A tunnel support optimization system based on a tunnel scanning robot and a BIM model

Abstract

The application relates to a tunnel support optimization system based on a tunnel scanning robot and a BIM model, aiming to solve the problems of low efficiency, poor precision and slow response in the stability analysis of surrounding rock in traditional drill-and-blast tunnel construction. The system integrates tracked robots, laser radars, rebound hammers, cameras and other devices to realize rapid and accurate collection of surrounding rock geological information. Combined with BIM technology, the system generates a dynamically updated tunnel model. The system automatically analyzes the collected data, performs real-time stability analysis of surrounding rock and optimization of support design, and pushes the latest design scheme to the construction site in real time through an intelligent terminal. The system also supports construction personnel to input on-site feedback information and automatically adjust the support design scheme. The system greatly improves the data collection efficiency, analysis precision and construction safety, ensures that the support design can respond to the changes of surrounding rock in real time, significantly improves the efficiency and accuracy of tunnel construction, and is especially suitable for tunnel construction under complex geological conditions.

Description

Technical Field

[0001] This application relates to the field of tunnel construction technology, and in particular to a tunnel support optimization system based on a tunnel scanning robot and a BIM model. Background Technology

[0002] Currently, in drill-and-blast tunnel construction, the time between the exposure of the excavation face after blasting and the commencement of support construction is typically only about one hour. This brief time window places extremely high demands on the efficiency and accuracy of surrounding rock stability analysis. Surrounding rock stability directly affects tunnel construction safety and progress; therefore, rapidly collecting and analyzing geological information from the excavation face within a limited time is a crucial step in the construction process. However, existing technologies primarily rely on manual operations, including manual data collection, analysis, and design decisions, which are insufficient to meet the demands for rapid response.

[0003] In traditional techniques, surrounding rock stability analysis typically employs manual geological surveys, measurements, and laboratory tests. While these methods provide fundamental data, they are limited by the efficiency and accuracy of manual operations. Data acquisition and analysis are often time-consuming and difficult to complete quickly. Furthermore, support design relies on human experience, making the analysis results susceptible to human error and lacking the ability to comprehensively and in real-time assess complex geological conditions. Especially during construction, surrounding rock conditions may dynamically change, and traditional static analysis methods cannot capture these changes in real time, thus affecting the reliability of the support design.

[0004] Although digital tools such as laser scanners and rebound hammers have been introduced in recent years to improve the accuracy and efficiency of geological data acquisition, these devices still rely on manual operation, and the data analysis and feedback processes lack a systematic workflow, real-time performance, and automation. Faced with complex geological conditions and tight construction deadlines, the limitations of these technologies are becoming increasingly apparent, especially when dealing with sudden geological problems during tunnel construction, where existing methods struggle to provide rapid and comprehensive decision support. Therefore, current technologies have significant shortcomings in data acquisition efficiency, analysis accuracy, and real-time response capabilities, hindering improvements in tunnel construction efficiency and safety.

[0005] To address the aforementioned issues, a tunnel support optimization system based on a tunnel scanning robot and a BIM model is designed. Summary of the Invention

[0006] This application provides a tunnel support optimization system based on a tunnel scanning robot and a BIM model to solve the problem that existing technologies are unable to achieve rapid acquisition, real-time analysis, and dynamic support design of surrounding rock geological information, which affects the efficiency and safety of tunnel construction.

[0007] Firstly, a tunnel support optimization system based on a tunnel scanning robot and a BIM model is provided, comprising:

[0008] The information acquisition module is used for data collection from the tunnel excavation face and the surrounding environment.

[0009] The model operation module establishes a communication connection with the information acquisition module, and uses BIM software to create a tunnel BIM model, which includes a geological body model, tunnel structure components and construction equipment components, and realizes data sharing and attribute expansion through IFC format files.

[0010] An information input module establishes a communication connection with the model operation module. The information input module includes monitoring and measurement, geological forecasting and working face measurement, and is used to collect and input basic geological and measurement information of the surrounding rock.

[0011] The IFC parsing and analysis module establishes a communication connection with the information input module and is used to parse the IFC format file through the cloud computing platform to complete three-dimensional point cloud reconstruction, joint parameter identification, geometric roughness extraction and surrounding rock classification index calculation.

[0012] The feedback analysis module establishes a communication connection with the IFC analysis and analysis module to receive the support design scheme and surrounding rock classification results from the IFC analysis and analysis module, and pushes the analysis results to the construction personnel in real time through the smart terminal, and records historical data during the construction process.

[0013] The human-machine assistance module establishes a communication connection with the feedback analysis module to interact with construction personnel, including receiving observation information and adjustment suggestions input by construction personnel, providing dynamic visualization of support design schemes, and feeding back the adjusted data to the model operation module.

[0014] Preferably, it also includes a wireless communication module, which establishes a communication connection with the information acquisition module, model operation module, information input module, IFC parsing and analysis module, feedback analysis module, and human-machine assistance module to realize real-time data transmission and synchronization between various modules, and supports multiple robot devices to collect data simultaneously, and to perform unified analysis after fusing multi-source data.

[0015] Preferably, the information acquisition module includes a tracked robot, a lidar scanning system, a robotic arm, a rebound spring, several cameras, and several lights. The lidar scanning system, the robotic arm, the several cameras, and the several lights are all mounted on the tracked robot, and the rebound spring is mounted on the robotic arm.

[0016] Preferably, the model operation module includes a geological body modeling module, a tunnel structure modeling module, a construction equipment module, a model integration module, and a real-time model update module;

[0017] The geological body modeling module is used to establish a three-dimensional geological model that includes the distribution of surrounding rock lithology, the spatial location and geometric parameters of joint surfaces, and fracture parameters. The tunnel structure modeling module is used to generate a standardized three-dimensional model that includes the initial support structure, secondary lining, and excavation cross-section design, and supports the adjustment of construction parameters, support thickness, and cross-sectional geometry. The construction equipment module is used to establish digital models of various equipment and machinery involved in tunnel construction. The model integration module is used to integrate the geological body model, support structure model, and construction design information through the IFC format standard to ensure data sharing and consistency among multiple modules and to support data interaction with other modules. The real-time model update module is used to dynamically update the lithology, joint parameters, and support structure design scheme of the BIM model based on the collected geological information and construction feedback data to adapt to actual construction needs.

[0018] Preferably, the information input module includes a geological monitoring module, a mechanical performance testing module, an environmental data acquisition module, and a comprehensive data processing module;

[0019] The geological monitoring module includes monitoring and measurement equipment for recording the dynamic deformation trend and stress field changes of the surrounding rock to support surrounding rock stability analysis and construction optimization. The mechanical performance testing module integrates a high-precision mechanical testing device to acquire the shear strength, elastic modulus, and rebound stiffness mechanical properties of the surrounding rock, and supports stress-strain relationship determination to evaluate the mechanical stability changes of the surrounding rock under different construction conditions in real time. The environmental data acquisition module is used to collect temperature, humidity, vibration, and noise parameters in the construction environment, analyze the impact of environmental factors on the stability of the surrounding rock, and provide early warning data of environmental anomalies. The comprehensive data processing module is used to verify, fuse, and normalize the collected multi-source data to ensure the consistency of geological, mechanical, and environmental data, and supports comparative analysis of real-time data and historical data to generate standardized input files that meet the requirements of graded calculation and support design.

