Management storage method and rendering method for mass point cloud data of transformer substation

Abstract

The invention discloses a transformer substation mass point cloud data management storage method and rendering method, and the method comprises the steps: dividing transformer substation point cloud data into ground point cloud data, power line point cloud data, wall point cloud data and equipment point cloud data, and building the index contents of the data; then storing the ground point cloud data, the power line point cloud data, the wall point cloud data and the equipment point cloud data as point cloud files according to different strategies, and finally performing point cloud data compression on each point cloud file; the rendering method comprises the following steps: decoding a compressed file corresponding to each point cloud file to obtain a decompressed point cloud file; and then judging an intersection range of the visual body and the point cloud data of the transformer substation, selecting the point cloud data to be loaded according to a viewpoint distance, and transmitting the point cloud data of the point cloud file selected in the step to a webpage end for rendering. According to the method, the processing efficiency of the mass point cloud data is improved, and the dependence of the mass point cloud data processing on a computer memory can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of substation cloud data processing methods, and in particular to a management and storage method and a rendering method for massive point cloud data of a substation. Background Art

[0002] The application of LiDAR in power grids is becoming more and more extensive. Efficient organization and management of point cloud data is the basis for efficient processing and application of point cloud data. Different processing and application requirements will adopt different point cloud data organization methods, usually gridding, quadtree, octree, KD tree, R tree and other data organization forms.

[0003] However, when applying point cloud data, the existing technology generally loads the point cloud data as a whole into memory and then renders or edits and analyzes it, which has the following problems: 1) High requirements for equipment performance: With the continuous improvement of hardware technology, more and more point cloud data is collected. The number of data points in a single substation is basically over 100 million. When the data is loaded into the memory, it takes up a large amount of memory. If the computer performance is low, insufficient memory will occur.

[0004] 2) When the original point cloud data storage size is higher than the effective operating memory of the computer hardware, the computer cannot process and render the display.

[0005] 3) Most of the existing point cloud indexing methods are aimed at all data, resulting in invalid grids and affecting data search speed.

[0006] 4) The point cloud data storage format is las, txt, ply and other formats, which takes up a lot of disk space.

[0007] 5) Data is stored on local disks, which cannot meet the grid’s need for unified data management. Summary of the invention

[0008] The present invention provides a management and storage method and a rendering method for massive point cloud data of a substation, so as to solve the problems of high dependence on computer memory and low rendering efficiency in the prior art of substation point cloud data processing.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is: The management and storage method of massive point cloud data of a substation includes the following steps: Step 1: After the substation cloud data is offset and rotated in the three-dimensional coordinate system, it is filtered to divide the substation cloud data into ground point cloud data and non-ground point cloud data; then, the power line point cloud data and non-power line point cloud data are extracted from the non-ground point cloud data; then, the wall point cloud data and the equipment point cloud data are obtained based on the non-power line point cloud data; finally, the ground point cloud data, the power line point cloud data, the wall point cloud data, and the equipment point cloud data are sorted according to their spatial positions in the three-dimensional coordinate system, and the index contents corresponding to the ground point cloud data, the power line point cloud data, the wall point cloud data, and the equipment point cloud data are established respectively; Step 2: Store the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 1 as point cloud files, respectively, where: For the ground point cloud data, according to the X, Y range of the ground point cloud data in the three-dimensional coordinate system, an index structure is established in a grid manner and stored as a point cloud file, and the corresponding index content is stored in the index file; For the power line point cloud data, directly store the power line point cloud data as a point cloud file, and store the corresponding index content in the index file; For the wall point cloud data, an index structure is established in a grid manner according to the outer and inner enclosing surfaces of the wall and stored as a point cloud file, and the corresponding index content is stored in the index file; The device point cloud data is divided into a plurality of point cloud clusters by clustering segmentation, and an index structure is established for each point cloud cluster according to the XYZ range of each point cloud cluster in the three-dimensional coordinate system and stored as a point cloud file, and the corresponding index content is stored in the index file; Step 3: compress the point cloud data respectively for the point cloud files formed by storing the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 2, and obtain corresponding compressed files; Furthermore, in step 1, the substation cloud data after displacement and rotation is filtered by cloth filtering CSF algorithm, thereby dividing the substation cloud data into ground point cloud data and non-ground point cloud data.

