Method for fusing three-dimensional geological model and WebGIS (Web Geographic Information System)

Abstract

The invention relates to the technical field of geological analysis, and discloses a method for fusing a three-dimensional geological model and a WebGIS (Web Geographic Information System), aiming at solving the problems of poor data intercommunity, limited spatial analysis capability, difficulty in real-time updating and low model loading efficiency of the existing method, and the scheme mainly comprises the following steps: extracting geological model data, and carrying out standardized format processing; lightweight processing is carried out, and geological attributes and model nodes are bound one by one; the format is converted into a binary format embedded with a metadata label, and data integrity verification and automatic error correction are carried out in the conversion process; constructing a non-uniform adaptive octree spatial index, a multi-level attribute data architecture and a real-time updating mechanism of attribute data; after the geological model and the terrain are precisely registered and fused, a dynamic loading mechanism, a high-performance rendering mechanism and a geological model real-time updating mechanism are constructed, and a three-dimensional geological analysis tool is integrated. According to the method, efficient loading, real-time rendering, dynamic updating and spatial analysis of the three-dimensional geologic model in the WebGIS are realized.

Description

Technical Field

[0001] The present invention relates to the field of geological analysis technology, and in particular to a method for fusing a three-dimensional geological model with WebGIS. Background Art

[0002] The development of 3D geological modeling technology has driven the refined representation and analysis of underground spaces. Mainstream geological modeling software, such as GoCAD and Petrel, can construct high-precision 3D geological models based on geological exploration data such as drillholes and profiles, accurately depicting the subsurface stratigraphic structure, lithologic distribution, and structural characteristics. Geological models contain rich geometric, topological, and attribute information, serving as a crucial basis for geological analysis and decision-making.

[0003] At the same time, the rise of WebGIS technology has revolutionized the online sharing and application of geographic information. Lightweight, browser-based GIS presents vast amounts of geographic data, enabling core GIS functions such as map browsing, spatial querying, and geographic analysis, significantly promoting the widespread application of geographic information. Integrating 3D geological models into WebGIS promises to enable online publication, professional analysis, and decision-making support for geological information. However, due to factors such as differences in data formats, high model complexity, and high real-time requirements, the integration of 3D geological models into WebGIS remains technically challenging.

[0004] In existing technologies, the integration of 3D geological models with WebGIS typically involves developing specialized data conversion tools to convert proprietary data generated by geological modeling software into a universal 3D format. The converted model files are then loaded directly into the WebGIS platform for display. This approach has the advantages of being simple to implement and leveraging the rendering capabilities of 3D engines (such as Cesium and three.js) to quickly visualize geological models on the Web. However, it has the following drawbacks:

[0005] (1) Data conversion losses are large, resulting in incomplete information. While the traditional universal 3D format is convenient for Web display, important information such as the geological model's topological structure and attribute fields is easily lost during the data conversion process. This is because the universal format is mainly oriented towards the general expression of geometric shapes and lacks specialized support for geological semantics. This information loss directly affects subsequent geological analysis and applications.

[0006] (2) The geometric meshing is simple, which is not conducive to rendering acceleration. Common progressive mesh generation algorithms are mostly based on the geometric characteristics of the model (such as curvature), while ignoring the distribution patterns of geological attributes. This results in the generated multi-level detail model being discontinuous in geological semantics, affecting rendering quality. In addition, the meshing granularity is difficult to dynamically adjust according to the actual geological conditions, resulting in poor rendering acceleration.

[0007] (3) Lack of spatial indexing leads to low query and analysis efficiency. Existing solutions such as direct loading and progressive transmission typically treat geological models as simple geometric objects and lack spatial indexing for geological objects. This makes it difficult to quickly retrieve model data in WebGIS, severely restricting the real-time response capabilities of complex geological analyses.

[0008] (4) Irrational data organization and difficulty in updating. To facilitate web display, geological models are organized using simple linear file formats (such as JSON). This disorganized data structure is not conducive to local modification and dynamic updating. When the geological interpretation changes, the entire model data often needs to be reprocessed.

[0009] (5) Lack of integration mechanism and weak analytical capabilities. Most solutions simply overlay geological models onto WebGIS, lacking integration with GIS data such as terrain and imagery. Geological elements are difficult to participate in spatial analysis and query operations in WebGIS, and the powerful analytical capabilities of GIS cannot be fully utilized. It is difficult for users to conduct professional geological analysis directly in the Web environment. Summary of the Invention

[0010] The present invention aims to solve the problems of poor data interoperability, limited spatial analysis capability, difficulty in real-time updating and low model loading efficiency in existing methods for integrating three-dimensional geological models with WebGIS, and proposes a method for integrating three-dimensional geological models with WebGIS.

[0011] The technical solution adopted by the present invention to solve the above technical problems is:

[0012] A method for integrating a three-dimensional geological model with WebGIS, the method comprising:

[0013] Acquiring a geological model data file, determining the type of the geological model data file and its corresponding data processing strategy based on a pre-built format feature library, extracting geological model data from the geological model data file according to the data processing strategy, and performing standardized format processing on the geological model data, wherein the geological model data includes geometric data, topological data, attribute data, and spatial reference data;

[0014] Lightweight processing is performed on the geological model data after being processed in a standardized format, and geological attributes are bound to model nodes one by one; the geological model data after being lightweight is converted into a binary format embedded with metadata tags, and data integrity verification and automatic error correction are performed during the conversion process, wherein the data integrity verification includes at least vertex coordinate range verification, triangular mesh topological relationship verification, and attribute value valid range verification;

[0015] The geological model data after lightweight processing is spatially divided, a non-uniform adaptive octree spatial index is constructed, and after classifying the attribute data in the geological model data, a standardized multi-level attribute data architecture based on the JSON structure and a real-time update mechanism for attribute data are constructed;

[0016] The geological model corresponding to the geological model data is accurately aligned with the terrain, and after fusing the geological model with the remote sensing image, a dynamic loading mechanism, a high-performance rendering mechanism, and a real-time update mechanism for the geological model are constructed, and three-dimensional geological analysis tools are integrated.

