Intelligent fusion of multi-source heterogeneous geological data and automatic construction system of three-dimensional entity model

Abstract

The application relates to the technical field of geology, and discloses an intelligent fusion and automatic construction system of a three-dimensional entity model of multi-source heterogeneous geological data, which is characterized by comprising a hardware platform and a software module running on the hardware platform; the hardware platform comprises a central processing unit (CPU), a graphics processing unit (GPU), a large-capacity memory (RAM), a high-speed storage system, a data input / output (I / O) interface and a man-machine interaction device; the software module is sequentially connected to form a technical closed loop, and comprises the following: S1: a data preprocessing and intelligent fusion module, which is used for receiving multi-source heterogeneous geological data; through the construction of a full-process automatic technical closed loop of "intelligent fusion-automatic modeling-simulation readiness", the application realizes high-precision intelligent fusion of multi-source heterogeneous geological data, automatically generates a three-dimensional entity model with correct geological logic and correct topological relationship, and directly outputs a "simulation readiness" model meeting the requirements of mainstream numerical simulation software.

Description

Technical Field

[0001] This invention relates to the field of geological technology, specifically to an intelligent fusion system for multi-source heterogeneous geological data and an automatic three-dimensional solid model construction system. Background Technology

[0002] As mineral resource exploration extends to deeper and concealed ore bodies, 3D geological modeling and metallogenic prediction have become core technologies in modern mineral exploration. Constructing high-precision, high-fidelity 3D geological entity models is the foundation for quantitative resource evaluation, mine design and mining, and numerical simulation of geological processes. However, the current process of converting multi-source heterogeneous geological data into 3D entity models that can be directly used for advanced analysis and simulation still faces many technical bottlenecks: geological data sources are wide-ranging and vary significantly in scale and accuracy; existing technologies lack a unified intelligent fusion framework, data fusion relies on human experience, resulting in low efficiency and poor repeatability; mainstream 3D geological modeling software is still mainly based on human-computer interaction, with core processes relying on manual operation by experts, resulting in low levels of automation and intelligence, and algorithms struggle to handle complex structures, requiring extensive manual repair; traditional modeling output focuses on geometric visualization, which is insufficient to meet the stringent requirements of advanced numerical analysis for model water tightness, mesh quality, and attribute partitioning, creating a gap in the conversion from visualized models to computable models; existing general modeling techniques do not fully integrate metallogenic knowledge, lack quantitative characterization and fusion mechanisms for key metallogenic thematic information, and are unable to support machine learning for metallogenic potential assessment.

[0003] To address this issue, we propose an intelligent fusion system for multi-source heterogeneous geological data and an automatic 3D solid model construction system. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent fusion system for multi-source heterogeneous geological data and an automatic three-dimensional solid model construction system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent fusion system for multi-source heterogeneous geological data and an automatic 3D solid model construction system, comprising a hardware platform and software modules running on the hardware platform. The hardware platform includes a central processing unit (CPU), a graphics processing unit (GPU), a large-capacity memory (RAM), a high-speed storage system, a data input / output (I / O) interface, and a human-computer interaction device. The software modules are sequentially connected to form a technical closed loop, including: S1: Data preprocessing and intelligent fusion module, used to receive multi-source heterogeneous geological data, perform data access and standardization, spatial registration, conflict detection, conflict resolution based on knowledge base and machine learning, feature extraction and semantic association processes, and generate a fused structured data body. Specifically, it is as follows: Start → Data access and standardization → Spatial registration → Conflict detection → Determine if there is a conflict. If so, perform conflict resolution based on knowledge base and machine learning. Otherwise, directly proceed to feature extraction and semantic association → Feature extraction and semantic association → Generate fused structured data body → End. S2: 3D modeling engine module, receives the structured data body, executes geological interface reasoning and construction, spatial topological relationship reasoning, and automatic generation of 3D solid model, and outputs the initial solid model. Specifically, it is: Start → Load structured data body → Geological interface reasoning and construction → Spatial topological relationship reasoning → Automatic generation of 3D solid model → Output the initial solid model → End. S3: Simulation-ready model generation and output module, receives the initial solid model, executes the model topology and geometry repair, automatic generation of computational mesh, simulation attribute partitioning and assignment, and simulation-ready model output process, and finally outputs a three-dimensional solid model that can be directly used for numerical simulation. Specifically, it is as follows: Start → Receive initial solid model → Model topology and geometry repair → Automatic generation of computational mesh → Simulation attribute partitioning and assignment → Output simulation-ready model → End.

[0006] Preferably, the central processing unit (CPU) of the hardware platform adopts a multi-core high-performance Intel Xeon or AMD EPYC series for complex logic judgment, data scheduling and geological rule reasoning; the graphics processing unit (GPU) adopts an NVIDIA RTX series or Tesla series for accelerating three-dimensional geometric calculation, spatial interpolation and real-time rendering.

