An AI-Driven Intelligent Modeling Method for Self-Distribution of 3D Printing Parameters of Complex Geological Structures

Abstract

The present invention proposes an AI-driven intelligent modeling method for self-distributing 3D printing parameters of complex geological structures, including the following steps: acquiring multi-source characteristic data of complex geological bodies and performing preprocessing; extracting geological geometric parameters and mapping them into a 3D printing voxel model, and converting them into geometric parameters that can be used for 3D printing; using intelligent algorithms to learn the non-linear mapping relationship between the physical parameters and material parameters of historical complex geological bodies, constructing and training an AI model to predict material distribution parameters; converting the geometric parameters and material distribution parameters into a three-dimensional model of complex geological structures and verifying; exporting parameterized full-model slice data; verifying the performance of the 3D printed complex geological structure model. Compared with the prior art, the present invention realizes the intelligent conversion from data to model, can accurately describe the complex spatial heterogeneity in geological disaster models, improves the adaptability and accuracy of the models, and provides a basis for subsequent prediction and prevention of complex geological body disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster prevention and control, and particularly to an AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures. Background Art

[0002] Geological disasters such as landslides and debris flows pose a serious threat to people's lives and property. Traditional geological disaster modeling methods rely on manual experience judgment and limited geological exploration data, making it difficult to comprehensively and accurately reflect the complexity and spatial heterogeneity of geological structures. With the development of geological exploration technologies and the diversification of data collection means, how to efficiently integrate multi-source geological data and intelligently extract key characteristic parameters has become an urgent problem to be solved in the field of geological disaster modeling. Existing methods usually adopt a homogenization hypothesis during modeling, making it difficult to accurately describe the complex spatial heterogeneity in geological disaster models and lacking an intelligent conversion from material distribution parameters to three-dimensional structures. Therefore, there is an urgent need for an AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures to achieve an intelligent conversion from data to model, accurately describe the complex spatial heterogeneity in geological disaster models, and improve the adaptability and accuracy of complex geological structure models. It provides a basis for subsequent scientific research, engineering design, disaster prediction and prevention of complex geological bodies. Summary of the Invention

[0003] The purpose of the present invention is to provide a method that can efficiently integrate multi-source geological data and intelligently extract key characteristic parameters to construct a complex geological model, realizing an intelligent conversion from data to model, so as to accurately describe the complex spatial heterogeneity in geological disaster models.

[0004] To achieve the above object, the present invention proposes an AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures, including the following steps:

[0005] S1: Obtain multi-source characteristic data of the actual complex geological body and perform preprocessing;

[0006] S2: Extract the geometric and physical parameters of the geological body based on the characteristic data of the geological body;

[0007] S3: Map the extracted existing geometric parameters of the geological body into a 3D printing voxel model and convert them into geometric parameters that can be used for 3D printing;

[0008] S4: Use an intelligent algorithm to autonomously learn the non-linear mapping relationship between the physical parameters of historical complex geological bodies and 3D printing material parameters, and construct and train an AI model;

[0009] S5: Input the existing physical parameters of the complex geological body and use the trained AI model to predict the 3D printing material distribution parameters;

[0010] S6: Form a printable three-dimensional voxel model with the determined existing complex geological body geometric parameters and material distribution parameters, realize the conversion from multi-source parameter distribution of the geological body to a complex geological model, and verify the converted three-dimensional voxel model;

[0011] S7: Import the slices exported from the verified three-dimensional voxel model into a 3D printing device to complete the 3D printing of the complex geological structure model;

[0012] S8: Verify the performance of the 3D printed complex geological structure model to ensure that the printed complex geological structure model can accurately reflect the complex spatial heterogeneity of the actual geological structure.

[0013] Furthermore, in step S1, the data acquisition methods include physical model tests, on-site exploration and measurement, core drilling and analysis, and remote sensing technology; the multi-source characteristic data includes ice-rock alternating structures, ice layer thickness, geological body structural plane distribution, fracture networks, porosity, and interlayer interface dip angles.

