Tunnel support optimization system based on tunnel scanning robot and BIM model

Abstract

The invention relates to a tunnel support optimization system based on a tunnel scanning robot and a BIM model, and aims to solve the problems of low surrounding rock stability analysis efficiency, poor precision and slow response in traditional drilling and blasting method tunnel construction. The system realizes rapid and accurate acquisition of surrounding rock geological information, generates a dynamically updated tunnel model in combination with a BIM technology, automatically analyzes acquired data, carries out surrounding rock stability analysis and support design optimization in real time, pushes a latest design scheme to a construction site in real time through an intelligent terminal, and also supports constructors to input site feedback information. According to the system, the data acquisition efficiency, the analysis precision and the construction safety are greatly improved, it is ensured that the support design can respond to surrounding rock changes in real time, the efficiency and the precision of tunnel construction are remarkably improved, and the system is particularly suitable for tunnel construction under complex geological conditions.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel construction, and particularly relates to a tunnel support optimization system based on a tunnel scanning robot and a BIM model. Background Art

[0002] Currently, in drill-and-blast tunnel construction, the time between the exposure of the excavation face after blasting and mucking and the start of support construction is usually only about 1 hour. This short time window poses extremely high requirements for the efficiency and accuracy of surrounding rock stability analysis. The stability of the surrounding rock is directly related to tunnel construction safety and progress. Therefore, quickly completing the collection and analysis of geological information on the excavation face within a limited time is a key link in the construction process. However, the existing technical system mainly relies on manual operations, including manual data collection, data analysis, and design judgment, and it is difficult to meet the requirements of rapid response.

[0003] In traditional technologies, the analysis of surrounding rock stability usually adopts manual geological exploration, surveying, and laboratory tests. Although these methods can provide basic data, they are limited by the efficiency and accuracy of manual operations. The data collection and analysis process often takes a long time and is difficult to complete in a short time. In addition, the support design relies on manual experience, and the analysis results are easily interfered by human factors, lacking the ability to comprehensively and real-time evaluate complex geological conditions. Especially during the construction process, the surrounding rock conditions may change dynamically, 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 collection, these devices still rely on manual operations, and the data analysis and feedback links do not form a systematic process, lacking real-time and automation. Facing complex geological conditions and short construction requirements, the limitations of these technical means are gradually emerging. Especially when dealing with sudden geological problems in tunnel construction, the existing methods are difficult to provide rapid and comprehensive decision-making support. Therefore, the existing technologies have obvious deficiencies in data collection efficiency, analysis accuracy, and real-time response ability, restricting the improvement of tunnel construction efficiency and safety.

[0005] In view of the above problems, a tunnel support optimization system based on a tunnel scanning robot and a BIM model is now designed. Summary of the Invention

[0006] The embodiments of the present application provide a tunnel support optimization system based on a tunnel scanning robot and a BIM model to solve the problems that the existing technologies are difficult to achieve rapid collection, real-time analysis, and dynamic support design of surrounding rock geological information, which affect tunnel construction efficiency and safety.

[0007] In a first aspect, a tunnel support optimization system based on a tunnel scanning robot and a BIM model is provided, which includes:

[0008] An information collection module for collecting data on the tunnel excavation face and the surrounding environment;

[0009] A model operation module, which establishes a communication connection with the information collection module, constructs a tunnel BIM model in combination with BIM software, including a geological body model, tunnel structure components, and construction equipment components, and realizes data sharing and attribute extension through IFC format files;

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

[0011] An IFC parsing and analysis module, which establishes a communication connection with the information input module, and is used to parse IFC format files through a cloud computing platform to complete 3D point cloud reconstruction, joint parameter identification, geometric roughness extraction, and surrounding rock classification index calculation;

[0012] A feedback analysis module, which establishes a communication connection with the IFC parsing and analysis module, and is used to receive the support design scheme and surrounding rock classification results from the IFC parsing and analysis module, and push the analysis results to construction personnel in real time through an intelligent terminal, and record the historical data during the construction process;

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

[0014] Preferably, it further includes a wireless communication module. The wireless communication module establishes communication connections with the information collection module, the model operation module, the information input module, the IFC parsing and analysis module, the feedback analysis module, and the human-machine assistance module, and is used to realize real-time data transmission and synchronization between each module, support simultaneous data collection by multiple robot devices, and perform unified analysis after fusing multi-source data.

[0015] Preferably, the information collection module includes a tracked robot, a lidar scanning system, a robotic arm, a rebound instrument, a plurality of cameras, and a plurality of lighting lamps. The lidar scanning system, the robotic arm, the plurality of cameras, and the plurality of lighting lamps are all arranged on the tracked robot, and the rebound instrument is arranged 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 model real-time update module;

[0017] The geological body modeling module is used to establish a three-dimensional geological model including the distribution of surrounding rock lithology, the spatial position and geometric parameters of joint planes, and fracture parameters. The tunnel structure modeling module is used to generate a standardized three-dimensional model including the primary support structure, the secondary lining, and the excavation section design, and supports the adjustment of construction parameters, support thickness, and section geometric shape. The construction equipment module is used to establish digital models of various equipment and machinery involved in the tunnel construction process. The model integration module is used to integrate the geological body model, the support structure model, and the construction design information through the IFC format standard to ensure data sharing and consistency among multiple modules and support data interaction with other modules. The model real-time update module is used to dynamically update the lithology, joint parameters, and support structure design scheme of the BIM model according to the collected geological information and construction feedback data to meet the actual construction requirements.

