Fusion display method and device for point cloud acquisition data and medium

Abstract

The embodiment of the invention discloses a fusion display method and device for point cloud acquisition data and a medium, and the method comprises the steps: determining the boundary of the point cloud acquisition data, and determining the total volume of the point cloud acquisition data based on a frame line corresponding to the boundary; based on scene demand information corresponding to the current digital twin scene, determining an expected point cloud number corresponding to the point cloud acquisition data, and determining the size of a voxel grid according to the expected point cloud number and the total volume; traversing the point cloud acquisition data and distributing each point cloud acquisition data to a corresponding voxel grid to collect representative points of each voxel grid, and obtaining point cloud acquisition data after downsampling; and carrying out elevation analysis on the point cloud acquisition data after downsampling to obtain DEM data corresponding to the point cloud acquisition data after downsampling, importing the DEM data into the current digital twinborn scene, and carrying out modification display on the current digital twinborn scene based on the DEM data.

Description

Technical Field

[0001] This specification relates to the technical field of point cloud data processing, and particularly to a method, device, and medium for fusing and displaying point cloud acquisition data. Background Art

[0002] In the hydrological industry, especially in the face of disaster weather, quickly and accurately obtaining and displaying post-disaster terrain data is of crucial significance for emergency event handling. Heavy rain and floods may cause river embankments to collapse, and debris flow disasters may inundate river channels. In these situations, it is necessary for staff to promptly master terrain changes in order to quickly take countermeasures. However, traditional methods for obtaining terrain data often take a long time and are difficult to meet the timeliness requirements of emergency handling.

[0003] In recent years, with the development of unmanned aerial vehicle (UAV) technology, UAVs equipped with lidar devices have become an efficient data acquisition means. By equipping UAVs with lidar devices, terrain data can be quickly scanned and obtained, providing a new solution for emergency response in the hydrological industry. However, although UAV lidar scanning can quickly obtain a large amount of data, the content of these raw data is complex. Direct use will result in low data processing and transmission efficiency, and low scene fusion efficiency. When the scene scale is large and the data is updated frequently, it is difficult to efficiently display the data in the digital twin scene. Summary of the Invention

[0004] To solve the above technical problems, one or more embodiments of this specification provide a method, device, and medium for fusing and displaying point cloud acquisition data.

[0005] One or more embodiments of this specification adopt the following technical solutions: One or more embodiments of this specification provide a method for fusing and displaying point cloud acquisition data, the method comprising: Determine the boundary of the point cloud acquisition data to determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary; Based on the scene requirement information corresponding to the current digital twin scene, determine the expected number of point clouds corresponding to the point cloud acquisition data, and determine the size of the voxel grid according to the expected number of point clouds and the total volume; Traverse the point cloud acquisition data and allocate each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid, obtaining the downsampled point cloud acquisition data; Perform elevation analysis on the downsampled point cloud acquisition data to obtain the DEM data corresponding to the downsampled point cloud acquisition data, import the DEM data into the current digital twin scene, and modify and display the current digital twin scene based on the DEM data.

[0006] Optionally, in one or more embodiments of the present specification, based on the scenario requirement information corresponding to the current digital twin scenario, determining the desired number of point cloud acquisition data specifically includes: Extracting the visualization requirement data and analysis requirement data corresponding to the digital twin scenario from the scenario requirement information; Based on the visualization requirement data, determining the visualization scenario corresponding to the point cloud acquisition data, and determining the quantity range of the point cloud acquisition data according to the visualization scenario; According to the visualization requirement data, determining whether the point cloud data has a local perspective area. If so, matching the accuracy data corresponding to each local perspective area with the quantity range of the point cloud acquisition data to obtain the first desired number of point clouds; According to the historical analysis data corresponding to the analysis requirement data, determining the second desired point cloud data corresponding to the analysis requirement data; Based on the proportions of the visualization requirement data and the analysis requirement data in the scenario requirement information, respectively performing weighted processing on the first desired number of point clouds and the second desired number of point clouds to obtain the desired number of point cloud acquisition data.

[0007] Optionally, in one or more embodiments of the present specification, determining the size of the voxel grid according to the desired number of point clouds and the total volume specifically includes: Inputting the desired number of point clouds and the total volume of the point cloud acquisition data into a preset voxel grid size formula to determine the size of the voxel grid; wherein, the preset voxel grid size formula is: ; wherein, VoxelSize is the size of the voxel grid, TotalVolume is the total volume of the point cloud acquisition data, and DesiredNumberOfPoints is the desired number of point clouds.

