Supporting safety monitoring system for tunneling rock burst

Abstract

The invention discloses a supporting safety monitoring system for tunneling rockburst, which relates to the technical field of tunneling rockburst supporting safety monitoring and comprises a tunnel survey module, a rockburst analysis module, a prevention processing analysis module and a processing execution module. In the tunneling process, although an existing support safety monitoring system evaluates the rockburst risk through multiple monitoring means such as stress, deformation, vibration and micro-seismic monitoring, obvious defects still exist, including the problems of inaccurate high-risk area recognition, insufficient monitoring precision, unreasonable support demand distribution, untimely support system response and the like. According to the invention, after analysis is carried out through the rockburst analysis module and the prevention processing analysis module, processing is carried out through the processing execution module; the balanced distribution of pressure is realized, and the pressure or displacement abnormal condition is quickly responded; and by means of integrated analysis and automatic supporting adjustment, the efficiency and safety of tunnel stability maintenance are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel excavation rock burst support safety monitoring technology, in particular to a support safety monitoring system for tunnel excavation rock burst. Background Art

[0002] During tunnel excavation, although the existing support safety monitoring system uses a variety of monitoring methods such as stress, deformation, vibration, and microseismic monitoring to assess rockburst risks, it still has significant defects, including inaccurate identification of high-risk areas, lack of comprehensive analysis methods, difficulty in accurately identifying and dividing high-risk areas, resulting in uncertainty in safety assessment and insufficient monitoring accuracy. Traditional monitoring methods often rely on only a single indicator and cannot comprehensively assess rockburst risks, resulting in insufficient accuracy and reliability of monitoring data, unreasonable allocation of support requirements, lack of comprehensive analysis of initial stress and rock mass strength in each area, resulting in unreasonable allocation of support materials, affecting support effects, untimely response of the support system, insufficient flexibility in manually adjusting the support system, and inability to quickly respond to sudden pressure or displacement anomalies, increasing the complexity and risk of operations, etc. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned problem and to provide a support safety monitoring system for rock burst in tunnel excavation.

[0004] The object of the present invention can be achieved by the following technical scheme: a support safety monitoring system for rock burst in tunnel excavation, comprising a tunnel survey module, a rock burst analysis module and a prevention and treatment analysis module; the tunnel survey module is used to collect data from tunnels to be mined and tunnels that have been opened, and distinguish the data, marking the data corresponding to the tunnel to be mined as T1, and marking the data corresponding to the opened tunnel as T2;

[0005] The rockburst analysis module is used to perform rockburst analysis on tunnels before and after mining, specifically:

[0006] Analysis of T1: Standardization of stress, rock mass strength and microseismic activity data to make their values ​​on a uniform scale; feature extraction from the standardized data;

[0007] Extract features from stress data: peak stress and stress concentration coefficient; extract from microseismic data: microseismic event frequency and energy release; then divide the extracted stress features and microseismic features into high stress areas and microseismic high activity areas; perform correlation analysis on stress features and microseismic features to obtain the Pearson correlation coefficient r;

[0008] Analyze T2: Statistically calculate the changes in displacement data in the time series, observe whether there is an abnormal increase trend in displacement, calculate the average rate of displacement change, fit the displacement data series into a straight line, and use linear regression to calculate the trend coefficient of displacement data; then set a safety threshold for the displacement change rate. If the set displacement change rate exceeds the corresponding threshold, the displacement change is considered abnormal, and a processing signal 1 is generated; then analyze the change trend of stress and strain data to identify concentrated areas of high stress or high strain; calculate the stress change, perform linear regression on the stress data to obtain the trend coefficient of the strain structure data, compare the safety threshold for the stress change rate with the stress change rate, and if the stress change rate exceeds the corresponding safety threshold, it indicates the risk of stress concentration or sudden increase, and a processing signal 2 is generated;

[0009] The preventive processing and analysis module is used to process and analyze the marked area, specifically:

[0010] The initial stress, rock strength and microseismic activity data collected in each area are obtained for comprehensive analysis to obtain the casting volume coefficient; the generated processing signal 1 and processing signal 2 are analyzed to obtain the support pressure.

