An intelligent support system and method for rock tunnels that adapts to geological conditions

Abstract

This application discloses an intelligent support system and method for rock tunnels adapted to geological conditions, relating to the fields of tunnel construction automation and intelligent technology. The system includes: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization module, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system, and an integrated control system. Through real-time monitoring and analysis by the intelligent advanced geological prediction module and the safety early warning system, potential geological risks and safety threats during tunnel construction can be detected in a timely manner. The adaptive support design optimization algorithm can dynamically adjust support parameters and construction plans based on real-time geological data and surrounding rock quality evaluation results. The integrated control system, as the core of the entire intelligent support system, achieves unified management and coordination of all sub-modules.

Description

Technical Field

[0001] This application relates to the fields of automation and intelligent technology in tunnel construction, and in particular to an intelligent support system and method for rock tunnels that adapts to geological conditions. Background Technology

[0002] With the development of modern engineering construction, tunnel engineering, as an important transportation and municipal infrastructure, faces increasingly prominent issues regarding construction safety and efficiency under complex geological conditions. Traditional tunnel support methods mainly rely on engineers' experience and on-site manual inspection. These methods are not only inefficient but also unable to cope with rapidly changing geological conditions and construction environments, posing significant safety hazards.

[0003] To improve the safety and efficiency of tunnel construction, the industry has begun exploring the application of intelligent technologies in recent years. However, most existing intelligent support systems are still in the research and development or small-scale testing stages, and a mature, comprehensive solution capable of large-scale commercial application has not yet been formed. Furthermore, tunnel construction involves multiple complex subsystems. These subsystems suffer from severe data silos and a lack of effective integration and collaboration, limiting the improvement of the level of intelligence in tunnel construction. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent support system and method for rock tunnels that adapts to geological conditions, enabling automation and intelligence in tunnel construction, with high integration, fast response speed, and strong adaptability.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides an intelligent support system for rock tunnels that adapts to geological conditions. The intelligent support system for rock tunnels that adapts to geological conditions includes: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization module, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system, and an integrated control system.

[0007] The intelligent advanced geological prediction module is used to: acquire geological data of the tunnel face, and use a deep learning network to predict the probability and risk level of geological changes based on the geological data of the tunnel face;

[0008] The intelligent surrounding rock quality evaluation system is used to: acquire surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data;

[0009] The adaptive support design optimization module is used to: obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index;

[0010] The intelligent construction equipment monitoring system is used to: acquire the operating parameters of the construction equipment, and issue an alarm when the operating parameters exceed a first preset threshold.

[0011] The environmental monitoring module is used to: acquire environmental parameters inside the tunnel and issue an alarm when the environmental parameters exceed a second preset threshold.

[0012] The security early warning system is used for:

[0013] Obtain safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel;

[0014] Using pattern recognition methods, the system identifies whether the security monitoring parameters pose potential security risks based on a historical case database.

[0015] The construction data analysis and decision support system is used for:

[0016] Obtain safety data by acquiring data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system;

[0017] Obtain construction data by acquiring data on construction progress, material usage, and worker activities;

[0018] Predict construction trends by combining historical construction data with the aforementioned safety data and construction data;

[0019] The material supply chain management system is used for:

[0020] Obtain the inventory levels and supply chain status of materials required for the construction project;

[0021] Utilize intelligent forecasting algorithms to predict future material demand based on material consumption rates and inventory levels;

[0022] Purchase orders are generated based on the future material needs and the inventory levels, and order information is sent to suppliers via electronic data interchange technology.

[0023] The personnel positioning and management system is used to: acquire the location information of each person, and send an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range;

[0024] The integrated control system is used to coordinate and control information sharing and collaborative work among the modules.

[0025] Secondly, this application provides an intelligent support method for rock tunnels that adapts to geological conditions, the intelligent support method for rock tunnels that adapts to geological conditions includes:

[0026] Geological data of the tunnel face is acquired, and a deep learning network is used to predict the probability and risk level of geological changes based on the geological data of the tunnel face.

[0027] Obtain surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data;

[0028] Obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index;

[0029] The system acquires the operating parameters of the construction equipment and issues an alarm when the operating parameters exceed a first preset threshold.

[0030] The system acquires environmental parameters inside the tunnel and issues an alarm when the environmental parameters exceed a second preset threshold.

[0031] Obtain safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel;

[0032] Using pattern recognition methods, the system identifies whether the security monitoring parameters pose potential security risks based on a historical case database.

[0033] Obtain safety data by acquiring data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system;

[0034] Obtain construction data by acquiring data on construction progress, material usage, and worker activities;

[0035] Predict construction trends by combining historical construction data with the aforementioned safety data and construction data;

[0036] Obtain the inventory levels and supply chain status of materials required for the construction project;

[0037] Utilize intelligent forecasting algorithms to predict future material demand based on material consumption rates and inventory levels;

[0038] Purchase orders are generated based on the future material needs and the inventory levels, and order information is sent to suppliers via electronic data interchange technology.

[0039] The system acquires the location information of each person and sends an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range.

[0040] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0041] This application provides an intelligent support system and method for rock tunnels adapted to geological conditions. The system includes: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization module, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system, and an integrated control system. The intelligent advanced geological prediction module is used to: acquire geological data of the tunnel face and use a deep learning network to predict the probability and risk level of geological changes based on the tunnel face geological data; the intelligent surrounding rock quality evaluation system is used to: acquire surrounding rock data at the tunnel perimeter and calculate a surrounding rock quality index based on the surrounding rock data; the adaptive support design optimization module is used to: acquire the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index; the intelligent construction equipment monitoring system is used to: acquire the operating parameters of the construction equipment and issue an alarm when the operating parameters exceed a first preset threshold; the environmental monitoring module is used to: acquire environmental parameters inside the tunnel and issue an alarm when the environmental parameters exceed a second preset threshold; the safety early warning system is used to: acquire safety monitoring parameters; the safety monitoring parameters include: the construction... The system monitors the operating parameters of the construction equipment and the environmental parameters inside the tunnel; it uses pattern recognition methods to identify potential safety risks in the safety monitoring parameters based on a historical case database; the construction data analysis and decision support system is used to: acquire data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system to obtain safety data; acquire data on construction progress, material usage, and worker activities to obtain construction data; and use historical construction data combined with the safety data and construction data to predict construction trends; the material supply chain management system is used to: acquire the inventory level and supply chain status of materials required in the construction project; use intelligent prediction algorithms to predict future material demand based on material consumption rate and inventory level; generate purchase orders based on the future material demand and inventory level, and send order information to suppliers through electronic data interchange technology; the personnel positioning and management system is used to: acquire the location information of each person and send alarms to the person when the person approaches a dangerous area or exceeds a preset safety range; the integrated control system is used to coordinate and control information sharing and collaborative work between various modules. Through the cooperation and coordination between these modules, the automation and intelligence of tunnel construction can be achieved, with high integration, fast response speed, and strong adaptability. Furthermore, this application can improve construction safety: through real-time monitoring and analysis of the intelligent advanced geological prediction module and safety early warning system, potential geological risks and safety threats during tunnel construction can be detected in a timely manner. These systems utilize advanced prediction algorithms and historical data learning to provide early warnings of possible disasters such as landslides and rock bursts, thereby significantly reducing the incidence of construction accidents and ensuring the safety of construction personnel and equipment.It optimizes construction efficiency: The adaptive support design optimization algorithm dynamically adjusts support parameters and construction plans based on real-time geological data and surrounding rock quality evaluation results. This intelligent design process not only shortens the design cycle but also improves the adaptability and reliability of the support structure, reducing construction delays and material waste caused by improper design, and significantly improving construction efficiency and economic benefits. It also strengthens construction process management: The integrated control system, as the core of the entire intelligent support system, achieves unified management and coordination of all sub-modules. Through data integration and intelligent decision support, various aspects of the construction process, such as material supply, personnel management, and environmental monitoring, can be automated and intelligently controlled. This refined management approach not only improves the transparency and controllability of the construction process but also further enhances the overall efficiency and response speed of the construction process by optimizing resource allocation and process coordination. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A system block diagram of an intelligent rock tunnel support system that adapts to geological conditions, provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart illustrating an intelligent support method for rock tunnels that adapts to geological conditions, provided as an embodiment of this application. Detailed Implementation

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

[0046] This application provides an intelligent support system and method for rock tunnels that adapts to geological conditions, aiming to improve the safety and efficiency of tunnel construction. The system employs an intelligent advanced geological prediction module, combining geophysical exploration technology and deep learning algorithms to achieve real-time prediction of geological conditions ahead of tunnel construction. An intelligent surrounding rock quality evaluation system automatically evaluates the quality of the surrounding rock using multi-source data fusion technology. The core of this application lies in the adaptive support design optimization algorithm, which, based on artificial intelligence technology, dynamically optimizes support design parameters to adapt to real-time changes in geological data. It also includes an intelligent construction equipment monitoring system that uses IoT technology to monitor the status of construction equipment in real time and achieves fault early warning and automatic control through big data mining. In particular, this application applies digital twin technology to create a digital twin model of the tunnel construction, enabling rapid acquisition, processing, and real-time updating of geological information and support design.

[0047] The purpose of this application is to provide an intelligent support system for rock tunnels that adapts to geological conditions. This system can respond to geological changes in real time, automatically adjust the support strategy, and improve construction safety and efficiency.

