Hanging line node matching and conductor reconstruction method and system based on multi-source point cloud constraint

Abstract

This invention relates to the field of three-dimensional image data processing technology, and particularly to a method and system for wire connection node matching and conductor reconstruction based on multi-source point cloud constraints. The method and system, by comprehensively utilizing conductor point cloud data, tower point cloud data, and tower 3D model data, achieve automatic identification and accurate reconstruction of conductor connection relationships between adjacent towers. It fully utilizes the real spatial morphology reflected by the conductor point cloud to establish a globally optimal matching relationship among candidate wire connection node sets of adjacent towers, and integrates structural priors and engineering constraints to accurately determine the actual conductor connection position and achieve continuous spatial reconstruction of the conductor. This solves the problems of low accuracy, poor robustness, and insufficient engineering applicability in existing technologies for conductor connection node matching.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional image data processing technology, and in particular to a method and system for matching wire nodes and reconstructing wires based on multi-source point cloud constraints. Background Technology

[0002] With the rapid development of digitalization, 3D visualization, and intelligent inspection technologies for power transmission lines, 3D modeling of conductors based on point cloud data, tower models, and spatial curve representation has become a hot research and application area in the industry. Current technologies mainly include the following typical approaches: (1) Traverse modeling based on design geometry rules In traditional transmission line design and modeling processes, conductors are typically modeled using geometric specifications or engineering experience from the design phase. These methods primarily generate conductors based on tower spacing, conductor suspension parameters, and standard geometric curve formulas, but do not consider the actual position and orientation of the towers in real space.

[0003] (2) Direct wire fitting based on laser point cloud With the widespread application of lidar point cloud acquisition technology, point cloud data collected from power transmission lines is extracted and segmented, and then the geometry of the conductor is reconstructed using point cloud fitting techniques. While this method reflects the actual location of the conductor using point cloud data, it often neglects point cloud noise, occlusion, and structural semantic information, easily leading to unstable or discontinuous reconstruction results.

[0004] (3) Scheme based on tower model and node extraction Potential wire connection points are extracted based on the 3D CAD / FBX model of the tower, and then the conductors are connected and fitted according to local distances or structural rules. This type of method incorporates tower structural information, but it usually adopts an independent matching method or empirical rule matching, which makes it difficult to guarantee a one-to-one correspondence between the conductors and the actual connection points of the tower.

[0005] Furthermore, existing technologies typically employ local matching or step-by-step judgment in the process of matching wire hanging nodes, lacking global constraints on the overall spatial morphology of the conductor. This makes it difficult to ensure the overall consistency of conductor connection relationships between adjacent towers, easily leading to matching results that are locally reasonable but overall unrealistic. While some methods incorporate 3D tower models or point cloud data, they fail to effectively integrate with the actual point cloud information of the conductor, resulting in deviations between the reconstructed results and the actual conductor suspension state. Existing technologies largely rely on the spatial positional relationships between towers, structural symmetry, or empirical rules to infer wire hanging nodes. In complex terrain, with deviations in tower attitude, or changes in the actual conductor shape, this can easily lead to incorrect correspondences of wire hanging nodes, thus affecting the continuity of the conductor spatial model and the reliability of the engineering.

[0006] Therefore, in order to address the problems of inaccurate determination of the connection relationship between conductors and adjacent towers and insufficient stability in the existing 3D modeling and conductor reconstruction technology for transmission lines, a new 3D modeling method for conductors is urgently needed to solve the problems of low matching accuracy of conductor connection nodes, poor robustness and insufficient engineering applicability in the existing technology. Summary of the Invention

[0007] Based on this, the present invention needs to provide a method and system for wire connection node matching and conductor reconstruction based on multi-source point cloud constraints to solve at least one of the above-mentioned technical problems. It can make full use of the real spatial morphology reflected by the conductor point cloud, establish a globally optimal matching relationship between the candidate wire connection node sets of adjacent towers, and integrate structural priors and engineering constraints to accurately determine the actual connection position of the conductor and realize the technical solution of continuous spatial reconstruction of the conductor.

