Safety step monitoring method and system for tunnel safety

Abstract

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel safety, and in particular relates to a tunnel safety-oriented safety step distance monitoring method and system. Background Art

[0002] During tunnel construction, it is necessary to monitor the displacement, strain and other structural parameters inside the tunnel as well as environmental parameters such as temperature and humidity in real time to ensure construction safety and quality. However, the internal environment of the tunnel is complex, the location and number of sensors are limited, and data transmission faces problems such as signal attenuation and interference. At the same time, tunnel construction is a dynamic process, and the monitoring system needs to adjust the sensor layout and data transmission path in real time according to the construction progress and environmental changes to ensure the real-time and accuracy of the monitoring data.

[0003] In addition, the processing and analysis of massive monitoring data also poses challenges to the system's computing and storage capabilities.

[0004] How to optimize the sensor deployment plan under limited sensor resources, establish a stable and reliable data transmission channel, and quickly identify abnormal situations from massive monitoring data are key technical issues facing the tunnel construction safety monitoring system. Summary of the invention

[0005] The purpose of this invention is to propose a safe step distance monitoring method and system for tunnel safety. By using multi-sensor data fusion, adaptive routing algorithm, edge computing, machine learning and other technologies, an intelligent tunnel construction safety monitoring system is constructed to provide reliable technical guarantee for the safety and quality of tunnel construction.

[0006] In order to achieve the above object, a first aspect of the present invention provides a safe step distance monitoring method for tunnel safety, the method comprising:

[0007] S1. Dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes, design a multi-objective optimization algorithm based on the sensor layout location, layout quantity, and data transmission path, and adjust the sensor layout in real time according to the optimization results;

[0008] S2. Based on the optimized sensor layout, an adaptive routing algorithm is used to dynamically select the optimal data transmission path for the wireless transmission environment between the edge sensor devices and the server inside the tunnel. By real-time evaluation of the transmission link quality, signal attenuation and interference are predicted, and the transmission path with the strongest signal and the smallest attenuation is selected to ensure the stability and reliability of data transmission.

[0009] S3. Deploy an edge computing module at the sensor edge node, preprocess and compress the collected monitoring data in the edge computing module, and perform data dimension reduction and feature extraction;

[0010] S4. Build a digital twin model of the tunnel environment and equipment status based on the extracted features. Through real-time interaction between physical and virtual information, realize comprehensive perception and dynamic tracking of the tunnel construction process, conduct fusion analysis of the sensor monitoring data, discover abnormal situations in time, and provide a basis for safety warning.

[0011] S5. Based on the detection data of the sensors used in the digital twin model, an incremental learning algorithm is used to analyze the monitoring data of the sensors online, and the anomaly detection model is dynamically updated based on the newly collected data samples, and the warning threshold is adaptively adjusted;

[0012] S6. Comprehensively apply multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations in the tunnel, and comprehensively evaluate the safety status of tunnel construction through data redundancy and complementarity;

[0013] S7. Establish a data processing architecture based on cloud-edge collaboration to store and calculate massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of data, and the cloud side is responsible for long-term storage and mining of data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

[0014] Furthermore, the S1 specifically includes:

[0015] Obtain tunnel construction progress and environmental change data, and determine whether the sensor deployment plan needs to be adjusted based on preset evaluation rules. The evaluation rules are whether the preset indicators exceed the preset thresholds.

[0016] If adjustment is needed, the dynamic optimization process of the sensor deployment plan is triggered;

[0017] Through a multi-objective optimization algorithm, a sensor deployment optimization model is established with sensor deployment location, deployment quantity and data transmission path as optimization variables, and monitoring data quality, transmission delay and energy consumption as optimization targets.

[0018] The particle swarm optimization algorithm is used to solve the sensor layout optimization model and output the optimized sensor layout plan. According to the optimized sensor layout plan, the sensor layout adjustment instructions are generated and sent to the corresponding sensor nodes:

[0019] For sensor nodes that need to be added, control their deployment and data collection behavior;

[0020] For the sensor nodes that need to be removed, control them to stop data collection and remove them from the network;

[0021] For sensor nodes that need to adjust their positions or data transmission paths, control them to perform corresponding adjustment operations;

[0022] During the tunnel construction and environmental monitoring process, the monitoring data collected by each sensor node is obtained, the data is cleared and processed, and the adaptability of the current sensor deployment plan is evaluated through data processing:

[0023] If the data quality is found to be degraded, the transmission delay is increased, or the energy consumption is abnormal during the monitoring process, that is, the data quality index is lower than the preset threshold, the transmission delay exceeds the preset threshold, or the energy consumption exceeds the preset threshold, the sensor deployment optimization process is re-triggered to dynamically adjust the sensor deployment to ensure the reliable operation of the monitoring system;

[0024] Track the progress of tunnel construction and environmental changes, and trigger sensor deployment optimization at regular intervals according to changes in monitoring needs. At the same time, if it is found during the monitoring process that the sensor deployment does not meet the current needs, it will also trigger sensor deployment optimization;

[0025] Dynamically adapt the sensor deployment plan through regular and irregular optimization.

[0026] Furthermore, the steps of the particle swarm optimization algorithm include:

[0027] Initialize the particle swarm, calculate the fitness of each particle, and update the position and velocity of the particle until the termination condition is met;

[0028] The particle position vector represents the sensor deployment scheme, and the fitness function is designed according to the optimization goal;

[0029] Through iterative optimization, the optimal sensor deployment plan that meets monitoring needs is obtained, and the deployment location, deployment quantity and data transmission path of each sensor are determined.

[0030] Furthermore, the S2 specifically includes:

[0031] According to the wireless signal transmission environment parameters inside the tunnel, multiple real-time link quality evaluation indicators are obtained;

[0032] Preprocess the acquired link quality assessment indicator data to improve data quality;

[0033] The preprocessed link quality evaluation index data is input into a pre-built link quality prediction model based on a long short-term memory neural network, and the predicted link quality value of each candidate transmission path in the future period of time is calculated by the model;

[0034] According to the predicted link quality value of each candidate transmission path, combined with the preset link quality threshold, the fuzzy hierarchical analysis method is used to comprehensively consider the link bandwidth and delay, and the comprehensive transmission performance of each candidate transmission path is scored to obtain the comprehensive score value of each candidate transmission path;

[0035] Sort the candidate transmission paths from high to low according to the comprehensive score, and select the candidate transmission path with the highest comprehensive score as the current optimal data transmission path;

[0036] If the comprehensive score of the current optimal transmission path is lower than the preset path switching threshold, the path switching mechanism is triggered, and the candidate transmission path with the second highest comprehensive score in the sorting result is selected as the new optimal data transmission path, and data transmission is switched to this path;

[0037] During data transmission, monitor the transmission quality of the current link and obtain real-time link quality parameters:

[0038] When the link quality drops below the preset threshold for more than a certain period of time, the adaptive routing algorithm is re-triggered to update the link quality prediction model and dynamically select the optimal transmission path;

[0039] At the same time, in response to the wireless signal interference factors inside the tunnel, the minimum mean square error adaptive filtering algorithm is used to adjust the equalization coefficient of the received signal in real time.

