A method and system for generating a three-dimensional geological topographic profile model

Abstract

The present application relates to the technical field of image data processing, in particular to a three-dimensional geological topographic profile model generation method and system, which solves the technical problem that the reasonable division of point cloud data is difficult to achieve in the prior art. The method comprises: obtaining three-dimensional point cloud data of a target geological area, the three-dimensional point cloud data comprising the spatial coordinates and intensity values of each three-dimensional point; performing iterative division on the three-dimensional point cloud data through an octree division algorithm to obtain divided three-dimensional point cloud data; the divided three-dimensional point cloud data is composed of multiple divided subspaces; in each iteration division process, for each subspace of the current iteration division, spatial feature and attribute feature analysis is performed based on the spatial coordinates and intensity values of the three-dimensional points in the subspace to determine a subspace division threshold of the subspace; and a three-dimensional geological topographic profile model is generated based on the divided three-dimensional point cloud data.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, specifically to a method and system for generating three-dimensional geological topographic profile models. Background Technology

[0002] Three-dimensional geological and topographic profile models, as a core tool for the three-dimensional visualization of surface topography and underground geological structures, can integrate surface elevation information with key data such as underground lithology, structure, and physical properties. This provides crucial data support and analytical basis for multiple fields, including geological exploration, mineral resource assessment, engineering geological investigation, environmental geological monitoring, disaster early warning, and urban underground space planning. Its core value lies in achieving intuitive presentation and in-depth analysis of geological and topographic information through precise three-dimensional digital modeling, thus contributing to the scientific nature of decision-making and the safety of implementation in related fields.

[0003] In the process of generating a 3D geological and topographic profile model, it is necessary to spatially divide the point cloud data so that the profile of the local area can be generated based on the divided subspaces. However, the existing technology is difficult to achieve reasonable division of point cloud data, resulting in the division space being too large or too small, which causes abnormal triangles or holes to appear when the profile is generated, resulting in poor modeling effect. Summary of the Invention

[0004] To address the technical problem of the difficulty in achieving reasonable partitioning of point cloud data in existing technologies, the present invention aims to provide a method and system for generating three-dimensional geological and topographic profile models. The specific technical solution adopted is as follows: This application provides a method for generating a three-dimensional geological and topographic profile model, including: Acquire three-dimensional point cloud data of the target geological area. The three-dimensional point cloud data includes the spatial coordinates and intensity values ​​of each three-dimensional point. The 3D point cloud data is iteratively partitioned using an octree partitioning algorithm to obtain the partitioned 3D point cloud data; the partitioned 3D point cloud data consists of multiple subspaces. In each iteration of partitioning, for each subspace partitioned in the current iteration, spatial features and attribute features are analyzed based on the spatial coordinates and intensity values ​​of the three-dimensional points in the subspace to determine the subspace partitioning threshold; the subspace partitioning threshold is used to determine whether the subspace should be partitioned in the current iteration. A three-dimensional geological and topographic profile model is generated based on the segmented three-dimensional point cloud data.

[0005] In one possible implementation, the method includes: For each 3D point in the subspace, a spherical analysis domain is constructed with the 3D point as the center and the radius as a preset radius; Based on the spatial coordinates of three-dimensional points in each spherical analysis domain, the density distribution and surface roughness are characterized and the necessity coefficient of geometric feature division is determined. Based on the intensity values ​​and spatial coordinates of the three-dimensional points in each spherical analysis domain, feature analysis is performed on the significance of intensity and the importance of location to determine the necessity coefficient of attribute feature division; The subspace partitioning threshold is determined based on the necessity coefficients for partitioning based on geometric features and attribute features.

[0006] In one possible implementation, the method includes: The density distribution dispersion coefficient of the subspace is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain. The density distribution dispersion coefficient is used to characterize the degree of dispersion of the three-dimensional points in the subspace. The surface roughness coefficient of the subspace is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain. The surface roughness coefficient is used to characterize the roughness of the surface where the three-dimensional points are located in the subspace. The necessity coefficient for geometric feature partitioning is determined based on the density distribution dispersion coefficient and the surface roughness coefficient.

[0007] In one possible implementation, the method includes: For each spherical analysis domain, based on the spatial coordinates of each three-dimensional point, the number of three-dimensional points within the spherical analysis domain is used as the distribution density coefficient of the three-dimensional points corresponding to the center of the sphere. The density distribution dispersion coefficient of the subspace is determined based on the density coefficient of each three-dimensional point in the subspace.

[0008] In one possible implementation, the method includes: For each spherical analysis domain, based on the spatial coordinates of the three-dimensional points in the spherical analysis domain, a surface fitting is performed on the three-dimensional points in the spherical analysis domain to obtain the surface normal vector of the three-dimensional point corresponding to the center of the sphere in the spherical analysis domain on the surface. For each spherical analysis domain, the deviation value of the surface normal vector of the three-dimensional point corresponding to the center of the spherical analysis domain is determined based on the surface normal vector of each three-dimensional point in the spherical analysis domain. The deviation value of the surface normal vector is used to characterize the degree of difference between the surface normal vector of the three-dimensional point corresponding to the center of the spherical analysis domain and the surface normal vector of other three-dimensional points in the spherical analysis domain. The surface roughness coefficient of the subspace is determined based on the deviation value of the surface normal vector of each three-dimensional point in the subspace.

[0009] In one possible implementation, the method includes: The intensity significance distribution dispersion coefficient of the subspace is determined based on the intensity values ​​and spatial coordinates of the three-dimensional points in each spherical analysis domain; the intensity significance distribution dispersion coefficient is used to characterize the degree of dispersion of the intensity values ​​of the three-dimensional points in the subspace; The dispersion coefficient of position importance distribution is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain; the dispersion coefficient of position importance distribution is used to characterize the degree of dispersion of the spatial position of the three-dimensional points in the subspace; The necessity coefficient for classifying attribute features is determined based on the dispersion coefficients of intensity significance distribution and location importance distribution.

