Distribution network equipment identification method and system based on laser point cloud data

Abstract

The invention relates to a distribution network equipment identification method and system based on laser point cloud data, and the method comprises the steps: collecting the laser point cloud data of a power transmission line corridor through employing an unmanned plane, and carrying out the data collection of a distribution network tower rod and a wire through employing a data collection device; the method comprises the following steps: extracting height, type, variable bracket, tower pole spacing and surrounding environment related data of a distribution network tower pole, establishing an intelligent identification model in combination with three-dimensional cutting and image identification technologies, performing laser point cloud identification in combination with actual conditions of a power transmission line corridor, and identifying distribution network tower pole data and power line data by using the established intelligent identification model; according to the method, the position information of each direction of the on-rack transformer is recognized through data preprocessing and feature recognition, and data support is provided for distribution line unmanned aerial vehicle autonomous inspection.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment identification, and in particular relates to a distribution network equipment identification method and system based on laser point cloud data. Background Art

[0002] Transmission line inspection requires not only 3D point cloud data for analyzing clearance safety distance, tower inclination, tower displacement, and tower base terrain changes, but also high-definition image data for analyzing hardware defects, foreign objects on towers and conductors, and short-circuit sparks, as well as infrared image analysis for broken conductors, and locating these defects. The existing line inspection method is to collect 3D laser point cloud data, high-definition image data, and infrared image data by aerial flight, and analyze them independently. Not only is the collection efficiency low and the flight safety factor high, but the number of redundant defect AI analysis is large, which makes the actual inspection cost high and the efficiency low, and it is impossible to accurately identify each distribution network equipment. Summary of the invention

[0003] In view of the problems of the above-mentioned prior art, the present invention provides a method and system for efficiently collecting multi-source data for transmission line inspection, which utilizes an established intelligent recognition model to identify distribution network tower data and power line data; performs data processing on the acquired data set to obtain evenly distributed point cloud data, and identifies the various position information of the on-rack transformer through data preprocessing and feature recognition, thereby providing data support for autonomous inspection of distribution line by drones.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A distribution network equipment identification method based on laser point cloud data, comprising:

[0006] Collecting laser point cloud data of the transmission line corridor using a drone, wherein the drone includes a data acquisition device mounted on the drone via a mounting structure;

[0007] Use control equipment to synchronously control infrared image and visible light image sensors, laser radar, and position and attitude measurement units, and use data acquisition equipment to collect data from distribution network towers and conductors;

[0008] The collected 3D laser point cloud data, 3D spatial position and posture data of the equipment are matched with the infrared image and visible light image; wherein, the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and wire, and the laser radar and position and posture measurement unit collect the 3D laser point cloud data, 3D spatial position and posture data of the distribution network tower and wire;

[0009] Extract data related to the height, type, transformer support, tower spacing, and surrounding environment of distribution network towers;

[0010] Combine 3D cutting and image recognition technology to establish an intelligent recognition model, combine the actual situation of the transmission line corridor to perform laser point cloud recognition, and use the established intelligent recognition model to identify distribution network tower data and power line data;

[0011] The acquired data set is processed to obtain evenly distributed point cloud data; feature recognition is performed on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment, and the various position information of the mounted transformers is identified to provide data support for autonomous inspection of distribution line drones.

[0012] Furthermore, a distribution network equipment identification method based on laser point cloud data is provided, and the establishment of an intelligent identification model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0013] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the generation of a laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0014] Furthermore, a power transmission line inspection method based on laser point cloud is provided, wherein the feature recognition comprises: classifying the laser point cloud in combination with actual objects in the transmission line corridor, establishing a high-precision feature recognition model, and classifying and identifying the tower point cloud and the power line point cloud.

[0015] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the classification and identification obtains different point cloud categories corresponding to different distribution network equipment, including: roughly classifying the processed laser point cloud to obtain the transmission line point cloud and the tower point cloud, filtering out the ground point cloud and the high and low vegetation point cloud; then finely classifying the transmission line point cloud and the tower point cloud, and using mathematical transformation and clustering to realize automatic classification of towers and transmission lines.

