A dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring

Abstract

This invention relates to a dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring. The system includes a monitoring module, a data processing module, a feedback module, and an execution module, designed to collect data on surrounding rock deformation, crown settlement, and seismic vibration during tunnel construction in real time. The monitoring module acquires data using devices such as stress sensors, accelerometers, and displacement gauges, and transmits the data to the data processing module via a wireless communication module. The data processing module predicts the stability of the surrounding rock and generates optimized support suggestions. The feedback module generates optimized support schemes based on the analysis results. The execution module implements the optimized schemes in on-site construction and dynamically adjusts support measures using automated equipment. Through real-time monitoring and dynamic feedback mechanisms, this system effectively improves the safety, stability, and emergency response capabilities of tunnel construction, and is particularly suitable for tunnel construction under earthquake-prone and complex geological conditions, showing broad application prospects.

Description

Technical Field

[0001] This invention belongs to the field of tunnel engineering technology, and in particular relates to a dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring. Background Technology

[0002] In earthquake-prone areas, tunnel construction faces complex geological conditions and extremely high engineering risks. Due to seismic forces and the instability of the surrounding rock, problems such as surrounding rock deformation, arch settlement, water and mud inrush, and rock bursts frequently occur during tunnel construction, threatening construction safety and the stability of the support structure.

[0003] Traditional tunnel support design relies on static geological analysis and construction experience, making it difficult to respond promptly to changes in the surrounding rock and dynamic impacts of earthquakes during construction. Especially during earthquakes, the mechanical properties of the surrounding rock change rapidly, and traditional support design and adjustment methods lag behind, easily leading to tunnel collapse and support failure.

[0004] In recent years, the application of real-time monitoring technology has made dynamic feedback and optimization during the construction process possible. 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 construction. In addition, optimization strategies for tunnel support design in earthquake-prone areas have not yet formed a standardized process, affecting 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 motion parameters, surrounding rock mechanics monitoring data, and construction process data to form a closed-loop monitoring, analysis, feedback, and optimization mechanism, dynamically adjust construction support schemes, and improve the safety and efficiency of tunnel construction in earthquake-prone areas. Summary of the Invention

[0006] To address the problems mentioned in the background art, this invention provides a dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring. This system can collect various monitoring data during construction in real time and generate support optimization schemes 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 dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring is provided, including but not limited to the following modules:

[0008] The monitoring module is used to collect data on surrounding rock deformation, arch settlement, perimeter convergence, seismic vibration acceleration, and groundwater inflow during tunnel construction.

[0009] The data processing module is used to perform multi-source fusion and analysis on the data collected by the monitoring module to generate surrounding rock stability predictions and support optimization suggestions;

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

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

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

[0013] Stress sensors are used to monitor the stress state of the surrounding rock.

[0014] Accelerometers are used to monitor vibration waves caused by earthquakes;

[0015] Displacement gauges are used to monitor crown settlement and perimeter convergence.

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

[0017] The monitoring equipment is deployed to cover key sections of the tunnel, such as rockburst sections, weak surrounding rock sections, lithological contact zones, and water and mud inrush sections, achieving full coverage of the monitoring area.

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

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

[0020] Data fusion algorithms are used to process multi-parameter monitoring data, reduce noise, and enhance data accuracy.

[0021] Trend prediction models are used to predict changes in the mechanical properties of surrounding rock and the impact range of earthquake shocks.

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

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

[0024] In rockburst areas, optimize the arrangement of energy-absorbing anchor bolts;

[0025] For sections with water inrush and mud leakage, adjust the structure of the waterproof layer and the layout of the drainage system;

[0026] In earthquake-affected areas, optimize the spacing of steel arch frames and the thickness of shotcrete.

[0027] The feedback module needs to combine preset support optimization rules, anchor density, steel arch spacing and shotcrete thickness to generate a dynamic support optimization scheme, dynamically adjust the anchor arrangement in weak surrounding rock and rockburst sections, optimize the distribution density of energy-absorbing anchors, optimize the shotcrete thickness and steel arch spacing in high-impact earthquake zones to ensure the seismic performance of the support structure, optimize the waterproofing and drainage design in water-rich sections through dynamic drainage measures, and send this to the execution module.