[0010] Furthermore, in step 1, a segmentation method based on point cloud shape features or a deep learning method is used to extract power line point cloud data and non-power line point cloud data from non-ground point cloud data.

[0011] Furthermore, in step 1, the non-power line point cloud data are clustered, and the cluster with the largest area is used as the wall point cloud data, and the remaining data is the equipment point cloud data.

[0012] Furthermore, in step 3, the compression process of the point cloud data in each point cloud file is as follows: (3.1) Quantifying the point cloud coordinates of the point cloud data in each point cloud file, quantizing the point cloud coordinate values ​​to be represented by integers, and obtaining the point cloud coordinate data range of the point cloud file; (3.2) Generate a coding table based on the point cloud coordinate data quantized in step (3.1); (3.3) According to the generated coding table, the quantized point cloud coordinate data is encoded to achieve compression of the point cloud data in each point cloud file.

[0013] A method for rendering substation massive point cloud data based on the above-mentioned method for managing and storing substation massive point cloud data comprises the following steps: (4.1) Determine the intersection range of the viewing volume and all point cloud data, obtain the single point cloud file to be loaded through the intersection range and index file, and then decode the single point cloud file to be loaded; (4.2) Retrieve the names of the point cloud files within the intersection range through the stored index file, and select the point cloud data within the intersection range to be loaded according to the viewpoint distance, i.e., the data from the viewpoint to the near plane of the viewing volume. When the viewpoint distance is far, select a part of the point cloud data from each of the multiple point cloud files within the intersection range for display. As the viewpoint distance decreases, gradually increase the point cloud data in the point cloud files within the intersection range for display. (4.3) The point cloud data of the point cloud file selected in step (4.3) is transferred to the web page for rendering.

[0014] Furthermore, in step (4.3), the speed of loading data on the web page is accelerated by caching the point cloud data.

[0015] Furthermore, when rendering on the web page in step (4.3), the following rules are followed: (a) Ground and wall point cloud data are not loaded by default and can be loaded when needed; (b) The number of point cloud data cannot exceed the set number threshold. When the total number of transmitted point cloud data exceeds the number threshold, a certain proportion of point cloud data is randomly sampled for display; (c) Record the list of point cloud data that have been loaded before the viewing volume changes. When the viewing volume changes, clear the point cloud data that is not in the visible area and load the point cloud data that has not been loaded in the visible area.

[0016] The present invention uses different strategies to index different targets and compresses and stores point cloud data, thereby improving the processing efficiency of massive point cloud data, effectively reducing the dependence of massive point cloud data processing on computer memory, and improving the robustness and efficiency of computer processing and rendering of point cloud data. Through the present invention, rapid retrieval of substation cloud data and rendering of massive point cloud data on the web page can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the grid division principle when storing wall point cloud data in an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of the sub-grid division principle when storing device point cloud data in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0020] This embodiment discloses a method for managing and storing massive point cloud data of a substation. Since the characteristics of the substation cloud data are that the overall shape is relatively regular, the ground points account for a large proportion, and the equipment point cloud data is relatively concentrated, this embodiment adopts different indexing methods for different areas based on the characteristics of the substation cloud data, and saves the processed data to the spatial database and the attribute database. The method for managing and storing massive point cloud data of a substation in this embodiment includes the following steps: Step 1: offset and rotate the substation cloud data in the three-dimensional coordinate system, and then filter it to divide the substation cloud data into ground point cloud data and non-ground point cloud data; then, extract the power line point cloud data and non-power line point cloud data from the non-ground point cloud data; then, obtain the wall point cloud data and equipment point cloud data based on the non-power line point cloud data; finally, sort the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data according to their spatial positions in the three-dimensional coordinate system, and establish the index contents corresponding to the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data, respectively.

[0021] In this embodiment, for the acquired substation cloud data, the XYZ range value of the overall point cloud data in the three-dimensional coordinate system is first obtained, and the minimum value of the XYZ range (i.e., the minimum value of the X coordinate, Y coordinate, and Z coordinate) is used as the offset to translate the substation cloud data, and the offset is saved to the overall configuration file, thereby completing the offset of the substation cloud data.

