A real scene modeling method for three-dimensional design of power transmission engineering considering entity attributes

Abstract

The application discloses a real scene modeling method for power transmission engineering three-dimensional design and considering entity attributes, point cloud data of different sources and different accuracies is registered and unified into a same coordinate system by using an iterative closest point algorithm; point cloud classification is performed on the point cloud data to obtain category point clouds including ground point clouds, building point clouds, vegetation point clouds, power transmission line point clouds, power transmission tower point clouds and unclassified point clouds; a digital elevation model is constructed by using the ground point clouds; monomer segmentation is performed on the category point clouds other than the ground point clouds to obtain a plurality of independent monomer clusters; entity attribute information of the monomer clusters is extracted; each monomer cluster is automatically reconstructed based on the category point clouds to which the monomer cluster belongs, and an entity model is obtained; and the entity model is associated with the entity attribute information of the corresponding monomer cluster. The method can make up for the current shortcomings of real scene modeling, enrich the achievements of the real scene model and meet the actual application requirements.

Description

Technical Field

[0001] This invention relates to a real-scene modeling method for 3D design of power transmission projects that takes into account entity attributes, and belongs to the field of 3D design and real-scene modeling technology for power transmission projects. Background Technology

[0002] Forward 3D design of power transmission projects has become the norm in the industry. Currently, the main scenario for 3D design is still 3D geographic information models. For the construction of 3D geographic information models, the most efficient technical means is to use laser point clouds or oblique photography and close-up photogrammetry to carry out 3D reconstruction based on the structure-from-motion (SfM) principle. This real-scene modeling method has the advantages of high automation, low cost and high efficiency. However, its disadvantage is that the constructed 3D model is a mesh model, which is not editable when used for power transmission line design. It is impossible to operate and analyze the specific model, and it cannot be adapted to spatial statistical and analytical applications.

[0003] To address the shortcomings of the aforementioned real-world models—namely, their lack of editability and flexibility—experts began researching model modularization. This involves dividing the overall real-world model into individual unit models using various methods after its construction. Some modularization methods achieve the effect solely through highlighting, a superficial approach that fails to meet subsequent analytical needs. Other methods employ post-processing, cutting the overall model into unit models after construction. While these unit models can satisfy analytical requirements, they are entirely manual, resulting in low efficiency. Furthermore, the segmented objects lack attributes, requiring manual completion later, leading to a large workload and low efficiency.

[0004] Generating 3D models using SfM (Sensitive Feature Mapping) relies on photogrammetry principles and computer vision algorithms to extract pixel information from images, generating point cloud data, and then reconstructing the scene structure to form a mesh model. Currently, software such as Terrasolid and GlobalMapper offer high efficiency in point cloud classification. For point cloud segmentation, there are segmentation algorithms based on geometric features, as well as algorithms based on deep learning. Summary of the Invention

[0005] To address the problems of current real-world models used in 3D design of power transmission projects, such as lack of attribute information, inability to be individualized, and lack of retrieval capabilities, this invention proposes a real-world modeling method for 3D design of power transmission projects that considers entity attributes. For forward 3D design of power transmission projects, the real-world model should be able to meet requirements such as building statistics, forest statistics, and intersection analysis. By integrating efficient point cloud processing algorithms into the real-world modeling process, the current shortcomings of real-world modeling can be overcome, enriching the results of real-world models and meeting practical application needs.

[0006] To achieve the above objectives, this invention provides a real-world modeling method for 3D design of power transmission projects that takes into account entity attributes, comprising the following steps:

[0007] Step 1: Use the iterative nearest point algorithm to register and unify point cloud data from different sources and with different precisions into the same coordinate system;

[0008] Step 2: Classify the point cloud data that has been registered and unified to the same coordinate system to obtain point clouds of categories including ground point clouds, building point clouds, vegetation point clouds, power transmission line point clouds, power transmission tower point clouds, and unclassified point clouds;

[0009] Step 3: Construct a digital elevation model using ground point clouds;

[0010] Step 4: Perform single-unit segmentation on point clouds other than ground point clouds to obtain multiple independent single-unit clusters;

[0011] Step 5: Combine the digital elevation model to extract the ground feature attribute information of individual clusters;

[0012] Step 6: Based on the point cloud of the category to which the individual cluster belongs, automatically reconstruct each individual cluster to obtain the entity model;

[0013] Step 7: Associate the entity model with the corresponding land feature attribute information of the individual cluster.

[0014] Prior to this, the feature attribute information includes entity category, classification code, label code, area, height, elevation, and sag.

