Power transmission line completion acceptance measurement method and device based on laser point cloud, and medium

Abstract

This invention relates to the field of power transmission line technology, specifically providing a method, equipment, and medium for power transmission line completion acceptance measurement based on laser point clouds. The method includes: storing three-dimensional point cloud data of the power transmission line corridor in a layered grid, and constructing a spatial index for each layer of grid data; using the spatial index and a pre-trained recognition model, identifying power transmission equipment in the non-ground point cloud data of the three-dimensional point cloud data to obtain entity recognition results of the non-ground point cloud data; extracting the suspension points of each insulator string in the non-ground point cloud data based on the entity recognition results; calculating the acceptance parameters of the power transmission line to obtain the completion acceptance results of the power transmission line. This invention significantly reduces the workload of manual tower climbing measurement and data processing, shortens the acceptance cycle, and avoids human subjective errors, achieving centimeter-level or even millimeter-level precision measurement of completion acceptance project parameters, ensuring the objectivity and accuracy of the data.

Description

Technical Field

[0001] This invention relates to the field of power transmission line technology, and in particular to a method, equipment and medium for power transmission line completion acceptance measurement based on laser point cloud. Background Technology

[0002] The final acceptance inspection of power transmission lines is a crucial step in comprehensively checking the quality of the engineering design and construction. Its results directly affect the safety and reliability of the line after commissioning and the realization of investment benefits. Acceptance items mainly include: conductor and ground wire sag, jumper clearance and conductor phase-to-phase distance, clearance between conductors and ground, vegetation, buildings, and other ground features, as well as the crossing of the line with other power lines, highways, railways, and other facilities.

[0003] Currently, the final acceptance of power transmission lines relies primarily on manual operations, using traditional equipment such as levels and theodolites for measurement. The measurement results are highly dependent on the experience and judgment of the operators, resulting in significant limitations in applicability and reliability. For example, critical parameters such as the minimum spatial distance between the diversion line and the tower usually require operators to climb the tower and use a marker for manual measurement or visual inspection. This method is not only labor-intensive and inefficient, but also lacks accuracy and struggles to accurately capture minimum electrical clearances, while posing safety hazards during tower climbing operations. Furthermore, in complex terrain or restricted environments where instruments are difficult to set up, some measurement targets cannot even be effectively covered, further exposing the significant shortcomings of existing methods in terms of measurement coverage and environmental adaptability. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, equipment and medium for the final acceptance measurement of power transmission lines based on laser point clouds, which solves the problems of low measurement efficiency and poor accuracy of the existing acceptance methods.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for measurement during the final acceptance of power transmission lines based on laser point clouds, comprising:

[0007] The 3D point cloud data of the target transmission line corridor is stored in a hierarchical grid, and a spatial index is built for each layer of grid data.

[0008] Using the spatial index and a pre-trained recognition model, power transmission equipment is identified in the non-ground point cloud data of the three-dimensional point cloud data to obtain entity recognition results of the non-ground point cloud data; wherein, the entity recognition results include towers, insulator strings, jumpers, conductors, and vegetation;

[0009] Based on the entity recognition results, the suspension points of each insulator string in the non-ground point cloud data are extracted;

[0010] Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated to obtain the completion acceptance results of the target transmission line.

[0011] In some embodiments of the present invention, the three-dimensional point cloud data of the target transmission line corridor is stored in a hierarchical grid format, including:

[0012] The three-dimensional space occupied by the three-dimensional point cloud data is divided into four grid levels with different resolutions; wherein, the first level of grid space resolution is meter level; the second level of grid space resolution is sub-meter level; the third level of grid space resolution is centimeter level; and the fourth level of grid space resolution is millimeter level.

[0013] Establish a two-way mapping relationship between grid cells at adjacent levels.

[0014] In some embodiments of the present invention, a spatial index is constructed for each layer of grid data, including:

[0015] The first level uses an R-tree as a spatial index; the second and third levels both use octrees as spatial indexes; and the fourth level uses a hash table as a spatial index.

[0016] In some embodiments of the present invention, power transmission equipment identification is performed on non-ground point cloud data in the three-dimensional point cloud data using a pre-trained recognition model, including:

[0017] A filtering algorithm is used to segment the 3D point cloud data into ground point cloud data and non-ground point cloud data;

[0018] Based on the tower register information of the target transmission line, extract the surrounding area point cloud data of each tower from the non-ground point cloud data;

[0019] The surrounding area point cloud data is input into a pre-trained recognition model to identify the category of each point cloud in the surrounding area point cloud data; wherein, the category includes towers, insulator strings, jumpers, conductors and vegetation.

[0020] In some embodiments of the present invention, after inputting the surrounding area point cloud data into a pre-trained recognition model to identify the category of each point cloud in the surrounding area point cloud data, the method further includes:

[0021] The remaining point cloud data in the non-ground point cloud data, excluding towers, insulator strings and jumpers, is divided into multiple clustering units based on a spatial clustering algorithm;

[0022] Based on the proportion of the classified point clouds in each cluster unit, all point clouds in the corresponding cluster unit are assigned to the category with the highest proportion.