[0048] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] In one exemplary embodiment, such as Figure 1 As shown, an intelligent support system for rock tunnels that adapts to geological conditions is provided, including: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization module, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system, and an integrated control system.

[0050] The intelligent advanced geological prediction module is used to: acquire geological data of the tunnel face, and use a deep learning network to predict the probability and risk level of geological changes based on the geological data of the tunnel face.

[0051] The intelligent advanced geological prediction module performs its functions according to the following specific steps: First, deployed geophysical exploration devices collect high-precision geological parameters, including rock stress, wave velocity, and electromagnetic properties, within a 50-meter range in front of the tunnel face at 1-meter intervals. This data is updated every second. The collected data is transmitted in real-time to the central processing unit via an encrypted wireless data transmission network at a transmission rate of 10 megabits per second. Within the central processing unit, the data undergoes preprocessing, including noise and outlier removal, followed by standardization to ensure a uniform scale within the range of -1 to 1. Next, deep learning analysis is performed on the data using a database containing over 100,000 historical geological events, occurring every 5 seconds, to identify geological patterns and predict potential risks. The preprocessing of the collected geological data, including noise removal, outlier handling, data standardization, and normalization, aims to improve data quality and make it suitable for deep learning analysis. Using deep learning models, convolutional neural networks (CNNs) automatically extract features from preprocessed data. These features may include key geological parameters such as rock physical properties, geological structure, and groundwater distribution. Pattern recognition of geological parameter data is then performed by training deep learning models, such as deep neural networks (DNNs). This may involve training the model using a large amount of historical geological event data, enabling it to identify different geological patterns, such as rock type, fault, and fracture development. After identifying geological patterns, the deep learning model can be further used to predict potential risks. This may include predicting geological changes and potential risks that may be encountered ahead of tunnel construction, such as landslides, rock bursts, and groundwater inrushes. In some cases, geomechanical information constraints are introduced to improve the predictive accuracy of the deep learning model. For example, by simplifying geomechanical equations and establishing physical constraint regularization terms, the ability to reason about and interpret unknown data can be improved. The deep learning model is trained and validated using a historical geological event database. This database may contain over 100,000 historical geological events, used to train the model to identify geological patterns and predict risks. Deep learning analysis is typically performed dynamically, updated every 5 seconds to respond in real-time to geological changes. This requires models capable of rapidly processing data and providing immediate predictions. In some advanced applications, multiple data sources, such as geophysical survey data, ground-penetrating radar data, and seismic wave velocity measurements, are fused to obtain more comprehensive geological information. Deep neural network models employ convolutional neural network structures with over 100 layers for feature learning and risk assessment in this process. Ultimately, the prediction results include the probability and risk level of geological changes. Historical geological event data is collected, including but not limited to rock type, faults, fissures, groundwater levels, and geostress. Environmental data related to geological changes, such as seismic activity and hydrogeological data, are also collected.Identify key factors influencing geological changes, such as the physical and mechanical properties of rocks and geological structures. Select statistical and geophysical parameters strongly correlated with geological changes as input features for the model. Train a machine learning model, such as a random forest, support vector machine (SVM), or deep neural network (DNN), using historical geological data. Apply cross-validation and tune model parameters to optimize model performance. Use the trained model to predict new geological data, estimating the probability of specific geological changes occurring. For example, the output layer of a deep learning model can yield a continuous value representing the likelihood of a specific geological change. Define different risk levels based on the probability of geological changes and their potential impact. Typically, risk levels can be categorized according to a combination of probability and impact, such as low risk, moderate risk, high risk, and extremely high risk. Use independent validation sets to test the model's predictive accuracy. Adjust model parameters and risk level classification criteria based on actual geological events.

[0052] Geological changes can include, but are not limited to, the following: rock fracturing and collapse, caused by in-situ stress, groundwater activity, or tectonic activity; rock bursts, where high-stress rock suddenly releases energy during tunnel excavation, causing rock fragments to be ejected rapidly; groundwater inrush, where aquifers in rock strata are breached, resulting in a large influx of groundwater into the tunnel; changes in in-situ stress, where crustal movement causes changes in in-situ stress around the tunnel, potentially affecting its stability; and fault activity, where earthquakes or tectonic activity cause fault displacement, potentially damaging the tunnel structure. Estimate the likelihood of specific geological changes occurring based on historical data and model predictions. Assess the potential impacts of geological changes on tunnel construction and operation, such as casualties, equipment damage, and construction delays. Consider whether geological changes can be detected by existing monitoring systems. Assess whether engineering measures can mitigate or avoid the impacts of geological changes.

[0053] Risk levels are categorized as follows: Low risk: low probability of occurrence, small potential impact, easy to detect and control. Medium risk: moderate probability of occurrence, controllable potential impact, requires certain monitoring and preventative measures. High risk: relatively high probability of occurrence, serious potential impact, requires enhanced monitoring and preventative measures. Extremely high risk: very high probability of occurrence, catastrophic potential impact, requires immediate emergency measures. The risk is displayed graphically and numerically through the user interface, for example, showing 'Rock fracture zone probability: 75%, recommended support level upgraded to Level III'. When the risk exceeds a preset threshold, such as when the stability index falls below 3.5, the risk level is assessed. The stability index is a key indicator in tunnel engineering, used to quantify the stability of the surrounding rock. This index is typically calculated by comprehensively analyzing various geological parameters affecting surrounding rock stability, such as uniaxial compressive strength, elastic modulus, fracture density, in-situ stress, and groundwater level. To obtain this index, engineers use statistical analysis or machine learning models, combined with historical data and field monitoring data, to build a mathematical model that reflects the stability of the surrounding rock. In practical applications, once the stability index is calculated, it is compared with a pre-set threshold. For example, if the stability index is below 3.5, it may indicate insufficient stability of the surrounding rock, posing a potential risk of collapse or rockburst. In this case, the automatic alarm system will be triggered to alert the construction team to take appropriate preventative measures, such as strengthening support or adjusting the construction plan. The forecast results are not limited to the stability index but also include a range of information related to geological risks. This may include the probability of geological changes ahead of tunnel construction, the risk level, recommended engineering measures, and warning information. These results are typically presented visually, such as charts, color coding, or 3D models, so that engineers can intuitively understand the geological conditions and potential risks. Furthermore, the forecast results are updated regularly to reflect real-time changes in geological conditions, ensuring the timeliness and accuracy of construction decisions. The automatic triggering of the alarm system notifies the construction team to take immediate preventative measures. This process ensures the accuracy of geological forecasts and the timeliness of construction response.

[0054] The intelligent surrounding rock quality evaluation system is used to: acquire surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data.

[0055] The operational process of the intelligent surrounding rock quality evaluation system is as follows: The system activates the multi-source data acquisition unit, deploys ground-penetrating radar and seismic wave velocity measurement equipment, and collects data every 0.5 meters along the tunnel perimeter at a frequency of 10 seconds, monitoring key parameters such as the density, moisture content, and compressive strength of the surrounding rock in real time. The acquired data is transmitted to the central processing station via a high-speed fiber optic network at a rate of at least 1 gigabits per second. At the central processing station, the data first undergoes preprocessing, including using a bandpass filter to remove 99% of high-frequency noise and low-frequency drift, followed by 0-to-1 normalization to ensure that the data is analyzed on a uniform scale. The data fusion processing unit uses an advanced fuzzy C-means clustering algorithm and adaptive neural network to comprehensively evaluate the normalized data and calculate the surrounding rock quality index (RMQI). The calculation of the surrounding rock quality index (RMQI) relies on several key geological data, including the uniaxial compressive strength (UCS) of the rock, fracture density, joint spacing (Jn), rock roughness (Jr), groundwater conditions (Jw), and geostress (σ). Data can be obtained through methods such as field geological surveys, borehole tests, and laboratory experiments. The process of calculating RMQI typically begins with the quantification of these parameters. The uniaxial compressive strength of the rock can be obtained through laboratory testing, while fracture density and joint spacing are usually assessed in the field by geological engineers. The influence of rock roughness and groundwater conditions can be quantified through field observations and historical data. The specific calculation process often uses the following formula to estimate the rock quality index (Q): Among them, RQD is the rock quality index, which reflects the continuity and integrity of the borehole core; UCS is the uniaxial compressive strength of the rock, representing the strength of the rock itself; and Jn, Jr, and Jw in the e-index are adjustment coefficients for joint spacing, roughness, and groundwater conditions, reflecting the influence of these factors on the stability of the surrounding rock. The calculated Q value needs to be adjusted and calibrated according to actual geological conditions and engineering experience to ensure that it accurately reflects the quality of the surrounding rock. Finally, based on the adjusted Q value, combined with in-situ stress and other relevant parameters, the RMQI is calculated. This index can comprehensively reflect the overall quality and stability of the surrounding rock, providing important decision-making basis for tunnel design and construction. Generally, the higher the RMQI value, the more stable the surrounding rock; conversely, the lower the RMQI value, the poorer the stability of the surrounding rock, which may require additional support measures. The index is a dimensionless value from 0 to 100, where 0 represents the lowest surrounding rock quality and 100 represents the best surrounding rock quality. The system sets five threshold levels for RMQI: above 80 is Grade A (Excellent), 60 to 79 is Grade B (Good), 40 to 59 is Grade C (Medium), 20 to 39 is Grade D (Poor), and below 20 is Grade E (Very Poor). When the RMQI is below 40, the system automatically rates the surrounding rock as Grade D or E and triggers an alarm mechanism, prompting the construction team to consider reinforcement measures. The evaluation results are displayed in real time through an intuitive digital dashboard and graphical interface, enabling the construction team to quickly grasp the quality status of the surrounding rock. For example, if the RMQI calculation result is 55, the system will display "Rock Quality Grade: Grade C (Medium)" and provide further analysis reports and recommended support schemes. This automated and numerical evaluation process not only improves the accuracy and efficiency of surrounding rock quality evaluation but also provides real-time, scientific decision support for tunnel construction.