[0028] In some embodiments, as a further preferred option, the execution module includes automated construction equipment for performing anchor bolt placement, shotcreting, and steel arch installation based on the support optimization scheme provided by the feedback module. Short-cut, low-explosion techniques are used in the through section and karst zone, combined with real-time monitoring to adjust the construction pace and support strength. Subsequently, the construction execution status needs to be transmitted back to the data processing module in real time for further analysis and optimization.

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

[0030] The monitoring module collects data on crown settlement, perimeter convergence, surrounding rock stress, seismic vibration acceleration, and groundwater inflow during tunnel construction.

[0031] In the data processing module, the monitoring data is fused from multiple sources and trend analyzed to predict the stability and deformation trend of the surrounding rock.

[0032] In the feedback module, a dynamic support optimization scheme is generated based on the analysis results, including adjustment of support materials and optimization of construction parameters;

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

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

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

[0036] In some embodiments, as a further preferred embodiment, the support optimization scheme generated by the feedback module is updated based on real-time data, including:

[0037] Adjustment of anchor bolt density;

[0038] Optimization of steel arch frame spacing;

[0039] The thickness of the sprayed concrete is dynamically adjusted, and the optimization scheme is continuously updated through real-time data.

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

[0041] This invention, through a real-time monitoring module and a wireless communication module, can collect data such as surrounding rock deformation, arch settlement, perimeter convergence, and seismic vibration parameters in real time during tunnel construction and rapidly transmit them to a data processing module. It can analyze the data and generate support optimization suggestions in a short time, achieving rapid processing and feedback of dynamic construction data, significantly improving construction response speed. Employing multi-source data fusion algorithms and trend prediction models, it analyzes and predicts monitoring data to generate surrounding rock stability assessment results and construction optimization schemes. Automated data analysis and feedback greatly reduce manual intervention, improving decision-making efficiency and accuracy.

[0042] Furthermore, through the surrounding rock stability prediction model and the early warning mechanism for high-risk areas, the system can promptly identify potential risks such as rock bursts, water inrushes, or seismic impacts. Support measures at the construction site can be dynamically adjusted based on real-time feedback from the module, effectively reducing potential safety hazards during tunnel construction.

[0043] This invention is applicable to tunnel construction in earthquake-prone areas and under complex geological conditions, enabling rapid response to sudden geological disasters through real-time monitoring and dynamic feedback. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0045] Figure 1 This is a schematic diagram of a module in one embodiment of the present invention. Detailed Implementation

[0046] The preferred embodiments of the present invention will be described in detail below to provide a clearer understanding of the purpose, features, and advantages of the invention. It should be understood that the following embodiments are not intended to limit the scope of the invention, but are merely illustrative of the essential spirit of the technical solution of the invention.

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

[0048] Throughout this specification, references to "an 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. Therefore, the appearance of "in an embodiment" or "an embodiment" in various places throughout the specification does not necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any manner in one or more embodiments.

[0049] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings. However, the present invention is not limited to these embodiments.

[0050] like Figure 1 The diagram shown is a schematic of a dynamic feedback system for tunnels in earthquake-prone areas based on real-time monitoring, according to an embodiment of the present invention. A specific implementation of an embodiment of the present invention includes the following steps:

[0051] 1. Monitoring Module

[0052] 1.1 Sensor Layout and Setup

[0053] Stress sensors: These are deployed in critical sections with poor surrounding rock stability, such as rockburst sections, weak surrounding rock sections, and near synclinal structures; one sensor is placed every 5-10 meters, and the placement should be adjusted according to the actual fracture risk of the surrounding rock. They monitor changes in stress inside and outside the surrounding rock to determine whether there is a risk of fracture. The acquisition frequency is set to once per second, which can be increased to 10 times per second in abnormal situations to monitor sudden changes.

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

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

[0056] 1.2 Wireless Communication Module

[0057] Communication Protocol: The LoRa communication protocol is adopted to achieve low-power, long-distance data transmission; sensor data is transmitted to the data processing module in real time via wireless communication; wireless communication base stations are deployed in the tunnel, with one base station every 500 meters, covering the entire tunnel area and supporting real-time uploading 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, remove outliers and improve data accuracy.

[0061] Sensor data is processed by a preprocessing module to remove noise; normalized using a time series algorithm; and then fused into a prediction algorithm module.

[0062] Real-time monitoring and alarms:

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

[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: Using an LSTM-based machine learning model, inputting fused multi-source data, and using historical surrounding rock data and seismic vibration experimental data for deep learning training; outputting the changing trend of the mechanical properties of the surrounding rock in the next stage and the possible impact range of seismic shock on the tunnel.