[0020] Preferably, the IFC parsing and analysis module includes a three-dimensional point cloud reconstruction module, a joint parameter identification module, a geometric roughness extraction module, and a surrounding rock grading module;

[0021] The 3D point cloud reconstruction module is used to generate a high-precision 3D digital model of the excavation face by combining 2D images and lidar point cloud data. The joint parameter identification module is used to extract the spatial location, geometric shape, and attitude parameters of the joint face and classify the joint face according to the clustering algorithm. The geometric roughness extraction module is used to calculate the surface roughness index of the joint face based on the geometric feature extraction algorithm. The surrounding rock grading module is used to calculate the BQ value, Q value, RMR, and GSI surrounding rock grading index according to the collected geological measurement information and to correct the grading results in combination with the ground stress.

[0022] Preferably, the surrounding rock classification module, combined with a cloud computing platform, automatically matches the optimal support design scheme based on real-time collected geological information and generates dynamic support design drawings adapted to the site conditions.

[0023] Preferably, the cloud computing platform is equipped with multi-threaded parallel processing capabilities, enabling it to simultaneously perform tasks such as 3D point cloud reconstruction, joint parameter extraction, and support design optimization. It also achieves rapid processing of large-scale geological data through a distributed computing framework. The cloud computing platform uses dynamic allocation of computing resources and data caching technology to optimize computing efficiency and supports real-time transmission and storage of computing results.

[0024] Preferably, the feedback analysis module includes a real-time feedback function module, a data tracking and optimization function module, and a user interaction function module;

[0025] The real-time feedback module is used to push the optimized design to the smart terminal at the construction site in real time based on the surrounding rock classification results and the support design scheme. The data tracking and optimization module is used to record the construction site data and compare it with historical data to provide support for support design optimization. The user interaction module is used to support construction personnel to input on-site feedback, automatically adjust the support design scheme, and update it in real time.

[0026] Preferably, the human-machine assistance module includes a construction feedback input function module, a support design scheme visualization display function module, a scheme adjustment and optimization support function module, and a real-time scheme update function module;

[0027] The construction feedback input module allows construction personnel to input on-site observation data and feedback information, including changes in surrounding rock, support effects, and construction progress, via smart terminals. The support design scheme visualization module provides a graphical display of the support design scheme and surrounding rock grading results. Construction personnel can view design changes and optimization suggestions through an interactive interface. The scheme adjustment and optimization support module automatically recommends adjustments to the support design scheme based on construction feedback and on-site data, and allows construction personnel to manually adjust design parameters. The real-time scheme update module automatically synchronizes the adjusted support design scheme to the construction terminal in real time, ensuring that on-site construction personnel receive the latest design information.

[0028] The beneficial effects of the technical solution provided in this application include:

[0029] 1. This application combines a tunnel scanning robot with an IFC model, enabling the system to rapidly collect and analyze geological information of the surrounding rock within a very short time window. The automated data acquisition and processing capabilities ensure that the stability analysis of the surrounding rock can be completed in a short time, avoiding the inefficiency and errors of manual operation and greatly improving the construction response speed.

[0030] 2. This system utilizes high-precision tools such as lidar and rebound hammers, combined with the IFC model, to capture real-time dynamic changes in the surrounding rock and conduct accurate mechanical performance evaluation. Compared with traditional static analysis methods, the system provides dynamic geological analysis capabilities, ensuring that the support design can respond to changes in the surrounding rock in real time, thereby improving the reliability of the design and the safety of construction.

[0031] 3. This application overcomes the limitations of manual analysis through automated data processing and analysis, realizing an automated process of data acquisition, analysis and support design. The IFC parsing and analysis module is based on a cloud computing platform, which can quickly process multi-source data from different devices (such as lidar scanning, rebound hammer, camera, etc.) and generate support design schemes in real time, effectively improving analysis accuracy and feedback efficiency.

[0032] 4. The system transmits data in real time through a wireless communication module, ensuring that construction personnel can always obtain the latest surrounding rock information and support design schemes. The support design results are pushed in real time through smart terminals, allowing construction personnel to adjust construction strategies in a timely manner, effectively avoiding construction risks caused by information lag, and improving safety and decision-making accuracy during the construction process.

[0033] 5. The system has a high degree of automation, which can reduce the reliance on manual operation for data collection, analysis and design, and avoid human factors from interfering with the analysis results. Through large-scale data processing and automated decision support, the system can generate support designs efficiently and accurately, which improves the scientificity and accuracy of the entire tunnel construction process.

[0034] 6. Through the integrated human-machine collaboration module, the system supports construction personnel to input feedback information in real time and automatically adjust the support design scheme. Changes at the construction site (such as the surrounding rock condition and construction progress) can be reflected in the support design in an instant, making the construction process more flexible and adaptable, while ensuring that the design matches the actual environment and improving construction efficiency and stability. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 System architecture diagram provided for embodiments of this application;

[0037] Figure 2 This is a schematic diagram of the operation of the information collection module provided in the embodiments of this application;

[0038] Figure 3 A schematic diagram of an information acquisition module provided in an embodiment of this application;

[0039] Figure 4 This is a schematic diagram illustrating the collaborative operation between modules provided in an embodiment of this application;

[0040] Figure 5 This is a schematic diagram of information collection at the tunnel excavation face provided in an embodiment of this application;

[0041] Figure 6 A schematic diagram of a three-dimensional point cloud digital twin model of an excavation face provided in an embodiment of this application;

[0042] Figure 7 This is a schematic diagram illustrating the intelligent extraction of joint orientation information of the excavation surface provided in an embodiment of this application.

[0043] In the picture: 1. Tracked robot; 2. LiDAR scanning system; 3. Robotic arm; 4. Rebound device; 5. Camera; 6. Lighting. Detailed Implementation

[0044] This application provides a tunnel support optimization system based on a tunnel scanning robot and a BIM model, which can solve the problem that existing technologies are unable to achieve rapid acquisition, real-time analysis and dynamic support design of surrounding rock geological information, thus affecting tunnel construction efficiency and safety.

[0045] Please see Figures 1-7A tunnel support optimization system based on a tunnel scanning robot and a BIM model includes: an information acquisition module, a model operation module, an information input module, an IFC parsing and analysis module, a feedback analysis module, and a human-machine assistance module.

