A power transmission and transformation engineering point cloud model key component rapid extraction method and system

Abstract

The application provides a power transmission and transformation engineering point cloud model key component rapid extraction method and system. The laser point cloud model scanned on the engineering construction site can extract and retain key components through multiple views (front view, side view and top view, etc.) according to actual needs, and the key component extraction of the point cloud model is realized by means of delimiting an irregular closed area composed of line segments in an arbitrary shape. The scheme can quickly identify the key components to be extracted, exert the advantages of simple and fast two-dimensional view image processing, reduce the mis-trimming and missed trimming conditions, and effectively improve the efficiency of three-dimensional point cloud model trimming.

Description

Technical Field

[0001] This invention relates to the field of power grid transmission and transformation engineering technology, and in particular to a method and system for rapid extraction of key components from point cloud models of power transmission and transformation projects. Background Technology

[0002] A 3D laser point cloud model is a 3D dataset composed of a large number of points, recording information such as the position, shape, and color of objects in space. In practical applications, 3D laser point cloud model data often contains a large number of invalid points, such as background information or components that do not need to be analyzed. Therefore, extracting key components from the point cloud model is an essential step and an important process.

[0003] The goal of key component extraction from point cloud models is to select and extract the set of points representing key components that need to be used for analysis from the entire 3D laser point cloud model dataset, thereby simplifying the data and improving processing efficiency. The essence of the key component extraction algorithm is to filter and select point cloud data, retaining only the parts used for analysis and removing redundant point cloud datasets.

[0004] In practical applications, the application scenarios for extracting key components from point cloud models are very extensive. For example, in fields such as robot navigation, autonomous driving, 3D reconstruction, and engineering flatness inspection, large-scale point cloud model data needs to be analyzed and processed to reduce the amount of data and improve computational efficiency, thereby facilitating subsequent analysis and processing.

[0005] However, extracting key components from point cloud models requires high accuracy, necessitating maximizing extraction efficiency while maintaining precision. High-precision extraction of key components demands more complex algorithms and computational resources, leading to slower extraction speeds; conversely, pursuing high efficiency may sacrifice some extraction accuracy. In some complex 3D scene applications, point cloud model data often contains significant amounts of noise and redundant information, further complicating the extraction process. Furthermore, occlusion and overlap between different objects also complicate the extraction process. Summary of the Invention

[0006] To address the shortcomings and deficiencies of existing technologies, this invention aims to maximize the extraction efficiency of point cloud models while ensuring extraction accuracy. It provides a method and system for rapid extraction of key components from point cloud models used in power transmission and transformation projects. For laser point cloud models obtained by scanning at the construction site, key components can be extracted and retained from multiple views (front view, side view, and top view, etc.) according to actual needs. Key component extraction is achieved by defining irregularly shaped closed regions composed of line segments. This solution can quickly identify the key components that need to be extracted, leveraging the simplicity and speed of 2D image processing, reducing erroneous and missed cropping, and effectively improving the efficiency of 3D point cloud model cropping.

[0007] The present invention specifically adopts the following technical solution:

[0008] A method for rapid extraction of key components from a point cloud model of a power transmission and transformation project includes the following steps:

[0009] S1. Obtain point cloud data of a given area during the construction phase of a power transmission and transformation project using a lidar scanning device;

[0010] S2. Correct the spatial position of the model based on the 3D point cloud model data obtained in step S1;

[0011] S3. Based on step S2, extract the key components as targets from the target area using the multi-view method; separate the key components from the 3D point cloud model data to obtain the point cloud model of the key area or component.

[0012] Furthermore, in step S1, the area of ​​the substation construction site to be acquired is first determined. After the area is delineated, the walking route of personnel is set to eliminate interference during the acquisition process. Then, point cloud data of the given area during the construction phase of the power transmission and transformation project is acquired by a lidar scanning device.