[0018] Preferably, the information input module includes a geological monitoring module, a mechanical property detection module, an environmental data collection module, and a comprehensive data processing module;

[0019] The geological monitoring module includes monitoring and measuring equipment for recording the dynamic deformation trend of the surrounding rock and the change of the in-situ stress field to support the analysis of surrounding rock stability and construction optimization. The mechanical property detection module integrates high-precision mechanical testing devices for obtaining the shear strength, elastic modulus, and rebound stiffness mechanical properties of the surrounding rock, and at the same time supports the determination of the stress-strain relationship to evaluate the change of the mechanical stability of the surrounding rock under different construction conditions in real time. The environmental data collection module is used to collect temperature, humidity, vibration, and noise parameters in the construction environment, analyze the influence of environmental factors on the stability of the surrounding rock, and provide early warning data for 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 at the same time supports the comparative analysis of real-time data and historical data to generate a standardized input file that meets the requirements of classification 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 classification module;

[0021] The three-dimensional point cloud reconstruction module is used to generate a high-precision three-dimensional digital model of the excavation surface by combining two-dimensional images and lidar point cloud data. The joint parameter identification module is used to extract the spatial position, geometric shape and attitude parameters of the joint surface, and classify the joint surface according to the 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. The surrounding rock classification module is used to calculate the surrounding rock classification indexes such as BQ value, Q value, RMR and GSI according to the collected geological measurement information, and correct the classification results in combination with the in-situ stress.

[0022] Preferably, the surrounding rock classification module combines with a cloud computing platform to automatically match the optimal support design scheme according to the real-time collected geological information, and generate a dynamic support design drawing adapted to the on-site conditions.

[0023] Preferably, the cloud computing platform is equipped with multi-thread parallel processing capabilities, can execute tasks such as three-dimensional point cloud reconstruction, joint parameter extraction and support design optimization simultaneously, and realizes the rapid processing of large-scale geological data through a distributed computing framework. The cloud computing platform adopts dynamic allocation of computing resources and data caching technology to optimize the computing efficiency, and supports the 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 function module is used to push the optimized design to the intelligent terminal at the construction site in real time according to the surrounding rock classification result and the support design scheme. The data tracking and optimization function module is used to record the construction site data and compare it with the historical data to provide support for the optimization of the support design. The user interaction function module is used to support the 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 function module supports construction personnel to input on-site observation data and feedback information through intelligent terminals, including surrounding rock changes, support effects, and construction progress. The support design plan visualization display function module provides graphical displays of the support design plan and surrounding rock classification results. Construction personnel can view design changes and optimization suggestions through the interaction interface. The plan adjustment and optimization support function module is used to automatically recommend adjustments to the support design plan based on construction feedback and on-site data, and allows construction personnel to manually adjust design parameters. The real-time plan update function module is used to automatically synchronize the adjusted support design plan to the construction terminal in real time to ensure that on-site construction personnel obtain the latest design information.

[0028] The beneficial effects brought by the technical solution provided by this application include:

[0029] 1. Through the combination of the tunnel scanning robot and the IFC model in this application, the system can quickly collect and analyze the geological information of the surrounding rock within an extremely short time window. The automated data collection and processing capabilities ensure that the analysis of the surrounding rock stability can be completed in a short time, avoiding the low efficiency and errors of manual operations, and greatly improving the construction response speed.

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

[0031] 3. Through automated data processing and analysis in this application, the system overcomes the limitations of manual analysis, realizes the automated processes of data collection, analysis, and support design. The IFC parsing and analysis module is based on a cloud computing platform, quickly processes multi-source data from different devices (such as lidar scanning, rebound hammer, camera, etc.), and generates a support design plan in real time, effectively improving the analysis accuracy and feedback efficiency.

[0032] 4. The system transmits data in real time through the wireless communication module to ensure that construction personnel can always obtain the latest surrounding rock information and support design plan. By pushing the support design results to the intelligent terminal in real time, construction personnel can 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, can reduce the dependence on manual operations for data collection, analysis, and design, avoid interference of human factors on analysis results, and through large-scale data processing and automated decision support, the system can generate support designs efficiently and accurately, improving the scientificity and accuracy of the entire tunnel construction process.

[0034] 6. The system, through an integrated human-machine collaboration module, supports construction workers to input feedback information in real time and automatically adjusts the support design plan. Changes at the construction site (such as the surrounding rock state and construction progress) can be immediately reflected in the support design, making the construction process more flexible and adaptable. At the same time, it ensures that the design matches the actual environment, improving construction efficiency and stability. Description of the Drawings

[0035] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is the system architecture diagram provided by the embodiment of the present application;

[0037] Figure 2 It is the working schematic diagram of the information acquisition module provided by the embodiment of the present application;

[0038] Figure 3 It is the device schematic diagram of the information acquisition module provided by the embodiment of the present application;

[0039] Figure 4 It is the collaborative working schematic diagram between modules provided by the embodiment of the present application;

[0040] Figure 5 It is the information acquisition schematic diagram of the tunnel excavation face provided by the embodiment of the present application;

[0041] Figure 6 It is the schematic diagram of the three-dimensional point cloud digital twin model of the excavation face provided by the embodiment of the present application;

[0042] Figure 7 It is the schematic diagram of intelligent extraction of joint attitude information of the excavation face provided by the embodiment of the present application.