[0014] Furthermore, in step S1, the preprocessing includes data cleaning, format conversion, and standardization processing to ensure the consistency and accuracy of the data.

[0015] Furthermore, in step S2, the geological body geometric and physical parameters include geometric parameters and physical parameters. The geometric parameters include size, geometric shape, and positional relationship; the physical parameters include porosity, fracture rate, and geological body dip angle.

[0016] Furthermore, step S3 specifically includes: determining the distribution of the complex geological body geometric dimensions in the 3D printing voxel model according to the extracted existing complex geological body geometric dimension data; determining the distribution of the complex geological body geometric shape in the 3D printing voxel model according to the extracted geometric shape data; and determining the positional relationship of the complex geological body in the 3D printing voxel model according to the extracted positional relationship data.

[0017] Furthermore, in step S4, use machine learning or deep learning intelligent algorithms to train an AI model with existing historical geological characteristic parameters and 3D printing material parameters. Specifically: use machine learning or deep learning intelligent algorithms to train an AI model with existing historical geological characteristic parameters and 3D printing material parameters, so that the AI model autonomously learns the non-linear mapping relationship between geological characteristic parameters (such as porosity, fracture rate, geological body dip angle, etc.) and the final printing material parameters (such as material type, density, hardness, etc.) through a large amount of historical data.

[0018] Further, in step S5, the preprocessed multi-source feature data is input into the trained AI model to predict the distribution parameters of the material, and the distribution parameters include material type, density, material permeability, material strength, etc.

[0019] Meanwhile, the material needs to meet the following two characteristics:

[0020] 1) Printability: Adapt to mainstream 3D printing technologies (such as stereolithography, fused deposition modeling, etc.);

[0021] 2) Performance tunability: Continuously regulate parameters such as hardness and density through material ratios (such as resin doping ratios) and structural designs (such as filling rates).

[0022] Further, in step S6, based on the determined geometric parameters, a continuous three-dimensional voxel geometric model is generated using geological modeling software (such as Petrel, Gocad, Move), and the material distribution parameters are distributed into the three-dimensional voxel geometric model through an interpolation algorithm (such as Kriging method, etc.) to establish a three-dimensional voxel model that can reflect the geometric and physical parameter distributions of complex geological bodies. Then, by comparing the actual geological data with the three-dimensional voxel model, the accuracy of the three-dimensional voxel model is verified to ensure that the obtained three-dimensional voxel model can accurately reflect the spatial heterogeneity of complex geological structures.

[0023] Further, in step S7, the STL file exported from the three-dimensional voxel model is sliced into a layered structure suitable for 3D printing and seamlessly docked with a 3D printer. The exported sliced data is imported into the 3D printer to print the 3D geological structure model, realizing high-precision printing of the geological disaster model.

[0024] Further, in step S8, the performance of the 3D printed complex geological structure model is verified by comparing the characteristics and parameters of the 3D printed complex geological structure model with those of the actual geological structure; the performance verification includes geometric accuracy verification, physical property verification, and mechanical property verification. Among them, the geometric accuracy verification specifically verifies whether the geometric dimensions, shapes, and structures of the 3D printed model meet the design requirements; the physical property verification specifically verifies whether the physical properties such as density, strength, and permeability of the 3D printed model are consistent with the actual geological structure. The mechanical property verification specifically verifies whether the mechanical properties of the 3D printed model can meet the requirements of engineering simulation and experiments.

[0025] Compared with the prior art, the advantages of the present invention are as follows:

[0026] 1. The present invention realizes the intelligent conversion from multi-source geological data to 3D printing models by the AI model autonomously learning the complex non-linear mapping relationship between geological parameters and 3D printing material parameters, avoiding the errors and low efficiency problems caused by relying on manual experience in traditional methods.

