Method for dynamically constructing coal mine geology digital twinborn body based on multi-source heterogeneous information

Abstract

The invention provides a method for dynamically constructing a coal mine geology digital twinborn body based on multi-source heterogeneous information. Comprising the steps of AIGC-assisted geological result reading, end-to-end structured information self-adaptive generation, primary geologic body model construction through a generalized triangular prism algorithm, geometric smooth subdivision based on Kriging interpolation, special geological element dynamic updating and coal mine digital twinborn matrix generation. According to the method, the AIGC is introduced to assist in reading the geological result innovatively from the remarkable characteristics of the data intensive normal form of the geological information, and intelligent generation of structured information matched with an automatic modeling updating algorithm is realized through big data assimilation of multi-source heterogeneous information; a quick response process of information processing, primary geologic body generation, geometric smoothing, abnormal geologic body encryption and global geologic body construction is formed, and a high-precision coal mine geologic digital model is dynamically constructed by utilizing geologic information of different geologic survey stages. And the requirements of high precision, renewability and automation of the coal mine digital twinborn body are met.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine digital geological model construction, and in particular to a method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information. Background Art

[0002] With the advancement of the intelligentization of coal mines, the industry has formed a coal mine digital twin technology route that relies on three-dimensional geological modeling and integrates full-time mining data, realizing the transformation from cognition to application. However, there are many non-end links and high maintenance costs in the geological modeling process. In practical applications, the model cannot be dynamically adjusted as geological cognition is updated. The lag of the model construction layer affects the equivalence of the twin system. Therefore, how to quickly and dynamically build a digital twin that can adapt to multi-time, multi-precision, and multi-format geological information has become a core issue that needs to be solved in order to fully realize the intelligentization of coal mines.

[0003] In terms of 3D geological modeling, the technology of 3D geological modeling of coal mines is relatively mature and can meet the demand for modeling accuracy. However, due to the multi-source heterogeneous characteristics of ground and air data, data preprocessing requirements are high, information utilization is low, and maintenance by professionals who have both geological knowledge and computer technology is required. There is still a lot of room for improvement in response speed and maintenance costs.

[0004] In terms of digital twin technology, the three-dimensional geological model, as the model construction layer of digital twin technology, assumes the role of geometric boundary and physical structure of the calculation layer. The accuracy of existing three-dimensional geological modeling technology can meet the application requirements, but with the advancement of excavation exposure and organizational exploration, the update speed of geological cognitive information does not match the response speed of the model construction layer, resulting in a lag in the "cognition-model" conversion, affecting the equivalence of the twin world and the accuracy of analytical decision-making.

[0005] Regarding the dynamic construction of coal mine geological digital twins, the full geological information at different stages is obtained through the "drilling, excavation, mining and falling" detection technology, and a three-dimensional quantitative random model is generated by combining various modeling algorithms. Although these methods have made some progress, due to the differences in information obtained at each stage and the limitations of modeling technology, the big data isomorphism of "four-along" multi-source dynamic geological information has not yet been achieved. Future research should promote the integration of multi-scale and different attribute data through the fusion strategy of data level, feature level and decision level, realize the dynamic correction and improvement of coal mine geological conditions, and thus promote the construction of coal mine digital twins.

[0006] In summary, the dynamic fusion of multi-source heterogeneous geological information and the construction of coal mine digital twins are important research directions for realizing the intelligent construction of coal mines, and have important theoretical value and practical significance. Summary of the invention

[0007] The inventor of this application has found through research that digital twin technology can significantly improve the efficiency of coal mines in risk assessment, decision support and mining scheduling by integrating three-dimensional modeling and real-time data. However, the existing geological modeling methods face problems such as delayed response and high maintenance costs, which limit the accuracy and practicality of digital twin systems. Therefore, how to dynamically update and integrate multi-period, multi-precision and multi-format geological data has become a key challenge in the intelligent construction of coal mines. To solve this problem, the present invention proposes a method for dynamically constructing coal mine geological digital twins based on multi-source heterogeneous information. The method introduces AIGC (Artificial Intelligence Generated Content) technology to assist in reading geological results, optimizes the data processing process by constructing an end-to-end structured information adaptive generation system, and uses the generalized tri-prism (Generalized Tri-Prism, GTP) algorithm to construct a primary geological body model, and combines the Kriging interpolation method to smoothly subdivide the geometric model. On this basis, special geological elements such as faults, collapse columns and anticlines are dynamically updated to finally construct a coal mine digital twin matrix. Through big data assimilation technology, this method can automatically process multi-source heterogeneous geological information, generate high-precision digital geological models, and realize real-time updating and optimization of the models through a rapid response mechanism, meeting the requirements of coal mine digital twins in terms of high precision, updatability and automated construction, and providing strong technical support for intelligent management and decision support of coal mines.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information includes the following steps:

