An intelligent safety step distance monitoring method and system for tunnel construction safety control

Abstract

This invention relates to the field of tunnel safety monitoring technology, and more particularly to an intelligent safety step distance monitoring method and system for tunnel construction safety control. The system includes a sensor deployment module, a data acquisition and transmission module, a safe step distance intelligent analysis and early warning module, and a dynamic control module. This safety step distance monitoring method and system for tunnel safety, by deploying multiple high-precision sensors at key locations in the tunnel and utilizing advanced data transmission and intelligent analysis technologies, can acquire key information such as the safe step distance and the stress state of the surrounding rock in real time and accurately. Once the safe step distance approaches or exceeds a reasonable range, the system immediately triggers multi-channel early warnings, enabling construction personnel to be aware of potential dangers at the first moment, thereby taking timely countermeasures and effectively avoiding serious safety accidents such as tunnel collapses, greatly protecting the lives of construction personnel.

Description

Technical Field

[0001] This invention relates to the field of tunnel safety monitoring technology, specifically to an intelligent safety step distance monitoring method and system for tunnel construction safety control. Background Technology

[0002] The starting point of tunnel construction safety step distance management is to ensure construction safety and prevent casualties caused by tunnel collapse. The core of step distance management is to control the distance between the tunnel face and the invert arch and secondary lining, and to control the cycle excavation length.

[0003] The safe step distance mainly refers to the reasonable spacing between the working face and the completed support structure behind it (such as secondary lining, invert arch, etc.). Traditional methods for monitoring the safe step distance have many significant drawbacks. On the one hand, they mainly rely on manual periodic measurements and experience-based judgment. Manual measurement is not only extremely inefficient, requiring a large amount of manpower and time, but the accuracy of the measurement results is also easily affected by human factors, such as the operator's skill level, fatigue, and the limitations of the measuring tools. On the other hand, experience-based judgment lacks scientific quantitative basis and is difficult to adapt to complex and changing geological conditions and construction conditions. Under complex geological conditions, the mechanical properties of the surrounding rock change rapidly and are difficult to predict. Traditional methods cannot capture the dynamic changes in the safe step distance in a timely manner, resulting in the inability to issue effective early warnings when the actual safe step distance exceeds the reasonable range.

[0004] This situation could easily trigger a series of serious safety accidents, such as tunnel collapses and large deformations of the surrounding rock, posing a direct and significant threat to the lives of construction workers. It would also lead to severe delays in the project schedule and substantial economic losses, including increased repair costs, compensation for delays, and waste of equipment and materials. Furthermore, it could cause adverse social impacts, such as traffic congestion (if a traffic tunnel is involved), environmental damage, and a crisis of public trust in the project.

[0005] There is an urgent need in the field of tunnel construction for an intelligent method for monitoring safe step distance, which can monitor changes in safe step distance in real time and accurately, and provide timely and effective early warning of potential safety risks. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent safety step distance monitoring method and system for tunnel construction safety control, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent safety step distance monitoring system for tunnel construction safety control, comprising a sensor layout module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module, and a dynamic control module.

[0008] The sensor arrangement module includes a laser rangefinder installed at the tunnel face to measure the distance between the tunnel face and the structure behind it, a displacement sensor installed on the lining trolley and the invert arch construction equipment to monitor their position changes, and a stress-strain sensor arranged in the surrounding rock of the tunnel to monitor the stress state of the surrounding rock. Each sensor is connected to a data acquisition terminal via wired or wireless means.

[0009] The data acquisition and transmission module, the data acquisition terminal is used to receive data collected by the sensor, and after preliminary processing and integration of the data, it uses 4G / 5G communication technology to transmit the data to the cloud server in real time.