[0027] 2. Traditional modeling methods usually adopt homogenization assumptions and are difficult to accurately reflect the complex spatial heterogeneity of geological structures (such as fracture networks, pore distributions, etc.). However, through a three-dimensional voxel model and AI-driven parameter allocation, the present invention can accurately reproduce the heterogeneous characteristics of actual geological bodies, providing a more realistic model basis for disaster prediction and engineering simulation.

[0028] 3. The present invention realizes the intelligent construction and verification of geological disaster models by efficiently integrating, processing, and analyzing geological structure feature data, and can be used for the evolution analysis of complex geological disaster bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of an AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be further described below.

[0031] This embodiment proposes an AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures. The method is as Figure 1 shown and includes the following steps:

[0032] Step 1: Extracting geological structure feature parameters, that is, obtaining multi-source feature data of actual geological bodies. The specific obtaining method is: based on physical model tests, on-site exploration and measurement, core drilling and analysis, remote sensing technology and other means, obtain multi-source feature data of actual complex geological bodies. Among them, the multi-source feature data includes ice-rock alternating structures, ice layer thicknesses, geological body structural plane distributions, fracture networks, porosities, and interlayer interface dips.

[0033] Step 2: Preprocessing multi-source data, that is, preprocessing the obtained multi-source feature data of actual geological bodies to ensure the consistency and accuracy of the data. Among them, the preprocessing methods include data cleaning, format conversion, and standardization processing. Specifically as follows:

[0034] Data cleaning: Remove noise data, fill in missing values, and correct incorrect data.

[0035] Format conversion: Convert data from different sources into a unified format for subsequent processing.

[0036] Standardization: Perform normalization processing on the data to ensure that the data is compared and analyzed under the same dimension.

[0037] Step 3: Extract geometric parameters such as size, geometric shape, and positional relationship, as well as physical parameters such as porosity, fracture rate, and dip angle of the geological body, based on characteristic data such as the distribution of structural planes of the geological body and the spatial distribution of multi-source data.

[0038] Step 4: Convert the distribution of geometric parameters of the three-dimensional voxel model, that is, map the extracted geological parameters to the spatial distribution of the 3D printing voxel model and convert them into parameters available for 3D printing; specifically including: determining the distribution of the geometric size of the complex geological body in the 3D printing voxel model according to the extracted geometric size data of the existing complex geological body; determining the distribution of the geometric shape of the complex geological body in the 3D printing voxel model according to the extracted geometric shape data; and determining the positional relationship of the complex geological body in the 3D printing voxel model according to the extracted positional relationship data.

[0039] Step 5: Use an intelligent algorithm to autonomously learn the non-linear mapping relationship between the physical parameters of historical complex geological bodies and 3D printing material parameters, and construct and train an AI model; specifically: use machine learning or deep learning intelligent algorithms to train the AI model with historical geological characteristic parameters and 3D printing material parameters, so that the AI model autonomously learns the non-linear mapping relationship between geological characteristic parameters (such as porosity, fracture rate, dip angle of the geological body, etc.) and the final printing material parameters (such as material type, density, hardness, etc.) through a large amount of historical data. For example, in this embodiment, the mapping relationship between geological parameters and material distribution parameters is shown in Table 1 below.

[0040] Table 1 Conventional geological parameters and material distribution parameters

[0041] Geological parameters Mapping logic Material selection Material distribution parameters Porosity Porosity reflects the void ratio of the geological body and directly affects the mechanical strength of the printed model Resin High porosity: photosensitive resin + a certain proportion of internal grid cavities (simulating permeability); low porosity: a certain proportion of filled ceramic-reinforced resin (simulating dense sandstone). Fracture rate Regions with high fracture rates need to simulate brittle failure behavior or fluid preferential channels Low fracture toughness materials (such as brittle resin) or embedded microcrack structures High fracture rate: preset microcrack paths during printing (simulating permeability); complex fracture network regions: brittle resin + different proportions of thick and thin grids (simulating different types of microfractured bodies). Dip angle of the geological body The dip angle and bedding direction result in the anisotropy of the mechanical properties of the geological body, which need to be reproduced through the printing direction or material distribution Carbon fiber-reinforced resin High-dip angle strata: orientedly print high-strength fibers along the bedding direction (simulating the compressive directionality); horizontal bedding: uniform material distribution (isotropic). Mineral composition The hardness and chemical properties of different minerals vary significantly Ceramic resin; water-absorbing resin Quartz-rich areas: high-hardness photocurable ceramic resin to simulate the strength of quartz; clay mineral areas: water-absorbing resin to simulate the change in permeability after clay absorbs water.