[0010] In one possible implementation, the method includes: For each spherical analysis domain, the intensity significance coefficient corresponding to the spherical analysis domain is determined based on the intensity values ​​of the three-dimensional points in the spherical analysis domain according to the significance analysis algorithm; The intensity significance distribution dispersion coefficient of the subspace is determined based on the intensity significance coefficient corresponding to each spherical analysis domain.

[0011] In one possible implementation, the method includes: For each spherical analysis domain, the positional importance coefficient of the spherical analysis domain is determined based on the spatial coordinates of the three-dimensional points in the spherical analysis domain; The discrete coefficients of the positional importance distribution of the subspace are determined based on the positional importance coefficients corresponding to each spherical analysis domain.

[0012] In one possible implementation, the method includes: Initialize the root node of the octree and use it as the initial node to be partitioned; the root node corresponds to the space where the 3D point cloud data of the target geological region is located; For each node to be partitioned in the current iteration partitioning process, if the number of 3D points in the node to be partitioned is less than or equal to the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is determined to be a leaf node; if the number of 3D points in the node to be partitioned is greater than the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is divided into eight child nodes, and the eight child nodes are used as the nodes to be partitioned in the next iteration partitioning process. Repeat the iterative partitioning process until there are no more nodes to be partitioned.

[0013] This application provides a three-dimensional geological topographic profile model generation system, including: The data acquisition module is used to acquire three-dimensional point cloud data of the target geological area. The three-dimensional point cloud data includes the spatial coordinates and intensity values ​​of each three-dimensional point. The iterative partitioning module is used to iteratively partition the 3D point cloud data using an octree partitioning algorithm to obtain the partitioned 3D point cloud data; wherein the partitioned 3D point cloud data consists of multiple partitioned subspaces; The dynamic threshold determination module is used to determine the subspace partitioning threshold of each subspace in each iteration by performing spatial feature and attribute feature analysis based on the spatial coordinates and intensity values ​​of the 3D points in the subspace. The subspace partitioning threshold is used to determine whether the subspace should be partitioned in the current iteration. The model generation module is used to generate a 3D geological and topographic profile model based on the divided 3D point cloud data.

[0014] The present invention has the following beneficial effects: To address the technical challenge of effectively partitioning point cloud data using existing technologies, this application provides a method and system for generating three-dimensional geological and topographic profile models. This method acquires three-dimensional point cloud data of a target geological region and iteratively partitions the data using an octree algorithm. During the partitioning process, the subspace partitioning threshold is dynamically determined based on the spatial and attribute features of the three-dimensional points, thereby dynamically adjusting the partitioning granularity. Subsequently, a three-dimensional geological and topographic profile model is generated based on the partitioned point cloud data. Compared to the limitations of using fixed thresholds in existing technologies, this method adjusts the partitioning threshold by combining the spatial and attribute features of the three-dimensional points. This allows the octree partitioning to adapt to the differences in geological features across different regions, ensuring the precision of partitioning complex geological regions while avoiding over-partitioning of simple regions. This improves the geometric accuracy of the model while reducing computational overhead, balancing the efficiency and quality of model generation and meeting the high-precision application requirements of three-dimensional geological and topographic profile models in various fields. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system architecture diagram of a three-dimensional geological topographic profile model generation system provided in one embodiment of the present invention; Figure 2 This is one of the flowcharts illustrating a method for generating a three-dimensional geological and topographic profile model according to an embodiment of the present invention. Figure 3This is a second schematic flowchart of a method for generating a three-dimensional geological and topographic profile model according to an embodiment of the present invention. Figure 4 This is the third flowchart illustrating a method for generating a three-dimensional geological and topographic profile model according to an embodiment of the present invention. Figure 5 This is the fourth flowchart illustrating a method for generating a three-dimensional geological and topographic profile model, as provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a three-dimensional geological topographic profile model generation method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] To address the technical challenge of effectively partitioning point cloud data using existing technologies, this application provides a method and system for generating three-dimensional geological and topographic profile models. This method acquires three-dimensional point cloud data of a target geological region and iteratively partitions the data using an octree algorithm. During the partitioning process, the subspace partitioning threshold is dynamically determined based on the spatial and attribute features of the three-dimensional points, thereby dynamically adjusting the partitioning granularity. Subsequently, a three-dimensional geological and topographic profile model is generated based on the partitioned point cloud data. Compared to the limitations of using fixed thresholds in existing technologies, this method adjusts the partitioning threshold by combining the spatial and attribute features of the three-dimensional points. This allows the octree partitioning to adapt to the differences in geological features across different regions, ensuring the precision of partitioning complex geological regions while avoiding over-partitioning of simple regions. This improves the geometric accuracy of the model while reducing computational overhead, balancing the efficiency and quality of model generation and meeting the high-precision application requirements of three-dimensional geological and topographic profile models in various fields.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for generating a three-dimensional geological and topographic profile model provided by this invention.

[0021] Please see Figure 1The diagram illustrates a system architecture of a three-dimensional geological and topographic profile model generation system according to an embodiment of the present invention. The three-dimensional geological and topographic profile model generation system 10 includes: a data acquisition module 11, an iterative partitioning module 12, a dynamic threshold determination module 13, and a model generation module 14.

[0022] The data acquisition module 11 is used to acquire three-dimensional point cloud data of the target geological area.