[0008] Optionally, in one or more embodiments of the present specification, traversing the point cloud acquisition data and allocating each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid to obtain the downsampled point cloud acquisition data specifically includes: Traversing the point cloud acquisition data to determine the voxel grid index corresponding to each point cloud acquisition data according to the coordinate data corresponding to each point cloud acquisition data and the size of the voxel grid; Based on the voxel grid index corresponding to each point cloud acquisition data, allocating each point cloud acquisition data to the corresponding voxel grid; Determining whether there is corresponding point cloud acquisition data in each voxel grid; If not, there is no representative point in the voxel grid; If so, obtain the centroid of each point cloud acquisition data in the voxel grid, and use the point cloud acquisition data corresponding to the centroid as the representative point; By collecting the representative points of each voxel grid, the downsampled point cloud acquisition data is obtained.

[0009] Optionally, in one or more embodiments of this specification, perform elevation analysis on the downsampled point cloud acquisition data to obtain DEM data corresponding to the downsampled point cloud acquisition data, specifically including: Obtain the average value of each point cloud acquisition data in the elevation direction within each voxel grid, and use the average value as the height information of the voxel grid; Determine the height values of the known points according to the height information of each voxel grid, and substitute the relevant data of the known points into the preset inverse distance weighted interpolation calculation formula to obtain the height values of the interpolation points; wherein, the preset inverse distance weighted interpolation calculation formula is: ; Z(x,y) is the height value at the interpolation point (x,y), is the height value at the known point n is the number of known points, and p is the power parameter; Combine the height values corresponding to each interpolation point according to the corresponding coordinate positions to obtain the DEM data corresponding to the downsampled point cloud acquisition data.

[0010] Optionally, in one or more embodiments of this specification, import the DEM data into the current digital twin scene and modify and display the current digital twin scene based on the DEM data, specifically including: Determine the data format of the current digital twin scene, and convert the DEM data into the to-be-imported DEM data corresponding to the data format; Based on the preset tool, load the to-be-imported DEM data into the current digital twin scene to align the existing model of the current digital twin scene with the to-be-imported DEM data; Correct the terrain height of the corresponding area in the current digital twin scene according to the height values of each grid in the DEM data, and correct the terrain texture of the corresponding area in the current digital twin scene based on the terrain data of the DEM data, and display the corrected current digital twin scene.

[0011] Optionally, in one or more embodiments of this specification, determine the boundary of the point cloud acquisition data, and determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary, specifically including: Traverse the point cloud acquisition data, and determine the boundaries of the point cloud acquisition data in each coordinate axis direction according to the maximum and minimum values of each point cloud acquisition data in each coordinate direction; Determine the frame lines enclosing the point cloud acquisition data based on the boundaries, and determine the lengths of each frame line based on the coordinate data of each vertex of the frame lines; Calculate the total volume of the point cloud acquisition data according to the lengths of each frame line.

[0012] Optionally, in one or more embodiments of this specification, before determining the boundaries of the point cloud acquisition data and determining the total volume of the point cloud acquisition data based on the frame lines corresponding to the boundaries, the method further includes: Perform filtering processing on the original point cloud acquisition data based on a preset bilateral filtering algorithm to obtain the filtered point cloud acquisition data; wherein, the preset bilateral filtering algorithm is: ; Wherein, is the intensity value of the filtered point cloud acquisition data, and are the intensity values of the original point cloud acquisition data point p and the original point cloud acquisition data point q respectively, S is the neighborhood point set of the original point cloud acquisition data point p, and are the range domain function and the spatial domain function respectively, is the normalization factor.

[0013] One or more embodiments of this specification provide a fusion display device for point cloud acquisition data, and the device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: execute any one of the above methods. A non-volatile computer storage medium provided by one or more embodiments of this specification stores computer-executable instructions, and the computer-executable instructions are configured to: be able to execute any one of the above methods.

