Earthquake frequent occurrence area tunnel dynamic feedback system based on real-time monitoring

Abstract

The invention relates to a real-time monitoring-based dynamic feedback system for tunnels in an earthquake-prone area. The system comprises a monitoring module, a data processing module, a feedback module and an execution module, and aims at collecting data such as surrounding rock deformation, vault settlement and earthquake vibration in tunnel construction in real time. The monitoring module obtains data through a stress sensor, an accelerometer, a displacement meter and other devices, and transmits the data to the data processing module through the wireless communication module. And the data processing module predicts the surrounding rock stability and generates a support optimization suggestion. And the feedback module generates a support optimization scheme according to the analysis result. And the execution module implements the optimization scheme to site construction, and dynamically adjusts support measures through automatic equipment. The system effectively improves the safety, stability and emergency response capability of tunnel construction through a real-time monitoring and dynamic feedback mechanism, is particularly suitable for tunnel construction under the conditions of frequent earthquakes and complex geological conditions, and has a wide application prospect.

Description

Technical Field

[0001] The invention belongs to the technical field of tunnel engineering, and in particular relates to a tunnel dynamic feedback system in an earthquake-prone area based on real-time monitoring. Background Art

[0002] In earthquake-prone areas, tunnel construction faces complex geological conditions and extremely high engineering risks. Due to earthquake action and surrounding rock instability, problems such as surrounding rock deformation, vault settlement, water and mud inrush, and rock bursts often occur during tunnel construction, posing a threat to construction safety and the stability of the support structure.

[0003] Traditional tunnel construction support design relies on static geological analysis and construction experience, and it is difficult to respond to the surrounding rock changes and earthquake dynamic impacts that occur during the construction process. Especially when an earthquake occurs, the mechanical properties of the surrounding rock change rapidly, and the traditional support design and adjustment methods lag behind, which can easily cause tunnel collapse and support failure.

[0004] In recent years, the application of real-time monitoring technology has made dynamic feedback and optimization possible during the construction process. However, existing monitoring systems often only target a single data source (such as displacement, stress, or seismic vibration) and lack the dynamic analysis function of multi-parameter fusion, making it difficult to achieve automatic feedback and optimization during the construction process. In addition, the optimization strategy for tunnel support design in earthquake-prone areas has not yet formed a standardized process, which affects the safety and efficiency of construction.

[0005] Therefore, there is an urgent need for a dynamic feedback system based on real-time monitoring that can integrate seismic parameters, surrounding rock mechanics monitoring data, and construction process data to form a closed-loop monitoring, analysis, feedback, and optimization mechanism, dynamically adjust the construction support plan, and improve the safety and efficiency of tunnel construction in earthquake-prone areas. Summary of the invention

[0006] In order to solve the problems mentioned in the background technology, the present invention provides a dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring. The system can collect a variety of monitoring data in real time during the construction process, and generate support optimization plans through data fusion and feedback algorithms, thereby realizing dynamic adjustment of support design and improving construction safety and adaptability.

[0007] Based on the first main aspect of the present invention, a tunnel dynamic feedback system for earthquake-prone areas based on real-time monitoring is provided, including but not limited to the following modules:

[0008] Monitoring module, used to collect surrounding rock deformation, vault settlement, perimeter convergence, seismic vibration acceleration, and groundwater inflow during tunnel construction;

[0009] The data processing module is used to integrate and analyze the data collected by the monitoring module from multiple sources to generate surrounding rock stability predictions and support optimization suggestions;

[0010] A feedback module generates a real-time construction support optimization plan based on the analysis results of the data processing module, wherein the plan includes adjusting the anchor density, the thickness of the shotcrete, and the spacing of the steel arches;

[0011] The execution module is used to transmit the optimization plan generated by the feedback module to the construction site and execute it, including the dynamic adjustment of support parameters and the optimization of construction technology.

[0012] In some embodiments, as a further preferred solution, the monitoring module includes:

[0013] Stress sensors are used to monitor the stress state of surrounding rocks;

[0014] accelerometers, which monitor vibration waves caused by earthquakes;

[0015] displacement meters to monitor vault settlement and perimeter convergence;

[0016] The wireless communication module is used to transmit the monitoring data to the data processing module in real time.