[0013] Further, in step S2, the 3D point cloud model data obtained in step S1 is loaded, and the 3D point cloud model data is converted into the architectural coordinate system of the substation project to correct the spatial position of the model. The specific steps are as follows:

[0014] S21. First, convert latitude and longitude to Geodetic 2000 coordinates using the following formula:

[0015] X=a*cos(B)*cos(L)+N*(1+e^2*cos^2(B))*sin(B)*cos(L)+e^2*N*sin^3(B)*cos(L)

[0016] Y=a*cos(B)*sin(L)+N*(1+e^2*cos^2(B))*sin(B)*sin(L)+e^2*N*sin^3(B)*sin(L)

[0017] Z=a*(1-e^2)*sin(B)+N*(1+e^2*cos^2(B))*cos(B)+e^2*N*sin(B)*cos^2(B)

[0018] In the formula, a is the semi-major axis of the ellipsoid, e is the first eccentricity of the ellipsoid, N is the radius of curvature of the prime meridian, B is the latitude, and L is the longitude.

[0019] S22. Select the coordinate origin X0 and Y0 of the substation project;

[0020] S23. The three-dimensional point cloud model data is converted into the building coordinate system of the substation project using the following formula;

[0021] X = X0 + Acosβ + Bsinβ

[0022] Y = Y0 - Asinβ + Bcosβ

[0023] In the formula, β is the angle between the Y-axis of the engineering coordinate system and the due north direction.

[0024] Furthermore, the origin of the coordinates is the point at the lower left corner of the fence of the substation project; when only a part of the substation project is scanned, the projection point of the corner point of a certain device on the design elevation of the project site is selected as the origin of the coordinates.

[0025] Furthermore, in step S3, an irregular closed shape composed of line segments is defined to identify the key components that need to be extracted; the entire point cloud dataset is traversed from left to right, from top to bottom, and from front to back from a specific viewpoint, and each point cloud data is checked to see if it is within the irregular closed shape; if the point cloud data is within the irregular closed shape, it is retained; if the point cloud data is not within the irregular closed shape, it is deleted or removed.

[0026] Furthermore, in the multi-view method, the three-dimensional point cloud model data is first mapped onto a two-dimensional plane to achieve coordinate transformation; secondly, starting from the perspective of the front view, the following process is performed in each view to delineate an irregular closed shape composed of line segments. Components inside the closed shape under the perspective of that view will be extracted, and components outside the closed shape will be deleted; until the key components in the three-dimensional point cloud model are extracted.

[0027] Furthermore, the specific algorithm for determining whether point cloud data lies within the defined irregular closed shape is as follows:

[0028] S31. Given a point p(x,y) and the set of endpoints of an irregular closed figure (p1;p2;p3;...;pn);

[0029] S32. Draw a ray L parallel to the X-axis with p as the starting point and infinity as the ending point: the starting point is p(x,y) and the ending point is (+∞,y); iteratively take the endpoints (p1;p2;p3;...;pn) of the irregular closed shape; determine whether p(x,y) is on the edge of the irregular closed shape. If the result is yes, the point cloud data is considered to be located within the defined irregular closed shape; if the result is no, proceed to step S33.

[0030] S33. Determine whether ray L intersects with all the edges of the irregular closed figure, and calculate the number of intersections;

[0031] S34. Determine the total number of intersection points. If the total number of intersection points is odd, the point cloud data is considered to be located within the defined irregular closed shape; if the total number of intersection points is even, the point cloud data is considered to be located outside the defined irregular closed shape.

[0032] A system for rapid extraction of key components from point cloud models of power transmission and transformation projects includes:

[0033] Point cloud data storage unit, used to store point cloud data of a given area acquired by a lidar scanning device during the construction phase of a power transmission and transformation project;

[0034] The model correction unit is used to correct the spatial position of the model based on the three-dimensional point cloud model data acquired by the point cloud data storage unit.