[0040] Furthermore, the S3 specifically includes:

[0041] If the amount of monitoring data collected by the sensor node is large, the edge computing module is deployed at the sensor node to preprocess and compress the monitoring data, and reduce the amount of data transmission through data dimension reduction and feature extraction:

[0042] First, according to the characteristics of the preprocessed monitoring data, the principal component analysis method is used to reduce the data dimension, extract the main features of the data, remove redundant information, and reduce the data dimension;

[0043] Then, the main features extracted by principal component analysis are used as input, and the data is compressed and encoded using the LZW lossless compression algorithm;

[0044] According to the CPU and memory resources of the edge computing module and the real-time requirements of the monitoring data, the number of principal components of the principal component analysis and the dictionary size of the LZW compression algorithm are dynamically adjusted to minimize the amount of data transmission while meeting the real-time requirements;

[0045] If the features of the monitoring data are more complex, the convolutional neural network algorithm is used for feature extraction. The high-level features of the data are automatically learned through convolution and pooling operations to improve the effect of data dimensionality reduction and compression:

[0046] The high-level feature map extracted by the convolutional neural network is fused with the results of principal component analysis as the input of the LZW compression algorithm;

[0047] Adaptively adjust the data transmission compression ratio, fragment size and retransmission times according to the parameters of the sensor node;

[0048] Through task offloading and load balancing between the edge computing module and the cloud server, computing-intensive tasks are offloaded to the cloud, reducing the processing pressure of the edge computing module. At the same time, storage-intensive tasks are performed on the edge side to reduce storage overhead in the cloud and improve the real-time and scalability of the overall system.

[0049] Furthermore, the S4 specifically includes:

[0050] By deploying various sensor devices at the tunnel construction site, the physical information of the tunnel is collected in real time, and the virtual information of the tunnel is obtained at the same time;

[0051] Matching and associating the collected physical information with the virtual information to construct a digital twin model of the tunnel construction process; wherein the digital twin model includes the three-dimensional geometric information, environmental parameters and equipment status of the tunnel;

[0052] Based on the constructed digital twin model, various monitoring data are integrated and analyzed;

[0053] The Kalman filter algorithm is used to set corresponding filter parameters according to the specific frequency range of the tunnel environment to remove noise from the monitoring data. At the same time, key features in the data are extracted and enhanced.

[0054] The association rules mining algorithm is used to analyze the association rules between tunnel environmental parameters and equipment status;

[0055] By setting support and confidence thresholds, frequently occurring parameter combination patterns are mined and a tunnel safety status assessment model is constructed; if the monitoring data exceeds the preset threshold, it is judged as an abnormal situation;

[0056] According to the tunnel construction schedule, the digital twin model is dynamically updated to achieve real-time tracking of the tunnel construction process;

[0057] Through 3D visualization technology, the construction status and environmental information of the tunnel can be directly displayed, providing intuitive decision-making support for construction managers;

[0058] When abnormal conditions are found in monitoring data analysis, the early warning mechanism is triggered in time;

[0059] Adopting the decision tree algorithm, through the branch structure of the decision tree, the corresponding emergency plan is automatically generated according to the early warning mechanism; wherein, the emergency plan includes the handling process of abnormal situations, the required resources and the division of labor of personnel;

[0060] Early warning information and emergency plans are pushed to relevant personnel in a timely manner to assist in tunnel safety management. At the same time, detailed information of abnormal situations is fed back to the digital twin model, and the abnormal situations are displayed through the model's visual interface, providing an intuitive basis for subsequent construction decisions.

[0061] Furthermore, the S5 specifically includes:

[0062] Acquire real-time monitoring data and use the real-time monitoring data as new data samples for the support vector machine incremental learning algorithm to dynamically update the parameters of the SVM anomaly detection model;

[0063] Use the updated SVM anomaly detection model to analyze the real-time monitoring data online and identify abnormal data;

[0064] For the identified abnormal data, the statistical process control method is used to adaptively adjust the warning threshold, and the accuracy of abnormal identification is improved by dynamically calculating new control limits;

[0065] The confusion matrix is ​​used to evaluate the anomaly recognition performance of the adjusted warning threshold. If both the precision and recall rates meet the requirements, the adjusted warning threshold is applied to subsequent anomaly detection.

[0066] If the performance still does not meet the requirements, the samples with identification errors are added to the incremental training set of SVM, and the anomaly detection model is optimized through incremental learning;

[0067] Iterate the above steps to continuously improve the performance of the anomaly detection system until it meets actual application requirements;

[0068] Finally, the optimized SVM anomaly detection model is deployed in the production environment to perform online anomaly analysis and early warning on real-time monitoring data, so as to timely discover and handle abnormal situations in the production process.

[0069] Furthermore, the S6 specifically includes:

[0070] Acquire monitoring data collected by tunnel sensors of different types and locations, and clean the acquired monitoring data;

[0071] Conduct exploratory analysis on the cleaned data, analyze the distribution characteristics and trend changes of the data through visualization and other methods, and perform feature selection and feature extraction;

[0072] Analyze the correlation between monitoring data from different sensors, and use association rule mining algorithms to discover frequent patterns and association rules in monitoring data, and mine the hidden security status characteristics in the data;

[0073] According to the results of correlation analysis and association rule mining, a machine learning algorithm such as decision tree or support vector machine is selected to establish a tunnel construction safety status assessment model, and the model is trained and optimized using methods such as cross-validation.

[0074] Normalize the monitoring data from different sensors and then fuse multi-source heterogeneous data;

[0075] On the basis of fusion, the Kalman filter algorithm is used to process the data and dynamically track the changing trend of the tunnel safety status;

[0076] Preset thresholds for tunnel construction safety status based on historical data and expert experience;

[0077] Compare the output of the security status assessment model with the preset threshold. When the assessment result exceeds the threshold, trigger the early warning mechanism.

[0078] Send early warning information to relevant personnel, prompting them to check and deal with safety hazards.

[0079] Furthermore, the S7 specifically includes:

[0080] Based on the characteristics of monitoring data from tunnel construction, a cloud-edge collaborative data processing architecture is designed to divide the responsibilities of the cloud and edge sides;

[0081] Deploy a real-time data processing module on the edge side, use Apache Flink to clean and filter the collected monitoring data, and use Kafka to process data streams and extract key features;

[0082] Through the preset threshold rules, determine whether there is an abnormal situation: if an abnormality is detected, an early warning is triggered immediately, and the relevant data is uploaded to the cloud through the MQTT protocol for further analysis;

[0083] The cloud receives monitoring data uploaded by the edge side, combines historical data with expert knowledge base, and uses the isolation forest algorithm to detect anomalies in the data, identify hidden abnormal patterns and trends, and assess the safety risks of tunnel construction;

[0084] According to the results of cloud analysis, the optimized data processing parameters are sent to the edge side through RESTful API, and the real-time data analysis strategy is dynamically adjusted to improve the efficiency and accuracy of edge computing;

[0085] Using visualization tools, the analysis results in the cloud are presented to users in the form of dashboards to assist safety decision-making at the construction site;

[0086] InfluxDB time series database is used to store massive monitoring data. Writing and querying capabilities are used to achieve efficient management and retrieval of monitoring data. Prometheus is used for real-time data collection and monitoring.