[0008] To achieve the above objectives, a method for matching wire nodes and reconstructing wires based on multi-source point cloud constraints is proposed, comprising the following steps: S1, Multi-source spatial data acquisition: Acquire multi-source spatial data corresponding to adjacent towers, including conductor point cloud data, tower point cloud data and tower 3D model data; S2, Extracting candidate wire connection nodes from the 3D model data of the tower: Based on the tower type information, constrain the structural regions related to conductor connections in the 3D model of the tower to obtain a spatial region with regional constraints, and extract multiple spatial nodes from the spatial region as candidate wire connection nodes. Each of the adjacent first towers forms a set of candidate wire-hanging nodes. The set of candidate hanging nodes of the second tower ; S3, Global matching of candidate hanging nodes based on conductor point cloud constraints: S31, for any pair of candidate wire-attached nodes Constructing a spatial reference path for the conductor ; S32 introduces engineering prior conditions as additional constraints, which can be used to construct a weighted matching cost function. ; S33 employs a globally optimal matching strategy within the candidate hanging node set. and Establish a one-to-one correspondence between them so that the overall matching result satisfies the following optimization objective: This yields the one-to-one correspondence between actual wire-hanging node pairs a and b between adjacent towers; where, This represents the set of matching relationships between candidate connection nodes. and These represent the actual hanging positions of the conductor on adjacent towers on both sides. , ; S4, Wire space reconstruction based on hanging node constraints: S41, through endpoint constraints The conductor is spatially reconstructed, and its shape in space is abstractly represented as a continuous three-dimensional spatial curve. : , .

[0009] Preferably, in step S1, The conductor point cloud data consists of multiple discrete three-dimensional sampling points, including descriptions of the overall orientation, sag trend and spatial continuity characteristics of the conductor between adjacent towers; The tower point cloud data includes spatial point information of the tower's main structure, crossarms, and related components. The three-dimensional model data of the tower is used to describe the structural topology of the tower and the geometric features of the components related to the conductor, including the geometric information of different structural parts of the tower that correspond one-to-one with the tower type, as well as the possible connection positions and structural constraints of the conductor.

[0010] Preferably, step S1 further includes: performing unified management and association processing on the conductor point cloud data, the tower point cloud data, and the tower 3D model data, so that they can be expressed in the same spatial coordinate system or a convertible coordinate system.

[0011] Preferably, step S2 includes: For the first and second towers, respectively, based on the tower type Preset spatial area related to wire connection , ;in, and The height constraint parameter corresponds to the tower type and is used to limit the spatial range where hanging wire structures may exist; M is a set of multiple spatial nodes that are abstractly represented by the three-dimensional model of the tower. , This represents the three-dimensional coordinate information of the i-th spatial node in the tower model. Let represent the three coordinates of the i-th spatial node; For the first and second towers, respectively from the spatial region Multiple spatial nodes are extracted as candidate hanging nodes. This forms a set of candidate hanging nodes. as well as .

[0012] Preferably, step S2 further includes: introducing a minimum spatial distance constraint. For candidate wired nodes Perform filtering and redundancy removal; among them, Represents Euclidean distance. This is the preset minimum node spacing threshold.

[0013] Preferably, in step S31, ,in, This represents the spatial connection path model determined by the candidate hanging nodes. This represents the spatial distance metric from the traverse point cloud to the path. The traverse point cloud data is represented as a set of multiple 3D sampling points. , This represents the k-th 3D sampling point. .

[0014] Preferably, in step S32, ;in, , , These are the weighting coefficients. This represents the height difference constraint term between two candidate nodes. This represents the spatial structure consistency constraint.

[0015] Preferably, in step S4, after step S41, there is also step S42, which introduces morphological constraints by minimizing the following objective function. Constrained traverse curve Spatial deviation between the point cloud and the guide line; This represents the spatial distance metric from a single 3D sampling point pk in the traverse point cloud to the reconstructed 3D traverse curve C; where... This represents the spatial distance metric from the conductor point cloud to the reconstructed curve. C(t)=(x(t),y(t),z(t)),t∈ [0,1] , is a specific symbol in the document for the conductor space reconstruction process, referring to the three-dimensional space curve model of the conductor ultimately reconstructed between the actual wire-hanging nodes of adjacent towers.