[0056] The adaptive support design optimization module is used to: obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index.

[0057] The specific operation flow of the adaptive support design optimization algorithm is as follows: The algorithm first receives real-time geological data and the Rock Quality Index (RMQI) from the intelligent surrounding rock quality evaluation system. The data update frequency is set to once every 10 seconds to ensure that the design parameters are synchronized with the latest geological conditions. The algorithm has a built-in parameter database that stores support design schemes corresponding to different RMQI values. For example, when the RMQI is in the range of 80 to 100, a Class A support scheme is recommended, requiring a support strength of 1.0 MPa and a rebar spacing of 150 mm. Next, the algorithm comprehensively evaluates the RMQI and other environmental parameters (such as groundwater level and in-situ stress) through a fuzzy logic controller. Other environmental parameters, such as groundwater level and in-situ stress, are typically obtained in the following stages: In the early stages of tunnel or engineering project planning, geological exploration is used to initially understand the geological conditions of the site, including the distribution of groundwater level and in-situ stress. This stage may include geological mapping, geophysical exploration, borehole testing, and other activities. Before construction begins, a more detailed geological survey and monitoring are conducted to obtain accurate groundwater level and in-situ stress data. This helps in designing more rational support structures and construction methods. During tunnel construction, continuous monitoring of groundwater levels and in-situ stress is crucial. This is typically achieved through monitoring equipment installed within the tunnel, such as water level gauges and stress sensors, to capture dynamic changes in environmental parameters in real time. After construction, it may also be necessary to periodically monitor these environmental parameters to assess long-term stability and structural safety. This is particularly important for tunnels in operation to prevent potential geological hazards. Determining the optimal support parameters under current geological conditions is essential. Groundwater level data is recorded periodically by placing water level gauges along the tunnel route. This data reflects changes in the pressure exerted by groundwater on the surrounding rock. In-situ stress gauges are used to measure in-situ stress in front of and around the tunnel face. This data is critical for assessing the stability of the surrounding rock. The Rock Quality Index (RMQI) is calculated, taking into account factors such as the uniaxial compressive strength of the rock, crack spacing, roughness, and groundwater conditions. For example, the formula can be used: RMQI = 100 × e - (a × crack spacing + b × roughness + c × groundwater conditions); where a, b, and c are weighting coefficients adjusted according to geological conditions. The RMQI, groundwater level, and in-situ stress are used as input variables. A fuzzy set is defined for each input variable, such as "high," "medium," and "low." Fuzzy rules are developed based on expert experience and historical data. For example, if the RMQI is "low" and the groundwater level is "high," then the surrounding rock stability is "poor." A fuzzy logic controller processes the input data, infers based on the fuzzy rules, and outputs a fuzzy value for the surrounding rock stability. The fuzzy output is converted into a clear numerical value, and the centroid method is used to calculate the specific stability index. Based on the defuzzified stability index, combined with engineering experience and design specifications, the optimal support parameters are determined.For example, if the stability index is below 3.5, it may be necessary to increase the thickness of the shotcrete, increase the density of anchor bolts, or use higher-strength steel supports. During construction, changes in groundwater level and in-situ stress are monitored in real time, and the support design is dynamically adjusted. For example, if a sudden rise in groundwater level is detected, drainage measures may need to be implemented immediately. A feedback mechanism is established to adjust the parameters and rules of the fuzzy logic controller based on actual performance and monitoring data during construction to improve the accuracy of the assessment. Project engineers and geological experts will make the final support decision based on the output of the fuzzy logic controller and site conditions. This may include changing construction methods, adjusting the support type, or adding additional monitoring measures.

[0058] For example, if a sudden rise in groundwater level leads to a decrease in surrounding rock stability, the algorithm will automatically increase the support strength to 1.2 MPa and adjust the rebar spacing to a denser configuration. Subsequently, the algorithm uses a genetic algorithm to optimize the support design, finding the lowest-cost support scheme while meeting safety requirements. The optimization process considers factors such as material costs, construction difficulty, and expected service life to ensure the economic efficiency and sustainability of the design. The algorithm outputs support design parameters including support type, support strength, rebar diameter, and spacing, which are transmitted to the construction management system in real time via a digital interface. Based on the design scheme provided by the algorithm, the construction team quickly adjusts the construction plan and support measures to ensure the safety and efficiency of the construction process. If real-time geological data shows that the RMQI drops to 55, the algorithm will automatically adjust the support design, outputting "Recommended support strength: 1.1 MPa, rebar diameter: 20 mm, spacing: 200 mm," and prompting the construction team to take appropriate measures. This automated design optimization process achieves intelligent and personalized support design, significantly improving the adaptability and safety of tunnel construction.

[0059] The intelligent construction equipment monitoring system is used to: acquire the operating parameters of the construction equipment, and issue an alarm when the operating parameters exceed a first preset threshold.

[0060] The intelligent construction equipment monitoring system uses IoT sensors installed in key components to accurately collect equipment operating parameters once per second, including temperature readings not exceeding 250°C, vibration frequencies not exceeding 50Hz, and pressure values ​​not exceeding 150 bar. If the temperature exceeds 250°C, the vibration frequency exceeds 50Hz, or the pressure exceeds 150 bar, the system will immediately issue an alarm, record the abnormal data, and may automatically shut down the equipment to prevent damage. Subsequently, the maintenance team will intervene to diagnose the fault and perform necessary repairs. Only after the problem is resolved will the equipment restart to continue its monitoring and data collection tasks. This design ensures safety and equipment health during construction. These key components include the engine and power system, which monitors speed, temperature, and pressure; the hydraulic system, which tracks pressure and flow; the electrical system, which detects current and voltage; the mechanical transmission system, which monitors vibration and noise; the working device, which assesses stress and wear; structural components, which monitor stress and deformation; the control system, which ensures operational precision; environmental monitoring devices, which protect the safety and health of operators; the positioning and navigation system, which provides precise equipment location information; and safety devices, which ensure rapid response in emergency situations. By collecting data from these sensors, the intelligent monitoring system can analyze and predict potential faults in real time, enabling preventative maintenance and improving construction efficiency and safety. This data is transmitted in real time to a central monitoring platform via a wireless network, whose processing power ensures analysis within a latency of no more than 100 milliseconds. Using machine learning algorithms, such as random forest classifiers, the system performs pattern recognition on the data. If a torque sensor reading on a tunnel boring machine cutterhead suddenly rises to 120% of a predetermined threshold, the system automatically flags it as an anomaly and calculates its failure probability. If the failure probability exceeds 75%, the system will issue a warning via a visual interface at least 10 hours before the predicted failure occurs and will also send the warning information to the maintenance team's mobile devices via an automatic notification system. The information includes the fault type, possible causes, and specific maintenance measures, ensuring the construction team can respond promptly and perform maintenance or repairs in advance, minimizing the risk of construction interruptions and equipment damage.

[0061] The environmental monitoring module is used to: acquire environmental parameters inside the tunnel, and issue an alarm when the environmental parameters exceed a second preset threshold.

[0062] The environmental monitoring module operates in a specific and systematic manner: It first collects environmental parameters in real time through multiple sensor nodes distributed throughout the tunnel. These sensors are specifically designed to accurately measure temperature (ranging from -20°C to 50°C), relative humidity (0% to 100%), and gas concentrations such as carbon monoxide (CO), methane (CH4), and other harmful gases (0 to 1000 ppm). Data acquisition is set to occur every 5 seconds to ensure rapid response to changes in the construction environment. The collected data undergoes preliminary processing by the module's built-in microcontroller, including data calibration and outlier removal, to ensure accuracy. Subsequently, the processed data is transmitted to the central monitoring station via a wireless communication network at a rate of at least 10 megabits per second. At the central monitoring station, an environmental data analysis system further analyzes the collected data, using statistical methods and trend prediction models to assess the stability and safety of the tunnel environment. If any monitored environmental parameter exceeds a preset safety threshold, such as temperature exceeding 40°C, humidity below 30%, or gas concentration exceeding safety standards, the system will automatically trigger an alarm and notify construction managers and workers via audible and visual signals and SMS messages. Simultaneously, the system will activate corresponding environmental control measures, such as activating the ventilation system to lower the temperature or increase humidity, ensuring that the construction environment quickly returns to a safe range. The environmental monitoring module also has data recording and historical trend analysis functions, providing the construction team with long-term trends and predictions of environmental changes, assisting in the formulation and adjustment of construction plans. Through this continuous and automated environmental monitoring and control process, this embodiment ensures the safety of the tunnel construction environment and the smooth progress of construction operations.

[0063] The safety early warning system is used to: acquire safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel; and use pattern recognition methods to identify whether the safety monitoring parameters have potential safety risks based on a historical case database.

