A method and system for retrieving massive laser point cloud data of electric power

Abstract

A method and system for retrieving massive laser point cloud data of electric power. The method includes building a Hadoop distributed architecture, deploying a spatial database based on the Hadoop distributed architecture, and using the spatial database as the primary storage structure for laser point cloud data of power grid equipment; constructing an 8-tree hierarchy based on the spatial database, storing slices of laser point cloud data of power grid equipment of different levels in the 8-tree hierarchy, and constructing a mapping from the spatial database nodes to the 8-tree hierarchy; and hierarchically retrieving the point cloud data of power grid equipment in the 8-tree hierarchy. The solution of the present invention realizes the data organization structure and data format conversion of the point cloud of power grid equipment, the organization and spatial index mechanism of the point cloud data model, and distributed storage.

Description

Technical Field

[0001] The present invention belongs to the field of massive point cloud data management in the electric power industry, and in particular relates to a retrieval method and system for massive laser point cloud data in the electric power industry. Background Art

[0002] With the rapid increase in the application of laser point cloud data, it is crucial to store and process laser point cloud data efficiently. Traditional relational databases are relatively weak in the storage and processing of massive spatial data. With the continuous development of computer technology and applications, traditional centralized storage methods have been difficult to meet the increasingly large and complex data storage needs. Distributed storage provides a new solution for the storage and processing of massive laser point cloud data. The parallelization algorithm of big data has become a hot topic in the field of big data and has obvious advantages in massive data processing. Compared with the single-core serial computing method, the use of parallel computing can effectively improve the processing efficiency of point cloud data.

[0003] In the existing technology, most of the distributed storage systems are based on relational databases. Relational databases have the advantages of low system development cost, support for multi-user concurrent access, good transactional and security, but there are many difficult to overcome problems when processing large-scale and high-concurrency data tasks. Therefore, with the development of technology, non-relational database systems are gradually used to store and process massive spatial data.

[0004] However, when it comes to storing massive data, the traditional solutions have obvious deficiencies in their rapid retrieval efficiency, and cannot meet application requirements. They are unable to integrate distributed spatial databases with indexing algorithms to leverage the advantages of both and maximize speed. In particular, the most fundamental speed-up effect of indexing algorithms has not been sufficiently improved. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and system for retrieving massive laser point cloud data of electric power, so as to realize the data organization structure and data format conversion of power grid equipment point cloud, the organization and spatial indexing mechanism of point cloud data model and the distributed storage of power grid equipment point cloud model.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions.

[0007] The present invention first discloses a method for retrieving massive laser point cloud data of electric power, which comprises the following steps:

[0008] Step 1, building a Hadoop distributed architecture, deploying a spatial database based on the Hadoop distributed architecture, and using the spatial database as a primary storage structure for laser point cloud data of power grid equipment;

[0009] Step 2, constructing an 8-tree hierarchical structure based on the spatial database, storing laser point cloud data slices of power grid equipment of different levels in the 8-tree hierarchical structure, and constructing a mapping from the spatial database nodes to the 8-tree hierarchical structure;

[0010] Step 3, hierarchically retrieve the power grid equipment point cloud data in the 80-20 tree hierarchy.

[0011] The present invention further includes the following preferred embodiments:

[0012] The method of using the spatial database as a primary storage structure for the laser point cloud data of power grid equipment further includes:

[0013] The massive point cloud data is managed and divided as a whole through the spatial database. When calling or retrieving the point cloud data, the spatial region or area range of the point cloud data is first determined, and the node position where the point cloud data is stored and the macro region are located from the spatial database.

[0014] The spatial database is a postgresql database.

[0015] The storing of laser point cloud data slices of power grid equipment of different levels in the 28-tree hierarchy structure further includes:

[0016] Through the nested 28-tree MNO algorithm, the input point cloud data is downsampled step by step based on the detail level LOD to form nodes of different levels. The density of the point cloud increases with the increase of the node level.