[0046] The information acquisition module is used for data acquisition of the tunnel excavation face and surrounding environment. The information acquisition module includes a tracked robot 1, a lidar scanning system 2, a robotic arm 3, a rebound spring 4, several cameras 5, and several lighting lamps 6. The lidar scanning system 2, the robotic arm 3, the several cameras 5, and the several lighting lamps 6 are all mounted on the tracked robot 1, and the rebound spring 4 is mounted on the robotic arm 3.

[0047] The tracked robot 1 is the mobile platform for the information acquisition module. It can move flexibly in different construction environments within the tunnel and adapt to various complex geological conditions, ensuring stable operation in narrow or uneven tunnel environments. It can also adjust its position as needed to quickly acquire surrounding rock information at different excavation faces. The lidar scanning system 2 is used to acquire high-precision three-dimensional point cloud data of the tunnel excavation face and its surrounding environment. The lidar scanning system 2 uses a laser beam to detect the surface of an object and converts the reflected signal into point cloud data, thereby generating a three-dimensional digital model of the excavation face and surrounding rock. Through this system, detailed geometric features of the surrounding rock can be obtained, especially in areas with irregular or complex structures. The lidar can accurately capture terrain details and provide more accurate spatial data. At the same time, in conjunction with the robotic arm 3 and the rebound hammer 4, it can be used to conduct dynamic rebound stiffness tests on the surrounding rock. The rebound hammer 4 assesses the mechanical properties of the surrounding rock by applying pressure and measuring the rebound results, obtaining important physical parameters such as the rock's elastic modulus and strength. It automatically records the measurement results and matches them with the position data of the robotic arm 3 for precise analysis of the surrounding rock's physical properties. By measuring the rebound stiffness of the surrounding rock, the system can evaluate the rock's elastic strength, providing a scientific basis for subsequent support design and construction plans. Furthermore, the robotic arm 3 has multi-degree-of-freedom motion capabilities, allowing it to be adjusted at different construction positions and angles as needed, enabling multi-point and multi-angle measurements to ensure the comprehensiveness and accuracy of rock mass performance data. Several cameras 5 and lighting lamps 6 are used to collect two-dimensional image data of the excavation face. The cameras 5 are mounted on the tracked robot 1, and the lighting lamps 6 provide sufficient illumination, ensuring high-quality image acquisition even in low-light environments. Through these cameras 5, the system can acquire high-resolution photos of the excavation face and combine them with LiDAR scanning results to achieve the fusion of two-dimensional images and three-dimensional point cloud data, further improving the accuracy and completeness of the data.

[0048] The tracked robot 1 serves as a mobile platform, integrating equipment such as a lidar scanning system 2, a robotic arm 3, a rebound hammer 4, a camera 5, and a lighting lamp 6. This ensures comprehensive data collection of the tunnel excavation face and surrounding environment. Furthermore, the collaborative work of these devices not only provides geometric data of the surrounding rock but also acquires physical property parameters of the surrounding rock, providing comprehensive and accurate basic data for subsequent surrounding rock analysis, support design, and construction optimization.

[0049] The model operation module establishes a communication connection with the information acquisition module to create a tunnel BIM model in conjunction with BIM software. This module ensures data sharing and consistency between various system modules and can dynamically adjust the model based on real-time geological information and construction feedback to optimize the support design scheme. The model operation module includes a geological body model, tunnel structure components and construction equipment components, and realizes data sharing and attribute expansion through IFC format files.

[0050] Specifically, the model operation module includes a geological body modeling module, a tunnel structure modeling module, a construction equipment module, a model integration module, and a real-time model update module;

[0051] The geological modeling module is used to create a three-dimensional geological model that includes the lithological distribution of the surrounding rock, the spatial location and geometric parameters of joint surfaces, and fracture parameters. This module utilizes BIM technology to convert information such as the lithological distribution, spatial location of joint surfaces, geometric parameters, and fracture parameters of the surrounding rock into a three-dimensional digital model. The geological model not only reflects the physical properties of the surrounding rock but also simulates rock mass deformation and related engineering responses, providing reference information for subsequent support design and construction.

[0052] Joint surface extraction: Based on 3D point cloud data and laser scanning results, the spatial location and geometric characteristics of joint surfaces can be extracted using the following clustering algorithm:

[0053]

[0054] Where D(x,y) represents the distance between two data points, N is the number of data points, and x i and y i Let ||·|| be the spatial coordinates of the point cloud data, and let ||·|| be the Euclidean distance function. Using this formula, the model can automatically identify and classify joint surfaces and extract their geometric characteristics (such as strike, dip angle, etc.).

[0055] By using the mass modeling module, the accuracy of surrounding rock analysis can be effectively improved, ensuring the stability of the surrounding rock during construction and providing accurate basic data for support design.

[0056] The tunnel structure modeling module is used to generate standardized 3D models that include the initial support structure, secondary lining, and excavation cross-section design. Using BIM technology, various components of the tunnel structure (such as support structure, lining layers, and anchor bolt arrangement) are modeled, and adjustments to construction methods, support thickness, and cross-sectional geometry are supported.

[0057] Initial support structure design: Initial support design usually includes the selection of support materials (such as shotcrete, steel bracing, etc.) and the layout of the support system. Through BIM model, the geometric dimensions of the support structure can be accurately calculated and the support parameters (such as thickness, strength, etc.) can be dynamically adjusted.

[0058] Secondary lining design: After excavation, secondary lining design is used to enhance the stability of the tunnel structure. The lining design scheme is automatically adjusted according to the actual conditions of tunnel excavation to ensure the structural strength and durability of the tunnel.

[0059] The tunnel structure modeling module enables more precise and flexible adjustments to the support design, ensuring that the support scheme at the construction site adapts to real-time changes, thereby optimizing the support design, avoiding construction risks caused by design lag, and improving the adaptability and flexibility of construction.

[0060] The construction equipment module is used to establish digital models of various equipment and machinery involved in tunnel construction, including tunneling equipment, support equipment, grouting equipment, etc., and to dynamically simulate and manage their location, status and operation process in combination with construction process parameters, providing digital support for construction planning, real-time monitoring and equipment optimization.

[0061] The model integration module is used to integrate geological body models, support structure models and construction design information through the IFC format standard, ensuring data sharing and consistency among multiple modules, and supporting data interaction with other modules. Using the IFC format ensures that multi-party data (such as geological data, support design, construction plan, etc.) of tunnel projects can be seamlessly connected, enhancing the collaborative work capabilities of different professional modules.

[0062] Data Consistency: The introduction of the IFC format enables seamless data exchange between different modules (such as geological analysis and structural design modules), avoiding information loss and duplication of work. Through this standardized format, multiple teams can simultaneously access, edit, and update relevant data in the BIM model, ensuring data consistency.

[0063] By applying the standardized IFC format, the model integration module can ensure data sharing and consistency, reduce errors and delays in data transmission, and improve decision-making efficiency and safety during construction.

