A method and system for measuring the length of a power line point cloud curve

Abstract

The present invention discloses a method and system for measuring the length of a power line point cloud curve, wherein the method comprises: collecting all point cloud data of a target operation area, and classifying the collected point cloud data to classify the conductor point cloud data of all power lines; using a Euclidean clustering method to perform a clustering segmentation on the above-classified conductor point cloud data, and segmenting to obtain a complete single power line curve; each single power line curve contains a plurality of laser point data; multiple power line curves are obtained based on multiple clustering; when processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud; based on the ordered point cloud, the length of the current single power line curve is calculated; the above-mentioned power line point cloud curve length measurement method significantly improves the measurement efficiency and measurement accuracy, and is suitable for large-scale point cloud data processing in big data working conditions such as power transmission lines.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line inspection, and in particular to a method and system for measuring the length of a power line point cloud curve. Background Art

[0002] Transmission lines and substations are critical energy transmission facilities, operating across vast geographical areas and impacting the lives and work of tens of thousands of people. They also play a crucial role in supporting industrial applications and development. Therefore, they require periodic inspection and analysis, and the length of the ground conductor and various substation transfer lines are crucial factors in assessing safety.

[0003] In the actual measurement process, the complex shapes and different curvatures of various line segments increase the measurement difficulty. At the same time, direct manual measurement is not possible near the conductors due to safety issues. Therefore, an efficient non-contact curve length measurement method is of great significance to the safety analysis and detection of power transmission work.

[0004] With the rapid development of science and technology, various detection methods are emerging. Among them, LiDAR technology, with its high precision and ease of use, has become popular in fields such as equipment detection and perception. Therefore, LiDAR technology can be used to directly acquire point clouds of power transmission lines. This provides high point cloud accuracy, a non-contact data collection process, and high security. The embodiments of the present invention utilize LiDAR technology to collect point clouds and then employ a curve measurement algorithm system for measurement and calculation.

[0005] After searching and analyzing, two prior art papers were found: "CN114972640A - A method for three-dimensional reconstruction and parameter calculation of cables based on point clouds" and "CN114429497A - A method for measuring the body size of live Qinchuan cattle based on a 3D camera." After summarizing and analyzing, it is believed that there are currently two main methods for calculating curve length based on point cloud data: one is to first extract the point cloud data, then project it onto the XOY plane, and finally perform curve fitting to calculate the length; the other is to segment the point cloud data, then use a path planning method to search for the wire path, and finally calculate the curve length. Since power line point clouds are not all in the same plane, using the method of projecting onto a two-dimensional plane will affect the accuracy of the calculation results; and the point cloud curve fitting method requires determining the curve order and segmenting the curve to meet the function definition. In practice, there are always curves with different curvatures and shapes, which makes it difficult to unify the above parameters and methods. In addition, curves may have problems such as missing and uneven density. When using the path planning method to construct a map, the grid resolution is difficult to calculate correctly. A larger resolution will result in large errors in the calculation results, and a smaller resolution may cause the same wire path to be broken, affecting the calculation results. Summary of the Invention

[0006] The present invention provides a method and system for measuring the length of a power line point cloud curve, which are used to solve the technical problems in the background technology.

[0007] To achieve the above object, according to a first aspect of the present invention, a method for measuring the length of a power line point cloud curve is proposed, comprising:

[0008] Collect all point cloud data of the target operation area, classify the collected point cloud data, and classify the conductor point cloud data of all power lines;

[0009] A European clustering method is used to perform a cluster segmentation on the wire point cloud data classified above, and a complete single power line curve is obtained by segmentation; each single power line curve contains a number of laser point data; multiple power line curves are obtained based on multiple clustering;

[0010] When processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud;

[0011] The length of the current single power line curve is calculated based on the ordered point cloud.

[0012] Preferably, the laser radar scanning device includes any one of an airborne laser radar scanning device and a vehicle-mounted laser radar scanning device.