[0017] Among them, the monitoring equipment is arranged to cover key sections of the tunnel, such as rock burst sections, soft surrounding rock sections, rock contact zones and water and mud gushing sections, to achieve full coverage of the monitoring area.

[0018] In some embodiments, as a further preferred solution, the data processing module performs trend analysis on the monitoring data based on a machine learning algorithm to predict changes in the mechanical properties of the surrounding rock.

[0019] In some embodiments, as a further preferred solution, the machine learning algorithm includes:

[0020] Data fusion algorithm, used to process multi-parameter monitoring data, reduce noise and enhance data accuracy;

[0021] Trend prediction model is used to predict the changes in mechanical properties of surrounding rocks and the impact range of earthquake shocks.

[0022] The algorithm integrates, analyzes and predicts the multi-source data collected by the monitoring module to generate surrounding rock stability assessment and construction support optimization suggestions.

[0023] In some embodiments, as a further preferred solution, the feedback module generates a construction support solution based on a preset optimization rule, including:

[0024] In rock burst areas, optimize the layout of energy-absorbing anchors;

[0025] In the water-gushing and mud-bursting section, adjust the waterproof layer structure and the layout of the drainage system;

[0026] In earthquake impact areas, optimize the spacing between steel arches and the thickness of shotcrete.

[0027] The feedback module needs to combine the preset support optimization rules, anchor density, steel arch spacing and shotcrete thickness to generate a dynamic support optimization plan, dynamically adjust the anchor arrangement method of the soft surrounding rock and rock burst section, optimize the distribution density of the energy-absorbing anchor, optimize the shotcrete thickness and steel arch arrangement spacing for the high-impact earthquake area, ensure the seismic performance of the support structure, optimize the drainage design in the water-rich section through dynamic drainage measures, and send this to the execution module.

[0028] In some embodiments, as a further preferred solution, the execution module includes automated construction equipment for executing anchor arrangement, concrete spraying and steel arch installation according to the support optimization solution provided by the feedback module. Short footage and weak blasting technology are used in the through section and karst zone, and the construction rhythm and support strength are adjusted in combination with real-time monitoring. Afterwards, the construction execution status needs to be fed back to the data processing module in real time for further analysis and optimization.

[0029] Based on the second main purpose of the present invention, a method for dynamic feedback of tunnels in earthquake-prone areas based on real-time monitoring is provided, comprising the following steps:

[0030] The monitoring module collects data on the vault settlement, perimeter convergence, surrounding rock stress, earthquake vibration acceleration, and groundwater inflow during tunnel construction;

[0031] In the data processing module, multi-source fusion and trend analysis are performed on the monitoring data to predict the stability and deformation trend of the surrounding rock;

[0032] In the feedback module, a dynamic support optimization plan is generated based on the analysis results, including support material adjustment and construction parameter optimization;

[0033] In the execution module, support measures are dynamically adjusted according to the optimization plan, including anchor density, steel arch spacing and concrete spraying thickness. Support optimization is implemented through automated construction equipment, and execution data is transmitted back to the data processing module in real time for verification.

[0034] In some embodiments, as a further preferred solution, the monitoring frequency of the monitoring module can be dynamically adjusted to achieve high-frequency monitoring of high-risk areas to ensure timely response to abnormal changes.

[0035] In some embodiments, as a further preferred solution, the data processing module generates a surrounding rock stability grade and issues a warning signal for high-risk areas based on historical data and real-time monitoring data. Intelligent processing of tunnel construction data reduces manual intervention and improves decision-making efficiency and accuracy.

[0036] In some embodiments, as a further preferred solution, the support optimization solution generated by the feedback module is updated according to real-time data, including:

[0037] Adjustment of anchor density;

[0038] Optimization of steel arch spacing;

[0039] Dynamic adjustment of concrete shotcrete thickness and continuous updating of optimization plans through real-time data.