[0014] The above at least one technical solution adopted by the embodiments of this specification can achieve the following beneficial effects: After determining the boundary and total volume of the point cloud acquisition data, the voxel grid size is set in combination with the expected number of point clouds, thereby achieving downsampling and effectively reducing the data volume. This makes the subsequent processing of a large amount of point cloud data efficient, reducing the consumption of computing resources and processing time. Determining the expected number of point clouds according to the scene requirements not only meets the requirements of the digital twin scene for data accuracy but also avoids excessive data redundancy. After importing the DEM data into the current digital twin scene, the terrain of the digital twin scene is accurately corrected by fusing the DEM data, improving the accuracy of the scene and making it more realistically simulate the real environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a schematic flowchart of a method for fusing and displaying point cloud acquisition data provided by an embodiment of this specification; Figure 2 It is a schematic diagram of obtaining point cloud acquisition data provided by an embodiment of this specification; Figure 3 It is a schematic diagram of displaying DEM data generated by an embodiment of this specification; Figure 4 It is a schematic diagram of the fusion of DEM data and the current digital twin scene provided by an embodiment of this specification; Figure 5 It is a schematic diagram of the structure of a device for fusing and displaying point cloud acquisition data provided by an embodiment of this specification; Figure 6 It is a schematic diagram of the structure of a non-volatile storage medium provided by an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Embodiments of this specification provide a method, device, and medium for fusing and displaying point cloud acquisition data.

[0017] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0018] As shown Figure 1 in the figure, the embodiment of this specification provides a schematic flowchart of a method for fusing and displaying point cloud acquisition data. It can be seen Figure 1 from this that in one or more embodiments of this specification, a method for fusing and displaying point cloud acquisition data includes the following steps: S101: Determine the boundary of the point cloud acquisition data to determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary.

[0019] In order to reduce the volume of point cloud data, before subsequent downsampling, it is necessary to determine the boundary of the point cloud acquisition data, so as to determine the total volume of the point cloud acquisition data according to the frame line corresponding to the boundary. In this process, by determining the boundary, the point cloud data irrelevant to the target area can be removed, avoiding unnecessary calculations on the irrelevant data in subsequent processing. Then, by calculating the total volume within the boundary, the scale and complexity of the point cloud data can be more accurately evaluated, providing a scientific basis for subsequent downsampling and analysis.

[0020] Further, in one or more embodiments of this specification, before determining the boundary of the point cloud acquisition data to determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary, in order to achieve the purpose of retaining the edge information of the point cloud acquisition data while effectively removing noise points, the method further includes the following process: As shown Figure 2 in the figure, in the embodiment of this specification, the original point cloud acquisition data can be obtained based on the lidar carried by the drone. Then, in order to filter out interference noise, the original point cloud acquisition data will be filtered based on a preset bilateral filtering algorithm to obtain the filtered point cloud acquisition data; where the preset bilateral filtering algorithm is: ; wherein is the intensity value of the filtered point cloud acquisition data, and are the intensity values of the original point cloud acquisition data point p and the original point cloud acquisition data point q respectively, S is the neighborhood point set of the original point cloud acquisition data point p, and are the range domain function and the spatial domain function respectively, is the normalization factor. In this process, the bilateral filtering algorithm is used to process the original point cloud acquisition data, which not only considers the spatial distance of the points but also the intensity of the points, enabling the algorithm to well retain the edges and geometric features of the point cloud data while smoothing the data.

[0021] Specifically, in one or more embodiments of this specification, the boundary of the point cloud acquisition data is determined to determine the total volume of the point cloud acquisition data based on the frame lines corresponding to the boundary. The specific process includes the following: First, traverse the point cloud acquisition data to determine the boundaries of the point cloud acquisition data in each coordinate axis direction according to the maximum and minimum values of each point cloud acquisition data in each coordinate direction. Then, determine the frame lines surrounding the point cloud acquisition data according to the boundaries, and determine the lengths of each frame line according to the coordinate data of each vertex of the frame line. Then, calculate the total volume of the point cloud acquisition data according to the lengths of each frame line. In this process, by traversing all the point cloud data, it is possible to ensure finding the maximum and minimum values in each coordinate direction, thus comprehensively covering the distribution range of the point cloud data.

[0022] S102: Based on the scenario requirement information corresponding to the current digital twin scenario, determine the expected number of point clouds corresponding to the point cloud acquisition data, and determine the size of the voxel grid according to the expected number of point clouds and the total volume.