[0022] In this embodiment, the oriented bounding box OBB of the offset substation cloud data is calculated, and through the relationship between the oriented bounding box OBB and the XY coordinate axes in the three-dimensional coordinate system, the offset substation cloud data is rotated to align with the XY coordinate axes, and the rotation parameters are saved to the configuration file, thereby completing the rotation of the offset substation cloud data.

[0023] In this embodiment, the substation cloud data after offset and rotation is filtered, thereby dividing the substation cloud data into ground point cloud data and non-ground point cloud data. Specifically, the filtering adopts the cloth filtering algorithm CSF method, and the specific algorithm process has been disclosed in the prior art document "An Easy-to-Use Airborne LiDAR Data Filtering MethodBased on Cloth Simulation", thereby obtaining ground point cloud data and non-ground point cloud data.

[0024] In this embodiment, the non-ground point cloud data is extracted by using a traditional algorithm or an artificial intelligence algorithm to extract power lines, and the non-ground point cloud data is divided into power line point cloud data and non-power line point cloud data. Specifically, the traditional algorithm uses a segmentation method based on point cloud shape features or a deep learning method, and the specific process has been disclosed in the prior art document "Research on Segmentation and Identification Algorithm of Substation Equipment Auxiliary Wires Based on Three-Dimensional Point Cloud"; the artificial intelligence algorithm uses PointNet++, and the specific process has been disclosed in the prior art document "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space". Thus, power line point cloud data and non-power line point cloud data are obtained.

[0025] In this embodiment, the non-power line point cloud data are clustered, and the cluster with the largest area is used as the wall point cloud data, and the remaining data is the equipment point cloud data.

[0026] In this embodiment, the ground point cloud data, power line point cloud data, wall point cloud data, and device point cloud data are sorted respectively, and the sorting rule for each type of point cloud data is: by priority X axis>Y axis>Z axis, and sorted from small to large according to the coordinates. Then, based on the sorted point cloud data, the index content corresponding to the ground point cloud data, power line point cloud data, wall point cloud data, and device point cloud data is established respectively.

[0027] Step 2: The ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 1 are stored as point cloud files using different strategies. The specific instructions are as follows: A. For the ground point cloud data, since the height difference of the ground point cloud data is small but the data volume is large, this embodiment uses a grid method to establish an index structure and store it as a point cloud file based on the X, Y range of the ground point cloud data in the three-dimensional coordinate system, and stores the corresponding index content in the index file.

[0028] Specifically, this embodiment first obtains the X, Y range of the ground point cloud data, takes the minimum value (X_min, Y_min) of the XY coordinates of the ground point cloud data as the reference point, divides the area corresponding to the ground point cloud data into multiple grids, and assumes that the size of each grid is g_d, then the number of rows of each grid is (D2 / g_d+1), and the number of columns is (D1 / g_d+1), where D1 is the range of the data in the X-axis direction, and D2 is the range of the data in the Y-axis direction.

[0029] Then, this embodiment distributes each ground point to the corresponding grid according to the spatial position, stores the sorted ground point cloud data of each grid into a point cloud file, stores the point cloud file in the spatial database, and stores the index content in the index file of the database.

[0030] When storing, when the number of ground points in each grid exceeds the given threshold grid_max_pt_number (for example, 100,000), the ground point cloud data in the grid is evenly divided into multiple point cloud files by random sampling, and the numbers of the multiple point cloud files are used as the third dimension of the grid, and the first dimension of each grid is the X coordinate range, and the second dimension is the Y coordinate range.

[0031] When the number of ground points in each grid is less than or equal to the given threshold grid_max_pt_number, it is directly stored as a file in a compressed file format.

[0032] B. Regarding the power line point cloud data, since the power line points account for a relatively small proportion in the substation data, hierarchical storage is not performed in this embodiment. Instead, the power line point cloud data is clustered to obtain multiple clusters. After clustering, each cluster data is directly stored as a point cloud file, and the point cloud file is stored in the database, and the corresponding index content is stored in the index file.

[0033] C. For the wall point cloud data, since the wall point cloud data is distributed in all directions and there is empty data in the middle, this embodiment establishes an index structure along the direction of the outer and inner enclosing surfaces of the wall and stores it as a point cloud file to avoid the appearance of empty space grid data, and stores the corresponding index content in the index file.