[0067] Model training: Based on historical monitoring data and current real-time data, the accuracy of model predictions is continuously optimized.

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

[0069] 3. Feedback Module

[0070] 3.1 Support Optimization Rules

[0071] Rockburst section: Optimize the density of energy-absorbing anchor bolts, increasing from one bolt every 3 meters to one bolt every 2 meters; thicken the shotcrete layer, increasing it from 20cm to 30cm, and add steel fibers to enhance strength.

[0072] Water inrush section: Add double-layer drainage pipes to the support design; adjust the waterproof layer material to high-density polyethylene (HDPE) to address the risk of sudden water inrush.

[0073] Earthquake impact zone: The spacing between steel arch frames was shortened from the original design of 2.5 meters to 1.5 meters; the seismic performance design of shotcrete was enhanced, and C35 concrete was used.

[0074] Rockburst section support optimization: Increase the density of energy-absorbing anchor bolts, placing one bolt 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 data processing module interact dynamically, updating the support plan every 10 minutes. The time from data analysis to feedback generation does not exceed 5 minutes to ensure real-time adjustments.

[0077] 4. Execution Module

[0078] 4.1 Automated construction equipment

[0079] Anchor bolt installation equipment: Equipped with a multi-functional robotic arm, it automatically drills holes and installs anchor bolts; it also has the ability to dynamically adjust the anchor bolt density.

[0080] Shotcrete equipment: Automatically controls spray 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 records support parameters (such as anchor spacing and concrete thickness) and transmits them to the data processing module in real time.

[0083] Results Verification: Data analysis was conducted to ensure that the construction execution was consistent with the optimized plan. Secondary monitoring of the support effect after implementation was performed to ensure that the optimized plan's effectiveness met the design objectives.

[0084] Implementation steps:

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

[0086] 1. Equipment installation and layout:

[0087] Stress sensors: preferentially deployed in unstable rock areas, rockburst sections, weak rock sections, and geological fault zones; accelerometers: deployed at tunnel entrances and key nodes within the tunnel body to ensure full coverage monitoring of seismic waves; displacement gauges: deployed at the tunnel arch, sidewalls, and invert, one every 10 meters; wireless communication base stations: one deployed every 500 meters to ensure seamless transmission of monitoring data.

[0088] 2. Data processing center setup:

[0089] Configure a high-performance server and install data processing and feedback system software; deploy an LSTM prediction model and upload historical data for initial training.

[0090] 3. Equipment debugging and calibration:

[0091] The sensor is calibrated using standardized test signals to ensure monitoring accuracy; data transmission tests are conducted to verify the stability and latency of wireless communication.

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

[0093] 1. Preliminary monitoring of surrounding rock:

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

[0095] 2. Adjustment of support design parameters:

[0096] Based on monitoring data, the anchor bolt density, shotcrete thickness, and steel arch spacing were optimized; seismic design was added to the earthquake impact zone, and drainage schemes were strengthened in the waterproof section.

[0097] 3. Technical briefing:

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

[0099] Step 3: Real-time monitoring and data acquisition: The construction environment and support effect are monitored in real time 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] Anomaly detection: When the data exceeds the set threshold, the system will automatically issue an alarm.

[0104] 2. Data transmission and storage:

[0105] Monitoring data is transmitted to the data processing center in real time via a wireless communication module; the data center uses 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 schemes and feed them back to the construction site.

[0107] 1. Data processing:

[0108] Data fusion: Normalize multi-source data from sensors and remove outliers;

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

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

[0111] Rockburst section: Increase the density of energy-absorbing anchor bolts; Water inrush section: Optimize the drainage system and waterproof layer structure; Earthquake impact zone: Adjust the spacing of steel arch frames and the thickness of sprayed concrete.

[0112] 3. Feedback to the site:

[0113] The optimized plan is sent to the construction equipment via a wireless communication module; the system generates an execution guidance document for the optimized plan for construction management personnel to refer to.

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

[0115] 1. Automated equipment execution:

[0116] Anchor bolt installation equipment: Equipped with a multi-functional robotic arm, it automatically drills holes and installs anchor bolts; it also has the ability to dynamically adjust the anchor bolt density.

[0117] Shotcrete equipment: Automatically controls spray 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 the support quality and make timely adjustments if deviations are found.