[0010] S1. AIGC assists in reading geological results: For massive, heterogeneous and professional coal mine geological results and maps, a pre-trained large-scale language model is used as the underlying architecture. According to different geological tasks, the output direction, depth and accuracy of the model are finely controlled by adjusting the prompt words; the general capabilities of large-scale language models are not enough to directly solve professional problems in the field of geology. A special annotated geological corpus is introduced. After transfer learning or fine-tuning, the model can more accurately understand the terms, formulas and methodologies in geology, and improve the accuracy and efficiency of solving problems in the geological field; a custom interface is designed to allow users to interact with the model according to task requirements, input geological data, and receive analysis results generated by the model, so as to realize the automatic extraction of geological element information based on geological prior knowledge;

[0011] S2. End-to-end adaptive generation of structured information: Based on step S1, adaptive matching of the input end of parametric modeling is performed, including the JSON format output of the geometric elements of boreholes, faults, collapse columns, and anticlines, to achieve the end-to-end production process of geological results and maps to geological information that can be used for digital modeling. On the basis of supporting manual input and correction of errors, structured data for geological modeling is generated through interactive interface + generative artificial intelligence to achieve information-model process experience without touching the non-end links of information;

[0012] S3. Construct a primary geological model using the generalized triangular prism algorithm: Perform secondary development of the Unreal Engine, divide the structured data obtained in step S2 into stratigraphic matrix data and abnormal encrypted data. The abnormal encrypted data includes faults, collapse columns, and anticlines. The stratigraphic matrix data includes point, line, and surface data of stratigraphic distribution obtained through drilling, trenching, pitting, and geophysical exploration methods, and convert the stratigraphic matrix data into discrete distribution data of the upper and lower surfaces of each stratigraphic layer as observation data; use the Bowyer-Woston algorithm to construct an irregular triangulated network according to the Delaunay triangulation principle and generate ground surface data. Based on the ground surface irregular triangulated network , the triangles are expanded downward along the direction of the borehole one by one to generate generalized triangular prism elements, and the overall description model of the stratum is constructed; the specific expansion rules are: if the three vertices of the current triangle have the same stratum number, the vertex of the new triangle expanded downward is the next point on the corresponding borehole; if the stratum numbers are different, the vertex with a smaller number is expanded along the corresponding borehole to the next point, and the vertex with a larger number remains unchanged; the modeling process includes extracting the initial triangle from the irregular triangulated network of the ground surface, expanding the new triangle according to its stratum number, recording and constructing the generalized triangular prism, and gradually updating and completing the model until all triangles are processed, thereby forming a primary geological body model;

[0013] S4. Geometric smoothing subdivision based on Kriging interpolation: In order to solve the problem of uneven changes in the stratigraphic interface of the primary geological body model, an algorithm is established to evaluate the spatial smoothness of the generalized triangular prism model. According to the threshold control and the model's precision requirements, the area to be interpolated is obtained through iterative calculation. The Kriging interpolation method is used, assuming that the interpolation point is z o Obeying the inherent stationary process, under the conditions of unbiased estimation and uniform distribution, calculate the optimal weight coefficient λ that minimizes the variance between the estimated value and the actual value i , calculate the stratigraphic distribution information of the interpolation point, and after multiple interpolations, make the transition between adjacent triangular prisms and boreholes smooth;

[0014] S5. Dynamic update of special geological elements: convert the abnormal body structured data obtained in step S2 into the constraint part of the generalized triangular prism model. Taking the fault as an example, the coal seam section is spatially fitted based on the structured data obtained in step S2, and the change of the stratum height along the section or vertical section is characterized by an exponential decay function. The geological structure is displayed in an encrypted form in the form of a virtual borehole to realize the construction of a high-precision geological model.