[0034] Specific as Figure 1As shown, in this embodiment, the outer enclosing surface AABB of the wall point cloud data is first extracted, and the inner enclosing surface AABB of the wall point cloud data is found by extending inward along the edge of the outer enclosing surface AABB 内 , the two enclosing surfaces form a rectangular ring.

[0035] Then, starting from the corner point close to the origin, a grid is established in the clockwise direction to segment the wall point cloud data.

[0036] Next, each wall point is distributed to its corresponding grid according to its spatial position.

[0037] Finally, the wall point cloud data in each grid is sorted, the sorted wall point cloud data of each grid is stored as a point cloud file, the point cloud file is stored in the spatial database, and the corresponding index content is stored in the index file of the database.

[0038] D. The device point cloud data is divided into multiple point cloud clusters by clustering segmentation, and an index structure is established for each point cloud cluster according to the XYZ range of each point cloud cluster in the three-dimensional coordinate system and stored as a point cloud file, and the corresponding index content is stored in the index file. Figure 2 As shown, the process is as follows: S1. First, obtain the XYZ range value of the point cloud data in each point cloud cluster (i.e., the XYZ range value of the outer bounding box), and make the following judgment: If the number of device points in a point cloud cluster is less than the set threshold D_grid_max_pt_number, the device point cloud data of the point cloud cluster is directly stored as a point cloud file.

[0039] If the number of device points in a point cloud cluster is greater than or equal to the given threshold D_grid_max_pt_number, the device point cloud data in the point cloud cluster is stored as a point cloud file in grid layers. The grid layering process is as follows: (A1) When the plane area of ​​the point cloud cluster is smaller than a given area (such as 5 square meters), the point cloud data of the point cloud cluster is evenly divided into different point cloud file units for storage in a random sampling manner with the standard that each file does not exceed the threshold D_grid_max_pt_number. In this way, the integrity of the device can be maintained when loading data.

[0040] (A2) Figure 2 As shown in the figure, when the plane area of ​​the point cloud cluster is larger than the given area, an outer bounding box is established according to the distribution of the point cloud data in the point cloud cluster in three-dimensional space, and the point cloud data in the point cloud cluster is segmented in three dimensions according to the segmentation values ​​of (X / 2, Y / 2, Z / 2) to obtain the segmented sub-grids, and the following judgments are made on the sub-grids: If the point data of the sub-grid is less than D_grid_max_pt_number*4, the point cloud data of the point cloud cluster is evenly divided into different point cloud file units for storage in a random sampling manner with each file not exceeding D_grid_max_pt_number; If the point data of the sub-grid is greater than or equal to D_grid_max_pt_number*4, repeat step (A2) until the point data of each sub-grid is less than D_grid_max_pt_number*4.

[0041] S2. Sort the device point cloud data in each stored point cloud file according to the spatial position.

[0042] S3. Store the sorted data of each file unit into a point cloud file, store the point cloud file in a spatial database, and store the corresponding index content in an index file in the database.

[0043] In this embodiment, the storage configuration files for ground point cloud data, power line point cloud data, wall point cloud data, and device point cloud data define the following rules: The configuration file is a json file, which stores information such as file / folder name, point cloud data range, grid level number, etc.

[0044] Point cloud file / folder naming rules: Point cloud data type_code / cluster_hierarchical code_row number_column number_third dimension number / number of layers in the Z direction.

[0045] Point cloud data type: ground files start with G, power line files start with P, wall files start with W, and device files start with D.

[0046] Code / Cluster: The serial number format of the cluster is defined as 001.

[0047] Hierarchical coding: The default value is 0, and it increases gradually with the increase of division depth.

[0048] Different names are separated by underscores.

[0049] Step 3: compress the point cloud data respectively formed by storing the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 2 to obtain their corresponding compressed files.