[0016] Furthermore, a distribution network equipment identification system based on laser point cloud data is provided, including: a data acquisition module, which uses a drone to collect laser point cloud data of a transmission line corridor, wherein the drone includes a data acquisition device mounted on the drone through a mounting structure; a data control module, which uses a control device to synchronously control an infrared image and a visible light image sensor, a laser radar, and a position and posture measurement unit, and uses a data acquisition device to collect data on distribution network towers and conductors; a data matching module, which matches the collected three-dimensional laser point cloud data, the three-dimensional spatial position and posture data of the equipment with the infrared image and the visible light image; wherein the infrared image and visible light image sensors collect infrared images and visible light images of distribution network towers and conductors, and the laser radar and the position and posture measurement unit collect 3D laser point cloud data of distribution network towers and conductors, 3D spatial position and posture data of equipment; data extraction module, extracts data related to the height, type, transformer bracket, tower spacing, and surrounding environment of distribution network towers; model building module, combines 3D cutting and image recognition technology to establish an intelligent recognition model, performs laser point cloud recognition based on the actual situation of the transmission line corridor, and uses the established intelligent recognition model to identify distribution network tower data and power line data; feature recognition module, processes the acquired data set to obtain evenly distributed point cloud data; performs feature recognition on evenly distributed point cloud data, obtains different point cloud categories corresponding to different distribution network equipment, identifies the various position information of the transformer on the rack, and provides data support for autonomous inspection of distribution line drones.

[0017] Furthermore, a distribution network equipment identification system based on laser point cloud data is provided, and the establishment of an intelligent identification model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0018] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the generation of a laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0019] Furthermore, a computer-readable storage medium is provided, on which a program is stored, characterized in that when the program is executed by a processor, the steps of the above-mentioned distribution network equipment identification method based on laser point cloud data are implemented.

[0020] Furthermore, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned steps of identifying a distribution network device based on laser point cloud data when executing the program.

[0021] Compared with the prior art, the advantages of the present invention are: a distribution network equipment identification method and system based on laser point cloud data, using drones to collect laser point cloud data of transmission line corridors; using control equipment to synchronously control infrared images and visible light image sensors, laser radars, and position and posture measurement units, and using data acquisition equipment to collect data on distribution network towers and conductors; the collected three-dimensional laser point cloud data, the three-dimensional spatial position and posture data of the equipment are matched with the infrared image and visible light image; wherein the infrared image and visible light image sensors collect infrared images and visible light images of distribution network towers and conductors, and the laser radar and position and posture measurement units ... The state measurement unit collects the three-dimensional laser point cloud data of the distribution network tower and conductor, the three-dimensional spatial position and posture data of the equipment; extracts the height, type, rack transformer bracket, tower spacing, and surrounding environment related data of the distribution network tower; combines three-dimensional cutting and image recognition technology to establish an intelligent recognition model, combines the actual situation of the transmission line corridor to perform laser point cloud recognition, and uses the established intelligent recognition model to identify the distribution network tower data and power line data; performs data processing on the acquired data set to obtain evenly distributed point cloud data; performs feature recognition on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment. The present invention improves the efficiency and accuracy of UAV inspection of transmission lines, and avoids the situation where multiple staff members are required to conduct on-site inspections along the line; secondly, through data preprocessing and feature recognition, the position information of the various positions of the rack transformer is identified, providing data support for the autonomous inspection of distribution line UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flow chart of a distribution network equipment identification method based on laser point cloud data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0025] In order to enable people in the technical field to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0026] Example 1

[0027] like Figure 1 As shown, a distribution network equipment identification method based on laser point cloud data includes:

[0028] Collecting laser point cloud data of the transmission line corridor using a drone, wherein the drone includes a data acquisition device mounted on the drone via a mounting structure;

[0029] Use control equipment to synchronously control infrared image and visible light image sensors, laser radar, and position and attitude measurement units, and use data acquisition equipment to collect data from distribution network towers and conductors;

[0030] The collected 3D laser point cloud data, 3D spatial position and posture data of the equipment are matched with the infrared image and visible light image; wherein, the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and wire, and the laser radar and position and posture measurement unit collect the 3D laser point cloud data, 3D spatial position and posture data of the distribution network tower and wire;

[0031] Extract data related to the height, type, transformer support, tower spacing, and surrounding environment of distribution network towers;

[0032] Combine 3D cutting and image recognition technology to establish an intelligent recognition model, combine the actual situation of the transmission line corridor to perform laser point cloud recognition, and use the established intelligent recognition model to identify distribution network tower data and power line data;

[0033] The acquired data set is processed to obtain evenly distributed point cloud data; feature recognition is performed on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment, and the various position information of the mounted transformers is identified to provide data support for autonomous inspection of distribution line drones.