[0015] Preferably, in step 1, the point cloud data includes laser point clouds and image point clouds;

[0016] Using laser point clouds as a reference, point cloud data from different sources and with different precisions are unified into the same coordinate system.

[0017] Prior to step 4, the density-based clustering algorithm DBSCAN is used to segment point clouds of other categories besides ground point clouds into individual clusters to obtain multiple independent individual clusters.

[0018] Preferred, the parameter values ​​for the density-based clustering algorithm DBSCAN are:

[0019] The neighborhood distance ε = 0.5m, the minimum number of points in a single cluster Minpts = 200, the distance between adjacent points in a single cluster is less than 0.5m, and the number of points in each single cluster is greater than 200.

[0020] Prioritize assigning a unique label code to each individual cluster in the point cloud of the same category. After individual cluster segmentation, the format of the individual cluster is as follows: point number, coordinate components, classification code, unique label code, color (RGB), intensity, and echo count.

[0021] Prioritizes step 5, which uses a digital elevation model to extract the land feature attribute information of individual clusters after individual segmentation, specifically includes:

[0022] Step 5-1: For the transmission line point cloud, extract the two points i(x) with the largest distance between them. i ,y i ,z i ) and j(x j ,y j ,z j Construct a straight line l using points i and j, calculate the distance d from each point in the transmission line point cloud to the straight line l, and select the maximum value d among the distances d. max As a sag;

[0023] Step 5-2: Project the vegetation point cloud, building point cloud, and transmission tower point cloud onto a two-dimensional plane, using... This represents vegetation point cloud, building point cloud, or power transmission tower point cloud, where c is the classification code and m is the tag code;

[0024] exist Retrieve the maximum value x on the x-axis max The minimum value x on the x-axis min The maximum value y on the y-axis max and the minimum value y on the y-axis min There are a total of four corner points. Use the four corner points to construct a rectangular bounding box, and then construct a grid of squares with a preset length and width within the rectangular bounding box.

[0025] Step 5-3: From point (x max y min Begin by searching in reverse S order. If a point is found within a grid, retrieve the maximum value Z on the Z-axis within that grid from the point cloud data. max Using the corresponding digital elevation model elevation value h, the height H = Z is calculated. max -h;

[0026] Z max Recorded in the elevation array In the above, H is recorded in the height array. middle;

[0027] Step 5-4: Select the maximum value in the elevation array Z as the elevation, and select the maximum value in the height array as the height; if there are n elements in the elevation array, then the area M of the category point cloud is M = n × the length of the grid × the width of the grid.

[0028] Firstly, step 5-5: Extract the land feature attribute information and save it to the database;

[0029] The database includes entity categories, classification codes, label codes, area, height, elevation, and sag. An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of any of the methods described above.

[0030] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0031] The beneficial effects achieved by this invention are as follows:

[0032] The method of this invention allows the established real-world model to be an independent entity, capable of participating in retrieval and statistical analysis, and taking into account the entity's attribute information. The model's attribute information can be directly utilized without the need for calculation through measurement. Compared with the real-world models currently used in the 3D design of power transmission projects, the model has richer attribute information, and compared with the current model individualization method, the entire process has a higher degree of automation.

[0033] This invention integrates a high-efficiency point cloud processing algorithm into the real-scene modeling process, which can make up for the current shortcomings of real-scene modeling, enrich the results of real-scene models, and meet the needs of practical applications. Attached Figure Description

[0034] Figure 1 This is a flowchart of the present invention;

[0035] Figure 2 This is a schematic diagram of the entity segmentation method of the present invention;

[0036] Figure 3 This is a schematic diagram of the extraction of land feature attribute information using the grid method in this invention. Detailed Implementation

[0037] The following embodiments are only used to illustrate the technical solutions of the present invention more clearly, and should not be used to limit the scope of protection of the present invention.

[0038] like Figure 1 As shown, this invention provides a real-world modeling method for 3D design of power transmission projects that takes into account entity attributes, including the following steps:

[0039] Step 1: Use the iterative nearest point algorithm to register and unify point cloud data from different sources and with different precisions into the same coordinate system;

[0040] Specifically, point cloud data from different data acquisition methods, such as airborne lidar, ground-based 3D laser scanning, and visible light imagery, are registered using the Iterative Closest Point (ICP) algorithm. During the registration process, airborne laser point cloud data, which has a wider data coverage and higher accuracy, is used as the benchmark, and other data are unified to the same coordinate system.