[0043] In the figure: 1. Crawler robot; 2. LiDAR scanning system; 3. Manipulator; 4. Rebound instrument; 5. Camera; 6. Lighting lamp. Detailed Embodiments

[0044] The embodiment of the present application provides a tunnel support optimization system based on a tunnel scanning robot and a BIM model, which can solve the problems in the prior art that it is difficult to quickly collect, analyze in real time and dynamically design the support for surrounding rock geological information, affecting the construction efficiency and safety of tunnels.

[0045] Please refer to Figures 1-7, a tunnel support optimization system based on a tunneling scanning robot and a BIM model, comprising: an information collection 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 collection module is used for collecting data of the tunnel excavation face and the surrounding environment. The information collection module includes a crawler robot 1, a lidar scanning system 2, a robotic arm 3, a rebound hammer 4, a plurality of cameras 5, and a plurality of lighting lamps 6. The lidar scanning system 2, the robotic arm 3, the plurality of cameras 5, and the plurality of lighting lamps 6 are all arranged on the crawler robot 1, and the rebound hammer 4 is arranged on the robotic arm 3.

[0047] The crawler robot 1 is a mobile platform of the information collection module, which can flexibly move in different construction environments in the tunnel and adapt to various complex geological conditions, ensuring stable operation in narrow or uneven tunnel environments. At the same time, it can adjust its position according to needs to quickly collect the surrounding rock information of the excavation face at different positions. The lidar scanning system 2 is used to obtain high-precision three-dimensional point cloud data of the tunnel excavation face and the surrounding environment. The lidar scanning system 2 uses laser beams to detect the surface of objects and converts the reflected signals into point cloud data, thereby generating a three-dimensional digital model of the excavation face and the 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, through the cooperation of the robotic arm 3 and the rebound hammer 4, it can be used to conduct a dynamic rebound stiffness test on the surrounding rock. The rebound hammer 4 evaluates the mechanical properties of the surrounding rock by applying a certain pressure and measuring the rebound result, obtains important physical parameters such as the elastic modulus and strength of the rock mass, can automatically record the measurement results, and matches them with the position data of the robotic arm 3 to accurately analyze the physical properties of the surrounding rock. By measuring the rebound stiffness of the surrounding rock, the system can evaluate the elastic strength of the rock mass and provide a scientific basis for subsequent support design and construction plans. In addition, the robotic arm 3 has the ability to move with multiple degrees of freedom and can be adjusted at different construction positions and angles according to needs, so as to conduct multi-point and multi-angle measurements to ensure the comprehensiveness and accuracy of the rock mass performance data. The plurality of cameras 5 and the lighting lamps 6 are respectively used for collecting two-dimensional image data of the excavation face. The cameras 5 are installed on the crawler robot 1 and cooperate with the lighting lamps 6 to provide sufficient light, which can ensure high-quality image collection even in low-light environments. Through these cameras 5, the system can obtain high-definition photos of the excavation face and combine them with the lidar scanning results to achieve the fusion of two-dimensional images and three-dimensional point cloud data, further improving the accuracy and integrity of the data.

[0048] The crawler robot 1, as a mobile platform, integrates devices such as a lidar scanning system 2, a robotic arm 3, a rebound hammer 4, a camera 5, and a lighting lamp 6, which can ensure the comprehensive collection of data on the tunnel excavation face and the surrounding environment. At the same time, the collaborative work of these devices not only provides geometric shape data of the surrounding rock, but also can obtain 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 collection module and is used to establish a tunnel BIM model in combination with BIM software. This module ensures data sharing and consistency among various system modules, and can dynamically adjust the model according to real-time geological information and construction feedback, optimizing the support design plan. The model operation module includes a geological body model, tunnel structure components, and construction equipment components, and realizes data sharing and attribute extension 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 model real-time update module;

[0051] The geological body modeling module is used to establish a three-dimensional geological model including the lithology distribution of the surrounding rock, the spatial position and geometric parameters of the joint plane, and the fracture parameters. This module uses BIM technology to convert information such as the lithology distribution of the surrounding rock, the spatial position of the joint plane, geometric parameters, and fracture parameters into a three-dimensional digital model. The geological model not only reflects the physical properties of the surrounding rock, but also can simulate the deformation of the rock mass and related engineering responses, providing reference information for subsequent support design and construction.

[0052] Joint plane extraction: Based on three-dimensional point cloud data and lidar scanning results, the spatial position and geometric characteristics of the joint plane can be extracted through the following clustering algorithm:

[0053]

[0054] Among them, D(x,y) represents the distance between two data points, N is the number of data points, x i and y i are the spatial coordinates of the point cloud data, and ||·|| is the Euclidean distance function. Through this formula, the model can automatically identify and classify the joint plane and extract its geometric characteristics (such as strike, dip angle, etc.).

[0055] In this way, through the geological body modeling module, the accuracy of surrounding rock analysis can be effectively improved, ensuring the effective guarantee of 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 a standardized 3D model including the primary support structure, secondary lining, and excavation section design. Through BIM technology, each component of the tunnel structure (such as the support structure, lining layer, bolt arrangement, etc.) is modeled, and adjustments to construction methods, support thickness, and section geometry are supported;

[0057] Primary support structure design: The primary support design usually includes the selection of support materials (such as shotcrete, steel supports, etc.) and the layout of the support system. Through the 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 the excavation is completed, the secondary lining design is used to enhance the stability of the tunnel structure. According to the actual conditions of the tunnel excavation, the lining design scheme is automatically adjusted to ensure the structural strength and durability of the tunnel.

