Artificial Intelligence-Based Simulation Twin Method and System for Coal Mine Tunneling Navigation

Abstract

This application discloses an AI-based simulation twin method and system for coal mine tunneling navigation, relating to the field of digital data processing. The method includes: using AI intelligent tools to parse coal mine tunneling operation procedure documents and extract tunneling content; using AI intelligent tools to verify the completeness of the coal mine tunneling operation procedure document based on the extracted tunneling content, obtaining a verification result; when the verification result is positive, retrieving the model corresponding to the tunneling content from a model database to generate a digital twin model; generating a dynamic simulation scene for coal mine tunneling navigation based on the digital twin model and the tunneling content; when the verification result is negative, using AI intelligent tools to obtain detection results of missing content in the coal mine tunneling operation procedure document based on the extracted tunneling content, and generating document improvement suggestions based on the detection results. This application facilitates better understanding of coal mine tunneling operation procedures by mine operators, improving the utilization efficiency and management level of coal mine tunneling operation procedures.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric digital data processing, in particular to a coal mine tunneling navigation simulation twin method and system based on artificial intelligence (AI). BACKGROUND

[0002] With the continuous improvement of coal mine safety production requirements, accurate understanding and execution of tunneling operation procedures become particularly important. Tunneling operation procedures cover safety elements such as construction technology, geological prediction, ventilation guarantee, and support design, and there is a phenomenon of technical standard superposition of multiple professional intersections, resulting in a large content system and low executability and high application threshold.

[0003] The current analysis methods of tunneling operation procedure files mainly include manual analysis and review methods, template comparison analysis methods, digital software assisted analysis methods, natural language processing (NLP) based intelligent analysis methods, and multi-system integrated analysis methods. Among them, the manual analysis and review method mainly reads and analyzes the procedure files one by one by professional technicians or review teams, and conducts compliance and feasibility evaluation combined with field experience, which has the disadvantages of low efficiency, strong subjectivity, and easy omission of details. The template comparison analysis method mainly uses standardized procedure templates to compare whether the existing files meet industry standards, which has the disadvantages of insufficient flexibility and lagging update. The digital software assisted analysis method mainly uses professional software (such as 2025B tunneling operation procedure software) to automatically extract key parameters (such as support design and blasting parameters) and perform logical verification, which has the disadvantages of relying on data quality and limited intelligence. The natural language processing (NLP) based intelligent analysis method automatically identifies key information (such as safety measures and geological description) in the text and stores it in categories, which has the disadvantages of immature technology and lack of industry adaptation. The multi-system integrated analysis method mainly combines geographic information system (GIS) and building information modeling (BIM) technologies to associate procedure content with three-dimensional tunnel models to realize visual verification, which has the disadvantages of high cost and poor data compatibility. SUMMARY

[0004] To solve the above-mentioned shortcomings of the prior art, the present application provides a coal mine tunneling navigation simulation twin method and system based on artificial intelligence, so as to facilitate the mine operation personnel to better understand the coal mine tunneling operation procedure, and to improve the utilization efficiency and management level of the coal mine tunneling operation procedure.

[0005] To achieve the above object, the application provides the following scheme:

[0006] In a first aspect, the application provides a coal mine tunneling navigation simulation twin method based on artificial intelligence, comprising:

[0007] Obtaining a coal mine tunneling operation procedure document;

[0008] Analyzing the coal mine tunneling operation procedure document by using an AI intelligent tool, and extracting tunneling content based on the analysis result;

[0009] Checking whether the coal mine tunneling operation procedure document is perfect based on the extracted tunneling content by using an AI intelligent tool, to obtain a checking result;

[0010] When the checking result is yes, a model corresponding to the tunneling content is called from a model database to generate a digital twin model; the model database is constructed based on a three-dimensional model of a coal mine tunneling device;

[0011] Based on the digital twin model, a dynamic simulation scene of coal mine tunneling navigation is generated in combination with the tunneling content;

[0012] When the checking result is no, a detection result of missing content in the coal mine tunneling operation procedure document is obtained based on the extracted tunneling content by using an AI intelligent tool, and a document perfecting suggestion is generated based on the detection result.

[0013] Optionally, the method further comprises:

[0014] Obtaining initial training data; the initial training data includes a plurality of perfect historical coal mine tunneling operation procedure documents corresponding to different coal mine tunneling conditions;

[0015] Extracting core content based on the initial training data to form a core content template; the core content is indispensable data in the coal mine tunneling operation procedure.