[0023] The 3D point cloud data includes the spatial coordinates and intensity values ​​of each 3D point. Spatial coordinates characterize the position of a 3D point in 3D space, for example, using (x, y, z) coordinates. Intensity values ​​represent the light intensity reflected back to the sensor when a laser pulse scans the surface of an object. The magnitude of this intensity value is related to factors such as the target material, roughness, incident angle, instrument emission energy, and laser wavelength, thus reflecting the geological characteristics (such as lithological differences and material density) corresponding to the 3D point.

[0024] In practical applications, the data acquisition module 11 can establish a communication connection with a 3D scanning device (such as a lidar), and control the 3D scanning device to scan the target geological area, collect and store the 3D point cloud data obtained from the scan.

[0025] The iterative partitioning module 12 is used to iteratively partition the 3D point cloud data using an octree partitioning algorithm to obtain the partitioned 3D point cloud data.

[0026] The divided 3D point cloud data consists of multiple subspaces.

[0027] For example, the octree partitioning algorithm includes: first, initializing the root node of the octree and using the root node as the initial node to be partitioned; then, for each node to be partitioned, determining whether the node needs to be further partitioned based on the subspace partitioning threshold provided by the dynamic threshold determination module 13, and if so, dividing the initial node to be partitioned into eight child nodes and using the child nodes as the nodes to be partitioned in the next round; repeating the above process until all nodes are leaf nodes.

[0028] The dynamic threshold determination module 13 is used to determine the subspace partitioning threshold of each subspace in each iteration by performing spatial feature and attribute feature analysis based on the spatial coordinates and intensity values ​​of the three-dimensional points in the subspace.

[0029] The subspace partitioning threshold is used to determine whether a subspace should be partitioned during the current iteration. The dynamic threshold determination module 13 can provide the calculated subspace partitioning threshold to the iterative partitioning module 12 in real time, providing a basis for iterative partitioning.

[0030] Model generation module 14 is used to generate a three-dimensional geological and topographic profile model based on the divided three-dimensional point cloud data.

[0031] The model generation module 14 can project the point cloud data contained in the divided leaf nodes onto a specified two-dimensional cross-sectional surface. This allows for the connection and optimization of the projected point cloud data by incorporating geological topological relationship processing rules, avoiding abnormal triangles or voids, and ultimately generating a three-dimensional geological and topographical profile model with smooth stratigraphic lines and correct topological relationships. The model generation module 14 can also perform format conversion or detail optimization on the generated model according to actual application needs to meet the requirements of different scenarios.

[0032] It should be noted that the various embodiments of this application can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0033] Please see Figure 2 The diagram illustrates a flowchart of a method for generating a three-dimensional geological topographic profile model according to an embodiment of the present invention. The method includes the following steps: Step 201: Obtain three-dimensional point cloud data of the target geological area.

[0034] The 3D point cloud data includes the spatial coordinates and intensity values ​​of each 3D point. This data forms the basis for constructing a 3D geological and topographic profile model. Spatial coordinates characterize the position of a 3D point in 3D space, for example, using (x, y, z) coordinates. Intensity values ​​represent the light intensity reflected back to the sensor when a laser pulse scans the surface of an object. The magnitude of this intensity value is related to factors such as the target material, roughness, incident angle, instrument emission energy, and laser wavelength, thus reflecting the geological characteristics (such as lithological differences and material density) corresponding to the 3D point.

[0035] For example, this application can acquire three-dimensional point cloud data of a target geological area using a three-dimensional scanning device, such as using a lidar to perform a full-range scan of the target area, to ensure that the collected three-dimensional point cloud data can comprehensively cover the relevant surface and underground geological information of the target geological area. In practical applications, the scanning accuracy and scanning range of the lidar can be adjusted according to factors such as the size of the target area and the geological complexity to ensure the integrity and validity of the three-dimensional point cloud data.

[0036] In some embodiments, this application may also perform preliminary preprocessing on the acquired 3D point cloud data, such as removing noise points and converting data formats, to ensure the validity and consistency of the data in subsequent processing. For example, this application may set data filtering rules to automatically remove 3D points whose spatial coordinates exceed the target area or whose intensity values ​​are abnormal, thereby improving data quality.

[0037] Step 202: Iteratively divide the 3D point cloud data using the octree partitioning algorithm to obtain the partitioned 3D point cloud data.

[0038] The resulting 3D point cloud data is composed of multiple subspaces. A subspace refers to the spatial region corresponding to each leaf node to be partitioned in the octree algorithm. The octree algorithm uses the 3D point cloud data as a root node and progressively divides it into multiple leaf nodes. Each leaf node corresponds to the spatial coordinates and intensity values ​​of each 3D point in a subspace.

[0039] In some embodiments, the iterative partitioning process of the above octree partitioning algorithm is as follows: First, initialize the root node of the octree and use it as the initial node to be partitioned.

[0040] The root node corresponds to the spatial location of the 3D point cloud data of the target geological area. The root node is the starting node in the octree partitioning. During initialization, the spatial extent of the root node needs to be determined to ensure complete coverage of all acquired 3D point cloud data. For example, the spatial boundary of the root node can be determined based on the maximum and minimum values ​​of the x, y, and z axis coordinates of each 3D point in the 3D point cloud data.

[0041] Subsequently, for each node to be partitioned in the current iteration partitioning process, if the number of 3D points in the node to be partitioned is less than or equal to the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is determined to be a leaf node.

[0042] If the number of 3D points within a node to be partitioned is less than or equal to the subspace partitioning threshold, it indicates that the geological features of the area corresponding to that node are relatively simple and do not require further partitioning, and therefore it will not participate in subsequent iterative partitioning.

