-dimensional coordinates (X / Y / Z values) of the cable feature points are solved simultaneously, establishing a correspondence between the image and the real three-dimensional space.

[0029] S23. Calculate the continuous three-dimensional point cloud sequence of the cable centerline through weighted triangulation, bundle adjustment and exponential decay smoothing, and fit it into a smooth spatial path curve; Specifically, in step S23, the camera pose and feature point 3D coordinates obtained in the previous step are optimized to generate a continuous and smooth spatial path for the cable centerline. Weighted triangulation assigns different weights to feature points based on their matching accuracy (e.g., feature points with small matching errors have high confidence), and recalculates the 3D coordinates to improve accuracy (points with high confidence have a greater impact on the final coordinates). Bundled adjustment simultaneously optimizes all camera poses and feature point 3D coordinates, eliminating the impact of single-image errors on the global result. For example, correcting minor deviations in the pose of a particular camera synchronously adjusts the coordinates of the corresponding feature points to ensure minimal overall error. Exponential decay smoothing processes the discrete feature point 3D coordinates, reducing local fluctuations, such as coordinate shifts in individual points caused by camera shake, ensuring continuous and orderly point coordinates. Finally, curve fitting techniques (such as Bézier curves and NURBS curves) are used to transform the continuous 3D point cloud sequence into a smooth curve, i.e., the spatial path of the cable centerline.

[0030] This embodiment eliminates interference through image preprocessing and establishes three-dimensional associations through motion recovery structure algorithm. It also optimizes data using methods such as weighted triangulation. Ultimately, it can accurately generate a continuous and smooth spatial path of the cable centerline, providing a high-precision path skeleton for subsequent construction of the cable three-dimensional model. This effectively avoids path distortion caused by poor image quality and coordinate calculation errors.

[0031] Furthermore, in some embodiments, the three-dimensional spatial coordinate sequence of the cable centerline is recovered using computer vision algorithms, and the specific calculation formula is as follows: in, For the coordinates of a point in three-dimensional space, For the first Coordinates of a two-dimensional image point. For projection function, For the camera intrinsic parameter matrix, It is a 3x3 rotation matrix. It is a 3D translation vector. For the first The weighting coefficient of each point For exponentially decaying weights, For the first Distance between feature points For attenuation scale parameters, Let the reprojection error function be... The path smoothing error function is... Let logarithmic regularization be the error function. The total number of point cloud sequences. For the first Three-dimensional point coordinates, For the first For the expected step size between adjacent points, The regularization coefficient is . The starting index for trigonometric summation. This represents the upper limit of the triangulation summation, indicating the total number of points in the image. The starting index for the exponentially decaying summation. The upper bound of the exponentially decaying summation is given by , which represents the total number of feature points. The starting index for summing the reprojection errors indicates that it starts from the first point cloud point. Let be the upper limit of the summation of reprojection errors, and represent the total number of point cloud sequences. The starting index for path smoothing summation indicates starting from the first adjacent pair. This is the starting index for the regularized summation, indicating that it starts from the first point cloud point.

[0032] Specifically, the calculation formula first determines the actual meaning, data source, and value rules of each key parameter in the formula to ensure that the calculation logic matches the actual needs of the substation cable scenario. Using a weighted triangulation formula, combined with the 2D point coordinates of multiple images and the camera pose, the 3D spatial coordinates of cable feature points are initially solved. By adjusting and optimizing the objective function in a bundled manner, all 3D spatial point coordinates and camera poses are simultaneously corrected, eliminating local errors in single-image calculations and improving global accuracy. The objective function consists of three parts: reprojection error (the difference between the coordinates of the 3D point projected onto the image and the actual 2D point, ensuring the 3D point matches the image), path smoothing error (the difference between the distance between adjacent 3D points and the expected step size, ensuring the continuity of the cable path), and logarithmic regularization error (preventing overfitting and balancing accuracy and stability). Exponential decay smoothing is used to further optimize the 3D point coordinates, focusing on correcting sequence fluctuations caused by abnormal feature point distances (such as distance misjudgments due to shooting angles). The coordinates of all three-dimensional points, after weighted triangulation, bundle adjustment, and exponential decay smoothing optimization, are arranged sequentially according to the cable laying order (e.g., from the trench entrance to the wall penetration hole) to form a continuous and orderly three-dimensional point cloud sequence. This sequence directly corresponds to the spatial distribution of the cable centerline, providing an accurate data basis for subsequent fitting of spatial path curves and avoiding path distortion caused by parameter ambiguity or calculation deviation.

[0033] Furthermore, in some embodiments, step S3, "based on image data, identifying and quantifying the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point," may specifically include: S31. Locate the blocking point and coiling area from the image data using the target detection algorithm, and extract the ring contour and number of coiling layers of the cable; Specifically, for step S31, the key areas of redundant cable coiling (blocking points and coiling areas) are accurately located from the acquired images, and the basic features of the coiling structure (ring contour, number of layers) are obtained to define the range and provide basic information for subsequent length calculation. The target detection algorithm is used to identify targets with specific shapes in the image. Blocking points (such as the circular opening of a wall penetration hole or the rectangular edge of a well entrance) are fixed references for cable coiling, and their positions need to be located first to determine the relative range of the coiling area. The coiling area is the ring-shaped cluster area formed by the cable around the blocking point, which needs to be distinguished from the background (such as walls or trench walls) and non-coiled cable segments by the algorithm. Extracting the ring contour involves capturing the edge shape of each coiled cable (mostly approximately circular or elliptical) to clarify the spatial range of a single coil. Determining the number of coiling layers involves counting the number of coils of cable stacked along the vertical direction (such as the axis of the wall penetration hole) within the coiling area to avoid omissions or duplicate calculations.