[0013] Preferably, all point cloud data of the target operation area is collected, and the collected point cloud data is classified to classify the conductor point cloud data of all power lines, specifically including:

[0014] Use laser radar scanning equipment to perform three-dimensional laser scanning on the scanning operation area and collect all point cloud data of the target operation area;

[0015] All point cloud data are denoised to obtain denoised point cloud data, and the denoised point cloud data are classified based on the denoised point cloud data to obtain a point cloud category of the power line. Finally, based on the point cloud category of the power line, it is determined whether the point cloud data is conductor point cloud data of the power line.

[0016] Preferably, a European clustering method is used to perform cluster segmentation on the above-classified wire point cloud data to obtain a complete single power line curve, specifically including:

[0017] A KD index tree is established based on all the wire point cloud data, and any point cloud among the current multiple wire point cloud data is determined as the initial point cloud. The initial point cloud is used as the initial search point and the set distance threshold r is used as the search radius to search all point clouds.

[0018] Then, the search is repeated based on the newly searched point cloud as the origin until no new points are added. After the search is completed, a point cloud classification set is obtained; the point clouds in the point cloud classification set constitute a complete single power line curve.

[0019] Preferably, when processing each single power line curve, performing a directional search on the single power line curve to obtain an ordered point cloud specifically includes:

[0020] Select any point on the current single power line curve as the initial search site, and search for all points within a neighborhood radius of r based on the initial search site; find the point p1 farthest from the current initial search site among all points within the neighborhood radius r, connect the current point and the farthest point p1 to calculate the direction d1, and determine the direction d1 as the target direction; then find another point p2 from far to near that is in the opposite direction of d1, and calculate the direction d2 based on the line connecting the current point and point p2;

[0021] Then, using p1 as the initial search point, search all points within a radius of r around p1, and find the point p3 with the same direction as the target and the farthest position from far to near. Calculate the direction d3 and determine direction d3 as the updated target direction. Similarly, search all points within a radius of r around p2, and determine the farthest point p4 with the same direction as d2. Calculate the new direction d4 by connecting the current point and the farthest point p4.

[0022] This process is repeated until no more points are added. At this point, all points obtained based on the search with the longest distance and the same direction are an ordered point cloud.

[0023] Preferably, computing the length of the current single power line curve based on the ordered point cloud specifically includes:

[0024] Performing center point normalization calculation on the ordered point cloud to obtain the center point of each ordered point cloud within a radius r', and then connecting all the center points to obtain a center point simulation dotted line whose shape is closer to the curve form;

[0025] Finally, a dashed line is simulated based on the center point, and the distances before and after the center point obtained from the ordered point cloud on the dashed line of the center point simulation are iteratively added to obtain the curve length.

[0026] Accordingly, the present invention provides a power line point cloud curve length measurement system, comprising a laser radar scanning device and a main controller; the main controller comprises an acquisition module, a classification processing module, a search processing module and a measurement calculation module;

[0027] LiDAR scanning equipment, used to perform three-dimensional laser scanning of the scanning operation area and collect all point cloud data of the target operation area;

[0028] The acquisition module is used to receive all point cloud data of the target operation area and classify the collected point cloud data to classify the conductor point cloud data of all power lines;

[0029] A classification processing module is used to perform a cluster segmentation on the wire point cloud data classified above using a Euclidean clustering method, and obtain a complete single power line curve by segmentation; each single power line curve contains a plurality of laser point data; multiple power line curves are obtained based on multiple clustering;

[0030] A search processing module is used to perform a directional search on each single power line curve to obtain an ordered point cloud when processing each single power line curve;

[0031] The measurement and calculation module is used to calculate the length of the current single power line curve based on the ordered point cloud.

[0032] Preferably, the acquisition module is further specifically used to perform denoising on all point cloud data to obtain denoised point cloud data, classify the denoised point cloud data based on the denoised point cloud data to obtain point cloud categories of power lines, and finally determine whether the point cloud data is conductor point cloud data of the power lines based on the point cloud categories of the power lines.