[0023] In some embodiments of the present invention, based on the entity recognition result, the suspension points of the insulator string in the non-ground point cloud data are extracted, including:

[0024] Based on the entity recognition results, each tower unit in the non-ground point cloud data is segmented, as well as multiple insulator string units and multiple conductor units spatially associated with each tower unit;

[0025] For each tower unit, a matching relationship between the insulator string unit and the conductor unit is established based on the spatial correspondence.

[0026] For each insulator string unit, the point in the point cloud of the insulator string unit that is closest to the tower unit is taken as the tower-side suspension point of the insulator string unit; the point in the point cloud of the insulator string unit that is closest to the corresponding matching conductor unit is taken as the conductor-side suspension point of the insulator string unit.

[0027] In some embodiments of the present invention, based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, acceptance parameters of the target transmission line are calculated to obtain the completion acceptance results of the target transmission line, including:

[0028] Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated. The acceptance parameters include: insulator string parameters, conductor and ground wire sag parameters, conductor-to-conductor safety distance parameters, jumper parameters, and distance parameters to ground and to objects.

[0029] The acceptance parameters are compared with the corresponding transmission line design specifications to determine whether each acceptance parameter is compliant, and the final acceptance result of the target transmission line is obtained.

[0030] In some embodiments of the present invention, after calculating the acceptance parameters of the target transmission line based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data to obtain the completion acceptance results of the target transmission line, the method further includes:

[0031] Based on the acceptance parameters and the completion acceptance results, a completion acceptance report for the target transmission line is generated; wherein, the completion acceptance report includes completion acceptance items, acceptance parameters, transmission line design code requirements, comparison results, hazard attributes, hazard levels, and hazard locations.

[0032] Secondly, the present invention also provides an electronic device, including: a processor, and a memory storing a program, the program including instructions, which, when executed by the processor, cause the processor to perform the above-described method for measuring the completion and acceptance of power transmission lines based on laser point clouds.

[0033] Thirdly, the present invention also provides a non-transient machine-readable medium storing computer instructions for causing the computer to execute the above-described laser point cloud-based transmission line completion acceptance measurement method.

[0034] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0035] In this embodiment of the invention, a hierarchical grid storage and hybrid index structure are used to achieve efficient management of massive point clouds. A dual-recognition model and cluster analysis are combined to classify the point clouds, and an octree nearest-point search algorithm is used to quickly calculate acceptance parameters. This results in a high degree of automation in the final acceptance process, significantly reducing the workload of manual tower climbing and data processing, shortening the acceptance cycle, and avoiding human subjective errors. It achieves centimeter-level and even millimeter-level precision measurements of key parameters such as sag, spacing, and electrical clearance, ensuring the objectivity and accuracy of the data. Relying on lidar data acquisition, the method of this invention does not require on-site instrument placement and can cover complex terrains such as mountains and canyons, as well as areas difficult for personnel to access. This overcomes the operational limitations of complex terrains and high-risk environments, achieving comprehensive acceptance along the entire corridor. Attached Figure Description

[0036] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a method for measuring the completion and acceptance of power transmission lines based on laser point clouds, provided in an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the process for extracting the suspension points of the insulator string in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of an octree;

[0040] Figure 4 This is a schematic diagram illustrating the effect of using the octree nearest neighbor search algorithm to calculate the phase-to-phase distance between adjacent conductors of a 1000kV transmission line in an embodiment of the present invention.

[0041] Figure 5 This is a schematic diagram of the straight jump arc in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of the sag of a single jump in an embodiment of the present invention;

[0043] Figure 7 This is a schematic diagram of the double jump arc in an embodiment of the present invention;

[0044] Figure 8 This is a schematic diagram of the sag of a hard jump in an embodiment of the present invention;

[0045] Figure 9 This is a point cloud diagram illustrating the calculation of the jump line sag in a straight jump according to an embodiment of the present invention;

[0046] Figure 10 This is a point cloud diagram illustrating the calculation of the minimum distance between the jumper wire and the tower component in a straight jump according to an embodiment of the present invention;

[0047] Figure 11 This is a point cloud diagram illustrating the calculation of the jump line sag for a single jump in an embodiment of the present invention;

[0048] Figure 12 This is a point cloud diagram illustrating the calculation of the minimum distance between the jumper wire and the tower component for a single jump in an embodiment of the present invention.

[0049] Figure 13 This is a point cloud diagram illustrating the calculation of the jump line sag in a double jump according to an embodiment of the present invention;

[0050] Figure 14 This is a point cloud diagram illustrating the calculation of the minimum distance between the jumper wire and the tower component for double jumps in an embodiment of the present invention.