[0015] S6. Generation of coal mine digital twin matrix: Utilize the large model with geological prior knowledge in step S1 to automatically extract and match the appropriate lithology description from the geological description of the borehole, and use the description as the input parameter to call the interface of the drawing large model to generate a map for rendering the coal mine geological digital twin matrix; the drawing large model generates high-quality maps that match the geological characteristics of the coal mine based on the lithology description to ensure that the geological characteristics of the map and the model correspond accurately; submit the generated maps and the three-dimensional geological model to the back-end system, and the back-end system performs high-performance computing and data processing to ensure the accurate display of the digital twin matrix during the coal mine geological analysis, demonstration and visualization process.

[0016] Furthermore, in step S2, end-to-end structured information is adaptively generated, and the user works through a visual process interface of original input-AI-assisted extraction-manual correction-upload. Taking faults as an example, the fault information that can be extracted from geological results and maps includes strike, dip, inclination, extended length, fault distance, and fault-coal intersection. The preset interface is called, and the general language model after fine-tuning and prompt word optimization can quickly focus on these key information and directly generate JSON format information that can be read by digital modeling software.

[0017] Furthermore, in step S4, the Kriging interpolation method is used to perform geometric smoothing for the uneven change of the stratum interface of the primary geological body model, assuming that z(x, y) is uniform in space and has the same variance σ 2 and expectation c, under the condition of unbiased estimation Under the condition of First, calculate the pairwise spatial correlation D of the observation data. ij and semivariance r ij =σ 2 -C ij , use the Gaussian model to fit the function Fitting, calculating the spatial correlation between unknown points and known points, calculating the semivariance r between unknown points and all known points i0 , substitute into the linear equations to calculate the optimal weight coefficient λ i , by Calculate the stratigraphic distribution information of the interpolation point, evaluate the spatial smoothness of the generalized triangular prism model, and iteratively calculate the interpolation area according to the accuracy requirements;

[0018] Where z is the height value of the stratum distribution, (x, y) is the plane coordinate, Var(·) is the function for calculating the variance, E(·) is the function for calculating the expectation, and C ij is the covariance of the ith observation and the jth observation, b0 is the nugget value, b is the arch height, f is the range, r i0 is the semivariance between the ith observation and the interpolation point, z i is the height value of the i-th observation data.

[0019] Furthermore, in step S5, the cross-section influence range parameter a is introduced and an exponential decay function Δh=h0e is used. -aΔL It describes the change of stratum height along the section or vertical section, h0 is the section distance, ΔL is the straight-line distance along the section or section dip.

[0020] Compared with the prior art, the method for dynamically constructing coal mine geological digital twins based on multi-source heterogeneous information provided by the present invention innovatively introduces AIGC technology to assist in reading geological results, and realizes intelligent generation of structured information matching the automated modeling update algorithm through big data assimilation of multi-source heterogeneous data. This process forms a rapid response process of "information processing-primary geological body generation-geometric smoothing-abnormal geological body encryption-global geological body construction", which can dynamically construct high-precision coal mine geological digital models based on geological data of different geological survey stages and different precisions, and make full use of geological information of all time periods. This method effectively solves the problems of high data preprocessing requirements and low information utilization in traditional geological body construction, meets the requirements of coal mine digital twin model construction in terms of high precision, updatability and automation, and can be used as the core technology for coal mine digital twin matrix construction. Therefore, compared with the prior art, it has the following advantages:

[0021] 1. Automated and efficient data processing capabilities: This method innovatively introduces AIGC technology and relies on a large language model to achieve automated processing of complex, multi-source and heterogeneous coal mine geological data. This mechanism optimizes the model output through prompt engineering and combines professional corpora in the field of geology to enhance the model's understanding of geological terms and methods through transfer learning or fine-tuning, enabling it to accurately and quickly extract and parse massive geological reports and maps. This automated data processing method not only greatly reduces manual intervention, but also significantly improves the efficiency of geological information acquisition and processing.