[0040] Advantages and beneficial effects of the present invention:

[0041] The present invention can collect data such as surrounding rock deformation, vault settlement, peripheral convergence and seismic vibration parameters in real time during tunnel construction through a real-time monitoring module and a wireless communication module, and quickly transmit them to a data processing module. It can analyze data and generate support optimization suggestions in a short time, realize rapid processing and feedback of construction dynamic data, and significantly improve the construction response speed. The multi-source data fusion algorithm and trend prediction model are used to analyze and predict the monitoring data, and generate surrounding rock stability assessment results and construction optimization plans. The automated analysis and feedback of data greatly reduces manual intervention and improves decision-making efficiency and accuracy.

[0042] Moreover, through the surrounding rock stability prediction model and the early warning mechanism of high-risk areas, the system can timely identify potential risks such as rock bursts, water inrush or earthquake shocks. The support measures at the construction site can be dynamically adjusted according to the real-time situation of the feedback module, thus effectively reducing the potential safety hazards that may occur during tunnel construction.

[0043] The present invention is suitable for tunnel construction in earthquake-prone areas and under complex geological conditions, and can achieve rapid response to sudden geological disasters through real-time monitoring and dynamic feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0045] Figure 1 It is a schematic diagram of modules in one embodiment of the present invention. DETAILED DESCRIPTION

[0046] The preferred embodiments of the present invention will be described in detail below so that the purpose, features and advantages of the present invention can be more clearly understood. It should be understood that the following embodiments are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0047] In the following description, certain specific details are set forth for the purpose of illustrating the various disclosed embodiments to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0048] References throughout the specification to "one embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0049] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0050] like Figure 1 FIG. 1 is a schematic diagram of a tunnel dynamic feedback system in an earthquake-prone area based on real-time monitoring provided by an embodiment of the present invention. The specific implementation steps of an embodiment of the present invention are as follows:

[0051] 1. Monitoring module

[0052] 1.1 Sensor layout and settings

[0053] Stress sensors: arranged in key sections with poor surrounding rock stability, rock burst sections, weak surrounding rock sections, and near syncline structures; arranged every 5-10 meters, and the arrangement should be adjusted according to the actual rupture risk of the surrounding rock; monitor the changes in stress inside and outside the surrounding rock to determine whether there is a risk of rupture. The acquisition frequency is set to 1 time per second, and can be increased to 10 times per second in abnormal circumstances to monitor sudden changes.

[0054] Accelerometers: They are arranged in the tunnel body and entrance sections with a density of one unit per 10 meters, monitoring seismic waves at a high frequency of 10-20 times per second; they can capture micro-earthquakes in real time and determine the impact of earthquakes on support; the sensors in the direction of the earthquake source must be arranged perpendicular to the tunnel axis to capture the main shock waves.

[0055] Displacement meters: They are mainly placed in areas where convergence deformation may occur; one is arranged every 10 meters and installed on the tunnel vault, side walls and invert sections to monitor deformation trends, periodically read the deformation of the vault and wall, and promptly capture signals of surrounding rock instability to prevent vault collapse.

[0056] 1.2 Wireless Communication Module

[0057] Communication protocol: The LoRa communication protocol is used to achieve low-power, long-distance data transmission; sensor data is transmitted to the data processing module in real time through wireless communication; wireless communication base stations are arranged in the tunnel, with one base station arranged every 500 meters, covering the entire tunnel and supporting real-time upload of large-capacity data.

[0058] 2. Data processing module

[0059] 2.1 Data Fusion and Analysis

[0060] Fusion algorithm: Bayesian weighting method is used to fuse data from different sensors to eliminate outliers and improve data accuracy.

[0061] The sensor data is processed through a preprocessing module to remove noise; it is normalized using a time series algorithm; and the fused data enters the prediction algorithm module.

[0062] Real-time monitoring and alarm:

[0063] Stability assessment indicators: abnormal stress (exceeding the design value by more than 30%) and abnormal displacement (greater than 50% of the allowable deformation value).

[0064] Early warning mechanism: When the monitoring value of a certain area approaches the set threshold, the system sends an alarm signal to the control center, triggering the feedback module.