[0034] Furthermore, a distribution network equipment identification method based on laser point cloud data is provided, and the establishment of an intelligent identification model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0035] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the generation of a laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0036] Furthermore, a power transmission line inspection method based on laser point cloud is provided, wherein the feature recognition comprises: classifying the laser point cloud in combination with actual objects in the transmission line corridor, establishing a high-precision feature recognition model, and classifying and identifying the tower point cloud and the power line point cloud.

[0037] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the classification and identification obtains different point cloud categories corresponding to different distribution network equipment, including: roughly classifying the processed laser point cloud to obtain the transmission line point cloud and the tower point cloud, filtering out the ground point cloud and the high and low vegetation point cloud; then finely classifying the transmission line point cloud and the tower point cloud, and using mathematical transformation and clustering to realize automatic classification of towers and transmission lines.

[0038] Example 2

[0039] A distribution network equipment identification system based on laser point cloud data is provided, comprising:

[0040] A data acquisition module, which uses a drone to collect laser point cloud data of the transmission line corridor, wherein the drone includes a data acquisition device on the drone;

[0041] The data control module uses control equipment to synchronously control the infrared image and visible light image sensors, laser radar, and position and posture measurement units, and uses data acquisition equipment to collect data from distribution network towers and conductors;

[0042] The data matching module matches the collected 3D laser point cloud data, the 3D spatial position and posture data of the equipment with the infrared image and the visible light image; wherein the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and the conductor, and the laser radar and the position and posture measurement unit collect the 3D laser point cloud data, the 3D spatial position and posture data of the equipment of the distribution network tower and the conductor;

[0043] Data extraction module, extracting data related to the height, type, rack-transformer bracket, tower spacing, and surrounding environment of distribution network towers;

[0044] The model building module combines 3D cutting and image recognition technology to build an intelligent recognition model, performs laser point cloud recognition based on the actual situation of the transmission line corridor, and uses the established intelligent recognition model to identify distribution network tower data and power line data;

[0045] The feature recognition module processes the acquired data set to obtain evenly distributed point cloud data; it performs feature recognition on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment, and identifies the various position information of the transformer on the rack, providing data support for autonomous inspection of distribution line drones.

[0046] Furthermore, a distribution network equipment identification system based on laser point cloud data is provided, and the establishment of an intelligent identification model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0047] Furthermore, a transmission line inspection method based on laser point cloud is provided, wherein the generation of a laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

[0048] Example 3

[0049] A distribution network equipment identification system based on laser point cloud data is provided, comprising: a data acquisition module, which uses a drone to collect laser point cloud data of a transmission line corridor, wherein the drone includes a data acquisition device mounted on the drone through a mounting structure; a data control module, which uses a control device to synchronously control an infrared image and a visible light image sensor, a laser radar, and a position and attitude measurement unit, and uses a data acquisition device to collect data on distribution network towers and conductors; a data matching module, which matches the collected three-dimensional laser point cloud data, the three-dimensional spatial position and attitude data of the equipment with the infrared image and the visible light image; wherein the infrared image and the visible light image sensor collect the infrared image and the visible light image of the distribution network tower and the conductor, and the laser radar and the position and attitude measurement unit collect the distribution network 3D laser point cloud data of towers and conductors, 3D spatial position and posture data of equipment; data extraction module, extracts data related to the height, type, transformer bracket, tower spacing, and surrounding environment of distribution network towers; model building module, combines 3D cutting and image recognition technology to establish an intelligent recognition model, and performs laser point cloud recognition based on the actual situation of the transmission line corridor, and uses the established intelligent recognition model to identify distribution network tower data and power line data; feature recognition module, processes the acquired data set to obtain evenly distributed point cloud data; performs feature recognition on evenly distributed point cloud data, obtains different point cloud categories corresponding to different distribution network equipment, identifies the various position information of the transformer on the rack, and provides data support for autonomous inspection of distribution line drones.