[0010] The intelligent analysis and early warning module for safe step distance has a built-in data analysis model on the cloud server that comprehensively considers factors such as tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model analyzes the received data to calculate the reasonable safe step distance range under the current construction state and compares it with the actual monitoring data. When the actual safe step distance is close to or exceeds the reasonable range, an early warning mechanism is automatically triggered, including audible and visual alarms at the construction site and pushes early warning information to the construction management personnel's mobile APP. The early warning information includes the warning location, step distance deviation, and possible risk level.

[0011] The dynamic control module can automatically adjust the construction progress when the safe step distance is exceeded, such as reducing the tunneling speed at the face, prioritizing the construction of secondary lining or invert arch, and combining the surrounding rock stress and strain monitoring data to automatically provide suggestions for optimizing support parameters when the stability of the surrounding rock is affected by the safe step distance, including increasing the length of anchor bolts and increasing the spacing of steel arch frames, and generating construction guidance plans for construction personnel to carry out support reinforcement operations.

[0012] Preferably, in the sensor arrangement module, the installation position of the laser rangefinder at the tunnel face should ensure that it can comprehensively and accurately measure the distance to the structure behind, with a measurement accuracy of not less than ±1 mm; the installation of the displacement sensor on the lining trolley and the invert arch construction equipment should be located at key nodes, with a measurement accuracy of not less than 0.001 mm; the arrangement of the stress and strain sensor in the tunnel surrounding rock should be selected at representative locations according to the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during construction.

[0013] Preferably, in the data acquisition and transmission module, the data acquisition terminal performs preliminary processing and integration of the data, including removing outliers and noise interference, and uses encryption technology to ensure data security and prevent data leakage or tampering when transmitting data using 4G / 5G communication technology.

[0014] Preferably, in the intelligent analysis and early warning module for safe step distance, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction techniques and support parameters, and the model can continuously learn and optimize itself based on new monitoring data to improve the accuracy of safe step distance calculation and the reliability of early warning.

[0015] Preferably, in the dynamic control module, the adjustment of the construction progress is achieved by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel, and during the implementation of dynamic control, the changes in the safe step distance and the surrounding rock condition are continuously monitored.

[0016] An intelligent safety step distance monitoring method for tunnel safety includes the following steps:

[0017] S1: Sensor installation and initialization steps. Based on the tunnel design drawings and geological survey report, determine the installation positions of the sensors in the tunnel face, secondary lining, invert arch and surrounding rock. Install the sensors according to the installation specifications and perform calibration and initialization settings, including setting the measurement accuracy and data acquisition frequency.

[0018] S2: Data acquisition and transmission steps: The sensor is activated to acquire data at a preset frequency and transmit it to the data acquisition terminal. The data acquisition terminal verifies and organizes the data and then transmits it to the cloud server via a 4G / 5G network. Encryption technology is used during the transmission process.

[0019] S3: Intelligent analysis and early warning steps. After receiving the data, the cloud server calls the data analysis model to process it, obtains a reasonable safe step range and compares it with the actual monitoring data. If the difference exceeds the preset threshold, the early warning program is triggered. Early warning information is sent through multiple channels and detailed information of the early warning event is recorded.

[0020] S4: Dynamic control steps. After receiving the early warning information, the construction management personnel organize the construction personnel to implement dynamic control measures according to the warning content and system suggestions. These measures include adjusting the construction schedule and carrying out operations according to the optimized support plan. During the implementation process, the safety step distance and changes in the surrounding rock condition are continuously monitored, and the monitoring data is fed back to the cloud server for re-analysis until the safety step distance meets the requirements and the surrounding rock remains stable.

[0021] Preferably, in the intelligent analysis and early warning step, the data analysis model first performs classification and feature extraction when processing data, and then performs comprehensive analysis and calculation in combination with tunnel-related parameter information.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] 1. This intelligent safety step distance monitoring method and system for tunnel construction safety control utilizes multiple high-precision sensors deployed at key locations within the tunnel and advanced data transmission and intelligent analysis technologies to acquire crucial information such as the safety step distance and the stress state of the surrounding rock in real time and accurately. Once the safety step distance approaches or exceeds a reasonable range, the system immediately triggers multi-channel early warnings, enabling construction personnel to be aware of potential dangers at the first moment and take timely countermeasures. This effectively prevents serious safety accidents such as tunnel collapses and greatly protects the lives of construction workers.