[0064] The safety early warning system operates with precision and automation: It first integrates real-time monitoring data streams, including key parameters such as temperature, humidity, and gas concentration provided by the environmental monitoring module, as well as equipment operating status information from the intelligent construction equipment monitoring system. The system's internal data analysis engine processes this data every minute, applying machine learning algorithms such as Support Vector Machines (SVM) and neural networks to perform in-depth analysis and identify potential anomaly patterns during construction. Anomaly pattern recognition in the intelligent construction equipment monitoring system is a multi-layered data analysis process that predicts and identifies potential problems by analyzing large amounts of data from the construction site in real time. First, the system collects data from sensors installed at key locations, which may include parameters such as temperature, vibration frequency, and pressure of the construction equipment. This data undergoes preprocessing, such as noise reduction and normalization, to ensure its accuracy and usability. The system uses feature extraction techniques to identify key information from the raw data, representing important variables and trends in the construction process. Then, by applying machine learning algorithms, the system performs in-depth analysis of these features to distinguish between normal operating conditions and anomaly patterns. During this process, the system references a historical case database containing data and recorded events from past projects to train and optimize the recognition model. Anomaly patterns may include various adverse conditions during construction, such as equipment performance degradation, construction delays, resource waste, safety hazards, or environmental impacts. These anomalies may manifest as sudden data changes, persistent deviations, or unexpected trends. For example, a sudden increase in vibration frequency may indicate internal wear or damage to equipment. Once the system identifies a potential anomaly pattern, it automatically triggers an alarm and sends real-time notifications to construction managers. This allows managers to take swift action, such as adjusting construction plans, performing equipment maintenance, or strengthening safety measures, to avoid potential risks and losses. The intelligent monitoring system possesses self-learning capabilities, continuously optimizing its anomaly detection capabilities based on newly collected data and historical cases. This continuous learning and optimization ensures the system adapts to changes during construction, improving the accuracy and efficiency of anomaly detection. The system also includes a historical case database storing safety events and solutions from past construction cases, providing rich reference information. Combining real-time data and historical cases, the safety early warning system uses pattern recognition technology to predict potential safety risks, such as structural instability, equipment failure, or environmental hazards. Once the system detects any abnormal or risky indicators, such as a carbon monoxide concentration in the tunnel exceeding the safety threshold of 200 ppm or a surrounding rock displacement rate exceeding 2 mm per day, it will automatically trigger an alarm mechanism. The alarm will be broadcast audibly via the tunnel's public address system, and an emergency notification will be sent to construction managers and safety officers via a wireless communication network.In addition, the system automatically activates emergency plans, such as evacuating construction personnel, suspending construction operations, or activating the ventilation system, to reduce risks and protect personnel safety. The safety early warning system also includes a feedback mechanism that allows on-site construction personnel to confirm alarms and provide real-time feedback, based on which the system adjusts its early warning strategies and emergency response measures. Through this process of continuous monitoring, intelligent analysis, and automatic response, the safety early warning system ensures high safety standards and rapid response capabilities during tunnel construction.

[0065] The construction data analysis and decision support system is used to: acquire data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system to obtain safety data; acquire data on construction progress, material usage, and worker activities to obtain construction data; and use historical construction data in combination with the safety data and the construction data to predict construction trends.

[0066] In this embodiment, the construction data analysis and decision support system plays a central role, and its workflow is as follows: The system first collects massive amounts of data generated during construction in real time from multiple data sources, including the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system. This data includes equipment status, environmental parameters, and safety events. This data flows into the system at a rate of millions of records per second, where it is cleaned, standardized, and integrated by the data preprocessing module to ensure data quality. The system then utilizes big data analytics techniques, such as data mining and pattern recognition, to conduct in-depth analysis of the integrated data, identifying key trends and potential problems during construction. Anomaly pattern recognition in the intelligent construction monitoring system is a meticulous and multi-layered process that relies on precise data acquisition and advanced analysis techniques. The system collects specific parameters in real time from sensors deployed at the construction site, such as equipment temperature readings (not exceeding 250°C), vibration frequency (not exceeding 50Hz), and pressure values ​​(not exceeding 150 bar). These parameters are key indicators for identifying whether equipment is operating normally. The data preprocessing step ensures the accuracy and consistency of this raw data by cleaning out noise and outliers and standardizing the data, preparing it for further analysis. During the feature extraction phase, the system analyzes data such as construction progress, material usage, and worker activities to identify key trends in project operation. For example, if equipment temperature readings consistently approach the upper limit of 250°C, the system flags this trend and, combined with vibration frequency and pressure data, uses machine learning algorithms for in-depth analysis to determine if overheating or abnormal wear issues exist. The system's predictive modeling function utilizes historical construction data, combined with currently monitored parameters, to predict potential future problems, such as equipment failures or construction delays. For instance, if both temperature and vibration frequency are abnormal, the system predicts an imminent equipment failure and immediately issues an alert. This allows the maintenance team to take action before problems occur, performing necessary inspections and repairs to avoid potential downtime and safety risks. The intelligent construction monitoring system provides decision support for project managers through real-time alerts and visual reports. These reports not only include the current construction status but may also include predictions of future risks and recommended preventative measures. This data-driven approach enables construction teams to proactively manage projects, optimize resource allocation, ensure construction safety, control costs, and reduce environmental impact. Meanwhile, artificial intelligence algorithms, including deep learning and natural language processing, intelligently analyze construction data, automatically extract features, and build predictive models. Internet of Things (IoT) devices, such as sensors and cameras, are used to collect real-time data on temperature, humidity, equipment status, and operating conditions at the construction site. RFID tags and GPS technology are used to track the transportation and use of materials and equipment. Drones are used for regular on-site monitoring, collecting image data to monitor construction progress. Outliers and noise are removed; for example, abnormal temperature readings caused by sensor malfunctions are excluded.Data from various sources (such as sensor data, operation logs, and weather information) is merged into a central database. Useful features are extracted from the raw data, for example, daily concrete usage from operation logs. An ARIMA model is used to predict construction progress; this model analyzes historical construction data to identify periodic and trend components to predict future construction activities. An LSTM network is used to predict resource demand; LSTM networks learn long-term dependencies in time-series data to predict the use of specific resources over a future period. Ensemble learning methods combine the predictions of multiple models, such as random forests and gradient boosting machines (GBM), to improve the accuracy and robustness of predictions. The model is trained using historical construction data, such as data from similar projects completed in the past few years. Model performance is evaluated using cross-validation to ensure high prediction accuracy across different datasets. Construction progress prediction: Based on the predictions of the ARIMA model, project managers can adjust construction plans, such as increasing labor or extending working hours, to ensure timely project completion. Based on the predictions of the LSTM network, necessary materials and equipment can be procured in advance to avoid construction delays due to resource shortages. By analyzing prediction results, potential risks, such as equipment failure or weather impacts, are identified, and countermeasures are developed in advance. Real-time monitoring of construction site data dynamically updates the prediction model to reflect the latest construction conditions. When actual construction progress deviates from the prediction results, the system automatically adjusts the prediction model to provide the latest forecast. For example, time series analysis is used to predict construction progress and resource requirements. The system's built-in decision support engine provides scientific decision-making suggestions to the construction management team based on the analysis results, such as adjusting construction plans, optimizing resource allocation, and preventing potential risks. These suggestions are presented in the form of visual reports and real-time notifications to ensure the timeliness and accuracy of construction decisions. The system also has self-learning capabilities, continuously optimizing the analysis model and decision suggestions based on feedback from the construction team and new construction data, improving the system's prediction accuracy and the effectiveness of decision support. The self-learning capability of the intelligent construction monitoring system is mainly achieved through machine learning and artificial intelligence technologies. The specific process includes data-driven model training, online learning, model optimization, and continuous feedback. The following are the specific steps for acquiring self-learning capabilities: The system collects a large amount of historical construction data and real-time monitoring data, which are used as a training set to train the machine learning model. Supervised learning allows models to learn patterns and relationships from labeled data, while unsupervised learning allows models to discover hidden patterns in unlabeled data. The system possesses online learning capabilities, meaning it can update the model instantly as new data arrives without retraining the entire dataset. This capability enables the system to adapt to changes during the construction process, capture new patterns, and improve prediction accuracy. Techniques such as cross-validation and grid search are used to optimize model parameters.The system continuously adjusts and tests different parameter combinations to find the optimal model configuration, thereby improving prediction performance. It adjusts the prediction model based on actual construction results and feedback. For example, if there is a significant difference between the actual and predicted construction progress, the system analyzes the reasons for the difference and updates the model to reduce future prediction errors. In some cases, the system may employ reinforcement learning techniques to learn optimal strategies through interaction with the environment. In construction monitoring, this means the system adjusts its behavior based on the results of construction operations to achieve better construction management and resource allocation. The system may use transfer learning techniques, which allow the model to apply knowledge learned in one project to another similar project. This is particularly useful for new or uncommon construction scenarios because it allows the system to quickly adapt to new environments. The system integrates the prediction results of multiple models, improving overall prediction performance through model fusion techniques such as stacking, boosting, or ensemble learning. The system can automatically discover and generate new features that may be more useful to the prediction model. For example, the system may identify the relationship between specific combinations of certain construction operations and resource consumption. Through this intelligent data analysis and decision support process, the construction data analysis and decision support system significantly improves the efficiency of construction management and the level of intelligence in the construction process.

[0067] The material supply chain management system is used to: obtain the inventory level and supply chain status of materials required in the construction project; predict future material demand based on material consumption rate and inventory level using intelligent prediction algorithms; generate purchase orders based on the future material demand and the inventory level; and send order information to suppliers through electronic data interchange technology.