[0050] In this embodiment, the point cloud file obtained in step 2 does not use compression formats such as las, ply, and txt, but uses a compression format file for point cloud data. This compression format can achieve at least 1:5 compression relative to las data. The compression process of point cloud data in each point cloud file in this embodiment is as follows: (3.1) Quantify the point cloud coordinates of the point cloud data in each point cloud file, quantize the point cloud coordinate values ​​of the point cloud data to a smaller range, so that the point cloud coordinate values ​​can be expressed by integers, and obtain the point cloud coordinate data range of the point cloud file. The process of obtaining the point cloud coordinate data range is as follows: Get the maximum and minimum XYZ values ​​of the point cloud coordinates of the point cloud data in each point cloud file, subtract the corresponding minimum value of xyz from all the point cloud data in each point cloud file; express the coordinates of each point (including attributes) by the difference between the coordinates of the previous point (including attributes) adjacent to it.

[0051] (3.2) Generate a coding table based on the point cloud coordinate data quantized in step (3.1). The process is as follows: Model the probability of symbols (coordinate values, attribute values ​​or other data) and generate a coding table. In the coding table, shorter codes are assigned to values ​​with higher frequencies, and longer codes are assigned to values ​​with lower frequencies. The established model is shown in the following formula:

[0052]

[0053]

[0054] in: Is the status value.

[0055] s is the value to be encoded.

[0056] X is a natural number.

[0057] M: Yes .

[0058] freq[s] / M: is actually the probability of the value s appearing.

[0059] CDF[s] is the sum of the frequencies of the symbols preceding s.

[0060] f(i) is the frequency of occurrence of the i-th symbol.

[0061] ENCODE() indicates encoding.

[0062] % is the remainder symbol.

[0063] (3.3) According to the generated coding table, the quantized point cloud coordinate data is encoded to achieve compression of the point cloud data in each point cloud file.

[0064] During decompression, the encoded compressed file data stream is decoded according to the coding table, thereby recovering the point cloud data in the original point cloud file.

[0065] This embodiment also discloses a method for rendering substation massive point cloud data on a web page based on the above-mentioned substation massive point cloud data management and storage method, comprising the following steps: Step (4.1) determines the intersection range of the viewing volume and all point cloud data, obtains the single point cloud file to be loaded through the intersection range and the index file, and then decodes the single point cloud file to be loaded.

[0066] Step (4.2) retrieves the name of the point cloud file within the intersection range through the stored index file, and selects the point cloud data within the intersection range to be loaded according to the viewpoint distance, that is, the data from the viewpoint to the near plane of the viewing volume. When the viewpoint distance is far, a part of the point cloud data is selected from each point cloud file in the multiple point cloud files within the intersection range for display. For example, if the equipment in a certain area within the viewing volume is divided into 4 point cloud files for storage, one point cloud file can be selected for rendering; as the viewpoint distance becomes smaller, the point cloud data in the point cloud files within the intersection range are gradually increased for display.

[0067] Step (4.3) transmits the point cloud data of the point cloud file selected in step (4.3) to the web page for rendering, and speeds up the data loading speed of the web page by caching the point cloud data.

[0068] When rendering on a web page, follow these rules: (a) Ground and wall point cloud data are not loaded by default and can be loaded when needed; (b) The number of point cloud data cannot exceed the set number threshold. When the total number of transmitted point cloud data exceeds the number threshold, a certain proportion of point cloud data is randomly sampled for display; (c) Record the list of point cloud data that have been loaded before the viewing volume changes. When the viewing volume changes, clear the point cloud data that is not in the visible area and load the point cloud data that has not been loaded in the visible area.

[0069] In this embodiment, the point cloud data can be edited in the visual interface after rendering. The editing content includes: modification of point cloud category attributes, modification of point cloud device attributes, whether the point cloud is valid data, etc. The modified data is saved to the cache and updated to the point cloud database and attribute database.