[0051] Figure 15 This is a schematic diagram of the table of contents of the completion acceptance report according to an embodiment of the present invention;

[0052] Figure 16 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0053] The above figures include the following reference numerals:

[0054] 1—Tension insulator string; 2—Tower crossarm; 3—Jumper wire; 4—Jumper wire insulator string; 5—Rigid support frame. Detailed Implementation

[0055] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0056] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for measurement during the final acceptance of power transmission lines based on laser point clouds. Figure 1 This is a flowchart illustrating the method for power transmission line completion acceptance measurement based on laser point clouds. This flowchart only shows the logical sequence of the method in this embodiment. In other possible embodiments of the invention, different methods may be used, provided they do not conflict with each other. Figure 1 Complete the steps shown or described in the order indicated.

[0057] See Figure 1 The method of this invention specifically includes the following steps:

[0058] Step S101: Store the 3D point cloud data of the target transmission line corridor in a layered grid and construct a spatial index for each layer of grid data.

[0059] This invention employs a drone equipped with a lidar device to autonomously fly along a target power transmission line corridor at a preset speed and altitude. During flight, the lidar emits laser beams in real time towards the power line and the corridor environment. Combined with a high-precision positioning system (GPS) and an inertial navigation system (INS), it simultaneously acquires the three-dimensional coordinates, echo intensity, and attitude information of the laser point cloud, completing the high-precision three-dimensional spatial point cloud data acquisition of the target power transmission line towers, conductors, insulator strings, jumpers, ground wires, as well as the surface, vegetation, buildings, highways, and crossing lines within the corridor.

[0060] Before storing the data in a hierarchical grid, the original 3D point cloud data is preprocessed, including statistical filtering to remove outlier noise points and point cloud normalization to eliminate the impact of noise on subsequent recognition and measurement accuracy.

[0061] The preprocessed 3D point cloud data of the target transmission line corridor was divided into four grid levels with different resolutions for storage. The first level has a meter-level grid resolution and can use a 1 cubic meter grid; the second level has a sub-meter-level grid resolution and can use a 0.1 meter grid; the third level has a centimeter-level grid resolution and can use a 0.01 meter grid; and the fourth level has a millimeter-level grid resolution and can use a 0.001 meter grid. A bidirectional mapping relationship was established between grid cells in adjacent levels to ensure data consistency and traceability.

[0062] The first-level grid resolution is suitable for global browsing, the second-level grid resolution is suitable for area analysis, the third-level grid resolution is suitable for component inspection, and the fourth-level grid resolution is suitable for precision measurement. The mesh generation process fully considers the characteristics of engineering measurements, automatically increasing storage accuracy for critical areas such as power equipment.

[0063] Accordingly, a spatial index is constructed for each layer of grid data. The first level uses an R-tree as the spatial index; the second and third levels both use an octree as the spatial index; and the fourth level uses a hash table as the spatial index.

[0064] Correspondingly, the top layer (first level) uses an R-tree to manage large-scale spatial relationships, the middle layer (second and third levels) uses a dynamic octree to process data of regular precision, and the bottom layer (fourth level) stores high-precision point clouds through a compact hash table. A hierarchical filtering strategy is employed during queries, first quickly locating the target region through a coarse-grained index, and then gradually refining the search range, greatly improving query efficiency.

[0065] In this embodiment, a differentiated compression strategy is adopted according to the characteristics of each level to compress and store the point cloud data at each level. The first level uses voxelization combined with Delta coding to achieve high-fold data compression while maintaining global shape features; the second level uses predictive coding technology based on principal component analysis to improve regional data compression efficiency while controlling distortion; the third level applies an improved Draco algorithm, which optimizes the linear features and component geometric distribution of transmission line point clouds based on the standard Draco point cloud compression framework, further improving compression efficiency and reducing reconstruction errors while preserving high-precision geometric details of key components such as conductors, towers, and insulator strings; the fourth level uses lossless compression storage to ensure that millimeter-level precision measurement data does not lose any original geometric information.

[0066] In addition, this embodiment of the invention introduces an intelligent caching mechanism, which dynamically schedules resources based on the data access frequency, retains frequently accessed data such as conductors, towers, insulator strings and hanging points in memory, and automatically archives infrequently accessed data such as large-area vegetation and remote ground.

[0067] This invention employs a four-level precision hierarchical architecture, progressively refining from meter-level to millimeter-level precision, with each level equipped with a dedicated spatial index and compression strategy. Through a dynamic precision adaptation mechanism, the appropriate processing level can be automatically selected based on the application scenario, optimizing storage efficiency while ensuring accuracy. This hierarchical architecture forms the foundational support structure of this invention, permeating the entire process from point cloud classification and insulator string suspension point extraction to acceptance parameter calculation. Dynamic precision adaptation and hierarchical index compression ensure processing efficiency and measurement accuracy at each stage, forming the core technological foundation for achieving efficient and high-precision final acceptance testing.

[0068] Step S102: Using spatial indexing and a pre-trained recognition model, power transmission equipment is identified in the non-ground point cloud data of the 3D point cloud data, and entity recognition results of the non-ground point cloud data are obtained.