[0042] The material needs to meet the following requirements:

[0043] 1) Printability: Adapt to mainstream 3D printing technologies (such as stereolithography, fused deposition modeling, etc.);

[0044] 2) Performance tunability: Continuously adjust parameters such as hardness and density through material ratio (such as resin doping ratio) and structural design (such as filling rate).

[0045] Step 6: Intelligent output of the material parameter distribution in the three-dimensional voxel model by the AI, that is, predict and output the material distribution parameters through the trained AI model; the specific method includes inputting the preprocessed physical parameters of the existing complex geological body into the trained AI model, and then using the AI model based on the rules learned during training (sample library) to directly output the matching optimal material distribution parameters for the newly input geological data, and predict and output the distribution parameters of the material, which include material type, density, material permeability, material strength, etc.

[0046] Step 7: Automatic conversion and verification of the full model of complex geological structures. Form a printable three-dimensional voxel model with the determined geometric parameters and material distribution parameters of existing complex geological bodies, and verify the converted three-dimensional voxel model. In this step, based on the determined geometric parameters, use geological modeling software (such as Petrel, Gocad, Move) to generate a continuous three-dimensional voxel geometric model, and distribute the material distribution parameters into the three-dimensional voxel geometric model through an interpolation algorithm (such as Kriging method, etc.) to establish a three-dimensional voxel model that can reflect the geometric and physical parameter distributions of complex geological bodies. Then, verify the accuracy of the three-dimensional voxel model by comparing the actual geological data with the three-dimensional voxel model to ensure that the obtained three-dimensional voxel model can accurately reflect the spatial heterogeneity of the complex geological structure.

[0047] Step 8: Export of parametric full model slice data. That is, import the slices exported from the verified three-dimensional voxel model into a 3D printing device to complete the 3D printing of the complex geological structure model. The specific operation method is as follows: Slice the STL and other files exported from the three-dimensional voxel model into a layered structure suitable for 3D printing, and seamlessly connect with the 3D printer. Import the exported slice data into the 3D printer to perform 3D printing of the geological structure model to ensure the smooth progress of the printing process and achieve high-precision printing of the geological disaster model.

[0048] Step 9: Performance verification of the 3D printed complex geological structure model. That is, verify the performance of the 3D printed geological structure model to ensure that the printed geological structure model can accurately reflect the complex spatial heterogeneity of the actual geological structure. The specific verification method is as follows: Verify the performance of the 3D printed complex geological structure model by comparing the characteristics and parameters of the 3D printed complex geological structure model with those of the actual geological structure. This performance verification includes geometric accuracy verification, physical property verification, and mechanical property verification. Among them, the geometric accuracy verification is specifically to verify whether the geometric dimensions, shapes, and structures of the 3D printed model meet the design requirements; the physical property verification is specifically to verify whether the physical properties such as density, strength, and permeability of the 3D printed model are consistent with those of the actual geological structure. The mechanical property verification is specifically to verify whether the mechanical properties of the 3D printed model can meet the requirements of engineering simulation and experiments.

[0049] The 3D printed complex geological structure model obtained by the method of the above embodiments can accurately describe the complex spatial heterogeneity in the geological disaster model.

[0050] The above are only the preferred embodiments of the present invention and do not impose any limitation on the present invention. Any person skilled in the art within the technical field, without departing from the technical solution of the present invention, makes any form of equivalent substitution or modification and other changes to the technical solution and technical content disclosed by the present invention, which are all within the content of the technical solution of the present invention and still fall within the protection scope of the present invention.