[0065] 2.2 Prediction Algorithm

[0066] Trend prediction model: Use an LSTM-based machine learning model, input the fused multi-source data, and use historical surrounding rock data and seismic vibration experimental data for deep learning training; output the changing trend of the surrounding rock mechanical properties in the next stage and the possible impact range of the earthquake shock on the tunnel.

[0067] Training model: Based on historical monitoring data and current real-time data, continuously optimize the accuracy of model predictions.

[0068] Multi-source data analysis: Real-time analysis of data from stress sensors, accelerometers and displacement meters to generate surrounding rock stability grades; when the surrounding rock stability is lower than the set threshold, the optimized support plan is automatically sent to the feedback module.

[0069] 3. Feedback module

[0070] 3.1 Support optimization rules

[0071] Rock burst section: optimize the density of energy-absorbing anchor rods from 1 every 3 meters to 1 every 2 meters; thicken the shotcrete layer from the original 20cm to 30cm, and add steel fiber to enhance strength.

[0072] Water gushing section: add double-layer drainage pipes in the support design; in view of the risk of water inrush, adjust the waterproof layer material to high-density polyethylene (HDPE).

[0073] Earthquake impact zone: shorten the distance between steel arch frames from the original 2.5 meters to 1.5 meters; increase the seismic performance design of shotcrete and use C35 concrete.

[0074] Optimization of rockburst section support: increase the density of energy-absorbing anchor rods, arranging one every 1 meter; increase the thickness of shotcrete from the original design of 20cm to 30cm.

[0075] 3.2 Dynamic Feedback Mechanism

[0076] Closed-loop mechanism: The feedback module and the data processing module form a dynamic interaction, and the support plan is updated every 10 minutes. The time from data analysis to feedback generation does not exceed 5 minutes to ensure the real-time adjustment.

[0077] 4. Execution module

[0078] 4.1 Automated construction equipment

[0079] Anchor arrangement equipment: equipped with a multifunctional robotic arm, which can automatically drill holes and install anchors; it has the ability to dynamically adjust the density of anchor arrangement.

[0080] Shotcrete equipment: Automatically controls the shotcrete thickness with an accuracy of ±5mm; equipped with a laser guidance system to achieve high-precision construction.

[0081] 4.2 Construction monitoring and feedback

[0082] Real-time data feedback: automatically record support parameters (such as anchor spacing, concrete thickness) and transmit them to the data processing module in real time.

[0083] Result verification: Conduct data analysis to ensure that the construction execution is consistent with the optimization plan. Conduct secondary monitoring of the support effect after execution to ensure that the execution effect of the optimization plan meets the design objectives.

[0084] Implementation steps:

[0085] Step 1: System deployment: Install and debug monitoring equipment, establish data processing and feedback systems, and ensure that the system has basic operating capabilities.

[0086] 1. Equipment installation and layout:

[0087] Stress sensors: are preferentially arranged in unstable surrounding rock areas, rock burst sections, weak surrounding rock sections, and geological fault zones; accelerometers: are arranged at tunnel entrances and key nodes of the tunnel body to ensure full coverage monitoring of seismic waves; displacement meters: are arranged at the tunnel vault, side walls, and invert positions, with one installed every 10 meters. Wireless communication base stations: wireless communication base stations are arranged every 500 meters to ensure that monitoring data is transmitted without blind spots.

[0088] 2. Data processing center construction:

[0089] Configure high-performance servers and install data processing and feedback system software; deploy LSTM prediction models and upload historical data for preliminary training.

[0090] 3. Equipment debugging and calibration:

[0091] Use standardized test signals to calibrate sensors to ensure monitoring accuracy; perform data transmission tests to verify the stability and latency of wireless communication.

[0092] Step 2: Construction preparation stage: Conduct surrounding rock monitoring, support design parameter optimization, and construction technology briefing in the early stage of construction.

[0093] 1. Preliminary monitoring of surrounding rock:

[0094] Construction monitoring: Conduct advanced geological forecasts and analyze surrounding rock stability based on preliminary sensor data; identify key construction areas based on geological reports and real-time monitoring results.