[0043] If the number of 3D points within the node to be partitioned is greater than the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is divided into eight child nodes, and the eight child nodes are used as the nodes to be partitioned in the next iteration partitioning process.

[0044] If the number of 3D points within a node to be partitioned exceeds the subspace partitioning threshold, it indicates that the geological features of the region corresponding to that node are relatively complex, thus requiring further refinement. In this case, the node can be divided into eight equally sized child nodes, and these eight child nodes will be used as the nodes to be partitioned in the next iteration. The spatial range of each child node is one-eighth of the spatial range of its parent node, ensuring that the partitioned subspace can discretly cover the spatial region of the parent node, avoiding spatial omissions or overlaps.

[0045] The iterative partitioning process is repeated until there are no more nodes to be partitioned. That is, when all nodes are marked as leaf nodes and no further partitioning is possible, the octree iterative partitioning is complete. At this point, the entire 3D space is divided into a series of leaf nodes containing point clouds of different densities and features, providing structured foundational data for the subsequent generation of 3D geological and topographic profile models.

[0046] It should be noted that, unlike the fixed threshold method in the prior art, the subspace partitioning threshold of this application is dynamically adjusted according to the actual characteristics of the three-dimensional points in the subspace during the iterative partitioning process, thereby achieving a more reasonable space partitioning.

[0047] Step 203: In each iteration of partitioning, for each subspace partitioned in the current iteration, spatial features and attribute features are analyzed based on the spatial coordinates and intensity values ​​of the three-dimensional points in the subspace to determine the subspace partitioning threshold of the subspace.

[0048] The subspace partitioning threshold is used to determine whether a subspace should be partitioned in the current iteration. Spatial features are mainly reflected in the spatial distribution of three-dimensional points (such as density distribution, positional relationships, etc.), while attribute features are mainly reflected in the differences in geological attributes reflected by the intensity values ​​of three-dimensional points. This application, through comprehensive analysis of these two types of features, can accurately determine the necessity of subspace partitioning, and thus determine the appropriate subspace partitioning threshold.

[0049] For example, if the 3D points within a subspace are not discrete and the geological features differ significantly, there may be situations where the local spatial point cloud information is too complex. If further subdivision is not performed, it may affect the modeling effect. Therefore, a smaller subspace subdivision threshold can be set. If the 3D points within a subspace are discrete and the geological features tend to be consistent, it indicates that the subdivision is more reasonable. Therefore, a larger subspace subdivision threshold can be set to avoid unnecessary subdivision operations.

[0050] Step 204: Generate a three-dimensional geological and topographic profile model based on the divided three-dimensional point cloud data.

[0051] After completing the iterative partitioning of the octree, each subspace can accurately reflect the geological and topographic features of the corresponding region. At this point, this application can project the partitioned 3D point cloud data onto a specified 2D cutting surface. In this way, the projected point cloud data can be connected and optimized by combining geological topological relationship processing rules to avoid abnormal triangles or voids, and finally generate a 3D geological and topographic profile model with smooth stratigraphic lines and correct topological relationships.

[0052] Based on the above technical solution, this application acquires 3D point cloud data of the target geological region and iteratively divides the 3D point cloud data based on the octree algorithm. During the division process, the subspace division threshold is dynamically determined based on the spatial and attribute features of the 3D points, thereby achieving dynamic adjustment of the division granularity. Subsequently, a 3D geological and topographic profile model is generated based on the divided 3D point cloud data. Compared with the limitations of using a fixed threshold in the prior art, this application can adjust the division threshold by combining the spatial and attribute features of the 3D points, enabling the octree division to adapt to the differences in geological features of different regions. This ensures the division precision of complex geological regions while avoiding over-division of simple regions, thereby improving the geometric accuracy of the model while reducing computational overhead, balancing the efficiency and quality of model generation, and meeting the high-precision application requirements of various fields for 3D geological and topographic profile models.

[0053] As one possible embodiment of this application, combined with Figure 2 ,like Figure 3 As shown, step 203 above can be achieved through the following steps: Step 301: For each three-dimensional point in the subspace, construct a spherical analysis domain with the three-dimensional point as the center and the radius as the preset radius.

[0054] The spherical analysis domain is used to focus on the feature information of a single 3D point and its surrounding adjacent 3D points, providing a precise local data range for subsequent feature analysis. The preset radius should be determined by comprehensively considering the density of the point cloud data and the complexity of the geological structure. For example, the preset radius can be set to 17cm to ensure sufficient coverage of adjacent 3D points while avoiding interference from irrelevant point cloud data. In practical applications, the preset radius can be adaptively adjusted according to the accuracy of the specific acquisition equipment and the geological conditions of the target area.

[0055] Step 302: Perform feature analysis on density distribution and surface roughness based on the spatial coordinates of three-dimensional points in each spherical analysis domain, and determine the necessity coefficient of geometric feature division.

[0056] Among them, the geometric feature partitioning necessity coefficient is used to quantify the partitioning requirement of subspace based on spatial geometric features. If the density distribution of three-dimensional points in the subspace is more discrete and the surface roughness is worse, it indicates that the geological structure of the region is more complex and the partitioning necessity is higher; conversely, if the density distribution of three-dimensional points in the subspace is more uniform and the surface roughness is better, it indicates that the geological structure of the region is simpler and the partitioning necessity is lower.

[0057] Therefore, this application uses feature analysis based on two dimensions: density distribution and surface roughness. By combining the analysis results of these two types of features, it can comprehensively reflect the geometric feature complexity of the subspace and provide a scientific basis for threshold adjustment.

[0058] Step 303: Based on the intensity value and spatial coordinates of the three-dimensional points in each spherical analysis domain, perform feature analysis on the significance of intensity and the importance of position, and determine the necessity coefficient of attribute feature division.