[0017] Furthermore, the geological model data processed in the standardized format is lightweighted, including:

[0018] Calculate the importance score of each model grid corresponding to the geological model data, eliminate the model grids with importance scores below the scoring threshold, and then use texture atlas technology to integrate textures of the same type or areas with similarity greater than the threshold into the same texture atlas, and apply texture compression algorithm to compress the corresponding texture files;

[0019] The calculation formula of the importance score is as follows:

[0020] R=w1C+w2D+w3G;

[0021] Among them, R represents the importance score, C represents the average curvature change rate of the model grid vertex area, D represents the density of geological edge vertices, G represents the amplitude of the geological attribute gradient change, and w1, w2 and w3 are the corresponding weight coefficients.

[0022] Furthermore, an octree space partitioning structure is constructed, including:

[0023] According to the spatial coordinate range of the geological model corresponding to the geological model data, an initial space box is constructed. The initial space box is the three-dimensional space range that envelops the entire geological model. An octree space partitioning structure is constructed according to the initial space box, and a spatial index and a relationship index are established.

[0024] Furthermore, an octree space partitioning structure is constructed based on the initial space box, including:

[0025] Divide the initial space box into 8 space boxes along the midpoints of the three coordinate axes to form the first layer of octree nodes;

[0026] Repeatedly evaluate the complexity of each space box based on the internal characteristics of the geological body, and determine whether to further subdivide the space box based on the complexity;

[0027] A recursive method is used to continuously subdivide the space box until the adaptive termination condition is met, and finally a non-uniform space division structure is formed.

[0028] Furthermore, spatial indexes and relational indexes are established, including:

[0029] Calculate the 3D center coordinates of each spatial box, normalize them, perform bit interleaving, convert them into Morton code, and establish a fast mapping relationship between the one-dimensional index and the spatial box.

[0030] Record the spatial adjacency relationship between each space box and the adjacent space boxes, specially mark the key geological structures, build a topological index, mark the geological contact relationship between adjacent geological units in the index table of the space box, and build a cross-scale LOD index link to record the parent-child relationship between space boxes at different LOD levels.

[0031] Furthermore, a standardized multi-level attribute data architecture based on JSON structure is constructed, including:

[0032] Design attribute layers according to the types of attribute data. The attribute layers include: basic attribute layer, extended attribute layer and derived attribute layer;

[0033] Based on the GeoJSON specification, the attribute data of each attribute layer is organized, the properties of GeoJSON are extended, and a unified JSON data template is designed.

[0034] Furthermore, the geological model corresponding to the geological model data is accurately aligned with the terrain, including:

[0035] According to the spatial coordinate range of the geological model, the model center coordinates are selected as the initial positioning point. Based on the geological model and terrain DEM elevation data, the bottom elevation benchmark of the geological model is preliminarily determined and roughly positioned in the WebGIS scene.

[0036] Based on the rough positioning, the model posture and position are fine-tuned according to the rotation parameters, translation parameters and scaling parameters obtained from the GIS interface;

[0037] The base range of the geological model was defined in WebGIS, and a 3D collision detection algorithm was used to quickly identify the spatial boundary between the geological model and the terrain model. A spatial interpolation algorithm was then used to construct a smooth transition surface at the boundary between the model and the terrain.

[0038] Histogram equalization is used to enhance the contrast of remote sensing images, and the scale-invariant feature transformation algorithm is used to obtain the key feature points in the remote sensing images and calculate the corresponding feature descriptors.

[0039] Automatically extract feature points on the surface projection of the geological model, and use the scale-invariant feature transformation feature point extraction algorithm to obtain the three-dimensional spatial coordinates and feature descriptors of the feature points on the surface of the geological model;

[0040] A fast nearest neighbor search algorithm is used to preliminarily match the feature points of the remote sensing image and the geological model, and a random sampling consistency algorithm is used to automatically filter out mismatched feature points and eliminate feature point pairs with large errors.

[0041] Based on the screened feature point pairs, the least squares method is used to optimize and calculate the affine transformation parameters. The remote sensing image and the geological model are accurately spatially aligned according to the affine transformation parameters. After alignment, the transparency grading technology and edge enhancement algorithm are used to display the spatial correspondence between the geological model and the remote sensing image.

[0042] Furthermore, the dynamic loading mechanism includes: determining the LOD level of the geological model, and determining the loading level of the geological model according to the LOD level. The calculation formula of the LOD level is as follows:

[0043] L=α·d -1 +β·e+γ·g+δ·i;

[0044] Where L represents the LOD level, d represents the distance from the viewpoint to the geological body, e represents the screen projection error estimate, g represents the complexity coefficient of the geological body, i represents the weight of the user's area of ​​interest, and α, β, γ, and δ represent the corresponding weight coefficients respectively;

[0045] The high-performance rendering mechanism includes: based on WebGL technology, using InstancedMesh instantiation batch rendering.