[0017] The method of constructing a 28-tree hierarchy structure based on the spatial database further includes:

[0018] Get the overall plane range of the point cloud data and get the maximum and minimum values ​​in the X and Y directions, which are X min , X max , Y min , Y max , get the minimum outer rectangle of the point cloud range (X min , Y min )(X min , Y max )(X max , Y max )(X max , Y min ), define the linked list pointer list of the total area;

[0019] Based on the minimum bounding rectangle and the size of the spatial area, sort the point cloud data of the entire area from small to large according to the value of X, then remove the median, and divide all points into two areas A and B at the N / 2th point position after rounding. N is the total number of points. Define the corresponding two linked lists listXin and listXax, which point to the common parent node list, and the parent node points to the two child nodes listXin and listXax;

[0020] Among them, the results of sorting from small to large according to the value of X are:

[0021] ,

[0022] The median is calculated as follows:

[0023] ,

[0024] In regions A and B, two-dimensional weighted segmentation in the Y direction is performed. For each region, two upper and lower regions are formed. At the same time, the region's linked list points to the corresponding child node linked lists listYin and listYax of the two regions.

[0025] When the number of points in the segmented area is greater than the preset point number threshold n, the above steps are iteratively performed; when the number of points in the segmented area is less than n:

[0026] In the current area, the spatial tree method is used to continue to perform three-dimensional refinement and segmentation downward. The three-dimensional weight segmentation is adopted. First, in the Z direction, the upper and lower areas are divided into upper and lower areas, and then divided according to the two-dimensional weight. After the cutting, the area is divided into eight areas. The number of point clouds in each area is exactly the same, or the number differs by one point. At the same time, eight corresponding linked lists list1-list8 are generated. The eight linked list nodes point to the parent node listS together, and the corresponding listS also points downward to the eight child nodes;

[0027] If the number of points in the eight regions is greater than the preset second point number threshold n', continue to repeat the above steps. When the number of points in the eight regions is less than n', stop further downward splitting and create corresponding child nodes and parent nodes until a complete 28-tree is finally created.

[0028] The present invention also discloses a retrieval system for electric power massive laser point cloud data using the above-mentioned retrieval method for electric power massive laser point cloud data, comprising:

[0029] A distributed architecture building module is used to build a Hadoop distributed architecture, deploy a spatial database based on the Hadoop distributed architecture, and use the spatial database as a primary storage structure for laser point cloud data of power grid equipment;

[0030] A hierarchical storage construction module is used to construct an 8-tree hierarchical structure based on the spatial database, store laser point cloud data slices of power grid equipment of different levels in the 8-tree hierarchical structure, and construct a mapping from the spatial database nodes to the 8-tree hierarchical structure;

[0031] The point cloud slice publishing module is used to hierarchically load the point cloud data slices of the power grid equipment in the 80-20 tree hierarchy structure and publish the point cloud data slices as 3DTiles files.

[0032] Accordingly, the present application also discloses a terminal, including a processor and a storage medium;

[0033] The storage medium is used to store instructions;

[0034] The processor is used to operate according to the instructions to execute the steps of the aforementioned method for retrieving massive laser point cloud data.

[0035] Correspondingly, the present application also discloses a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the aforementioned method for retrieving massive power laser point cloud data are implemented.

[0036] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for retrieving massive laser point cloud data of electric power. Based on a distributed architecture, the present invention combines a spatial database and an 80-20 tree to build a new multi-source index, thereby realizing distributed and rapid retrieval of massive point cloud data, realizing safe and rapid retrieval and query for the management of massive data, and effectively reducing the amount of data under the same conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the 80-20 tree structure in the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] The embodiments described in this application are only some embodiments of the present invention, not all embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0040] In view of the shortcomings of the prior art, the present invention proposes a method and system for retrieving massive laser point cloud data of electric power, which combines different data storage methods according to the characteristics of electric power data and business features to achieve complementary advantages of different indexes. The distributed spatial database and the hierarchical construction method of the 28-tree are used to maximize the speed of data retrieval and query.

[0041] Aiming at the massive point cloud data model of power grid equipment, a spatial index mechanism for massive 3D point clouds is established. Aiming at the characteristics of massive point clouds, based on the performance characteristics of distributed file storage database, a multi-layer indexing method is designed to achieve efficient organization and management of point cloud data.

[0042] The method for retrieving massive laser point cloud data of electric power disclosed in the present invention comprises the following steps:

[0043] Step 1: Build a Hadoop distributed architecture, deploy a spatial database based on the Hadoop distributed architecture, and use the spatial database as the primary storage structure of the laser point cloud data of power grid equipment.

[0044] Hadoop is an open source software framework that supports data-intensive distributed applications and is released under the Apache 2.0 license agreement. It includes the Hadoop kernel, MapReduce, and the Hadoop Distributed File System (HDFS). Point cloud data contains rich geographic information and belongs to the category of spatial data. A distributed storage strategy is used to build a storage model to realize the application of non-relational databases in a distributed environment.