[0023] After obtaining the total volume of the point cloud acquisition data based on the above steps, in order to achieve downsampling of the point cloud acquisition data, the expected number of point clouds corresponding to the point cloud acquisition data will be determined based on the scenario requirement information corresponding to the current digital twin scenario, and then the size of the voxel grid will be determined according to the expected number of point clouds and the total volume. In this way, by determining the expected number of point clouds, the amount of point cloud data can be controlled within a reasonable range, reducing the computational workload of subsequent processing and analysis. In addition, according to different digital twin scenario requirements, the expected number of point clouds and the voxel grid size can be flexibly adjusted to meet the accuracy and performance requirements of different scenarios.

[0024] Specifically, in one or more embodiments of this specification, based on the scenario requirement information corresponding to the current digital twin scenario, determining the expected number of point clouds corresponding to the point cloud acquisition data specifically includes the following process: Extract the visualization requirement data and analysis requirement data corresponding to the digital twin scenario from the scenario requirement information. Among them, the visualization requirement data is related to the visual effect that the digital twin scenario finally presents to the user, such as the fineness of the scenario, the presentation requirements of color and material, etc.; the analysis requirement data focuses on various analyses using point cloud data, such as the requirements for data in structural stress analysis, computational fluid dynamics simulation, etc. Then, in order to determine whether the scenario corresponding to the current visualization requirement data is a high-precision scenario or a low-precision scenario, the visualization scenario corresponding to the point cloud acquisition data will be determined based on the visualization requirement data, so as to determine the quantity range of the point cloud acquisition data according to the visualization scenario. For example, a visualization scenario is a digital twin scenario for virtual tourism display. Since this visualization scenario requires highly restoring every detail of the scenic spot, including the texture of the building, the form of the plants, etc. Therefore, this high-fineness visualization scenario has a high requirement for the data volume, and then the quantity range of the point cloud acquisition data is determined. If it is only used for the preliminary display of macro-region planning, then at this time the visualization scenario has low requirements for details, and the quantity range of the point cloud acquisition data is also relatively low. Specifically, the quantity range can be obtained by querying based on the preset data volume table corresponding to the visualization scenario. Then, since in some scenarios, a close-up display of local key areas is required, it is necessary to determine whether the point cloud data has local perspective areas according to the visualization requirement data. If so, match the accuracy data corresponding to each local perspective area with the quantity range of the point cloud acquisition data to obtain the first expected point cloud quantity. Then, according to the historical analysis data corresponding to the analysis requirement data, determine the second expected point cloud data corresponding to the analysis requirement data. Then, according to the proportions of the visualization requirement data and the analysis requirement data in the scenario requirement information, perform weighted processing on the first expected point cloud quantity and the second expected point cloud quantity respectively to obtain the expected point cloud quantity corresponding to the point cloud acquisition data. And in different digital twin scenarios, the importance degrees of the visualization requirement and the analysis requirement are different, so it is necessary to determine their proportions in the scenario requirement information. If it is a digital twin scenario of a virtual exhibition mainly for display, the proportion of the visualization requirement may account for 70%, and the proportion of the analysis requirement accounts for 30%. According to this proportion, perform weighted processing on the first expected point cloud quantity and the second expected point cloud quantity, that is, multiply the first expected point cloud quantity by the proportion of the visualization requirement, multiply the second expected point cloud quantity by the proportion of the analysis requirement, and then add the two to get the result, which is the expected point cloud quantity corresponding to the point cloud acquisition data.

[0025] In this process, the desired number of point clouds is determined by comprehensively considering the requirements of visualization and analysis, avoiding the one-sidedness of determining the data volume based on a single factor. By differentiating the fineness of the visualization scene, the range of the number of point clouds is determined, and separate precision considerations are carried out for local perspective areas, which can flexibly adapt to the diverse needs of different types of digital twin scenarios. And the second desired number of point clouds is determined based on the historical analysis data corresponding to the analysis requirement data, drawing on past experience to make the decision more scientific and reasonable. Weighted processing is carried out according to the proportion of visualization requirements and analysis requirements in the scene. This method can flexibly adjust the influence degree of the two requirements on the desired number of point clouds, so that the determination of the desired number of point clouds is more in line with the core goal of the scene.