[0016] Optionally, checking whether the coal mine tunneling operation procedure document is perfect based on the extracted tunneling content by using an AI intelligent tool, to obtain a checking result, comprises:

[0017] Performing semantic matching on the extracted tunneling content and the core content template by using an AI intelligent tool, and determining a matching similarity;

[0018] When the matching similarity is greater than a set threshold, the checking result is yes;

[0019] When the matching similarity is less than or equal to the set threshold, the checking result is no.

[0020] Optionally, an AI intelligent tool is used to obtain a detection result of missing content in the coal mine tunneling operation procedure document based on the extracted tunneling content, including:

[0021] An AI intelligent tool is used to obtain missing content in the coal mine tunneling operation procedure document based on the core content template and the extracted tunneling content.

[0022] Optionally, the construction process of the model database includes:

[0023] Obtain tunneling equipment covered by the coal mine tunneling face and attribute data of the tunneling equipment; the attribute data includes technical parameters, performance indicators, component information, model, size, and name;

[0024] Construct a three-dimensional model of the tunneling equipment according to a set proportion;

[0025] Generate a label based on the attribute data of the tunneling equipment;

[0026] Use the label to classify and store the constructed three-dimensional model to form the model database.

[0027] Optionally, the constructed three-dimensional model is classified and stored using a tree structure.

[0028] Optionally, an AI intelligent tool is used to analyze the coal mine tunneling operation procedure document and extract tunneling content based on the analysis result, including:

[0029] An AI intelligent tool is used to analyze the structure of the coal mine tunneling operation procedure document to obtain an analysis result; the analysis result includes chapters, paragraphs, and lists;

[0030] Perform semantic extraction on the analysis result to obtain the tunneling content; the tunneling content includes engineering overview, equipment tunneling machine process, geological and hydrological conditions, roadway layout and support instructions, construction technology, support components, safety technical measures, and labor organization and technical indicators.

[0031] Optionally, a model corresponding to the tunneling content is called from the model database to generate a digital twin model, including:

[0032] Call a three-dimensional model corresponding to the tunneling equipment involved in the equipment tunneling machine process from the model database to obtain a model calling result;

[0033] Construct a mathematical model corresponding to the tunneling content;

[0034] Based on the model calling result and the mathematical model, construct a digital twin model in combination with the tunneling content.

[0035] In a second aspect, the application provides an artificial intelligence-based coal mine tunneling navigation simulation twin system, comprising:

[0036] a user terminal for uploading a coal mine tunneling operation procedure document;

[0037] a server for data interaction with the user terminal, for implementing the artificial intelligence-based coal mine tunneling navigation simulation twin method provided above, generating a dynamic simulation scene or document improvement suggestion of coal mine tunneling navigation, and sending the dynamic simulation scene or document improvement suggestion of coal mine tunneling navigation to the user terminal for display.

[0038] Optionally, the user terminal is one or more of various desktop computers, notebook computers, smartphones, tablet computers, Internet of Things devices, and portable wearable devices.

[0039] According to the specific embodiments provided by the application, the application has the following technical effects:

[0040] The application provides an artificial intelligence-based coal mine tunneling navigation simulation twin method and system. By using an AI intelligent tool to analyze a coal mine tunneling operation procedure document and extracting tunneling content based on the analysis result, coal mine tunneling operation procedure document verification, etc. can be performed, which can solve the problems of low efficiency, strong subjectivity, easy omission of details, insufficient flexibility, and lagging update in the analysis process of coal mine tunneling operation procedures. Based on the model corresponding to the tunneling content retrieved from the model database and the extracted tunneling content, a digital twin model is generated, and dynamic simulation of coal mine tunneling navigation is realized, so that mine personnel can better understand the coal mine tunneling operation procedure, and the problems of lack of industry adaptation, high cost, and poor data compatibility of existing systems can be overcome. Moreover, by using an AI intelligent tool to obtain a detection result of missing content in the coal mine tunneling operation procedure document based on the extracted tunneling content, a document improvement suggestion is generated based on this detection result, which can help users quickly find defects in the coal mine tunneling operation procedure, further improve the generation and modification efficiency of the coal mine tunneling operation procedure, and thus improve the utilization efficiency and management level of the coal mine tunneling operation procedure. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0042] Figure 1 A flowchart of an artificial intelligence-based coal mine tunneling navigation simulation twin method provided by an embodiment of the application;

[0043] Figure 2 A schematic diagram of the structure of an artificial intelligence-based coal mine tunneling navigation simulation twin system provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] In one exemplary embodiment, this application provides an artificial intelligence-based simulation twin method for coal mine tunneling navigation. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is described using a server as an example. Figure 1 As shown, the method includes:

[0048] Step 100: Obtain the coal mine tunneling operation procedure document. The coal mine tunneling operation procedure document can be in PDF, Word, or image format.