[0041] Step 2: Classify the point cloud data that has been registered and unified to the same coordinate system to obtain category point clouds including ground point clouds, building point clouds, vegetation point clouds, power transmission line point clouds, power transmission tower point clouds and unclassified point clouds. The classification codes refer to the internationally accepted codes defined by the Photogrammetry and Remote Sensing Association.

[0042] Step 3: Construct a Digital Elevation Model (DEM) using ground point clouds. More specifically, construct the DEM using point cloud processing software based on the ground point clouds classified in Step 2.

[0043] Step 4: As Figure 2 As shown, point clouds of other types besides ground point clouds are segmented into individual units to obtain multiple independent individual clusters;

[0044] Specifically, the density-based clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used for segmentation and clustering. This algorithm only requires two parameters: the neighborhood distance ε and the minimum number of points (Minpts) contained in an entity. Based on engineering experience, we choose ε = 0.5m and Minpts = 200. Points with a distance of less than 0.5 meters between adjacent points and more than 200 points constitute independent entities. After the point cloud data of each independent entity is segmented, each point is assigned the same tag code. The point cloud data format is as follows:

[0045]

[0046] Step 5: As Figure 3 As shown, the ground feature attribute information of individual clusters is extracted by combining the digital elevation model;

[0047] Step 6: Based on the point cloud of the category to which the individual cluster belongs, automatically reconstruct each individual cluster to obtain the entity model;

[0048] Step 7: Associate the entity model with the corresponding land feature attribute information of the individual cluster.

[0049] Furthermore, the feature attribute information in this implementation includes entity category, classification code, label code, area, height, elevation, and sag.

[0050] Furthermore, in step 1 of this implementation, the point cloud data includes laser point clouds and image point clouds;

[0051] Using laser point clouds as a reference, point cloud data from different sources and with different precisions are unified into the same coordinate system.

[0052] Furthermore, in step 4 of this implementation, the density-based clustering algorithm DBSCAN is used to segment point clouds of other categories besides ground point clouds into individual clusters to obtain multiple independent individual clusters.

[0053] Furthermore, in this implementation, the parameter values ​​for the density-based clustering algorithm DBSCAN are:

[0054] The neighborhood distance ε = 0.5m, the minimum number of points in a single cluster Minpts = 200, the distance between adjacent points in a single cluster is less than 0.5m, and the number of points in each single cluster is greater than 200.

[0055] Furthermore, in this implementation, each individual cluster in the same category of point cloud is assigned a unique label code. After individual cluster segmentation, the format of the individual cluster is as follows: point number, coordinate components, classification code, unique label code, color (RGB), intensity, and echo count.

[0056] Furthermore, step 5 in this embodiment, in conjunction with the digital elevation model, extracts the land feature attribute information of the segmented individual clusters, specifically including:

[0057] Step 5-1: For the transmission line point cloud, extract the two points i(x) with the largest distance between them. i ,y i ,z i ) and j(x j ,y j ,z j Construct a straight line l using points i and j, calculate the distance d from each point in the transmission line point cloud to the straight line l, and select the maximum value d among the distances d. max As a sag;

[0058] Step 5-2: Project the vegetation point cloud, building point cloud, and transmission tower point cloud onto a two-dimensional plane, using... This represents vegetation point cloud, building point cloud, or power transmission tower point cloud, where c is the classification code and m is the tag code;

[0059] exist Retrieve the maximum value x on the x-axis max The minimum value x on the x-axismin The maximum value y on the y-axis max and the minimum value y on the y-axis min There are a total of four corner points. Use the four corner points to construct a rectangular bounding box, and then construct a grid of squares with a preset length and width within the rectangular bounding box.

[0060] Step 5-3: From point (x max y min Begin by searching in reverse S order. If a point is found within a grid, retrieve the maximum value Z on the Z-axis within that grid from the point cloud data. max Using the corresponding digital elevation model elevation value h, the height H = Z is calculated. max -h;

[0061] Z max Recorded in the elevation array In the above, H is recorded in the height array. middle;

[0062] Step 5-4: Select the maximum value in the elevation array Z as the elevation, and select the maximum value in the height array as the height; if there are n elements in the elevation array, then the area M of the category point cloud is M = n × the length of the grid × the width of the grid.

[0063] Furthermore, in step 5-5 of this implementation: extract the attribute information of the land features and save it to the database; the database includes entity category, classification code, label code, area, height, elevation and sag.

[0064] The first item in each row of the database lists the entity category, and the first item in each column lists the classification code, label code, area, height, elevation, and sag.