[0087] In a second aspect of the present invention, a safe step distance monitoring system for tunnel safety is provided, the system comprising:

[0088] The sensor layout optimization module is used to dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes. It designs a multi-objective optimization algorithm based on the sensor layout location, layout quantity, and data transmission path, and adjusts the sensor layout in real time according to the optimization results.

[0089] The adaptive routing module is used to dynamically select the optimal data transmission path based on the optimized sensor layout and the wireless transmission environment between the edge sensor devices inside the tunnel and the server using an adaptive routing algorithm. It evaluates the transmission link quality in real time, predicts signal attenuation and interference, and selects the transmission path with the strongest signal and the least attenuation to ensure the stability and reliability of data transmission.

[0090] An edge computing module is used to deploy the edge computing module at the edge node of the sensor, pre-process and compress the collected monitoring data in the edge computing module, and perform data dimension reduction and feature extraction;

[0091] The digital twin module is used to build a digital twin model of the tunnel environment and equipment status based on the extracted features. Through the real-time interaction of physical and virtual information, it can realize the comprehensive perception and dynamic tracking of the tunnel construction process, conduct fusion analysis on the monitoring data of the sensors, timely discover abnormal situations, and provide a basis for safety warning;

[0092] The incremental learning module is used to analyze the sensor monitoring data online based on the detection data of the sensor used in the digital twin model using an incremental learning algorithm, dynamically update the anomaly detection model based on the newly collected data samples, and adaptively adjust the warning threshold;

[0093] The safety assessment module is used to comprehensively apply multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations in the tunnel, and comprehensively evaluate the safety status of tunnel construction through data redundancy and complementarity;

[0094] The cloud-edge collaboration module is used to establish a data processing architecture based on cloud-edge collaboration, which stores and calculates massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of data, and the cloud side is responsible for long-term storage and mining of data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

[0095] The beneficial technical effects of the present invention are at least as follows:

[0096] The present invention dynamically optimizes the sensor deployment scheme, adopts an adaptive routing algorithm to select the optimal data transmission path, and deploys edge computing modules at sensor nodes for data preprocessing and compression. At the same time, a digital twin model of the tunnel environment and equipment status is constructed to achieve comprehensive perception and dynamic tracking. An incremental learning algorithm is used to analyze the monitoring data online and dynamically update the anomaly detection model. Multi-sensor data fusion technology is used to improve the reliability and accuracy of monitoring data. A data processing architecture based on cloud-edge collaboration is established to achieve efficient management and utilization of monitoring data. The present invention can adapt to the progress of tunnel construction and environmental changes, optimize sensor layout and data transmission, improve the accuracy and real-time performance of anomaly identification, and provide comprehensive data support and early warning basis for tunnel construction safety. Through organic combination, the present invention forms an efficient, accurate and reliable tunnel safety step monitoring system, which provides comprehensive technical support for tunnel construction safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0098] Figure 1 The present invention is a flow chart of a safe step distance monitoring method for tunnel safety.

[0099] Figure 2 A schematic diagram of a safe step distance monitoring method and system for tunnel safety according to the present invention.

[0100] Figure 3 It is another schematic diagram of a safe step distance monitoring method and system for tunnel safety according to the present invention.

[0101] Figure 4 The present invention is a schematic structural diagram of a safe step distance monitoring system for tunnel safety. DETAILED DESCRIPTION

[0102] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0103] like Figure 1-3 As shown, a safe step distance monitoring system for tunnel safety provided by an embodiment of the present invention may specifically include:

[0104] S101. Dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes. Through the multi-objective optimization algorithm, comprehensively consider the sensor layout location, layout quantity, data transmission path and other factors to obtain the optimal sensor layout plan that meets the monitoring needs, and adjust the sensor layout in real time according to the optimization results.

[0105] Obtain tunnel construction progress and environmental change data, and determine whether the sensor deployment plan needs to be adjusted based on preset evaluation rules. The evaluation rules include whether indicators such as monitoring data quality, transmission delay and energy consumption exceed preset thresholds.

[0106] If adjustment is required, the dynamic optimization process of the sensor deployment plan is triggered.

[0107] The quality of monitoring data is measured by the accuracy Q and completeness C of the data. The transmission delay is measured by the average transmission time T of the data from the sensor node to the data center. The energy consumption is measured by the average power consumption E of the sensor node. It is expressed as follows:

[0108]

[0109] Among them, D i , Represent the actual value and predicted value of the i-th data point, N represents the total number of data points, and t i represents the transmission time of the ith data point, e i represents the power consumption of the i-th sensor node.

[0110] Through the multi-objective optimization algorithm, the sensor deployment optimization model is established with the sensor deployment location, deployment quantity and data transmission path as optimization variables, and the monitoring data quality, transmission delay and energy consumption as optimization goals. Among them, the monitoring data quality can be measured by indicators such as data accuracy and completeness, the transmission delay can be measured by the average transmission time of data from the sensor node to the data center, and the energy consumption can be measured by the average power consumption of the sensor node.

[0111] Among them, the optimization variables are: sensor layout position P, layout number N and data transmission path R.

[0112] Optimization objectives: monitoring data quality Q, transmission delay T and energy consumption E. It is expressed as follows:

[0113]

[0114] Among them, the α, β, and γ weight coefficients are used to balance the importance of different optimization objectives.

[0115] The sensor layout optimization model is established as follows:

[0116]

[0117] The particle swarm optimization algorithm is used to solve the above optimization model. The key steps of the particle swarm optimization algorithm include:

[0118] Initialize the particle swarm, calculate the fitness of each particle, and update the particle's position and velocity until the termination condition is met. i =(P i ,N i ,R i ), particle speed v i , fitness function:

[0119] f(x i )=(αQ(x i )+βT(x i )+γE(x i ))

[0120] The particle position vector represents the sensor layout plan, and the fitness function is designed according to the optimization goal. The update rules are as follows:

[0121] v i (t+1)=ωv i (t)+c1r1(p i -x i (t))+c2r2(p best -x i (t))

[0122] x i (t+1)=x i (t)+v i (t+1)

[0123] Among them, ω represents the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p i is the optimal position of particle i, p best is the global best position.

[0124] Through iterative optimization, the optimal sensor deployment plan that meets monitoring needs is obtained, and the deployment location, deployment quantity and data transmission path of each sensor are determined.