[0024] 2. This intelligent safety step distance monitoring method and system for tunnel construction safety control, combined with surrounding rock stress and strain monitoring data, automatically generates highly targeted and scientifically sound optimization suggestions for support parameters. Construction personnel can better adapt to changes in surrounding rock under complex geological conditions by following these suggestions for support reinforcement, enhancing the stability of the tunnel support structure, fundamentally reducing safety risks caused by insufficient support, and further improving the overall safety during tunnel construction. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a diagram of the safe step distance monitoring system architecture of the present invention;

[0027] Figure 2 This is a schematic diagram of the cyclic operation of the mechanized supporting equipment in the tunnel according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figures 1-2 The present invention provides a technical solution: an intelligent safety step distance monitoring system for tunnel construction safety control, comprising a sensor layout module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module, and a dynamic control module.

[0030] The sensor deployment module includes a laser rangefinder installed at the tunnel face to measure the distance between the tunnel face and the structure behind it, a displacement sensor installed on the lining trolley and invert construction equipment to monitor their position changes, and a stress-strain sensor installed in the surrounding rock of the tunnel to monitor the stress state of the surrounding rock. All sensors are connected to the data acquisition terminal via wired or wireless means.

[0031] In the sensor deployment module, the installation position of the laser rangefinder at the tunnel face should ensure that it can comprehensively and accurately measure the distance to the structure behind, with a measurement accuracy of not less than ±1 mm; the installation of the displacement sensor on the lining trolley and invert construction equipment should be located at key nodes, with a measurement accuracy of not less than 0.001 mm; the arrangement of the stress and strain sensors in the tunnel surrounding rock should be selected at representative locations based on the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during construction.

[0032] The data acquisition and transmission module, the data acquisition terminal, is used to receive data collected by sensors, and after preliminary processing and integration of the data, it uses 4G / 5G communication technology to transmit the data to the cloud server in real time.

[0033] In the data acquisition and transmission module, the data acquisition terminal performs preliminary processing and integration of the data, including removing outliers and noise interference. When transmitting data using 4G / 5G communication technology, encryption technology is used to ensure data security and prevent data leakage or tampering.

[0034] The intelligent analysis and early warning module for safe step distance has a built-in data analysis model on the cloud server that comprehensively considers factors such as tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model analyzes the received data to calculate the reasonable safe step distance range under the current construction state and compares it with the actual monitoring data. When the actual safe step distance is close to or exceeds the reasonable range, the early warning mechanism is automatically triggered. The early warning information includes the warning location, step distance deviation, and possible risk level.

[0035] Remove outliers from sensor-acquired data. For example, for laser rangefinder sensor data, if the difference between a measurement and a measurement at an adjacent time point exceeds a certain threshold (e.g., 5 meters, set based on experience), the value is considered an outlier and is deleted or corrected (using methods such as mean substitution).

[0036] For displacement sensor and stress-strain sensor data, a similar method is used, setting an outlier threshold based on the sensor's accuracy range and historical data fluctuations.

[0037] Data from different sensors is standardized to the same units and numerical range. For example, distance data (distance between the tunnel face and the structure behind) is normalized to the [0,1] interval using the following formula: Where x is the original data, x min and x max These are the minimum and maximum values ​​of this type of data, respectively, and x1 is the normalized data.

[0038] Geological condition parameterization:

[0039] Based on the geological survey report, geological factors such as rock type and the degree of joint and fracture development are quantified. For example, rock type is classified into soft rock (assigned value 1), hard rock (assigned value 3), and medium-strength rock (assigned value 2); the degree of joint and fracture development is classified into high (assigned value 0.8), medium (assigned value 0.5), and low (assigned value 0.2) based on indicators such as fracture density and width.