[0034] S32. Separate the coiled structure using an image segmentation algorithm, and calculate the radius and circumference of each coiled loop; Specifically, in step S32, background interference is eliminated by separating the coiled cable from the image, thereby accurately calculating the actual dimensions (radius, circumference) of each loop of cable, providing basic data for the subsequent total length calculation. The image segmentation algorithm distinguishes the coiled cable from the background (walls, air, other devices) at the pixel level, generating a segmentation mask that only contains the coiled cable, avoiding background elements (such as wall textures and shadows) from affecting the size measurement. The radius is calculated based on the annular contour of the segmented single loop of cable, measuring the average distance from its outermost edge to the coiling center (such as the center of the wall hole) (i.e., the single loop radius). The circumference is calculated by combining the formula for the circumference of a circle with the single loop radius to obtain the actual length of each loop of cable. Assuming the cable cross-section is circular, the circumference of the annular contour is approximately equal to the unfolded length of the single loop of cable.

[0035] S33. Sum the lengths of all coils and obtain the total redundant coil length through viewpoint correction and overlay compensation; Specifically, for step S33, based on the accumulation of single-loop lengths, the measurement errors caused by shooting angle deviation and cable overlap are corrected to finally obtain the accurate total redundant winding length. Accumulating the length of all winding loops involves summing the calculated circumference of each loop to obtain the preliminary total length; angle correction corrects for the distortion of the ring contour (e.g., a perfect circle becomes an ellipse) caused by the camera's tilted shooting angle (e.g., not shooting directly at the winding area), correcting the measured elliptical circumference to the actual circular circumference through angle conversion; overlap compensation corrects for errors caused by partial overlap of adjacent cable loops (e.g., cables pressing against each other during winding, causing some lengths to be obscured and not measured), supplementing the calculation of the missed lengths based on the degree of overlap.

[0036] This embodiment can accurately identify and quantify the total redundant coil length of the cable at the sealing point, avoiding inaccurate measurements caused by positioning deviation, background interference, or uncorrected errors, and providing accurate redundant length parameters for subsequent construction of the cable 3D model.

[0037] Furthermore, in some embodiments, the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point is identified and quantified, and the specific calculation formula is as follows: in, Let be the loss function of the object detection algorithm. For bounding box loss, For classifying losses, For confidence loss, For regularization loss, These are the weighting coefficients, For the first One intersection and comparison, It is a binary cross-entropy function. For the first The probability of each true / predicted class For the first True / Predicted Confidence Level For regularization weights, For the first One bounding box deviation, To segment the output mask, , , For convolution, upsampling, and encoding operations, For cable image data, For exponential weighting coefficients, For the first Gradient values, For scale parameters, For the first Circle length, For the first Circle radius, For the first Interlayer overlap compensation The logarithmic compensation coefficient is... For the first Circle layers, For the total length of redundant coiling, For the first A viewpoint correction function, For the first Each shooting angle For correction factors, For exponential decay parameters, The starting index for summing the bounding box loss. The upper bound of the summation of bounding box losses, representing the total number of intersection-union ratios. The starting index for summing the classification loss. The summation of the classification loss is the upper bound, representing the total number of class probabilities. The starting index for summing the confidence loss. The upper bound of the summation of confidence loss is given by , where represents the total number of confidence levels. This is the starting index for summing the regularization loss. The upper bound of the summation of regularization losses is given by , which represents the total number of bounding box deviations. The starting index for the exponentially weighted summation. The upper bound of the exponentially weighted summation is given by , which represents the total number of gradient values. The starting index for summing the single-loop length and the total redundancy length. This represents the upper limit of the sum of the total redundant lengths, and indicates the total number of coil turns.

[0038] Specifically, the calculation formula first defines the actual meaning, data source, and value rules of each key parameter in the formula to ensure that the calculation logic matches the actual scenario of cable coiling at the substation blockage point. Through the target detection loss function formula, the bounding box localization, category judgment, confidence, and model regularization effects are comprehensively optimized to ensure that the algorithm can accurately locate the blockage point and coiling area in the image. Using the image segmentation formula, the cable image features are extracted through convolution, upsampling, and encoding operations. The edge accuracy is optimized by combining exponential weighted gradient to generate a segmentation mask containing only the coiled cable, achieving pixel-level separation of the cable from the background (wall, air). Based on the single-loop contour determined by the segmentation mask, the length of each loop of cable is calculated by combining the formula. Errors are corrected by inter-layer overlap compensation and logarithmic compensation (such as the length omission caused by the compression and overlap of adjacent loops of cable). The length deviation caused by the tilt of the shooting angle is corrected by the viewpoint correction function. The weights of different shooting angles are balanced by exponential decay. Finally, the single-loop lengths are accumulated to obtain the total redundant length.

[0039] This embodiment can accurately quantify the total redundant coil length of the cable at the sealing point, effectively eliminating errors caused by positioning deviation, background interference, overlapping obstruction, and viewing angle tilt, and providing high-precision redundant length parameters for the cable 3D model.