[0011] As a preferred embodiment of the present invention, it also includes a processing execution module; during the support process, the processing execution module automatically adjusts the support force according to the real-time monitored pressure and displacement to maintain the balanced stress distribution in each area; the processing execution module is also responsible for the dynamic adjustment of material filling and pouring pressure; during the pouring process, the processing execution module is also used to calculate the actual pouring volume V of the pouring area, and perform a comprehensive calculation based on the pouring volume coefficient JZZ, the cross-sectional area A of the tunnel, and the pouring pressure P1 of the pouring area, through the formula: Output the actual pouring volume V of the pouring area, where P1=β0×P2, P1 is the required pouring pressure, P2 is the collected actual pressure of the area, β0 is the pouring adjustment coefficient, and P0 is the reference pressure.

[0012] As a preferred embodiment of the present invention, the specific process of standardizing the stress, rock mass strength and microseismic activity data to make their values ​​on a unified scale is as follows:

[0013] The mean μ and standard deviation σ of each data point are calculated by the formula: Where x is a data point, N is the total number of data points, i = 1, 2, ..., N; then each data point is standardized: Each data point x i Normalized to z i .

[0014] As a preferred embodiment of the present invention, the specific process of extracting the features: peak stress and stress concentration factor from stress data, and extracting: microseismic event frequency and energy release from microseismic data is as follows:

[0015] The peak stress is calculated by finding the maximum value c in the stress data set c. max =max(c), the stress concentration factor is calculated by the ratio of the maximum principal stress to the compressive strength of the material, and the formula Output stress concentration factor SCF,c max is the calculated peak stress, C is the compressive strength of the material; the frequency of earthquake events is calculated by counting the number of microseismic events occurring within a certain period of time, using the formula: Output the microseismic event frequency WPL, H is the total number of microseismic events within the time interval Δt; energy release is calculated by calculating the energy release E of each microseismic event and summing it within a certain time period: From the formula: Output energy release ESF, s = 1, 2, ..., H, H is the total number of microseismic events.

[0016] As a preferred embodiment of the present invention, the specific process of dividing the extracted stress characteristics and microseismic characteristics into high stress areas and microseismic high activity areas is as follows:

[0017] In the model, the stress distribution map is determined to identify the area with high stress, which is divided into a high stress area; the frequency and energy release of microseismic events are counted and compared to obtain the area with frequent microseismic activities, which is divided into a microseismic high activity area; the high stress area and the microseismic high activity area are superimposed; if the two areas overlap highly, it indicates that the rock burst risk in this area is relatively high, and it is divided into a key attention activity area, a general attention activity area, or an ultra-key attention activity area. The division is judged by the correlation between the stress characteristics and the microseismic characteristics, and the correlation is calculated to obtain the Pearson correlation coefficient r. When r=1, it means a complete positive correlation, and it is divided into an ultra-key attention activity area, indicating that the stress characteristics and microseismic characteristics increase and decrease synchronously under certain circumstances; when r=-1, it means a complete negative correlation, and it is divided into a general attention activity area, indicating that when one feature increases, the other will decrease; when r=0, it means no correlation, and it is divided into a key attention activity area, indicating that there is no obvious relationship between the two.

[0018] As a preferred embodiment of the present invention, the specific process of obtaining the initially collected stress, rock mass strength and microseismic activity data in each area and performing comprehensive analysis to obtain the casting volume coefficient is as follows:

[0019] By formula: The output calculation results in the casting volume coefficient JZZ, c' is the initial stress value, S is the acquired rock mass strength, f is the frequency of microseismic activity, E is the energy release, ω1, ω2 and ω3 are the preset weight coefficients, Indicates the relative pressure on the rock mass.

[0020] As a preferred embodiment of the present invention, the specific process of analyzing the generated processing signal 1 and the processed signal 2 to obtain the support pressure is:

[0021] Analyze the processing signal 1: obtain the initial position data of the mark, calculate the distance between the mobile support and the position corresponding to the processing signal 1; the processing execution module moves the support system to the specified position through the positioning module; when the support system is in place, apply initial pressure, according to the pressure demand and balance analysis of each area of ​​the support, by the formula: P 支护 =α×P 监测 , output support pressure P 支护 , P 监测 is the monitored regional pressure, and α is the control coefficient; if the pressure or displacement is found to exceed the target range during the support process, the support system controlled by the processing execution module will adjust the support force;

[0022] Analyze processing signal 2: Same as processing signal 1, move the support system to the specified position; when the support system is in place, apply initial pressure first; based on the pressure demand and balance analysis of each support area, the hydraulic support system will adjust the support force.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention conducts a comprehensive analysis of tunnel stress, rock mass strength and microseismic activity through standardized processing, feature extraction and regional division of the rockburst analysis module, thereby achieving accurate identification of high-risk areas. This module not only evaluates important indicators such as peak stress, stress concentration factor, microseismic frequency and energy release, but also determines the rockburst risk level in different areas through correlation analysis, providing a scientific basis for tunnel safety assessment. At the same time, by detecting and generating processing signals using displacement and stress change trends, early warning can be given before risks rise and processing measures can be triggered, effectively ensuring the long-term stability and safety of the tunnel structure.