[0050] The above system is used to carry out a transmission line inspection system based on laser point cloud, including:

[0051] The preprocessing module is configured to: preprocess the collected laser point cloud data of the transmission line corridor;

[0052] The three-dimensional laser model building module is configured to: solve and process the pre-processed laser point cloud data to build a three-dimensional laser model of the transmission line corridor;

[0053] The laser point cloud tower model building module is configured to: classify the laser point cloud in combination with the actual objects in the transmission line corridor, build a high-precision laser point cloud tower model, and classify and identify the tower point cloud and the power line point cloud;

[0054] The UAV autonomous inspection trajectory module is configured to: plan the trajectory based on the data of the established high-precision laser point cloud tower model, confirm each point that needs to be photographed, and determine the UAV autonomous inspection trajectory by connecting each photographing point.

[0055] Example 4

[0056] A distribution network equipment identification method based on laser point cloud data, comprising:

[0057] Collecting laser point cloud data of the transmission line corridor using a drone, wherein the drone includes a data acquisition device mounted on the drone via a mounting structure;

[0058] Use control equipment to synchronously control infrared image and visible light image sensors, laser radar, and position and attitude measurement units, and use data acquisition equipment to collect data from distribution network towers and conductors;

[0059] The collected 3D laser point cloud data, 3D spatial position and posture data of the equipment are matched with the infrared image and visible light image; wherein, the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and wire, and the laser radar and position and posture measurement unit collect the 3D laser point cloud data, 3D spatial position and posture data of the distribution network tower and wire;

[0060] Extract data related to the height, type, transformer support, tower spacing, and surrounding environment of distribution network towers;

[0061] Combine 3D cutting and image recognition technology to establish an intelligent recognition model, combine the actual situation of the transmission line corridor to perform laser point cloud recognition, and use the established intelligent recognition model to identify distribution network tower data and power line data;

[0062] The acquired data set is processed to obtain evenly distributed point cloud data; feature recognition is performed on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment, and the various position information of the mounted transformers is identified to provide data support for autonomous inspection of distribution line drones.

[0063] The original laser point cloud data of the transmission line is selected based on the laser point cloud to obtain the best point cloud data, specifically: constructing a laser point cloud pyramid to obtain the number of layers and tile range of the laser point cloud pyramid;

[0064] Based on the user's perspective information, the optimal number of pyramid layers and tile range are determined to obtain the best point cloud data.

[0065] Furthermore, a laser point cloud pyramid is constructed to obtain the number of layers and tile range of the laser point cloud pyramid, specifically:

[0066] Get the two-dimensional boundary of the original point cloud data, that is, the maximum and minimum values ​​of the point cloud coordinates X and Y, and assign a unique number to each point cloud; specify the size of the first layer unit tile, the thinning factor between adjacent layers and the point cloud thinning method, combine the two-dimensional boundary range of the original point cloud with the specified parameters, and obtain the configuration parameters of all pyramid structures, including the total number of pyramid layers, the average point cloud density of each layer, the tile index coordinates of each layer and the two-dimensional boundary range of the tile, and save them to the pyramid configuration file; according to the two-dimensional boundary range of each layer of tiles, start from the bottom layer and thin out the original data layer by layer to each tile of each layer of the pyramid; the bottom layer data of the pyramid comes directly from the original data, and the data of the i+1th layer is generated by the i-th layer data through the thinning algorithm.

[0067] Example 5

[0068] A computer-readable storage medium is provided, on which a program is stored, characterized in that when the program is executed by a processor, the steps of the above-mentioned distribution network equipment identification method based on laser point cloud data are implemented.

[0069] Example 6

[0070] An electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned steps of identifying a distribution network device based on laser point cloud data when executing the program.