[0125] According to the optimized sensor deployment plan, a sensor deployment adjustment instruction is generated and sent to the corresponding sensor nodes.

[0126] For sensor nodes that need to be added, their deployment and data collection behavior are controlled; for sensor nodes that need to be removed, they are controlled to stop data collection and be removed from the network; for sensor nodes that need to adjust their positions or data transmission paths, they are controlled to perform corresponding adjustment operations.

[0127] During the tunnel construction and environmental monitoring process, the monitoring data collected by each sensor node is continuously acquired, and the data is cleaned, fused and analyzed.

[0128] Data cleaning methods include outlier detection, noise filtering, etc., data fusion methods include Kalman filtering, Bayesian estimation, etc., and data analysis methods include statistical analysis, trend analysis, etc.

[0129] Through data processing, the adaptability of the current sensor deployment plan is evaluated.

[0130] If problems such as data quality degradation, increased transmission delay or abnormal energy consumption are found during the monitoring process, that is, the data quality index is lower than the preset threshold, the transmission delay exceeds the preset threshold or the energy consumption exceeds the preset threshold, the sensor deployment optimization process will be re-triggered and the sensor deployment will be dynamically adjusted to ensure the reliable operation of the monitoring system.

[0131] Continuously track the progress of tunnel construction and environmental changes, and trigger sensor deployment optimization at regular intervals (such as one week or one month) according to changes in monitoring needs. At the same time, if it is found that the sensor deployment does not meet the current needs during the monitoring process, sensor deployment optimization is also triggered. Through regular and irregular optimization, the sensor deployment plan can dynamically adapt to the characteristics of each stage of tunnel construction and provide continuous and effective monitoring services.

[0132] S102. In view of the complex and changeable wireless transmission environment inside the tunnel, an adaptive routing algorithm is used to dynamically select the optimal data transmission path. By evaluating the link quality in real time, signal attenuation and interference are predicted, and the transmission path with the strongest signal and the least attenuation is selected to ensure the stability and reliability of data transmission.

[0133] According to the wireless signal transmission environment parameters inside the tunnel, multiple real-time link quality evaluation indicators X such as signal strength S, signal-to-noise ratio SNR, bit error rate BER, etc. are obtained.

[0134] The acquired link quality assessment indicator data is preprocessed, including data cleaning, denoising, normalization, etc., to improve data quality.

[0135] The preprocessed link quality evaluation index data is input into the pre-built link quality prediction model based on the long short-term memory neural network (LSTM), and the predicted link quality value of each candidate transmission path in the future period of time is calculated by the model. It is expressed as follows:

[0136]

[0137] in, Indicates the link quality prediction value in the future period. Represents the link quality evaluation indicator data after preprocessing; represents the link quality evaluation index data from tn to time t; f LSTM Represents the prediction function of the LSTM model.

[0138] According to the predicted link quality value of each candidate transmission path, combined with the preset link quality threshold, the fuzzy analytic hierarchy process (FAHP) is used to comprehensively consider factors such as link bandwidth and delay to score the comprehensive transmission performance of each candidate transmission path and obtain the comprehensive score value of each candidate transmission path. It is expressed as follows:

[0139]

[0140] Among them, w1, w2 and w3 are weight coefficients, representing link bandwidth B, delay D and link quality prediction value respectively. The importance of.

[0141] The candidate transmission paths are sorted from high to low according to the comprehensive score, and the candidate transmission path with the highest comprehensive score is selected as the current optimal data transmission path. If the comprehensive score of the current optimal transmission path is lower than the preset path switching threshold (such as 6), the path switching mechanism is triggered, and the candidate transmission path with the second highest comprehensive score in the sorting result is selected as the new optimal data transmission path, and data transmission is switched to this path.

[0142] During data transmission, the transmission quality of the current link is continuously monitored to obtain real-time link quality parameters.

[0143] When the link quality drops below a preset threshold (such as a signal-to-noise ratio below 10dB) for more than a certain period of time (such as 5 seconds), the adaptive routing algorithm is re-triggered to update the link quality prediction model and dynamically select the optimal transmission path to ensure transmission stability.

[0144] In view of the wireless signal interference factors such as Doppler frequency shift and near-far effect in the tunnel, the minimum mean square error (LMS) adaptive filtering algorithm is used to adjust the equalization coefficient of the received signal in real time to reduce signal distortion, improve the signal-to-noise ratio, and enhance the reliability of data transmission. It is expressed as follows:

[0145] w(n+1)=w(n)+μ·e(n)·x(n)

[0146] Where w(n) is the equalization coefficient vector, μ is the step size parameter, e(n) is the error signal, and x(n) is the input signal vector;

[0147] The error signal is calculated as follows:

[0148] e(n)=d(n)-w T(n)·x(n)

[0149] Where d(n) is the desired signal.

[0150] S103. Deploy edge computing modules at sensor nodes to preprocess and compress the collected monitoring data, reduce the data transmission volume through data dimension reduction and feature extraction, alleviate data transmission pressure, and improve data transmission efficiency and real-time performance.

[0151] If the amount of monitoring data collected by the sensor node is large, the edge computing module is deployed at the sensor node to preprocess and compress the monitoring data. By reducing the dimension of the data and extracting features, the amount of data transmission can be reduced and the pressure of data transmission can be alleviated:

[0152] Firstly, according to the characteristics of the preprocessed monitoring data, principal component analysis is used to reduce the data dimension, extract the main features of the data, remove redundant information, and reduce the data dimension.

[0153] Then, the main features extracted by principal component analysis are used as input, and the data is compressed and encoded through the LZW lossless compression algorithm, which can minimize the amount of data transmission and improve data transmission efficiency while ensuring data integrity.

[0154] Among them, the LZW lossless compression algorithm takes the main features extracted by principal component analysis as input, compresses and encodes the data through the LZW lossless compression algorithm, and minimizes the amount of data transmission while ensuring data integrity, thereby improving data transmission efficiency. The dictionary size of the LZW compression algorithm is dynamically adjusted according to the CPU and memory resources of the edge computing module and the real-time requirements of the monitoring data.

[0155] According to the CPU and memory resources of the edge computing module and the real-time requirements of the monitoring data, the number of principal components of the principal component analysis and the dictionary size of the LZW compression algorithm are dynamically adjusted to minimize the amount of data transmission while meeting the real-time requirements.

[0156] If the features of the monitoring data are more complex, the convolutional neural network algorithm is used for feature extraction. The high-level features of the data are automatically learned through convolution and pooling operations to improve the effect of data dimensionality reduction and compression. It is expressed as follows:

[0157] O=σ(X*W+b)

[0158] Among them, X is the input data, W is the convolution kernel, b is the bias term, σ activation function, and O is the convolution layer output;

[0159] Pooling operation, P cnn is the pooling output:

[0160] P cnn=Pool(O)

[0161] Among them, Pool is a pooling function, such as maximum pooling or average pooling.