[0059] The tunnel structure modeling module enables the support design to be adjusted more precisely and flexibly, ensuring that the support plan at the construction site adapts to real-time changes, thereby optimizing the support design, avoiding construction risks caused by design lag, and enhancing the adaptability and flexibility of construction.

[0060] The construction equipment module is used to establish digital models of various equipment and machinery involved in the tunnel construction process, including tunneling equipment, support equipment, grouting equipment, etc., and dynamically simulate and manage their positions, states, and operation processes 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 the geological body model, support structure model, 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 seamless docking of multi-party data (such as geological data, support design, construction plan, etc.) for tunnel projects, enhancing the collaborative working ability of different professional modules;

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

[0063] In this way, through the standardized application of the IFC format, the model integration module can ensure data sharing and consistency, reduce errors and delays in data transmission, and improve the decision-making efficiency and safety during the construction process.

[0064] The model real-time update module is used to dynamically update the lithology, joint parameters and support structure design scheme of the BIM model according to the collected geological information and construction feedback data to meet the actual construction requirements. This module ensures that the BIM model always reflects the latest situation at the construction site, enabling the support design to be optimized and adjusted according to the actual site conditions. The model real-time update module can dynamically optimize the support design, ensuring that the design during construction is always consistent with the actual surrounding rock conditions, effectively coping with changes in complex geological and construction environments, thereby improving construction safety and efficiency;

[0065] Real-time data update: As construction progresses, the surrounding rock conditions and construction conditions change. The model real-time 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 completed by connecting to real-time data sources (such as sensors and monitoring devices);

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

[0067] The model operation module of the present invention effectively solves problems in aspects such as 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 update. Through highly integrated and automated data processing, the system can provide real-time feedback, ensuring dynamic matching of the support design scheme with the on-site situation, and thus 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 prediction and face measurement, and is used to collect and input the basic geological and measurement information of the surrounding rock. Among them, the information input module includes a geological monitoring module, a mechanical property detection module, an environmental data collection module and a comprehensive data processing module, ensuring comprehensive analysis and real-time feedback of the surrounding rock during construction, thereby providing accurate data support for support design and construction optimization. The establishment of a communication connection between the information input module and the model operation module enables the collected data to be transmitted to the BIM model in real time, promoting data sharing and dynamic optimization of the support design scheme;

[0069] The geological monitoring module includes monitoring and measuring equipment, which is used to record the dynamic deformation trend of surrounding rock and the change of in-situ stress field to support the analysis of surrounding rock stability and construction optimization. The geological monitoring module records the dynamic deformation trend of surrounding rock and the change of in-situ stress field in real time through the monitoring and measuring equipment, and provides detailed geological data for the analysis of surrounding rock stability and construction optimization. These data not only help to identify potential dangerous areas, but also support the optimization of construction plans, ensuring that the support design can cope with the changes in the geological environment. The geological monitoring module provides real-time data to ensure that the dynamic characteristics of surrounding rock can be reflected in the BIM model in time, helping construction personnel to adjust construction strategies and reduce potential risks;

[0070] The mechanical property detection module integrates high-precision mechanical testing devices, which are used to obtain mechanical properties such as shear strength, elastic modulus and rebound stiffness of surrounding rock, and at the same time support the determination of stress-strain relationship to evaluate the change of mechanical stability of surrounding rock under different construction conditions in real time. Through these data, the support design can be dynamically adjusted to ensure the stability of surrounding rock. The mechanical property detection module provides important mechanical parameter support for the support design to ensure 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 influence of environmental factors on the stability of surrounding rock, and provide early warning data for environmental anomalies. Environmental factors such as temperature changes and humidity fluctuations may cause deformation and crack expansion of 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 evaluate the potential influence of the external environment on the stability of surrounding rock, and optimize the support design;

[0072] Evaluation of temperature and humidity influence: The influence of temperature and humidity on surrounding rock can be quantified by the coefficient of thermal expansion and the humidity coefficient. The calculation formula for the volume change of 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 change amounts of temperature and humidity respectively. This formula helps to evaluate the influence of environmental factors on the stability of surrounding rock.

[0075] The comprehensive data processing module is used to check, fuse and normalize the multi-source data collected to ensure the consistency of geological, mechanical and environmental data. At the same time, it supports the comparative analysis of real-time data and historical data to generate standardized input files that meet the requirements of hierarchical calculation and support design. The comprehensive data processing module ensures the consistency of multi-source data, reduces the error between data, and improves the accuracy of surrounding rock analysis and support design;

[0076] Data normalization: To eliminate the differences between different measurement units and magnitudes, the data is normalized. The commonly used normalization formula is:

[0077]

[0078] Among them, x′ is the normalized data, x is 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 property detection module, environmental data acquisition module, and comprehensive data processing module, the information input module can provide 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 the consistency and comparability of different data sources, providing a strong guarantee for the safety and construction optimization during the tunnel construction process.

[0080] The IFC parsing and analysis module establishes a communication connection with the information input module, and is used to parse IFC format files through the cloud computing platform to 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 the effective control of surrounding rock stability during the construction process, and at the same time optimize the support design to improve construction efficiency and safety;

[0081] Furthermore, 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 classification module;

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

[0083] Three-dimensional reconstruction algorithm: Three-dimensional point cloud reconstruction generates spatial coordinates by jointly analyzing two-dimensional images. The commonly used reconstruction formula is:

[0084]

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

[0086] The joint parameter identification module is used to extract the spatial position, geometric shape, and attitude parameters of the joint surface, and classify the joint surface according to the clustering algorithm. Through this module, the possible joint surfaces in the surrounding rock can be automatically identified and classified 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 the joint surface, avoiding the inefficiency and errors of traditional manual identification, and improving the accuracy and efficiency of data collection.