[0069] In this embodiment of the invention, identifying power transmission equipment from non-ground point cloud data in three-dimensional point cloud data using a pre-trained recognition model may include steps S1021 to S1023.

[0070] Step S1021: Use a filtering algorithm to segment the 3D point cloud data into ground point cloud data and non-ground point cloud data.

[0071] In the scenario of a power transmission line corridor, ground targets and non-ground targets exhibit significant spatial separability. While the ground surface has natural undulations, it exhibits a macroscopic, continuous, and gradual trend, forming the basic geometric surface of the scene. Non-ground elements such as power lines, towers, and vegetation appear as discrete protruding structures attached to this basic geometric surface, creating local elevation abrupt changes and discontinuities. The filtering algorithm quantifies these elevation differences to segment the ground point cloud from the non-ground point cloud.

[0072] Filtering algorithms can employ progressive morphological filtering, cloth simulation filtering, and other methods. Progressive morphological filtering calculates the elevation difference within a moving window that gradually increases in size, separating areas with gentle elevation changes from areas with abrupt elevation changes, such as towers and vegetation, based on a set threshold. Cloth simulation filtering simulates the physical process of virtual cloth covering an inverted point cloud under gravity, using the final shape of the cloth to represent the ground surface.

[0073] Considering the complex terrain of the transmission line corridor and the presence of a large number of suspended power lines, multiple filtering algorithms can be compared and verified or used in series to improve segmentation accuracy and robustness.

[0074] Step S1022: Based on the tower register information of the target transmission line, extract the surrounding area point cloud data of each tower from the non-ground point cloud data.

[0075] The power transmission line tower ledger is a database used for the refined management of power transmission lines, recording detailed information on the design, construction, and operation and maintenance of each tower. This invention utilizes the tower ledger of the target transmission line to obtain the three-dimensional coordinate reference position and corresponding tower type of each tower, thereby determining the extraction range of point cloud data for the surrounding area.

[0076] Considering that power transmission towers are rigid, vertical structures, and taking into account the engineering scenario of power transmission lines, the extraction area is a cylindrical region centered on the tower's three-dimensional coordinates. A preset radius is set horizontally, and the vertical area covers the ground surface to a preset height above the tower top, ensuring complete inclusion of the point cloud of the entire tower and surrounding components. Based on this extraction area, point clouds located within the cylindrical region are selected from non-ground point cloud data to serve as the point cloud data for the tower's surrounding area.

[0077] For each tower on the target transmission line, the coordinates of the tower in the ledger are used as the query reference. The spatial index of the first-level grid is used to quickly locate the macro-region grid block where the tower is located. Then, through the bidirectional mapping relationship between grids, it is guided from top to bottom to a higher resolution level, and the high-precision point cloud data around the tower is dynamically loaded.

[0078] Step S1023: Input the surrounding area point cloud data into the pre-trained recognition model to identify the category of each point cloud in the surrounding area point cloud data.

[0079] This invention employs a supervised training point cloud semantic segmentation model as the recognition model, which can utilize network architectures with excellent local feature extraction capabilities, such as PointNet++, RandLA-Net, and KPConv. The recognition model is used for component-level fine classification of point clouds in the area surrounding the tower, to accurately distinguish spatially interwoven objects such as the tower body, insulator strings, conductors, jumpers, ground wires, and attached vegetation.

[0080] Considering that transmission lines include various tower types such as straight-line towers, tension towers, angle towers, and terminal towers, and that the spatial structure, component arrangement, size ratios, and connection relationships of different tower types vary significantly, using a general model can easily lead to misidentification or omission of key components such as insulator strings and jumpers. Therefore, this invention trains dedicated recognition models for different tower types, enabling each model to learn the prior structural features of the corresponding tower type through deep learning, such as the vertical distribution characteristics of suspension strings in straight-line towers and the spatial orientation characteristics of jumpers in tension towers, thereby improving the accuracy and reliability of component classification.

[0081] In this embodiment of the invention, step S1023 may include: performing standardized preprocessing on the point cloud data of the area surrounding the tower, sequentially performing point cloud translation, height scaling, and height normalization operations to eliminate the influence of spatial location and scale differences on recognition; simultaneously constructing a KD-Tree local nearest neighbor retrieval structure to improve the efficiency of neighborhood feature calculation in the recognition model. The preprocessed point cloud data of the area surrounding the tower is then input into the recognition model matching the current tower type for inference, with each point labeled with a category tag, completing the fine classification of components such as the tower body, insulator strings, conductors, jumpers, ground wires, and vegetation.

[0082] After identifying each tower on the target transmission line, this embodiment of the invention may further include:

[0083] Step S1024: Divide the remaining point cloud data (excluding towers, insulator strings, and jumpers) in the non-ground point cloud data into multiple clustering units based on a spatial clustering algorithm.

[0084] The remaining point cloud data includes some point clouds with identified model-labeled categories, as well as unlabeled point clouds.