[0040] Calculate the comprehensive geological parameter G, assuming a weighted average method is used, such as G = 0.6 × rock type assignment + 0.4 × joint and fracture development degree assignment.

[0041] Construction process parameterization:

[0042] For the tunneling method, assign a value of 1 if it is drill-and-blast tunneling, and assign a value of 2 if it is shield tunneling.

[0043] The efficiency of the support installation process is quantified based on the time required for support installation per meter of tunnel. For example, if the installation time per meter of support is t hours, its reciprocal 1 / t can be calculated as the support installation efficiency parameter S.

[0044] Quantification of support parameters:

[0045] For anchor bolts, the support strength coefficient R is calculated using the following formula: Where L is the anchor length (meters), d is the anchor diameter (cm), n is the number of anchors per square meter, and k1 is an empirical coefficient (e.g., 0.5).

[0046] For steel arch frames, the formula for calculating their support stiffness coefficient R1 is as follows: Where I is the moment of inertia of the steel arch section (m) 4 ), where k is the spacing between steel arch frames (meters), and k2 is an empirical coefficient (e.g., 1.2).

[0047] Using a multiple linear regression model, we assume the relationship between the safety step size D and the above parameters is as follows:

[0048] D = a × G + b × s + c × R + d × R1 + e

[0049] Among them, a, b, c, d, and e are regression coefficients obtained through big data analysis and training.

[0050] The model was trained using a large amount of existing tunnel construction data (including actual values ​​of safe step distance and corresponding parameter values ​​under different geological conditions, construction techniques and support parameters). The regression coefficients were solved using methods such as the least squares method to minimize the sum of squared errors between the model-predicted safe step distance and the actual safe step distance.

[0051] A portion of known tunnel construction data (data not used in training) is used as a test set and input into the trained model. Evaluation metrics such as the root mean square error (RMSE) and mean absolute error (MAE) between the model's predicted safe step distance and the actual safe step distance are calculated.

[0052]

[0053] Where D 1,i D is the prediction safety step size for the i-th sample. 2,i is the actual safe step size for the i-th sample, and n is the number of test samples.

[0054] During tunnel construction, real-time sensor data is acquired and, after undergoing the aforementioned preprocessing, feature extraction, and parameterization steps, is input into a trained model to calculate the reasonable safe step distance D under the current construction condition. T .

[0055] D T The actual monitored safe step distance D R To make a comparison, if |D T -D R The warning mechanism will be triggered if the set threshold (e.g., 2 meters, set according to engineering experience and safety standards) is reached.

[0056] In the intelligent analysis and early warning module for safe step distance, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction techniques and support parameters. The model can continuously learn and optimize itself based on new monitoring data to improve the accuracy of safe step distance calculation and the reliability of early warning.

[0057] The dynamic control module can automatically adjust the construction progress when the safe step distance is exceeded, such as reducing the tunneling speed at the face, prioritizing the construction of secondary lining or invert arch, and combining the surrounding rock stress and strain monitoring data to automatically provide suggestions for optimizing support parameters when the stability of the surrounding rock is affected by the safe step distance, including increasing the length of anchor bolts and increasing the spacing of steel arch frames, and generating construction guidance plans for construction personnel to carry out support reinforcement operations.

[0058] In the dynamic control module, the construction progress is adjusted by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel. During the implementation of dynamic control, the changes in the safety step distance and the surrounding rock condition are continuously monitored.

[0059] Specific implementation steps:

[0060] Three laser rangefinders were evenly distributed at the working face to ensure comprehensive and accurate measurement of the distance between the working face and structures at different locations behind it. Displacement sensors were installed at key nodes of the lining trolley and invert construction equipment, for a total of five sensors. Stress-strain sensors were installed at ten representative locations within the surrounding rock, based on geological structure and stress distribution characteristics. After installation, all sensors underwent rigorous calibration and initialization. The measurement accuracy of the laser rangefinders was calibrated to ±0.05 meters, the accuracy of the displacement sensors was set to ±0.01 millimeters, and the measurement range of the stress-strain sensors was reasonably set according to the expected stress conditions of the surrounding rock. The data acquisition frequency was uniformly set to once every 5 minutes.