[0022] 2. End-to-end structured information generation process: This method realizes end-to-end automation from geological data extraction to model construction, forming a highly integrated structured information generation system. By designing a large model interface and combining it with a visual interface, users can directly extract and convert geological data such as drill holes and faults into structured formats such as JSON required for geological modeling, reducing complex intermediate processing steps.

[0023] 3. High-precision three-dimensional modeling and spatial smoothing optimization: In terms of geological modeling, this method constructs a primary geological body model based on the generalized triangular prism algorithm, which can handle complex structures in coal mine geology and generate detailed geological body descriptions. At the same time, the geological model is geometrically smoothed by the Kriging interpolation method, so that the stratigraphic interface of the model has a high degree of continuity and smoothness. This two-layer modeling optimization mechanism ensures the accuracy of the model structure, especially in the processing of complex strata and multi-level spatial relationships, effectively improving the authenticity and practicality of the geological model.

[0024] 4. Dynamic update capability of special geological elements: In view of the variability of complex geological elements such as faults, collapse columns, and anticlines in coal mines, this method has a dynamic update mechanism. Through real-time detection of geological features and structured data generation, combined with mathematical models such as exponential decay functions to accurately fit the changing trends of geological structures, the geological model can continuously reflect the latest geological knowledge. This dynamic update mechanism enables the digital twin of coal mines to be continuously optimized and updated during the operation process.

[0025] 5. High-quality digital twin visualization: In terms of visualization, the high-quality maps generated by AIGC technology are combined with the three-dimensional geological model to ensure that the digital twin has highly consistent geological characteristics and visualization effects. This model is not only intuitive in presenting geological data, but also enhances the transparency of information in the decision-making process, providing strong support for coal mine geological analysis, operation demonstration and intelligent management, and significantly improving the practicality of the digital twin. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of a method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0028] Please refer to Figure 1 As shown, the present invention provides a method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information, comprising the following steps:

[0029] S1. AIGC assists in reading geological results: For massive, heterogeneous and professional coal mine geological results and maps, a pre-trained large language model (Large Language Models (LLMs) such as the GPT series are used as the underlying architecture. The model output is optimized through prompt engineering. According to different geological tasks such as profile analysis, borehole extraction, and seismic data processing, the output direction, depth, and accuracy of the model are finely controlled by adjusting the prompt words. The general capabilities of large language models are not enough to directly solve professional problems in the field of geology. Specialized geological corpora with annotations such as academic papers, technical reports, and industry standards are introduced. After transfer learning or fine-tuning, the model can more accurately understand the terms, formulas, and methodologies in geology, and improve the accuracy and efficiency of solving problems in the geological field. Custom interfaces are designed to allow users to interact with the model according to task requirements, input geological data such as exploration data, stratigraphic information, seismic data, drilling maps, and geophysical reports, obtain the stratigraphic distribution and element information and description of geological anomalies of boreholes, trenching, pit exploration, and geophysical exploration, and receive the analysis results generated by the model to realize the automatic extraction of geological element information based on geological prior knowledge.

[0030] S2. End-to-end adaptive generation of structured information: Based on step S1, adaptive matching of the input end of parametric modeling is performed, including the output of geometric elements such as boreholes, faults, collapse columns, and anticlines in JSON (JavaScript Object Notation) format, to achieve the end-to-end production process of geological results and maps to geological information that can be used for digital modeling. On the basis of supporting manual input and correction of errors, structured data for geological modeling is assisted by interactive interface + generative artificial intelligence (Generative Artificial Intelligence), realizing the information-model process experience without touching non-end links such as intermediate information processing;