[0040] Furthermore, in some embodiments, step S4, "constructing a mechanical model to simulate the natural sag shape of the cable based on the material physical properties of the cable and the spacing between suspension points," may specifically include: S41. Input the material parameters of the cable and the distance between adjacent suspension points; Specifically, step S41 provides basic physical and geometric data for constructing the mechanical model, ensuring that the model accurately reflects the actual physical characteristics and laying scenario of the cable, laying a data foundation for subsequent sag simulation. Among these, the cable's material parameters are key indicators reflecting its physical properties, including cable density (mass per unit volume, determining the cable's own weight's impact on sag), elastic modulus (measuring the material's resistance to deformation, affecting the cable's tensile and bending degrees under stress), and cross-sectional area (the size of the cable's cross-section, directly related to its load-bearing capacity and deformation pattern). The distance between adjacent suspension points is a core geometric parameter for cable suspension, referring to the straight-line distance between two fixed cable suspension structures (such as brackets or hooks), determining the span range of cable sag calculation, and serving as the geometric basis for sag morphology simulation.

[0041] S42. Establish a catenary mechanical model to simulate the bending shape of the cable under gravity, and calculate the sag curve through temperature compensation and iterative calculation. Specifically, for step S42, based on the flexible physical characteristics of the cable and the law of gravity, its true shape of natural sag is restored, while the interference of ambient temperature changes on sag is corrected to ensure that the simulation results fit the actual working conditions. The catenary mechanical model is a classic model describing the equilibrium shape of a homogeneous flexible object under the action of gravity alone. Unlike the parabolic model, it can accurately reflect the smooth bending trajectory formed by the cable's own weight when both ends are fixed. The tension at each point of the cable changes along the curve, which conforms to the stress characteristics of actual flexible cables. Temperature compensation is used to adjust the influence of ambient temperature fluctuations on sag. When the temperature rises, the cable will lengthen due to thermal expansion and contraction, resulting in increased sag. When the temperature drops, the cable will shorten, resulting in decreased sag. The effective length and tension of the cable need to be corrected through the compensation formula to eliminate temperature interference. Iterative calculation involves repeatedly fine-tuning the model parameters (such as the average tension of the cable and the sag height) to gradually reduce the deviation between the calculation results and the cable's stress equilibrium conditions until a stable and continuous sag curve is obtained, that is, the regular curve of the sag height changing with the horizontal position.

[0042] S43. Optimize the catenary mechanical model by combining the finite element analysis method to adapt to different laying environments; Specifically, for step S43, the limitations of the catenary model in complex laying environments are addressed to make the simulation results closer to the diverse actual construction scenarios of substations. The catenary model usually only considers the effect of gravity and assumes a simple laying environment (such as no additional external forces and horizontal alignment of suspension points). However, the actual laying environment of a substation may have complex interference factors, such as gust loads in outdoor substations, slight contact forces between cables and other pipelines in cable trenches, and non-horizontal arrangement of suspension points due to construction errors. The finite element analysis method discretizes the cable into multiple small units (such as dividing a 10m long cable into 50 units of 0.2m length), calculates all external forces (such as gravity, wind force, and contact force) and deformations on each unit, and then integrates the force and deformation results of all units to back-optimize the key parameters of the catenary model (such as local tension distribution and bending curvature). This allows the model to adapt to the force characteristics of different environments and ensures that the simulated sag shape is highly consistent with the actual laying state.

[0043] This embodiment can efficiently and accurately simulate the natural sag shape of cables under different laying environments, providing morphological data that conforms to physical laws for subsequent construction of cable 3D models, and avoiding deviations between the 3D model and the actual cable shape due to inaccurate sag simulation.

[0044] Furthermore, in some embodiments, a mechanical model is constructed based on the material physical properties of the cable and the spacing between suspension points to simulate the natural sag of the cable. The specific calculation formula is as follows: in, Vertical height Let be the catenary constant. The exponential compensation coefficient is used. For the first Local tension For temperature-scale parameters, For average tension, For density, It is the acceleration due to gravity. For cross-sectional area, The logarithmic correction factor is... For elastic modulus, For maximum verticality, For logarithmic summation weights, For the first Spacing between individuals The total spacing between suspension points. The derivative of verticality. This is the exponential disturbance coefficient. For spatial scale parameters, This is the actual length of the cable. The starting index for the exponentially compensated summation. The upper limit of the exponentially compensated summation represents the total local tension. The starting index for the logarithmic summation. represents the upper limit of the logarithmic summation, and represents the total number of sub-intervals.

[0045] Specifically, the calculation formula first defines the actual meaning, data source, and value rules of each key parameter in the formula to ensure that the calculation logic is fully matched with the physical characteristics of the cable and the laying scenario. Using the catenary constant calculation formula, combined with cable tension, material properties, and correction coefficients, the core parameters reflecting the cable sag shape are determined. Based on the basic principles of the catenary and combined with exponential compensation to correct for temperature effects, a functional relationship between sag height and horizontal position is constructed to calculate the cable sag at any horizontal position. By logarithmically summing and integrating the sag contributions of each sub-spacing, the maximum sag of the cable is calculated. Using the boundary conditions of the actual cable length, the catenary constant is iteratively adjusted to eliminate the deviation between the calculated length and the actual length, ensuring that the sag simulation conforms to physical reality and avoiding deviations between the model and the actual shape due to inaccurate sag calculation.

