Method and Platform for Simulating and Mining Geological Models Based on Dynamic Boolean Operations

Abstract

The present invention discloses a method and platform for simulating and mining the excavation process of a geological model based on dynamic Boolean operations. The method includes: dividing a large-scale geological model into finite sub-spaces by spatial bucketing and establishing a corresponding hash table, and establishing an index for each sub-space based on the R-Tree; obtaining the bounding box where the excavation machine is located in real time; using the collision detection function of the R-Tree to detect the sub-space index that intersects with the bounding box; and finding the intersecting geological space model in the hash table; extracting the triangles that intersect with the bounding box and converting them into multi-scale point clouds; hierarchically dividing the multi-scale point clouds into spherical node spaces based on the Ball-Tree algorithm to construct a point cloud index in a tree-like structure; using the point cloud index to search each spherical node, and deleting the point clouds within the threshold range from the geological space model data to achieve dynamic update of the Boolean operations for the intersecting sub-spaces. The present invention improves the efficiency of dynamic Boolean operations for large-scale geological models.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional geological body dynamic modeling, and particularly relates to a method and platform for simulating the excavation process of a geological model based on dynamic Boolean operation. Background Art

[0002] The construction and operation and maintenance of underground spaces are developing towards intelligence, informatization, digitalization, etc. As one of the important digital carriers, geological models have been widely used in the analysis and visualization of actual projects. With the emergence of new digital technologies such as digital twins and construction simulation, the behavior of physical entities in geology can be simulated in the digital space, such as the tunneling process of a shield machine. This helps engineers better understand the underground geological conditions, guide the excavation process and predict possible risks. However, although significant progress has been made in simulating construction equipment such as shield machines with digital twin technology, current research and inventions mainly focus on the state of the shield machine itself and its impact on the surrounding environment. Less attention has been paid to how the space excavated by the excavation machine in the real world is mapped to the model in real time. Specifically, the excavation behavior is essentially a process of performing Boolean operations on the geological model. However, the digital twin excavation process of underground spaces often involves large-scale geological models, which poses a challenge to the efficiency of existing Boolean operations.

[0003] Although existing inventions have achieved good results in Boolean operations, there is relatively little current research and work on Boolean operations between large-scale grid models. Large-scale geological models are usually represented by a large number of small triangles, and performing Boolean operations may bring huge computational burdens and time costs, and it is difficult to balance accuracy and cost. In addition, current inventions also rarely focus on the application of Boolean operations in the field of digital twins, which is important in reflecting the construction process of underground spaces. Because a large part of the construction of underground spaces involves the excavation of geology, and the excavation situation of geology needs to be mapped to the model in real time.

[0004] Currently, many commercial 3D software is mainly applicable to the Boolean operations of regular geometric bodies, while the Boolean operations of complex geological bodies often lead to errors such as holes. The method based on Constructive Solid Geometry (CSG) can construct complex geometric forms through Boolean operations such as intersection, union, and difference of multiple simple geometric entities. However, the stability and efficiency of this method have become bottlenecks and it is difficult to meet the needs of large-scale models. The geological conditions in reality are more complex, and usually a large number of triangles are required to accurately restore the changes in geological layers, which poses higher requirements for existing Boolean operation algorithms. In the field of geological engineering, some inventions have begun to explore methods for accelerating Boolean operations for block-shaped models. At the same time, in other engineering fields, there are also inventions adopting different innovative methods to improve the efficiency of Boolean operations. However, even with these methods, it is still difficult to avoid the mesh patch intersection operation and subsequent fragment meshing steps for large-scale Boolean operations, resulting in the inability to achieve the desired fast Boolean operation effect. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method and platform for simulating the excavation process of a geological model based on dynamic Boolean operations, aiming to propose an efficient Boolean operation method to meet the real-time simulation of the digital twin excavation process of underground space.