[0031] S3, generalized triangular prism algorithm to build a primary geological model: secondary development of Unreal Engine (Unreal Engine), the structured data obtained in step S2 is divided into stratigraphic matrix data and abnormal encrypted data, the abnormal encrypted data includes faults, collapse columns, anticlines, etc., the stratigraphic matrix data includes the point, line, and surface data of stratigraphic distribution obtained by drilling, trenching, pit exploration, geophysical exploration and other methods, and the stratigraphic matrix data is converted into discrete distribution data of the upper and lower surfaces of each stratigraphic layer as observation data; the Bowyer-Woston algorithm is used to construct an irregular triangulated network (TIN) according to the Delaunay triangulation principle and generate ground surface data, based on the ground surface irregular triangulated network, the triangles are converted one by one The shape is extended downward along the direction of the borehole to generate a generalized triangular prism element, and an overall description model of the stratum is constructed; the specific extension rule is: if the three vertices of the current triangle have the same stratum number, the vertex of the new triangle extended downward is the next point on the corresponding borehole; if the stratum numbers are different, the vertex with a smaller number is extended to the next point along the corresponding borehole, and the vertex with a larger number remains unchanged; the modeling process includes extracting the initial triangle from the irregular triangulated network of the ground surface, extending the new triangle according to its stratum number, recording and constructing the generalized triangular prism, and gradually updating and completing the model until all triangles are processed, thereby forming a primary geological body model;

[0032] S4. Geometric smoothing subdivision based on Kriging interpolation: In order to solve the problem of uneven changes in the stratigraphic interface of the primary geological body model, an algorithm is established to evaluate the spatial smoothness of the generalized triangular prism model. According to the threshold control and the model's precision requirements, the area to be interpolated is obtained through iterative calculation. The Kriging interpolation method is used, assuming that the interpolation point in the interpolation area is z o Obeying the intrinsically stationary process, under the conditions of unbiased estimation and uniform distribution, calculate the optimal weight coefficient λ that minimizes the variance between the estimated value and the actual value i , calculate the stratigraphic distribution information of the interpolation point, and after multiple interpolations, make the transition between adjacent triangular prisms and boreholes smooth;

[0033] S5. Dynamic update of special geological elements (including faults, collapse columns, and anticlines): convert the structural data of the abnormal body obtained in step S2 into the constraint part of the generalized triangular prism model. Taking the fault as an example, the strike, dip, dip angle, extension length, gap, and coal-break intersection point based on the structural data obtained in step S2 are used to fit the coal seam section in space, and an exponential decay function is used to characterize the change of the stratum height along the section or vertical section. The geological structure is displayed in an encrypted form in the form of virtual boreholes to realize the construction of a high-precision geological model.

[0034] S6. Generation of coal mine digital twin matrix: Utilize the large model with geological prior knowledge in step S1 to automatically extract and match the appropriate lithology description from the geological description of the borehole, and use the description as the input parameter to call the interface of the drawing large model to generate a map for rendering the coal mine geological digital twin matrix; the drawing large model generates a high-quality map that matches the coal mine geological characteristics based on the lithology description, and combines the high-quality map with the high-precision three-dimensional geological model to ensure that the geological characteristics of the map and the model accurately correspond; submit the generated map and the three-dimensional geological model to the back-end system, and the back-end system performs high-performance computing and data processing to ensure the accurate display of the digital twin matrix during the coal mine geological analysis, demonstration and visualization process.

[0035] As a specific embodiment, end-to-end structured information is adaptively generated in step S2, and the user works through a visual process interface of original input-AI assisted extraction-manual correction-upload. Taking faults as an example, the fault information that can be extracted from geological results and maps includes strike, dip, inclination, extension length, fault distance, fault coal intersection, etc., and the preset interface is called. The general language model after fine-tuning and prompt word optimization can quickly focus on these key information and directly generate JSON format information that can be read by digital modeling software.

[0036] As a specific embodiment, in step S4, the Kriging interpolation method is used to perform geometric smoothing for the uneven change of the stratum interface of the primary geological body model, assuming that z(x, y) is uniform in space and has the same variance σ 2 and the expectation c (which is the expectation of z), satisfying the unbiased estimation condition Under the condition of First, calculate the pairwise spatial correlation (Euclidean distance) of the observed data D ij and semivariance r ij =σ 2 -C ij , use the Gaussian model to fit the function Fitting, calculating the spatial correlation (Euclidean distance) between unknown points and known points, calculating the semivariance r between unknown points and all known points i0 , substitute into the linear equations to calculate the optimal weight coefficient λ i , by Calculate the stratigraphic distribution information of the interpolation point, evaluate the spatial smoothness of the generalized triangular prism model, and iteratively calculate the interpolation area according to the accuracy requirements;

[0037] Where z is the height value of the stratum distribution, (x, y) is the plane coordinate, Var(·) is the function for calculating the variance, E(·) is the function for calculating the expectation, and C ijis the covariance of the ith observation and the jth observation, b0 is the nugget value, b is the arch height, f is the range, r i0 is the semivariance between the ith observation and the interpolation point, z i is the height value of the i-th observation data.