[0025] 2. The present invention calculates the casting volume coefficient JZZ through the preventive treatment analysis module, comprehensively analyzes the initial stress, rock mass strength and microseismic activity in each area, provides support requirements adapted to the risk level, and ensures the reasonable allocation of support materials. At the same time, the module automatically analyzes the generated processing signals, uses the positioning module to accurately move the support system into place and dynamically adjust the support force to achieve a balanced distribution of pressure and quickly respond to abnormal pressure or displacement. This integrated analysis and automatic support adjustment method greatly improves the efficiency and safety of tunnel stability maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0027] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0030] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0031] See also Figure 1 As shown, a support safety monitoring system for rock burst in tunnel excavation includes a tunnel survey module, a rock burst analysis module, a prevention and treatment analysis module and a treatment execution module;

[0032] The tunnel survey module is used to collect data on tunnels that are about to be mined and those that have been opened, and to distinguish the data. Specifically:

[0033] For tunnels that need to be mined, the survey team first goes to the site to conduct a preliminary safety assessment and environmental inspection. They can use drones to take aerial photos to obtain an overview and preliminary data of the area. Next, they measure the terrain, select multiple measurement points in front of the tunnel to be mined, cover the entire area, use a laser rangefinder or total station to measure, and record the coordinates, ground height and relative position of each measurement point. The measurement data is imported into a computer and processed using geographic information system (GIS) software to draw a detailed topographic map, mark important terrain features (such as mountains, rivers, roads) and potential safety areas, and mark the data corresponding to the mined tunnel as T1.

[0034] For tunnels that have been opened, displacement sensors need to be installed at key locations such as the arch, side walls and floor to monitor possible structural displacement or deformation, and data should be recorded regularly and their changing trends observed to detect potential structural problems such as settlement, uplift or lateral displacement. In addition, stress strain sensors are installed on the tunnel walls to monitor internal stress changes in real time, and the data corresponding to the opened tunnel is marked as T2;

[0035] The rockburst analysis module is used to perform rockburst analysis on tunnels before and after mining, specifically:

[0036] Analysis of T1: Standardize the stress, rock strength and microseismic activity data (different features have different units and magnitudes, which lead to some features dominating the model training and thus affecting the prediction results; data standardization can convert all features to the same scale so that the model can treat each feature more fairly), so that their values ​​are on a unified scale, calculate the mean μ and standard deviation σ of each data point, and use the formula: Where x is a data point, N is the total number of data points, i = 1, 2, ..., N; then each data point is standardized: Each data point x i Normalized to z i ;(the standardized value z i Indicates the degree of deviation of the data point from the mean. The mean of the transformed data is 0 and the standard deviation is 1);

[0037] Then extract features from the standardized data.

[0038] Extract features from stress data: peak stress and stress concentration factor, where peak stress is: find the maximum value in stress data set c (peak stress is the maximum stress that rock mass bears during excavation and is an important indicator for evaluating rock mass stability), c max =max(c); The stress concentration factor is calculated by the ratio of the maximum principal stress to the compressive strength of the material, which is given by the formula Output stress concentration factor SCF (SCF is an important parameter for assessing the potential rock burst risk in high stress areas. The larger the value, the lower the rock mass stability). max is the peak stress calculated previously, and C is the compressive strength of the material;

[0039] Extract from microseismic data: microseismic event frequency and energy release, where the frequency of microseismic events is calculated by counting the number of microseismic events occurring within a certain period of time, using the formula: Output the frequency of microseismic events WPL (the frequency of microseismic events reflects the active degree of stress release inside the rock mass. When the frequency is high, it may indicate an increased risk of rock burst), H is the total number of microseismic events within the time interval Δt; energy release is calculated by calculating the energy release E of each microseismic event and summing it within a certain time period: From the formula: Output energy release ESF (the total amount of energy release can reflect the energy accumulation inside the rock mass, and a large energy release may indicate a potential rock burst), H is the total number of microseismic events, s = 1, 2, ..., H;