[0016] The present invention also provides a system for matching wire nodes and reconstructing conductors based on multi-source point cloud constraints, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for matching wire nodes and reconstructing conductors based on multi-source point cloud constraints as described above.

[0017] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the hanging node matching and wire reconstruction method based on multi-source point cloud constraints as described above.

[0018] This invention proposes a method for matching wire connection nodes and reconstructing conductors based on multi-source point cloud constraints, applicable to applications such as digital modeling, inspection analysis, and 3D visualization of transmission lines. Given the tower type, by comprehensively utilizing conductor point cloud data, tower point cloud data, and tower 3D model data, the method achieves automatic identification and accurate reconstruction of conductor connection relationships between adjacent towers.

[0019] In this invention, firstly, point cloud data of conductors, point cloud data of towers, and 3D model data of towers corresponding to adjacent towers are acquired. The point cloud data of conductors reflects the actual direction and suspension shape of the conductors in space; the point cloud data of towers characterizes the spatial distribution of towers in the actual scene; and the 3D model data of towers describes the structural features of the towers and the geometric information of their suspension components.

[0020] Based on this, using prior information about the tower type, structural regions related to conductor connections are extracted from the 3D model of the tower, and multiple candidate wire-hanging nodes are generated within these structural regions. These candidate wire-hanging nodes represent possible conductor hanging positions, and their acquisition methods can include model node extraction, structural feature point calculation, or geometric rule generation; this invention does not limit the specific methods used.

[0021] Subsequently, traverse point cloud data is introduced as core constraint information to perform global matching of candidate wire-hanging nodes between adjacent towers. Unlike matching methods based solely on spatial distance or local features, this invention analyzes the overall spatial orientation and morphological characteristics reflected in the traverse point cloud to construct a matching cost model between candidate wire-hanging node pairs. Combined with engineering constraints such as high consistency and spatial structural rationality, and under the premise of satisfying a one-to-one correspondence, the true and effective wire-hanging node matching results between adjacent towers are determined. This globally optimal matching method avoids errors or instability in wire-hanging relationships caused by locally optimal selection.

[0022] After obtaining the actual wire-hanging nodes corresponding one-to-one between adjacent towers, this invention further reconstructs the conductor spatially under the constraints of these actual wire-hanging nodes. The conductor is abstracted as a continuous three-dimensional spatial curve satisfying endpoint constraints, and by introducing conductor point clouds as morphological constraints, the reconstructed conductor maintains consistency with the actual conductor in terms of overall orientation, spatial continuity, and sag trend. Simultaneously, the conductor model can be optimized and adjusted by combining prior conditions such as conductor smoothness, physical rationality, and engineering specifications, thereby generating a conductor spatial model that meets the requirements of engineering applications.

[0023] Through the above technical solution, this invention can accurately establish the correspondence between conductors and hanging nodes between adjacent towers in complex tower structures and point cloud noise environments, and achieve high-precision reconstruction of conductor spatial morphology. This reduces manual intervention, improves modeling efficiency and result reliability, and provides effective technical support for the digital management and intelligent operation and maintenance of transmission lines. This solution, under the condition of known actual hanging nodes, fully utilizes the real spatial morphology information contained in the conductor point cloud to achieve accurate modeling of conductors between adjacent towers, avoiding conductor offset problems caused by inaccurate endpoints or simple fitting. This provides a reliable data foundation for subsequent conductor simulation analysis, operation and maintenance inspection, and digital modeling. Compared with the prior art, this invention has at least the following beneficial effects: (i) Avoid mismatch problems caused by relying solely on the spatial relationship of towers. In existing technologies, the determination of conductor connection points between adjacent towers is usually based on inferences made from the spatial positional relationship between towers or the geometric symmetry of the tower model. In complex terrain or when tower attitudes deviate, errors in the corresponding connection points can easily occur. This invention uses the actual spatial morphology reflected in the conductor point cloud data as the core constraint, establishing a global matching relationship among the candidate connection point sets. This effectively avoids the mismatch problem caused by local judgments based solely on the relative positions of the towers.