[0061] During construction, sensors continuously collect data at a set frequency and transmit the data wirelessly to a data acquisition terminal. The data acquisition terminal quickly processes the received data, removing outliers and noise interference, and then transmits the data in real-time to a cloud server using a 5G network. The data analysis model on the cloud server is trained and optimized based on the tunnel's geological conditions (e.g., the tunnel mainly traverses soft rock strata with low rock strength and well-developed joints and fissures), construction techniques (drill-and-blast excavation, combined anchor bolt and steel arch support, and concrete pouring for lining), and support parameters (anchor bolts are 3 meters long, 22 millimeters in diameter, and spaced 1 meter apart; steel arch spacing is 0.8 meters). When the model analysis detects that the actual safe step distance between the tunnel face and the secondary lining exceeds the reasonable range by more than 2 meters at a certain moment, the system immediately triggers an early warning.

[0062] "The safe step distance between the tunnel face and the secondary lining exceeds the reasonable range by 2 meters, located at tunnel kilometer marker K5+300. The risk level is medium. Please take immediate action." Based on the warning information, management personnel quickly issued instructions to reduce the tunneling speed at the tunnel face and to increase personnel and equipment to accelerate the secondary lining construction. Simultaneously, the system recommended increasing the anchor bolt length to 3.5 meters and reducing the spacing of the steel arch frames to 0.6 meters based on the surrounding rock stress-strain data. Construction personnel operated according to the optimized support plan, continuously monitoring the safe step distance and the surrounding rock condition during the operation, and feeding the monitoring data back to the cloud server for re-analysis every 30 minutes. After a period of adjustment and construction, the safe step distance gradually returned to the reasonable range, and the stability of the surrounding rock was effectively guaranteed, ensuring the safe and smooth progress of the tunnel construction.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent safety step distance monitoring system for tunnel construction safety control, comprising a sensor layout module, a data acquisition and transmission module, a safety step distance intelligent analysis and early warning module, and a dynamic control module, characterized in that: The sensor deployment module includes laser rangefinders installed at the tunnel face to measure the distance between the tunnel face and the structure behind it; displacement sensors installed on the lining trolley and invert construction equipment to monitor their position changes; and stress-strain sensors deployed within the tunnel surrounding rock to monitor the stress state of the surrounding rock. All sensors are connected to a data acquisition terminal via wired or wireless means. The distance data between the tunnel face and the structure behind it is normalized to the [0,1] interval using the following formula: ,in It is the raw data. and These are the minimum and maximum values ​​of this type of data, respectively. It is normalized data; The data acquisition and transmission module, the data acquisition terminal is used to receive data collected by the sensor, and after preliminary processing and integration of the data, it uses 4G / 5G communication technology to transmit the data to the cloud server in real time. The intelligent analysis and early warning module for safe step distance has a built-in data analysis model on the cloud server that comprehensively considers tunnel geological conditions, construction technology, and support parameters. Through big data analysis and machine learning algorithms, the model analyzes the received data to calculate the reasonable safe step distance range under the current construction state and compares it with the actual monitoring data. When the actual safe step distance exceeds the reasonable range, an early warning mechanism is automatically triggered, including audible and visual alarms at the construction site and pushes early warning information to the construction management personnel's mobile APP. The early warning information includes the warning location, step distance deviation, and possible risk level. The dynamic control module can automatically adjust the construction progress when the safe step distance exceeds the standard, reduce the tunneling speed at the face, prioritize the construction of secondary lining or invert arch, and combine the surrounding rock stress and strain monitoring data to automatically provide suggestions for optimizing support parameters when the stability of the surrounding rock is affected by the safe step distance, including increasing the length of anchor bolts, increasing the spacing of steel arch frames, and generating construction guidance plans for construction personnel to carry out support reinforcement operations. In the sensor arrangement module, the installation position of the laser rangefinder at the tunnel face should ensure that it can comprehensively and accurately measure the distance to the structure behind, with a measurement accuracy of not less than ±1 mm; the installation of the displacement sensor on the lining trolley and invert construction equipment should be located at key nodes, with a measurement accuracy of not less than 0.001 mm; the arrangement of the stress and strain sensor in the tunnel surrounding rock should be selected at representative locations based on the geological structure and stress distribution characteristics, and its measurement range should cover the stress and strain change range that may occur in the surrounding rock during construction. In the intelligent analysis and early warning module for safe step distance, the training data of the data analysis model comes from a large number of tunnel engineering cases with different geological conditions, construction technology and support parameters. The model can continuously learn and optimize itself based on new monitoring data to improve the accuracy of safe step distance calculation and the reliability of early warning. In the dynamic control module, the construction progress is adjusted by automatically controlling the operating parameters of the construction equipment and rationally allocating the work tasks of the construction personnel. During the implementation of dynamic control, the changes in the safe step distance and the surrounding rock condition are continuously monitored.