[0046] Furthermore, in some embodiments, step S5, "generating a three-dimensional model of the cable through parametric modeling based on the spatial path, redundant winding length, and natural sag shape, so as to digitally control the construction quality of concealed cable engineering in substations through the three-dimensional model of the cable," may specifically include: S51. Use the spatial path as a skeleton, superimpose redundant winding length as a local adjustment parameter, and incorporate natural sag shape as a shape correction. Specifically, for step S51, key morphological data of cable laying are integrated to provide a complete and realistic basic framework for 3D modeling. Among them, the spatial path is the core orientation benchmark of the cable in 3D space, which determines the overall trajectory of the cable from the starting point to the ending point like a skeleton, such as the continuous path from the substation equipment cabinet to the wall penetration hole and then to the shaft, ensuring that the overall position of the model is consistent with the actual laying; the redundant coiling length is the basis for local adjustment at specific locations (wall penetration hole, shaft entrance). Cables at these locations need to reserve coiling length for convenient subsequent maintenance. Coiling structures (such as rings) need to be added at the corresponding positions of the skeleton according to the calculated redundant length to avoid the model omitting key local morphological features; the natural sag morphology is the morphological correction of the suspended cable section. The cable will sag due to its own weight between suspension points. The simulated sag curve (such as the shape of sag in the middle and flat at both ends) needs to be integrated into the suspended section of the skeleton to make the model get rid of the ideal state of rigid straight line and conform to the actual physical bending characteristics.

[0047] S52. Generate a 3D model of the cable using parametric modeling tools; Specifically, in step S52, the integrated skeleton, local adjustments, and shape correction data are transformed into a visualized and quantifiable 3D solid model of the cable. The parametric modeling tool can automatically generate the 3D structure by inputting key parameters (such as path curve, coiling radius / number of turns, and sag curve equation), eliminating the need for manual drawing of details. Parameters can be adjusted in real time; for example, the number of coiling turns is automatically updated when the redundant length changes. During the modeling process, the tool adds surface attributes based on the actual specifications of the cable (such as diameter and material color), enabling the model to not only reflect its shape but also its appearance features, such as the metallic luster of copper core cables and the black texture of the insulation layer. Ultimately, a 1:1 scale 3D model of the actual cable is generated, supporting multi-angle viewing, zooming, and cross-sectional analysis.

[0048] S53. Compare the generated 3D model of the cable with the design model. When the deviation obtained from the comparison exceeds the preset threshold, an early warning is triggered and a deviation analysis report is generated. Specifically, for step S53, the system compares the actual installation with the design requirements to identify discrepancies, providing timely warnings and tracing the problem. The design model is an ideal 3D model generated from engineering drawings, such as a sag design value of 0.15m for section AB and a redundant length design value of 1.2m for point B. During comparison, the system automatically calculates the deviations of the two models at key locations, such as spatial coordinate deviation, sag height deviation, and redundant length deviation, and compares them with preset thresholds (such as coordinate deviation ≤ 3cm, sag deviation ≤ 0.05m, and redundant length deviation ≤ 0.1m). If a deviation exceeds the threshold, such as the actual redundant length at point B being 1.5m while the design value is 1.2m, resulting in a deviation of 0.3m > 0.1m, the system immediately triggers a warning (such as a pop-up notification or audible and visual alarm). Simultaneously, a deviation analysis report is generated, clearly identifying the deviation location (wall penetration at point B), deviation type (excessive redundant length), deviation value (0.3m), and potential impacts (poor heat dissipation due to dense coiling), providing a clear basis for construction adjustments.

[0049] This embodiment can intuitively and accurately detect deviations in the construction of concealed cables in substations, and realize digital control of construction quality. It avoids the omissions and subjective errors of traditional manual inspections, and can quickly locate problems and guide rectification through reports, providing traceable technical support for ensuring the quality of concealed works.

[0050] To facilitate understanding of the intelligent 3D modeling method for concealed cable engineering in substations provided in this embodiment, this embodiment will be described in detail with specific examples.

[0051] The first step involves deploying image acquisition equipment at the construction site to obtain image data during cable laying. To ensure the comprehensiveness and accuracy of the data, this embodiment employs a combined fixed and mobile deployment strategy. Multiple high-definition cameras and infrared sensors are fixedly installed at key locations in the substation's concealed works, such as cable trenches, wall penetrations, shaft entrances, and suspension points. These fixed devices form a preliminary monitoring network capable of continuously recording construction activities at key nodes. To compensate for potential blind spots in the fixed monitoring network, mobile image acquisition equipment is also deployed, such as handheld cameras operated by on-site personnel or drones used where permitted. These mobile devices can flexibly supplement the image capture at any location during the dynamic laying process, thus forming a comprehensive monitoring network together with the fixed equipment. All acquisition devices transmit the captured multi-angle, high-resolution image data and video sequences to the back-end processing system in real time via a wireless network deployed on-site. To ensure data traceability, the system automatically labels each frame of image or video segment with a precise timestamp and the location coordinates at the time of acquisition, ensuring that the data covers the entire process of cable laying from installation to final fixing and sealing, providing a reliable data source for subsequent analysis and modeling.

[0052] After acquiring comprehensive image data, the second step is to use computer vision algorithms to reconstruct the three-dimensional spatial coordinate sequence of the cable centerline based on this cable image data, thereby forming its precise spatial path. This process first preprocesses the acquired raw image data, including image denoising to eliminate the impact of environmental noise on image quality; contrast enhancement to highlight the difference between the cable outline and the background; and feature point extraction to identify stable feature points on the cable surface or edges. Subsequently, an advanced structure for motion reconstruction (SFM) algorithm is applied. This algorithm can simultaneously estimate the camera pose and the three-dimensional coordinates of feature points on the cable at various moments from a multi-view image sequence without needing to know the precise camera position beforehand. To improve the accuracy and robustness of the path, after initially obtaining the three-dimensional point cloud, a series of optimization calculations are performed, including weighted triangulation, bundle adjustment, and exponential decay smoothing. This series of calculations optimizes the discrete feature point coordinates into a continuous and smooth three-dimensional point cloud sequence, and finally fits it into a smooth spatial path curve that accurately represents the cable centerline, ensuring that the final generated path has centimeter-level accuracy and meets the stringent requirements of engineering control.