[0085] A spatial clustering algorithm based on Euclidean distance is adopted to automatically divide the remaining point cloud data into multiple physically independent clustering units according to the spatial density and proximity relationship of the point cloud.

[0086] Step S1025: Based on the proportion of the classified point clouds in each cluster unit, classify all point clouds in the corresponding cluster unit into the category with the highest proportion.

[0087] For each cluster unit, the percentage of point cloud categories accurately labeled by the identified model within that unit is calculated. Based on the category dominance principle, all point clouds within that cluster unit are uniformly assigned to the category with the highest percentage. For example, if the majority of the identified and labeled point clouds in a certain cluster unit are of the "wire" category, then all point clouds in that cluster unit are classified as wire point clouds.

[0088] To further ensure the accuracy and engineering usability of the classification results, this embodiment of the invention adds a manual verification and correction step. The non-ground point cloud data after full-coverage classification is visually inspected, with a focus on verifying categories prone to misclassification or omission, such as conductors, ground wires, vegetation, buildings, and crossing lines. Particular attention is paid to potential issues such as category confusion and misjudgment of noise points that may occur during clustering supplementary classification. For misjudgments, mixed classifications, and omissions discovered during the verification process, manual corrections are performed, invalid noise point clouds are removed, and category labeling errors are calibrated to ensure the accuracy and reliability of the final classification results.

[0089] Step S103: Based on the entity recognition results, extract the suspension points of each insulator string in the non-ground point cloud data.

[0090] See Figure 2 In this embodiment of the invention, step S103 may include steps S1031 to S1033.

[0091] Step S1031: Based on the entity recognition results, segment each tower unit in the non-ground point cloud data, as well as multiple insulator string units and multiple conductor units spatially associated with each tower unit.

[0092] Based on the entity recognition and category labeling results of the aforementioned non-ground point cloud data, and using the tower's ledger number as the retrieval criterion, three core point cloud sets corresponding to the tower were selected from the point cloud: conductor points, insulator string points, and tower points.

[0093] Subsequently, spatial clustering analysis was performed on conductor points and insulator string points respectively. For conductor points, based on the spatial continuity, linear distribution characteristics, and Euclidean distance constraints of the point cloud, multiple independent single conductor units were obtained through clustering. For insulator string points, based on the strip distribution and spatial connectivity of the point cloud, multiple independent insulator string point sets were obtained through clustering. Simultaneously, spatial connectivity aggregation was performed on tower points to obtain complete tower units. Finally, using the tower units as the core, conductor units and insulator string units within their spatial association range were selected to complete the segmentation and spatial association of the three types of entity units.

[0094] Step S1032: For each tower unit, establish the matching relationship between the insulator string unit and the conductor unit based on the spatial correspondence.

[0095] Using tower units as the core and combining the topology of transmission lines, spatial matching is performed on the segmented conductor units and insulator string units.

[0096] First, based on spatial location, route consistency, and stringing area constraints, a one-to-one correspondence is established between each conductor unit and its corresponding insulator string unit. This ensures that the matching conforms to the structural characteristics of different tower types, such as straight-line towers and tension towers, avoiding mismatches across spans or towers. Then, combining engineering parameters such as the number of circuits and voltage type, the target conductor units and insulator string units requiring stringing point calculations are selected. Irrelevant and redundant component units are eliminated, completing the verification and optimization of the matching relationship, forming a stable "tower-insulator string-conductor" topology link.

[0097] Step S1033: For each insulator string unit, take the point in the point cloud of the insulator string unit that is closest to the tower unit as the tower-side suspension point of the insulator string unit; take the point in the point cloud of the insulator string unit that is closest to its matching conductor unit as the conductor-side suspension point of the insulator string unit.

[0098] For insulator string units that have been matched, this embodiment of the invention uses an octree nearest point search algorithm to extract two types of suspension points: First, with tower units as the target set, the point in the insulator string unit point cloud that is closest to the tower unit is retrieved and determined as the connection point between the insulator string and the tower, i.e., the tower-side suspension point; Second, with matched conductor units as the target set, the point in the insulator string unit point cloud that is closest to the conductor unit is retrieved and determined as the connection point between the conductor and the insulator string, i.e., the conductor-side suspension point.

[0099] After extracting the suspension points of the insulator strings, all suspension points are uniformly sorted and stored in a standardized manner according to engineering logic. The specific sorting principles are as follows: First, along the line direction, all suspension points are globally sorted according to the ascending order of tower numbers (from smallest to largest tower); second, in the cross-sectional direction of the tower, if there are multiple parallel loops, the loops are numbered sequentially from left to right and from top to bottom; for multi-phase conductors within the same loop, the order of suspension points for each phase is determined according to their actual spatial arrangement on the tower, usually from top to bottom or from left to right. After sorting, the suspension point data is associated with other attributes of the insulator strings (such as string type, length, voltage level, etc.) for storage, ensuring the orderliness and traceability of the data, and providing standardized and accurate benchmark data for subsequent calculations of acceptance parameters such as insulator string length and electrical clearance.