[0035] Additionally, a point cloud key component extraction unit is used to extract key components as targets from the target area using a multi-view method based on model correction; the key components are separately segmented from the 3D point cloud model data to obtain the point cloud model of the key area or component.

[0036] Furthermore, the model correction unit converts the three-dimensional point cloud model data into the building coordinate system of the substation project to correct the spatial position of the model.

[0037] Furthermore, the point cloud key component extraction unit identifies the key components to be extracted by defining irregular closed shapes composed of line segments; it then traverses the entire point cloud dataset from left to right, top to bottom, and front to back from a specific viewpoint, checking whether each point cloud data point is within the irregular closed shape; if the point cloud data point is within the irregular closed shape, it is retained; otherwise, it is deleted or removed.

[0038] Compared to existing technologies, this invention and its preferred embodiment can crop point clouds by displaying three views (typically front, side, and top views) of the target object in the original point cloud. This method combines the advantages of two-dimensional image processing and three-dimensional point cloud processing, improving the accuracy and efficiency of point cloud cropping to a certain extent. The three-view approach visually displays the distribution of point cloud data in three main directions, helping users quickly identify the areas that need to be cropped. By combining information from multiple views, the point cloud data to be retained and cropped can be determined more accurately, reducing incorrect and missed cropping. Users can perform cropping operations using multiple views according to their actual needs. After cropping, the algorithm outputs a point cloud dataset containing only the portion of the original point cloud located within the specified region, greatly simplifying subsequent processing and analysis. Attached Figure Description

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0040] Figure 1 This is a schematic diagram of image segmentation in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of image processing in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0043] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0046] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0047] The following describes the present invention in further detail through two specific embodiments:

[0048] First, we introduce the method for rapid extraction of key components from point cloud models of power transmission and transformation projects provided in this embodiment of the invention, including the following steps:

[0049] S1. Obtain point cloud data of key areas during the construction phase of power transmission and transformation projects using lidar scanning devices, such as... Figure 1 As shown,;

[0050] S2. Based on the acquired 3D point cloud model data, find the horizontal plane of the model and correct the orientation of the model;

[0051] The spatial positioning of the calibration model involves converting it into the building coordinate system commonly used in substation engineering. The specific steps are as follows:

[0052] 1) First, convert latitude and longitude to Geodetic 2000 coordinates using the following formula:

[0053] X=a*cos(B)*cos(L)+N*(1+e^2*cos^2(B))*sin(B)*cos(L)+e^2*N*sin^3(B)*cos(L)

[0054] Y=a*cos(B)*sin(L)+N*(1+e^2*cos^2(B))*sin(B)*sin(L)+e^2*N*sin^3(B)*sin(L)

[0055] Z=a*(1-e^2)*sin(B)+N*(1+e^2*cos^2(B))*cos(B)+e^2*N*sin(B)*cos^2(B)

[0056] In the formula, a is the semi-major axis of the ellipsoid, e is the first eccentricity of the ellipsoid, N is the radius of curvature of the prime meridian, B is the latitude, and L is the longitude.

[0057] 2) Select the origin of the substation project coordinates, which is usually the lower left corner of the substation project perimeter wall. When only scanning a part of the substation project, the projection point of a corner point of a certain equipment on the design elevation of the project site can be selected as the origin of the coordinates.

[0058] 3) The coordinates of the origin, X0 and Y0, are the coordinates of the point in the geodetic 2000 coordinate system.

[0059] X = X0 + Acosβ + Bsinβ

[0060] Y = Y0 - Asinβ + Bcosβ

[0061] In the formula, β is the angle between the Y-axis of the engineering coordinate system and the due north direction.

[0062] The above steps achieve the goal of converting the coordinates (longitude, latitude, and elevation) of points in the point cloud model obtained through 3D point cloud laser scanning technology into the building coordinate system commonly used in substation engineering.