[0046] Furthermore, the real-time update mechanism of the attribute data includes differential real-time update and model update conflict handling;

[0047] The real-time differential update includes: dividing the geological model space into multiple spatial sub-blocks, calculating the unique hash value of the geometric coordinates, topological relationship and attribute data of each spatial sub-block, realizing rapid encoding of the model space state, and automatically recalculating and comparing the hash value of each spatial sub-block when the model data is updated, locating the changed area, and automatically extracting the newly added, deleted or modified key geometric nodes and attribute data in the changed area, generating incremental differential data, and pushing it to the client in real time via a WebSocket long connection. After receiving the differential data, the client uses a local refresh technology to only update the data cache and three-dimensional visualization content of the corresponding spatial area;

[0048] The model update conflict handling includes: when the client submits updated data, the server first checks whether the version number of the updated data submitted by the client is consistent with the current latest version number. If inconsistent, it is determined to be a data conflict, and a visual conflict handling interface is provided through the WebGIS platform to display the conflict area and specific conflict content.

[0049] Furthermore, the three-dimensional geological analysis tool is used for virtual drilling analysis, custom sectioning, model explosion analysis, real-time interactive query of attribute data, and rolling curtain analysis.

[0050] The beneficial effects of the present invention are as follows: the method for integrating a three-dimensional geological model with WebGIS is compatible with mainstream geological modeling software formats at home and abroad (such as GOCAD and Petrel), based on the automatic recognition of a format feature library, thereby improving the efficiency of accessing multi-source heterogeneous data; the standardized processing flow achieves unified expression of data in different formats, and adopts methods such as adaptive hierarchical analysis and reversible compression to fully preserve the geometry, topology, and attribute information of the geological model, ensuring that the geological professional semantics are not distorted, and providing a high-quality data foundation for subsequent geological analysis; the three-level integrity verification mechanism ensures model accuracy, and the automatic error correction function can repair common data defects, reducing the cost of manual intervention; The non-uniform adaptive octree index realizes LOD dynamic scheduling, achieves efficient organization, retrieval, transmission and display of geological model data, improves model loading efficiency and the smoothness and visual realism of model roaming; the real-time update mechanism of JSON attribute architecture and attribute data can realize rapid update of the model according to changes in local geological interpretation, support real-time application needs such as engineering monitoring and dynamic resource assessment, and improve the dynamic nature of geological data management; through deep integration with WebGIS, the spatial analysis advantages of GIS are fully utilized, and three-dimensional geological analysis tools are integrated, so that geologists can perform analysis directly in the Web environment, greatly expanding the depth of WebGIS industry applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a method for integrating a three-dimensional geological model with WebGIS provided in an embodiment;

[0052] Figure 2 A schematic diagram of the principle framework of the method for integrating a three-dimensional geological model with WebGIS provided in the embodiment;

[0053] Figure 3 A schematic diagram showing the effect of integrating the 3D geological model provided in the embodiment into a WebGIS scene;

[0054] Figure 4 A schematic diagram illustrating the effect of the virtual drilling analysis function provided in the embodiment;

[0055] Figure 5 A schematic diagram showing the effect of the custom cutting function provided in the embodiment;

[0056] Figure 6 A schematic diagram showing the effect of the model explosion analysis function provided in the embodiment;

[0057] Figure 7This is a schematic diagram of the effect of the real-time interactive query function of attributes provided in the embodiment. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0059] Some of the processes described in the specification of the present invention and the figures above include multiple operations that appear in a specific order. However, it should be understood that these operations may not be performed in the order in which they appear herein or may be performed in parallel. The sequence numbers of the operations are merely used to distinguish between different operations and do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0060] This invention aims to provide an efficient, dynamic, and scalable method for integrating 3D geological models with WebGIS, addressing issues such as poor data interoperability, limited spatial analysis capabilities, difficulty in real-time updates, and inefficient model loading. This method enables efficient loading, real-time rendering, dynamic updating, and spatial analysis of 3D geological models in WebGIS, improving the management and application of geological information in WebGIS systems.

[0061] Before actual application, it is necessary to deploy the system and provide security guarantees to provide operational support, including: (1) Front-end interface development: Using the Cesium.js and Three.js fusion frameworks, build a responsive front-end platform to display the model data loading status in real time. (2) Back-end service deployment: Using a distributed load balancing architecture, the server implements a RESTful API to support high-concurrency access; using Redis distributed cache technology to improve model access efficiency. (3) Security mechanism: The data interface supports the JWT authentication mechanism to ensure data access security; sensitive data is encrypted and stored using AES-256, and data transmission uses the TLS1.3 encryption protocol.

[0062] Figure 1 A flow chart showing a method for integrating a 3D geological model with WebGIS is shown in Figure 1 , the method comprises the following steps:

[0063] Step 1: Obtain a geological model data file, determine the type of the geological model data file and its corresponding data processing strategy based on a pre-built format feature library, extract geological model data from the geological model data file according to the data processing strategy, and perform standardized format processing on the geological model data.

[0064] See also Figure 2This embodiment automatically identifies and parses geological model data files of different formats based on a multi-layer parsing framework. The multi-layer parsing framework includes three submodules: a format recognition layer, a data extraction layer, and a standard conversion layer.

[0065] The format recognition layer collects typical features of files generated by mainstream geological modeling software such as GoCAD, Petrel, and Leapfrog, including file header identifiers, version information, data segment start markers, and structural metadata, to establish a standardized format feature library. The layer then analyzes the geological model data files to be processed, extracts the file header byte sequence, and matches the patterns in the feature library byte by byte to determine the data file type and corresponding processing strategy.

[0066] The data extraction layer extracts geometric data, topological data, geological attributes and spatial reference information from geological model data in a hierarchical manner by designing a dedicated data parsing engine, ensuring that the original characteristics of the model are fully preserved during the parsing process.