[0045] A spatial database is deployed on the server and nodes of the Hadoop distributed architecture. In a preferred embodiment, a postgresql database is used. The spatial database is used to manage and divide the entire mass point cloud data. When calling or retrieving point cloud data, the search condition position such as the spatial area or area range is first determined. With the assistance of the database, the node position where the mass data is stored and the macroscopic approximate area are quickly located to achieve rapid positioning.

[0046] Step 2: construct an 80-tree hierarchy based on the spatial database, store laser point cloud data slices of power grid equipment of different levels in the 80-tree hierarchy, and construct a mapping from the spatial database nodes to the 80-tree hierarchy.

[0047] In the next layer of the spatial database, the granularity is further reduced and refined by constructing a 28-tree, and the large-scale laser point cloud data in each node is divided into designated areas. The specific 28-tree construction method is as follows:

[0048] Step 2.1: Get the overall plane range of the point cloud data and get the maximum and minimum values ​​in the X and Y directions, which are Xmin , X max , Y min , Y max Then we get the minimum bounding rectangle (X min , Y min )(X min , Y max )(X max , Y max )(X max , Y min ) four consecutive points and define the linked list pointer list of the total area.

[0049] Step 2.2: Two-dimensional weighted segmentation. Based on the minimum bounding rectangle and the size of the spatial area, first sort the point cloud data of the entire area from small to large according to the value of X. If the total number of points is N, sort them according to the median, and divide all points into two areas A and B at the N / 2 (rounded)th point position, and define two corresponding linked lists listXin and listXax, which point to the common parent node list. Similarly, the parent node points to two child nodes listXin and listXax.

[0050] Among them, the results of sorting from small to large according to the value of X are:

[0051] ,

[0052] The median is calculated as follows:

[0053] ,

[0054] Step 2.3: Perform two-dimensional weighted segmentation in the Y direction in regions A and B respectively. Taking region A as an example, two regions C and D are formed. At the same time, the linked list of region A points to the child node linked lists listYin and listYax of the corresponding two regions. Similarly, perform the same operation on region B.

[0055] Step 2.4: When the number of points in the segmented area is greater than the preset point number threshold n, iteratively execute steps 2.2-2.3; when the number of points in the segmented area is less than n, execute step 2.5.

[0056] Step 2.5: When the total number of point clouds in area S is less than n, the spatial tree is used to continue to perform three-dimensional refinement and segmentation downward in the current area. The three-dimensional weight segmentation is used. First, in the Z direction, the upper and lower areas are divided into upper and lower areas, and then divided according to the two-dimensional weight. After the cutting, area S is divided into eight areas of different sizes. The number of point clouds in each area is exactly the same, or the number differs by one point. At the same time, eight corresponding linked lists list1-list8 are generated. The eight linked list nodes point to the parent node listS together, and the corresponding listS also points downward to eight child nodes.

[0057] Step 2.6: When the number of points in the irregular eight regions is less than the preset second point number threshold n', stop further downward segmentation. If the number of points in the eight regions is greater than n', continue to repeat step 2.5 to create corresponding child nodes and parent nodes until a complete 28-tree is finally created, such as Figure 1 shown.

[0058] Step 3: hierarchically retrieve the power grid equipment point cloud data in the 80-20 tree hierarchy.

[0059] The linked list structure is used to achieve fast data retrieval, and the retrieval is completed from coarse to fine until the root node is reached according to the upper and lower leaf node levels.

[0060] The 28-tree structure of the present invention ensures the consistency of the order of magnitude and data volume of point clouds at the same level, and effectively controls the data rendering load within the scope of dynamic data retrieval or rendering visualization query. The 28-tree method also realizes the combination of two-dimensional weights and three-dimensional weight segmentation at different depths, realizes a new retrieval method, and greatly improves the balance of leaf nodes and the stability of hierarchical depth. Better distance retrieval and neighborhood retrieval are achieved. Combined with the application of distributed deployment of spatial databases, data security and stability are achieved, and a fast retrieval method with retrieval areas progressively from large to small is achieved.

[0061] For the case where the search is in three-dimensional space, perform the following steps:

[0062] Step 3.1: Determine the three non-coplanar sides of the rectangle to determine the position and orientation of the rectangle. From these three sides, deduce the six faces of the rectangle, that is, three pairs of parallel planes.