[0006] According to the first aspect of the embodiments of the present disclosure, there is provided a method for simulating the excavation process of a geological model based on dynamic Boolean operations, the method comprising the following steps:

[0007] Use spatial bucketing to divide a large-scale geological model into finite subspaces, establish a corresponding hash table for each subspace, and establish an index for each subspace based on the R-Tree;

[0008] Obtain the bounding box where the excavation machine is located in real time; use the collision detection function of the R-Tree to detect the subspace index intersecting with the bounding box; based on the detected subspace index, find the intersecting geological space model in the hash table; extract the triangles intersecting with the bounding box from the geological space model;

[0009] Convert the triangles into multi-scale point clouds;

[0010] Based on the Ball-Tree algorithm, gradually divide the multi-scale point clouds into spherical node spaces to construct a tree-shaped point cloud index; set the threshold range for the point clouds to be deleted; use the point cloud index to search each spherical node, and delete the point clouds within the threshold range from the geological space model data to achieve dynamic update of the Boolean operation of the intersecting subspaces.

[0011] In some embodiments, the spatial bucketing specifically includes: obtaining the bounding box of the large-scale geological model; determining the size of the subspace according to the bounding box and obtaining the number of subspaces on the X, Y, and Z axes; establishing a hash table according to the subspace size and number; circularly dividing and saving the multi-dimensional data of the subspace; and saving the hash table.

[0012] In some embodiments, the K-Means algorithm is used to cluster the point cloud after Boolean operation, find the outlier points of the point cloud after Boolean operation and delete them, further optimizing the Boolean operation.

[0013] According to the second aspect of the embodiments of the present disclosure, there is provided a digital twin platform for simulating and excavating a large-scale geological model based on dynamic Boolean operation, and the digital twin platform includes:

[0014] A large-scale geological model, including the excavation location and the surrounding geological model;

[0015] A cloud server configured to implement the steps of the method for simulating and excavating a large-scale geological model based on dynamic Boolean operation.

[0016] In some embodiments, the digital twin platform further includes:

[0017] A sensor module for obtaining the real-time position information of the excavation machine;

[0018] In some embodiments, the digital twin platform further includes a dynamic rendering module for visualizing the implementation result of the cloud server in real time and updating the large-scale geological model.

[0019] In some embodiments, the communication module uses the Websocket communication protocol for data transmission and message communication between the sensor module and the cloud server.

[0020] In some embodiments, the dynamic rendering module asynchronously loads the large-scale geological model and the dynamically updated subspace through WebGL technology, and finally presents the real-time effect of the excavation on the user interface.

[0021] A method for simulating the excavation process of a geological model based on dynamic Boolean operations provided by an embodiment of the present disclosure is an efficient dynamic Boolean operation method for large-scale underground geological models based on spatial hash indexing and multi-scale point clouds, which can improve the efficiency of dynamic Boolean operations on large-scale geological models and serve as technical support for the digital twin excavation process of underground spaces. First, the large-scale geological model is divided into finite sub-spaces by using the spatial bucket algorithm, and then the spatial triangle data is efficiently managed by the R-Tree algorithm; the collision detection function of the R-Tree is used to detect and extract the sub-spaces intersecting with the excavator, and the triangles are converted into point clouds; finally, the point clouds are searched for and deleted within the threshold accuracy by the Ball-Tree algorithm and the K-Mean algorithm to complete the Boolean operation of the intersection of the excavator and the large geological model. The present invention emphasizes the analysis of the Boolean operation speed using point clouds of different scales, indicating that adjusting the accuracy of the Boolean operation can significantly improve the efficiency to adapt to different scenarios. The present invention adopts a local Boolean operation method, avoiding exhaustive intersection detection in the entire geological model. Instead, the bounding box collision method is used to quickly identify the intersecting areas, bypassing the cumbersome global intersection detection process. In addition, the present invention simplifies the operation process by adopting point cloud indexing instead of the traditional triangle difference set operation, making the Boolean operation simpler and more efficient. The digital twin platform for simulating the excavation process of a large-scale geological model based on dynamic Boolean operations provided by the present invention exhibits robustness and versatility, and can display the real-time effect of excavation in real time, which is not only applicable to underground spaces but also can be extended to other projects that require simulating excavation and drilling on large models.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0023] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0024] Figure 1 is a schematic flow chart of the method for simulating the excavation process of a geological model based on dynamic Boolean operations in an embodiment of the present invention;