[0026] Specifically, in one or more embodiments of the present specification, the size of the voxel grid is determined according to the desired number of point clouds and the total volume, which specifically includes the following process: First, the desired number of point clouds and the total volume of the point cloud acquisition data are input into a preset voxel grid size formula to determine the size of the voxel grid. Among them, it should be noted that the preset voxel grid size formula is: ; where VoxelSize is the size of the voxel grid, TotalVolume is the total volume of the point cloud acquisition data, and DesiredNumberOfPoints is the desired number of point clouds.

[0027] In the above process, since the desired number of point clouds reflects the user's requirements for the density and detail level of the final point cloud data, and the total volume reflects the space range occupied by the point cloud data. Therefore, by combining these two parameters to determine the voxel grid size, it can be ensured that the generated voxel grid can reasonably divide the original point cloud, neither losing too much detail information due to too large a voxel grid, nor causing the problem of low processing efficiency due to too small a voxel grid size.

[0028] S103: Traverse the point cloud acquisition data and allocate each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid, and obtain the downsampled point cloud acquisition data.

[0029] After determining the size of the voxel grid based on the above steps, the point cloud acquisition data can be traversed and each point cloud acquisition data can be assigned to the corresponding voxel network to collect the representative points of each voxel grid, thereby obtaining the downsampled point cloud acquisition data. Since the amount of the original point cloud acquisition data is often extremely large, direct processing will consume a large amount of computing resources, storage resources, and time. Assigning the point cloud data to the voxel grid and collecting the representative points for downsampling can significantly reduce the amount of data. Moreover, during the downsampling process, by reasonably collecting the representative points of the voxel grid, the key geometric features of the point cloud data can be retained, ensuring that the key point cloud acquisition data will not be lost due to downsampling.

[0030] Specifically, in one or more embodiments of this specification, traversing the point cloud acquisition data and assigning each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid and obtain the downsampled point cloud acquisition data specifically includes the following process: First, traverse the point cloud acquisition data, and thus determine the voxel grid index corresponding to each point cloud acquisition data according to the coordinate data corresponding to each point cloud acquisition data and the size of the voxel grid. Then, according to the voxel grid index corresponding to each point cloud acquisition data, assign each point cloud acquisition data to the corresponding voxel grid. Determine whether there is corresponding point cloud acquisition data in each voxel grid. If there is no corresponding point cloud acquisition data, then there is no representative point in this voxel grid. If there is corresponding point cloud acquisition data, obtain the centroid of each point cloud acquisition data in this voxel grid, and use the point cloud acquisition data corresponding to the centroid as the representative point. By collecting the representative points of each voxel grid, the downsampled point cloud acquisition data can be obtained.

[0031] S104: Perform elevation analysis on the downsampled point cloud acquisition data to obtain the DEM data corresponding to the downsampled point cloud acquisition data, so as to import the DEM data into the current digital twin scene and modify and display the current digital twin scene based on the DEM data.

[0032] After the above steps are implemented to downsample the point cloud acquisition data, elevation analysis will be performed on the downsampled point cloud acquisition data, thereby obtaining the DEM data corresponding to the downsampled point cloud acquisition data. Then, the DEM data will be imported into the current digital twin scene, and the current digital twin scene will be modified and displayed according to the DEM data. In this process, the DEM data accurately reflects the elevation information of the terrain. After importing it into the digital twin scene, it can accurately reshape the terrain and landforms in the scene.

[0033] Specifically, in one or more embodiments of this specification, performing elevation analysis on the downsampled point cloud acquisition data to obtain the DEM data corresponding to the downsampled point cloud acquisition data specifically includes the following process: The average value of each point cloud acquisition data in each voxel grid in the elevation direction is obtained, and the average value is used as the height information of the voxel grid. Then, the height value of the known point is determined according to the height information of each voxel grid, and the relevant data of the known point is substituted into the preset inverse distance weighted interpolation calculation formula to obtain the height value of the interpolation point; wherein, the preset inverse distance weighted interpolation calculation formula is: ; Z(x,y) is the height value at the interpolation point (x,y), For known points The height value at the point, n is the number of known points, and p is the power parameter. In different application scenarios, the accuracy requirements for terrain data are different. This process can flexibly control the smoothness and accuracy of the interpolation result by adjusting the power parameter p in the preset inverse distance weighted interpolation calculation formula. Then the height values ​​corresponding to each interpolation point are combined according to the corresponding coordinate positions to obtain the following Figure 3 The DEM data corresponding to the downsampled point cloud collection data shown.