[0007] Preferably, the hardware platform has a large-capacity memory (RAM) of ≥64GB, the high-speed storage system adopts a solid-state drive (SSD) array, the data input / output (I / O) interface includes a scanner interface, a network and storage interface, and a field sensor communication interface, and the human-computer interaction device includes a high-resolution display, a keyboard, a mouse, and an optional three-dimensional spatial interaction device.

[0008] Preferably, the conflict resolution process of the data preprocessing and intelligent fusion module includes: Step A: Query the prior uncertainty weight of this geological element in the geological knowledge base; Step B: Call the pre-trained machine learning model to analyze the conflict context and output the optimal adjustment suggestion vector; Step C: Integrate prior weights and model recommendations, automatically adjust the contribution of each data source, and calculate a geologically reasonable and consistent boundary location.

[0009] Preferably, in the geological interface reasoning and construction process of the 3D modeling engine module, a geological rule inference engine is called, and a spatial interpolation algorithm or a structural restoration algorithm is selected to generate a geologically reasonable 3D surface, combining the stratigraphic regional attitude trend and fault fault information.

[0010] Preferably, in the spatial topological relationship reasoning process of the 3D modeling engine module, Boolean operations are automatically performed based on the rules and semantic relationships of the geological knowledge base. Fault surfaces are used to cut strata surfaces and intrusive body surfaces are used to cut surrounding rock surfaces, so that the cut geological interfaces split into independent surfaces on both sides of the corresponding structures and inherit the original ID and attributes.

[0011] Preferably, in the automatic generation process of the computational grid of the simulation-ready model generation and output module, a hexahedral dominant body grid, a tetrahedral grid, or a hybrid grid is generated according to the preset grid size requirements or adaptive rules, and local densification is performed in key parts of faults and contact zones.

[0012] Preferably, in the simulation attribute partitioning and assignment process of the simulation-ready model generation and output module, material attribute partitions are divided based on geological unit IDs, and corresponding partitions are assigned values ​​in batches according to preset material attribute templates such as elastic modulus, permeability coefficient, and thermal conductivity. Among them, fault structures are defined as interface units or assigned weak attributes.

[0013] Preferably, the model format output by the simulation-ready model generation and output module includes the proprietary formats of mainstream numerical simulation software such as FLAC3D, ANSYS, COMSOL, and ABAQUS, as well as lightweight 3D visualization files in WebGL format.

[0014] This invention provides an intelligent fusion system for multi-source heterogeneous geological data and an automatic 3D solid model construction system. This system offers the following advantages:

[0015] This invention achieves high-precision intelligent fusion of multi-source heterogeneous geological data by constructing a closed-loop automated technology system of "intelligent fusion - automatic modeling - simulation ready". It automatically generates three-dimensional solid models with correct geological logic and topological relationships, and directly outputs "simulation ready" models that meet the requirements of mainstream numerical simulation software. This not only shortens the modeling cycle from weeks / months to hours / minutes, significantly reducing manpower and time costs by more than 70%, eliminating subjective human errors, and ensuring model consistency and reliability, but also strengthens the quantitative representation of mineralization-related thematic information. It provides high-quality structured data support for deep mineralization prediction, mine dynamic management, and geological scientific research, and promotes the digital and intelligent transformation of the geological exploration and resource development industry. Attached Figure Description

[0016] Figure 1 This is a hardware platform architecture diagram of the system of the present invention; Figure 2 This is a system architecture block diagram of the present invention; Figure 3 This is a flowchart of the data preprocessing and intelligent fusion process of the present invention; Figure 4 This is a flowchart of the 3D modeling engine workflow of the present invention; Figure 5 This is a flowchart illustrating the generation process of the simulation-ready model for this invention. Detailed Implementation