[0071] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0072] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network equipment identification method based on laser point cloud data, characterized in that: include: Collecting laser point cloud data of power transmission line corridors using drones, where the drones include data collection equipment; Use control equipment to synchronously control infrared image and visible light image sensors, laser radar, and position and attitude measurement units, and use data acquisition equipment to collect data from distribution network towers and conductors; The collected 3D laser point cloud data, 3D spatial position and posture data of the equipment are matched with the infrared image and visible light image; wherein, the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and wire, and the laser radar and position and posture measurement unit collect the 3D laser point cloud data, 3D spatial position and posture data of the distribution network tower and wire; Extract data related to the height, type, transformer support, tower spacing, and surrounding environment of distribution network towers; Combine 3D cutting and image recognition technology to establish an intelligent recognition model, combine the actual situation of the transmission line corridor to perform laser point cloud recognition, and use the established intelligent recognition model to identify distribution network tower data and power line data; The acquired data set is processed to obtain evenly distributed point cloud data; feature recognition is performed on the evenly distributed point cloud data, and classification and identification are performed to obtain different point cloud categories corresponding to different distribution network equipment, and the various position information of the mounted transformers is identified to provide data support for autonomous inspection of distribution line drones.

2. A distribution network equipment identification based on laser point cloud data as claimed in claim 1, characterized in that: The establishment of the intelligent recognition model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

3. A power transmission line inspection method based on laser point cloud as claimed in claim 2, characterized in that: The generating of the laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate the laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

4. A power transmission line inspection method based on laser point cloud as claimed in claim 1, characterized in that: The feature recognition includes: classifying laser point clouds in combination with actual objects in the transmission line corridor, establishing a high-precision feature recognition model, and classifying and identifying tower point clouds and power line point clouds.

5. A power transmission line inspection method based on laser point cloud as claimed in claim 4, characterized in that: The classification and identification obtains different point cloud categories corresponding to different distribution network equipment, including: roughly classifying the processed laser point cloud to obtain the transmission line point cloud and the tower point cloud, filtering out the ground point cloud and the high and low vegetation point cloud; then finely classifying the transmission line point cloud and the tower point cloud, and using mathematical transformation and clustering to realize automatic classification of towers and transmission lines.

6. A distribution network equipment identification system based on laser point cloud data, characterized in that: include: The data acquisition module uses a drone to collect laser point cloud data of the transmission line corridor, where the drone includes a data acquisition device; The data control module uses control equipment to synchronously control the infrared image and visible light image sensors, laser radar, and position and posture measurement units, and uses data acquisition equipment to collect data from distribution network towers and conductors; The data matching module matches the collected 3D laser point cloud data, the 3D spatial position and posture data of the equipment with the infrared image and the visible light image; wherein the infrared image and visible light image sensors collect the infrared image and visible light image of the distribution network tower and the conductor, and the laser radar and the position and posture measurement unit collect the 3D laser point cloud data, the 3D spatial position and posture data of the equipment of the distribution network tower and the conductor; Data extraction module, extracting data related to the height, type, rack-transformer bracket, tower spacing, and surrounding environment of distribution network towers; The model building module combines 3D cutting and image recognition technology to build an intelligent recognition model, performs laser point cloud recognition based on the actual situation of the transmission line corridor, and uses the established intelligent recognition model to identify distribution network tower data and power line data; The feature recognition module processes the acquired data set to obtain evenly distributed point cloud data; it performs feature recognition on the evenly distributed point cloud data to obtain different point cloud categories corresponding to different distribution network equipment, and identifies the various position information of the transformer on the rack, providing data support for autonomous inspection of distribution line drones.

7. A distribution network equipment identification system based on laser point cloud data as claimed in claim 6, characterized in that: The establishment of the intelligent recognition model includes: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate a laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

8. A power transmission line inspection method based on laser point cloud as claimed in claim 7, characterized in that: The generating of the laser point cloud three-dimensional model comprises: using an open source three-dimensional rendering engine to perform scene rendering on the pre-processed point cloud data to generate the laser point cloud three-dimensional model; and using paging technology to dynamically adjust the laser point cloud three-dimensional model.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of a distribution network equipment identification method based on laser point cloud data as described in any one of claims 1 to 5 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of identifying network distribution equipment based on laser point cloud data as described in any one of claims 1 to 5 are implemented.

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