[0087] Clustering algorithm: The density peak-based clustering algorithm is commonly used for the classification and identification of joint surfaces. The basic steps of this algorithm are to calculate the local density and distance of each point for adaptive clustering to identify the position of the joint surface. The clustering algorithm formula is:

[0088]

[0089] where D i is the clustering distance of the i-th point, N is the number of data points, x i and y i are the spatial coordinates in the point cloud data respectively. Through this formula, the algorithm can effectively classify the joint surface.

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

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

[0092]

[0093] where R is the roughness index, A is the area of the joint surface, is the gradient of the surface normal vector of the joint surface, and x and y are the surface coordinates. This formula evaluates the roughness of the joint surface by calculating the change of the surface normal vector.

[0094] The surrounding rock classification module is used to calculate the BQ value, Q value, RMR, and GSI surrounding rock classification indicators based on the collected geological measurement information, and correct the classification results in combination with the in-situ stress. This module provides a quality assessment of the surrounding rock for the support design, helping construction personnel select appropriate support schemes. The surrounding rock classification module automatically generates classification results based on real-time geological information, avoiding the cumbersome and inaccurate traditional manual calculations.

[0095] Calculation of surrounding rock classification: The classification of the surrounding rock is calculated through the following formula:

[0096]

[0097] BQ is the surrounding rock classification index, and RMR and GSI are the rock mass and structure indices respectively.

[0098] More specifically, the surrounding rock classification module combines with the cloud computing platform to automatically match the optimal support design scheme according to the real-time collected geological information, and generate dynamic support design drawings adapted to the on-site conditions. The cloud computing platform is equipped with multi-thread parallel processing capabilities, and can simultaneously execute tasks such as three-dimensional point cloud reconstruction, joint parameter extraction, and support design optimization, and achieve 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 the computing efficiency, supports real-time transmission and storage of calculation results. The cloud computing platform can process a large amount of data in real-time and in parallel, significantly improving the computing speed, ensuring the real-time and accuracy of surrounding rock analysis and support design;

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

[0100]

[0101] where, R i is the resource allocation ratio of the i-th computing node, C i is the computing power of this node, and N is the total number of computing nodes. Through this formula, 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 module, the system can accurately extract the geometric information of the surrounding rock from the three-dimensional point cloud data, and accurately classify the surrounding rock according to its geological characteristics. At the same time, the cloud computing platform provides powerful computing support to ensure real-time analysis and processing of various data, optimize the support design scheme, and automatically adjust according to changes in construction conditions. Through this series of technical means, this system greatly 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 parsing and analysis module, which is used to receive the support design plan and surrounding rock classification results from the IFC parsing and analysis module, and push the analysis results to the construction personnel in real time through the intelligent terminal. In addition, the feedback analysis module also records the historical data during the construction process to support subsequent construction decision-making 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 function module is used to push the optimized design to the intelligent terminal at the construction site in real time according to the surrounding rock classification results and the support design plan. Through this module, the construction personnel can view the latest support design plan on the intelligent terminal, including key parameters such as support type, support layout, bolt length, and support thickness. This real-time feedback mechanism ensures that the construction personnel can react in a timely manner according to the latest data and design plan, avoiding construction inconsistencies caused by information lag. The real-time feedback function ensures that the construction personnel can quickly obtain the optimization results of the support design on site;

[0106] Optimized support design push: The optimized support design plan is dynamically adjusted according to the classification index and construction environment. The optimization push process can be expressed by the following formula:

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

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

[0109] The data tracking and optimization function module is used to record the construction site data and compare it with the historical data to provide support for optimizing the support design. By comparing the on-site real-time data and historical data, the execution effect of the support design plan is evaluated, and optimization adjustments are made according to the results. For example, after monitoring the dynamic changes of the surrounding rock, the system can adjust the support plan in real time according to 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, can accurately evaluate the effectiveness of the support design, and optimize according to on-site changes to improve construction safety and efficiency;

[0110] Data comparison and optimization: By comparing the on-site data and historical data, the optimization algorithm can automatically adjust the support design. Its optimization formula is:

[0111]

[0112] where ΔD is the support design adjustment amount, and D current and D historical 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 the continuous optimization of the support plan.

[0113] The user interaction function module is used to support construction personnel to input on-site feedback, automatically adjust the support design plan, and update it in real time, such as surrounding rock changes, support effects, and construction progress. These feedback messages will be automatically processed, and the support design plan will be adjusted according to the on-site situation. Construction personnel can not only see the dynamic display of the support design plan, but also manually adjust the design plan. The system will automatically update the support design according to these adjustments and push the modified plan to the site through intelligent terminals. The user interaction function module enhances the interaction between construction personnel and the system, enabling construction personnel to flexibly respond to on-site changes and adjust the support design plan in real time, thus ensuring the flexibility and adaptability of construction;

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

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

[0116] where D adjusted is the adjusted support design, α is the adjustment coefficient, and feedback is the feedback data input by construction personnel. This formula realizes the seamless connection between on-site feedback and the support design plan, ensuring that the design plan is updated in real time and matches the actual construction conditions.