[0067] In this embodiment, the geological model data includes geometric data, topological data, attribute data and spatial reference data. Among them, geometric data extraction includes: using a line-by-line or segmented parsing method to accurately extract vertex coordinates and triangular mesh index data from the original file to establish a standard geometric data structure. Topological relationship extraction includes: according to the topological characteristics of the geological body, parsing and extracting the topological relationship between strata, such as the contact relationship, adjacency relationship, and inclusion relationship of the layers, and establishing a topological map structure for comprehensive recording and preservation. Attribute data extraction includes: according to the attribute definition format of the geological professional software, automatically identifying and extracting lithology categories, age information, physical and mechanical properties, etc. Spatial reference information extraction: extracting geographic coordinate system, projection method, spatial transformation parameters, etc. to ensure the accuracy of spatial positioning.

[0068] Using universal 3D data formats (such as FBX) as an intermediate format, a unified intermediate data format conversion engine is developed to convert geological model data into a widely compatible standard format. This accurately preserves the geometric details, topology, and attribute information of the geological model. Topology-preserving algorithms (such as half-edge data structures and boundary tracking algorithms) are utilized to ensure that spatial relationships between geological volumes are not distorted during the conversion process.

[0069] The standardized data generated by the conversion includes four types of structured data: geometry, topology, attributes, and spatial reference. Its data organization and metadata description specifications provide a feature extraction benchmark for lightweight models, ensuring that models generated by different geological software can be efficiently loaded and used in a WebGIS environment. The topology verification log generated during the conversion process will serve as a basis for version comparison during subsequent model updates.

[0070] This embodiment adopts methods such as adaptive hierarchical analysis and reversible compression to fully preserve the geometric, topological and attribute information of the geological model, ensure that the geological professional semantics are not distorted, and provide a high-quality data foundation for subsequent geological analysis.

[0071] Step 2: Perform lightweight processing on the geological model data after standardized format processing, and bind the geological attributes to the model nodes one by one; convert the lightweight geological model data into a binary format embedded with metadata tags, and perform data integrity verification and automatic error correction during the conversion process.

[0072] Based on the standardized data structure output in step 1, this step focuses on solving the redundancy problem of the original data. The processing result directly affects the spatial index efficiency of the subsequent steps.

[0073] In this embodiment, the geological model data processed in the standardized format is subjected to lightweight processing, including:

[0074] The importance score of each model grid corresponding to the geological model data is calculated, and the model grids with importance scores lower than the scoring threshold are eliminated. Then, the texture atlas technology is used to integrate the textures of the same type or areas with similarity greater than the threshold into the same texture atlas, effectively reducing the number of drawing calls. The texture compression algorithm is applied to compress the volume of the corresponding texture files, optimizing the texture file volume by 30% to 60%.

[0075] The calculation formula of the importance score is as follows:

[0076] R=w1C+w2D+w3G;

[0077] Where R represents the importance score, C represents the average curvature change rate of the model mesh vertex area, which can be calculated using local surface fitting methods to calculate the average curvature change rate of the local area of ​​each vertex. D represents the density of geological edge vertices, which can be obtained by calculating the shortest vertical distance from the vertex to the nearest geological interface (such as a fault or fold surface). G represents the magnitude of the geological attribute gradient change, which can be calculated based on spatial interpolation. w1, w2, and w3 are the corresponding weight coefficients, which can be flexibly adjusted according to the characteristics of the geological model and are set to 0.3, 0.5, and 0.2 by default. Vertex importance at faults is automatically increased by 300% to prevent oversimplification of the fault structure.

[0078] In this example, geological attributes are bound to model nodes using glTF extended metadata features, ensuring a one-to-one correspondence between attributes and geometry nodes, facilitating efficient client queries. Finally, the lightweight standard model data is converted into a glTF binary format that complies with the 3DTiles standard. The metadata tags embedded in the generated glTF binary format provide a basis for LOD grading during dynamic loading in subsequent steps. Data integrity validation rules are defined during the conversion, such as vertex coordinate range verification, triangle mesh topology relationship checking, and valid attribute value range detection. An automatic error correction mechanism is also designed to repair geometric and topological errors in the model, such as self-intersecting surfaces and dangling points.

[0079] Step 3: Spatially divide the geological model data after lightweight processing, build a non-uniform adaptive octree spatial index, classify the attribute data in the geological model data, and then build a standardized multi-level attribute data architecture based on JSON structure and a real-time update mechanism for attribute data.

[0080] This step is used to reconstruct the lightweight data obtained in step 2 into a spatially indexable structure, including a non-uniform adaptive octree spatial index and a multi-level attribute data architecture, to support the alignment accuracy and dynamic loading efficiency of subsequent steps. The non-uniform adaptive octree spatial index constructs a dynamic spatial partitioning structure based on the complexity of the geological model, automatically subdividing complex structural areas into finer-grained spatial blocks, and using Morton coding to establish a fast mapping relationship between one-dimensional indexes and three-dimensional space, greatly improving spatial retrieval speed. The multi-level attribute data architecture is designed to target the professional attributes of geological information, with a three-level JSON data structure consisting of a basic attribute layer, an extended attribute layer, and a derived attribute layer, supporting flexible expansion, real-time updating, and efficient querying of attribute data.

[0081] In this embodiment, constructing an octree space partitioning structure includes: constructing an initial space box according to the spatial coordinate range of the geological model corresponding to the geological model data, wherein the initial space box is a three-dimensional space range that envelops the entire geological model, constructing the octree space partitioning structure according to the initial space box, and establishing a spatial index and a relationship index.

[0082] An octree spatial partitioning structure is constructed based on the initial spatial box, including: dividing the initial spatial box into 8 spatial boxes along the midpoints of the three coordinate axes to form the first layer of octree nodes; repeatedly evaluating the complexity of each spatial box based on the internal characteristics of the geological body, and determining whether to further subdivide the spatial box based on the complexity; and using a recursive method to continuously subdivide the spatial box until an adaptive termination condition is met, ultimately forming a non-uniform spatial partitioning structure.