[0024] (ii) Improve the accuracy of wire hanging node matching by utilizing the actual shape constraints of the conductor. This invention directly incorporates conductor point cloud data as constraint information. The conductor point cloud can realistically reflect the suspension state and spatial orientation of the conductor in the actual scene. By integrating the conductor point cloud constraints into the candidate hanging node matching process, the matching results are consistent with the actual physical shape of the conductor, thereby significantly improving the accuracy and stability of the hanging node correspondence.

[0025] (iii) Adopt a globally optimal matching strategy to avoid local optima. Compared to the common local matching or greedy matching methods in existing technologies, this invention constructs a global matching cost function and performs global optimization among the candidate wire hanging node sets of adjacent towers to achieve a one-to-one optimal matching result. This ensures the rationality of the matching relationship from an overall perspective and avoids the impact of local optimal solutions on the overall conductor connection relationship.

[0026] (iv) Fully integrate structural prior knowledge and engineering constraints to enhance applicability. This invention not only considers the spatial constraints of conductor point clouds during the matching process, but also incorporates the prior condition of known tower type, and introduces constraints such as height consistency, spatial structure consistency and engineering rules, so that the matching results meet the actual needs of power engineering and improve the versatility and reliability of the method under different tower types and different application scenarios. Attached Figure Description

[0027] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0028] Figure 1 This is a flowchart illustrating a method for matching wire nodes and reconstructing wires based on multi-source point cloud constraints provided by the present invention.

[0029] Figure 2 This is a schematic diagram of the hardware structure of a system running a method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints, according to an embodiment of the present invention.

[0030] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0031] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0032] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0033] To achieve the above objectives, please refer to Figure 1 and Figure 2 The present invention provides a method for matching wire nodes and reconstructing wires based on multi-source point cloud constraints, including the following steps S1 to S4.

[0034] S1, Multi-source spatial data acquisition: Acquire multi-source spatial data corresponding to adjacent towers, including conductor point cloud data, tower point cloud data, and tower 3D model data.

[0035] Specifically, in this embodiment, to achieve accurate modeling of the spatial relationship between conductors and towers, multi-source spatial data corresponding to adjacent towers is first acquired, including conductor point cloud data, tower point cloud data, and tower 3D model data. These multi-source spatial data collectively constitute the basic data input for analyzing the spatial relationship between conductors and towers.

[0036] Conductor point cloud data is used to reflect the spatial distribution of conductors in real-world scenarios. This data consists of multiple discrete three-dimensional sampling points and describes the overall orientation, sag trend, and spatial continuity of conductors between adjacent towers. Because conductor point clouds are directly derived from real-world scenarios, the spatial information they contain can accurately reflect the physical morphology of conductors under natural and engineering conditions.

[0037] Tower point cloud data is used to describe the spatial location and overall structural form of towers in actual scenarios, and can also be acquired through laser scanning. The tower point cloud data includes spatial point information of the tower's main structure, crossarms, and related components, used to determine the spatial location range of the tower in a unified spatial coordinate system, and to provide a reference for the subsequent spatial association between the conductor point cloud and the tower model data.

[0038] The 3D model data of the tower is used to describe the structural topology of the tower and the geometric features of the components related to conductor suspension. It can be a standard model built based on design drawings, pre-established model library data, or a structural model obtained through 3D modeling. The 3D model data of the tower contains geometric information of different structural parts of the tower and corresponds one-to-one with the tower type, used to characterize the possible suspension positions of the conductors and structural constraints.