[0100] When extracting insulator string suspension points and conducting transmission line acceptance analysis, distance calculation between multiple points is a core and essential operation. Transmission line point cloud data is massive in scale; directly calculating using a full traversal method would be extremely computationally intensive and time-consuming. To significantly improve the efficiency of spatial retrieval and distance calculation, this invention employs an octree-based nearest-point search algorithm.

[0101] See Figure 3 An octree is a hierarchical tree-like data structure for three-dimensional space. Its core principle is to recursively divide a specified three-dimensional space region into 8 octants. Each non-leaf node manages 8 subspace nodes, and the three-dimensional unit corresponding to each subspace is called a voxel.

[0102] Octrees achieve hierarchical subdivision of three-dimensional space through recursive partitioning: the initial three-dimensional space is continuously divided into 8 sub-cubes. If the point cloud distribution attributes within a certain voxel are consistent, the voxel is used as a leaf node and the partitioning stops; otherwise, the partitioning continues until the preset resolution or the maximum number of partitioning times n is reached, and finally a complete hierarchical spatial index structure with the root node as the top layer is formed.

[0103] After organizing and storing point cloud data in an octree structure, when performing nearest point search, it is only necessary to search the point cloud within the voxel where the target point is located and its adjacent voxels, without traversing the entire point cloud data. Spatial pruning greatly reduces invalid calculations and can significantly improve the efficiency of nearest point search and distance calculation.

[0104] The octree corresponds to the second and third level spatial indexes of this invention, and together with the global R tree and the underlying hash table, it constitutes a hierarchical retrieval system.

[0105] K-Nearest Neighbor Search is a commonly used spatial nearest neighbor retrieval algorithm used to find the K points in a point set that are closest to a target point. In this embodiment of the invention, K=1 is chosen, meaning that only the single point closest to the target point is searched. This nearest-point search can be directly used for extracting insulator string suspension points and calculating line safety distances.

[0106] Figure 4 This diagram illustrates the effect of using the octree nearest neighbor search algorithm to calculate the phase-to-phase distance between adjacent conductors in a 1000kV transmission line.

[0107] Step S104: Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, calculate the acceptance parameters of the target transmission line to obtain the completion acceptance results of the target transmission line.

[0108] This invention determines whether the acceptance parameters are compliant by comparing them with the corresponding transmission line design specifications, and uses this as the final acceptance result of the target transmission line.

[0109] The core geometric dimensional parameters for the final acceptance of transmission lines mainly include: insulator string parameters, conductor and ground wire sag parameters, conductor-to-conductor safety distance parameters, jumper parameters, and distance parameters to ground and to objects.

[0110] Insulator string parameters include: suspension string skew angle and offset, tension string tilt angle, length difference and skew angle difference of multiple strings, and minimum clearance between insulator strings and tower components; conductor and ground wire sag parameters include: conductor sag, ground wire sag, and sag ratio between ground wire and conductor; conductor safety distance parameters include: minimum horizontal and vertical distance between phase conductors, spacing between phase split conductors, minimum distance between conductor and ground wire, and minimum clearance for line crossings; jumper parameters include: jumper sag and minimum distance between jumper and tower components; distance parameters to ground and objects include: minimum vertical distance between conductor and ground wire and ground, and minimum clearance between conductor and ground wire and objects within the line corridor and crossings. Detailed acceptance parameters can be found in industry standards, which will not be elaborated upon here.

[0111] The suspension points of the insulator strings serve as the reference control points for the three-dimensional geometric calculation of transmission lines. They are used to calculate insulator string parameters, conductor and ground wire sag parameters, conductor-to-conductor safety distance parameters, jumper parameters, etc. Ground point cloud data serves as the elevation and terrain benchmark for calculating transmission line acceptance parameters. It is used to construct a ground elevation benchmark and calculate distance parameters to the ground and to objects.

[0112] The following section uses jumper sag and minimum distance between the jumper and tower components as examples to explain the process of obtaining jumper completion acceptance results.

[0113] Step S1041: Determine the jumper structure through software matching or manual specification so that a matching mathematical calculation model for final acceptance can be adopted for different jumper structures.

[0114] In power transmission line engineering, to ensure that the jumper and tower meet the safe distance requirements under various operating conditions, different jumper structure designs must be adopted according to the voltage level and tower type characteristics. These different structural designs, in turn, determine the different methods for calculating jumper sag. Common jumper structures mainly include four types: straight jumper, single jumper, double jumper, and rigid jumper, which differ in their stress characteristics, spatial arrangement, and sag control.