[0059] Besides geometric features, attribute features also affect subsequent modeling results. The attribute feature partitioning necessity coefficient is used to quantify the need for partitioning subspaces based on geological attribute features. If the intensity significance distribution of 3D points within a subspace is more discrete and the difference in positional importance is greater, it indicates that the differences in geological attributes in that area are more obvious, and the partitioning necessity is higher. Conversely, if the intensity significance distribution of 3D points within a subspace is more uniform and the difference in positional importance is smaller, it indicates that the differences in geological attributes in that area are less obvious, and the partitioning necessity is lower.

[0060] Among them, intensity significance reflects the importance of the geological attributes represented by the intensity value of the three-dimensional point, and location importance reflects the criticality of the three-dimensional point in the spatial structure. This application can accurately capture the differences in geological attributes within the subspace through comprehensive analysis of these two types of features.

[0061] Step 304: Determine the subspace partitioning threshold based on the necessity coefficients for geometric feature partitioning and attribute feature partitioning.

[0062] This application, through the fusion calculation of two types of partitioning necessity coefficients—geometric feature partitioning necessity coefficient and attribute feature partitioning necessity coefficient—can comprehensively consider the geometric feature complexity and geological attribute differences of the quantum space, thereby determining the optimal partitioning threshold suitable for the subspace. This subspace partitioning threshold can satisfy the fine partitioning requirements of complex regions while avoiding over-partitioning of simple regions, ensuring the rationality and efficiency of octree partitioning.

[0063] In some embodiments, this application may determine the threshold correction coefficient of the subspace based on the necessity coefficient of geometric feature partitioning and the necessity coefficient of attribute feature partitioning, and determine the subspace partitioning threshold of the subspace based on the threshold correction coefficient and the preset partitioning threshold.

[0064] For example, the threshold correction coefficient satisfies the following formula: in, This is the threshold correction coefficient for the subspace, with a value between 0 and 1. Determine the necessity coefficients for the geometric features of the subspace. The necessity coefficient for dividing the attribute features of a subspace.

[0065] The subspace partitioning threshold satisfies the following formula: in, The subspace partitioning threshold represents the subspace. The preset segmentation threshold can be adjusted based on the point cloud data density; for example, it can be 50. This is the threshold correction coefficient for the subspace. When the threshold correction coefficient is greater than 0.5, A value less than 0 indicates that the subspace partitioning threshold needs to be lowered to make the subspace easier to partition; conversely, when the threshold correction coefficient is less than 0.5, A value greater than 0 indicates that the subspace partitioning threshold needs to be increased to make the subspace more difficult to partition.

[0066] Based on the above technical solution, this application constructs spherical analysis domains and analyzes the necessity of partitioning each spherical analysis domain from two dimensions: geometric features and attribute features, ultimately determining the subspace partitioning threshold. This solution makes the threshold adjustment basis more comprehensive and logically rigorous, enabling more precise adaptation to the feature differences of different subspaces, further improving the rationality of octree partitioning, ensuring the scientific nature of the subspace partitioning threshold determination, and providing stronger guarantees for the accuracy and efficiency of subsequent model generation, allowing the model to more accurately reflect the true characteristics of geological topography.

[0067] As one possible embodiment of this application, combined with Figure 3 ,like Figure 4 As shown, step 302 above can be achieved through the following steps: Step 401: Determine the density distribution dispersion coefficient of the subspace based on the spatial coordinates of the three-dimensional points in each spherical analysis domain.

[0068] The density distribution discreteness coefficient is used to characterize the degree of dispersion of three-dimensional points in the subspace.

[0069] It should be noted that if the subspace has a discrete distribution, it will result in a high local spatial density, meaning that the local space contains too much information. This may affect geometric accuracy. For example, when modeling a detailed local region, leaf node coverage can lead to coarseness, distorted patterns, or omissions in the generated profile. Therefore, this application can evaluate the discreteness of the subspace density distribution as a basis for subsequently determining the subspace partitioning threshold for deciding whether to continue partitioning the subspace.

[0070] In one possible implementation, this application can, for each spherical analysis domain, use the number of three-dimensional points in the spherical analysis domain as the distribution density coefficient of the three-dimensional points corresponding to the center of the sphere of the spherical analysis domain based on the spatial coordinates of each three-dimensional point, and then determine the density distribution dispersion coefficient of the subspace based on the distribution density coefficient of each three-dimensional point in the subspace.

[0071] For example, a larger distribution density coefficient indicates a denser point cloud around the 3D point, and vice versa. This application can use the variance of the distribution density coefficient of each 3D point in the subspace, and normalize the variance (e.g., by normalizing the maximum and minimum values), as the density distribution dispersion coefficient of the subspace. A smaller variance of the distribution density coefficient of each 3D point in the subspace indicates a more uniform distribution of nodes within the subspace, and a smaller density distribution dispersion coefficient. Conversely, a larger variance of the distribution density coefficient of each 3D point in the subspace indicates a more discrete distribution of nodes within the subspace, and a larger density distribution dispersion coefficient.

[0072] Step 402: Determine the surface roughness coefficient of the subspace based on the spatial coordinates of the three-dimensional points in each spherical analysis domain.

[0073] Among them, the surface roughness coefficient is used to characterize the roughness of the surface where the three-dimensional point is located in the subspace.

[0074] In one possible implementation, this application can perform surface fitting on the three-dimensional points in the spherical analysis domain based on the spatial coordinates of the three-dimensional points in the spherical analysis domain, thereby obtaining the surface normal vector of the three-dimensional point corresponding to the center of the sphere on the surface.

[0075] The surface normal vector reflects the spatial orientation of the surface at that point.