[0039] After acquiring the aforementioned multi-source spatial data, the traverse point cloud data, tower point cloud data, and tower 3D model data are uniformly managed and correlated, ensuring they are expressed within the same or a convertible spatial coordinate system. This provides a consistent data foundation for subsequent candidate wire-hanging node extraction, traverse point cloud constraint matching, and traverse spatial reconstruction. By introducing multi-source spatial data, this invention can comprehensively utilize real-world scene information and prior structural information, improving the accuracy and stability of the spatial relationship modeling between traverses and towers.

[0040] Preferably, in step S1, The conductor point cloud data consists of multiple discrete three-dimensional sampling points, including descriptions of the overall orientation, sag trend and spatial continuity characteristics of the conductor between adjacent towers; The tower point cloud data includes spatial point information of the tower's main structure, crossarms, and related components. The three-dimensional model data of the tower is used to describe the structural topology of the tower and the geometric features of the components related to the conductor, including the geometric information of different structural parts of the tower that correspond one-to-one with the tower type, as well as the possible connection positions and structural constraints of the conductor.

[0041] Preferably, step S1 further includes: performing unified management and association processing on the conductor point cloud data, the tower point cloud data, and the tower 3D model data, so that they can be expressed in the same spatial coordinate system or a convertible coordinate system.

[0042] S2, Extracting candidate wire connection nodes from the 3D model data of the tower: Based on the tower type information, constrain the structural regions related to conductor connections in the 3D model of the tower to obtain a spatial region with regional constraints, and extract multiple spatial nodes from the spatial region as candidate wire connection nodes. Each of the adjacent first towers forms a candidate set of wire-hanging nodes. Set of candidate hanging nodes for the second tower .

[0043] Preferably, in this embodiment, the three-dimensional model of the tower is used to describe the geometric shape and spatial positional relationship of each structural component of the tower in three-dimensional space. The three-dimensional model of the tower can be abstractly represented as a set composed of multiple spatial nodes: ;in, This represents the three-dimensional coordinate information of the i-th spatial node in the tower model.

[0044] Since the type of each tower is known, and different types of towers differ in crossarm structure, insulator arrangement, and conductor connection methods, this invention constrains and limits the structural areas related to conductor connections in the tower model based on tower type information. Specifically, this can be done according to the tower type. Preset spatial areas related to wire connections: ;in, and The height constraint parameter corresponds to the tower type and is used to limit the spatial range where hanging wire structures may exist.

[0045] Based on the above structural region constraints, multiple spatial nodes are extracted from the spatial region as candidate wire-hanging nodes, forming a candidate wire-hanging node set: The candidate hanging node These can be the endpoints, geometric center points, and feature points obtained through geometric calculations of structural components in a tower model.

[0046] To improve the spatial representativeness and stability of the candidate wire-attached node set, redundancy removal can be performed on the candidate nodes. A minimum spatial distance constraint is introduced to filter the candidate nodes: ;in, Represents Euclidean distance. This is a preset minimum node spacing threshold. This constraint helps prevent candidate hanging nodes from becoming excessively concentrated in local areas.

[0047] By using the above method, a set of candidate wire-hanging nodes related to conductor connections is obtained from the three-dimensional model of the tower, providing reliable basic data support for the spatial matching of wire-hanging nodes between adjacent towers and the spatial reconstruction of conductors.

[0048] In this embodiment, the correspondence between the hanging nodes of adjacent towers is not determined solely by the spatial relationship between the towers. Instead, conductor point cloud data is introduced as the core constraint information, and a stable and reasonable one-to-one correspondence is established between the candidate hanging node sets of the towers on both sides through a global optimal matching method.

[0049] Traverse point cloud data can be represented as a set of multiple three-dimensional sampling points: .

[0050] The conductor point cloud accurately reflects the suspension shape and orientation characteristics of the conductor in space, providing a direct physical constraint for matching conductor hanging nodes. After obtaining the candidate hanging node sets for two adjacent towers, they are denoted as follows: The matching cost is constructed by the degree of spatial correlation between the conductor point cloud and the candidate hanging nodes.