[0162] The high-level feature map extracted by the convolutional neural network is fused with the result of principal component analysis as the input of the LZW compression algorithm. It is expressed as follows:

[0163] F fusion =concat(X pca , P cnn )

[0164] Among them, X pca represents the data after dimensionality reduction, F fusion Represents fused data, and concat represents feature fusion operation.

[0165] According to the parameters of the sensor node such as network bandwidth, signal strength and transmission delay, the compression ratio, fragment size and number of retransmissions of data transmission are adaptively adjusted to improve the efficiency and real-time performance of data transmission while ensuring the reliability of data transmission.

[0166] Through task offloading and load balancing between the edge computing module and the cloud server, computing-intensive tasks such as data compression and feature extraction are offloaded to the cloud, reducing the processing pressure on the edge computing module.

[0167] At the same time, storage-intensive tasks such as data decompression and storage are performed on the edge side to reduce storage overhead in the cloud and improve the real-time and scalability of the overall system.

[0168] S104. Build a digital twin model of the tunnel environment and equipment status. Through real-time interaction of physical and virtual information, achieve comprehensive perception and dynamic tracking of the tunnel construction process, conduct fusion analysis of monitoring data, discover abnormal situations in a timely manner, and provide a basis for safety warnings.

[0169] By deploying various sensor devices at the tunnel construction site, physical information such as tunnel environmental parameters and equipment operating status can be collected in real time, while virtual information such as BIM models and construction schedules can be obtained.

[0170] The collected physical information is matched and associated with the virtual information to build a digital twin model of the tunnel construction process. The model contains multi-dimensional data such as the tunnel's three-dimensional geometry, environmental parameters, and equipment status.

[0171] Based on the constructed digital twin model, various monitoring data are integrated and analyzed.

[0172] The Kalman filter algorithm is used to set corresponding filter parameters according to the specific frequency range of the tunnel environment to remove noise from the monitoring data.

[0173] At the same time, key features in the data, such as temperature, humidity, vibration frequency, etc., are extracted and enhanced to provide more effective data support for subsequent analysis.

[0174] Association rule mining algorithms, such as the Apriori algorithm, are used to analyze the association between tunnel environmental parameters and equipment status. Frequently occurring parameter combination patterns are mined by setting support and confidence thresholds to build a tunnel safety status assessment model.

[0175] If the monitoring data exceeds the preset threshold, it is judged as an abnormal situation. According to the tunnel construction schedule, the digital twin model is dynamically updated to achieve real-time tracking of the tunnel construction process.

[0176] Through 3D visualization technology, the construction status and environmental information of the tunnel can be displayed intuitively, providing intuitive decision-making support for construction managers.

[0177] When abnormal situations are found in monitoring data analysis, the early warning mechanism is triggered in time.

[0178] Decision tree algorithms, such as the ID3 algorithm, are used to comprehensively consider the type, severity, duration and other characteristics of abnormal situations, and automatically generate corresponding emergency plans through the branch structure of the decision tree. The emergency plan includes the handling process of abnormal situations, required resources, and personnel division of labor.

[0179] The early warning information and emergency plans will be pushed to relevant personnel in a timely manner, and notifications will be sent via SMS, email, etc. to assist in tunnel safety management.

[0180] At the same time, detailed information of the abnormal situation, such as abnormality type, occurrence time, location, etc., is fed back to the digital twin model, and the abnormal situation is displayed through the model's visual interface, providing an intuitive basis for subsequent construction decisions.

[0181] S105. Use incremental learning algorithms to perform online analysis of monitoring data, dynamically update anomaly detection models based on newly collected data samples, and adaptively adjust warning thresholds to improve the accuracy and real-time performance of anomaly identification and reduce false positives and missed positives.

[0182] Real-time monitoring data is obtained and used as new data samples for the support vector machine (SVM) incremental learning algorithm to dynamically update the parameters of the SVM anomaly detection model.

[0183] The updated SVM anomaly detection model is used to analyze the real-time monitoring data online to identify abnormal data. For the identified abnormal data, the statistical process control (SPC) method is used to adaptively adjust the warning threshold, and the accuracy of anomaly identification is improved by dynamically calculating new control limits. The control limits are calculated as follows:

[0184]

[0185] in, is the sample mean, s is the sample standard deviation, and k is the control limit coefficient (usually 3).

[0186] The adaptive adjustments are as follows:

[0187] New Threshold=AdjustT hreshold(UCL,LCL,X new )

[0188] Among them, X new is real-time monitoring data, X new ={x1, x2, ..., x new}.

[0189] The confusion matrix is ​​used to evaluate the anomaly recognition performance of the adjusted warning threshold. If both the accuracy and recall rate meet the requirements, the adjusted warning threshold is applied to subsequent anomaly detection. If the performance still does not meet the requirements, the samples with incorrect recognition are added to the incremental training set of the SVM, and the anomaly detection model is further optimized through incremental learning. The confusion matrix is ​​expressed as follows:

[0190]

[0191] Among them, TP is a true positive example, FP is a false positive example, FN is a false negative example, and TN is a true negative example.

[0192] The above steps are continuously iterated to continuously improve the performance of the anomaly detection system until it meets the actual application requirements. Finally, the optimized anomaly detection system is deployed in the production environment to perform online anomaly analysis and early warning on real-time monitoring data, timely discover and handle abnormal situations in the production process, and ensure production safety and product quality.

[0193] S106. Comprehensively apply multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations. Through data redundancy and complementarity, the reliability and accuracy of monitoring data can be improved to achieve a comprehensive assessment of the safety status of tunnel construction.

[0194] The monitoring data collected by tunnel sensors of different types and locations are obtained. Data cleaning techniques such as median filtering and outlier detection are used to remove noise and outliers in the monitoring data, thereby improving the quality and reliability of the monitoring data.

[0195] Conduct exploratory analysis on the cleaned data, understand the distribution characteristics, trend changes, etc. of the data through visualization and other means, and perform feature selection and feature extraction to lay the foundation for subsequent analysis.

[0196] The correlation between monitoring data from different sensors is analyzed using indicators such as the Pearson correlation coefficient, and association rule mining algorithms such as Apriori are used to discover frequent patterns and association rules in the monitoring data and mine the implicit security status characteristics in the data.

[0197] According to the results of correlation analysis and association rule mining, machine learning algorithms such as decision trees or support vector machines are selected to establish a tunnel construction safety status assessment model. The model is trained and optimized using methods such as cross-validation to improve the accuracy and reliability of safety status assessment.

[0198] The monitoring data of different sensors are normalized to eliminate dimensional differences, and then data fusion methods such as weighted average or principal component analysis are used to fuse multi-source heterogeneous data. Through fusion, the complementarity of different sensor data can be fully utilized, the uncertainty of single sensor data can be reduced, and more comprehensive and reliable tunnel safety status information can be obtained. On the basis of fusion, the Kalman filter algorithm is used to further process the data and dynamically track the changing trend of the tunnel safety status. Through Kalman filtering, the safety status can be predicted and updated in real time, providing a basis for timely warning.