[0070] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, and such combinations should also be regarded as the contents disclosed in the present disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0071] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A method for managing and storing massive point cloud data of a substation, characterized in that: The following steps are involved: Step 1: After the substation cloud data is offset and rotated in the three-dimensional coordinate system, it is filtered to divide the substation cloud data into ground point cloud data and non-ground point cloud data; then, the power line point cloud data and non-power line point cloud data are extracted from the non-ground point cloud data; then, the wall point cloud data and the equipment point cloud data are obtained based on the non-power line point cloud data; finally, the ground point cloud data, the power line point cloud data, the wall point cloud data, and the equipment point cloud data are sorted according to their spatial positions in the three-dimensional coordinate system, and the index contents corresponding to the ground point cloud data, the power line point cloud data, the wall point cloud data, and the equipment point cloud data are established respectively; Step 2: Store the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 1 as point cloud files, respectively, where: For the ground point cloud data, according to the X, Y range of the ground point cloud data in the three-dimensional coordinate system, an index structure is established in a grid manner and stored as a point cloud file, and the corresponding index content is stored in the index file; For the power line point cloud data, directly store the power line point cloud data as a point cloud file, and store the corresponding index content in the index file; For the wall point cloud data, an index structure is established in a grid manner according to the outer and inner enclosing surfaces of the wall and stored as a point cloud file, and the corresponding index content is stored in the index file; The device point cloud data is divided into a plurality of point cloud clusters by clustering segmentation, and an index structure is established for each point cloud cluster according to the XYZ range of each point cloud cluster in the three-dimensional coordinate system and stored as a point cloud file, and the corresponding index content is stored in the index file; Step 3: compress the point cloud data respectively formed by storing the ground point cloud data, power line point cloud data, wall point cloud data, and equipment point cloud data obtained in step 2 to obtain their corresponding compressed files.

2. The method for managing and storing massive point cloud data of a substation according to claim 1, characterized in that: In step 1, the substation cloud data after offset and rotation is filtered by cloth filtering CSF algorithm, thereby dividing the substation cloud data into ground point cloud data and non-ground point cloud data.

3. The method for managing and storing massive point cloud data of a substation according to claim 1, characterized in that: In step 1, a segmentation method based on point cloud shape features or a deep learning method is used to extract power line point cloud data and non-power line point cloud data from non-ground point cloud data.

4. The method for managing and storing massive point cloud data of a substation according to claim 1, characterized in that: In step 1, the non-power line point cloud data are clustered, and the cluster with the largest area is used as the wall point cloud data, and the remaining data is the equipment point cloud data.

5. The method for managing and storing massive point cloud data of a substation according to claim 1, characterized in that: In step 3, the compression process of point cloud data in each point cloud file is as follows: (3.1) Quantifying the point cloud coordinates of the point cloud data in each point cloud file, quantizing the point cloud coordinate values ​​to be represented by integers, and obtaining the point cloud coordinate data range of the point cloud file; (3.2) Generate a coding table based on the point cloud coordinate data quantized in step (3.1); (3.3) According to the generated coding table, the quantized point cloud coordinate data is encoded to achieve compression of the point cloud data in each point cloud file.

6. A method for rendering substation massive point cloud data based on the method for managing and storing substation massive point cloud data according to any one of claims 1 to 5, characterized in that: The following steps are involved: (4.1) Determine the intersection range of the viewing volume and all point cloud data, obtain the single point cloud file to be loaded through the intersection range and index file, and then decode the single point cloud file to be loaded; (4.2) Retrieve the names of the point cloud files within the intersection range through the stored index file, and select the point cloud data within the intersection range to be loaded according to the viewpoint distance, i.e., the data from the viewpoint to the near plane of the viewing volume. When the viewpoint distance is far, select a part of the point cloud data from each of the multiple point cloud files within the intersection range for display. As the viewpoint distance decreases, gradually increase the point cloud data in the point cloud files within the intersection range for display. (4.3) The point cloud data of the point cloud file selected in step (4.3) is transferred to the web page for rendering.

7. The method for rendering massive point cloud data of a substation according to claim 6, characterized in that: In step (4.3), the point cloud data is cached to speed up the data loading on the web page.

8. The method for rendering massive point cloud data of a substation according to claim 6, characterized in that: When rendering on the web page in step (4.3), follow the following rules: (a) Ground and wall point cloud data are not loaded by default and can be loaded when needed; (b) The number of point cloud data cannot exceed the set number threshold. When the total number of transmitted point cloud data exceeds the number threshold, a certain proportion of point cloud data is randomly sampled for display; (c) Record the list of point cloud data that have been loaded before the viewing volume changes. When the viewing volume changes, clear the point cloud data that is not in the visible area and load the point cloud data that has not been loaded in the visible area.

Images