[0083] Specifically, the steps of constructing the octree space partitioning structure include:

[0084] Step 31: Determine the initial spatial boundary, specifically including: constructing an initial spatial box based on the spatial coordinate range of the three-dimensional geological model to enclose the three-dimensional spatial range of the entire geological model. The length, width, and height of the spatial box are determined by the maximum and minimum coordinates of the model.

[0085] Step 32: Complexity analysis and adaptive subdivision, i.e., determining the spatial division granularity through geological body internal feature evaluation methods, specifically including:

[0086] Calculate the density of mesh vertices in the space: perform density statistics on the initial space box. Areas where the vertex density exceeds the preset threshold are identified as complex areas and need to be further subdivided.

[0087] Analyze the rate of change of geological attributes: perform spatial interpolation on vertex attributes within a spatial box, calculate attribute gradient changes, and prioritize subdividing areas with drastic attribute changes;

[0088] Fault and geological interface identification: Topological analysis is performed based on the location of geological interfaces. If there are multiple fault intersections or important geological interfaces within the space box, fine-grained subdivision is prioritized.

[0089] Set an adaptive subdivision strategy: Based on the above analysis, the spatial boxes of complex areas are gradually subdivided into finer levels (up to 8 layers), while simple homogeneous areas are retained at a coarser granularity level (such as 2-3 layers).

[0090] Step 33: Construct an octree spatial partitioning structure. This includes: dividing the initial spatial box into 8 sub-blocks along the midpoints of the three coordinate axes to form the first layer of octree nodes; repeating the complexity evaluation for each sub-space box to determine whether to further subdivide; and recursively continuing to subdivide until the adaptive termination condition is met, ultimately forming a non-uniform spatial partitioning structure. The specific partitioning depth range is generally 3 to 8 layers, and complex fault areas are generally subdivided to a maximum of 8 layers.

[0091] In this embodiment, establishing a spatial index and a relational index includes:

[0092] The three-dimensional center point coordinates of each spatial box are calculated, and after normalization, the three-dimensional center point coordinates are bit-interleaved and converted into Morton code to establish a fast mapping relationship between the one-dimensional index and the spatial box. The spatial adjacency relationship between each spatial box and the adjacent spatial box is recorded, and key geological structures are specially marked to construct a topological index. The geological contact relationship between adjacent geological units is marked in the index table of the spatial box, and a cross-scale LOD index link is constructed to record the parent-child relationship between spatial boxes at different LOD levels.

[0093] Specifically, this embodiment first uses Morton coding to establish a spatial index, which specifically includes:

[0094] Calculate the center point coordinates of the space box: extract the center point coordinates of each space box;

[0095] Spatial coordinate normalization: normalize the three-dimensional coordinates to a unified integer grid coordinate system (such as 32-bit integers) to ensure efficient calculation of Morton codes;

[0096] Morton code generation: Bit-interleaving is performed on the 3D coordinate data of each spatial box to convert the 3D coordinates into 1D integer values, i.e., Morton codes, and establish a fast mapping relationship between the 1D index and the 3D spatial box.

[0097] Then, establish the adjacency and topology relationship index, including:

[0098] Establish a spatial box adjacency graph to record the spatial adjacency relationship between each spatial box and the surrounding spatial boxes;

[0099] Specially mark key geological structures such as faults and stratigraphic interfaces, construct a topological index, and clearly mark the geological contact relationship between adjacent geological units in the spatial box index table;

[0100] Build cross-scale LOD index links, record the parent-child relationship between different LOD level space boxes, and support real-time LOD level switching.

[0101] Based on the above spatial structure, when the client requests model data, Morton encoding is used for fast spatial index search. The specific process is as follows:

[0102] First, the 3D coordinates of the client's query location are determined based on user interaction. The 3D coordinates are then quickly converted to Morton code values, and a one-dimensional code is quickly searched through the index library to quickly locate the corresponding spatial box and its adjacent area data. Finally, based on the spatial adjacency graph, the surrounding spatial boxes are quickly traversed to extract relevant geological structure and attribute information, enabling efficient spatial query and dynamic loading of model data.

[0103] Build a standardized multi-level attribute data architecture based on JSON structure, including:

[0104] Design attribute layers according to the types of attribute data. The attribute layers include: basic attribute layer, extended attribute layer, and derived attribute layer. Organize the attribute data of each attribute layer based on the GeoJSON specification, extend the properties attribute of GeoJSON, and design a unified JSON data template to ensure the consistency and standardization of front-end and back-end data exchange.

[0105] Specifically, this embodiment designs a three-level JSON data structure of basic attribute layer, extended attribute layer, and derived attribute layer for the professional attributes of geological information, which supports flexible expansion, real-time update and efficient query of attribute data. Among them, the basic attribute layer: includes basic geological attributes extracted directly from geological modeling software or exploration data, such as stratigraphic age, lithology category, structural type, etc. The extended attribute layer: includes detailed physical and mechanical parameters of rock formations, hydrogeological parameters, etc. obtained by field experiments, laboratory tests or field exploration measurements. The derived attribute layer: derived attributes obtained through spatial analysis or numerical calculation, such as model unit volume, fault area, geological body dip angle, etc., attribute data obtained by dynamic calculation.

[0106] In this embodiment, a real-time update mechanism for attribute data is created, specifically a version snapshot mechanism for attribute data is designed, which quickly identifies the attribute data change area through hash values, extracts only the changed attribute fields, and generates differential JSON data fragments for real-time transmission.