[0033] Preferably, the classification processing module is further specifically used to establish a KD index tree based on all the wire point cloud data, determine any one point cloud among the current multiple wire point cloud data as the initial point cloud, use the initial point cloud as the initial search point and use the set distance threshold r as the search radius to search all point clouds; then, based on the newly searched point cloud as the origin, continue to search repeatedly until no new points are added, and obtain a point cloud classification set after the search is completed; the point clouds in the point cloud classification set constitute a complete single power line curve.

[0034] Preferably, the search processing module is further specifically used to select any point on the current single power line curve as the initial search site, search for all points within a neighborhood radius of r based on the initial search site; find the point p1 farthest from the current initial search site among all points within the neighborhood radius of r, connect the current point and the farthest point p1 to calculate the direction d1, and determine the direction d1 as the target direction; then find another point p2 from far to near that is in the opposite direction of d1, and calculate the direction d2 based on the line connecting the current point and point p2; then use p1 as the target direction. The search is performed initially at the search site. All points within a neighborhood radius of r with p1 as the origin are searched. From far to near, the point p3 with the same direction as the target and the farthest position is found. The direction d3 is calculated and determined as the updated target direction. Similarly, all points within a neighborhood radius of r with p2 as the origin are searched, and the farthest point p4 with the same direction as d2 is determined. The new direction d4 is calculated by connecting the current point and the farthest point p4. This process is repeated until no points are added. At this time, all points obtained based on the search with the farthest distance and the same direction are an ordered point cloud.

[0035] The measurement and calculation module is also specifically used to perform center point normalization calculation on the ordered point cloud to obtain the center point of the point cloud within a radius r' range near each ordered point cloud, and then connect all the center points to obtain a center point simulated dotted line whose shape is closer to the curve shape; finally, based on the center point simulated dotted line, the front and rear distances of the center point obtained from the ordered point cloud on its center point simulated dotted line are iteratively added to obtain the curve length.

[0036] Compared with the prior art, the embodiments of the present invention have at least the following technical effects:

[0037] The present invention provides a method and system for measuring the length of a power line point cloud curve, wherein the method includes:

[0038] All point cloud data of the target operation area are collected and classified to obtain conductor point cloud data of all power lines; the classified conductor point cloud data are clustered and segmented using a Euclidean clustering method to obtain a complete single power line curve; each single power line curve contains a number of laser point data; multiple power line curves are obtained based on multiple clustering; when processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud; and the length of the current single power line curve is calculated based on the ordered point cloud.

[0039] This paper proposes a method for measuring the length of power line point cloud curves. Using a point cloud of the conductor to be measured, the method uses Euclidean clustering to segment individual conductors. Based on the characteristic that only forward and backward directions exist during the conductor search, a directional search is performed on the individual curves to calculate an ordered conductor point cloud. The ordered point cloud then calculates the conductor center points within a given range. Finally, the distances between these center points are sequentially added to calculate the conductor length. This method can measure the length of three-dimensional conductors of arbitrary curvature and effectively solves the problem of missing parts of the conductor point cloud. It has low algorithm complexity, high execution efficiency, and a wide range of applications.

[0040] Among them, "CN114972640A-A method for three-dimensional reconstruction and parameter calculation of cables based on point clouds" uses a depth camera to obtain point clouds, then segments the wire point clouds, projects part of the wire point clouds to calculate their diameters to establish an octree, and finally uses the A* algorithm to find the optimal path, and finally interpolates to calculate the curve length. This method can calculate curves of arbitrary curvature, but its algorithm complexity is high and depends on the accuracy of wire diameter calculation. At the same time, when the point cloud density is inconsistent or there are partial missing wire point clouds, the same wire path will split into multiple ones. However, the method of the embodiment of the present invention can effectively avoid the problem of wire path splitting by setting the clustering radius. At the same time, the method of the embodiment of the present invention has low complexity, higher execution efficiency, and stronger applicability.