[0034] In this process, the height value of the interpolation point is inferred by using the height value of the known point through the inverse distance weighted interpolation algorithm, which fully considers the distance relationship between points. The closer the known point is to the interpolation point, the greater the impact on the interpolation result, which is in line with the continuity of the actual terrain changes. And the DEM data obtained based on this process can more accurately reflect the ups and downs of the terrain. Even if the amount of data is reduced after downsampling, a high-precision terrain model can still be constructed, providing a reliable data basis for subsequent terrain analysis and application.

[0035] Furthermore, in one or more embodiments of the present specification, DEM data is imported into the current digital twin scene, and the current digital twin scene is modified and displayed based on the DEM data, specifically including the following processes: Different digital twin platforms or software support different data formats. If the DEM data format does not match, it will not be able to be imported into the scene. Therefore, it is necessary to determine the data format of the current digital twin scene in order to convert the DEM data into DEM data to be imported that corresponds to the data format. Then, the DEM data to be imported is loaded into the current digital twin scene according to the preset tool, so as to align the existing model of the current digital twin scene with the DEM data to be imported. According to the height value of each grid in the DEM data, the terrain height of the corresponding area in the current digital twin scene is corrected, and the terrain of the corresponding area in the current digital twin scene is texture corrected based on the terrain data of the DEM data, so as to perform the corrected current digital twin scene as follows: Figure 4The display shown. In this process, through format conversion, DEM data can be smoothly integrated into the current digital twin scenario, laying a foundation for subsequent terrain construction and scenario optimization. Aligning the existing model with the DEM data enables the objects in the current digital twin scenario to be closely combined with the terrain. Modifying the terrain height and texture based on the height values of each grid in the DEM data can provide a more accurate reference for each digital twin scenario.

[0036] As Figure 5 shown, an embodiment of this specification provides a schematic structural diagram of a fusion display device for point cloud acquisition data. As Figure 5 can be seen, in one or more embodiments of this specification, a fusion display device for point cloud acquisition data, the device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: execute any one of the above methods.

[0037] As Figure 6 shown, an embodiment of this specification provides a schematic structural diagram of a non-volatile storage medium. As Figure 6 can be seen, in one or more embodiments of this specification, a non-volatile storage medium stores computer-executable instructions 601, and the computer-executable instructions 601 can: execute any one of the above methods.

[0038] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0039] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0040] The above are only one or more embodiments of this specification and are not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A method for fusing and displaying point cloud acquisition data, characterized in that The method includes: Determine the boundary of the point cloud acquisition data, and determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary; Based on the scenario requirement information corresponding to the current digital twin scenario, determine the desired number of point clouds corresponding to the point cloud acquisition data, and determine the size of the voxel grid according to the desired number of point clouds and the total volume; Traverse the point cloud acquisition data and allocate each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid, and obtain the downsampled point cloud acquisition data; Perform elevation analysis on the downsampled point cloud acquisition data to obtain the DEM data corresponding to the downsampled point cloud acquisition data, import the DEM data into the current digital twin scenario, and modify and display the current digital twin scenario based on the DEM data.

2. The method for fusion display of point cloud acquisition data according to claim 1, wherein Based on the scenario requirement information corresponding to the current digital twin scenario, determine the desired number of point clouds corresponding to the point cloud acquisition data, specifically including: Extract the visualization requirement data and analysis requirement data corresponding to the digital twin scenario from the scenario requirement information; Determine the visualization scenario corresponding to the point cloud acquisition data based on the visualization requirement data, and determine the quantity range of the point cloud acquisition data according to the visualization scenario; Determine whether the point cloud data has a local perspective area according to the visualization requirement data. If so, match the accuracy data corresponding to each local perspective area with the quantity range of the point cloud acquisition data to obtain the first desired number of point clouds; Determine the second desired point cloud data corresponding to the analysis requirement data according to the historical analysis data corresponding to the analysis requirement data; Based on the proportions of the visualization requirement data and the analysis requirement data in the scenario requirement information, perform weighted processing on the first desired number of point clouds and the second desired number of point clouds respectively to obtain the desired number of point clouds corresponding to the point cloud acquisition data.