[0076] Then, for each spherical analysis domain, the deviation value of the surface normal vector of the three-dimensional point corresponding to the center of the sphere is determined based on the surface normal vector of each three-dimensional point in the spherical analysis domain.

[0077] The surface normal vector deviation value is used to characterize the degree of difference between the surface normal vector of the 3D point corresponding to the center of the spherical analysis domain and the surface normal vectors of other 3D points within the spherical analysis domain. This surface normal vector deviation value can be determined based on the vector difference between the surface normal vectors of the 3D point corresponding to the center of the spherical analysis domain and all other 3D points within the spherical analysis domain. A larger vector difference indicates a greater difference in orientation between the surface where the center of the sphere and the surrounding points are located, resulting in a rougher surface; conversely, a smaller difference indicates a smoother surface. For example, this application can use the difference between 1 and the absolute value of the cosine similarity between two surface normal vectors to characterize the vector difference between the two surface normal vectors.

[0078] For example, the deviation value of the surface normal vector satisfies the following formula: in, The three-dimensional point corresponding to the center of the spherical analysis domain. The deviation value of the surface normal vector indicates a one-to-one correspondence between the three-dimensional points in the subspace and the spherical analysis domain. For three-dimensional points In the corresponding spherical analysis domain, excluding three-dimensional points The number of 3D points other than those in the above three dimensions In the spherical analysis domain, excluding three-dimensional points Other three-dimensional points With three-dimensional points The cosine similarity between the surface normal vectors Characterizing the spherical analysis domain excluding three-dimensional points Other three-dimensional points With three-dimensional points The vector difference between the surface normal vectors. This is a normalization function (e.g., maximum / minimum normalization) used to normalize the surface normal vector deviation values ​​to between 0 and 1.

[0079] Thus, this application can determine the surface roughness coefficient of the subspace based on the deviation value of the surface normal vector of each three-dimensional point in the subspace.

[0080] For example, the surface roughness coefficient satisfies the following formula: in, For subspace The surface roughness coefficient, Let be the variance of the deviation values ​​of the surface normal vectors of the three-dimensional points in the subspace. The number of three-dimensional points in the subspace. For three-dimensional points The deviation value of the surface normal vector. This is a normalization function (e.g., maximum / minimum normalization) used to normalize the surface roughness coefficients to between 0 and 1. , For the weighting coefficients, satisfying The specific value can be adjusted according to the actual application scenario; for example, it can be set to... , To balance the dispersion of deviation values ​​with the overall level, the smaller the deviation value of the surface normal vector of the three-dimensional point in the subspace, and the smaller the variance value of the deviation value of the surface normal vector of the three-dimensional point in the subspace, the lower the roughness of the surface where the three-dimensional point is located in the subspace.

[0081] Step 403: Determine the necessity coefficient for geometric feature division based on the density distribution dispersion coefficient and the surface roughness coefficient.

[0082] For example, this application can use the product of the density distribution discreteness coefficient and the surface roughness coefficient as the geometric feature partitioning necessity coefficient. The larger the geometric feature partitioning necessity coefficient, the more discrete the density distribution of the subspace, the rougher the surface, the more complex the geometric features, and the higher the partitioning necessity; the smaller the geometric feature partitioning necessity coefficient, the simpler the geometric features of the subspace, and the lower the partitioning necessity.

[0083] Based on the above technical solutions, the density distribution discreteness coefficient in this application can accurately reflect the spatial distribution state of the point cloud, and the surface roughness coefficient can effectively capture the structural features of the geological surface. The combination of the two makes the analysis of geometric features more comprehensive, ensures the accuracy of subsequent threshold adjustment, and enables the octree to perform more refined division in complex geological areas and reduce unnecessary division in simple areas, further improving the accuracy and efficiency of model generation, while enhancing the feasibility of the solution.

[0084] As one possible embodiment of this application, combined with Figure 3 ,like Figure 5 As shown, step 303 above can be achieved through the following steps: Step 501: Determine the intensity significance distribution dispersion coefficient of the subspace based on the intensity values ​​and spatial coordinates of the three-dimensional points in each spherical analysis domain.

[0085] Among them, the intensity significance distribution dispersion coefficient is used to characterize the degree of dispersion of the intensity values ​​of three-dimensional points in the subspace.

[0086] It should be noted that the structural information contained in each three-dimensional point in the subspace is of different importance. For example, the three-dimensional points that represent the details of the geological profile are usually of higher importance. By further subdividing the more important three-dimensional points, the data details of these more important three-dimensional points can be enhanced. Therefore, this application can further combine the intensity values ​​of the three-dimensional points to evaluate whether further subdivision is needed.

[0087] In one possible implementation, this application can determine the intensity significance coefficient of each spherical analysis domain based on the intensity values ​​of the three-dimensional points in the spherical analysis domain according to a significance analysis algorithm. Then, the intensity significance distribution discreteness coefficient of the subspace is determined based on the intensity significance coefficients of each spherical analysis domain.

[0088] The significance analysis algorithm employs a statistical method based on local intensity differences to compare the intensity value of the center point within a spherical analysis domain with the average intensity value of all points in its neighborhood, thereby quantifying the intensity significance of that point within a local region. For example, for the center point corresponding to the spherical analysis domain, the absolute value of the difference between the center point's intensity value and the average intensity value of all three-dimensional points within the spherical analysis domain is used as the intensity significance coefficient of that center point. A larger intensity significance coefficient indicates a more significant difference between the center point's intensity value and the intensity values ​​of surrounding points, and the more critical the geological attributes it represents (such as lithological variations, differences in material density, etc.), reflecting key geological features; conversely, a smaller coefficient indicates that the geological attributes of the center point tend to be consistent with those of the surrounding area.