[0038] As a specific embodiment, in step S5, special geological elements (such as faults, collapse columns, and anticlines) are dynamically updated, and the abnormal body structured data obtained in step S2 is converted into the constraint part of the generalized triangular prism model; taking the fault as an example, the coal seam section is spatially fitted by obtaining structured data such as strike, dip, dip angle, extension length, fault distance, and coal-fault intersection, and by introducing the section influence range parameter a, an exponential decay function Δh=h0e is used. -aΔL Characterizes the change of stratum height along the section or vertical section; where h0 is the section distance and ΔL is the straight-line distance along the section or section dip.

[0039] In order to better reflect the technical effect of the present invention, a field test will be carried out in the Mengcun Coal Mine in Shaanxi Province. The specific steps are as follows:

[0040] S1. Collect and organize historical geological survey data, including multi-level geological drilling column charts with different spacing, three-dimensional geological exploration reports and maps, transient resistivity exploration results, channel wave exploration maps, etc., upload them to the large model with the help of a predetermined visual interface, and extract geological element information; divide the strata into 11 levels, and obtain the following effective geological elements: 43 first-level boreholes (spacing 750m), 23 second-level boreholes (spacing 100m), 698 third-level boreholes (spacing 10m), and 15 reliable faults.

[0041] S2. According to the input requirements of the Unreal Engine modeling platform, the geological feature information is output in JSON format, and the primary model is constructed using the drilling data and the generalized triangular prism voxel algorithm. The spatial smoothness of the model is calculated, and the interpolation area is calculated according to the threshold control and accuracy requirements. The geometric smoothing is performed using the Kriging interpolation method, and it is iterated 2 to 3 times to achieve a smooth transition between adjacent triangular prisms and drilling holes.

[0042] S3. Based on the geometric element data of the fault anomaly, fit the coal seam section in space, set the section influence range parameter a, and use the exponential decay function Δh=h0e -aΔL Describe the change of stratum height along the section or vertical section, inversely calculate the stratum distribution on both sides of the fault, and encrypt the geological structure through virtual drilling, ultimately realizing the construction of a high-precision geological model.

[0043] S4. Lithology information is automatically extracted from the borehole geological description and used as input to generate the coal mine geological digital twin base map. The map matches the coal mine geological characteristics and is combined with the 3D geological model to ensure consistency. Finally, the map and 3D model are submitted to the back-end system, providing a basis for the subsequent construction of the digital twin system.

[0044] Compared with the prior art, the method for dynamically constructing coal mine geological digital twins based on multi-source heterogeneous information provided by the present invention innovatively introduces AIGC technology to assist in reading geological results, and realizes intelligent generation of structured information matching the automated modeling update algorithm through big data assimilation of multi-source heterogeneous data. This process forms a rapid response process of "information processing-primary geological body generation-geometric smoothing-abnormal geological body encryption-global geological body construction", which can dynamically construct high-precision coal mine geological digital models based on geological data of different geological survey stages and different precisions, and make full use of geological information of all time periods. This method effectively solves the problems of high data preprocessing requirements and low information utilization in traditional geological body construction, meets the requirements of coal mine digital twin model construction in terms of high precision, updatability and automation, and can be used as the core technology for coal mine digital twin matrix construction. Therefore, compared with the prior art, it has the following advantages:

[0045] 1. Automated and efficient data processing capabilities: This method innovatively introduces AIGC technology and relies on a large language model to achieve automated processing of complex, multi-source and heterogeneous coal mine geological data. This mechanism optimizes the model output through prompt engineering and combines professional corpora in the field of geology to enhance the model's understanding of geological terms and methods through transfer learning or fine-tuning, enabling it to accurately and quickly extract and parse massive geological reports and maps. This automated data processing method not only greatly reduces manual intervention, but also significantly improves the efficiency of geological information acquisition and processing.