[0025] Figure 2 is a schematic diagram of establishing a sub-space hash based on spatial bucketing in an embodiment of the present invention;

[0026] Figure 3 is a schematic flow chart of the R-Tree range search in an embodiment of the present invention;

[0027] Figure 4 is a flow chart of updating nodes of the R-Tree in an embodiment of the present invention;

[0028] Figure 5 It is the flowchart of the intersecting subspace triangle index in the embodiment of the present invention;

[0029] Figure 6 It is the schematic diagram of converting a subspace triangle into a multi-scale point cloud in the embodiment of the present invention;

[0030] Figure 7 It is the flowchart of the Ball-Tree range search in the embodiment of the present invention;

[0031] Figure 8 It is the schematic diagram of multi-scale point cloud indexing and deletion in the embodiment of the present invention;

[0032] Figure 9 It is the schematic diagram of clustering the point cloud after Boolean operation by the K-Means algorithm in the embodiment of the present invention;

[0033] Figure 10 It is the schematic diagram of the digital twin platform structure for the geological model simulation and excavation process based on dynamic Boolean operation in the embodiment of the present invention;

[0034] Figure 11 It is the flowchart of the method of the digital twin platform for the geological model simulation and excavation process based on dynamic Boolean operation in the embodiment of the present invention. Detailed implementation manners

[0035] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the convenience of description, only parts related to the present invention rather than all structures are shown in the drawings.

[0036] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0037] The embodiment of the present invention provides the following embodiments for a method of geological model simulation and excavation process based on dynamic Boolean operation:

[0038] Embodiment 1: A method of geological model simulation and excavation process based on dynamic Boolean operation, as Figure 1 shown, the method includes the following steps:

[0039] Step 1: Use spatial bucketing to divide the large-scale geological model into finite sub-spaces, create corresponding hash tables for each sub-space, and establish sub-space indexes based on the R-Tree;

[0040] Step 2: Obtain the bounding box where the excavation machine is located in real time; use the collision detection function of the R-Tree to detect the sub-space indexes that intersect with the bounding box; based on the detected sub-space indexes, find the intersecting geological space models in the hash table; extract the triangles that intersect with the bounding box from the geological space models;

[0041] Specifically, the core data structure of the R-Tree is a tree structure, where each node represents a large bounding box, and the child nodes are small bounding boxes inside the large bounding box, and so on until the nodes of the created tree meet the requirements. Through this algorithm, the sub-space models after spatial bucketing are organized, and it is known which node in the tree each sub-space is in. When performing collision detection, the search function of the R-Tree will be used to calculate whether the bounding box of the excavation machine intersects with the sub-space. If they intersect, it means that there is a collision between the excavation machine and the sub-space of the geological model. At this time, the child node index in the R-Tree can be taken out, and this index is also the index of the sub-space in the hash table.

[0042] Step 3: Convert the triangles into multi-scale point clouds;

[0043] Step 4: Based on the Ball-Tree algorithm, gradually divide the multi-scale point clouds into spherical node spaces to construct a tree-shaped point cloud index; set the threshold range for the point clouds to be deleted; use the point cloud index to search each spherical node, and delete the point clouds within the threshold range from the geological space model data to achieve dynamic update of the Boolean operation of the intersecting sub-spaces.

[0044] To simplify the complexity of the Boolean operation as much as possible and limit it to a small range to meet the real-time visualization requirements at a specific frequency. It is necessary to manage the multi-dimensional space data of the large-scale geological model in detail. To effectively manage these data, a variety of technologies can be comprehensively used, including spatial bucketing and spatial indexing.