[0051] Preferably, step S2 includes: For the first and second towers, respectively, based on the tower type Preset spatial area related to wire connection , ;in, and The height constraint parameter corresponds to the tower type and is used to limit the spatial range where hanging wire structures may exist; M is a set of multiple spatial nodes that are abstractly represented by the three-dimensional model of the tower. , This represents the three-dimensional coordinate information of the i-th spatial node in the tower model. Let represent the three coordinates of the i-th spatial node; For the first and second towers, respectively from the spatial region Multiple spatial nodes are extracted as candidate hanging nodes. This forms a set of candidate hanging nodes. as well as .

[0052] Preferably, step S2 further includes: introducing a minimum spatial distance constraint. For candidate wired nodes Perform filtering and redundancy removal; among them, Represents Euclidean distance. This is the preset minimum node spacing threshold.

[0053] S3, Global matching of candidate hanging nodes based on conductor point cloud constraints: S31, for any pair of candidate wire-attached nodes Constructing a spatial reference path for the conductor ; S32 introduces engineering prior conditions as additional constraints, which can be used to construct a weighted matching cost function. ; S33 employs a globally optimal matching strategy within the candidate hanging node set. and Establish a one-to-one correspondence between them so that the overall matching result satisfies the following optimization objective: This yields the one-to-one correspondence between actual wire-hanging node pairs a and b between adjacent towers; where, This represents the set of matching relationships between candidate connection nodes. and These represent the actual hanging positions of the conductor on adjacent towers on both sides. , .

[0054] Specifically, for any pair of candidate hanging nodes This can be considered as a potential connection endpoint of the traverse, and a spatial reference path for the traverse can be constructed based on this assumption. Furthermore, the spatial deviation from each sampling point in the traverse point cloud to this assumed connection path can be calculated, and the following cost function is defined: ;in, This represents the spatial connection path model determined by the candidate hanging nodes. This represents the spatial distance metric from the point cloud of the conductor to the path.

[0055] Based on the aforementioned conductor constraint cost, engineering prior conditions can be further introduced as additional constraint terms, thus constructing a weighted matching cost function: ;in, , , These are the weighting coefficients. This represents the height difference constraint term between two candidate nodes. This represents a spatial structure consistency constraint term. The spatial structure consistency constraint term... It is used to characterize the degree of matching between candidate wire-hanging node pairs and the overall spatial structure features of the conductor. It can be constructed based on the spatial orientation, morphological features and prior information of the engineering structure of the conductor point cloud, in order to suppress the matching relationship of candidate wire-hanging nodes that are close in spatial distance but unreasonable in conductor structure.

[0056] Based on the aforementioned comprehensive matching cost function, this scheme adopts a globally optimal matching strategy in the candidate hanging node set. and Establish a one-to-one correspondence between them so that the overall matching result satisfies the following optimization objective: ;in, This represents the set of matching relationships between candidate wire connection nodes. Through global optimization, it avoids errors or instability in wire connection relationships caused by local optimal matching.

[0057] By introducing traverse point clouds as core constraint information and combining global optimal matching and multiple engineering constraints, this scheme can accurately establish a one-to-one correspondence between hanging nodes between adjacent towers in complex tower structures and noisy point cloud environments, providing reliable and realistic endpoint constraints for subsequent traverse space reconstruction.

[0058] In this embodiment, after completing the global matching of candidate wire-attached nodes based on traverse point cloud constraints, one-to-one pairs of actual wire-attached nodes corresponding to adjacent towers can be obtained. These actual wire-attached node pairs are located on adjacent towers and are used to characterize the actual attachment positions of the traverse at both ends, providing explicit endpoint constraints for traverse spatial reconstruction. Let the actual wire-attached nodes matched between adjacent towers be as follows: , ; in, and These represent the actual hanging positions of the conductors on adjacent towers on both sides.

[0059] Preferably, in step S31, ,in, This represents the spatial connection path model determined by the candidate hanging nodes. This represents the spatial distance metric from the traverse point cloud to the path. The traverse point cloud data is represented as a set of multiple 3D sampling points. , This represents the k-th 3D sampling point. .