[0117] In this way, the feedback analysis module ensures that the support design in tunnel construction can respond to changes in the surrounding rock state and construction conditions in a timely and accurate manner through real-time feedback, data tracking and optimization, and user interaction functions. The real-time feedback function provides rapid push of the support design, the data tracking and optimization function helps optimize the design plan and provides real-time monitoring of construction data, and the user interaction function allows construction personnel to make flexible adjustments according to the on-site situation. Overall, the feedback analysis module greatly improves the adaptability and accuracy of the support design plan, ensuring the safety, efficiency, and efficient decision-making of tunnel construction.

[0118] The human-machine assistance module establishes a communication connection with the feedback analysis module, which is used to interact with construction workers, including receiving the observation information and adjustment suggestions input by construction workers, providing a dynamic visual display of the support design plan, and feeding back the adjusted data to the model operation module. This module supports construction workers to input on-site feedback information and dynamically adjusts the support design plan according to data such as the surrounding rock changes, support effect, and construction progress on-site. In addition, the human-machine assistance module also provides a visual display of the support design plan, helps construction workers understand and operate the support design plan, and synchronously updates the latest design information in real time through intelligent terminals;

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

[0120] The construction feedback input function module supports construction workers to input on-site observation data and feedback information through intelligent terminals. Construction workers can record and report key information such as surrounding rock changes, support effect, and construction progress, and feed these data back to the system. Through this function module, construction workers can monitor the changes on the construction site in real time and share real-time data with the system, thereby prompting the timely adjustment of the support design plan. This function module improves the sense of participation of on-site construction workers and ensures the real-time docking of the support design plan with the actual construction conditions;

[0121] Integration of construction feedback data: After the construction feedback data is integrated, it is used as the input for adjusting the design plan. The data integration process can be expressed by the following formula:

[0122]

[0123] Among them, F input is the integrated construction feedback data, feedback i is the i-th feedback item, w i is the weight of the feedback item, and n is the number of feedback items. Through this formula, the system can integrate different feedbacks input by construction workers into input data that can be used to adjust the support design.

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

[0125] Graphical display of the design solution: The visual display of the support design can be achieved through the following algorithm:

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

[0127] where D visualized is the visualized design solution, D optimized is the optimized support design, and parameters are the key information of the support design solution (such as layout, thickness, etc.). Through this formula, the system converts the optimized support design into graphical data for the convenience of construction personnel to view and operate.

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

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

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

[0131] where D adjusted is the adjusted support design, D current is the current design solution, α is the adjustment coefficient, and F input is the construction feedback input data. Through this formula, the system can automatically adjust the support design solution according to the feedback data at the construction site.

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

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

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

[0135] where D updated is the updated support design, D current is the current design, D adjusted is the adjusted design, and β is the weight updated in real time. This formula ensures that the design scheme can quickly respond to changes at the construction site and be promptly transmitted to the construction personnel.

[0136] In this way, the human-machine assistance module provides a flexible and efficient support design adjustment and real-time feedback mechanism for construction personnel through four main functional modules: construction feedback input, visualization display of support design schemes, support for scheme adjustment and optimization, and real-time scheme update. Through the interaction between construction personnel and the system, the support design scheme can be continuously optimized according to 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. The wireless communication module establishes communication connections with the information collection module, model operation module, information input module, IFC parsing and analysis module, feedback analysis module, and human-machine assistance module, and is used to achieve real-time transmission and synchronization of data between various modules, and support multiple robot devices to collect data simultaneously, and perform unified analysis after fusing multi-source data;

[0138] Real-time data transmission and synchronization: The wireless communication module ensures real-time transmission and synchronization of data 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 through the wireless network, and the input data of other modules will be updated in a timely manner. For example, when the information collection module collects data, it transmits these data to the IFC parsing and analysis module for subsequent processing, and finally pushes them to the feedback analysis module for construction personnel. Real-time data transmission and synchronization ensure that there is no lag in information between different modules, ensuring the efficient operation of the system and improving the construction decision-making efficiency;

[0139] Support for multiple robot devices to work simultaneously: The wireless communication module supports multiple robot devices to work in parallel. During tunnel construction, multiple robot devices can collect data simultaneously, covering a larger area. The data collected by each robot is summarized to the cloud through the wireless communication module for centralized analysis. Supporting multiple robot devices to work in parallel improves the data collection efficiency. Especially in large-scale tunnel projects, multiple working 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 conducting unified analysis through the cloud computing platform. The system can integrate geological monitoring data, mechanical data, environmental data, etc., in order to provide more accurate information for support design. Through this data fusion, the support design plan can be dynamically adjusted according to real-time feedback. Data fusion improves the accuracy and integrity of the system and provides comprehensive data support for support design;

[0141] Promote collaboration between modules: The wireless communication module connects each module, enabling efficient collaboration between different modules. Each module relies on the data support of other modules for analysis and decision-making to ensure that the system can work in coordination and respond to changes at the construction site in a timely manner. Through the efficient collaboration between modules, information flows more smoothly, reducing information silos and enhancing the system's response ability;