[0045] Spatial bucketing is a method of spatial data management that divides spatial data into different sub-spaces, making the data in each sub-space more independent, and creating corresponding hash tables for each sub-space, which can more effectively locate and retrieve the data of each sub-space. As Figure 2 shown, a large space is divided into multiple small spaces for storage, and then located and retrieved based on the hash table. In the large-scale geological model, this helps to quickly and accurately obtain data of specific regions or attributes in complex geological structures, improving the efficiency of query operations.

[0046] Specifically, spatial bucketing generally can be divided into five steps: obtaining the bounding box of a large-scale geological model; determining the size of the subspaces according to the bounding box and obtaining the number of subspaces on the X, Y, and Z axes; establishing a hash table according to the subspace size and number; loop-dividing and saving the multi-dimensional data of the subspaces; saving the hash table.

[0047] Specifically, establish indexes for each subspace based on the R-Tree. The R-Tree is a balanced tree-like data structure for spatial data indexing and is widely used in geographic information systems and database management systems to achieve range indexing, nearest neighbor indexing, and spatial join query indexing for multi-dimensional spatial data. In a large-scale geological model, this helps to more quickly locate and access the required spatial data during the Boolean operation process, thereby accelerating the operation process and improving the calculation efficiency. Generally, using the R-Tree for efficient indexing includes processes such as construction, search, and deletion. Figure 3 This is a process of R-Tree range search, where the shaded area is the query bounding box, and inside the query bounding box is the path to find all intersecting leaf nodes. It can be found that the shaded area intersects with the root node and At this time, there will be two paths. Taking path ① as an example, first traverse the child nodes in the root node to find the child node that intersects with the shaded area , and then traverse the child node to find the leaf nodes that intersect with the shaded area , , . Taking Figure 4 as an example, first find the leaf node to be deleted, then update the child nodes and the root node, and finally obtain the new R-Tree and the original data.

[0048] Adopt spatial data management methods such as spatial bucketing and R-Tree to effectively manage the multi-dimensional spatial data of large-scale geological bodies. The introduction of this method has greatly improved the efficiency of data indexing and provides key support for mining machines to perform dynamic Boolean operations with geological layers. Before the mining machine performs dynamic Boolean operations with the geological layer, the primary task is to efficiently index the data of the intersecting subspaces. To achieve this goal, the embodiment designs a detailed intersecting subspace triangle indexing process, as Figure 5As shown. Specifically, first, the bounding box of the excavation machine at a specific moment was obtained. Then, the collision detection function of the R-Tree was used to quickly and accurately detect the subspace indices that intersect with the bounding box. This step helps to narrow down the search range, thereby improving the overall indexing efficiency. Subsequently, based on these indices, the intersecting geological space models were found in the pre-constructed hash table. This step is to accurately locate the data containing the required information and make full preparations for the subsequent Boolean operations. Finally, the triangles that intersect with the bounding box were extracted from the intersecting geological space models, and these triangles will become the key elements of the subsequent Boolean operations.

[0049] Convert the subspace triangles into multi-scale point clouds. Specifically, due to the geometric complexity and data scale, performing Boolean operations between spatial triangles is a challenging task. Handling various different geometric relationships requires considering the mutual relationships between edges and edges, vertices and vertices, involving complex geometric operations such as intersection, union, and difference. In this case, performing Boolean operations on each pair of triangles will result in a significant increase in the computational amount, thus affecting the computational efficiency. In contrast, the dimension of points is lower and the logic is simpler, and calculating the relationships between points is more intuitive. The Boolean operation between points only needs to determine whether they are equal or within a certain distance range. Therefore, when dealing with large-scale and complex geometric data, choosing the operation method between points may be more efficient. However, although the Boolean operation between points is relatively simple, as the number of points increases, not only will the efficiency of the Boolean operation decrease, but the data volume of the subspace model will also increase. Therefore, it is necessary to convert the triangles into point clouds of different scales according to the Boolean operation accuracy required by the scenario, as Figure 6 shown. Different scales refer to the distance between point clouds. The smaller the scale, the more it can represent the spatial relationship of the original triangle. To maximize the use of the triangles restored by the point clouds, the embodiments of the present invention adopt uniformly growing point clouds inside the triangles.