[0063] S3. Irregularly closed shapes are defined to identify key components that need to be extracted. The extraction algorithm traverses the entire point cloud dataset from left to right, top to bottom, and front to back from a specific viewpoint, checking each point cloud data point to see if it falls within an irregularly closed shape. If the point cloud data is within an irregularly closed shape, it is retained; if it is not, it is deleted or removed. Figure 2 As shown.

[0064] The process involves locating key components within the target area and extracting them using multiple views. First, the 3D point cloud model data is mapped onto a 2D plane, achieving coordinate transformation. From the front view, an irregular closed shape composed of line segments of arbitrary shape is defined. Components inside this closed shape are extracted, while those outside are deleted. Next, from the left view, the same process is repeated, extracting components inside and deleting them from the top view. This continues until all key components in the 3D point cloud model are extracted.

[0065] As a preferred embodiment, the specific algorithm for determining whether point cloud data is located within a defined irregular closed shape is as follows:

[0066] 1) Given a point p(x,y) and a set of endpoints of an irregular closed figure (p1;p2;p3;...;pn).

[0067] 2) Draw a ray L parallel to the X-axis, starting at point p and ending at infinity, with the starting point at p(x,y) and the ending point at (+∞,y). Iteratively select the endpoints (p1;p2;p3;...;pn) of the irregular closed shape. Determine if p(x,y) lies on the edge of the irregular closed shape. If yes, the point cloud data is considered to be within the defined irregular closed shape. If no, proceed to step 3).

[0068] 3) Determine whether ray L intersects with all the edges of the irregular closed figure, and calculate the number of intersections.

[0069] 4) Determine the total number of intersection points. If the total number of intersection points is odd, the point cloud data is considered to be located within the defined irregular closed shape; if the total number of intersection points is even, the point cloud data is considered to be located outside the defined irregular closed shape.

[0070] The above solution addresses the problem that existing similar technologies are only effective for rectangular frames, and cannot 100% determine whether point cloud data has been selected for pentagonal or larger polygonal or concave polygonal frames.

[0071] This step involves extracting the target analysis region (i.e., key components) from the 3D point cloud model data, and then segmenting the key components separately from the 3D point cloud model data to obtain the point cloud model of the key region or component.

[0072] like Figure 3 As shown, this embodiment provides a system design corresponding to the above methods, including...

[0073] Point cloud acquisition unit;

[0074] Point cloud data storage unit, used to store point cloud data of a given area acquired by a lidar scanning device during the construction phase of a power transmission and transformation project;

[0075] The model correction unit is used to correct the spatial position of the model based on the three-dimensional point cloud model data acquired by the point cloud data storage unit.

[0076] Additionally, a point cloud key component extraction unit is used to extract key components as targets from the target area using a multi-view method based on model correction; the key components are separately segmented from the 3D point cloud model data to obtain the point cloud model of the key area or component.

[0077] The point cloud acquisition unit generally refers to a lidar scanning device, which is existing technology. The following section focuses on further describing the special design of the system of this invention:

[0078] As a preferred embodiment, the model correction unit converts the three-dimensional point cloud model data into the architectural coordinate system of the substation project to correct the spatial position of the model.

[0079] As a preferred embodiment, the point cloud key component extraction unit identifies the key components to be extracted by defining an irregular closed shape composed of line segments; by traversing the entire point cloud dataset from left to right, from top to bottom, and from front to back from a specific viewpoint, it checks whether each point cloud data is within the irregular closed shape; if the point cloud data is within the irregular closed shape, it is retained; if the point cloud data is not within the irregular closed shape, it is deleted or discarded.