[0049] Step 101: Use AI intelligent tools to parse the coal mine tunneling operation procedure document and extract the tunneling content based on the parsing results.

[0050] Step 102: Use AI intelligent tools to verify the completeness of the coal mine tunneling operation procedure document based on the extracted tunneling content, and obtain the verification results.

[0051] Step 103: When the verification result is yes, retrieve the model corresponding to the tunneling content from the model database and generate a digital twin model. The model database is constructed based on the 3D model of coal mine tunneling equipment.

[0052] Step 104: Based on the digital twin model, generate a dynamic simulation scenario for coal mine tunneling navigation by combining the tunneling content.

[0053] Step 105: When the verification result is negative, use AI intelligent tools to obtain the detection results of missing content in the coal mine tunneling operation procedure document based on the extracted tunneling content, and generate document improvement suggestions based on the detection results.

[0054] By implementing steps 100 to 105 above, mine operators can better understand the coal mine tunneling operation procedures. Furthermore, applying AI-powered tools to read and analyze the coal mine tunneling operation procedure documents, extracting core content, and ultimately generating corresponding 3D simulation animation scenes based on the extracted core content can improve the utilization efficiency and management level of the coal mine tunneling operation procedures.

[0055] In another exemplary embodiment of this application, in order to further improve the accuracy of extracting tunneling content from coal mine tunneling operation procedure documents, the artificial intelligence-based coal mine tunneling navigation simulation twin method provided in this application may further include the following steps 200 and 201.

[0056] Step 200: Obtain initial training data. The initial training data includes multiple complete historical coal mine tunneling operation procedure documents corresponding to different coal mine tunneling conditions.

[0057] Step 201: Extract core content based on the initial training data to form a core content template. The core content consists of necessary (indispensable) data in the coal mine tunneling operation procedures.

[0058] Furthermore, based on the descriptions of steps 200 and 201 above, in step 105, AI intelligent tools can be used to obtain the missing content in the coal mine tunneling operation procedure document based on the core content template and the extracted tunneling content.

[0059] In another exemplary embodiment of this application, to avoid the problem of omitting key content in coal mine tunneling operation procedure documents due to different description methods, this application uses semantic matching instead of the traditional keyword matching method for verifying coal mine tunneling operation procedure documents. Based on this, in step 102 above, the process of using AI intelligent tools to verify the completeness of the coal mine tunneling operation procedure document based on the extracted tunneling content and obtaining the verification result includes:

[0060] AI-powered tools are used to semantically match the extracted tunneling content with the core content template and determine the matching similarity. When the matching similarity is greater than a set threshold, the verification result is "yes." When the matching similarity is less than or equal to the set threshold, the verification result is "no." The specific value of the set threshold can be determined based on the actual requirements of coal mine tunneling operations.

[0061] In another exemplary embodiment of this application, in order to form a more comprehensive database, the process of constructing the model database includes:

[0062] Step 300: Obtain the tunneling equipment covered by the coal mine tunneling face and the attribute data of the tunneling equipment. The attribute data includes: technical parameters, performance indicators, component information, model, size and name.

[0063] Step 301: Construct a three-dimensional model of the tunneling equipment according to the set scale.

[0064] In practical applications, a 1:1 full-scale, high-precision 3D model of the tunneling equipment can be constructed using general-purpose modeling software, ensuring that the overall dimensional error of the model does not exceed ±2%. Specifically, the error of key components (such as cutting drums, rakes, and shovels) must be controlled within ±1%. Furthermore, refined modeling techniques must be employed during the modeling process to avoid low-poly simplification, fully meeting the multiple needs of industrial design visualization, technical solution demonstration, and simulation analysis. The final 3D model should possess accuracy in mechanical structure, high fidelity in appearance, and strong visual aesthetic appeal.

[0065] Step 302: Generate tags based on the attribute data of the tunneling equipment.