[0017] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0018] A preferred embodiment of the intelligent fusion of multi-source heterogeneous geological data and automatic construction system for three-dimensional solid models provided by this invention is as follows: Figures 1 to 5 The system described is an intelligent fusion system for multi-source heterogeneous geological data and an automatic 3D solid model construction system. It includes a hardware platform and software modules running on the hardware platform. The hardware platform's central processing unit (CPU) uses a multi-core high-performance Intel Xeon or AMD EPYC series CPU for complex logic judgment, data scheduling, and geological rule reasoning. The graphics processing unit (GPU) uses an NVIDIA RTX or Tesla series GPU for accelerating 3D geometric calculations, spatial interpolation, and real-time rendering. The hardware platform has a large-capacity RAM (≥64GB), a high-speed storage system using a solid-state drive (SSD) array, and data input / output (I / O) interfaces including a scanner interface, network and storage interfaces, and field sensor communication interfaces. The human-computer interaction device includes a high-resolution display, keyboard, mouse, and optional 3D spatial interaction device. The hardware platform includes a CPU, GPU, RAM, high-speed storage system, data input / output (I / O) interfaces, and human-computer interaction device. The software modules are sequentially connected to form a closed-loop technology, including: S1: Data preprocessing and intelligent fusion module, used to receive multi-source heterogeneous geological data, perform data access and standardization, spatial registration, conflict detection, conflict resolution based on knowledge base and machine learning, feature extraction and semantic association processes, and generate a fused structured data body. Specifically, it is as follows: Start → Data access and standardization → Spatial registration → Conflict detection → Determine if there is a conflict. If so, perform conflict resolution based on knowledge base and machine learning. Otherwise, directly proceed to feature extraction and semantic association → Feature extraction and semantic association → Generate fused structured data body → End. The conflict resolution process between the data preprocessing and intelligent fusion modules includes: Step A: Query the prior uncertainty weight of this geological element in the geological knowledge base; Step B: Call the pre-trained machine learning model, analyze the conflict context, and output the optimal adjustment suggestion vector; Step C: Integrate prior weights and model suggestions, automatically adjust the contribution of each data source, and calculate the geologically reasonable and consistent boundary location; Furthermore, in the data access and standardization process, the adapter reads borehole, profile, geological map, geophysical inversion body, and geochemical point cloud data, and converts them into internally defined standardized data objects. Furthermore, in the spatial registration process, coordinate transformation algorithms are used to correct all data objects to a unified three-dimensional spatial coordinate system, and feature point matching algorithms are used for spatial alignment of historical data that lacks precise coordinates. S2: 3D modeling engine module, receives the structured data body, executes geological interface reasoning and construction, spatial topological relationship reasoning, and automatic generation of 3D solid model, and outputs the initial solid model. Specifically, it is: Start → Load structured data body → Geological interface reasoning and construction → Spatial topological relationship reasoning → Automatic generation of 3D solid model → Output the initial solid model → End. In the geological interface reasoning and construction process of the 3D modeling engine module, the geological rule reasoner is called, and the spatial interpolation algorithm or the structural restoration algorithm is selected to generate a geologically reasonable 3D surface, which is combined with the stratigraphic regional attitude trend and fault fault information. Furthermore, in the spatial topological relationship reasoning process of the 3D modeling engine module, Boolean operations are automatically performed based on the rules and semantic relationships of the geological knowledge base. Fault surfaces are used to cut strata surfaces and intrusive body surfaces are used to cut surrounding rock surfaces, so that the cut geological interfaces split into independent surfaces on both sides of the corresponding structures and inherit the original ID and attributes. Furthermore, in the automatic generation process of 3D solid models, 3D volume elements are formed by stitching together the interfaces of upper and lower strata and closing the lateral boundaries (model boundaries or faults). Each volume element corresponds to an independent geological unit and is automatically assigned a unit ID and associated lithology, density, strength and other attributes. S3: Simulation-ready model generation and output module, receives the initial solid model, executes model topology and geometry repair, automatic generation of computational mesh, simulation attribute partitioning and assignment, and simulation-ready model output process, and finally outputs a three-dimensional solid model that can be directly used for numerical simulation. Specifically, it is as follows: Start → Receive initial solid model → Model topology and geometry repair → Automatic generation of computational mesh → Simulation attribute partitioning and assignment → Output simulation-ready model → End. In the automatic generation process of the computational grid in the simulation-ready model generation and output module, a hexahedral dominant body grid, a tetrahedral grid, or a hybrid grid is generated according to the preset grid size requirements or adaptive rules, and local refinement is performed in key parts of faults and contact zones. Furthermore, in the simulation attribute partitioning and assignment process of the simulation-ready model generation and output module, material attribute partitions are divided based on geological unit IDs. According to the preset material attribute templates such as elastic modulus, permeability coefficient, and thermal conductivity, values ​​are assigned to the corresponding partitions in batches. Among them, fault structures are defined as interface units or assigned weak attributes. Furthermore, the simulation-ready model generation and output module outputs models in formats including those specific to mainstream numerical simulation software such as FLAC3D, ANSYS, COMSOL, and ABAQUS, as well as lightweight 3D visualization files in WebGL format. Furthermore, in the model topology and geometry repair process, geometric defects such as zero-area triangles, duplicate nodes, and non-manifold edges are automatically detected and repaired to ensure that the model is strictly watertight and geometrically valid.

[0019] In this embodiment, the software module also includes a geological knowledge base, which encodes basic geological rules such as stratigraphic order, fault cutting relationships, and intrusive body contact relationships, providing rule support for conflict resolution in data fusion and topological relationship reasoning in 3D modeling.

[0020] The above description is merely an illustrative embodiment of the present invention and is not intended to limit the scope of the invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention. Furthermore, it should be noted that the components of the present invention are not limited to the overall application described above. Each technical feature described in the specification can be used individually or in combination as needed. Therefore, the present invention naturally covers other combinations and specific applications related to the inventive points of this case.