[0063] Step 3.2: For each face, construct a plane equation:

[0064] Ax + By + Cz + D = 0, where (A, B, C) is the normal vector of the plane and D is a constant term of the plane.

[0065] Step 3.3: Substitute the coordinates of the point to be determined into the equation of each plane and, based on the sign of the result (positive, negative, or zero), determine the position of the point relative to the plane, that is, on which side of the plane it is located, or on the plane.

[0066] Step 3.4: Based on the judgment results of all faces, if the point is on the correct side (i.e., inside) of all six planes, it is determined that the point is inside the rectangle and is directly located at the leaf node.

[0067] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for retrieving massive laser point cloud data of electric power. Based on a distributed architecture, the present invention combines a spatial database and an 80-20 tree to build a new multi-source index, thereby realizing distributed and rapid retrieval of massive point cloud data, realizing safe and rapid retrieval and query for the management of massive data, and effectively reducing the amount of data under the same conditions.

[0068] The present invention may be a system, a method and / or a computer program product. The present invention also discloses a retrieval system for electric power massive laser point cloud data based on the aforementioned retrieval method for electric power massive laser point cloud data, comprising:

[0069] A distributed architecture building module is used to build a Hadoop distributed architecture, deploy a spatial database based on the Hadoop distributed architecture, and use the spatial database as a primary storage structure for laser point cloud data of power grid equipment;

[0070] A hierarchical storage construction module is used to construct an 8-tree hierarchical structure based on the spatial database, store laser point cloud data slices of power grid equipment of different levels in the 8-tree hierarchical structure, and construct a mapping from the spatial database nodes to the 8-tree hierarchical structure;

[0071] The point cloud slice publishing module is used for hierarchical retrieval of the power grid equipment point cloud data files in the 80-tree hierarchy structure.

[0072] Based on the spirit of the present invention, those skilled in the art can easily think that a computer program product can be obtained based on the aforementioned method for retrieving electric power mass laser point cloud data. The computer program product may include a computer-readable storage medium, which carries computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure. That is, the present application also includes a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned method for retrieving electric power mass laser point cloud data.

[0073] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, - but not limited to - electrical storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as punch card or groove protrusion structure with instructions stored thereon and any suitable combination of the above. Computer readable storage medium used here is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

[0074] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0075] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0076] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for retrieving massive laser point cloud data of electric power, characterized in that: The following steps are involved: Step 1, building a Hadoop distributed architecture, deploying a spatial database based on the Hadoop distributed architecture, and using the spatial database as a primary storage structure for laser point cloud data of power grid equipment; Step 2, constructing an 8-tree hierarchical structure based on the spatial database, storing laser point cloud data slices of power grid equipment of different levels in the 8-tree hierarchical structure, and constructing a mapping from the spatial database to the 8-tree hierarchical structure; Step 3, hierarchically retrieving the power grid equipment point cloud data in the 80-20 tree hierarchy structure; The method of using the spatial database as a primary storage structure for the laser point cloud data of power grid equipment further includes: The massive point cloud data is managed and divided as a whole through a spatial database. When calling or retrieving point cloud data, the spatial region or area range of the point cloud data is first determined, and the node position where the point cloud data is stored and the macro region are located from the spatial database; The spatial database is a postgresql database; The storing of laser point cloud data slices of power grid equipment of different levels in the 28-tree hierarchy structure further includes: Through the nested 28-tree MNO algorithm, the input point cloud data is downsampled level by level based on the detail level LOD to form nodes of different levels. The density of the point cloud increases with the increase of the node level. The method of constructing a 28-tree hierarchy structure based on the spatial database further includes: Step 2.1: Get the overall plane range of the point cloud data and get the maximum and minimum values ​​in the X and Y directions, which are X min , X max , Y min , Y max , get the minimum outer rectangle of the point cloud range (X min , Y min )(X min , Y max )(X max , Y max )(X max , Y min ), define the linked list pointer list of the total area; Step 2.2: Based on the minimum bounding rectangle and the size of the spatial area, sort the point cloud data of the entire area from small to large according to the value of X, then remove the median, and divide all points into two areas A and B at the N / 2th point position after rounding. N is the total number of points. Define the corresponding two linked lists listXin and listXax, which point to the common parent node list, and the parent node points to the two child nodes listXin and listXax; Among them, the results of sorting from small to large according to the value of X are: The median is calculated as follows: Step 2.3: Perform two-dimensional weighted segmentation in the Y direction in regions A and B respectively. For each region, two upper and lower regions are formed. At the same time, the region's linked list points to the child node linked lists listYin and listYax corresponding to the two regions. Step 2.4: When the number of points in the segmented area is greater than the preset point number threshold n, iteratively execute the above steps 2.2-2.3; Step 2.5: When the number of points in the segmented region is less than n: In the current area, the spatial tree method is used to continue to perform three-dimensional refinement and segmentation downward. The three-dimensional weight segmentation is adopted. First, in the Z direction, the upper and lower areas are divided into upper and lower areas, and then divided according to the two-dimensional weight. After the cutting, the area is divided into eight areas. The number of point clouds in each area is exactly the same, or the number differs by one point. At the same time, eight corresponding linked lists list1-list8 are generated. The eight linked list nodes point to the parent node listS together, and the corresponding listS also points downward to the eight child nodes; Step 2.6: If the number of points in the eight regions is greater than the preset second point number threshold n', continue to repeat the above step 2.