[0068] In this embodiment, the material supply chain management system plays a crucial role in ensuring construction continuity and efficiency. Its workflow is as follows: First, the system connects to the supplier database via a real-time data interface, automatically tracking the inventory levels and supply chain status of materials required for the construction project. Using preset thresholds and intelligent prediction algorithms, the system analyzes material consumption rates and inventory levels every two hours to predict future material needs and ensure timely replenishment. The system's built-in order management module automatically generates purchase orders based on prediction results and safety stock levels, and sends order information to suppliers via Electronic Data Interchange (EDI) technology for rapid response. Simultaneously, the system monitors logistics status, updating the transportation progress and estimated arrival time of materials in real time to ensure accurate material supply. After materials arrive at the construction site, the system uses RFID technology or barcode scanning for warehousing management, ensuring the accuracy and traceability of material information. Furthermore, the system supports inventory optimization, automatically adjusting inventory strategies based on construction progress and material consumption patterns to reduce inventory costs and improve material turnover. The system also includes a risk management module to assess potential risks in the supply chain, such as supplier delays and logistics disruptions, and develop corresponding response strategies. This comprehensive supply chain management process ensures the continuity, timeliness, and accuracy of material supply during tunnel construction, thereby supporting the smooth progress of the construction project as planned.

[0069] The personnel positioning and management system is used to: acquire the location information of each person, and send an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range.

[0070] The personnel positioning and management system plays a crucial role in this invention, and its workflow is as follows: First, the system equips each construction worker entering the tunnel with an RFID tag or other type of positioning device. These devices have unique identification and can send location signals at least once per second. Multiple readers are installed inside the tunnel, arranged in a predetermined grid to ensure coverage of the entire construction area and receive signals from the tags in real time. At the core of the system is a central monitoring platform that uses advanced positioning algorithms to process the received signals to determine the precise location of each tag, with an accuracy within 1 meter. Once the positioning data is determined, the system updates the location information of the construction workers in real time and displays the current location of each person on a 3D map of the tunnel. The system also integrates a safety early warning module. When construction workers approach dangerous areas or exceed preset safety limits, the system automatically sends alarms to relevant personnel and notifies construction management personnel to take necessary safety measures. The system also has an emergency evacuation function; in the event of an emergency within the tunnel, the system can quickly guide personnel to evacuate to a safe area. To improve management efficiency, the system also provides personnel attendance and work hour tracking functions, automatically recording personnel entry and exit times and work hours, providing data support for project management and resource planning. This embodiment ensures the safety of tunnel construction personnel and improves the efficiency and response speed of construction management through this comprehensive personnel positioning and management system.

[0071] The adaptive geological condition rock tunnel intelligent support system also includes a digital twin technology application module.

[0072] In this embodiment, the digital twin technology application module plays a crucial role in achieving precise digital mapping of tunnel construction. Its workflow is as follows: First, the system collects physical data from the tunnel construction site using integrated 3D scanning technology and high-precision sensors. This data includes, but is not limited to, the tunnel's dimensions, shape, and the geological characteristics of the surrounding rock. Then, using advanced data processing software, the collected physical data is converted into a precise 3D digital model, which realistically reflects the actual condition of the tunnel at a 1:1 scale. The system rapidly extracts geological information from the generated 3D digital model, automatically identifying the type, structure, and potential geological defects of the surrounding rock through machine learning and pattern recognition technologies. The system also supports the visualization of geological information, clearly displaying the geological structure and potential risk points inside the tunnel using different colors and markers. In the digital twin module of the intelligent construction monitoring system, the geological structure and potential risk points inside the tunnel are displayed using carefully designed visual markers. Different colors are typically used to distinguish various types of rocks and geological features; for example, blue represents hard granite, yellow represents softer sandstone, and red may be used to mark fracture zones or faults. As for the identification of risk points, specific symbols or patterns may be used, such as exclamation marks to indicate potential collapse areas and water droplet patterns to mark possible seepage points. This data on geological structures and risk points is acquired through high-precision geophysical exploration devices such as 3D scanning technology, ground-penetrating radar, seismic wave velocity meters, and geostress gauges. These devices are arranged at certain intervals around the tunnel to collect parameters such as the density, moisture, and strength of the surrounding rock in real time, and transmit the data to a central processing station. There, after preprocessing and analysis, the data is used to generate a 3D digital model of the tunnel, incorporating information on geological structures and risk points, providing the construction team with an intuitive basis for risk assessment and planning. The digital twin module also has a real-time update function, dynamically adjusting the digital model based on new geological data and monitoring results during construction to ensure that the model always reflects the latest construction status. The system also supports integration with construction data analysis and decision support systems, using the digital twin model as an intuitive basis for decision-making. The application of the digital twin model in construction decision-making is a comprehensive and multi-step process, involving multiple stages such as data collection, model building, analysis and prediction, and decision execution. First, detailed data on the tunnel and its surrounding geological environment were collected using high-precision 3D scanning technology, ground-penetrating radar, seismic wave velocity meters, and geostress gauges. This data included key geological parameters such as rock type, fracture distribution, groundwater level, and geostress. Then, this data was used to create a 1:1 scale 3D digital model of the actual tunnel. In this model, different colors and markers were used to distinguish various geological structures and risk points; for example, fracture zones might be marked in red, high-stress areas in purple, and potential seepage points were marked with teardrop patterns.

[0073] Once a digital twin model is created, it can be used to simulate the construction process and predict potential problems. By simulating different construction schemes, such as different excavation paths or support designs, the project team can assess the geological risks and construction difficulties of each scheme. For example, the model might predict the risk of landslides in a specific area due to rock fracturing and high stress conditions. Furthermore, the model can be used to simulate environmental impacts, such as the impact of construction activities on groundwater level changes. Based on the analysis results of the digital twin model, project managers can make more informed decisions. For example, they might decide to take additional support measures in high-risk areas or adjust the construction path to avoid areas with the most complex geological conditions. In addition, the digital twin model can assist in resource allocation decisions, helping project managers optimize the scheduling of materials and equipment by predicting resource requirements at different construction stages. During construction, the digital twin model can also be updated based on real-time monitoring data to reflect the latest geological conditions and construction progress, ensuring that decisions are always based on the most accurate information. The digital twin model not only plays a role before and during construction but can also be used for decision support during the operation and maintenance phases after construction is completed. For example, by analyzing long-term geological stability data in the model, managers can develop preventative maintenance plans to extend the tunnel's lifespan. Simultaneously, the digital twin model can serve as a communication and training tool, helping project team members better understand geological conditions and construction plans, improving overall team collaboration efficiency. It also assists construction teams in conducting more accurate risk assessments and construction planning. The digital twin technology application module provides a highly realistic and interactive virtual environment for tunnel construction, enabling construction teams to conduct scenario simulations, risk analysis, and decision-making in virtual space, significantly improving construction safety and efficiency.

[0074] The adaptive geological condition rock tunnel intelligent support system also includes a machine vision and intelligent recognition technology module.

[0075] In this embodiment, the machine vision and intelligent recognition technology module plays a crucial role in automatically identifying surrounding rock features and intelligently classifying them. Its workflow is as follows: The system first deploys high-definition cameras at the tunnel construction face to capture real-time image data of the surrounding rock surface. These images are transmitted to the central processing unit via an image acquisition module at a rate of at least 30 frames per second, ensuring the continuity and real-time nature of the image data. In the central processing unit, image preprocessing algorithms first perform noise reduction, contrast enhancement, and edge detection on the received images to improve the recognizability of geological features. Subsequently, a deep learning convolutional neural network (CNN) is applied to extract features and perform pattern recognition on the images, automatically identifying fissures, joints, and other surrounding rock features. The system's built-in intelligent classification algorithm, based on the identified features and combined with geomechanical parameters, intelligently classifies the surrounding rock, collecting key geological data, including the uniaxial compressive strength, elastic modulus, fissure spacing, roughness, and groundwater conditions of the rock. This data is obtained through high-precision geological exploration equipment and on-site monitoring instruments, providing a scientific basis for surrounding rock classification. An intelligent classification model is developed using machine learning or deep learning algorithms combined with an expert system. This model can automatically and objectively classify surrounding rock based on input geomechanical parameters. Typically, surrounding rock is classified into five levels: Level I indicates intact rock with high strength, suitable for rapid construction; Level II indicates relatively intact rock requiring some support measures; Level III indicates moderately fractured rock requiring moderate support; Level IV indicates severely fractured rock requiring extensive support and reinforcement; and Level V indicates extremely fractured rock with extremely poor stability, requiring special treatment and reinforced support. The classification is primarily based on rock strength, integrity, geostress state, groundwater conditions, and historical geological events. For example, high uniaxial compressive strength and low fracture spacing usually indicate high rock strength and good integrity, thus being classified as a higher level. Conversely, low strength and high fracture spacing may indicate poor rock stability, requiring more support measures. This classification reflects the stability of the surrounding rock and the necessary support measures. For example, the system might classify surrounding rock into five grades, from Grade I (Excellent) to Grade V (Very Poor), and automatically recommend corresponding construction schemes and support designs. Specifically, it collects detailed data on the tunnel surrounding rock from geological exploration and on-site monitoring equipment, including parameters such as uniaxial compressive strength, elastic modulus, fracture density, roughness, groundwater level, and in-situ stress. This data is transmitted in real-time to a central processing unit for preprocessing to ensure accuracy and usability. Using machine learning algorithms, combined with geomechanical parameters and historical construction data, the system automatically grades the surrounding rock. For example, based on rock strength and fracture density, the surrounding rock might be classified into Grade I (Excellent) to Grade V (Very Poor). The system has a built-in construction scheme database containing recommended construction methods and support designs for different surrounding rock grades and geological conditions.Based on the surrounding rock classification results, the system automatically queries the database to match the most suitable construction plan. Furthermore, the system considers real-time monitored environmental changes, such as sudden rises in groundwater levels or increases in ground stress, to further optimize the recommended plan. When recommending a plan, the system simultaneously evaluates its safety and economic viability. The safety assessment considers whether the plan can ensure the safety of construction personnel and the stability of the tunnel, while the economic assessment considers the cost-effectiveness of the plan to ensure that construction costs are controlled while meeting safety requirements. Recommended construction plans and support designs are presented to the construction team through an intuitive user interface. These plans include detailed technical parameters, construction steps, required material specifications, and expected construction time. The construction team can quickly make decisions and adjust the construction plan based on this information. During construction, the system continuously monitors changes in geological conditions and dynamically adjusts the recommended plan based on new data. For example, if a sudden increase in ground stress is detected, the system may suggest increasing the support strength or changing the support design to ensure construction safety. The system also has self-learning and optimization capabilities, continuously adjusting its identification algorithm and classification standards based on feedback from construction personnel and new geological data to improve identification accuracy and the scientific nature of the classification. Through this automated machine vision and intelligent recognition process, the present invention significantly improves the efficiency and accuracy of surrounding rock assessment in tunnel construction.