[0064] The real-time model update module dynamically updates the lithology, joint parameters, and support structure design scheme of the BIM model based on collected geological information and construction feedback data to adapt to actual construction needs. This module ensures that the BIM model always reflects the latest situation on the construction site, allowing the support design to be optimized and adjusted according to the actual site conditions. The real-time model update module can dynamically optimize the support design, ensuring that the design remains consistent with the actual surrounding rock conditions during construction, effectively responding to changes in complex geological and construction environments, thereby improving construction safety and efficiency.

[0065] Real-time data updates: As construction progresses, the surrounding rock conditions and construction conditions will change. The real-time model update module can receive feedback data from the information input module and the construction site in real time, and update the design parameters in the BIM model based on this information. This process is usually accomplished by connecting to real-time data sources (such as sensors and monitoring equipment).

[0066] Dynamic optimization design: The real-time updated BIM model not only helps construction personnel to grasp the current construction status, but also enables the optimization of support design based on newly collected surrounding rock data. It can dynamically adjust parameters such as support thickness, anchor bolt arrangement and lining design to ensure that the design scheme is consistent with the actual construction conditions, thereby maximizing construction safety and efficiency.

[0067] The model operation module of this invention effectively solves problems related to surrounding rock analysis, support design, and construction management during tunnel construction through multiple functions such as mass modeling, tunnel structure modeling, model integration, and real-time updates. Through highly integrated and automated data processing, the system can provide real-time feedback, ensuring dynamic matching between the support design scheme and site conditions, thereby achieving efficient, safe, and intelligent tunnel construction.

[0068] The information input module establishes a communication connection with the model operation module. The information input module includes monitoring and measurement, geological forecasting, and face measurement functions, used to collect and input basic geological and measurement information of the surrounding rock. Specifically, the information input module includes a geological monitoring module, a mechanical property testing module, an environmental data acquisition module, and a comprehensive data processing module. This ensures comprehensive analysis and real-time feedback of the surrounding rock during construction, providing accurate data support for support design and construction optimization. The communication connection between the information input module and the model operation module allows the collected data to be transmitted to the BIM model in real time, promoting data sharing and dynamic optimization of support design schemes.

[0069] The geological monitoring module includes monitoring and measurement equipment to record the dynamic deformation trends and stress field changes of the surrounding rock, supporting surrounding rock stability analysis and construction optimization. The module records these changes in real time, providing detailed geological data for stability analysis and construction optimization. This data not only helps identify potential hazardous areas but also supports the optimization of construction plans, ensuring that the support design can cope with changes in the geological environment. The real-time data provided by the geological monitoring module ensures that the dynamic characteristics of the surrounding rock are reflected in the BIM model in a timely manner, helping construction personnel adjust construction strategies and reduce potential risks.

[0070] The mechanical performance testing module integrates a high-precision mechanical testing device to obtain the mechanical properties of the surrounding rock, such as shear strength, elastic modulus, and resilience stiffness. It also supports stress-strain relationship determination and real-time assessment of the mechanical stability changes of the surrounding rock under different construction conditions. With this data, the support design can be dynamically adjusted to ensure the stability of the surrounding rock. The mechanical performance testing module provides important mechanical parameter support for the support design, ensuring the reliability and adaptability of the design.

[0071] The environmental data acquisition module is used to collect parameters such as temperature, humidity, vibration and noise in the construction environment, analyze the impact of environmental factors on the stability of the surrounding rock, and provide early warning data of environmental anomalies. Environmental factors, such as temperature changes and humidity fluctuations, may cause deformation and crack propagation of the surrounding rock. Therefore, real-time monitoring of these parameters helps construction personnel to take timely measures to prevent disasters. The environmental data acquisition module provides comprehensive environmental monitoring for construction, helps to assess the potential impact of the external environment on the stability of the surrounding rock, and optimizes the support design.

[0072] Assessment of the impact of temperature and humidity: The effects of temperature and humidity on the surrounding rock can be quantified using the coefficients of thermal expansion and humidity. The formula for calculating the volume change of the surrounding rock is:

[0073] ΔV=α·V0·ΔT+β·V0·ΔH

[0074] Where ΔV is the volume change, α is the coefficient of thermal expansion, β is the coefficient of humidity expansion, V0 is the initial volume, and ΔT and ΔH are the changes in temperature and humidity, respectively. This formula helps to assess the impact of environmental factors on the stability of surrounding rock.

[0075] The integrated data processing module is used to verify, fuse, and normalize the collected multi-source data to ensure the consistency of geological, mechanical, and environmental data. It also supports the comparative analysis of real-time and historical data and generates standardized input files that meet the requirements of graded calculation and support design. The integrated data processing module ensures the consistency of multi-source data, reduces errors between data, and improves the accuracy of surrounding rock analysis and support design.

[0076] Data normalization: To eliminate differences between different units of measurement and orders of magnitude, data is normalized. Commonly used normalization formulas are:

[0077]

[0078] Where x′ represents the normalized data, x represents the original data, and min(x) and max(x) are the minimum and maximum values ​​in the dataset, respectively.

[0079] The information input module plays an indispensable role in the tunnel support optimization system. Through the collaborative work of the geological monitoring module, mechanical performance testing module, environmental data acquisition module, and integrated data processing module, the information input module provides comprehensive and accurate real-time data support for surrounding rock stability analysis and support design. Combining advanced algorithms and data processing technologies, this module ensures consistency and comparability across different data sources, providing strong support for safety and construction optimization during tunnel construction.

[0080] The IFC parsing and analysis module establishes a communication connection with the information input module to parse IFC format files through a cloud computing platform, complete three-dimensional point cloud reconstruction, joint parameter identification, geometric roughness extraction, and surrounding rock classification index calculation. These processing steps can provide accurate surrounding rock analysis and support design support for tunnel construction, ensure that the stability of the surrounding rock is effectively controlled during construction, and optimize support design to improve construction efficiency and safety.

[0081] Furthermore, the IFC parsing and analysis module includes a 3D point cloud reconstruction module, a joint parameter identification module, a geometric roughness extraction module, and a surrounding rock grading module;

[0082] The 3D point cloud reconstruction module is used to generate a high-precision 3D digital model of the excavation face by combining 2D images and LiDAR point cloud data. This module combines 2D images collected from the tracked robot and point cloud data obtained from LiDAR scanning, and uses a virtual multi-view algorithm to reconstruct the 2D images from multiple angles to generate accurate 3D point cloud data. The 3D point cloud reconstruction module can provide an accurate 3D geometric model for subsequent joint parameter identification, geometric roughness extraction and surrounding rock classification, ensuring that the spatial position and morphology of the surrounding rock can be fully described, and providing the necessary spatial information for support design.