[0080] In further design:

[0081] The point cloud key component extraction unit is used to divide or label the point cloud region, separating the point cloud into regions and clearly marking the region. This region is usually a rectangle or polygon used to identify the target part. This process is crucial to ensuring data accuracy and efficiency. During the clipping operation, some complex geometric calculations and data processing problems may be encountered. Especially when the point cloud region is a polygon, it is necessary to accurately determine the positional relationship between points and polygons to ensure the accuracy of clipping. Therefore, the point cloud clipping method designed above is provided. Furthermore, when processing particularly large amounts of point cloud data, spatial indexing or parallel computing techniques can be used to improve the speed and efficiency of the clipping operation. Finally, after the clipping operation, a new point cloud dataset is output. This dataset only contains the portion of the original point cloud located within the specified region, thus greatly simplifying subsequent processing and analysis.

[0082] When extracting the target area, the actual operation involves importing the original point cloud into a desktop application. The application converts the point cloud data in three-dimensional space into a two-dimensional image using algorithms and rules, and at the same time, it crops out the unnecessary parts as needed, retaining the target area for saving.

[0083] The extraction and processing unit acquires the point cloud data that needs to be cropped. This data is essentially a series of three-dimensional coordinate points. These three-dimensional point cloud data are mapped onto a two-dimensional plane, achieving coordinate transformation. In this step, the x and y coordinate values ​​are translated to ensure that the minimum coordinate value becomes 0. Through linear or non-linear mapping, the z-coordinate value of each point is mapped to an image pixel value, achieving an effective conversion from three-dimensional to two-dimensional. Based on actual needs and engineering characteristics, a shape-based cropping method is selected for the cropping operation to ensure accurate extraction of key components. This method not only improves the efficiency and accuracy of extraction but also provides strong data support for subsequent analysis and design of power transmission and transformation projects.

[0084] In this design, key components are extracted using a three-view approach: a top view, a left view, and a side view. These views are obtained by observing the object from different angles, each providing information about the object's shape and position in different directions. Using these three views allows for a more comprehensive understanding of the object's spatial layout, ensuring more precise cropping.

[0085] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

[0086] The computational and logical judgment parts of the system and method provided in this embodiment can be stored in a computer-readable storage medium in the form of code, implemented in the form of a computer program, and the basic parameter information required for the calculation can be input through computer hardware, and the calculation results can be output.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

[0090] This patent is not limited to the above-described preferred embodiments. Anyone can derive other forms of methods and systems for rapid extraction of key components from point cloud models of power transmission and transformation projects based on the inspiration of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A method for rapid extraction of key components from a point cloud model of a power transmission and transformation project, characterized in that: Includes the following steps: S1. Obtain point cloud data of a given area during the construction phase of a power transmission and transformation project using a lidar scanning device; S2. Correct the spatial position of the model based on the 3D point cloud model data obtained in step S1; S3. Based on step S2, extract the key components as the target from the target area using the multi-view method; separate the key components from the 3D point cloud model data to obtain the point cloud model of the key area or component. In step S1, the area of ​​the substation construction site to be acquired is first determined. After the area is delineated, the walking route of personnel is set to eliminate interference during the data acquisition process. Then, the point cloud data of the given area during the construction phase of the power transmission and transformation project is acquired by the lidar scanning device. In step S2, the 3D point cloud model data obtained in step S1 is loaded, and the 3D point cloud model data is converted into the architectural coordinate system of the substation project to correct the spatial position of the model. The specific steps are as follows: S21. First, convert latitude and longitude to Geodetic 2000 coordinates using the following formula: X=a*cos(B)*cos(L)+N*(1+e^2*cos^2(B))*sin(B)*cos(L)+e^2*N*sin^3(B)*cos(L) Y=a*cos(B)*sin(L)+N*(1+e^2*cos^2(B))*sin(B)*sin(L)+e^2*N*sin^3(B)*sin(L) Z=a*(1-e^2)*sin(B)+N*(1+e^2*cos^2(B))*cos(B)+e^2*N*sin(B)*cos^2(B) In the formula, a is the semi-major axis of the ellipsoid, e is the first eccentricity of the ellipsoid, N is the radius of curvature of the prime meridian, B is the latitude, and L is the longitude. S22. Select the coordinate origin X0 and Y0 of the substation project; S23. The three-dimensional point cloud model data is converted into the building coordinate system of the substation project using the following formula; X = X0 + A'cosβ + B'sinβ Y = Y0 - A'sinβ + B'cosβ In the formula, β is the angle between the Y-axis of the engineering coordinate system and the due north direction; In step S3, an irregular closed shape composed of line segments is defined to identify the key components that need to be extracted. The entire point cloud dataset is traversed from left to right, from top to bottom, and from front to back from a specific viewpoint, and each point cloud data is checked to see if it is within the irregular closed shape. If the point cloud data is within the irregular closed shape, it is retained; if the point cloud data is not within the irregular closed shape, it is deleted or removed. In the multi-view method, the three-dimensional point cloud model data is first mapped onto a two-dimensional plane to achieve coordinate transformation. Then, starting from the perspective of the front view, the following process is performed in each view: an irregular closed shape composed of line segments is defined, and the components inside the closed shape are extracted and the components outside the closed shape are deleted from the perspective of that view until the key components in the three-dimensional point cloud model are extracted.