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

[0120] 1. Verification of construction effect:

[0121] The system uses sensors to perform secondary monitoring of the supported area and record changes in the stability of the surrounding rock; 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 results are 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 an optimization suggestion report for future construction reference.

[0127] Step 7: Emergency Management during Construction: Responding to emergencies during construction, such as rock bursts, water inrushes, or earthquake impacts.

[0128] 1. Emergency monitoring:

[0129] The system increases the monitoring frequency to 5 times per second when an event occurs; it records the entire event process in real time, providing a basis for subsequent analysis.

[0130] 2. Rapid response mechanism:

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

[0132] 3. Incident Analysis and Improvement:

[0133] Afterwards, conduct in-depth analysis of the monitoring data to optimize the incident handling process and improve the system's emergency response capabilities.

[0134] The implementation steps outlined in this application ensure comprehensive coverage from system deployment to dynamic optimization, maximizing construction safety and efficiency. Each step incorporates actual construction needs and cutting-edge technological support, and can be further customized and optimized based on project characteristics.

[0135] In detail, the modules in the real-time monitoring-based dynamic feedback system for tunnels in earthquake-prone areas described in this embodiment of the invention employ the same technical means as those in the real-time monitoring-based dynamic feedback method for tunnels in earthquake-prone areas shown in the accompanying drawings, 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 separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0138] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0139] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic feedback method for tunnels in earthquake-prone areas based on real-time monitoring, characterized in that, Includes the following modules: The monitoring module is used to collect data on surrounding rock deformation, arch settlement, perimeter convergence, seismic vibration acceleration, and groundwater inflow during tunnel construction. The data processing module is used to perform multi-source fusion and analysis on the data collected by the monitoring module to generate surrounding rock stability predictions and support optimization suggestions; The feedback module generates a real-time construction support optimization plan based on the analysis results of the data processing module. The plan includes adjusting the anchor bolt density, shotcrete thickness, and steel arch spacing. 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; The monitoring module includes: Stress sensors are used to monitor the stress state of the surrounding rock. Accelerometers are used to monitor vibration waves caused by earthquakes; Displacement gauges are used to monitor crown settlement and perimeter convergence. The wireless communication module is used to transmit monitoring data to the data processing module in real time. The data processing module performs trend analysis on the monitoring data based on machine learning algorithms; The machine learning algorithm includes: Data fusion algorithms are used to process multi-parameter monitoring data, reduce noise, and enhance data accuracy. Trend prediction models are used to predict changes in the mechanical properties of surrounding rock and the impact range of earthquake shocks. It also includes the following steps: The monitoring module collects data on crown settlement, perimeter convergence, surrounding rock stress, seismic vibration acceleration, and groundwater inflow during tunnel construction. In the data processing module, the monitoring data is fused from multiple sources and trend analyzed to predict the stability and deformation trend of the surrounding rock. In the feedback module, a dynamic support optimization scheme is generated based on the analysis results, including adjustment of support materials and optimization of construction parameters; In the execution module, the support measures are dynamically adjusted according to the optimization plan, including anchor bolt density, steel arch spacing and shotcrete thickness. The support optimization is implemented through automated construction equipment, and the execution data is sent back to the data processing module for verification in real time.

2. The method according to claim 1, characterized in that, The feedback module generates a construction support scheme based on preset optimization rules, including: In rockburst areas, optimize the arrangement of energy-absorbing anchor bolts; For sections with water inrush and mud leakage, adjust the structure of the waterproof layer and the layout of the drainage system; In earthquake-affected areas, optimize the spacing of steel arch frames and the thickness of shotcrete.

3. The method according to claim 1, characterized in that, The execution module includes automated construction equipment for performing anchor bolt placement, concrete spraying, and steel arch frame installation based on the support optimization scheme provided by the feedback module.

4. The method according to claim 1, characterized in that, The monitoring frequency of the monitoring module can be dynamically adjusted to achieve high-frequency monitoring in high-risk areas, ensuring timely response to abnormal changes.

5. The method according to claim 1, characterized in that, The data processing module generates the surrounding rock stability level based on historical data and real-time monitoring data, and issues early warning signals for high-risk areas.

6. The method according to claim 1, characterized in that, The support optimization plan generated by the feedback module is updated based on real-time data, including: Adjustment of anchor bolt density; Optimization of steel arch frame spacing; The thickness of the shotcrete is dynamically adjusted, and the optimization plan is continuously updated through real-time data.

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