[0107] Step 4: Accurately align the geological model corresponding to the geological model data with the terrain, fuse the geological model with the remote sensing image, build a dynamic loading mechanism, a high-performance rendering mechanism, and a real-time update mechanism for the geological model, and integrate 3D geological analysis tools.

[0108] In this embodiment, the geological model corresponding to the geological model data is accurately aligned with the terrain, including:

[0109] Step 41: Based on the spatial coordinate range of the geological model, select the model center coordinates (longitude, latitude, altitude) as the initial positioning point. Based on the geological model and terrain DEM elevation data, preliminarily determine the bottom elevation benchmark of the geological model and roughly locate it in the WebGIS scene.

[0110] Step 42: Based on the rough positioning, the model's attitude and position are fine-tuned using the rotation parameters (rotation angles X, Y, and Z), translation parameters, and scaling parameters obtained from the GIS interface. To improve adjustment accuracy, the rotation angle fine-tuning step size is set to 0.1°, the latitude and longitude adjustment accuracy is ±0.0001°, and the elevation adjustment accuracy is ±0.1m. Real-time feedback of the adjustment results is displayed in the WebGIS scene. Table 1 shows a schematic diagram of the parameters for the 3D geological model integration WebGIS scene interface.

[0111] Table 1 Parameters of 3D geological model fusion WebGIS scene interface

[0112] Parameter name Parameter value Model longitude 98.91223° Model latitude 30.59327° Model height 2649m Rotation angle X 39.5° Rotation angle Y 30.75° Rotation angle Z -77° Zoom 1 Model lifting height 10 Model base depth 200 Model base range Array Initial perspective default Explosion Perspective default

[0113] Step 43: Define the base of the geological model in WebGIS. Use a 3D collision detection algorithm to quickly identify the spatial boundary between the geological model and the terrain model. Use a spatial interpolation algorithm to construct a smooth transition surface at the boundary between the model and the terrain, eliminating any gaps or discontinuities between the model and the terrain. Geological structural constraints are incorporated into the interpolation process to ensure that the interpolation results are consistent with the changing trends of the geological structure and surface topography, avoiding sudden changes and discontinuities in the spatial structure.

[0114] The above steps manually or automatically input the latitude, longitude, and elevation information of the model location, and use the rotation angle and scaling ratio to accurately adjust the model posture and position, develop an automatic boundary splicing algorithm for the geological model and terrain, and accurately calculate the boundary area between the model and terrain based on three-dimensional collision detection and spatial interpolation algorithms to achieve seamless integration of the model and terrain.

[0115] Step 44: Use histogram equalization to perform contrast enhancement processing on the remote sensing image to improve image clarity and detail recognition. Use the scale-invariant feature transformation algorithm to obtain key feature points in the remote sensing image and calculate the corresponding feature descriptors to form the basis for feature point matching.

[0116] Step 45: Automatically extract feature points on the surface projection of the geological model, and use a scale-invariant feature transformation feature point extraction algorithm to obtain the three-dimensional spatial coordinates and feature descriptors of the feature points on the surface of the geological model.

[0117] Step 46: Use a fast nearest neighbor search algorithm to preliminarily match the feature points of the remote sensing image and the geological model, and use a random sampling consistency algorithm to automatically filter out mismatched feature points and eliminate feature point pairs with large errors to ensure the quality of feature point matching.

[0118] Step 47: Based on the screened feature point pairs, the least squares method is used to optimize and calculate the affine transformation parameters (including translation, rotation, and scaling). The remote sensing image and the geological model are accurately spatially aligned based on the affine transformation parameters, and the alignment accuracy reaches the sub-pixel level. After alignment, the transparency grading technology and edge enhancement algorithm are used to display the spatial correspondence between the geological model and the remote sensing image, enhance the visual hierarchy between the surface image and the underground structure, and achieve a more intuitive geological interpretation effect.

[0119] The above steps define the base of the geological model in WebGIS and use a 3D collision detection algorithm to quickly identify the spatial interface between the geological model and the terrain model. A spatial interpolation algorithm is used to construct a smooth transition surface at the interface between the model and the terrain, eliminating any gaps or discontinuities between the model and the terrain. Geological structural constraints are incorporated into the interpolation process to ensure that the interpolation results are consistent with the changing trends of the geological structure and surface topography, avoiding sudden changes and discontinuities in the spatial structure. Figure 3A schematic diagram showing the effect of integrating a three-dimensional geological model into a WebGIS scene.

[0120] In this embodiment, the dynamic loading mechanism includes: determining the LOD level of the geological model, and determining the loading level of the geological model according to the LOD level. The calculation formula of the LOD level is as follows:

[0121] L=α·d -1 +β·e+γ·g+δ·i;

[0122] Among them, L represents the LOD level, d represents the distance from the viewpoint to the geological body, e represents the screen projection error estimate, g represents the complexity coefficient of the geological body (calculated based on geometric complexity and attribute change rate), i represents the weight of the user's interest area, and α, β, γ, and δ represent the corresponding weight coefficients (dynamically adjusted according to the user's interaction history).

[0123] The high-performance rendering mechanism includes: based on WebGL technology, using InstancedMesh instantiation batch rendering to reduce the number of GPU calls; through texture compression technology, reducing the volume of texture data and improving loading performance.

[0124] In this embodiment, the real-time update mechanism of the attribute data includes differential real-time update and model update conflict handling;

[0125] The differential real-time update includes: dividing the geological model space into multiple spatial sub-blocks, calculating the unique hash value of the geometric coordinates, topological relationship and attribute data of each spatial sub-block, and realizing rapid encoding of the model space state; when the model data is updated, automatically recalculating and comparing the hash value of each spatial sub-block, locating the changed area, and automatically extracting the newly added, deleted or modified key geometric nodes and attribute data in the changed area, generating incremental differential data, and pushing it to the client in real time via a WebSocket long connection. After receiving the differential data, the client uses local refresh technology to only update the data cache and three-dimensional visualization content of the corresponding spatial area, thereby improving the efficiency of model data update and user interaction experience.