3. The method for fusion display of point cloud acquisition data according to claim 1, characterized in that, Determine the size of the voxel grid according to the desired number of point clouds and the total volume, specifically including: Input the desired number of point clouds and the total volume of the point cloud acquisition data into the preset voxel grid size formula to determine the size of the voxel grid; where the preset voxel grid size formula is: ； Where VoxelSize is the size of the voxel grid, TotalVolume is the total volume of the point cloud acquisition data, and DesiredNumberOfPoints is the desired number of point clouds.

4. A method for fusing and displaying point cloud acquisition data according to claim 1, characterized in that, Traverse the point cloud acquisition data and allocate each point cloud acquisition data to the corresponding voxel grid to collect the representative points of each voxel grid, and obtain the downsampled point cloud acquisition data, specifically including: Traverse the point cloud acquisition data to determine the voxel grid index corresponding to each point cloud acquisition data according to the coordinate data corresponding to each point cloud acquisition data and the size of the voxel grid; Based on the voxel grid index corresponding to each point cloud acquisition data, allocate each point cloud acquisition data to the corresponding voxel grid; Determine whether there is corresponding point cloud acquisition data in each voxel grid; If not, there is no representative point in the voxel grid; If so, obtain the centroid of each point cloud acquisition data in the voxel grid, and use the point cloud acquisition data corresponding to the centroid as the representative point; By collecting the representative points of each voxel grid, the downsampled point cloud acquisition data is obtained.

5. A method for fusing and displaying point cloud acquisition data according to claim 1, characterized in that Perform elevation analysis on the downsampled point cloud acquisition data to obtain the DEM data corresponding to the downsampled point cloud acquisition data, specifically including: Obtain the average value of each point cloud acquisition data in the elevation direction within each voxel grid, and use the average value as the height information of the voxel grid; Determine the height value of the known points according to the height information of each voxel grid, so as to substitute the relevant data of the known points into the preset inverse distance weighted interpolation calculation formula to obtain the height value of the interpolation points; wherein, the preset inverse distance weighted interpolation calculation formula is: ; Z(x, y) is the height value at the interpolation point (x, y), is the known point at the height value, n is the number of known points, and p is the power parameter; Combine the height values corresponding to each interpolation point according to the corresponding coordinate positions to obtain the DEM data corresponding to the downsampled point cloud acquisition data.

6. A method for fusing and displaying point cloud acquisition data according to claim 1, characterized in that, Import the DEM data into the current digital twin scenario, and modify and display the current digital twin scenario based on the DEM data, specifically including: Determine the data format of the current digital twin scenario, and convert the DEM data into the to-be-imported DEM data corresponding to the data format; Based on a preset tool, load the to-be-imported DEM data into the current digital twin scenario to align the existing model in the current digital twin scenario with the to-be-imported DEM data; Correct the terrain height of the corresponding area in the current digital twin scenario according to the height values of each grid in the DEM data, and perform texture correction on the terrain of the corresponding area in the current digital twin scenario based on the terrain data of the DEM data, so as to display the corrected current digital twin scenario.

7. A method for fusing and displaying point cloud acquisition data according to claim 1, characterized in that, Determine the boundary of the point cloud acquisition data, and determine the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary, specifically including: Traverse the point cloud acquisition data, and determine the boundary of the point cloud acquisition data in each coordinate axis direction according to the maximum and minimum values of each point cloud acquisition data in each coordinate direction; Determine the frame line surrounding the point cloud acquisition data based on the boundary, and determine the length of each frame line based on the coordinate data of each vertex of the frame line; Calculate the total volume of the point cloud acquisition data according to the lengths of each frame line.

8. A method for fusion display of point cloud acquisition data according to claim 1, characterized in that Before determining the boundary of the point cloud acquisition data and determining the total volume of the point cloud acquisition data based on the frame line corresponding to the boundary, the method further includes: Perform filtering processing on the original point cloud acquisition data based on a preset bilateral filtering algorithm to obtain the filtered point cloud acquisition data; wherein, the preset bilateral filtering algorithm is: ； Among them, is the intensity value of the filtered point cloud acquisition data, and are the intensity values of the original point cloud acquisition data points p and q respectively. S is the neighborhood point set of the original point cloud acquisition data point p, and are the range domain function and the spatial domain function respectively, is the normalization factor.

9. A fusion display device for point cloud acquisition data, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: execute the method according to any one of claims 1-8 above.

10. A non-volatile memory stores computer-executable instructions, characterized in that, The computer executable can execute the method according to any one of claims 1-8 above.

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