[0089] For example, this application can normalize the variance of the intensity significance coefficients corresponding to each spherical analysis domain (e.g., normalize the maximum and minimum values) as the intensity significance distribution dispersion coefficient of that subspace. The smaller the variance, the smaller the intensity significance difference between regions within the subspace, the more uniform the intensity value distribution, and the smaller the intensity significance distribution dispersion coefficient; the larger the variance, the greater the intensity significance difference, the more discrete the distribution, and the larger the intensity significance distribution dispersion coefficient.

[0090] Step 502: Determine the dispersion coefficient of position importance distribution based on the spatial coordinates of the three-dimensional points in each spherical analysis domain.

[0091] Among them, the position importance distribution dispersion coefficient is used to characterize the degree of dispersion of the spatial position of a three-dimensional point in the subspace.

[0092] In one possible implementation, this application can determine the positional importance coefficient of each spherical analysis domain based on the spatial coordinates of three-dimensional points in the spherical analysis domain, and then determine the positional importance distribution discreteness coefficient of the subspace based on the positional importance coefficient of each spherical analysis domain.

[0093] For example, this application can, for each spherical analysis domain, uniformly divide the spherical analysis domain into 8 statistical analysis subspaces based on the spatial coordinates of the three-dimensional points within the spherical analysis domain (the division method can refer to the node division method of an octree). Then, determine the number of three-dimensional points in each statistical analysis subspace, calculate the variance of the number of three-dimensional points in these 8 statistical analysis subspaces, and use this variance as the positional importance coefficient corresponding to the spherical analysis domain.

[0094] Subsequently, this application can normalize the variance of the positional importance coefficients corresponding to each spherical analysis domain (e.g., normalize the maximum and minimum values) as the positional importance distribution dispersion coefficient of that subspace. The larger the variance, the greater the difference in positional importance among regions within the subspace, the more discrete the spatial positional distribution of the three-dimensional points within the spherical analysis domain, the more critical the position of some regions, and the larger the positional importance distribution dispersion coefficient; the smaller the variance, the more uniform the positional distribution, the smaller the difference in positional importance, and the smaller the positional importance distribution dispersion coefficient.

[0095] Step 503: Determine the necessity coefficient for attribute feature classification based on the dispersion coefficient of intensity significance distribution and the dispersion coefficient of location importance distribution.

[0096] For example, this application can use the product of the dispersion coefficient of intensity significance distribution and the dispersion coefficient of location importance distribution as the attribute feature partitioning necessity coefficient. The larger the attribute feature partitioning necessity coefficient, the more uneven the intensity significance distribution and location importance distribution of the three-dimensional points in the subspace, the more obvious the differences in geological attributes, and the higher the partitioning necessity; the smaller the attribute feature partitioning necessity coefficient, the more consistent the geological attribute characteristics of the subspace, and the lower the partitioning necessity.

[0097] Based on the above technical solutions, the intensity significance analysis in this application can accurately identify three-dimensional points that reflect key geological attributes, and the location importance analysis can capture key locations in the spatial structure, further improving the accuracy of dynamic threshold adjustment, ensuring that the octree partitioning can accurately adapt to the differences in geological attributes of the subspace, making the generated three-dimensional geological and topographic profile model more prominent in terms of key geological features, while avoiding unnecessary computational overhead, and balancing the accuracy and generation efficiency of the model.

[0098] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for generating a three-dimensional geological and topographic profile model, characterized in that, include: Acquire three-dimensional point cloud data of the target geological area, wherein the three-dimensional point cloud data includes the spatial coordinates and intensity values ​​of each three-dimensional point; The 3D point cloud data is iteratively divided using an octree partitioning algorithm to obtain partitioned 3D point cloud data; wherein, the partitioned 3D point cloud data consists of multiple partitioned subspaces; In each iteration of partitioning, for each subspace partitioned in the current iteration, spatial features and attribute features are analyzed based on the spatial coordinates and intensity values ​​of the three-dimensional points in the subspace to determine the subspace partitioning threshold; the subspace partitioning threshold is used to determine whether the subspace is partitioned in the current iteration partitioning process; A three-dimensional geological and topographic profile model is generated based on the divided three-dimensional point cloud data.

2. The method for generating a three-dimensional geological and topographic profile model according to claim 1, characterized in that, Based on the spatial coordinates and intensity values ​​of three-dimensional points in the subspace, spatial feature and attribute feature analysis is performed to determine the subspace partitioning threshold of the subspace, including: For each three-dimensional point in the subspace, a spherical analysis domain is constructed with the three-dimensional point as the center and the radius as a preset radius; Based on the spatial coordinates of three-dimensional points in each spherical analysis domain, the density distribution and surface roughness are characterized and the necessity coefficient of geometric feature division is determined. Based on the intensity values ​​and spatial coordinates of the three-dimensional points in each spherical analysis domain, feature analysis is performed on the significance of intensity and the importance of location to determine the necessity coefficient of attribute feature division; The subspace partitioning threshold of the subspace is determined based on the necessary coefficients for partitioning the geometric features and the necessary coefficients for partitioning the attribute features.

3. The method for generating a three-dimensional geological and topographic profile model according to claim 2, characterized in that, Based on the spatial coordinates of three-dimensional points in each spherical analysis domain, the density distribution and surface roughness are characterized, and the necessity coefficients for geometric feature partitioning are determined, including: The density distribution dispersion coefficient of the subspace is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain, wherein the density distribution dispersion coefficient is used to characterize the degree of dispersion of the three-dimensional points in the subspace. The surface roughness coefficient of the subspace is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain, wherein the surface roughness coefficient is used to characterize the roughness of the surface where the three-dimensional points are located in the subspace. The necessity coefficient for dividing the geometric features is determined based on the density distribution discreteness coefficient and the surface roughness coefficient.