[0066] Step 303: Use tags to classify and store the constructed 3D models to form a model database.

[0067] In another exemplary embodiment of this application, in order to improve the accuracy of model matching, a tree structure can be used to classify and store the constructed 3D models. For example, the resulting tree-like classification and storage results are shown in Table 1. In practical applications, the classification and storage results can also be stored as a tree-like image.

[0068] Table 1. Tree-like classification storage structure

[0069]

[0070] In another exemplary embodiment of this application, the implementation process of step 101 described above may include:

[0071] Step 400: Use AI-powered tools to analyze the structure of the coal mine tunneling operation procedure document and obtain the analysis results. The analysis results include: chapters, paragraphs, and lists.

[0072] Step 401: Semantic extraction is performed on the parsing results to obtain the tunneling content. The tunneling content includes: project overview, tunneling machine technology, geological and hydrological conditions, tunnel layout and support description, construction technology, support components, safety technical measures, and labor organization and technical indicators.

[0073] The project overview includes basic information such as tunnel name, excavation purpose, design length, and service life. The equipment and tunneling machine process includes the type of equipment used, the relative working positions of various equipment, equipment models, electrical parameters, mechanical parameters, and hydraulic parameters. The geological and hydrological conditions include information on coal seam occurrence characteristics, gas level, ground temperature, and ground pressure. The tunnel layout and support specifications include key support information such as tunnel cross-section, support type, and anchor bolt parameters. The construction technology includes construction methods, drilling methods, and blasting operations. Support components include support location, support type (anchor mesh or anchor lock), anchor bolt size, anchor bolt material, and anchor bolt model. Safety technical measures include safety technical measures such as ventilation and dust control, water prevention management, and electromechanical management. The labor organization and technical indicators include personnel allocation for roles such as team leader, deputy team leader, technician, material handler, driver, and electrician.

[0074] Furthermore, the AI ​​tools used in this application can be Deepseek, Kimi, Doubao, etc. For example, calling the Deepseek interface to identify user-uploaded coal mine tunneling operation procedure documents can accurately parse the document structure, including chapters, paragraphs, lists, etc. For coal mine tunneling operation procedure documents of a general size, the total time for Deepseek to parse and extract key content is no more than 1 minute, and the accuracy rate of key content extraction is no less than 90%.

[0075] In another exemplary embodiment of this application, in order to facilitate users' further understanding of the coal mine tunneling operation procedures, coal mine tunneling simulation can be achieved by constructing a digital twin model corresponding to the coal mine tunneling operation procedures. Based on this, the process of generating the digital twin model in step 103 includes:

[0076] Step 500: Retrieve the 3D model corresponding to the tunneling equipment involved in the tunneling machine process from the model database, and obtain the model retrieval result.

[0077] Step 501: Construct a mathematical model corresponding to the excavation content.

[0078] Step 502: Based on the model retrieval results and mathematical model, construct a digital twin model by combining the tunneling content. The final constructed digital twin model comprehensively covers all aspects of the tunneling project involved in the coal mine tunneling operation procedures document.

[0079] Based on the same inventive concept, this application also provides an AI-based coal mine tunneling navigation simulation twin system for implementing the aforementioned AI-based coal mine tunneling navigation simulation twin method. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more AI-based coal mine tunneling navigation simulation twin system embodiments provided below can be found in the limitations of the AI-based coal mine tunneling navigation simulation twin method described above, and will not be repeated here.

[0080] In one exemplary embodiment, such as Figure 2 As shown, an artificial intelligence-based coal mine tunneling navigation simulation twin system is provided, comprising: a user terminal 1 and a server terminal 2. The server terminal 2 interacts with the user terminal 1 for data exchange.

[0081] User-side 1 is used to upload coal mine tunneling operation procedures documents and display dynamic simulation scenarios or document improvement suggestions for coal mine tunneling navigation. Server-side 2 is used to implement the aforementioned AI-based coal mine tunneling navigation simulation twin method to generate dynamic simulation scenarios or document improvement suggestions for coal mine tunneling navigation.

[0082] As an optional implementation, user terminal 1 can be one or more of various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Among these, IoT devices can be smart TVs, smart in-vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc.

[0083] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores AI-based coal mine tunneling navigation simulation twin data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an AI-based coal mine tunneling navigation simulation twin method.