[0076] The adaptive geological condition rock tunnel intelligent support system also includes an integrated control system, which coordinates and controls information sharing and collaborative work among the modules.

[0077] The integrated control system, serving as the core hub of this embodiment, is responsible for data integration, intelligent decision-making, and rapid response among its various sub-modules. Specifically, the system first collects data streams in real time from modules such as intelligent advanced geological forecasting, surrounding rock quality evaluation, construction equipment monitoring, environmental monitoring, safety early warning, construction data analysis, material supply chain management, personnel positioning management, digital twin technology, and machine vision and intelligent recognition through a central data bus. Utilizing efficient data fusion technology, the integrated control system integrates these heterogeneous data sources to form a unified construction data view. The system's built-in intelligent decision engine employs advanced optimization algorithms and artificial intelligence technology to perform in-depth analysis of the integrated data and generate optimal decision-making solutions during the construction process. For example, based on geological forecasts and surrounding rock evaluation results, it intelligently adjusts support parameters; based on environmental and safety monitoring data, it provides real-time warnings of potential risks and triggers emergency responses. The integrated control system also possesses powerful real-time response capabilities, enabling it to rapidly implement decision results through automated actuators such as automatic valves, robotic arms, and robots, based on instructions from the intelligent decision engine. For instance, when the safety early warning system issues an alarm, the integrated control system can quickly mobilize emergency resources, guide personnel evacuation, and simultaneously adjust construction equipment and environmental control systems to ensure construction safety. The system also includes a user interface that graphically displays the construction status and decision results, facilitating monitoring and operation by construction management personnel. Through the coordination of the integrated control system, a high degree of information sharing and collaborative work is achieved among the modules, ensuring intelligent, automated, and efficient management of the entire tunnel construction process.

[0078] In one exemplary embodiment, such as Figure 2 As shown, an intelligent support method for rock tunnels that adapts to geological conditions is provided. This method includes:

[0079] Geological data of the tunnel face is acquired, and a deep learning network is used to predict the probability and risk level of geological changes based on the geological data of the tunnel face.

[0080] Obtain surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data;

[0081] Obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index;

[0082] The system acquires the operating parameters of the construction equipment and issues an alarm when the operating parameters exceed a first preset threshold.

[0083] The system acquires environmental parameters inside the tunnel and issues an alarm when the environmental parameters exceed a second preset threshold.

[0084] Obtain safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel;

[0085] Using pattern recognition methods, the system identifies whether the security monitoring parameters pose potential security risks based on a historical case database.

[0086] Obtain safety data by acquiring data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system;

[0087] Obtain construction data by acquiring data on construction progress, material usage, and worker activities;

[0088] Predict construction trends by combining historical construction data with the aforementioned safety data and construction data;

[0089] Obtain the inventory levels and supply chain status of materials required for the construction project;

[0090] Utilize intelligent forecasting algorithms to predict future material demand based on material consumption rates and inventory levels;

[0091] Purchase orders are generated based on the future material needs and the inventory levels, and order information is sent to suppliers via electronic data interchange technology.

[0092] The system acquires the location information of each person and sends an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range.

[0093] Specifically, the intelligent support method for rock tunnels that adapts to geological conditions provided in this embodiment can also be described as follows:

[0094] S1. Intelligent Advanced Geological Prediction: The intelligent advanced geological prediction module is activated to collect geological data in front of the tunnel face in real time using geophysical exploration technology. The data is analyzed through deep learning algorithms to predict possible geological changes and potential risks, and the geological prediction information is updated every 5 seconds.

[0095] S2. Surrounding Rock Quality Evaluation: The intelligent surrounding rock quality evaluation system automatically collects geological data, including rock stress and wave velocity, and conducts comprehensive analysis through data fusion technology. It automatically evaluates the surrounding rock quality every 10 seconds and adjusts the support parameters based on the evaluation results.

[0096] S3. Support Design Optimization: The adaptive support design optimization algorithm dynamically adjusts the support structure design, such as support strength and reinforcement layout, based on real-time geological data and surrounding rock quality evaluation, to ensure the optimal support scheme. The design parameters are updated every minute.

[0097] S4. Construction Equipment Monitoring: The intelligent construction equipment monitoring system uses IoT sensors to monitor the operating status of construction equipment in real time, predicts potential faults through big data analysis, performs a status assessment every 15 seconds, and provides timely warnings.

[0098] S5. Environmental Status Monitoring: The environmental monitoring module monitors the temperature, humidity and gas concentration inside the tunnel, collecting data every 5 seconds to ensure a safe construction environment.

[0099] S6. Safety Risk Warning: The safety warning system combines real-time monitoring data and historical cases to predict potential safety risks, such as gas accumulation or unstable surrounding rock. Once a risk is detected, an alarm is triggered immediately.

[0100] S7. Construction Decision Support: The construction data analysis and decision support system analyzes construction data and provides decision-making suggestions, such as adjustments to the construction plan and resource allocation, providing a decision support report every 30 minutes.

[0101] S8. Material Supply Monitoring: The material supply chain management system monitors material inventory and supply status, and updates material demand forecasts every 2 hours to ensure timely material supply.

[0102] S9. Personnel Safety Management: The personnel positioning and management system uses RFID technology to locate personnel in the tunnel in real time, updating the location information every 10 seconds to ensure personnel safety.

[0103] S10. Application of Digital Twin Technology: The digital twin technology application module creates a three-dimensional digital model of tunnel construction, updates geological information in real time, and provides a digital mapping of the construction process.

[0104] S11. Machine Vision and Intelligent Recognition: The machine vision and intelligent recognition technology module automatically identifies surrounding rock features, such as fissures and joints, through high-definition camera equipment and performs intelligent classification, completing one recognition every 20 seconds.

[0105] S12. Integrated Control: The integrated control system integrates the data and functions of all the above modules to perform data integration, intelligent decision-making, and rapid response, such as automatically adjusting the construction process and resource allocation to ensure intelligent management of the entire tunnel construction process.

[0106] Through this series of specific and coordinated steps, this method enables adaptive management of various geological and construction conditions during rock tunnel construction, thereby improving construction safety and efficiency.

[0107] Through the system and method of this invention, tunnel construction can be intelligently managed under various geological conditions, improving the level of automation in construction, reducing safety risks, and enhancing construction efficiency and quality.

[0108] In one exemplary embodiment, an intelligent support system for rock tunnels adapting to geological conditions is provided, comprising: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization algorithm, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management system, a personnel positioning and management system, a digital twin technology application module, a machine vision and intelligent recognition technology module, and an integrated control system. The intelligent advanced geological prediction module collects and analyzes geological data using geophysical exploration and deep learning technologies to provide early warnings of geological risks for the system. The intelligent surrounding rock quality evaluation system automatically completes real-time evaluation of the surrounding rock quality based on the advanced geological prediction data and its own collected multi-source data. The adaptive support design optimization algorithm receives the geological prediction and surrounding rock evaluation results and dynamically adjusts the support... The system incorporates various technologies to ensure the safety and adaptability of the tunnel construction. The intelligent construction equipment monitoring system uses IoT sensors to monitor equipment status in real time, predict malfunctions, and ensure construction continuity. The environmental monitoring module collects tunnel environmental parameters and integrates with the safety early warning system to assess the safety of the construction environment in real time. The construction data analysis and decision support system integrates data from various modules to provide scientific construction decision support. The material supply chain management system monitors material supply to ensure the timeliness and accuracy of construction materials. The personnel positioning and management system uses RFID and other technologies to locate personnel in real time, ensuring their safety. The digital twin technology application module creates a three-dimensional digital model of the tunnel construction, enabling rapid extraction of geological information and digital mapping of the construction process. The machine vision and intelligent recognition technology module automatically identifies and classifies surrounding rock characteristics, providing intuitive geological information for support design.