Claims

1. A system for intelligent fusion of multi-source heterogeneous geological data and automatic construction of 3D solid models, characterized in that, It includes a hardware platform and software modules running on the hardware platform. The hardware platform includes a central processing unit (CPU), a graphics processing unit (GPU), a large-capacity memory (RAM), a high-speed storage system, a data input / output (I / O) interface, and a human-computer interaction device. The software modules are sequentially connected to form a closed-loop technology, including: S1: Data preprocessing and intelligent fusion module, used to receive multi-source heterogeneous geological data, perform data access and standardization, spatial registration, conflict detection, conflict resolution based on knowledge base and machine learning, feature extraction and semantic association processes, and generate a fused structured data body. Specifically, it is as follows: Start → Data access and standardization → Spatial registration → Conflict detection → Determine if there is a conflict. If so, perform conflict resolution based on knowledge base and machine learning. Otherwise, directly proceed to feature extraction and semantic association → Feature extraction and semantic association → Generate fused structured data body → End. S2: 3D modeling engine module, receives the structured data body, executes geological interface reasoning and construction, spatial topological relationship reasoning, and automatic generation of 3D solid model, and outputs the initial solid model. Specifically, it is: Start → Load structured data body → Geological interface reasoning and construction → Spatial topological relationship reasoning → Automatic generation of 3D solid model → Output the initial solid model → End. S3: Simulation-ready model generation and output module, receives the initial solid model, executes the model topology and geometry repair, automatic generation of computational mesh, simulation attribute partitioning and assignment, and simulation-ready model output process, and finally outputs a three-dimensional solid model that can be directly used for numerical simulation. Specifically, it is as follows: Start → Receive initial solid model → Model topology and geometry repair → Automatic generation of computational mesh → Simulation attribute partitioning and assignment → Output simulation-ready model → End.

2. The system according to claim 1, characterized in that, The central processing unit (CPU) of the hardware platform adopts a multi-core high-performance Intel Xeon or AMD EPYC series for complex logic judgment, data scheduling and geological rule reasoning; the graphics processing unit (GPU) adopts an NVIDIA RTX series or Tesla series for accelerating three-dimensional geometric calculations, spatial interpolation and real-time rendering.

3. The system according to claim 1, characterized in that, The hardware platform has a large-capacity memory (RAM) of ≥64GB, a high-speed storage system using a solid-state drive (SSD) array, and data input / output (I / O) interfaces including a scanner interface, a network and storage interface, and a field sensor communication interface. The human-computer interaction devices include a high-resolution display, a keyboard, a mouse, and optional three-dimensional spatial interaction devices.

4. The system according to claim 1, characterized in that, The conflict resolution process of the data preprocessing and intelligent fusion module includes: Step A: Query the prior uncertainty weight of this geological element in the geological knowledge base; Step B: Call the pre-trained machine learning model to analyze the conflict context and output the optimal adjustment suggestion vector; Step C: Integrate prior weights and model recommendations, automatically adjust the contribution of each data source, and calculate a geologically reasonable and consistent boundary location.

5. The system according to claim 1, characterized in that, In the geological interface reasoning and construction process of the 3D modeling engine module, the geological rule inference engine is called, and the spatial interpolation algorithm or the structural restoration algorithm is selected to generate a geologically reasonable 3D surface, which is combined with the stratigraphic regional attitude trend and fault fault information.

6. The system according to claim 1, characterized in that, In the spatial topological relationship reasoning process of the 3D modeling engine module, Boolean operations are automatically performed based on the rules and semantic relationships of the geological knowledge base. Fault surfaces are used to cut strata surfaces and intrusive body surfaces are used to cut surrounding rock surfaces, so that the cut geological interfaces split into independent surfaces on both sides of the corresponding structures and inherit the original ID and attributes.

7. The system according to claim 1, characterized in that, In the automatic generation process of the computational grid of the simulation-ready model generation and output module, a hexahedral dominant body grid, a tetrahedral grid, or a hybrid grid is generated according to the preset grid size requirements or adaptive rules, and local densification is performed in key parts of faults and contact zones.

8. The system according to claim 1, characterized in that, In the simulation attribute partitioning and assignment process of the simulation-ready model generation and output module, material attribute partitions are divided based on geological unit IDs. According to preset material attribute templates such as elastic modulus, permeability coefficient, and thermal conductivity, values ​​are assigned to the corresponding partitions in batches. Among them, fault structures are defined as interface units or assigned weak attributes.

9. The system according to claim 1, characterized in that, The model output formats of the simulation-ready model generation and output module include those specific to mainstream numerical simulation software such as FLAC3D, ANSYS, COMSOL, and ABAQUS, as well as lightweight 3D visualization files in WebGL format.

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