2. The method for rapid extraction of key components from a point cloud model of a power transmission and transformation project according to claim 1, characterized in that: The origin of the coordinate system is the point at the lower left corner of the fence of the substation project; when only a part of the substation project is scanned, the projection point of the corner point of a certain device on the design elevation of the project site is selected as the origin of the coordinate system.

3. The method for rapid extraction of key components from a point cloud model of a power transmission and transformation project according to claim 1, characterized in that: The specific algorithm for determining whether point cloud data lies within a defined irregular closed shape is as follows: S31. Given a point p(x,y) and the set of endpoints of an irregular closed figure (p1;p2;p3;...;pn); S32. Draw a ray R parallel to the X-axis with p as the starting point and infinity as the ending point: the starting point is p(x,y) and the ending point is (+∞,y); iteratively take the endpoints (p1;p2;p3;...;pn) of the irregular closed shape; determine whether p(x,y) is on the edge of the irregular closed shape. If the result is yes, the point cloud data is considered to be located within the defined irregular closed shape; if the result is no, proceed to step S33. S33. Determine whether ray R intersects with all the edges of the irregular closed figure, and calculate the number of intersections; S34. Determine the total number of intersection points. If the total number of intersection points is odd, the point cloud data is considered to be located within the defined irregular closed shape; if the total number of intersection points is even, the point cloud data is considered to be located outside the defined irregular closed shape.

4. A rapid extraction system for key components of a point cloud model of a power transmission and transformation project, using the rapid extraction method for key components of a point cloud model of a power transmission and transformation project as described in any one of claims 1-3, characterized in that... include: Point cloud data storage unit, used to store point cloud data of a given area acquired by a lidar scanning device during the construction phase of a power transmission and transformation project; The model correction unit is used to correct the spatial position of the model based on the three-dimensional point cloud model data acquired by the point cloud data storage unit. Additionally, a point cloud key component extraction unit is used to extract key components as targets from the target area using a multi-view method based on model correction; the key components are separately segmented from the 3D point cloud model data to obtain the point cloud model of the key area or component.

5. The rapid extraction system for key components of a point cloud model of a power transmission and transformation project according to claim 4, characterized in that: The model correction unit converts the 3D point cloud model data into the building coordinate system of the substation project to correct the spatial position of the model.

6. The rapid extraction system for key components of a point cloud model of a power transmission and transformation project according to claim 4, characterized in that: The point cloud key component extraction unit identifies the key components to be extracted by defining irregular closed shapes composed of line segments. It then traverses the entire point cloud dataset from left to right, top to bottom, and front to back from a specific viewpoint, checking each point cloud data point to see if it is within the irregular closed shape. If the point cloud data is within the irregular closed shape, it is retained; otherwise, it is deleted or removed.

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