[0083] 3D Reconstruction Algorithm: 3D point cloud reconstruction generates spatial coordinates through joint analysis of 2D images. Commonly used reconstruction formulas are:

[0084]

[0085] Where P(X, Y, Z) represents a point in three-dimensional space, f(x, y) is a point in the image, D is the view distance, and Z is the depth value. Using this formula, a two-dimensional image can be converted into three-dimensional coordinate points, generating high-precision point cloud data.

[0086] The joint parameter identification module is used to extract the spatial location, geometric shape, and attitude parameters of joint surfaces, and classify the joint surfaces according to the clustering algorithm. Through this module, it can automatically identify the joint surfaces that may exist in the surrounding rock and classify them according to their geometric characteristics, providing accurate input for the mechanical analysis of the surrounding rock. The joint parameter identification module can accurately extract the spatial distribution and geometric shape of joint surfaces, avoiding the inefficiency and errors of traditional manual identification, and improving the accuracy and efficiency of data acquisition.

[0087] Clustering Algorithm: Density peak-based clustering algorithms are commonly used for the classification and identification of joint surfaces. The basic steps of this algorithm are to adaptively cluster by calculating the local density and distance of each point to identify the location of the joint surface. The clustering algorithm formula is:

[0088]

[0089] Among them, D i x is the cluster distance of the i-th point, N is the number of data points, and x i and y i These are the spatial coordinates in the point cloud data. Using this formula, the algorithm can effectively classify joint surfaces.

[0090] The geometric roughness extraction module is used to calculate the surface roughness index of joint surfaces based on geometric feature extraction algorithms. This module provides accurate geometric roughness data for surrounding rock classification and support design, helping construction personnel assess the stability of the rock mass and make corresponding design adjustments.

[0091] Roughness calculation: The roughness of a joint surface can be calculated using the following formula:

[0092]

[0093] Where R is the roughness index and A is the region of the joint surface. Let x and y be the gradient of the surface normal vector of the joint surface, and let x and y be the surface coordinates. This formula evaluates the roughness of the joint surface by calculating the change in the surface normal vector.

[0094] The surrounding rock grading module is used to calculate the BQ value, Q value, RMR and GSI surrounding rock grading index based on the collected geological survey information, and to correct the grading results in combination with the ground stress. This module provides a quality assessment of the surrounding rock for support design, helping construction personnel to select appropriate support schemes. The surrounding rock grading module automatically generates grading results based on real-time geological information, avoiding the tediousness and inaccuracy of traditional manual calculation.

[0095] Rock mass classification calculation: The classification of rock mass is calculated using the following formula:

[0096]

[0097] BQ is the surrounding rock grading index, while RMR and GSI are the rock quality and structure indices, respectively.

[0098] More specifically, the surrounding rock classification module, combined with a cloud computing platform, automatically matches the optimal support design scheme based on real-time collected geological information and generates dynamic support design drawings adapted to site conditions. The cloud computing platform is equipped with multi-threaded parallel processing capabilities, enabling it to simultaneously perform 3D point cloud reconstruction, joint parameter extraction, and support design optimization tasks. It also achieves rapid processing of large-scale geological data through a distributed computing framework. The cloud computing platform uses dynamic allocation of computing resources and data caching technology to optimize computing efficiency, supports real-time transmission and storage of calculation results, and can process large amounts of data in parallel in real time, significantly improving computing speed and ensuring the real-time performance and accuracy of surrounding rock analysis and support design.

[0099] Computing resource allocation: The cloud platform allocates computing resources through a load balancing algorithm to ensure efficient system operation. The load balancing algorithm can be expressed by the following formula:

[0100]

[0101] Among them, R i C represents the resource allocation ratio for the i-th computing node. i Where N is the computing power of the node and N is the total number of computing nodes, the platform can dynamically adjust computing resources to ensure the rapid completion of tasks.

[0102] Through the collaborative work of the IFC parsing and analysis modules, the system can accurately extract the geometric information of the surrounding rock from 3D point cloud data and precisely classify the surrounding rock according to geological characteristics. Simultaneously, the cloud computing platform provides powerful computational support, ensuring real-time analysis and processing of various data, optimizing support design schemes, and automatically adjusting according to changes in construction conditions. Through this series of technical means, the system significantly improves the efficiency, safety, and accuracy of tunnel construction, providing strong data support for tunnel support design.

[0103] The feedback analysis module establishes a communication connection with the IFC analysis and interpretation module to receive support design schemes and surrounding rock classification results from the IFC analysis and interpretation module, and pushes the analysis results to construction personnel in real time via smart terminals. In addition, the feedback analysis module also records historical data during the construction process to support subsequent construction decisions and design optimization.

[0104] Preferably, the feedback analysis module includes a real-time feedback function module, a data tracking and optimization function module, and a user interaction function module;

[0105] The real-time feedback module pushes the optimized design to the smart terminal at the construction site in real time based on the surrounding rock classification results and support design scheme. Through this module, construction personnel can view the latest support design scheme on the smart terminal, including key parameters such as support type, support layout, anchor bolt length, and support thickness. This real-time feedback mechanism ensures that construction personnel can react promptly based on the latest data and design scheme, avoiding inconsistencies in construction caused by information lag. The real-time feedback function ensures that construction personnel can quickly obtain the optimized support design results on-site.

[0106] Optimized support design submission: The optimized support design scheme is dynamically adjusted based on grading indicators and construction environment. The optimization submission process can be represented by the following formula:

[0107] Doptimized=f(Q,RMR,GSI,parameters)

[0108] Among them, D optimized For the optimized support design, Q, RMR, and GSI are the surrounding rock classification indicators, and parameters are other construction-related parameters. This formula adjusts the design scheme through real-time data to ensure the optimal support scheme for on-site construction.

[0109] The data tracking and optimization module records construction site data and compares it with historical data to provide support for support design optimization. By comparing real-time and historical data, it evaluates the effectiveness of the support design scheme and makes optimization adjustments based on the results. For example, after monitoring dynamic changes in the surrounding rock, the system can adjust the support scheme in real time based on these changes to ensure that the support structure can always cope with complex geological conditions. The data tracking module provides real-time tracking and historical backtracking functions for construction data, enabling accurate evaluation of the effectiveness of the support design and optimization based on site changes, thereby improving construction safety and efficiency.

[0110] Data Comparison and Optimization: By comparing on-site data and historical data, the optimization algorithm can automatically adjust the support design. The optimization formula is as follows:

[0111]

[0112] Where ΔD is the adjustment amount for the support design, D current and D historical These are the current and historical design parameters, respectively, and n is the number of data points. This formula dynamically adjusts the support design through real-time data feedback to ensure continuous optimization of the support scheme.

[0113] The user interaction module supports construction personnel in inputting on-site feedback, automatically adjusting the support design scheme, and updating it in real time, including information such as changes in surrounding rock, support effectiveness, and construction progress. This feedback is processed automatically, and the support design scheme is adjusted according to the site conditions. Construction personnel can not only see a dynamic display of the support design scheme but also manually adjust it. The system automatically updates the support design based on these adjustments and pushes the modified scheme to the site via smart terminals. This user interaction module enhances the interactivity between construction personnel and the system, enabling them to flexibly respond to changes on-site and adjust the support design scheme in real time, thereby ensuring the flexibility and adaptability of construction.