5. When the number of points in the eight regions is less than n', stop further downward splitting and create corresponding child nodes and parent nodes until a complete 28-tree is finally created.

2. A retrieval system for massive laser point cloud data of electric power, characterized in that: include: A distributed architecture building module is used to build a Hadoop distributed architecture, deploy a spatial database based on the Hadoop distributed architecture, and use the spatial database as a primary storage structure for laser point cloud data of power grid equipment; A hierarchical storage construction module is used to construct an 8-tree hierarchical structure based on the spatial database, store laser point cloud data slices of power grid equipment of different levels in the 8-tree hierarchical structure, and construct a mapping from the spatial database to the 8-tree hierarchical structure; A point cloud slice publishing module, used for hierarchical retrieval of power grid equipment point cloud data in the 28-tree hierarchy structure; The distributed architecture building module is further used to: The massive point cloud data is managed and divided as a whole through a spatial database. When calling or retrieving point cloud data, the spatial region or area range of the point cloud data is first determined, and the node position where the point cloud data is stored and the macro region are located from the spatial database; The spatial database is a postgresql database; The hierarchical storage building module is further used to: Through the nested 28-tree MNO algorithm, the input point cloud data is downsampled level by level based on the detail level LOD to form nodes of different levels. The density of the point cloud increases with the increase of the node level. The hierarchical storage building module is further used to: Step 2.1: Get the overall plane range of the point cloud data and get the maximum and minimum values ​​in the X and Y directions, which are X min , X max , Y min , Y max , get the minimum outer rectangle of the point cloud range (X min , Y min )(X min , Y max )(X max , Y max )(X max , Y min ), define the linked list pointer list of the total area; Step 2.2: Based on the minimum bounding rectangle and the size of the spatial area, sort the point cloud data of the entire area from small to large according to the value of X, then remove the median, and divide all points into two areas A and B at the N / 2th point position after rounding. N is the total number of points. Define the corresponding two linked lists listXin and listXax, which point to the common parent node list, and the parent node points to the two child nodes listXin and listXax; Among them, the results of sorting from small to large according to the value of X are: The median is calculated as follows: Step 2.3: Perform two-dimensional weighted segmentation in the Y direction in regions A and B respectively. For each region, two upper and lower regions are formed. At the same time, the region's linked list points to the child node linked lists listYin and listYax corresponding to the two regions. Step 2.4: When the number of points in the segmented area is greater than the preset point number threshold n, iteratively execute the above steps 2.2-2.3; Step 2.5: When the number of points in the segmented region is less than n: In the current area, the spatial tree method is used to continue to perform three-dimensional refinement and segmentation downward. The three-dimensional weight segmentation is adopted. First, in the Z direction, the upper and lower areas are divided into upper and lower areas, and then divided according to the two-dimensional weight. After the cutting, the area is divided into eight areas. The number of point clouds in each area is exactly the same, or the number differs by one point. At the same time, eight corresponding linked lists list1-list8 are generated. The eight linked list nodes point to the parent node listS together, and the corresponding listS also points downward to the eight child nodes; Step 2.6: If the number of points in the eight regions is greater than the preset second point number threshold n', continue to repeat the above step 2.

5. When the number of points in the eight regions is less than n', stop further downward splitting and create corresponding child nodes and parent nodes until a complete 28-tree is finally created.

3. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method for retrieving electric power massive laser point cloud data according to claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for retrieving massive laser point cloud data of electric power described in claim 1 are implemented.

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