[0142] Therefore, the wireless communication module plays a bridging role in the entire system, ensuring data transmission and synchronization between different modules. It supports multiple robotic devices to work in parallel, 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 collection: After the tunnel construction blasting and mucking process is completed, the tracked robot 1 cooperates with workers to take multiple photos of the excavation face at different positions through the information collection module. Through the lidar scanning system 2 and camera 5 integrated in the tracked robot 1, two-dimensional photos (8 - 12 pieces) of the excavation face surface and high-precision three-dimensional point cloud data are obtained. At the same time, the Schmidt hammer rock mass rebound test is carried out through the rebound instrument 4 equipped on the robotic arm 3 to obtain the dynamic rebound stiffness data of the surrounding rock (the average value of 20 measurement values). These data are used for subsequent rock mass stability analysis and support design optimization;

[0145] 2. Data upload: Through the intelligent mobile terminal, using the high-speed wireless network in the tunnel, the above-mentioned collected two-dimensional photos, three-dimensional point cloud data, rock mass rebound stiffness data, and mileage information are uploaded to the cloud computing platform for analysis. This upload process usually takes 2 - 5 minutes to complete, ensuring the real-time and integrity of the data;

[0146] 3. Data parsing and model construction: After the cloud computing platform receives the uploaded data, the IFC parsing and analysis module parses these data. Through the virtual multi-view algorithm and the reconstruction technology combining two-dimensional images and three-dimensional point cloud data, a three-dimensional digital model of the excavation face is generated. Further, the joint parameter identification algorithm is used to extract the spatial position, geometric shape, and attitude parameters of the joint surface, and the joint surface is intelligently classified through the clustering algorithm. In addition, based on the geometric feature extraction algorithm, the surface roughness index of the joint surface is calculated;

[0147] 4. Surrounding rock classification and support design generation: Based on the analyzed data, the surrounding rock classification module automatically calculates surrounding rock classification indexes such as BQ value, Q value, RMR, and GSI, and corrects the classification results by combining information such as in-situ stress collected in real time. Using the cloud computing platform, the support design scheme is automatically generated and matched with the classification indexes to ensure the automatic selection of the most suitable support type and support structure design according to the actual surrounding rock conditions;

[0148] 5. Real-time push of support design: The feedback analysis module real-time pushes the support design scheme and surrounding rock classification results to the construction site through intelligent terminals. Construction personnel can view the support design scheme through intelligent terminals, including key construction parameters such as support type, support layout, bolt length, and support thickness. This module also records the 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 intelligent terminals. Construction personnel input on-site observation information and adjustment suggestions (such as surrounding rock changes and support effects), and the system adjusts the support design scheme according to the feedback. Construction personnel can view the design changes and optimization suggestions in real time through the interaction interface and can also manually adjust the design parameters;

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

[0151] 8. Real-time update of support design scheme: During the construction process, 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 according to 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 the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present application. Unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, or a communication connection can be established; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0153] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A tunnel support optimization system based on a tunneling scanning robot and a BIM model, characterized in that, It includes: An information collection module for collecting data on the tunnel excavation face and the surrounding environment; A model operation module that establishes a communication connection with the information collection module, constructs a tunnel BIM model in combination with BIM software, including a geological body model, tunnel structure components, and construction equipment components, and realizes data sharing and attribute extension through IFC format files; An information input module that establishes a communication connection with the model operation module. The information input module includes monitoring and measurement, geological prediction, and heading face measurement, and is used to collect and input the basic geological and measurement information of the surrounding rock; An IFC parsing and analysis module that establishes a communication connection with the information input module and is used to parse IFC format files through a cloud computing platform to complete 3D point cloud reconstruction, joint parameter identification, geometric roughness extraction, and surrounding rock classification index calculation; A feedback analysis module that establishes a communication connection with the IFC parsing and analysis module, is used to receive the support design scheme and surrounding rock classification results from the IFC parsing and analysis module, and push the analysis results to construction personnel in real time through an intelligent terminal, and record the historical data during the construction process; A human-machine assistance module that establishes a communication connection with the feedback analysis module and is used to interact with construction personnel, including receiving the observation information and adjustment suggestions input by construction personnel, providing a dynamic visualization display of the support design scheme, and feeding back the adjusted data to the model operation module.

2. The tunnel support optimization system according to claim 1, characterized in that: It further includes a wireless communication module. The wireless communication module establishes communication connections with the information collection module, the model operation module, the information input module, the IFC parsing and analysis module, the feedback analysis module, and the human-machine assistance module, and is used to realize real-time transmission and synchronization of data between each module, support simultaneous data collection by multiple robot devices, and perform unified analysis after fusing multi-source data.

3. The tunnel support optimization system according to claim 1, characterized in that: The information collection module includes a tracked robot (1), a lidar scanning system (2), a robotic arm (3), a rebound hammer (4), a plurality of cameras (5), and a plurality of lighting lamps (6). The lidar scanning system (2), the robotic arm (3), the plurality of cameras (5), and the plurality of lighting lamps (6) are all arranged on the tracked robot (1), and the rebound hammer (4) is arranged on the robotic arm (3).

4. The tunnel support optimization system according to 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 model real-time update module; The geological body modeling module is used to establish a three-dimensional geological model including the distribution of surrounding rock lithology, the spatial position 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 primary support structure, secondary lining, and excavation section design, and supports the adjustment of construction methods, support thickness, and section geometric shapes; The construction equipment module is used to establish digital models of various equipment and machinery involved in the tunnel construction process; The model integration module is used to integrate the geological body model, the support structure model and the construction design information through the IFC format standard, and support data interaction with other modules; The model real-time update module is used to dynamically update the lithology, joint parameters and support structure design scheme of the BIM model according to the collected geological information and construction feedback data to meet the actual construction requirements.