[0199] Based on historical data and expert experience, the threshold of tunnel construction safety status is preset. The output of the safety status assessment model is compared with the preset threshold. When the assessment result exceeds the threshold, the early warning mechanism is triggered. The early warning information can be sent to relevant personnel in a timely manner through SMS, email, etc., prompting them to check and deal with safety hazards.

[0200] During the entire safety status assessment process, data visualization technologies such as dashboards and heat maps are used to intuitively display the real-time safety status of the tunnel, sensor data change trends, early warning information, etc., to provide intuitive decision support for managers. Visualization should be used throughout data analysis, modeling, early warning and other links to improve the availability and interpretability of the system. Through visualization, managers can fully grasp the safety dynamics of tunnel construction, detect anomalies in a timely manner, and make correct decisions.

[0201] S107. Establish a data processing architecture based on cloud-edge collaboration to store and calculate massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of data, and the cloud side is responsible for long-term storage and mining of data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

[0202] According to the characteristics of monitoring data of tunnel construction, a cloud-edge collaborative data processing architecture is designed to reasonably divide the responsibilities of the cloud and edge sides.

[0203] A real-time data processing module is deployed on the edge side, Apache Flink is used to clean and filter the collected monitoring data, and Kafka is used to process data streams and extract key features.

[0204] Through the preset threshold rules, it is determined whether there is an abnormal situation. If an abnormality is detected, an early warning is triggered immediately, and the relevant data is uploaded to the cloud through the MQTT protocol for further analysis.

[0205] The cloud receives monitoring data uploaded by the edge side, combines historical data with expert knowledge base, and uses the isolation forest algorithm to detect anomalies in the data, identify hidden abnormal patterns and trends, and assess the safety risks of tunnel construction.

[0206] According to the results of cloud analysis, the optimized data processing parameters are sent to the edge side through RESTful API, and the real-time data analysis strategy is dynamically adjusted to improve the efficiency and accuracy of edge computing.

[0207] Using visualization tools such as Grafana, the analysis results in the cloud are presented to users in the form of intuitive dashboards to assist in safety decision-making at the construction site.

[0208] InfluxDB time series database is used to store massive monitoring data, and its high-performance writing and query capabilities are used to achieve efficient management and retrieval of monitoring data. At the same time, Prometheus is used for real-time data collection and monitoring to ensure the reliability of the data processing process.

[0209] Through cloud-edge collaborative architecture design, combined with stream data processing, machine learning algorithms, and time series database technologies, we have built an efficient, intelligent, and reliable tunnel construction safety monitoring data processing system. The system can detect abnormal situations in a timely manner, provide accurate safety warnings, and provide visual decision support for construction sites to ensure the safety and efficiency of tunnel construction.

[0210] Specifically, the edge side is responsible for real-time data processing, cleaning, filtering, and preliminary anomaly detection to ensure the real-time and initial accuracy of the data.

[0211] The cloud is responsible for in-depth data analysis, historical data management, expert knowledge base application and optimization parameter distribution, providing advanced analysis and decision support.

[0212] Data visualization uses tools such as Grafana to present analysis results to users in an intuitive form, assisting safety decisions at construction sites.

[0213] InfluxDB and Prometheus are used for data storage and monitoring to achieve efficient management and real-time monitoring of monitoring data and ensure the reliability of the data processing process.

[0214] To better implement the above method, Figure 4 As shown, the present invention also proposes a safe step distance monitoring system for tunnel safety, the system comprising:

[0215] The sensor layout optimization module 101 is used to dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes, design a multi-objective optimization algorithm according to the layout position, layout quantity and data transmission path of the sensors, and adjust the sensor layout in real time according to the optimization results;

[0216] The adaptive routing module 102 is used to dynamically select the optimal data transmission path based on the optimized sensor layout and the wireless transmission environment between the edge sensor device inside the tunnel and the server by using an adaptive routing algorithm, and to select the transmission path with the strongest signal and the smallest attenuation by real-time evaluation of the transmission link quality, prediction of signal attenuation and interference, so as to ensure the stability and reliability of data transmission;

[0217] The edge computing module 103 is used to deploy the edge computing module at the sensor edge node, pre-process and compress the collected monitoring data in the edge computing module, and perform data dimension reduction and feature extraction;

[0218] The digital twin module 104 is used to construct a digital twin model of the tunnel environment and equipment status based on the extracted features, realize comprehensive perception and dynamic tracking of the tunnel construction process through real-time interaction of physical information and virtual information, perform fusion analysis on the monitoring data of the sensors, timely discover abnormal situations, and provide a basis for safety warning;

[0219] The incremental learning module 105 is used to use the incremental learning algorithm to perform online analysis on the monitoring data of the sensor based on the detection data of the sensor used in the digital twin model, dynamically update the anomaly detection model based on the newly collected data samples, and adaptively adjust the warning threshold;

[0220] The safety assessment module 106 is used to comprehensively use multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations in the tunnel, and comprehensively assess the safety status of tunnel construction through data redundancy and complementarity;

[0221] The cloud-edge collaboration module 107 is used to establish a data processing architecture based on cloud-edge collaboration, which stores and calculates massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of the data, and the cloud side is responsible for long-term storage and mining of the data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

[0222] In summary, the data collection and layout optimization of the present invention provides a basic data source for the system to ensure the comprehensiveness and reliability of the data. Based on the optimized layout scheme, the stability and reliability of data transmission are ensured. The data is preprocessed and compressed at the edge node to reduce the amount of data transmission and improve system efficiency. Using the preprocessed data, a digital twin model is constructed to achieve comprehensive perception and dynamic tracking. Based on the digital twin model, online analysis and anomaly detection are performed to improve the accuracy and real-time nature of early warning. Multi-sensor data is integrated to improve the reliability and accuracy of monitoring data. Through cloud-edge collaboration, efficient data management and utilization are achieved to ensure the overall performance and reliability of the system. Through the organic combination of these steps, an efficient, accurate and reliable tunnel safety step monitoring system is formed, which provides comprehensive technical support for tunnel construction safety.