[0046] 2. End-to-end structured information generation process: This method realizes end-to-end automation from geological data extraction to model construction, forming a highly integrated structured information generation system. By designing a large model interface and combining it with a visual interface, users can directly extract and convert geological data such as drill holes and faults into structured formats such as JSON required for geological modeling, reducing complex intermediate processing steps.

[0047] 3. High-precision three-dimensional modeling and spatial smoothing optimization: In terms of geological modeling, this method constructs a primary geological body model based on the generalized triangular prism algorithm, which can handle complex structures in coal mine geology and generate detailed geological body descriptions. At the same time, the geological model is geometrically smoothed by the Kriging interpolation method, so that the stratigraphic interface of the model has a high degree of continuity and smoothness. This two-layer modeling optimization mechanism ensures the accuracy of the model structure, especially in the processing of complex strata and multi-level spatial relationships, effectively improving the authenticity and practicality of the geological model.

[0048] 4. Dynamic update capability of special geological elements: In view of the variability of complex geological elements such as faults, collapse columns, and anticlines in coal mines, this method has a dynamic update mechanism. Through real-time detection of geological features and structured data generation, combined with mathematical models such as exponential decay functions to accurately fit the changing trends of geological structures, the geological model can continuously reflect the latest geological knowledge. This dynamic update mechanism enables the digital twin of coal mines to be continuously optimized and updated during the operation process.

[0049] 5. High-quality digital twin visualization: In terms of visualization, the high-quality maps generated by AIGC technology are combined with the three-dimensional geological model to ensure that the digital twin has highly consistent geological characteristics and visualization effects. This model is not only intuitive in presenting geological data, but also enhances the transparency of information in the decision-making process, providing strong support for coal mine geological analysis, operation demonstration and intelligent management, and significantly improving the practicality of the digital twin.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information, characterized in that: The following steps are involved: S1. AIGC assists in reading geological results: For massive, heterogeneous and professional coal mine geological results and maps, a pre-trained large-scale language model is used as the underlying architecture. According to different geological tasks, the output direction, depth and accuracy of the model are finely controlled by adjusting the prompt words. The general capabilities of large-scale language models are not enough to directly solve professional problems in the field of geology. The introduction of annotated specialized geological corpus, through transfer learning or fine-tuning, enables the model to more accurately understand the terms, formulas and methodologies in geology, and improve the accuracy and efficiency of solving problems in the field of geology. Design a custom interface that allows users to interact with the model according to task requirements, input geological data, and receive analysis results generated by the model, thereby achieving automated extraction of geological element information based on prior geological knowledge; S2. End-to-end adaptive generation of structured information: Based on step S1, adaptive matching of the input end of parametric modeling is performed, including the JSON format output of the geometric elements of boreholes, faults, collapse columns, and anticlines, to achieve the end-to-end production process of geological results and maps to geological information that can be used for digital modeling. On the basis of supporting manual input and correction of errors, structured data for geological modeling is generated through interactive interface + generative artificial intelligence to achieve information-model process experience without touching the non-end links of information; S3. Construct a primary geological model using the generalized triangular prism algorithm: Perform secondary development of the Unreal Engine, divide the structured data obtained in step S2 into stratigraphic matrix data and abnormal encrypted data. The abnormal encrypted data includes faults, collapse columns, and anticlines. The stratigraphic matrix data includes point, line, and surface data of stratigraphic distribution obtained through drilling, trenching, pitting, and geophysical exploration methods, and convert the stratigraphic matrix data into discrete distribution data of the upper and lower surfaces of each stratigraphic layer as observation data; use the Bowyer-Woston algorithm to construct an irregular triangulated network according to the Delaunay triangulation principle and generate ground surface data. Based on the ground surface irregular triangulated network , the triangles are expanded downward along the direction of the borehole one by one to generate generalized triangular prism elements, and the overall description model of the stratum is constructed; the specific expansion rules are: if the three vertices of the current triangle have the same stratum number, the vertex of the new triangle expanded downward is the next point on the corresponding borehole; if the stratum numbers are different, the vertex with a smaller number is expanded along the corresponding borehole to the next point, and the vertex with a larger number remains unchanged; the modeling process includes extracting the initial triangle from the irregular triangulated network of the ground surface, expanding the new triangle according to its stratum number, recording and constructing the generalized triangular prism, and gradually updating and completing the model until all triangles are processed, thereby forming a primary geological body model; S4. Geometric smoothing subdivision based on Kriging interpolation: In order to solve the problem of uneven changes in the stratigraphic interface of the primary geological body model, an algorithm is established to evaluate the spatial smoothness of the generalized triangular prism model. According to the threshold control and the model's precision requirements, the area to be interpolated is obtained through iterative calculation. The Kriging interpolation method is used, assuming that the interpolation point is z o Obeying the inherent stationary process, under the conditions of unbiased estimation and uniform distribution, calculate the optimal weight coefficient λ that minimizes the variance between the estimated value and the actual value i , calculate the stratigraphic distribution information of the interpolation point, and after multiple interpolations, make the transition between adjacent triangular prisms and boreholes smooth; S5. Dynamic update of special geological elements: convert the abnormal body structured data obtained in step S2 into the constraint part of the generalized triangular prism model. Taking the fault as an example, the coal seam section is spatially fitted based on the structured data obtained in step S2, and the change of the stratum height along the section or vertical section is characterized by an exponential decay function. The geological structure is displayed in an encrypted form in the form of a virtual borehole to realize the construction of a high-precision geological model. S6. Generation of coal mine digital twin matrix: Utilize the large model with geological prior knowledge in step S1 to automatically extract and match the appropriate lithology description from the geological description of the borehole, and use the description as the input parameter to call the interface of the drawing large model to generate a map for rendering the coal mine geological digital twin matrix; the drawing large model generates high-quality maps that match the geological characteristics of the coal mine based on the lithology description to ensure that the geological characteristics of the map and the model correspond accurately; submit the generated maps and the three-dimensional geological model to the back-end system, and the back-end system performs high-performance computing and data processing to ensure the accurate display of the digital twin matrix during the coal mine geological analysis, demonstration and visualization process.