[0084] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0085] In one exemplary embodiment, this application may provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0086] In one exemplary embodiment, this application may also provide a computer program product comprising a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0088] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0089] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A simulated twin method for coal mine tunneling navigation based on artificial intelligence, characterized in that, include: Obtain coal mine tunneling operation procedures documents; AI-powered tools were used to analyze the coal mine tunneling operation procedure document, and tunneling content was extracted based on the analysis results. AI-powered tools were used to verify the completeness of the coal mine tunneling operation procedure document based on the extracted tunneling content, and the verification results were obtained. When the verification result is yes, the model corresponding to the tunneling content is retrieved from the model database to generate a digital twin model; the model database is constructed based on the three-dimensional model of the coal mine tunneling equipment. Based on the digital twin model, a dynamic simulation scenario for coal mine tunneling navigation is generated by combining the tunneling content. When the verification result is negative, AI intelligent tools are used to obtain the detection result of missing content in the coal mine tunneling operation procedure document based on the extracted tunneling content, and document improvement suggestions are generated based on the detection result. The method further includes: Acquire initial training data; the initial training data includes multiple complete historical coal mine tunneling operation procedure documents corresponding to different coal mine tunneling conditions; Based on the initial training data, core content is extracted to form a core content template; the core content is indispensable data in the coal mine tunneling operation procedures. AI-powered tools were used to detect missing content in the coal mine tunneling operation procedure document based on extracted tunneling data. The results included: AI-powered tools were used to extract missing content from coal mine tunneling operation procedures documents based on core content templates and extracted tunneling content.

2. The artificial intelligence-based simulation twin method for coal mine tunneling navigation according to claim 1, characterized in that, AI-powered tools are used to verify the completeness of the coal mine tunneling operation procedure document based on extracted tunneling content, yielding verification results including: AI-powered tools are used to perform semantic matching between the extracted content and the core content template, and the matching similarity is determined. When the matching similarity is greater than the set threshold, the verification result is yes; When the matching similarity is less than or equal to a set threshold, the verification result is negative.

3. The artificial intelligence-based simulation twin method for coal mine tunneling navigation according to claim 1, characterized in that, The process of constructing the model database includes: Obtain the tunneling equipment covered by the coal mine tunneling face and the attribute data of the tunneling equipment; the attribute data includes: Technical parameters, performance indicators, component information, model, dimensions and names; Construct a three-dimensional model of the tunneling equipment according to the set scale; Tags are generated based on the attribute data of the tunneling equipment; The constructed 3D models are categorized and stored using the aforementioned tags to form the model database.

4. The artificial intelligence-based simulation twin method for coal mine tunneling navigation according to claim 3, characterized in that, A tree structure is used to categorize and store the constructed 3D models.

5. The artificial intelligence-based simulation twin method for coal mine tunneling navigation according to claim 1, characterized in that, AI-powered tools were used to analyze the coal mine tunneling operation procedure document, and tunneling content was extracted based on the analysis results, including: AI-powered tools were used to analyze the structure of the coal mine tunneling operation procedure document, resulting in the analysis results, which included chapters, paragraphs, and lists. The semantics of the parsing results are extracted to obtain the tunneling content; the tunneling content includes: project overview, equipment tunneling machine technology, geological and hydrological conditions, tunnel layout and support description, construction technology, support components, safety technical measures, and labor organization and technical indicators.

6. The artificial intelligence-based simulation twin method for coal mine tunneling navigation according to claim 5, characterized in that, Retrieve the model corresponding to the excavation content from the model database to generate a digital twin model, including: Retrieve the three-dimensional model corresponding to the tunneling equipment involved in the tunneling machine process from the model database to obtain the model retrieval result; Construct a mathematical model corresponding to the excavation content; Based on the model retrieval results and the mathematical model, a digital twin model is constructed in conjunction with the tunneling content.

7. A coal mine tunneling navigation simulation twin system based on artificial intelligence, characterized in that, include: The user-side application is used to upload documents related to coal mine tunneling operations. The server interacts with the user terminal to implement the AI-based coal mine tunneling navigation simulation twin method as described in any one of claims 1-6, generating a dynamic simulation scenario or document improvement suggestions for coal mine tunneling navigation, and sending the dynamic simulation scenario or document improvement suggestions for coal mine tunneling navigation to the user terminal for display.

8. The artificial intelligence-based coal mine tunneling navigation simulation twin system according to claim 7, characterized in that, The user terminal can be one or more of various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices.

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