[0223] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0224] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the current technical solution. The current computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0225] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are orientations or positional relationships that are commonly placed when the current invention products are used. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0226] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A safe step distance monitoring method for tunnel safety, characterized in that: The method comprises: S1. Dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes, design a multi-objective optimization algorithm based on the sensor layout location, layout quantity, and data transmission path, and adjust the sensor layout in real time according to the optimization results; S2. Based on the optimized sensor layout, an adaptive routing algorithm is used to dynamically select the optimal data transmission path for the wireless transmission environment between the edge sensor devices and the server inside the tunnel. By real-time evaluation of the transmission link quality, signal attenuation and interference are predicted, and the transmission path with the strongest signal and the smallest attenuation is selected to ensure the stability and reliability of data transmission. S3. Deploy an edge computing module at the sensor edge node, preprocess and compress the collected monitoring data in the edge computing module, and perform data dimension reduction and feature extraction; S4. Build a digital twin model of the tunnel environment and equipment status based on the extracted features. Through real-time interaction between physical and virtual information, realize comprehensive perception and dynamic tracking of the tunnel construction process, conduct fusion analysis of the sensor monitoring data, discover abnormal situations in time, and provide a basis for safety warning. S5. Based on the detection data of the sensors used in the digital twin model, an incremental learning algorithm is used to analyze the monitoring data of the sensors online, and the anomaly detection model is dynamically updated based on the newly collected data samples, and the warning threshold is adaptively adjusted; S6. Comprehensively apply multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations in the tunnel, and comprehensively evaluate the safety status of tunnel construction through data redundancy and complementarity; S7. Establish a data processing architecture based on cloud-edge collaboration to store and calculate massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of data, and the cloud side is responsible for long-term storage and mining of data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

2. A tunnel safety-oriented safety step distance monitoring method according to claim 1, characterized in that: Said S1 specifically includes: Obtain tunnel construction progress and environmental change data, and determine whether the sensor deployment plan needs to be adjusted based on preset evaluation rules. The evaluation rules are whether the preset indicators exceed the preset thresholds. If adjustment is needed, the dynamic optimization process of the sensor deployment plan is triggered; Through a multi-objective optimization algorithm, a sensor deployment optimization model is established with sensor deployment location, deployment quantity and data transmission path as optimization variables, and monitoring data quality, transmission delay and energy consumption as optimization targets. The particle swarm optimization algorithm is used to solve the sensor layout optimization model and output the optimized sensor layout plan. According to the optimized sensor layout plan, the sensor layout adjustment instructions are generated and sent to the corresponding sensor nodes: For sensor nodes that need to be added, control their deployment and data collection behavior; For the sensor nodes that need to be removed, control them to stop data collection and remove them from the network; For sensor nodes that need to adjust their positions or data transmission paths, control them to perform corresponding adjustment operations; During the tunnel construction and environmental monitoring process, the monitoring data collected by each sensor node is obtained, the data is cleared and processed, and the adaptability of the current sensor deployment plan is evaluated through data processing: If the data quality is found to be degraded, the transmission delay is increased, or the energy consumption is abnormal during the monitoring process, that is, the data quality index is lower than the preset threshold, the transmission delay exceeds the preset threshold, or the energy consumption exceeds the preset threshold, the sensor deployment optimization process is re-triggered to dynamically adjust the sensor deployment to ensure the reliable operation of the monitoring system; Track the progress of tunnel construction and environmental changes, and trigger sensor deployment optimization at regular intervals according to changes in monitoring needs. At the same time, if it is found during the monitoring process that the sensor deployment does not meet the current needs, it will also trigger sensor deployment optimization; Dynamically adapt the sensor deployment plan through regular and irregular optimization.

3. A tunnel safety-oriented safety step distance monitoring method according to claim 2, characterized in that: The steps of the particle swarm optimization algorithm include: Initialize the particle swarm, calculate the fitness of each particle, and update the position and velocity of the particle until the termination condition is met; The particle position vector represents the sensor deployment scheme, and the fitness function is designed according to the optimization goal; Through iterative optimization, the optimal sensor deployment plan that meets monitoring needs is obtained, and the deployment location, deployment quantity and data transmission path of each sensor are determined.

4. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S2 specifically includes: According to the wireless signal transmission environment parameters inside the tunnel, multiple real-time link quality evaluation indicators are obtained; Preprocess the acquired link quality assessment indicator data to improve data quality; The preprocessed link quality evaluation index data is input into a pre-built link quality prediction model based on a long short-term memory neural network, and the predicted link quality value of each candidate transmission path in the future period of time is calculated by the model; According to the predicted link quality value of each candidate transmission path, combined with the preset link quality threshold, the fuzzy hierarchical analysis method is used to comprehensively consider the link bandwidth and delay, and the comprehensive transmission performance of each candidate transmission path is scored to obtain the comprehensive score value of each candidate transmission path; Sort the candidate transmission paths from high to low according to the comprehensive score, and select the candidate transmission path with the highest comprehensive score as the current optimal data transmission path; If the comprehensive score of the current optimal transmission path is lower than the preset path switching threshold, the path switching mechanism is triggered, and the candidate transmission path with the second highest comprehensive score in the sorting result is selected as the new optimal data transmission path, and data transmission is switched to this path; During data transmission, monitor the transmission quality of the current link and obtain real-time link quality parameters: When the link quality drops below the preset threshold for more than a certain period of time, the adaptive routing algorithm is re-triggered to update the link quality prediction model and dynamically select the optimal transmission path; At the same time, in response to the wireless signal interference factors inside the tunnel, the minimum mean square error adaptive filtering algorithm is used to adjust the equalization coefficient of the received signal in real time.

5. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S3 specifically includes: If the amount of monitoring data collected by the sensor node is large, the edge computing module is deployed at the sensor node to preprocess and compress the monitoring data, and reduce the amount of data transmission through data dimension reduction and feature extraction: First, according to the characteristics of the preprocessed monitoring data, the principal component analysis method is used to reduce the data dimension, extract the main features of the data, remove redundant information, and reduce the data dimension; Then, the main features extracted by principal component analysis are used as input, and the data is compressed and encoded using the LZW lossless compression algorithm; According to the CPU and memory resources of the edge computing module and the real-time requirements of the monitoring data, the number of principal components of the principal component analysis and the dictionary size of the LZW compression algorithm are dynamically adjusted to minimize the amount of data transmission while meeting the real-time requirements; If the features of the monitoring data are more complex, the convolutional neural network algorithm is used for feature extraction. The high-level features of the data are automatically learned through convolution and pooling operations to improve the effect of data dimensionality reduction and compression: The high-level feature map extracted by the convolutional neural network is fused with the results of principal component analysis as the input of the LZW compression algorithm; Adaptively adjust the data transmission compression ratio, fragment size and retransmission times according to the parameters of the sensor node; Through task offloading and load balancing between the edge computing module and the cloud server, computing-intensive tasks are offloaded to the cloud, reducing the processing pressure of the edge computing module. At the same time, storage-intensive tasks are performed on the edge side to reduce storage overhead in the cloud and improve the real-time and scalability of the overall system.

6. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S4 specifically includes: By deploying various sensor devices at the tunnel construction site, the physical information of the tunnel is collected in real time, and the virtual information of the tunnel is obtained at the same time; Matching and associating the collected physical information with the virtual information to construct a digital twin model of the tunnel construction process; wherein the digital twin model includes the three-dimensional geometric information, environmental parameters and equipment status of the tunnel; Based on the constructed digital twin model, various monitoring data are integrated and analyzed; The Kalman filter algorithm is used to set corresponding filter parameters according to the specific frequency range of the tunnel environment to remove noise from the monitoring data. At the same time, key features in the data are extracted and enhanced. The association rules mining algorithm is used to analyze the association rules between tunnel environmental parameters and equipment status; By setting support and confidence thresholds, frequently occurring parameter combination patterns are mined and a tunnel safety status assessment model is constructed; if the monitoring data exceeds the preset threshold, it is judged as an abnormal situation; According to the tunnel construction schedule, the digital twin model is dynamically updated to achieve real-time tracking of the tunnel construction process; Through 3D visualization technology, the construction status and environmental information of the tunnel can be directly displayed, providing intuitive decision-making support for construction managers; When abnormal conditions are found in monitoring data analysis, the early warning mechanism is triggered in time; Adopting the decision tree algorithm, through the branch structure of the decision tree, the corresponding emergency plan is automatically generated according to the early warning mechanism; wherein, the emergency plan includes the handling process of abnormal situations, the required resources and the division of labor of personnel; Early warning information and emergency plans are pushed to relevant personnel in a timely manner to assist in tunnel safety management. At the same time, detailed information of abnormal situations is fed back to the digital twin model, and the abnormal situations are displayed through the model's visual interface, providing an intuitive basis for subsequent construction decisions.

7. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S5 specifically includes: Acquire real-time monitoring data and use the real-time monitoring data as new data samples for the support vector machine incremental learning algorithm to dynamically update the parameters of the SVM anomaly detection model; Use the updated SVM anomaly detection model to analyze the real-time monitoring data online and identify abnormal data; For the identified abnormal data, the statistical process control method is used to adaptively adjust the warning threshold, and the accuracy of abnormal identification is improved by dynamically calculating new control limits; The confusion matrix is ​​used to evaluate the anomaly recognition performance of the adjusted warning threshold. If both the precision and recall rates meet the requirements, the adjusted warning threshold is applied to subsequent anomaly detection. If the performance still does not meet the requirements, the samples with identification errors are added to the incremental training set of SVM, and the anomaly detection model is optimized through incremental learning; Iterate the above steps to continuously improve the performance of the anomaly detection system until it meets actual application requirements; Finally, the optimized SVM anomaly detection model is deployed in the production environment to perform online anomaly analysis and early warning on real-time monitoring data, so as to timely discover and handle abnormal situations in the production process.

8. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S6 specifically includes: Acquire monitoring data collected by tunnel sensors of different types and locations, and clean the acquired monitoring data; Conduct exploratory analysis on the cleaned data, analyze the distribution characteristics and trend changes of the data through visualization and other methods, and perform feature selection and feature extraction; Analyze the correlation between monitoring data from different sensors, and use association rule mining algorithms to discover frequent patterns and association rules in monitoring data, and mine the hidden security status characteristics in the data; According to the results of correlation analysis and association rule mining, a machine learning algorithm such as decision tree or support vector machine is selected to establish a tunnel construction safety status assessment model, and the model is trained and optimized using methods such as cross-validation. Normalize the monitoring data from different sensors and then fuse multi-source heterogeneous data; On the basis of fusion, the Kalman filter algorithm is used to process the data and dynamically track the changing trend of the tunnel safety status; Preset thresholds for tunnel construction safety status based on historical data and expert experience; Compare the output of the security status assessment model with the preset threshold. When the assessment result exceeds the threshold, trigger the early warning mechanism. Send early warning information to relevant personnel, prompting them to check and deal with safety hazards.

9. The method for monitoring safe step distance for tunnel safety according to claim 1, characterized in that: The S7 specifically includes: Based on the characteristics of monitoring data from tunnel construction, a cloud-edge collaborative data processing architecture is designed to divide the responsibilities of the cloud and edge sides; Deploy a real-time data processing module on the edge side, use Apache Flink to clean and filter the collected monitoring data, and use Kafka to process data streams and extract key features; Through the preset threshold rules, determine whether there is an abnormal situation: if an abnormality is detected, an early warning is triggered immediately, and the relevant data is uploaded to the cloud through the MQTT protocol for further analysis; The cloud receives monitoring data uploaded by the edge side, combines historical data with expert knowledge base, and uses the isolation forest algorithm to detect anomalies in the data, identify hidden abnormal patterns and trends, and assess the safety risks of tunnel construction; According to the results of cloud analysis, the optimized data processing parameters are sent to the edge side through RESTful API, and the real-time data analysis strategy is dynamically adjusted to improve the efficiency and accuracy of edge computing; Using visualization tools, the analysis results in the cloud are presented to users in the form of dashboards to assist safety decision-making at the construction site; InfluxDB time series database is used to store massive monitoring data. Writing and querying capabilities are used to achieve efficient management and retrieval of monitoring data. Prometheus is used for real-time data collection and monitoring.

10. A safety step distance monitoring system for tunnel safety, characterized in that: The system comprises: The sensor layout optimization module is used to dynamically optimize the sensor layout plan according to the tunnel construction progress and environmental changes. It designs a multi-objective optimization algorithm based on the sensor layout location, layout quantity, and data transmission path, and adjusts the sensor layout in real time according to the optimization results. The adaptive routing module is used to dynamically select the optimal data transmission path based on the optimized sensor layout and the wireless transmission environment between the edge sensor devices inside the tunnel and the server using an adaptive routing algorithm. It evaluates the transmission link quality in real time, predicts signal attenuation and interference, and selects the transmission path with the strongest signal and the least attenuation to ensure the stability and reliability of data transmission. An edge computing module is used to deploy the edge computing module at the edge node of the sensor, pre-process and compress the collected monitoring data in the edge computing module, and perform data dimension reduction and feature extraction; The digital twin module is used to build a digital twin model of the tunnel environment and equipment status based on the extracted features. Through the real-time interaction of physical and virtual information, it can realize the comprehensive perception and dynamic tracking of the tunnel construction process, conduct fusion analysis on the monitoring data of the sensors, timely discover abnormal situations, and provide a basis for safety warning; The incremental learning module is used to analyze the sensor monitoring data online based on the detection data of the sensor used in the digital twin model using an incremental learning algorithm, dynamically update the anomaly detection model based on the newly collected data samples, and adaptively adjust the warning threshold; The safety assessment module is used to comprehensively apply multi-sensor data fusion technology to correlate and analyze sensor data of different types and locations in the tunnel, and comprehensively evaluate the safety status of tunnel construction through data redundancy and complementarity; The cloud-edge collaboration module is used to establish a data processing architecture based on cloud-edge collaboration, which stores and calculates massive monitoring data in layers. The edge side is responsible for real-time processing and analysis of data, and the cloud side is responsible for long-term storage and mining of data. Through cloud-edge collaboration, efficient management and utilization of monitoring data can be achieved, providing comprehensive data support for tunnel construction safety.

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