[0050] Build a point cloud index based on Ball-Tree. Ball-Tree is a tree-like data structure for indexing spatial data, mainly applied to the analysis of point sets or objects in high-dimensional spaces, such as clustering and proximity search. By using hyperplanes or decomposing all data points into two clusters. This plane is often called a hyperplane, and each cluster represents two nodes of the tree. Generally, the efficient indexing using Ball-Tree includes processes such as construction and search. Figure 7 This is a process of Ball-Tree range search, where the pentagram is the query point, the circle is the threshold circle of the query point, and the arrow is the path to find all intersecting leaf nodes.

[0051] The dynamic Boolean operation of large-scale geological space models requires finding the triangles where the excavation machine intersects with the subspace models, converting them into multi-scale point clouds according to the scenario requirements, then indexing the point clouds within the threshold range and deleting them. As Figure 8 shown. First, the multi-scale point clouds are hierarchically divided into spherical node spaces through the Ball-Tree algorithm to construct an efficient tree structure for quickly retrieving the point clouds within a specific threshold range. Then, the threshold range is defined, that is, the numerical range of the point clouds that need to be retained or deleted is determined. This threshold range can be set based on specific application requirements and the characteristics of the geological space model. Next, using the established Ball-Tree index, each spherical node is searched to find the point clouds that fall within the specified threshold range. Finally, the intersecting point clouds obtained from the search are deleted from the geological space model data to achieve the effect of Boolean operation.

[0052] Furthermore, the K-Means algorithm is used to cluster the point clouds after Boolean operation, find the outliers in the point clouds after Boolean operation and delete them to further optimize the Boolean operation. Specifically, although the Ball-Tree can search for most of the point clouds within the threshold and delete them from the growing point cloud set, due to the limitations of spherical search, there are some situations that cannot be excluded, such as the points between spheres. Even after performing range search and deletion on all the point clouds of the excavation machine, there may still be some redundant outliers left. This situation often occurs in point cloud processing, so additional measures need to be taken to improve the quality of the point clouds after Boolean operation. In the process of solving this problem, the embodiments of the present invention introduce the K-Means algorithm to cluster the point clouds after Boolean operation, as Figure 9 shown. The clustering process divides the point clouds after Boolean operation into two categories, where the category with a smaller number is identified as outliers, and the category with a larger number is the point clouds that need to be retained. By introducing K-Means clustering, it is ensured that the redundant outliers are excluded from the Boolean operation results, and finally a more perfect large-scale geological space Boolean operation model is formed.

[0053] Another embodiment is used to illustrate a digital twin platform for simulating the excavation process of a large-scale geological model based on dynamic Boolean operation, as Figure 10 shown. The digital twin platform includes:

[0054] A large-scale geological model, including the excavation location and the surrounding geological model;

[0055] A cloud server, configured to implement the steps of the method for simulating the excavation process of a large-scale geological model based on dynamic Boolean operation described in Embodiment 1.

[0056] In some embodiments, the digital twin platform further includes:

[0057] A sensor module for obtaining the real-time position information of the excavation machine;

[0058] Preferably, the digital twin platform further includes a dynamic rendering module for real-time visualizing the implementation results of the cloud server and updating the large-scale geological model.

[0059] Preferably, the communication module uses the Websocket communication protocol for data transmission and message communication between the sensor module and the cloud server.

[0060] Preferably, the dynamic rendering module asynchronously loads the large-scale geological model and the dynamically updated subspace through WebGL technology, and finally presents the real-time excavation effect on the user interface.