[0109] The intelligent advanced geological prediction module includes a geophysical exploration device for collecting geological data and a deep learning network for processing data and making predictions. The intelligent surrounding rock quality evaluation system includes a sensor array for collecting geological data and an intelligent algorithm for comprehensively analyzing geological data and automatically evaluating surrounding rock quality. The adaptive support design optimization algorithm includes an interface for receiving geological and surrounding rock quality data in real time and an optimization engine for dynamically adjusting the support design scheme based on this data. The intelligent construction equipment monitoring system includes a sensor network for monitoring the status of construction machinery and an intelligent diagnostic system for analyzing status data and predicting equipment failures. The environmental monitoring module includes a sensor group for detecting environmental parameters inside the tunnel and monitoring software for real-time feedback of environmental status. The safety early warning system includes an expert system for analyzing construction risks and a response mechanism for automatically triggering alarms when an anomaly is detected. The construction data analysis and decision support system includes a data warehouse for storing and processing construction data and analytical tools for providing decision-making suggestions. The material supply chain management system includes a logistics tracking system for tracking the flow of materials and an intelligent prediction algorithm for predicting material demand.

[0110] The intelligent advanced geological prediction module deploys a series of high-precision geophysical exploration devices, positioned at predetermined locations on the tunnel face, to collect key geological parameters in real time, such as geological structure, rock mechanical properties, and groundwater distribution. The collected raw data is then transmitted to a central processing unit via a secure data transmission network. Within the central processing unit, deep learning algorithms preprocess the data, including data cleaning, standardization, and feature extraction, to enhance the accuracy and reliability of the signals. Next, the preprocessed data is fed into a trained deep neural network model. This model, based on the characteristics of the geological data and in conjunction with a historical geological event database, performs pattern recognition and risk assessment to predict potential geological changes and risks that may be encountered ahead of tunnel construction. Finally, the prediction results generated by the module are displayed intuitively through a user interface and notified to the construction team in the form of real-time alerts, ensuring that appropriate preventative measures are taken before adverse changes in geological conditions occur.

[0111] The intelligent surrounding rock quality evaluation system initiates a multi-source data acquisition unit. This unit is equipped with high-precision ground-penetrating radar, seismic wave velocity measuring instruments, geostress gauges, and groundwater level monitors. These devices are arranged around the tunnel at 0.5-meter intervals to collect parameters such as density, moisture content, strength, elastic modulus, geostress state, and groundwater level of the surrounding rock in real time. The data acquisition frequency is set to once every 10 seconds. Subsequently, the collected raw data is transmitted to the central processing station via a fiber optic communication network at a rate of 1 gigabit per second. At the central processing station, the data first undergoes preprocessing, including filtering, noise reduction, and normalization, to ensure data quality. Then, the data fusion processing unit uses fuzzy comprehensive evaluation and neural network algorithms to comprehensively analyze the multi-source data. This algorithm considers the correlation and weight of geological data to calculate the Rock Mass Quality Index (RMQI), which ranges from 0 to 100, where 0 represents extremely poor surrounding rock quality and 100 represents extremely good surrounding rock quality. The system automatically classifies the surrounding rock into five levels, from A (excellent) to E (poor), based on the RMQI value, and updates the surrounding rock quality evaluation results in real time. Finally, the evaluation results are displayed through a visual interface, and the system automatically issues an alarm when the RMQI value falls below a preset threshold of 40, prompting the construction team to take appropriate reinforcement measures.

[0112] Upon receiving real-time geological data and the Rock Quality Index (RMQI) from the intelligent surrounding rock quality evaluation system, the adaptive support design optimization algorithm initiates a dynamic design adjustment process every 10 seconds. The algorithm accesses its built-in parameter database and, based on the specific RMQI value (e.g., RMQI between 80 and 100), automatically selects a Class A support scheme, recommending a support strength of 1.0 MPa and a rebar spacing of 150 mm. Using a fuzzy logic controller, the algorithm comprehensively evaluates the RMQI and other environmental parameters. If rising groundwater levels lead to decreased stability, the algorithm increases the support strength to 1.2 MPa and adjusts the rebar spacing to enhance the support. Further optimization using a genetic algorithm, considering cost, construction difficulty, and durability, results in a set of economical and safe support design parameters. Finally, the construction team quickly implements the adjusted support measures based on the precise design scheme provided by the algorithm, such as "recommended support strength: 1.1 MPa, rebar diameter: 20 mm, spacing: 200 mm".

[0113] The intelligent construction equipment monitoring system collects key operating parameters of construction equipment in real time, such as temperature, vibration, and pressure, through a series of sophisticated IoT sensors, updating the data every second to ensure continuous monitoring of equipment status. This data is transmitted in real time to the central monitoring platform via a high-speed wireless network at a rate of at least 10 megabits per second. On the platform, a big data mining analyzer uses machine learning algorithms, such as random forest classifiers, to perform in-depth analysis of the data stream to identify abnormal patterns in equipment behavior. For example, if the cutterhead torque sensor data of a tunnel boring machine shows an abnormal increase, the analyzer will quickly assess this deviation, predict possible faults, and issue an early warning 10 hours in advance. The warning information is promptly conveyed to the maintenance team through a visual interface and an automatic notification system, providing detailed guidance such as the fault type, cause, and recommended maintenance measures. This enables the construction team to quickly take preventative or remedial measures, thereby improving the reliability and efficiency of construction equipment and reducing unexpected downtime.

[0114] The environmental monitoring module uses a uniformly distributed sensor network within the tunnel to accurately collect environmental parameters such as temperature, humidity, and gas concentration every 5 seconds. These sensors cover a temperature range from -20°C to 50°C, monitor relative humidity from 0% to 100%, and harmful gas concentrations from 0 to 1000 ppm. The data is transmitted to a central monitoring station and updated in real-time via a wireless communication network at a rate of at least 10 megabits per second. At the monitoring station, the environmental data analysis system uses statistical analysis and trend prediction models to evaluate the data, ensuring environmental stability and safety. If any parameter exceeds a preset safety threshold, such as a temperature reaching 40°C or a gas concentration exceeding 500 ppm, the system immediately triggers an audible and visual alarm and notifies construction management personnel and workers via SMS. Simultaneously, it automatically initiates environmental control measures, such as increasing ventilation to lower the temperature or increasing humidity, to quickly restore safe environmental standards. The environmental monitoring module also features long-term data recording and trend analysis capabilities, supporting construction planning and ensuring the construction environment remains continuously safe.

[0115] The safety early warning system integrates data streams from the environmental monitoring module and the intelligent construction equipment monitoring system in real time. Utilizing machine learning algorithms such as Support Vector Machines (SVM) and neural networks, it performs in-depth analysis every minute to identify abnormal patterns during construction. Combined with a historical case database, the system uses pattern recognition technology to predict potential safety risks. When an anomaly is detected, such as a carbon monoxide concentration exceeding 200 ppm or a surrounding rock displacement rate exceeding 2 mm / day, the system automatically triggers an alarm, issues an emergency notification via tunnel broadcasts and wireless network, and simultaneously activates emergency plans, such as evacuating personnel or increasing ventilation. The system also includes a feedback mechanism, allowing on-site personnel to provide real-time feedback so that the system can adjust its early warning strategies.

[0116] The construction data analysis and decision support system aggregates massive amounts of construction data in real time from multiple data sources, including intelligent construction equipment monitoring systems, environmental monitoring modules, and safety early warning systems. It preprocesses the data with a capacity of millions of records per second to ensure accuracy. Utilizing big data technologies and artificial intelligence algorithms, including deep learning and natural language processing, the system conducts in-depth data analysis, identifies key trends, and builds predictive models, such as time series analysis, to forecast construction progress and resource requirements. The built-in decision support engine provides the construction management team with visualized reports and real-time notifications, recommending scientific adjustments to construction plans and resource optimization strategies. The system also possesses self-learning capabilities, continuously optimizing models based on team feedback and new data to improve the accuracy of predictions and recommendations, thereby enhancing construction management efficiency and intelligence.

[0117] The materials supply chain management system connects to the supplier database via a real-time data interface, automatically analyzing and predicting material needs and inventory levels for the tunnel construction project every two hours to ensure timely replenishment. The built-in order management module automatically generates purchase orders based on the analysis results and quickly places orders with suppliers via EDI technology. Logistics status is monitored in real time to update material transportation and estimated arrival times. Upon arrival, the system uses RFID or barcode technology for precise warehousing management, while also supporting inventory optimization by automatically adjusting inventory strategies based on actual consumption patterns to reduce costs and improve efficiency. The risk management module assesses potential supply chain risks and develops response strategies to ensure continuous and accurate material supply for the construction project, supporting project progress as planned.

[0118] The personnel positioning and management system equips each construction worker with RFID tags or other positioning devices to achieve real-time tracking of personnel locations within the tunnel. These devices send signals at least once per second, which are received by multiple readers arranged in a pre-defined grid within the tunnel. The central monitoring platform processes these signals using advanced positioning algorithms to determine and update the location of each person on a 3D map in real time with an accuracy within 1 meter. A safety early warning module automatically issues alarms when personnel approach hazardous areas and guides evacuation to safe areas in emergencies. The system also includes personnel attendance and work hour tracking functions, automatically recording and providing the data required for project management, thereby ensuring the safety of construction workers and improving construction management efficiency and response speed.