[0095] 2. Adjustment of support design parameters:

[0096] Based on the monitoring data, the density of anchor arrangement, thickness of shotcrete and spacing of steel arch frames are optimized; seismic-resistant design is added in the earthquake impact zone, and the drainage scheme is strengthened in the waterproof section.

[0097] 3. Technical disclosure:

[0098] Explain in detail the operating logic of the dynamic feedback system to construction management personnel, on-site technicians and construction teams; provide detailed parameters and construction priorities of the optimized construction plan.

[0099] Step 3: Real-time monitoring and data collection: Real-time monitoring of the construction environment and support effects during the construction process to ensure the continuity and accuracy of the data.

[0100] 1. Monitoring process:

[0101] Data collection frequency: routine monitoring: once every 10 minutes; high-risk areas: once every 1 minute.

[0102] Data types: stress, displacement, vibration waveform, groundwater flow.

[0103] Abnormal monitoring: When the data exceeds the set threshold, the system automatically issues an alarm.

[0104] 2. Data transmission and storage:

[0105] The monitoring data is transmitted to the data processing center in real time through the wireless communication module; the data center adopts distributed storage to ensure high capacity and high security.

[0106] Step 4: Data processing and feedback: Use fusion algorithms and prediction models to generate support optimization plans and feed them back to the construction site.

[0107] 1. Data processing:

[0108] Data fusion: normalize the multi-source data of sensors and eliminate outliers;

[0109] Trend prediction: Use the LSTM model to predict the stability of the surrounding rock in the next stage; analyze the impact range of seismic waves on the support structure.

[0110] 2. Support optimization generation: The system automatically generates support optimization solutions based on real-time data and set rules:

[0111] Rock burst section: increase the density of energy-absorbing anchor rods; water gushing section: optimize the drainage system and waterproof layer structure; earthquake impact area: adjust the spacing between steel arch frames and the thickness of concrete spraying.

[0112] 3. Feedback to the site:

[0113] The optimization plan is sent to the construction equipment through the wireless communication module; the system generates an execution guidance document for the optimization plan for reference by construction management personnel.

[0114] Step 5: Construction optimization and dynamic adjustment: Dynamically adjust construction support parameters according to the feedback plan to achieve efficient and safe construction.

[0115] 1. Automated equipment execution:

[0116] Anchor arrangement equipment: equipped with a multifunctional robotic arm, which can automatically drill holes and install anchors; it has the ability to dynamically adjust the density of anchor arrangement.

[0117] Shotcrete equipment: Automatically controls the shotcrete thickness with an accuracy of ±5mm; equipped with a laser guidance system to achieve high-precision construction.

[0118] 2. Manual assistance and verification: Increase manual inspection and support in high-risk areas to ensure the effectiveness of the optimization plan; use portable monitoring equipment to verify support quality and make timely adjustments if deviations are found.

[0119] Step 6: Closed-loop feedback and continuous optimization: Verify the implementation effect of the support optimization plan and continuously improve the model and system.

[0120] 1. Construction effect verification:

[0121] The sensors are used to conduct secondary monitoring of the support completed area and record changes in the surrounding rock stability; the system automatically analyzes whether the support effect meets expectations and generates a construction report.

[0122] 2. Data feedback and model optimization:

[0123] The execution result data is uploaded to the data processing module; the system updates the LSTM model based on the new data to improve the accuracy of future predictions.

[0124] 3. Construction summary and optimization suggestions:

[0125] Regularly summarize construction progress and problems;

[0126] Submit optimization suggestion report for subsequent construction reference.

[0127] Step 7: Emergency management during construction: respond to emergencies during construction, such as rock bursts, water gushing or earthquake shocks.

[0128] 1. Emergency monitoring:

[0129] When an event occurs, the system increases the monitoring frequency to 5 times per second and records the entire event process in real time to provide a basis for subsequent analysis.

[0130] 2. Quick response mechanism:

[0131] The system automatically generates an emergency support plan; construction personnel cooperate to implement the optimized emergency plan.

[0132] 3. Event analysis and improvement:

[0133] Conduct in-depth analysis of monitoring data afterwards to optimize the event handling process and enhance the system's emergency response capabilities.