[0053] The specific calculation model for restoring the three-dimensional spatial coordinate sequence of the cable centerline is as follows: The specific formula for weighted triangulation is: in, For the coordinates of a point in three-dimensional space, For this three-dimensional point in the th... The corresponding pixel coordinates on a two-dimensional image This is a projection function that maps points in three-dimensional space onto a two-dimensional image plane. This is the camera intrinsic parameter matrix, specifically a 3x3 matrix, containing internal parameters such as the camera's focal length and principal point offset. It is a 3x3 rotation matrix. It is a 3D translation vector. For the first The weight coefficients for each point are assigned based on the confidence level of feature point detection; the higher the confidence level, the greater the weight. These are exponentially decaying weights used to balance the reprojection error and the smoothing term. For the first Distance between feature points This is the attenuation scale parameter.

[0054] The objective function for the bundled adjustment is: The objective function consists of three parts. This is the reprojection error function, used to minimize the difference between the coordinates of a 3D point reprojected back into the image and its actual observed coordinates. This is the path smoothing error function, used to ensure that the recovered path is smooth and continuous. This is a logarithmic regularization error function used to prevent overfitting of the model. The total number of point cloud sequences. The first point in the point cloud sequence Three-dimensional point coordinates, For the first For the expected step size between adjacent points, This is the regularization coefficient, used to adjust the strength of the regularization term. The starting index for trigonometric summation. This represents the upper limit of the triangulation summation, indicating the total number of points in the image. The starting index for the exponentially decaying summation. The upper bound of the exponentially decaying summation is given by , which represents the total number of feature points. The starting index for summing the reprojection errors indicates that it starts from the first point cloud point. Let be the upper limit of the summation of reprojection errors, and represent the total number of point cloud sequences. The starting index for path smoothing summation indicates starting from the first adjacent pair. `index` represents the starting index for the regularized summation, indicating the start point from the first point cloud point. By minimizing the total error function, the coordinates of all 3D points and the poses of all cameras can be optimized simultaneously to obtain the optimal 3D path.

[0055] Next, the third step is to identify and quantify the complex shape of the cable at a specific location, which mainly includes two aspects: quantification of redundant coil length and simulation of natural sag shape.

[0056] First, the identification and quantification of redundant coil length primarily targets the reserved coiled portion of the cable at sealing points such as wall penetrations or shaft entrances. The system first uses an advanced target detection algorithm, such as the YOLOv8 model, to automatically locate the sealing points and the coiled area of ​​the cable from the acquired cable image data. After localization, the algorithm further extracts the ring-shaped contour formed by the cable coil and the approximate number of coil layers. Subsequently, a sophisticated image segmentation algorithm, such as the U-Net network model, is applied to perform pixel-level separation of the coiled cable structure, clearly distinguishing it from the background and other objects. Based on the segmented mask image, the system can accurately calculate the radius and circumference of each coil. Finally, by accumulating the calculated lengths of all coils and applying exponential viewpoint correction and logarithmic stacking compensation algorithms to eliminate measurement errors caused by shooting angle and multiple cable stacks, the accurate total redundant coil length is obtained. This quantification result will serve as an important parameter for subsequent local morphological adjustments of the 3D model.

[0057] The specific calculation model involved in this process is as follows.

[0058] The loss function of the YOLOv8 object detection algorithm is: Among them, the total loss function It consists of four parts: For bounding box loss, For classifying losses, For confidence loss, This is the regularization loss. These are the weighting coefficients, For the first The intersection-union ratio of each predicted bounding box to the ground truth bounding box. It is a binary cross-entropy function. For the first The probability of each true / predicted class For the first True / Predicted Confidence Level For regularization weights, For the first One bounding box deviation.

[0059] The output formula of the U-Net image segmentation algorithm is expressed as: in, The output mask for segmentation is a matrix of the same size as the original image, where each element has a value between 0 and 1, representing the probability that the pixel belongs to a cable. , , For convolution, upsampling, and encoding operations, For cable image data, For exponential weighting coefficients, For the first Gradient values, This is the scale parameter.

[0060] The formula for calculating the length of a single coil is: in, For the first Circle length, For the first Circle radius, For the first Interlayer overlap compensation is typically taken as 0.01 to 0.05 meters. The logarithmic compensation coefficient is... For the first The number of coil layers is used to correct for length calculation errors caused by multiple tightly coiled layers.

[0061] The formula for calculating the total redundancy length is: in, For the total length of redundant coiling, For the first A viewpoint correction function, For the first Each shooting angle For correction factors, For exponential decay parameters, The starting index for summing the bounding box loss. The upper bound of the summation of bounding box losses, representing the total number of intersection-union ratios. The starting index for summing the classification loss. The summation of the classification loss is the upper bound, representing the total number of class probabilities. The starting index for summing the confidence loss. The upper bound of the summation of confidence loss is given by , where represents the total number of confidence levels. This is the starting index for summing the regularization loss. The upper bound of the summation of regularization losses is given by , which represents the total number of bounding box deviations. The starting index for the exponentially weighted summation. The upper bound of the exponentially weighted summation is given by , which represents the total number of gradient values. The starting index for summing the single-loop length and the total redundancy length. This represents the upper limit of the sum of the total redundant lengths, and indicates the total number of coil turns.