[0041] Furthermore, "CN114429497A - A 3D Camera-Based Method for Measuring the Body Dimensions of Live Qinchuan Cattle" uses a 3D camera to acquire a point cloud, transform it spatially, and perform curve fitting and surface reconstruction on the point cloud data to complete the measurement. However, this method, which uses point cloud projection and curve fitting, has low generalizability and cannot be applied to length measurements of point clouds that are not in the same plane.

[0042] Therefore, the power line point cloud curve length measurement method provided by the present invention significantly improves the measurement efficiency and measurement accuracy, and is suitable for large-scale point cloud data processing in big data working conditions such as transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0044] Figure 1 This is a schematic diagram of the main process of the method for measuring the length of a power line point cloud curve provided by an embodiment of the present invention;

[0045] Figure 2This is a flow chart of a specific example of a method for measuring the length of a power line point cloud curve provided by an embodiment of the present invention;

[0046] Figure 3 This is a schematic flow chart of a specific example of segmenting a power line point cloud curve length measurement method provided by an embodiment of the present invention to obtain a complete single power line curve;

[0047] Figure 4 This is a schematic flow chart of a specific example of a method for measuring the length of a power line point cloud curve provided by an embodiment of the present invention, in which a single power line curve is searched with a direction to obtain an ordered point cloud;

[0048] Figure 5 This is a schematic flow chart of a specific example of a method for measuring the length of a power line point cloud curve provided by an embodiment of the present invention based on the length of a current single power line curve calculated by the ordered point cloud;

[0049] Figure 6 Schematic diagram of a power line point cloud curve length measurement system provided by an embodiment of the present invention;

[0050] Reference numerals: laser radar scanning device 10 ; main controller 20 ; acquisition module 21 ; classification processing module 22 ; search processing module 23 ; measurement calculation module 24 . DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] In the description of the present invention, it should be noted that the terms "center", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0054] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0055] like Figure 1 To achieve the above-mentioned purpose, the present invention proposes a method for measuring the length of a power line point cloud curve, which includes the following steps:

[0056] S100, collecting all point cloud data of the target operation area, and classifying the collected point cloud data to classify the conductor point cloud data of all power lines;

[0057] S200, performing a cluster segmentation on the wire point cloud data classified above using a Euclidean clustering method, and obtaining a complete single power line curve by segmentation; each single power line curve includes a plurality of laser point data; and obtaining multiple power line curves based on multiple clustering;

[0058] Specifically, the Euclidean clustering method sets a given distance threshold, r, and uses a kd-tree to search for all points within a radius of r. The search is then repeated based on the found points until no new points are added. Each search results in this method classifying the points as a complete curve (i.e., a single power line).

[0059] S300, when processing each single power line curve, performing a directional search on the single power line curve to obtain an ordered point cloud;

[0060] S400: Calculate the length of the current single power line curve based on the ordered point cloud.

[0061] The specific technical solution and specific technical effects of the method for measuring the length of a power line point cloud curve provided by an embodiment of the present invention are described in detail below:

[0062] Preferably, as an implementable solution, the laser radar scanning device includes any one of an airborne laser radar scanning device and a vehicle-mounted laser radar scanning device.

[0063] See also Figure 2 , collect all point cloud data of the target operation area, and classify the collected point cloud data to classify the conductor point cloud data of all power lines, including:

[0064] S101, performing three-dimensional laser scanning on the scanning operation area using a laser radar scanning device to collect all point cloud data of the target operation area;

[0065] S102, performing denoising processing on all point cloud data to obtain denoised point cloud data, classifying the denoised point cloud data to obtain a point cloud category of power lines, and finally determining whether the point cloud data is conductor point cloud data of power lines based on the point cloud category of power lines (i.e., the purpose of classification is to distinguish power lines from other entities, thereby screening out power lines);

[0066] (1) Scan the operating area with 3D LiDAR to collect point cloud data mainly of transmission lines and substations, including poles, conductors, ground, vegetation, buildings, transformer boxes, meters and other ground features.