[0060] Preferably, in step S32, ;in, , , These are the weighting coefficients. This represents the height difference constraint term between two candidate nodes. This represents the spatial structure consistency constraint.

[0061] S4, Wire space reconstruction based on hanging node constraints: S41, through endpoint constraints The conductor is spatially reconstructed, and its shape in space is abstractly represented as a continuous three-dimensional spatial curve. : , .

[0062] Specifically, after completing the global matching of candidate wire-attached nodes based on traverse point cloud constraints, one-to-one pairs of actual wire-attached nodes corresponding to adjacent towers can be obtained. These actual wire-attached node pairs are located on adjacent towers and are used to characterize the actual attachment positions of the traverse at both ends, providing explicit endpoint constraints for traverse spatial reconstruction. Let the actual wire-attached nodes matched between adjacent towers be as follows: , ;in, and These represent the actual hanging positions of the conductors on adjacent towers on both sides.

[0063] Under the constraints of the aforementioned actual wire connection nodes, this invention performs spatial reconstruction of the conductor. The shape of the conductor in space can be abstractly represented as a continuous three-dimensional spatial curve: , And satisfy the following endpoint constraints: The above constraints ensure that the reconstructed conductor model is accurately connected in space to the actual wire-hanging nodes of adjacent towers.

[0064] In addition to endpoint constraints, traverse point cloud data, as crucial information reflecting the true overhang morphology of the traverse, is further incorporated as a morphological constraint. During the traverse spatial reconstruction process, the traverse curves are constrained... The spatial deviation between the reconstructed point cloud and the actual traverse point cloud ensures that the overall orientation and overhang trend of the reconstructed result are consistent with the real traverse. This can be achieved by minimizing the following objective function: ;in, This represents the spatial distance metric from the point cloud of the conductor to the reconstructed curve.

[0065] By using the above-mentioned conductor space reconstruction method, this scheme can fully utilize the real spatial morphology information contained in the conductor point cloud under the condition of knowing the real wire hanging nodes, realize the accurate modeling of conductors between adjacent towers, avoid conductor offset problems caused by inaccurate endpoints or simple fitting, and thus provide a reliable data foundation for subsequent conductor simulation analysis, operation and maintenance inspection and digital modeling.

[0066] Preferably, in step S4, after step S41, there is also step S42, which introduces morphological constraints by minimizing the following objective function. Constrained traverse curve Spatial deviation between the point cloud and the guide line; This represents the spatial distance metric from a single 3D sampling point pk in the traverse point cloud to the reconstructed 3D traverse curve C; where... This represents the spatial distance metric from the conductor point cloud to the reconstructed curve. C(t)=(x(t),y(t),z(t)),t∈ [0,1], is a specific symbol in the document for the conductor space reconstruction process, referring to the three-dimensional space curve model of the conductor ultimately reconstructed between the actual wire-hanging nodes of adjacent towers.

[0067] The present invention also provides a system for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints. The system is built on a computer system and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, it implements the steps of the method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints as described above.

[0068] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer.

[0069] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0070] The memory can be an internal storage unit, such as a hard drive or RAM; it can also be an external storage device, such as an external hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a Flash Card. Furthermore, the memory may include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output.

[0071] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the hanging node matching and wire reconstruction method based on multi-source point cloud constraints as described above.