5. The tunnel support optimization system according to claim 1, characterized in that: The information input module includes a geological monitoring module, a mechanical property detection module, an environmental data acquisition module and a comprehensive data processing module; The geological monitoring module includes monitoring and measuring equipment for recording the dynamic deformation trend of the surrounding rock and the change of the in-situ stress field to support the analysis of the surrounding rock stability and construction optimization; The mechanical property detection module integrates high-precision mechanical testing devices for obtaining the shear strength, elastic modulus and rebound stiffness mechanical properties of the surrounding rock, and at the same time supports the determination of the stress-strain relationship and real-time evaluation of the change of the mechanical stability of the surrounding rock under different construction conditions; The environmental data acquisition module is used to collect the temperature, humidity, vibration and noise parameters in the construction environment and analyze the influence of environmental factors on the stability of the surrounding rock, that is; Quantify the influence of temperature and humidity on the surrounding rock through the coefficient of thermal expansion and the coefficient of humidity, and evaluate the influence of environmental factors on the stability of the surrounding rock. The expression is: ΔV = α·V0·ΔT + β·V0·ΔH Where, ΔV is the volume change of the surrounding rock; α is the coefficient of thermal expansion; β is the coefficient of humidity expansion; V0 is the initial volume; ΔT and ΔH are the change amounts of temperature and humidity respectively; The comprehensive data processing module is used to check, fuse and normalize the collected multi-source data, and at the same time support the comparative analysis of real-time data and historical data to generate a standardized input file that meets the requirements of hierarchical calculation and support design.

6. The tunnel support optimization system according to claim 1, characterized in that: 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 position, geometric shape and occurrence parameters of the joint surface and classify the joint surface according to the 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, evaluate the roughness index of the joint surface by calculating the change of the surface normal vector. The expression is: where R is the roughness index; A is the area of the joint surface; is the gradient of the surface normal vector of the joint surface; x and y are surface coordinates; The surrounding rock grading module is used to calculate the surrounding rock grading indexes of BQ value, Q value, RMR and GSI according to the collected geological measurement information, and correct the grading result in combination with the in-situ stress. The grading of the surrounding rock is calculated by the following formula: Where, BQ is the surrounding rock grading index; RMR and GSI are the rock mass and structure indexes respectively.

7. The tunnel support optimization system according to claim 6, characterized in that: The surrounding rock classification module, in combination with the cloud computing platform, automatically matches the optimal support design plan according to the real-time collected geological information and generates dynamic support design drawings adapted to the on-site conditions.

8. The tunnel support optimization system according to claim 7, wherein: The cloud computing platform is equipped with multi-thread parallel processing capabilities, can simultaneously execute three-dimensional point cloud reconstruction, joint parameter extraction, and support design optimization tasks, and realizes the 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 the real-time transmission and storage of computing results.

9. The tunnel support optimization system according to claim 1, wherein: 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 function module is used to push the optimized design to the intelligent terminal at the construction site in real time according to the surrounding rock classification result and the support design plan. The optimization push process is represented by the following formula: Doptimized = f(Q, RMR, GSI, parameters) Wherein, Doptimized is the optimized support design; Q, RMR, and GSI are surrounding rock classification indicators; parameters are other construction-related parameters; The data tracking and optimization function module is used to record the construction site data and compare it with the historical data to provide support for optimizing the support design. The expression is: Wherein, ΔD is the support design adjustment amount; Dcurrent and Dhistorical are the current and historical design parameters respectively; n is the number of data points; The user interaction function module is used to support the construction personnel to input on-site feedback, automatically adjust the support design plan, and update it in real time. The adjustment process is represented by the following formula: Dadjusted = Dcurrent + α·(feedback) Wherein, Dadjusted is the adjusted support design; α is the adjustment coefficient; feedback is the feedback data input by the construction personnel.

10. The tunnel support optimization system according to claim 1, wherein: The human-machine assistance module includes a construction feedback input function module, a support design plan visualization display function module, a plan adjustment and optimization support function module, and a real-time plan update function module; The construction feedback input function module supports the construction personnel to input on-site observation data and feedback information through the intelligent terminal, including surrounding rock changes, support effects, and construction progress. After the construction feedback data is integrated, it is used as the input for adjusting the design plan. The data integration process can be represented by the following formula: Among them, Finput is the integrated construction feedback data; feedback i is the i-th feedback item; w i is the weight of the feedback item; n is the number of feedback items; The support design plan visualization display function module provides a graphical display of the support design plan and the surrounding rock classification result. The visualization display of the support design is realized through the following algorithm: Dvisualized = f(Doptimized, parameters) Among them, Dvisualized is the visualized design solution, Doptimized is the optimized support design, and parameters are the key information of the support design solution; The scheme adjustment and optimization support function module is used to automatically recommend the adjustment of the support design solution according to the construction feedback and on-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: Dadjusted = Dcurrent + α·(Finput) Among them, Dadjusted is the adjusted support design, Dcurrent is the current design solution, α is the adjustment coefficient, and Finput is the construction feedback input data; The real-time scheme update function module is used to automatically synchronize the adjusted support design solution to the construction terminal in real time to ensure that on-site construction personnel obtain the latest design information. The real-time update of the support design solution is expressed by the following formula: Dupdated = Dcurrent + β·(Dadjusted - Dcurrent) Among them, Dupdated is the updated support design, Dcurrent is the current design, Dadjusted is the adjusted design, and β is the weight of real-time update.

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