[0061] In order to improve the efficiency of large-scale geological dynamic Boolean operations to meet the requirement of realizing near-real-time visualization of the excavation process in the digital twin platform. In this process, the real-time position of the excavation machine in the actual scene is transmitted back to the digital twin platform through feedback, and at the same time, Boolean operations are performed on the large-scale geological model at the corresponding position in the virtual space. This not only provides support for the real-time monitoring of the excavation machine but also helps to deeply analyze the characteristics and structure of the geological layers. However, achieving this goal faces a series of challenges, such as insufficient Boolean operation accuracy resulting in residual triangles and the large-scale triangle representation of the surface causing time-consuming. To solve these problems, the embodiment first adopts efficient algorithms such as spatial bucketing, hash tables, R-Trees, and Ball-Trees to efficiently manage the multi-dimensional spatial data in the large-scale geological model. Subsequently, the logical calculation speed of the Boolean operation is increased through multi-scale point clouds, and finally, the original large-scale geological model and the real-time updated sub-model are asynchronously loaded through WebGL.

[0062] Specifically, as Figure 11As shown, the digital twin platform for simulating the excavation process of large-scale geological models based on dynamic Boolean operations involves obtaining the real-time position information of excavation machines from the actual excavation scenario and transmitting this information to the digital twin platform, which involves the acquisition and processing of sensor data to ensure the accuracy of the real-time position. At the same time, the original large-scale geological space model also needs to be loaded into the platform to prepare for subsequent dynamic Boolean operations. In the Boolean operation part in the cloud server, a method of comprehensively applying spatial bucketing, indexing, and multi-scale point clouds is adopted. First, the multi-dimensional spatial data of the large-scale geological model is efficiently managed through spatial bucketing and indexing techniques to quickly locate the subspace that needs to perform Boolean operations. Subsequently, the logical calculation of the intersection area is carried out through multi-scale point cloud technology, thereby improving the operation efficiency. The goal of this step is to complete the Boolean operation on the geological model in the cloud server to obtain accurate excavation results. In this process, considering the importance of data transmission and message communication, the Websocket communication protocol and other technologies are used to ensure the timely transmission and storage of the real-time position of the excavation machine and the relevant information of the geological model. Finally, in the dynamic rendering part of the digital twin platform, the results of the Boolean operation and the updated geological model are transmitted to the front end to achieve real-time visualization. Through technologies such as WebGL, the digital twin platform can asynchronously load the original large-scale geological space model and the dynamically updated subspace model, thus presenting the real-time effect of excavation on the user interface.

[0063] In addition to the above modules, the digital twin platform for simulating the excavation process of large-scale geological models based on dynamic Boolean operations may also include other components. However, since these components are not related to the content of the embodiments of the present disclosure, their illustrations and descriptions are omitted here.

[0064] For the other specific working processes of the digital twin platform for simulating the excavation process of large-scale geological models based on dynamic Boolean operations, refer to the description of Method Embodiment 1 for simulating the excavation process of large-scale geological models based on dynamic Boolean operations above, and details will not be repeated.

[0065] Based on the technical solutions provided in the above embodiments, a method for simulating the excavation process of a geological model based on dynamic Boolean operations is an efficient dynamic Boolean operation method for large-scale underground geological models based on spatial hashing indexes and multi-scale point clouds, which can improve the efficiency of dynamic Boolean operations on large-scale geological models and serve as technical support for the digital twin excavation process of underground spaces. First, the large-scale geological model is divided into finite sub-spaces by using the spatial bucket algorithm, and then the spatial triangle data is efficiently managed by the R-Tree algorithm; the R-Tree's collision detection function is used to detect and extract the sub-spaces intersecting with the excavator, and the triangles are converted into point clouds; finally, the Ball-Tree algorithm and the K-Mean algorithm are used to search for and delete the point clouds within the threshold accuracy, completing the Boolean operation of the intersection of the excavator and the large geological model. The present invention emphasizes the analysis of the Boolean operation speed using point clouds of different scales, indicating that adjusting the accuracy of the Boolean operation can significantly improve the efficiency to adapt to different scenarios. The present invention adopts a local Boolean operation method, avoiding exhaustive intersection detection in the entire geological model. Instead, the bounding box collision method is used to quickly identify the intersecting areas, bypassing the cumbersome global intersection detection process. In addition, the present invention simplifies the operation process by using point cloud indexing instead of the traditional triangle difference set operation, making the Boolean operation simpler and more efficient. The digital twin platform for simulating the excavation process of large-scale geological models based on dynamic Boolean operations provided by the present invention exhibits robustness and versatility, can display the real-time excavation effect in real time, and is not only applicable to underground spaces but also can be extended to other projects that require simulating excavation and drilling on large models.