[0067] (2) Classify the collected point cloud data and classify the wire data required for measurement.

[0068] Specifically, laser point cloud technology is used to measure the spatial position of objects, describing their absolute, true location within the terrain. After acquiring the point cloud data, noise removal is typically required before point cloud classification. Currently, classification methods primarily include automatic point cloud classification and manual classification. Automatic classification uses traditional point cloud clustering methods or deep learning point cloud classification methods to isolate wire categories. Manual classification primarily involves manual operation to separate point clouds into different categories.

[0069] See also Figure 3 , using the Euclidean clustering method to perform a cluster segmentation on the wire point cloud data classified above, and segmenting to obtain a complete single power line curve, specifically including:

[0070] S201, establishing a KD index tree based on all wire point cloud data, determining any point cloud among the current plurality of wire point cloud data as an initial point cloud, using the initial point cloud as an initial search point and using a set distance threshold r as a search radius, searching all point clouds;

[0071] S202 , continuing to search repeatedly based on the newly searched point cloud as the origin until no new points are added, and obtaining a point cloud classification set after the search is completed; the point clouds in the point cloud classification set constitute a complete single power line curve.

[0072] Specifically, the Euclidean clustering method sets a given distance threshold, r, and uses a kd-tree to search for all points within a radius of r. The search is then repeated based on the newly found points until no new points are added. Each search results in this method classifying the points as a complete curve (i.e., a single power line).

[0073] See also Figure 4When processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud, specifically including:

[0074] S301, selecting any point on the current single power line curve as an initial search site, searching for all points within a neighborhood radius r based on the initial search site; finding the point p1 farthest from the current initial search site among all points within the neighborhood radius r, connecting the current point and the farthest point p1 to calculate a direction d1, and determining direction d1 as the target direction; then finding another point p2 in the opposite direction to d1 from far to near, and calculating direction d2 based on the line connecting the current point and point p2;

[0075] S302, search again with p1 as the initial search point, search all points within a neighborhood radius of r with p1 as the origin, find point p3 with the same direction as the target and the farthest position from far to near, calculate direction d3, and determine direction d3 as the updated target direction; similarly search all points within a neighborhood radius of r with p2 as the origin, determine the farthest point p4 in the same direction as d2, and calculate the new direction d4 by connecting the current point and the farthest point p4;

[0076] S303, repeating this process until no more points are added, at which point all points obtained based on the search with the longest distance and the same direction form an ordered point cloud.

[0077] The same direction means that the dot product of the directions is less than 0, that is, the angle between them is less than 90 degrees.

[0078] To explain this, there are only two possible search directions for any point on a curve segment: one in the forward direction and the other in the opposite direction. Using this logic, a search is performed at any point on the curve (the initial search point) to obtain all points within a neighborhood radius of r. The farthest point, p1, is found. A line is connected between the current point and p1 to calculate direction d1 (determining direction d1 as the target direction). Next, a point, p2, is found, pointing in the opposite direction of d1, and direction d2 is calculated. The search continues with p1 as the initial search point. All points within a neighborhood radius of r, with p1 as the origin, are searched to find the farthest point, p3, pointing in the same direction as d1 (the target direction), and direction d3 is calculated. Similarly, all points within a neighborhood radius of r, with p2 as the origin, are searched to determine the farthest point, p4, pointing in the same direction as d2. A new direction d4 is calculated by connecting the current point and p4. This calculation repeats until no more points are added. It should be noted that "same direction" here means that the dot product of the directions is less than 0, meaning that the angle between them is less than 90 degrees.