[0062] Secondly, the simulation of the natural sag shape aims to realistically reflect the natural sag of the cable due to its own weight between two suspension points. This process first requires inputting the cable's material physical properties, such as its density per unit length, elastic modulus, and cross-sectional area, as well as the horizontal distance between two adjacent suspension points. Based on these parameters, the system establishes a catenary mechanical model. This model can accurately simulate the natural bending shape of a homogeneous flexible cable under gravity. To make the simulation results closer to actual working conditions, exponential temperature compensation and logarithmic iterative calculations are incorporated into the model to correct for the influence of ambient temperature changes on cable length and tension, thereby calculating an accurate sag curve. In some complex laying environments, the catenary model can be further optimized using finite element analysis to ensure that the final simulated shape is highly consistent with the actual laying state of the cable.

[0063] The specific calculation model for simulating the natural sag of cables is as follows.

[0064] The equation of the catenary is: The catenary equation describes the sag height. As the horizontal position x changes. The constant of the catenary determines the shape of the catenary. The exponential compensation coefficient is used. For the first Local tension This is a temperature-scale parameter.

[0065] The formula for calculating the catenary constant is: in, For average tension, For density, It is the acceleration due to gravity. For cross-sectional area, The logarithmic correction factor is... It is the elastic modulus.

[0066] The formula for calculating the maximum sag is: in, For maximum verticality, For logarithmic summation weights, For the first Spacing between individuals This represents the total spacing between suspension points.

[0067] To solve for the catenary constant, iterative calculations using boundary conditions are required, and the formula is as follows: The physical meaning of this formula is that the integral of the arc length along the catenary curve should be equal to the actual length of the cable. .in, The derivative of verticality. This is the exponential disturbance coefficient. For spatial scale parameters, This is the actual length of the cable. The starting index for the exponentially compensated summation. The upper limit of the exponentially compensated summation represents the total local tension. The starting index for the logarithmic summation. represents the upper limit of the logarithmic summation, and represents the total number of sub-intervals.

[0068] Finally, after completing spatial path recovery, redundant length quantification, and sag morphology simulation, the fourth step of the method is to organically combine these three aspects, generating a final high-precision 3D model of the cable through parametric modeling, thereby achieving digital control over the construction quality of concealed cable engineering in substations. In this step, the recovered spatial path is first used as the central skeleton of the 3D model. Then, the calculated redundant coiling length is used as a local adjustment parameter, precisely superimposed on the corresponding wall penetration or shaft entrance positions in the model to form the coiling shape. Simultaneously, the simulated natural sag morphology is used as a shape correction, integrated into the suspended cable segments to exhibit a natural sag consistent with physical laws. Using professional parametric modeling tools, such as surface generation techniques based on Bézier curves or NURBS curves, a complete 3D mesh model of the cable is constructed based on the integrated data. To enhance the realism of the model, appropriate materials and textures are added for rendering. The generated model is optimized to support smooth interactive visualization operations, including rotation at any angle, free scaling, and cross-sectional analysis, ensuring that the model accuracy fully meets the various requirements of digital engineering control.

[0069] Based on the generated 3D model, digital management and control of construction quality can be achieved. Specifically, the system compares the real-time generated "as-built" 3D model with the pre-imported "design" 3D model in the same coordinate system. Algorithms automatically calculate key indicators such as path deviation, length difference, and shape discrepancies between the two. When any deviation exceeds a preset threshold, for example, greater than 5%, the system automatically triggers an early warning mechanism and generates a detailed deviation analysis report, notifying management personnel to handle it promptly. To ensure the immutability and traceability of all data and process records, the system uses advanced technologies such as blockchain or digital signatures to encrypt and store the generated model data, comparison results, and processing records. This enables full lifecycle quality traceability and auditing of concealed works from construction to operation and maintenance, supports remote querying and compliance verification, and greatly improves the transparency and reliability of project management.

[0070] To facilitate better implementation of the intelligent 3D modeling method for concealed substation cable works according to the embodiments of this application, this application also provides an intelligent 3D modeling system for concealed substation cable works based on the aforementioned intelligent 3D modeling method. The meanings of the terms used are the same as in the aforementioned intelligent 3D modeling method for concealed substation cable works, and specific implementation details can be found in the descriptions within the method embodiments.

[0071] Please see Figure 3 , Figure 3This is a schematic diagram of the intelligent 3D modeling system for concealed cable works in substations provided in this application embodiment. Specifically, the intelligent 3D modeling system for concealed cable works in substations may include an image acquisition module 201, a spatial path module 202, a length calculation module 203, a sag simulation module 204, and a model construction module 205, as follows: Image acquisition module 201 is used to acquire image data of the cable during the laying process; The spatial path module 202 is used to recover the three-dimensional spatial coordinate sequence of the cable centerline based on image data and through computer vision algorithms to form a spatial path. The length calculation module 203 is used to identify and quantify the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point based on image data; The sag simulation module 204 is used to build a mechanical model based on the material physical properties of the cable and the spacing between suspension points to simulate the natural sag shape of the cable. The model building module 205 is used to generate a three-dimensional model of the cable through parametric modeling based on the spatial path, redundant winding length and natural sag shape, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable.