[0114] On-site feedback and scheme adjustment: Based on on-site feedback input by construction personnel, the support design scheme can be automatically adjusted. The adjustment process can be represented by the following formula:

[0115] D adjusted =D current +α·(feedback)

[0116] Among them, D adjusted The adjusted support design uses α as the adjustment coefficient and feedback as the feedback data input by the construction personnel. This formula achieves seamless integration between on-site feedback and support design scheme, ensuring that the design scheme is updated in real time and matches the actual construction conditions.

[0117] This feedback analysis module, through real-time feedback, data tracking and optimization, and user interaction, ensures that the support design during tunnel construction can respond promptly and accurately to changes in the surrounding rock condition and construction conditions. The real-time feedback function provides rapid updates to the support design, the data tracking and optimization function helps optimize the design scheme and provides real-time monitoring of construction data, and the user interaction function allows construction personnel to make flexible adjustments based on site conditions. Overall, the feedback analysis module significantly improves the adaptability and accuracy of the support design scheme, ensuring the safety, efficiency, and effective decision-making during tunnel construction.

[0118] The human-machine assistance module establishes a communication connection with the feedback analysis module for interaction with construction personnel. This includes receiving observation information and adjustment suggestions from construction personnel, providing a dynamic and visual display of the support design scheme, and feeding back the adjusted data to the model operation module. This module supports construction personnel inputting on-site feedback information and dynamically adjusting the support design scheme based on data such as changes in the surrounding rock, support effectiveness, and construction progress. Furthermore, the human-machine assistance module provides a visual display of the support design scheme to help construction personnel understand and operate it, and synchronizes the latest design information in real time via smart terminals.

[0119] The human-machine assistance module in this application includes a construction feedback input function module, a support design scheme visualization display function module, a scheme adjustment and optimization support function module, and a real-time scheme update function module.

[0120] The construction feedback input module allows construction personnel to input on-site observation data and feedback information through smart terminals. Construction personnel can record and report key information such as changes in surrounding rock, support effect, and construction progress, and feed this data back to the system. Through this module, construction personnel can monitor changes on the construction site in real time and share real-time data with the system, thereby prompting timely adjustments to the support design scheme. This module enhances the sense of participation of on-site construction personnel and ensures real-time alignment between the support design scheme and actual construction conditions.

[0121] Construction feedback data integration: After integration, construction feedback data serves as input for adjusting the design scheme. The data integration process can be represented by the following formula:

[0122]

[0123] Among them, F input For the integrated construction feedback data, feedback i For the i-th feedback item, w i Here, n represents the weight of each feedback item, and n represents the number of feedback items. This formula allows the system to integrate different feedback inputs from construction personnel into input data that can be used to adjust the support design.

[0124] The support design scheme visualization module provides a graphical display of support design schemes and surrounding rock classification results. Construction personnel can view design changes and optimization suggestions through the interactive interface. This module presents key information of the support design (such as support type, support layout, support thickness, etc.) in a graphical way, enabling construction personnel to intuitively understand the support design scheme. The visualization function helps construction personnel quickly understand complex support design schemes, thereby making more efficient and accurate construction decisions.

[0125] Graphical representation of the design scheme: The visualization of the support design can be achieved through the following algorithms:

[0126] D visualized =f(D optimized (parameters)

[0127] Among them, D visualized For the visualized design scheme, D optimized For the optimized support design, parameters are the key information of the support design scheme (such as layout, thickness, etc.). Through this formula, the system converts the optimized support design into graphical data, so that construction personnel can view and operate it.

[0128] The scheme adjustment and optimization support module is used to automatically recommend adjustments to the support design scheme based on construction feedback and site data, and allows construction personnel to manually adjust the design parameters. This module can automatically calculate the optimized support design scheme based on real-time data and construction feedback, and display the optimized design scheme to the construction personnel through a smart terminal. The construction personnel can manually adjust the design parameters according to the actual situation to ensure that the support design scheme matches the actual construction conditions. The design optimization method that combines automatic recommendation and manual adjustment improves the flexibility and adaptability of the support design scheme.

[0129] Automatic optimization recommendation: The automatic optimization recommendation of support design can be achieved through the following formula:

[0130] D adjusted =D current +α·(F input )

[0131] Among them, D adjusted For the adjusted support design, D current For the current design scheme, α is the adjustment coefficient, and F input This formula provides input data for construction feedback. Based on this formula, the system can automatically adjust the support design scheme according to feedback data from the construction site.

[0132] The real-time scheme update module is used to automatically synchronize the adjusted support design scheme to the construction terminal in real time, ensuring that on-site construction personnel have access to the latest design information. Through this module, construction personnel can obtain the latest support design scheme at any time and perform corresponding construction operations based on real-time data. The real-time synchronization function ensures that construction personnel can always obtain the latest design scheme, reducing the construction risks caused by information lag.

[0133] Real-time update algorithm: The real-time update of the support design scheme can be expressed by the following formula:

[0134] D updated =Dcurrent +β·(D adjusted -D current )

[0135] Among them, D updated For the updated support design, D current For the current design, D adjusted For the adjusted design, β represents the weight that is updated in real time. This formula ensures that the design scheme can quickly respond to changes on the construction site and be promptly communicated to the construction personnel.

[0136] This human-machine collaboration module provides construction personnel with a flexible and efficient mechanism for adjusting support designs and providing real-time feedback through four main functional modules: construction feedback input, visualization of support design schemes, scheme adjustment and optimization support, and real-time scheme updates. Through the interaction between construction personnel and the system, the support design scheme can be continuously optimized based on real-time data and feedback, ensuring a high degree of matching between the design and construction conditions. The combination of automation and manual adjustment makes the support design more accurate and adaptable.

[0137] Furthermore, this application also includes a wireless communication module, which establishes a communication connection with the information acquisition module, model operation module, information input module, IFC parsing and analysis module, feedback analysis module, and human-machine assistance module to realize real-time data transmission and synchronization between the various modules, and supports multiple robot devices to collect data simultaneously, and to perform unified analysis after fusing multi-source data.

[0138] Real-time data transmission and synchronization: The wireless communication module ensures real-time data transmission and synchronization between various modules of the system. Data collected or generated by each module (such as surrounding rock images, point cloud data, etc.) will be transmitted to the cloud in real time via the wireless network and the input data of other modules will be updated in a timely manner. For example, when the information acquisition module collects data, it transmits this data to the IFC parsing and analysis module for subsequent processing, and finally pushes it to the feedback analysis module for construction personnel. Real-time data transmission and synchronization ensures that there is no information delay between different modules, ensuring the efficient operation of the system and improving the efficiency of construction decision-making.