[0079] See also Figure 5 , computing the length of the current single power line curve based on the ordered point cloud, specifically including:

[0080] S401, performing center point normalization calculation on the ordered point cloud to obtain the center point of each ordered point cloud (or simply ordered point) within a radius r', and then connecting all the center points to obtain a center point simulation dashed line whose shape is closer to a curve;

[0081] S102. Finally, based on the center point simulated dotted line, the front and rear distances of the center points obtained from the ordered point cloud on the center point simulated dotted line are iteratively added to obtain the curve length (i.e., if there are 1, 2, 3, 4...n center points on the center point simulated dotted line, first calculate the front and rear distance (i.e., straight-line distance) between points 1 and 2, and then calculate the front and rear distance (straight-line distance) between points 2 and 3. In this way, the straight-line distance between two adjacent points is calculated, and finally the distance between points 1 to n is obtained by superposition. Obviously, the above distances are not the true curve lengths, but this method is sufficient to make the calculation more accurate).

[0082] Specifically, because the ordered point cloud is searched based on the longest distance and the same direction, most of the ordered points are located on the surface of the curve, resulting in a broken line effect. Direct addition of the distances will result in large errors. Therefore, by calculating the center point of the point cloud within a radius r' around each ordered point and then connecting all the center points, the shape is closer to the curve, resulting in a more accurate result.

[0083] Example 2

[0084] See also Figure 6 , Embodiment 2 of the present invention provides a power line point cloud curve length measurement system, including a laser radar scanning device 10 and a main controller 20; the main controller 20 includes an acquisition module 21, a classification processing module 22, a search processing module 23 and a measurement calculation module 24;

[0085] LiDAR scanning equipment, used to perform three-dimensional laser scanning of the scanning operation area and collect all point cloud data of the target operation area;

[0086] The acquisition module is used to receive all point cloud data of the target operation area and classify the collected point cloud data to classify the conductor point cloud data of all power lines;

[0087] A classification processing module is used to perform a cluster segmentation on the wire point cloud data classified above using a Euclidean clustering method, and obtain a complete single power line curve by segmentation; each single power line curve contains a plurality of laser point data; multiple power line curves are obtained based on multiple clustering;

[0088] A search processing module is used to perform a directional search on each single power line curve to obtain an ordered point cloud when processing each single power line curve;

[0089] The measurement and calculation module is used to calculate the length of the current single power line curve based on the ordered point cloud.

[0090] Preferably, the acquisition module is further specifically used to perform denoising on all point cloud data to obtain denoised point cloud data, classify the denoised point cloud data based on the denoised point cloud data to obtain point cloud categories of power lines, and finally determine whether the point cloud data is conductor point cloud data of the power lines based on the point cloud categories of the power lines.

[0091] Preferably, the classification processing module is further specifically used to establish a KD index tree based on all the wire point cloud data, determine any one point cloud among the current multiple wire point cloud data as the initial point cloud, use the initial point cloud as the initial search point and use the set distance threshold r as the search radius to search all point clouds; then, based on the newly searched point cloud as the origin, continue to search repeatedly until no new points are added, and obtain a point cloud classification set after the search is completed; the point clouds in the point cloud classification set constitute a complete single power line curve.

[0092] Preferably, the search processing module is further specifically used to select any point on the current single power line curve as the initial search site, search for all points within a neighborhood radius of r based on the initial search site; find the point p1 farthest from the current initial search site among all points within the neighborhood radius of r, connect the current point and the farthest point p1 to calculate the direction d1, and determine the direction d1 as the target direction; then find another point p2 from far to near that is in the opposite direction of d1, and calculate the direction d2 based on the line connecting the current point and point p2; then use p1 as the target direction. The search is performed initially at the search site. All points within a neighborhood radius of r with p1 as the origin are searched. From far to near, the point p3 with the same direction as the target and the farthest position is found. The direction d3 is calculated and determined as the updated target direction. Similarly, all points within a neighborhood radius of r with p2 as the origin are searched, and the farthest point p4 with the same direction as d2 is determined. The new direction d4 is calculated by connecting the current point and the farthest point p4. This process is repeated until no points are added. At this time, all points obtained based on the search with the farthest distance and the same direction are an ordered point cloud.