[0065] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of any of the methods described above.

[0066] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0067] The classification codes refer to the internationally accepted codes defined by the Photogrammetry and Remote Sensing Association. The classification values ​​corresponding to different categories of point clouds are as follows:

[0068] In this embodiment, a grid of "0.2m × 0.2m" is constructed within a rectangular bounding box, and the area of ​​the point cloud M = n * 0.2 * 0.2;

[0069] The database format is as follows:

[0070]

[0071] Based on the category to which the point cloud belongs, automatic reconstruction is performed sequentially according to the label code. Each model has a unique identification code, which consists of a classification code and a label code.

[0072] After the model is built, the entity attribute database is linked to the entity model library according to the classification code and label code.

[0073] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] 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.

[0075] 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.

[0076] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A real scene modeling method for power transmission engineering three-dimensional design considering entity attributes, characterized in that, The method comprises the following steps: Step 1: using an iterative closest point algorithm to register and unify point cloud data of different sources and different accuracies into the same coordinate system; Step 2: classifying the point cloud data registered and unified into the same coordinate system to obtain category point clouds including ground point clouds, building point clouds, vegetation point clouds, power transmission line point clouds, power transmission tower point clouds and unclassified point clouds; Step 3: constructing a digital elevation model using the ground point clouds; Step 4: performing single-body segmentation on the category point clouds other than the ground point clouds to obtain multiple independent single-body clusters; Step 5: extracting feature attribute information of the single-body clusters in combination with the digital elevation model; Step 6: automatically reconstructing each single-body cluster based on the category point clouds to which the single-body cluster belongs to obtain a solid model; Step 7: associating the solid model with the feature attribute information of the corresponding single-body cluster; In Step 4, a density-based clustering algorithm DBSCAN is used to perform single-body segmentation on the category point clouds other than the ground point clouds to obtain multiple independent single-body clusters; Parameter values of the density-based clustering algorithm DBSCAN are as follows: Neighborhood distance , the minimum number of points contained in a monomer cluster , the distance between adjacent points in a monomer cluster is less than 0.5 meters, and the number of points in each monomer cluster is greater than 200; A unique label code is assigned to each single-body cluster in the same category point cloud, and the format of the single-body cluster after single-body segmentation is as follows: point number, coordinate component, classification code, unique label code, color RGB, intensity and echo number; In Step 5, the feature attribute information of the single-body cluster after single-body segmentation is extracted in combination with the digital elevation model, and specifically includes: Step 5-1: For the power transmission line point cloud, extract the two points with the largest distance between them. and ,by and Construct a straight line from two points The distance of each point in the point cloud of the transmission line from the straight line is calculated. distance Select distance The maximum value in As a sag; Step 5-2: Project the vegetation point cloud, the building point cloud, and the power tower point cloud to a two-dimensional plane, use to represent the vegetation point cloud, the building point cloud, or the power tower point cloud, c is a classification code, and m is a label code; In the maximum value on the x-axis is searched , the minimum value on the x-axis , the maximum value on the y-axis and the minimum value on the y-axis corresponding to a total of four corner points, a rectangular bounding box is constructed using the four corner points, and a square grid with a predetermined length and width is constructed in the rectangular bounding box; Step 5-3: Starting from point (x0, y0) , ), search in reverse S order, if there are points in the grid, search the maximum value on the Z axis in the point cloud data in the grid and the corresponding digital elevation model elevation value h, calculate the height ; Z max recorded in the elevation array H is recorded in the height array ​ Step 5-4: selecting the maximum value in the elevation array Z as the elevation and selecting the maximum value in the height array as the height; the number of elements in the elevation array is n, and then the area M of the category point cloud is n×length of the square grid×width of the square grid.

2. The real scene modeling method for three-dimensional design of power transmission engineering considering entity attributes according to claim 1, characterized in that, The feature attribute information includes entity category, classification code, label code, area, height, elevation and sag.

3. The real scene modeling method for three-dimensional design of power transmission engineering considering entity attributes according to claim 1, characterized in that, In Step 1, the point cloud data includes laser point cloud and image point cloud; The point cloud data of different sources and different accuracies is unified into the same coordinate system with the laser point cloud as the reference.

4. The real scene modeling method for three-dimensional design of power transmission engineering considering entity attributes according to claim 1, characterized in that, Step 5-5: extracting the feature attribute information and saving it to a database; The database includes entity category, classification code, label code, area, height, elevation and sag.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1 to 4 when executing the program.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the steps of the method of any one of claims 1 to 4 when executed by the processor.

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