[0072] Specific limitations regarding the intelligent 3D modeling system for concealed cable works in substations can be found in the limitations of the intelligent 3D modeling method for concealed cable works in substations mentioned above, and will not be repeated here. Each module in the aforementioned intelligent 3D modeling system for concealed cable works in substations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0073] The intelligent 3D modeling system for concealed cable engineering in substations provided in this embodiment acquires image data of the cable laying process by deploying image acquisition equipment, restores the 3D spatial path of the cable centerline with the help of computer vision algorithms, identifies and quantifies the redundant coiling length of the cable sealing points, and constructs a mechanical model to simulate natural sag by combining cable attributes and the spacing between suspension points. Then, by combining the three factors, a 3D model of the cable is generated through parametric modeling, ultimately realizing digital control of the construction quality of concealed cable engineering in substations, improving the accuracy, efficiency and traceability of construction quality control.

[0074] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0075] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and intelligent 3D modeling methods for concealed cable engineering in substations by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0076] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0077] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0078] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows: The process involves acquiring image data of the cable during installation; using computer vision algorithms to reconstruct the three-dimensional spatial coordinate sequence of the cable centerline, forming a spatial path; identifying and quantifying the redundant coiling length of the cable at wall penetration holes or shaft entrance sealing points based on the image data; constructing a mechanical model based on the cable's material physical properties and suspension point spacing to simulate the cable's natural sag shape; and generating a three-dimensional cable model through parametric modeling based on the spatial path, redundant coiling length, and natural sag shape, thereby enabling digital control of the construction quality of concealed cable engineering in substations.

[0079] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0080] This application embodiment acquires image data of the cable laying process by deploying image acquisition equipment, restores the three-dimensional spatial path of the cable centerline with the help of computer vision algorithms, identifies and quantifies the redundant winding length of the cable sealing points, and constructs a mechanical model to simulate natural sag by combining cable attributes and suspension point spacing. Then, by combining the three, a three-dimensional model of the cable is generated through parametric modeling, and finally realizes the digital control of the construction quality of concealed cable engineering in substations, improving the accuracy, efficiency and traceability of construction quality control.

[0081] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0082] Therefore, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the intelligent 3D modeling methods for concealed cable works in substations provided in embodiments of this application. For example, the instructions can execute the following steps: The process involves acquiring image data of the cable during installation; using computer vision algorithms to reconstruct the three-dimensional spatial coordinate sequence of the cable centerline, forming a spatial path; identifying and quantifying the redundant coiling length of the cable at wall penetration holes or shaft entrance sealing points based on the image data; constructing a mechanical model based on the cable's material physical properties and suspension point spacing to simulate the cable's natural sag shape; and generating a three-dimensional cable model through parametric modeling based on the spatial path, redundant coiling length, and natural sag shape, thereby enabling digital control of the construction quality of concealed cable engineering in substations.

[0083] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0084] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0085] Since the instructions stored in the storage medium can execute the steps in any of the intelligent three-dimensional modeling methods for concealed substation cable projects provided in the embodiments of this application, the beneficial effects that any of the intelligent three-dimensional modeling methods for concealed substation cable projects provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0086] The above provides a detailed description of an intelligent 3D modeling method and system for concealed cable engineering in substations, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent three-dimensional modeling of concealed cable works in substations, characterized in that, include: Acquire image data during the cable laying process; Based on the image data, the three-dimensional spatial coordinate sequence of the cable centerline is recovered by computer vision algorithms to form a spatial path; Based on the image data, the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point is identified and quantified. Based on the material physical properties of the cable and the spacing between suspension points, a mechanical model is constructed to simulate the natural sag shape of the cable. Based on the spatial path, the redundant winding length, and the natural sag shape, a three-dimensional model of the cable is generated through parametric modeling, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable.

2. The intelligent three-dimensional modeling method for concealed cable engineering in substations according to claim 1, characterized in that, The acquisition of image data during cable laying includes: Multiple image acquisition devices were fixedly installed at key locations in the concealed works of the substation to form a preliminary coverage monitoring network; Mobile image acquisition devices are configured to supplement blind spots in the initial coverage monitoring network, forming a full coverage monitoring network; The image data is transmitted in real time via a wireless network.

3. The intelligent three-dimensional modeling method for concealed cable works in substations according to claim 1, characterized in that, The process of recovering the three-dimensional spatial coordinate sequence of the cable centerline using computer vision algorithms based on the image data to form a spatial path includes: The image data is preprocessed, including denoising, contrast enhancement, and feature point extraction. The camera pose and the three-dimensional coordinates of cable feature points are estimated from the image data using a motion recovery structure algorithm. By using weighted triangulation, bundle adjustment, and exponential decay smoothing, a continuous three-dimensional point cloud sequence of the cable centerline is calculated and fitted into a smooth spatial path curve.

4. The intelligent three-dimensional modeling method for concealed cable engineering in substations according to claim 3, characterized in that, The specific calculation formula for recovering the three-dimensional spatial coordinate sequence of the cable centerline using computer vision algorithms is as follows: in, For the coordinates of a point in three-dimensional space, For the first Coordinates of a two-dimensional image point For projection function, For the camera intrinsic parameter matrix, It is a 3x3 rotation matrix. It is a 3D translation vector. For the first The weighting coefficient of each point For exponentially decaying weights, For the first Distance between feature points For attenuation scale parameters, Let the reprojection error function be... The path smoothing error function is... Let logarithmic regularization be the error function. The total number of point cloud sequences. For the first Three-dimensional point coordinates, For the first For the expected step size between adjacent points, The regularization coefficient is . The starting index for trigonometric summation. This represents the upper limit of the triangulation summation, indicating the total number of points in the image. The starting index for the exponentially decaying summation. The upper bound of the exponentially decaying summation is given by , which represents the total number of feature points. The starting index for summing the reprojection errors indicates that it starts from the first point cloud point. Let be the upper limit of the summation of reprojection errors, and represent the total number of point cloud sequences. The starting index for path smoothing summation indicates starting from the first adjacent pair. This is the starting index for the regularized summation, indicating that it starts from the first point cloud point.