[0139] Supports multiple robots working simultaneously: The wireless communication module supports multiple robots working in parallel. During tunnel construction, multiple robots can collect data simultaneously, covering a larger area. Each robot collects data and aggregates it to the cloud via the wireless communication module for centralized analysis. Supporting multiple robots working in parallel improves data collection efficiency, especially in large-scale tunnel projects, where multiple work areas can be covered simultaneously.

[0140] Multi-source data fusion and unified analysis: The wireless communication module is also responsible for fusing data from different modules and performing unified analysis through the cloud computing platform. The system can integrate geological monitoring data, mechanical data, and environmental data to provide more accurate information for support design. Through this data fusion, the support design scheme can be dynamically adjusted based on real-time feedback. Data fusion improves the accuracy and completeness of the system and provides comprehensive data support for support design.

[0141] Facilitating inter-module collaboration: The wireless communication module connects various modules, enabling efficient collaboration between different modules. Each module relies on data support from other modules for analysis and decision-making, ensuring that the system can work in a coordinated manner and respond promptly to changes on the construction site. Through efficient collaboration between modules, information flows more smoothly, reducing information silos and improving the system's responsiveness.

[0142] Therefore, the wireless communication module acts as a bridge in the whole system, ensuring data transmission and synchronization between different modules. It supports the parallel operation of multiple robotic devices, promotes data fusion and module collaboration, thereby improving the efficiency, safety and flexibility of tunnel construction.

[0143] The working principle of this application is as follows:

[0144] 1. Data Acquisition: After the blasting and muck removal process in tunnel construction is completed, the tracked robot 1, in conjunction with the manual personnel, takes multiple photos of the excavation face at different locations through the information acquisition module. The tracked robot 1 uses the integrated LiDAR scanning system 2 and camera 5 to acquire 2D photos (8-12 photos) of the excavation face surface and high-precision 3D point cloud data. At the same time, the rebound test of the rock mass is carried out by the rebound hammer 4 equipped with the robotic arm 3 to obtain the dynamic rebound stiffness data of the surrounding rock (average of 20 measurements). These data are used for subsequent rock mass stability analysis and support design optimization.

[0145] 2. Data Upload: Using a smart mobile terminal and the high-speed wireless network inside the tunnel, the collected two-dimensional photos, three-dimensional point cloud data, rock mass rebound stiffness data, and station information are uploaded to the cloud computing platform for analysis. This upload process is usually completed within 2-5 minutes, ensuring the real-time nature and integrity of the data.

[0146] 3. Data Analysis and Model Building: After receiving the uploaded data, the cloud computing platform's parsing and analysis module analyzes the data and generates a 3D digital model of the excavation face using a virtual multi-view algorithm and reconstruction technology combining 2D images and 3D point cloud data. Further, a joint parameter identification algorithm is used to extract the spatial location, geometric shape, and attitude parameters of the joint surfaces, and a clustering algorithm is used to intelligently classify the joint surfaces. In addition, based on a geometric feature extraction algorithm, the surface roughness index of the joint surfaces is calculated.

[0147] 4. Rock Grading and Support Design Generation: Based on the analyzed data, the rock grading module automatically calculates rock grading indicators such as BQ value, Q value, RMR and GSI, and corrects the grading results by combining real-time collected information such as ground stress. Using the cloud computing platform, the support design scheme is automatically generated and matched with the grading indicators to ensure that the most suitable support type and support structure design are automatically selected according to the actual rock conditions.

[0148] 5. Real-time push of support design: The feedback analysis module pushes the support design scheme and surrounding rock classification results to the construction site in real time through the smart terminal. Construction personnel can view the support design scheme through the smart terminal, including key construction parameters such as support type, support layout, anchor bolt length and support thickness. This module will also record historical data during the construction process to provide a basis for future construction decisions.

[0149] 6. Dynamic support design and construction adjustment: The human-machine assistance module interacts with construction personnel through a smart terminal. Construction personnel input on-site observation information and adjustment suggestions (such as changes in surrounding rock and support effect). The system adjusts the support design scheme based on the feedback. Construction personnel can view design changes and optimization suggestions in real time through the interactive interface, and can also manually adjust design parameters.

[0150] 7. Data Synchronization and Optimization: Throughout the construction process, the wireless communication module is responsible for real-time data transmission and synchronization between modules, ensuring that the collected data can be updated in real time and shared. Multiple robotic devices simultaneously collect and process data, transmit the data to the cloud via wireless communication, and perform unified analysis. All modules work together to achieve dynamic adjustment of surrounding rock information and support design schemes, ensuring efficiency and safety during the construction process.

[0151] 8. Real-time updates of support design schemes: During construction, the surrounding rock conditions and construction progress will change dynamically. The system can collect construction data in real time through the feedback analysis module and update the support design scheme based on the real-time feedback using the model operation module. The updated scheme will be pushed to the site through the human-machine assistance module to ensure that construction personnel can obtain the latest design drawings and carry out on-site construction.

[0152] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or the establishment of a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0153] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A tunnel support optimization system based on a tunnel scanning robot and a BIM model, characterized in that, It includes: The information acquisition module is used for data acquisition of the tunnel excavation face and the surrounding environment. The information acquisition module includes a tracked robot (1), a laser radar scanning system (2), a robotic arm (3), a rebound spring (4), several cameras (5) and several lights (6). The laser radar scanning system (2), the robotic arm (3), the several cameras (5) and the several lights (6) are all mounted on the tracked robot (1), and the rebound spring (4) is mounted on the robotic arm (3). The model operation module establishes a communication connection with the information acquisition module, and uses BIM software to create a tunnel BIM model, which includes a geological body model, tunnel structure components and construction equipment components, and realizes data sharing and attribute expansion through IFC format files. An information input module establishes a communication connection with the model operation module. The information input module includes monitoring and measurement, geological forecasting and working face measurement, and is used to collect and input basic geological and measurement information of the surrounding rock. The IFC parsing and analysis module establishes a communication connection with the information input module and is used to parse the IFC format file through the cloud computing platform to complete three-dimensional point cloud reconstruction, joint parameter identification, geometric roughness extraction and surrounding rock classification index calculation. The IFC parsing and analysis module includes a three-dimensional point cloud reconstruction module, a joint parameter identification module, a geometric roughness extraction module, and a surrounding rock grading module; The three-dimensional point cloud reconstruction module is used to generate a high-precision three-dimensional digital model of the excavation face by combining two-dimensional images and lidar point cloud data. The joint parameter identification module is used to extract the spatial location, geometric shape, and attitude parameters of the joint surface, and to classify the joint surface according to a clustering algorithm; The geometric roughness extraction module is used to calculate the surface roughness index of the joint surface based on the geometric feature extraction algorithm. That is, it evaluates the roughness index of the joint surface by calculating the change of the surface normal vector. The expression is: ； in, R Roughness index; A The region is the joint surface; The gradient of the surface normal vector of the joint surface; x and y For surface coordinates; The surrounding rock grading module is used to calculate the BQ value, Q value, RMR, and GSI surrounding rock grading index based on the collected geological survey information, and to correct the grading results by combining the in-situ stress. The grading of the surrounding rock is calculated using the following formula: ； in, BQ The surrounding rock classification index; RMR and GSI These are rock quality and structural index, respectively; The feedback analysis module establishes a communication connection with the IFC analysis and analysis module to receive the support design scheme and surrounding rock classification results from the IFC analysis and analysis module, and pushes the analysis results to the construction personnel in real time through the smart terminal, and records historical data during the construction process. The human-machine assistance module establishes a communication connection with the feedback analysis module to interact with construction personnel, including receiving observation information and adjustment suggestions input by construction personnel, providing dynamic visualization of support design schemes, and feeding back the adjusted data to the model operation module.