[0134] The implementation steps of this application ensure full coverage of the process from system deployment to dynamic optimization, maximizing the safety and efficiency of construction. Each step combines actual construction needs and cutting-edge technical support, and can be further customized and optimized according to project characteristics.

[0135] In detail, the modules described in the dynamic feedback system of tunnels in earthquake-prone areas based on real-time monitoring described in an embodiment of the present invention adopt the same technical means as the dynamic feedback method of tunnels in earthquake-prone areas based on real-time monitoring described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0136] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0138] The matters not described in detail in the present invention are all known technologies to those skilled in the art.

[0139] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A tunnel dynamic feedback system in earthquake-prone areas based on real-time monitoring, characterized in that: Including but not limited to the following modules: Monitoring module, used to collect surrounding rock deformation, vault settlement, perimeter convergence, seismic vibration acceleration, and groundwater inflow during tunnel construction; The data processing module is used to integrate and analyze the data collected by the monitoring module from multiple sources to generate surrounding rock stability predictions and support optimization suggestions; A feedback module generates a real-time construction support optimization plan based on the analysis results of the data processing module, wherein the plan includes adjusting the anchor density, the thickness of the shotcrete, and the spacing of the steel arches; The execution module is used to transmit the optimization plan generated by the feedback module to the construction site and execute it, including the dynamic adjustment of support parameters and the optimization of construction technology.

2. The earthquake-prone area tunnel dynamic feedback system based on real-time monitoring according to claim 1 is characterized in that: The monitoring module comprises: Stress sensors are used to monitor the stress state of surrounding rocks; accelerometers, which monitor vibration waves caused by earthquakes; displacement meters to monitor vault settlement and perimeter convergence; The wireless communication module is used to transmit the monitoring data to the data processing module in real time.

3. The system according to claim 1 or 2, characterized in that: The data processing module performs trend analysis on the monitoring data based on a machine learning algorithm.

4. The system according to claim 3, characterized in that The machine learning algorithm includes: Data fusion algorithm, used to process multi-parameter monitoring data, reduce noise and enhance data accuracy; Trend prediction model is used to predict the changes in mechanical properties of surrounding rocks and the impact range of earthquake shocks.

5. The system according to claim 1, characterized in that The feedback module generates a construction support scheme based on preset optimization rules, including: In rock burst areas, optimize the layout of energy-absorbing anchors; In the water-gushing and mud-bursting section, adjust the waterproof layer structure and the layout of the drainage system; In earthquake impact areas, optimize the spacing between steel arches and the thickness of shotcrete.

6. The system according to claim 1, characterized in that The execution module includes automated construction equipment for executing anchor arrangement, concrete spraying and steel arch installation according to the support optimization solution provided by the feedback module.

7. A tunnel dynamic feedback method in earthquake-prone areas based on real-time monitoring, characterized in that: The following steps are involved: The monitoring module collects data on the vault settlement, perimeter convergence, surrounding rock stress, earthquake vibration acceleration, and groundwater inflow during tunnel construction; In the data processing module, multi-source fusion and trend analysis are performed on the monitoring data to predict the stability and deformation trend of the surrounding rock; In the feedback module, a dynamic support optimization plan is generated based on the analysis results, including support material adjustment and construction parameter optimization; In the execution module, support measures are dynamically adjusted according to the optimization plan, including anchor density, steel arch spacing and concrete spraying thickness. Support optimization is implemented through automated construction equipment, and execution data is transmitted back to the data processing module in real time for verification.

8. The method according to claim 7, characterized in that The monitoring frequency of the monitoring module can be adjusted dynamically to achieve high-frequency monitoring of high-risk areas and ensure timely response to abnormal changes.

9. The method according to claim 7, characterized in that: The data processing module generates surrounding rock stability grades and issues early warning signals for high-risk areas based on historical data and real-time monitoring data.

10. The method according to claim 7, characterized in that The support optimization plan generated by the feedback module is updated based on real-time data, including: Adjustment of anchor density; Optimization of steel arch spacing; Dynamic adjustment of concrete shotcrete thickness and continuous updating of optimization plans through real-time data.

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