[0126] The model update conflict handling process includes the following: When a client submits updated data, the server first checks whether the version number of the updated data submitted by the client is consistent with the current latest version number. If not, it is determined to be a data conflict. The server then provides a visual conflict handling interface through the WebGIS platform, displaying the conflict area and specific conflict content. For example, the conflict area and specific conflict content can be intuitively displayed using color highlighting or annotations, allowing users to interactively select conflict resolution strategies, including manual overwrite submission, accepting the latest data, and manual data merging. An optimistic locking mechanism is used to perform version checks on update requests submitted by the client to address data conflicts that may arise from collaborative editing by multiple people.

[0127] In this embodiment, the three-dimensional geological analysis tool is used for virtual drilling analysis, custom sectioning, model explosion analysis, real-time interactive query of attribute data, and rolling curtain analysis.

[0128] Figure 4 A schematic diagram of the effect of a virtual drilling analysis function is shown. The user specifies the drilling location and depth, and the system automatically generates drilling profile geological information based on spatial interpolation, displaying the changes in strata and properties at the drilling site in real time.

[0129] Figure 5 The figure shows a schematic diagram of the effect of a custom cutting function, which provides the interactive definition function of any spatial cutting plane. The system generates a cross-sectional view effect in real time based on the user-defined cutting plane, clearly showing the internal details of the geological structure.

[0130] Figure 6 A schematic diagram of the effect of a model explosion analysis function is shown. This function supports automatic or manual explosion of models according to multiple dimensions such as strata, lithology, and structural units, helping users to intuitively analyze complex underground geological structures.

[0131] Figure 7 The figure shows a schematic diagram of the effect of a real-time interactive query function for attributes. Users can click on any point on the model to obtain attribute information such as lithology, age, physical and mechanical parameters, and support annotation and export.

[0132] The rolling curtain analysis function refers to the comparative review of geological model version traceability changes, realizing time series evolution comparison and differentiated evaluation of multiple scheme models.

[0133] In summary, the method for integrating a 3D geological model with WebGIS provided in this embodiment has at least the following advantages:

[0134] (1) Lossless representation of geological information. Adaptive hierarchical analysis and reversible compression are used to fully preserve the geometric, topological, and attribute information of the geological model, ensuring that the geological semantics are not distorted, and providing a high-quality data foundation for subsequent geological analysis.

[0135] (2) Efficient model scheduling and rendering. The system deeply integrates multiple technologies such as octree indexing, LOD model scheduling, and WebGL rendering optimization to achieve efficient organization, retrieval, transmission, and display of geological model data, improving the smoothness and visual realism of model roaming.

[0136] (3) Convenient model modification and updating. The innovative incremental transmission and local update mechanism can quickly update the model according to changes in local geological interpretation, supporting real-time application needs such as engineering monitoring and dynamic resource assessment, and improving the dynamic nature of geological data management.

[0137] (4) GIS analysis capabilities are comprehensively enhanced. Through deep integration with WebGIS, the spatial analysis advantages of GIS are fully utilized, and a series of geological professional analysis tool sets are integrated, enabling geologists to conduct analysis directly in the Web environment, greatly expanding the depth of WebGIS industry applications.

[0138] (5) The system architecture is advanced and reliable. The microservice decoupling design concept is adopted to achieve full decoupling of the data layer, computing layer, and application layer. It has the technical advantages of high concurrency, high availability, and scalability, providing a solid and efficient system guarantee for complex geological applications.

[0139] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

Claims

1. A method for integrating a three-dimensional geological model with WebGIS, characterized in that: The method comprises: Acquiring a geological model data file, determining the type of the geological model data file and its corresponding data processing strategy based on a pre-built format feature library, extracting geological model data from the geological model data file according to the data processing strategy, and performing standardized format processing on the geological model data, wherein the geological model data includes geometric data, topological data, attribute data, and spatial reference data; Lightweight processing is performed on the geological model data after being processed in a standardized format, and geological attributes are bound to model nodes one by one; the geological model data after being lightweight is converted into a binary format embedded with metadata tags, and data integrity verification and automatic error correction are performed during the conversion process, wherein the data integrity verification includes at least vertex coordinate range verification, triangular mesh topological relationship verification, and attribute value valid range verification; The geological model data after lightweight processing is spatially divided, a non-uniform adaptive octree spatial index is constructed, and after classifying the attribute data in the geological model data, a standardized multi-level attribute data architecture based on the JSON structure and a real-time update mechanism for attribute data are constructed; The geological model corresponding to the geological model data is accurately aligned with the terrain, and after fusing the geological model with the remote sensing image, a dynamic loading mechanism, a high-performance rendering mechanism, and a real-time update mechanism for the geological model are constructed, and three-dimensional geological analysis tools are integrated.

2. The method for integrating a three-dimensional geological model with WebGIS according to claim 1, wherein: Lightweight processing of geological model data in standardized format, including: Calculate the importance score of each model grid corresponding to the geological model data, eliminate the model grids with importance scores below the scoring threshold, and then use texture atlas technology to integrate textures of the same type or areas with similarity greater than the threshold into the same texture atlas, and apply texture compression algorithm to compress the corresponding texture files; The calculation formula of the importance score is as follows: R=w1C+w2D+w3G; Among them, R represents the importance score, C represents the average curvature change rate of the model grid vertex area, D represents the density of geological edge vertices, G represents the amplitude of the geological attribute gradient change, and w1, w2 and w3 are the corresponding weight coefficients.