[0115] See Figures 5 to 8 A straight jumper refers to a jumper 3 whose two ends are directly connected to the two sides of the tension tower and fixed to the tension clamps at the ends of the tension insulator strings 1 on the tower crossarm 2. There is no jumper insulator string 4 or rigid support frame 5 in the middle; it hangs naturally from a single section of flexible conductor, resembling a catenary. A single jumper refers to a jumper 3 suspended and fixed in the middle by a string of jumper insulator strings 4, dividing the complete jumper into two independent flexible jumper segments. A double jumper refers to a jumper 3 suspended and fixed by two strings of jumper insulator strings 4, dividing the complete jumper into three independent flexible jumper segments. A rigid jumper refers to a double jumper with support, that is, a rigid support frame 5 is added to the double jumper string. The rigid support frame 5 provides multi-point rigid support for the jumper 3, limiting the sag of the jumper. The overall shape of the jumper is close to rigid, with only slight natural sag between the support points.

[0116] Using the line connecting the suspension endpoints of the jumper (segment) as the reference chord, the vertical distance from the lowest point of the actual catenary shape of the jumper to this reference chord is the sag of the jumper (segment). Figures 5 to 8 The red line segment in the diagram represents the sag of the corresponding jumper (segment).

[0117] Step S1042: Select the jumper point cloud and the tower point cloud as the core calculation objects, and calculate the jumper sag and the minimum distance between the jumper and the tower component based on the octree nearest point search algorithm.

[0118] The minimum distances between jumpers and tower components include: the minimum distance between the jumper and the upper crossarm of the tower, the minimum distance between the jumper and the tower body, and the minimum distance between the jumper and the lower crossarm of the tower.

[0119] In this embodiment of the invention, straight jumps, single jumps, and double jumps were selected for analysis and calculation, and Tables 1 to 3 show the calculation results. A straight jump in an arc; and The two jump line segments are sags in a single jump; , and The three jump segments are sags for a double jump; This is the minimum distance from the jumper cable to the crossarm on the tower; This is the minimum distance from the jumper cable to the tower body; This is the minimum distance from the jumper cable to the crossarm under the tower.

[0120] See Figure 9 and Figure 10 Table 1 shows the calculation results of the jumper sag and the minimum distance between the jumper and the tower components for straight jumps.

[0121] Table 1. Calculation results of jumper sag and minimum distance between jumper and tower components for straight jumps.

[0122]

[0123] See Figure 11 and Figure 12 Table 2 shows the calculation results of the jumper sag and the minimum distance between the jumper and the tower components for a single jump.

[0124] Table 2. Calculation results of jumper sag and minimum distance between jumper and tower components for a single jump.

[0125]

[0126] See Figure 13 and Figure 14 Table 3 shows the calculation results of the jumper sag and the minimum distance between the jumper and the tower components for double jumpers.

[0127] Table 3. Calculation results of jumper sag and minimum distance between jumper and tower components for double jumpers.

[0128]

[0129] Step S1043: Compare the calculated results of jumper sag and minimum distance between jumper and tower components with the corresponding items in the transmission line design code to determine whether the jumper electrical clearance and sag meet the code requirements, thereby achieving automated final acceptance of jumper installation quality.

[0130] See Figure 15 In some embodiments of the invention, a completion acceptance report for the target transmission line is generated based on the acceptance parameters and completion acceptance results of the target transmission line; wherein, the completion acceptance report includes completion acceptance items, acceptance parameters, transmission line design code requirements, comparison results, hazard attributes, hazard level, and hazard location.

[0131] This invention, through automatic comparison with design specifications, identification of potential hazards, and generation of structured reports, comprehensively promotes the standardization of the acceptance process and the intelligentization of decision-making.

[0132] The laser point cloud-based transmission line completion acceptance measurement method provided in this invention has the following advantages compared to traditional completion acceptance methods: Acceptance of a single tower requires at least 3-5 people; acceptance of a single section of the line requires at least 3-5 people, and acceptance of a corridor requires at least 2 people. This invention utilizes airborne laser point cloud data to assist in completion acceptance, reducing the number of people required for each section by 2 and the time required for each section by 0.3 days. During the acceptance process, only the degree of equipment damage and connection status needs to be checked; distance analysis of other complete objects identifiable by the laser point cloud can be largely completed using the results of this invention, improving acceptance efficiency and accuracy, and shortening the acceptance cycle. It also solves the problem of high workload for corridor acceptance personnel in special terrains, and the difficulty for acceptance personnel to reach remote areas such as mountains and canyons; this invention can also be used for acceptance in these areas.

[0133] An embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the laser point cloud-based transmission line completion acceptance measurement method of an embodiment of the present invention.

[0134] An embodiment of the present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the laser point cloud-based transmission line completion acceptance measurement method of an embodiment of the present invention.

[0135] Embodiments of this invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the laser point cloud-based transmission line completion acceptance measurement method according to embodiments of this invention.

[0136] refer to Figure 16The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0137] like Figure 16 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0138] Multiple components in the electronic device are connected to I / O interface 105, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information into the electronic device. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, disks and optical discs. Communication unit 109 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0139] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 102 and / or communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0140] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0142] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0143] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are subject to strict compliance with relevant laws, regulations, and regulatory requirements in their collection, storage, use, processing, transmission, provision, and disclosure, and adhere to the principles of legality, legitimacy, necessity, and good faith. The acquisition of relevant information and data is premised on the user's explicit consent or other legitimate reasons, and a clear and convenient authorization management approach is provided to the user, allowing the user to independently choose to consent, withdraw consent, or refuse to provide relevant information. For functions that rely on user information, if the user does not authorize or withdraws authorization, the corresponding technical function cannot be implemented, and the technical solution of this invention is not applicable in this scenario.