2. The tunnel support optimization system as described in claim 1, characterized in that: It also includes a wireless communication module, which establishes communication connections with the information acquisition module, model operation module, information input module, IFC parsing and analysis module, feedback analysis module, and human-machine assistance module to realize real-time data transmission and synchronization between various modules, and supports multiple robot devices to collect data simultaneously, and to perform unified analysis after fusing multi-source data.

3. The tunnel support optimization system as described in claim 1, characterized in that: The model operation module includes a geological body modeling module, a tunnel structure modeling module, a construction equipment module, a model integration module, and a real-time model update module. The geological body modeling module is used to establish a three-dimensional geological model that includes the distribution of surrounding rock lithology, the spatial location and geometric parameters of joint surfaces, and fracture parameters. The tunnel structure modeling module is used to generate a standardized three-dimensional model including the initial support structure, secondary lining and excavation cross-section design, and supports the adjustment of construction methods, support thickness and cross-sectional geometry. The construction equipment module is used to establish digital models of various equipment and machinery involved in tunnel construction. The model integration module is used to integrate geological body models, support structure models, and construction design information using the IFC format standard, and supports data interaction with other modules. The real-time model update module is used to dynamically update the lithology, joint parameters, and support structure design scheme of the BIM model based on the collected geological information and construction feedback data, so as to adapt to actual construction needs.

4. The tunnel support optimization system as described in claim 1, characterized in that: The information input module includes a geological monitoring module, a mechanical performance testing module, an environmental data acquisition module, and a comprehensive data processing module; The geological monitoring module includes monitoring and measurement equipment for recording the dynamic deformation trend and changes in the geostress field of the surrounding rock, in order to support the stability analysis of the surrounding rock and construction optimization. The mechanical performance testing module integrates a high-precision mechanical testing device to obtain the shear strength, elastic modulus and rebound stiffness mechanical properties of the surrounding rock. It also supports the determination of stress-strain relationship and real-time evaluation of the mechanical stability changes of the surrounding rock under different construction conditions. The environmental data acquisition module is used to collect temperature, humidity, vibration and noise parameters in the construction environment, and analyze the impact of environmental factors on the stability of the surrounding rock. The influence of temperature and humidity on surrounding rock is quantified using the coefficient of thermal expansion and the coefficient of humidity, thus assessing the impact of environmental factors on surrounding rock stability. The expression is: ； Where, Δ V This refers to changes in the volume of the surrounding rock. α The coefficient of thermal expansion; β The coefficient of humidity expansion; V 0 represents the initial volume; Δ T and Δ H These represent the changes in temperature and humidity, respectively. The integrated data processing module is used to verify, fuse, and normalize the collected multi-source data. It also supports comparative analysis of real-time data and historical data, and generates standardized input files that meet the requirements of hierarchical calculation and support design.

5. The tunnel support optimization system as described in claim 1, characterized in that: The surrounding rock classification module, combined with a cloud computing platform, automatically matches the optimal support design scheme based on real-time collected geological information and generates dynamic support design drawings adapted to site conditions.

6. The tunnel support optimization system as described in claim 5, characterized in that: The cloud computing platform is equipped with multi-threaded parallel processing capabilities, enabling it to simultaneously perform tasks such as 3D point cloud reconstruction, joint parameter extraction, and support design optimization. It also achieves rapid processing of large-scale geological data through a distributed computing framework. The cloud computing platform uses dynamic allocation of computing resources and data caching technology to optimize computing efficiency and supports real-time transmission and storage of calculation results.

7. The tunnel support optimization system as described in claim 1, characterized in that: The feedback analysis module includes a real-time feedback function module, a data tracking and optimization function module, and a user interaction function module. The real-time feedback module is used to push the optimized design to the smart terminal at the construction site in real time based on the surrounding rock classification results and the support design scheme. The optimization push process is represented by the following formula: ； in, For the optimized support design; Q , RMR and GSI These are the surrounding rock classification indicators; parameters are other construction-related parameters. The data tracking and optimization module records construction site data and compares it with historical data to provide support for support design optimization. The expression is: ； Where, Δ D Adjustments to the support design; and These are the current and historical design parameters, respectively. n The number of data points; The user interaction module is used to support construction personnel in inputting on-site feedback, automatically adjusting the support design scheme, and updating it in real time. The adjustment process is represented by the following formula: ； in, The revised support design; The adjustment coefficient is used; feedback is the feedback data entered by the construction personnel.

8. The tunnel support optimization system as described in claim 1, characterized in that: The human-machine assistance module includes a construction feedback input function module, a support design scheme visualization display function module, a scheme adjustment and optimization support function module, and a real-time scheme update function module. The construction feedback input module allows construction personnel to input on-site observation data and feedback information via smart terminals, including changes in surrounding rock, support effectiveness, and construction progress. After integration, the construction feedback data serves as input for adjusting the design scheme. The data integration process can be represented by the following formula: ； in, This refers to the integrated construction feedback data; i For the first i One feedback item; w i The weight of the feedback item; n The number of feedback items; The visualization module for the support design scheme provides a graphical display of the support design scheme and the surrounding rock classification results. The visualization of the support design is achieved through the following algorithm: ； in, For the visualized design scheme, For the optimized support design, parameters are the key information of the support design scheme; The scheme adjustment and optimization support module is used to automatically recommend adjustments to the support design scheme based on construction feedback and site data, and allows construction personnel to manually adjust the design parameters. The automatic optimization recommendation of the support design is achieved through the following formula: ； in, For the adjusted support design, For the current design scheme, To adjust the coefficient, Input data for construction feedback; The real-time scheme update module is used to automatically synchronize the adjusted support design scheme to the construction terminal in real time, ensuring that on-site construction personnel have access to the latest design information. The real-time update of the support design scheme is expressed by the following formula: ； in, For the updated support design, For the current design, For the revised design, The weights are updated in real time.

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