[0066] In this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a step or method including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such step or method.

[0067] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for simulating and mining the process of a large-scale geological model based on dynamic Boolean operations, characterized in that The method includes the following steps: Using spatial bucketing to divide a large-scale geological model into finite sub-spaces, establishing corresponding hash tables for each sub-space, and building indexes for each sub-space based on the R-Tree. The process of R-Tree range search includes setting the shadow area as the query bounding box, finding all root nodes within the query bounding box, traversing the child nodes in the root nodes, obtaining the child nodes that intersect with the shadow area, and traversing the child nodes that intersect with the shadow area to find the leaf nodes that intersect with the shadow area. After finding the leaf nodes to be deleted, update the child nodes and root nodes to obtain a new R-Tree and the original data; Obtaining the bounding box where the excavation machine is located in real time; using the collision detection function of the R-Tree to detect the sub-space indexes that intersect with the bounding box; based on the detected sub-space indexes, finding the intersecting geological space models in the hash table; extracting the triangles that intersect with the bounding box from the geological space models; converting the triangles into multi-scale uniform growth point clouds, where different scales represent the distance between point clouds and are inversely proportional to the spatial relationship with the original triangles; Based on the Ball-Tree algorithm, hierarchically dividing the point cloud into spherical node spaces to construct a tree-structured point cloud index; setting the threshold range for the point cloud to be deleted; using the point cloud index to search each spherical node, and deleting the point cloud within the threshold range from the data of the geological space model to achieve dynamic update of the intersecting sub-space Boolean operation; Using the K-Means algorithm to cluster the point cloud after the Boolean operation, finding and deleting the outliers of the point cloud after the Boolean operation to further optimize the Boolean operation.

2. The method for simulating and mining the process of a large-scale geological model based on dynamic Boolean operations according to claim 1, wherein The spatial bucketing specifically includes: obtaining the bounding box of the large-scale geological model; determining the size of the sub-space according to the bounding box and obtaining the number of sub-spaces on the X, Y, and Z axes; establishing a hash table according to the sub-space size and number; circularly dividing and saving the multi-dimensional data of the sub-space; saving the hash table.

3. A digital twin platform for simulating and mining the process of a large-scale geological model based on dynamic Boolean operations, characterized in that, The digital twin platform includes: A large-scale geological model, including the excavation location and the surrounding geological model; A cloud server configured to implement the steps of the method for simulating the excavation process of a large-scale geological model based on dynamic Boolean operation according to any one of claims 1-2.

4. The digital twin platform for simulating and mining the process of large-scale geological models based on dynamic Boolean operations according to claim 3, characterized in that, The digital twin platform further includes: A sensor module for obtaining the real-time position information of the excavation machine; A communication module for data transmission and message communication between the sensor module and the cloud server.

5. The digital twin platform for simulating and mining the process of large-scale geological models based on dynamic Boolean operations according to claim 3, characterized in that The digital twin platform further includes a dynamic rendering module for visualizing the implementation results of the cloud server in real time and updating the large-scale geological model.

6. The digital twin platform for simulating the excavation process of a large-scale geological model based on dynamic Boolean operations according to claim 4, characterized in that, The communication module uses the Websocket communication protocol for data transmission and message communication between the sensor module and the cloud server.

7. The digital twin platform for simulating and mining the process of large-scale geological models based on dynamic Boolean operations according to claim 5, characterized in that The dynamic rendering module asynchronously loads the large-scale geological model and the dynamically updated sub-spaces through WebGL technology, and finally presents the real-time effect of the excavation on the user interface.

Images