4. The method for generating a three-dimensional geological and topographic profile model according to claim 3, characterized in that, The density distribution discreteness coefficient of the subspace is determined based on the spatial coordinates of three-dimensional points in each spherical analysis domain, including: For each spherical analysis domain, based on the spatial coordinates of each three-dimensional point, the number of three-dimensional points within the spherical analysis domain is used as the distribution density coefficient of the three-dimensional points corresponding to the center of the spherical analysis domain. The density distribution discreteness coefficient of the subspace is determined based on the distribution density coefficient of each three-dimensional point in the subspace.

5. The method for generating a three-dimensional geological and topographic profile model according to claim 3, characterized in that, The surface roughness coefficient of the subspace is determined based on the spatial coordinates of three-dimensional points in each spherical analysis domain, including: For each spherical analysis domain, based on the spatial coordinates of the three-dimensional points in the spherical analysis domain, a surface fitting is performed on the three-dimensional points in the spherical analysis domain to obtain the surface normal vector of the three-dimensional point corresponding to the center of the spherical analysis domain on the surface. For each spherical analysis domain, the deviation value of the surface normal vector of the three-dimensional point corresponding to the center of the spherical analysis domain is determined based on the surface normal vector of each three-dimensional point in the spherical analysis domain; the deviation value of the surface normal vector is used to characterize the degree of difference between the surface normal vector of the three-dimensional point corresponding to the center of the spherical analysis domain and the surface normal vectors of other three-dimensional points in the spherical analysis domain. The surface roughness coefficient of the subspace is determined based on the deviation value of the surface normal vector of each three-dimensional point in the subspace.

6. The method for generating a three-dimensional geological and topographic profile model according to claim 2, characterized in that, Based on the intensity values ​​and spatial coordinates of the three-dimensional points in each spherical analysis domain, feature analysis is performed on the significance of intensity and the importance of location to determine the necessity coefficients for attribute feature division, including: The intensity significance distribution dispersion coefficient of the subspace is determined based on the intensity value and spatial coordinates of the three-dimensional points in each spherical analysis domain; the intensity significance distribution dispersion coefficient is used to characterize the degree of dispersion of the intensity value of the three-dimensional points in the subspace; The position importance distribution dispersion coefficient is determined based on the spatial coordinates of the three-dimensional points in each spherical analysis domain; the position importance distribution dispersion coefficient is used to characterize the degree of dispersion of the spatial positions of the three-dimensional points within the subspace; The necessity coefficient for classifying the attribute features is determined based on the dispersion coefficient of the intensity significance distribution and the dispersion coefficient of the location importance distribution.

7. The method for generating a three-dimensional geological and topographic profile model according to claim 6, characterized in that, The dispersion coefficient of the intensity significance distribution of the subspace is determined based on the intensity values ​​and spatial coordinates of three-dimensional points in each spherical analysis domain, including: For each spherical analysis domain, the intensity significance coefficient corresponding to the spherical analysis domain is determined based on the intensity values ​​of the three-dimensional points in the spherical analysis domain according to the significance analysis algorithm; The intensity significance distribution dispersion coefficient of the subspace is determined based on the intensity significance coefficient corresponding to each spherical analysis domain.

8. The method for generating a three-dimensional geological and topographic profile model according to claim 6, characterized in that, The dispersion coefficient of positional importance distribution is determined based on the spatial coordinates of three-dimensional points in each spherical analysis domain, including: For each spherical analysis domain, the positional importance coefficient corresponding to the spherical analysis domain is determined based on the spatial coordinates of the three-dimensional points in the spherical analysis domain; The discrete coefficient of the positional importance distribution of the subspace is determined based on the positional importance coefficient corresponding to each spherical analysis domain.

9. The method for generating a three-dimensional geological and topographic profile model according to claim 1, characterized in that, The 3D point cloud data is iteratively partitioned using an octree partitioning algorithm to obtain partitioned 3D point cloud data, including: Initialize the root node of the octree and use the root node as the initial node to be partitioned; the root node corresponds to the space where the three-dimensional point cloud data of the target geological region is located; For each node to be partitioned in the current iteration partitioning process, if the number of 3D points in the node to be partitioned is less than or equal to the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is determined to be a leaf node; if the number of 3D points in the node to be partitioned is greater than the subspace partitioning threshold of the node to be partitioned in the current iteration partitioning process, the node to be partitioned is divided into eight child nodes, and the eight child nodes are used as the nodes to be partitioned in the next iteration partitioning process. Repeat the iterative partitioning process until there are no more nodes to be partitioned.

10. A three-dimensional geological topographic profile model generation system, characterized in that, include: The data acquisition module is used to acquire three-dimensional point cloud data of the target geological area, wherein the three-dimensional point cloud data includes the spatial coordinates and intensity values ​​of each three-dimensional point; The iterative partitioning module is used to iteratively partition the 3D point cloud data using an octree partitioning algorithm to obtain partitioned 3D point cloud data; wherein the partitioned 3D point cloud data consists of multiple partitioned subspaces. The dynamic threshold determination module is used to determine the subspace partitioning threshold of each subspace in each iteration partitioning process by performing spatial feature and attribute feature analysis based on the spatial coordinates and intensity values ​​of the three-dimensional points in the subspace. The subspace partitioning threshold is used to determine whether the subspace is partitioned in the current iteration partitioning process. The model generation module is used to generate a three-dimensional geological and topographic profile model based on the divided three-dimensional point cloud data.

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