Claims

1. An AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures, characterized in that, It includes the following steps: S1: Obtain multi-source characteristic data of the actual complex geological body and perform preprocessing; S2: Extract the geometric and physical parameters of the geological body based on the characteristic data of the geological body; S3: Map the extracted geometric parameters of the existing geological body into the 3D printing voxel model and convert them into geometric parameters that can be used for 3D printing; S4: Use intelligent algorithms to autonomously learn the non-linear mapping relationship between the physical parameters of historical complex geological bodies and the 3D printing material parameters, and construct and train an AI model; S5: Input the physical parameters of the existing complex geological body, and use the trained AI model to predict the 3D printing material distribution parameters; S6: Form a printable three-dimensional voxel model with the determined geometric parameters and material distribution parameters of the existing complex geological body, realize the conversion from the multi-source parameter distribution of the geological body to the complex geological model, and verify the converted three-dimensional voxel model; S7: Import the slices exported from the verified three-dimensional voxel model into the 3D printing device to complete the 3D printing of the complex geological structure model; S8: Verify the performance of the 3D printed complex geological structure model to ensure that the printed complex geological structure model can accurately reflect the complex spatial heterogeneity of the actual geological structure.

2. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, wherein In step S1, the data acquisition methods include physical model experiments, on-site exploration and measurement, core drilling and analysis, and remote sensing technology; the multi-source characteristic data includes ice-rock layer alternating structure, ice layer thickness, distribution of geological body structural planes, fracture network, porosity, and dip angle of the interlayer interface.

3. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, characterized in that In step S1, the preprocessing includes data cleaning, format conversion, and standardization processing.

4. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, characterized in that In step S2, the geometric and physical parameters of the geological body include geometric parameters and physical parameters. The geometric parameters include size, geometric shape, and positional relationship; the physical parameters include porosity, fracture rate, and dip angle of the geological body.

5. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, characterized in that, Step S3 specifically includes: determining the distribution of the geometric size of the complex geological body in the 3D printing voxel model according to the extracted geometric size data of the existing complex geological body; determining the distribution of the geometric shape of the complex geological body in the 3D printing voxel model according to the extracted geometric shape data; and determining the positional relationship of the complex geological body in the 3D printing voxel model according to the extracted positional relationship data.

6. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, wherein In step S4, use machine learning or deep learning intelligent algorithms to train the AI model with the existing historical geological characteristic parameters and 3D printing material parameters, and establish the non-linear mapping relationship between the physical parameters of the geological body and the 3D printing material parameters.

7. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, characterized in that In step S5, input the preprocessed physical parameters of the existing complex geological body into the trained AI model to predict the distribution parameters of the material. The distribution parameters include material type, density, material permeability, and material strength.

8. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, wherein In step S6, based on the determined geometric parameters, a continuous three-dimensional voxel geometric model is generated using geological modeling software. The material distribution parameters are assigned to the three-dimensional voxel geometric model through an interpolation algorithm to establish a three-dimensional voxel model that can reflect the geometric and physical parameter distributions of complex geological bodies. Then, by comparing the actual geological data with the three-dimensional voxel model, the accuracy of the three-dimensional voxel model is verified to ensure that the obtained three-dimensional voxel model can accurately reflect the spatial heterogeneity of complex geological structures.

9. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, wherein In step S7, the STL file exported from the three-dimensional voxel model is sliced into a layered structure suitable for 3D printing and seamlessly docked with a 3D printer. The exported sliced data is imported into the 3D printer to complete the 3D printing of the complex geological structure model.

10. The AI-driven intelligent modeling method for self-distribution of 3D printing parameters of complex geological structures according to claim 1, characterized in that, In step S8, by comparing the characteristics and parameters of the 3D printed complex geological structure model with those of the actual geological structure, the performance of the 3D printed complex geological structure model is verified; the performance verification includes geometric accuracy verification, physical property verification, and mechanical property verification.

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