[0119] The digital twin technology application module collects physical data from the tunnel construction site using 3D scanning and high-precision sensors, transforming this data into a precise 3D digital model of the actual tunnel at a 1:1 scale. This module utilizes machine learning technology to automatically identify and extract geological information from the model, such as surrounding rock type and structure, while providing visualizations that highlight geological structures and risk areas with different colors and markers. The module, capable of real-time updates, dynamically adjusts the model based on the latest geological data and monitoring results to ensure it reflects the current construction status. Furthermore, this module integrates with a decision support system, providing the construction team with intuitive risk assessment and planning support, enabling efficient construction plan simulations and decision-making in a virtual environment, thereby improving construction safety and efficiency.

[0120] The machine vision and intelligent recognition technology module uses high-definition camera equipment to capture images of the tunnel construction face and transmits them to the processing unit in real time at a rate of 30 frames per second. In this unit, the images undergo preprocessing including noise reduction, contrast enhancement, and edge detection. Subsequently, a deep learning convolutional neural network performs feature extraction and pattern recognition, automatically identifying surrounding rock features such as cracks and joints. Based on these features and geomechanical parameters, the intelligent grading algorithm intelligently grades the surrounding rock, from Grade I (excellent) to Grade V (poor), and recommends corresponding construction and support schemes. The system also has a self-learning function, continuously optimizing the algorithm based on feedback and new data to improve the accuracy of identification and grading, thereby enhancing the efficiency and scientific rigor of construction assessment.

[0121] The integrated control system, serving as the central hub of this invention, integrates data streams from various sub-modules in real time via a central data bus, using data fusion technology to form a unified construction data view. The built-in intelligent decision engine employs optimization algorithms and artificial intelligence to deeply analyze the integrated data, generating and executing optimal construction decision schemes. For example, it automatically adjusts support strategies based on real-time geological and surrounding rock evaluation data, or provides real-time risk warnings and triggers emergency measures based on environmental and safety monitoring data. The integrated control system also possesses rapid response capabilities, quickly implementing decisions through automated actuators, such as rapidly allocating emergency resources and guiding personnel evacuation during safety warnings. The user interface displays the construction status in a graphical format, facilitating monitoring and operation, ensuring intelligent, automated, and efficient management of the construction process.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An intelligent support system for rock tunnels that adapts to geological conditions, characterized in that, The adaptive geological condition rock tunnel intelligent support system includes: an intelligent advanced geological prediction module, an intelligent surrounding rock quality evaluation system, an adaptive support design optimization module, an intelligent construction equipment monitoring system, an environmental monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system, and an integrated control system. The intelligent advanced geological prediction module is used to: acquire geological data of the tunnel face, and use a deep learning network to predict the probability and risk level of geological changes based on the geological data of the tunnel face; The intelligent surrounding rock quality evaluation system is used to: acquire surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data; The adaptive support design optimization module is used to: obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index; The intelligent construction equipment monitoring system is used to: acquire the operating parameters of the construction equipment, and issue an alarm when the operating parameters exceed a first preset threshold. The environmental monitoring module is used to: acquire environmental parameters inside the tunnel and issue an alarm when the environmental parameters exceed a second preset threshold. The security early warning system is used for: Obtain safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel; Using pattern recognition methods, the system identifies whether the security monitoring parameters pose potential security risks based on a historical case database. The construction data analysis and decision support system is used for: Obtain safety data by acquiring data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system; Obtain construction data by acquiring data on construction progress, material usage, and worker activities; Predict construction trends by combining historical construction data with the aforementioned safety data and construction data; The material supply chain management system is used for: Obtain the inventory levels and supply chain status of materials required for the construction project; Utilize intelligent forecasting algorithms to predict future material demand based on material consumption rates and inventory levels; Purchase orders are generated based on the future material needs and the inventory levels, and order information is sent to suppliers via electronic data interchange technology. The personnel positioning and management system is used to: acquire the location information of each person, and send an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range; The integrated control system is used to coordinate and control information sharing and collaborative work among the modules.

2. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The adaptive geological condition intelligent support system for rock tunnels also includes: a digital twin technology application module; The digital twin technology application module is used for: Obtain the tunnel's dimensions, shape, and the geological characteristics of the surrounding rock to obtain physical data; A three-dimensional digital model is constructed using the physical data; Machine learning and pattern recognition methods are used to identify the type, structure, and potential geological defects of the surrounding rock based on the three-dimensional digital model, thereby obtaining geological information; The geological information is visualized, and the geological structure and risk points inside the tunnel are displayed in the three-dimensional digital model using different colors and labels.

3. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The adaptive geological condition rock tunnel intelligent support system also includes: a machine vision and intelligent recognition technology module; The machine vision and intelligent recognition technology module is used for: Acquire image data of the surrounding rock surface; The image data is subjected to feature extraction and pattern recognition using a convolutional neural network to obtain image features; the image features include: crack and joint features in the image data; The surrounding rock is intelligently classified based on the image features and geomechanical parameters using an intelligent classification algorithm to obtain the classification results. Based on the classification results, corresponding construction plans and support designs are recommended.

4. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The intelligent advanced geological prediction module is specifically used for: The geological data of the tunnel face is acquired at a frequency of once per second; the geological data of the tunnel face is the data within a preset range in front of the tunnel face; the geological data of the tunnel face includes rock stress, wave velocity and electromagnetic properties; Geological parameter features of the tunnel face geological data were extracted using a convolutional neural network. The geological parameter characteristics include: rock physical property characteristics, geological structure characteristics, and groundwater distribution characteristics; The geological parameter features are input into a trained deep learning network model to obtain the probability and risk level of geological changes at the corresponding location of the tunnel face geological data; the deep learning network model is trained from a historical geological event database; the geological changes include: rock fracturing, collapse, rock burst, groundwater inrush, and fault activity.

5. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The intelligent surrounding rock quality evaluation system is specifically used for: Acquire surrounding rock data at the tunnel perimeter; the surrounding rock data includes: density, moisture content, and compressive strength of the surrounding rock; Calculate the surrounding rock quality index based on the surrounding rock data; An alarm mechanism is triggered when the surrounding rock quality index is lower than a preset value.

6. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The personnel positioning and management system is specifically used for: Obtain the location information of each person; The location information of each person is processed using a positioning algorithm, and the current location of each person is displayed on a 3D map of the tunnel. An alarm is sent to the person when they approach a dangerous area or go beyond a preset safety zone. In the event of an emergency inside the tunnel, personnel will be guided to evacuate to a safe area based on the location information provided. Record personnel entry and exit times and working hours, and transmit these times and hours to the construction data analysis and decision support system to provide data support for project management and resource planning.

7. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The aforementioned supply chain management system is also used for: Real-time monitoring and updates of the transportation progress and estimated arrival time of materials; and warehousing management of materials after they arrive at the construction site using RFID technology or barcode scanning. Adjust inventory strategies based on construction progress and material consumption patterns; Use the risk management module to assess potential risks in the supply chain; The potential risks include supplier delays and logistical disruptions.

8. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The intelligent construction equipment monitoring system is specifically used for: Obtain the operating parameters of the construction equipment; the operating parameters include: engine and power system speed, temperature and pressure; hydraulic system pressure and flow rate; electrical system detection current and voltage; mechanical transmission system vibration and noise; An alarm will be triggered when the operating parameters exceed the first preset threshold.

9. The intelligent rock tunnel support system adapting to geological conditions according to claim 1, characterized in that, The environmental monitoring module is specifically used for: The environmental parameters inside the tunnel are acquired; these parameters include temperature, relative humidity, and gas concentration; and are collected by several sensors distributed inside the tunnel. An alarm will be triggered and environmental control measures will be activated when the temperature exceeds 40°C, the relative humidity is below 30%, or the gas concentration exceeds the safety standard.

10. A method for intelligent support of rock tunnels that adapts to geological conditions, characterized in that, The adaptive intelligent support method for rock tunnels under geological conditions includes: Geological data of the tunnel face is acquired, and a deep learning network is used to predict the probability and risk level of geological changes based on the geological data of the tunnel face. Obtain surrounding rock data at the tunnel perimeter and calculate the surrounding rock quality index based on the surrounding rock data; Obtain the surrounding rock quality index and recommend the required support scheme based on the surrounding rock quality index; The system acquires the operating parameters of the construction equipment and issues an alarm when the operating parameters exceed a first preset threshold. The system acquires environmental parameters inside the tunnel and issues an alarm when the environmental parameters exceed a second preset threshold. Obtain safety monitoring parameters; the safety monitoring parameters include: the operating parameters of the construction equipment and the environmental parameters inside the tunnel; Using pattern recognition methods, the system identifies whether the security monitoring parameters pose potential security risks based on a historical case database. Obtain safety data by acquiring data from the intelligent construction equipment monitoring system, environmental monitoring module, and safety early warning system; Obtain construction data by acquiring data on construction progress, material usage, and worker activities; Predict construction trends by combining historical construction data with the aforementioned safety data and construction data; Obtain the inventory levels and supply chain status of materials required for the construction project; Utilize intelligent forecasting algorithms to predict future material demand based on material consumption rates and inventory levels; Purchase orders are generated based on the future material needs and the inventory levels, and order information is sent to suppliers via electronic data interchange technology. The system acquires the location information of each person and sends an alarm to the person when the person approaches a dangerous area or exceeds a preset safe range.

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