[0093] The measurement and calculation module is also specifically used to perform center point normalization calculation on the ordered point cloud to obtain the center point of the point cloud within a radius r' range near each ordered point cloud, and then connect all the center points to obtain a center point simulated dotted line whose shape is closer to the curve shape; finally, based on the center point simulated dotted line, the front and rear distances of the center point obtained from the ordered point cloud on its center point simulated dotted line are iteratively added to obtain the curve length.

[0094] In summary, the method and system for measuring the length of a power line point cloud curve provided by the present invention, on the one hand, measures curve length based on point cloud data and is applicable to three-dimensional curves of arbitrary curvature. Furthermore, by clustering and segmenting the point cloud, setting an appropriate radius effectively addresses issues such as missing or broken points in the curve. Third, the method in this embodiment utilizes only a Euclidean clustering algorithm and a directional search algorithm, resulting in low algorithm complexity, high execution efficiency, and a wide range of applicability.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third etc. does not indicate any order. These words may be interpreted as names.

[0100] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for measuring the length of a power line point cloud curve, characterized in that: The steps are as follows: Collect all point cloud data of the target operation area, classify the collected point cloud data, and classify the conductor point cloud data of all power lines; A European clustering method is used to perform a cluster segmentation on the wire point cloud data classified above, and a complete single power line curve is obtained by segmentation; each single power line curve contains a number of laser point data; multiple power line curves are obtained based on multiple clustering; When processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud; Calculating the length of the current single power line curve based on the ordered point cloud; When processing each single power line curve, a directional search is performed on the single power line curve to obtain an ordered point cloud, specifically including: Select any point on the current single power line curve as the initial search site, and search for all points within a neighborhood radius of r based on the initial search site; find the point p1 farthest from the current initial search site among all points within the neighborhood radius r, connect the current point and the farthest point p1 to calculate the direction d1, and determine the direction d1 as the target direction; then find another point p2 from far to near that is in the opposite direction of d1, and calculate the direction d2 based on the line connecting the current point and point p2; Then, using p1 as the initial search point, search all points within a radius of r around p1, and find the point p3 with the same direction as the target and the farthest position from far to near. Calculate the direction d3 and determine direction d3 as the updated target direction. Similarly, search all points within a radius of r around p2, and determine the farthest point p4 with the same direction as d2. Calculate the new direction d4 by connecting the current point and the farthest point p4. This process is repeated until no more points are added. At this point, all points found based on the longest distance and the same direction are an ordered point cloud. The length of the current single power line curve is calculated based on the ordered point cloud, specifically including: Performing center point normalization calculation on the ordered point cloud to obtain the center point of each ordered point cloud within a radius r', and then connecting all the center points to obtain a center point simulation dotted line whose shape is closer to the curve form; Finally, a dashed line is simulated based on the center point, and the distances before and after the center point obtained from the ordered point cloud on the dashed line of the center point simulation are iteratively added to obtain the curve length.

2. The method for measuring the length of a power line point cloud curve according to claim 1, wherein: Collect all point cloud data of the target operation area and classify the collected point cloud data to classify the conductor point cloud data of all power lines, including: Use laser radar scanning equipment to perform three-dimensional laser scanning on the scanning operation area and collect all point cloud data of the target operation area; All point cloud data are denoised to obtain denoised point cloud data, and the denoised point cloud data are classified based on the denoised point cloud data to obtain a point cloud category of the power line. Finally, based on the point cloud category of the power line, it is determined whether the point cloud data is conductor point cloud data of the power line.

3. The method for measuring the length of a power line point cloud curve according to claim 2, wherein: The laser radar scanning device includes any one of an airborne laser radar scanning device and a vehicle-mounted laser radar scanning device.