2. The method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information according to claim 1 is characterized in that: In step S2, end-to-end structured information is adaptively generated, and the user works through a visual process interface of original input-AI-assisted extraction-manual correction-upload. Taking faults as an example, the fault information that can be extracted from geological results and maps includes strike, dip, inclination, extended length, fault distance, and fault-coal intersection. The preset interface is called, and the general language model after fine-tuning and prompt word optimization can quickly focus on these key information and directly generate JSON format information that can be read by digital modeling software.

3. The method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information according to claim 1 is characterized in that: In step S4, the Kriging interpolation method is used to perform geometric smoothing for the uneven change of the stratum interface of the primary geological body model, assuming that z(x, y) is uniform in space and has the same variance σ 2 and expectation c, under the condition of unbiased estimation Under the condition of First, calculate the pairwise spatial correlation D of the observation data. ij and semivariance r ij =σ 2 -C ij , use the Gaussian model to fit the function Fitting, calculating the spatial correlation between unknown points and known points, calculating the semivariance r between unknown points and all known points i0 , substitute into the linear equations to calculate the optimal weight coefficient λ i , by Calculate the stratigraphic distribution information of the interpolation point, evaluate the spatial smoothness of the generalized triangular prism model, and iteratively calculate the interpolation area according to the accuracy requirements; Where z is the height value of the stratum distribution, (x, y) is the plane coordinate, Var(·) is the function for calculating the variance, E(·) is the function for calculating the expectation, and C ij is the covariance of the ith observation and the jth observation, b0 is the nugget value, b is the arch height, f is the range, r i0 is the semivariance between the ith observation and the interpolation point, z i is the height value of the i-th observation data.

4. The method for dynamically constructing a coal mine geological digital twin based on multi-source heterogeneous information according to claim 1 is characterized in that: In step S5, the cross-section influence range parameter a is introduced and an exponential decay function Δh=h0e is used. -aΔL It describes the change of stratum height along the section or vertical section, h0 is the section distance, ΔL is the straight-line distance along the section or section dip.

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