[0144] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0145] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0146] The above embodiments merely illustrate several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for measurement during the final acceptance of power transmission lines based on laser point clouds, characterized in that, include: The 3D point cloud data of the target transmission line corridor is stored in a hierarchical grid, and a spatial index is built for each layer of grid data. Using the spatial index and a pre-trained recognition model, power transmission equipment is identified in the non-ground point cloud data of the three-dimensional point cloud data to obtain entity recognition results of the non-ground point cloud data; wherein, the entity recognition results include towers, insulator strings, jumpers, conductors, and vegetation; Based on the entity recognition results, the suspension points of each insulator string in the non-ground point cloud data are extracted; Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated to obtain the completion acceptance results of the target transmission line. The 3D point cloud data of the target transmission line corridor is stored in a hierarchical grid format, including: The three-dimensional space occupied by the three-dimensional point cloud data is divided into four grid levels with different resolutions; wherein, the first level of grid space resolution is meter level; the second level of grid space resolution is sub-meter level; the third level of grid space resolution is centimeter level; and the fourth level of grid space resolution is millimeter level. Establish a two-way mapping relationship between grid cells at adjacent levels; Build a spatial index for each layer of grid data, including: The first level uses an R-tree as a spatial index; the second and third levels both use octrees as spatial indexes; and the fourth level uses a hash table as a spatial index.

2. The method for measurement and acceptance of power transmission lines based on laser point clouds according to claim 1, characterized in that, Using a pre-trained recognition model, power transmission equipment identification is performed on non-ground point cloud data within the 3D point cloud data, including: A filtering algorithm is used to segment the 3D point cloud data into ground point cloud data and non-ground point cloud data; Based on the tower register information of the target transmission line, extract the surrounding area point cloud data of each tower from the non-ground point cloud data; The surrounding area point cloud data is input into a pre-trained recognition model to identify the category of each point cloud in the surrounding area point cloud data; wherein, the category includes towers, insulator strings, jumpers, conductors and vegetation.

3. The method for measurement and acceptance of power transmission lines based on laser point clouds according to claim 2, characterized in that, After inputting the surrounding area point cloud data into a pre-trained recognition model to identify the category of each point cloud in the surrounding area point cloud data, the process further includes: The remaining point cloud data in the non-ground point cloud data, excluding towers, insulator strings and jumpers, is divided into multiple clustering units based on a spatial clustering algorithm; Based on the proportion of the classified point clouds in each cluster unit, all point clouds in the corresponding cluster unit are assigned to the category with the highest proportion.

4. The method for measurement and acceptance of power transmission lines based on laser point clouds according to claim 2, characterized in that, Based on the entity recognition results, the suspension points of the insulator strings in the non-ground point cloud data are extracted, including: Based on the entity recognition results, each tower unit in the non-ground point cloud data is segmented, as well as multiple insulator string units and multiple conductor units spatially associated with each tower unit; For each tower unit, a matching relationship between the insulator string unit and the conductor unit is established based on the spatial correspondence. For each insulator string unit, the point in the point cloud of the insulator string unit that is closest to the tower unit is taken as the tower-side suspension point of the insulator string unit; the point in the point cloud of the insulator string unit that is closest to the corresponding matching conductor unit is taken as the conductor-side suspension point of the insulator string unit.

5. The method for measurement and acceptance of power transmission lines based on laser point clouds according to claim 1, characterized in that, Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated to obtain the final acceptance results of the target transmission line, including: Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated. The acceptance parameters include: insulator string parameters, conductor and ground wire sag parameters, conductor-to-conductor safety distance parameters, jumper parameters, and distance parameters to ground and to objects. The acceptance parameters are compared with the corresponding transmission line design specifications to determine whether each acceptance parameter is compliant, and the final acceptance result of the target transmission line is obtained.

6. The method for measurement and acceptance of power transmission lines based on laser point clouds according to claim 5, characterized in that, Based on the entity recognition results, the suspension points of the insulator string, and the ground point cloud data in the three-dimensional point cloud data, the acceptance parameters of the target transmission line are calculated to obtain the final acceptance results of the target transmission line. The process further includes: Based on the acceptance parameters and the completion acceptance results, a completion acceptance report for the target transmission line is generated; wherein, the completion acceptance report includes completion acceptance items, acceptance parameters, transmission line design code requirements, comparison results, hazard attributes, hazard levels, and hazard locations.

7. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the laser point cloud-based transmission line completion acceptance measurement method according to any one of claims 1 to 6.

8. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the transmission line completion acceptance measurement method based on laser point clouds according to any one of claims 1 to 6.

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