4. The method for measuring the length of a power line point cloud curve according to claim 3, wherein: The Euclidean clustering method is used to perform a cluster segmentation on the wire point cloud data classified above, and a complete single power line curve is obtained by segmentation, specifically including: A KD index tree is established based on all the wire point cloud data, and any point cloud among the current multiple wire point cloud data is determined as the initial point cloud. The initial point cloud is used as the initial search point and the set distance threshold r is used as the search radius to search all point clouds. Then, the search is repeated based on the newly searched point cloud as the origin until no new points are added. After the search is completed, a point cloud classification set is obtained; the point clouds in the point cloud classification set constitute a complete single power line curve.

5. A power line point cloud curve length measurement system, characterized in that: It is operated by using the power line point cloud curve length measurement method according to any one of claims 1 to 4; the power line point cloud curve length measurement system includes a laser radar scanning device and a main controller; the main controller includes an acquisition module, a classification processing module, a search processing module and a measurement calculation module; LiDAR scanning equipment, used to perform three-dimensional laser scanning of the scanning operation area and collect all point cloud data of the target operation area; The acquisition module is used to receive all point cloud data of the target operation area and classify the collected point cloud data to classify the conductor point cloud data of all power lines; A classification processing module is used to perform a cluster segmentation on the wire point cloud data classified above using a Euclidean clustering method, and obtain a complete single power line curve by segmentation; each single power line curve contains a plurality of laser point data; multiple power line curves are obtained based on multiple clustering; A search processing module is used to perform a directional search on each single power line curve to obtain an ordered point cloud when processing each single power line curve; The measurement and calculation module is used to calculate the length of the current single power line curve based on the ordered point cloud.

6. The power line point cloud curve length measurement system according to claim 5, characterized in that: The acquisition module is further specifically used to perform denoising on all point cloud data to obtain denoised point cloud data, classify the denoised point cloud data based on the denoised point cloud data to obtain the point cloud category of the power line, and finally determine whether the point cloud data is the conductor point cloud data of the power line based on the point cloud category of the power line.

7. The power line point cloud curve length measurement system according to claim 6, characterized in that: The classification processing module is further specifically used to establish a KD index tree based on all the wire point cloud data, determine any one point cloud among the current multiple wire point cloud data as the initial point cloud, use the initial point cloud as the initial search point and the set distance threshold r as the search radius to search all point clouds; then, based on the newly searched point cloud as the origin, continue to search repeatedly until no new points are added, and obtain a point cloud classification set after the search is completed; the point clouds in the point cloud classification set constitute a complete single power line curve.

8. The power line point cloud curve length measurement system according to claim 7, characterized in that: The search processing module is further specifically used to select any point on the current single power line curve as the initial search site, search for all points within a neighborhood radius of r based on the initial search site; find the farthest point p1 from the current initial search site among all points within the neighborhood radius r, connect the current point and the farthest point p1 to calculate the direction d1, and determine the direction d1 as the target direction; then find another point p2 from far to near that is opposite to the direction of d1, and calculate the direction d2 based on the line connecting the current point and point p2; then use p1 as the initial search site. Search the site to search, search all points within a radius of r with p1 as the origin, find the point p3 with the same direction as the target and the farthest position from far to near, calculate the direction d3, and determine the direction d3 as the updated target direction; similarly, search all points within a radius of r with p2 as the origin, determine the farthest point p4 in the same direction as d2, and calculate the new direction d4 by connecting the current point and the farthest point p4; repeat this process until no more points are added, at which point all points obtained based on the search with the farthest distance and the same direction are an ordered point cloud; The measurement and calculation module is also specifically used to perform center point normalization calculation on the ordered point cloud to obtain the center point of the point cloud within a radius r' range near each ordered point cloud, and then connect all the center points to obtain a center point simulated dotted line whose shape is closer to the curve shape; finally, based on the center point simulated dotted line, the front and rear distances of the center point obtained from the ordered point cloud on its center point simulated dotted line are iteratively added to obtain the curve length.

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