5. The intelligent three-dimensional modeling method for concealed cable works in substations according to claim 1, characterized in that, The step of identifying and quantifying the redundant coil length of the cable at the wall penetration hole or shaft entrance sealing point based on the image data includes: The blocking point and coiling area are located from the image data using a target detection algorithm, and the ring outline and number of coiling layers of the cable are extracted. The coiled structure is separated using an image segmentation algorithm, and the radius and circumference of each coil are calculated. The lengths of all coils are summed, and the total length of redundant coils is obtained through viewpoint correction and overlay compensation.

6. The intelligent three-dimensional modeling method for concealed cable engineering in substations according to claim 5, characterized in that, The specific calculation formula for identifying and quantifying the redundant coil length of the cable at the wall penetration hole or shaft entrance sealing point is as follows: in, Let be the loss function of the object detection algorithm. For bounding box loss, For classifying losses, For confidence loss, For regularization loss, These are the weighting coefficients, For the first One intersection and comparison, It is a binary cross-entropy function. For the first The probability of each true / predicted class For the first True / Predicted Confidence Level For regularization weights, For the first One bounding box deviation, To segment the output mask, , , For convolution, upsampling, and encoding operations, For cable image data, For exponential weighting coefficients, For the first Gradient values, For scale parameters, For the first Circle length, For the first Circle radius, For the first Interlayer overlap compensation The logarithmic compensation coefficient is... For the first Circle layers, For the total length of redundant coiling, For the first A viewpoint correction function, For the first Each shooting angle For correction factors, For exponential decay parameters, The starting index for summing the bounding box loss. The upper bound of the summation of bounding box losses, representing the total number of intersection-union ratios. The starting index for summing the classification loss. The summation of the classification loss is the upper bound, representing the total number of class probabilities. The starting index for summing the confidence loss. The upper bound of the summation of confidence loss is given by , where represents the total number of confidence levels. This is the starting index for summing the regularization loss. The summation of regularization losses is the upper bound, representing the total number of bounding box deviations. The starting index for the exponentially weighted summation. The upper bound of the exponentially weighted summation is given by , which represents the total number of gradient values. The starting index for summing the single-loop length and the total redundancy length. This represents the upper limit of the sum of the total redundant lengths, and indicates the total number of coil turns.

7. The intelligent three-dimensional modeling method for concealed cable works in substations according to claim 1, characterized in that, The mechanical model constructed based on the material physical properties of the cable and the spacing between suspension points to simulate the natural sag of the cable includes: Input the material parameters of the cable and the distance between adjacent suspension points; A catenary mechanical model was established to simulate the bending shape of the cable under gravity, and the sag curve was calculated through temperature compensation and iterative calculation. The mechanical model of the catenary was optimized by combining the finite element analysis method to adapt to different laying environments.

8. The intelligent three-dimensional modeling method for concealed cable works in substations according to claim 7, characterized in that, Based on the material physical properties of the cable and the spacing between suspension points, a mechanical model is constructed to simulate the natural sag of the cable. The specific calculation formula is as follows: in, Vertical height Let be the catenary constant. The exponential compensation coefficient is used. For the first Local tension For temperature-scale parameters, For average tension, For density, It is the acceleration due to gravity. For cross-sectional area, The logarithmic correction factor is... For elastic modulus, For maximum verticality, For logarithmic summation weights, For the first Spacing between individuals The total spacing between suspension points. The derivative of verticality. This is the exponential disturbance coefficient. For spatial scale parameters, This is the actual length of the cable. The starting index for the exponentially compensated summation. The upper limit of the exponentially compensated summation represents the total local tension. The starting index for the logarithmic summation. represents the upper limit of the logarithmic summation, and represents the total number of sub-intervals.

9. The intelligent three-dimensional modeling method for concealed cable works in substations according to claim 1, characterized in that, The process involves generating a 3D cable model through parametric modeling based on the spatial path, redundant winding length, and natural sag shape. This model is then used to digitally control the construction quality of concealed cable projects in substations. The spatial path is used as a skeleton, the redundant winding length is superimposed as a local adjustment parameter, and the natural sag shape is incorporated as a shape correction. Generate a 3D model of the cable using parametric modeling tools; The generated 3D model of the cable is compared with the design model. When the deviation obtained from the comparison exceeds the preset threshold, an early warning is triggered and a deviation analysis report is generated.

10. An intelligent three-dimensional modeling system for concealed cable engineering in substations, characterized in that, include: The image acquisition module is used to acquire image data of the cable during the laying process; The spatial path module is used to recover the three-dimensional spatial coordinate sequence of the cable centerline based on the image data using computer vision algorithms, thereby forming a spatial path. The length calculation module is used to identify and quantify the redundant coiling length of the cable at the wall penetration hole or shaft entrance sealing point based on the image data. The sag simulation module is used to construct a mechanical model based on the material physical properties of the cable and the spacing between suspension points to simulate the natural sag shape of the cable. The model building module is used to generate a three-dimensional model of the cable through parametric modeling based on the spatial path, the redundant winding length, and the natural sag shape, so as to digitally control the construction quality of the concealed cable project in the substation through the three-dimensional model of the cable.

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