3. The method for integrating a three-dimensional geological model with WebGIS according to claim 1, wherein: Construct an octree space partitioning structure, including: According to the spatial coordinate range of the geological model corresponding to the geological model data, an initial space box is constructed. The initial space box is the three-dimensional space range that envelops the entire geological model. An octree space partitioning structure is constructed according to the initial space box, and a spatial index and a relationship index are established.

4. The method for integrating a three-dimensional geological model with WebGIS according to claim 3, wherein: Construct an octree space partitioning structure based on the initial space box, including: Divide the initial space box into 8 space boxes along the midpoints of the three coordinate axes to form the first layer of octree nodes; Repeatedly evaluate the complexity of each space box based on the internal characteristics of the geological body, and determine whether to further subdivide the space box based on the complexity; A recursive method is used to continuously subdivide the space box until the adaptive termination condition is met, and finally a non-uniform space division structure is formed.

5. The method for integrating a three-dimensional geological model with WebGIS according to claim 4, wherein: Establish spatial indexes and relational indexes, including: Calculate the 3D center coordinates of each spatial box, normalize them, perform bit interleaving, convert them into Morton code, and establish a fast mapping relationship between the one-dimensional index and the spatial box. Record the spatial adjacency relationship between each space box and the adjacent space boxes, specially mark the key geological structures, build a topological index, mark the geological contact relationship between adjacent geological units in the index table of the space box, and build a cross-scale LOD index link to record the parent-child relationship between space boxes at different LOD levels.

6. The method for fusing a three-dimensional geological model with WebGIS according to claim 1, wherein: Build a standardized multi-level attribute data architecture based on JSON structure, including: Design attribute layers according to the types of attribute data. The attribute layers include: basic attribute layer, extended attribute layer and derived attribute layer; Based on the GeoJSON specification, the attribute data of each attribute layer is organized, the properties of GeoJSON are extended, and a unified JSON data template is designed.

7. The method for integrating a three-dimensional geological model with WebGIS according to claim 1, wherein: Accurately align the geological model and terrain corresponding to the geological model data, including: According to the spatial coordinate range of the geological model, the model center coordinates are selected as the initial positioning point. Based on the geological model and terrain DEM elevation data, the bottom elevation benchmark of the geological model is preliminarily determined and roughly positioned in the WebGIS scene. Based on the rough positioning, the model posture and position are fine-tuned according to the rotation parameters, translation parameters and scaling parameters obtained from the GIS interface; The base range of the geological model was defined in WebGIS, and a 3D collision detection algorithm was used to quickly identify the spatial boundary between the geological model and the terrain model. A spatial interpolation algorithm was then used to construct a smooth transition surface at the boundary between the model and the terrain. Histogram equalization is used to enhance the contrast of remote sensing images, and the scale-invariant feature transformation algorithm is used to obtain the key feature points in the remote sensing images and calculate the corresponding feature descriptors. Automatically extract feature points on the surface projection of the geological model, and use the scale-invariant feature transformation feature point extraction algorithm to obtain the three-dimensional spatial coordinates and feature descriptors of the feature points on the surface of the geological model; A fast nearest neighbor search algorithm is used to preliminarily match the feature points of the remote sensing image and the geological model, and a random sampling consistency algorithm is used to automatically filter out mismatched feature points and eliminate feature point pairs with large errors. Based on the screened feature point pairs, the least squares method is used to optimize and calculate the affine transformation parameters. The remote sensing image and the geological model are accurately spatially aligned according to the affine transformation parameters. After alignment, the transparency grading technology and edge enhancement algorithm are used to display the spatial correspondence between the geological model and the remote sensing image.

8. The method for fusing a three-dimensional geological model with WebGIS according to claim 1, characterized in that: The dynamic loading mechanism includes: determining the LOD level of the geological model, and determining the loading level of the geological model according to the LOD level. The calculation formula of the LOD level is as follows: L=α·d -1 +β·e+γ·g+δ·i; Where L represents the LOD level, d represents the distance from the viewpoint to the geological body, e represents the screen projection error estimate, g represents the complexity coefficient of the geological body, i represents the weight of the user's area of ​​interest, and α, β, γ, and δ represent the corresponding weight coefficients respectively; The high-performance rendering mechanism includes: based on WebGL technology, using InstancedMesh instantiation batch rendering.

9. The method for integrating a three-dimensional geological model with WebGIS according to claim 1, wherein: The real-time update mechanism of the attribute data includes differential real-time update and model update conflict handling; The real-time differential update includes: dividing the geological model space into multiple spatial sub-blocks, calculating the unique hash value of the geometric coordinates, topological relationship and attribute data of each spatial sub-block, realizing rapid encoding of the model space state, and automatically recalculating and comparing the hash value of each spatial sub-block when the model data is updated, locating the changed area, and automatically extracting the newly added, deleted or modified key geometric nodes and attribute data in the changed area, generating incremental differential data, and pushing it to the client in real time via a WebSocket long connection. After receiving the differential data, the client uses a local refresh technology to only update the data cache and three-dimensional visualization content of the corresponding spatial area; The model update conflict handling includes: when the client submits updated data, the server first checks whether the version number of the updated data submitted by the client is consistent with the current latest version number. If inconsistent, it is determined to be a data conflict, and a visual conflict handling interface is provided through the WebGIS platform to display the conflict area and specific conflict content.

10. The method for fusing a three-dimensional geological model with WebGIS according to claim 1, wherein: The three-dimensional geological analysis tool is used for virtual drilling analysis, custom sectioning, model explosion analysis, real-time interactive query of attribute data, and rolling curtain analysis.

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