2. The intelligent safety step distance monitoring system for tunnel construction safety control according to claim 1, characterized in that: In the data acquisition and transmission module, the data acquisition terminal performs preliminary processing and integration of the data, including removing outliers and noise interference. When transmitting data using 4G / 5G communication technology, encryption technology is used to ensure data security and prevent data leakage or tampering.

3. The monitoring method of the intelligent safety step distance monitoring system for tunnel construction safety control according to claim 2, characterized in that, Includes the following steps: S1: Sensor installation and initialization steps. Based on the tunnel design drawings and geological survey report, determine the installation positions of the sensors in the tunnel face, secondary lining, invert arch and surrounding rock. Install the sensors according to the installation specifications and perform calibration and initialization settings, including setting the measurement accuracy and data acquisition frequency. S2: Data acquisition and transmission steps: The sensor is activated to acquire data at a preset frequency and transmit it to the data acquisition terminal. The data acquisition terminal verifies and organizes the data and then transmits it to the cloud server via a 4G / 5G network. Encryption technology is used during the transmission process. S3: Intelligent analysis and early warning steps. After receiving the data, the cloud server calls the data analysis model to process it, obtains a reasonable safe step range and compares it with the actual monitoring data. If the difference exceeds the preset threshold, the early warning program is triggered. Early warning information is sent through multiple channels and detailed information of the early warning event is recorded. S4: Dynamic control steps. After receiving the early warning information, construction management personnel organize construction workers to implement dynamic control measures based on the warning content and system suggestions. These measures include adjusting the construction schedule and operating according to the optimized support plan. During implementation, the safety step distance and changes in the surrounding rock condition are continuously monitored, and the monitoring data is fed back to the cloud server for re-analysis until the safety step distance meets the requirements and the surrounding rock remains stable. The calculation formula is: ; in These are regression coefficients obtained through big data analysis and training; The stiffness coefficient of the steel arch support is... This represents the anchor bolt support strength coefficient. To integrate geological parameters, The spacing between the steel arch frames is in meters. in ; It is the length of the anchor bolt, in meters; It is the diameter of the anchor bolt, in centimeters; It refers to the number of anchor bolts per square meter. It is an empirical coefficient; ,in It is the moment of inertia of the steel arch section. ; The spacing between the steel arch frames is in meters. It is an empirical coefficient.

4. The monitoring method of an intelligent safety step distance monitoring system for tunnel construction safety control according to claim 3, characterized in that: In the intelligent analysis and early warning steps, the data analysis model first classifies and extracts features when processing data, and then performs comprehensive analysis and calculation in combination with tunnel-related parameter information.

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