[0072] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0073] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0075] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / system and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0079] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0080] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method of hanging line node matching and conductor reconstruction based on multi-source point cloud constraints, characterized in that, Including the following steps: S1, Multi-source spatial data acquisition: Acquire multi-source spatial data corresponding to adjacent towers, including conductor point cloud data, tower point cloud data and tower 3D model data; S2, extracting candidate wire hanging nodes from the tower three-dimensional model data: based on the tower type information, the structure region related to the wire connection in the tower three-dimensional model is constrained and limited, the spatial region of the region constraint is obtained, and a plurality of spatial nodes in the spatial region are extracted as candidate wire hanging nodes , respectively forming a set of candidate wire hanging nodes of the adjacent first tower and a set of candidate wire hanging nodes of the second tower ; S3, Global matching of candidate hanging nodes based on conductor point cloud constraints: S31, for any pair of candidate hanging line nodes , construct a spatial reference path of the guide line ; S32, the engineering prior condition is introduced as an additional constraint term to construct a weighted matching cost function ; S33, a global optimal matching strategy is adopted to establish one-to-one correspondence between the candidate hanging line node set and , so that the overall matching result meets the following optimization objective: , obtaining a one-to-one corresponding real hanging line node pair a and b between adjacent towers; wherein, represents the matching relationship set between the candidate hanging line nodes, and respectively represent the real hanging line position of the conductor on the adjacent two side towers, , ; S4, Wire space reconstruction based on hanging node constraints: S41, pass through the endpoint constraint condition The wire is spatially reconstructed, and the shape of the wire in space is abstractly represented as a continuous three-dimensional space curve : , .

2. The method of claim 1, wherein, In step S1 The conductor point cloud data consists of multiple discrete three-dimensional sampling points, including descriptions of the overall orientation, sag trend and spatial continuity characteristics of the conductor between adjacent towers; The tower point cloud data includes spatial point information of the tower's main structure, crossarms, and related components. The three-dimensional model data of the tower is used to describe the structural topology of the tower and the geometric features of the components related to the conductor, including the geometric information of different structural parts of the tower that correspond one-to-one with the tower type, as well as the possible connection positions and structural constraints of the conductor.

3. The method of claim 2, wherein, Step S1 further includes: performing unified management and association processing on the conductor point cloud data, the tower point cloud data, and the tower three-dimensional model data, so that they can be expressed in the same spatial coordinate system or a convertible coordinate system.

4. The multi-source point cloud constraint based hanging line node matching and conductor reconstruction method according to claim 1, characterized in that, Step S2 includes: For the first tower and the second tower, according to the tower type Pre-set space area related to wire connection , ; wherein, and are height constraint parameters corresponding to the tower type, used to limit the space range where the wire hanging structure may exist; M is a set composed of multiple space nodes, which is abstractly represented by the three-dimensional model of the tower, , represents the three-dimensional coordinate information of the i-th space node in the tower model, respectively represent the three-axis coordinates of the i-th space node. For the first and second towers, respectively from the spatial region Multiple spatial nodes are extracted as candidate hanging nodes. This forms a set of candidate hanging nodes. as well as .

5. The method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints according to claim 4, characterized in that, Step S2 further includes: introducing a minimum spatial distance constraint. For candidate wired nodes Perform filtering and redundancy removal; among them, Represents Euclidean distance. This is the preset minimum node spacing threshold.

6. The method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints according to claim 1, characterized in that, In step S31 ,in, This represents the spatial connection path model determined by the candidate hanging nodes. This represents the spatial distance metric from the traverse point cloud to the path. The traverse point cloud data is represented as a set of multiple 3D sampling points. , This represents the k-th 3D sampling point. .

7. The method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints according to claim 6, characterized in that, In step S32 ;in, , , These are the weighting coefficients. This represents the height difference constraint term between two candidate nodes. This represents the spatial structure consistency constraint.

8. The method for matching hanging nodes and reconstructing conductors based on multi-source point cloud constraints according to claim 7, characterized in that, In step S4, after step S41, there is also step S42, which introduces morphological constraints by minimizing the following objective function. Constrained traverse curve Spatial deviation between the point cloud and the guide line; This represents the spatial distance metric from a single 3D sampling point pk in the traverse point cloud to the reconstructed 3D traverse curve C; where... This represents the spatial distance metric from the conductor point cloud to the reconstructed curve. C(t)=(x(t),y(t),z(t)),t∈[0,1] , is a specific symbol in the document for the conductor space reconstruction process, referring to the three-dimensional space curve model of the conductor ultimately reconstructed between the actual wire-hanging nodes of adjacent towers.

9. A wire node matching and wire reconstruction system based on multi-source point cloud constraints, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the hanging node matching and conductor reconstruction method based on multi-source point cloud constraints as described in any one of claims 1 to 8.

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