[0040] The extracted stress characteristics and microseismic characteristics are then divided into regions: the stress distribution map is determined in the model to identify the area with high stress, and it is divided into "high stress area"; the frequency and energy release of microseismic events are counted, and the area with frequent microseismic activities is compared, and it is divided into "microseismic high activity area"; the high stress area and the microseismic high activity area are superimposed; if the two areas are highly overlapped, it indicates that the rock burst risk in this area is high, and it is divided into "key attention activity area" or "general attention activity area" or "super key attention activity area", and the division is judged by the correlation between stress characteristics and microseismic characteristics;

[0041] Correlation analysis between stress characteristics and microseismic characteristics: Through the formula Output Pearson correlation coefficient r, x i and i Represents a single data point of two features, x i Represents the stress concentration factor SCF of a certain area; y i represents the frequency of microseismic events in the area; and They represent the average values ​​of these characteristics, which are used to calculate the degree of deviation of the data. The Pearson coefficient r reflects the correlation between the two characteristics: when r = 1, it indicates a complete positive correlation, which is divided into the "super-focused activity area", indicating that the stress characteristics and microseismic characteristics increase and decrease synchronously under certain circumstances; when r = -1, it indicates a complete negative correlation, which is divided into the "general attention activity area", indicating that when one characteristic increases, the other characteristic decreases; when r = 0, it indicates no correlation, which is divided into the "focused activity area", indicating that there is no obvious relationship between the two.

[0042] Analysis of T2: Displacement sensors are installed at key locations such as the tunnel vault, side wall, and floor, and marked as position data; stress and strain sensors record changes in the horizontal and vertical displacements of the tunnel structure to detect whether there is settlement, uplift, or lateral displacement, and are marked as strain structure data; the first recorded data collected after the sensor is installed is marked as initial position data, and the first recorded data collected after the stress and strain sensor is installed is marked as initial strain structure data;

[0043] Then analyze the changes in displacement data in time series to observe whether there is an abnormal increase trend in displacement; calculate the average displacement by setting the displacement at time t j The displacement data collected is d j , and calculate the average displacement increment Δd in each time period: Where k is the number of observation time intervals, and j = 1, 2, ..., k, d j+1 -d j is the difference between two consecutive displacement data; then analyze the displacement change and calculate the average rate v of displacement change: Where Δt1 is the time interval between two observation time points; finally, the displacement data sequence is fitted into a straight line, and linear regression is used to calculate the trend coefficient β of the displacement data; then the regression equation of linear regression is: d(t1)=α+βt1, where d(t) is the displacement value at time t, β is the rate of change of displacement over time, a positive value indicates an increase in displacement, and a negative value indicates a decrease in displacement; then a safety threshold of the displacement change rate v is set, and if v exceeds the corresponding threshold, the displacement change is considered abnormal, and a processing signal 1 is generated;

[0044] Then, by analyzing the changing trends of stress and strain data, we can identify the concentrated areas of high stress or high strain. Let the stress data collected at time tj be cj, and calculate the average stress change Δc in each time period: Then calculate the stress change rate v c : Similarly, linear regression is performed on the stress data to calculate the trend coefficient β of the strain structure data. c , then the regression equation of linear regression is: c(t1)=αc+βct1 where β c Indicates the rate of change of stress over time. A positive value indicates an increase in stress, and a negative value indicates a decrease in stress. Set the stress change rate β c The safety threshold and stress change rate β c For comparison, if β c Exceeding the corresponding safety threshold indicates the risk of stress concentration or sudden increase, and generates processing signal 2;

[0045] The preventive treatment and analysis module is used to process and analyze the marked areas, which are collectively referred to as "high stress areas", "microseismic high activity areas", "key attention activity areas", "general attention activity areas" and "super key attention activity areas". Specifically:

[0046] The initially collected stress, rock mass strength and microseismic activity data in each area are obtained for comprehensive analysis, using the formula: The output calculation obtains the casting volume coefficient JZZ, where c' is the initial stress value, S is the collected rock strength, f is the frequency of microseismic activity, E is the energy release, ω1, ω2 and ω3 are preset weight coefficients, and the specific values ​​can be adjusted according to the risk level of the region; c' / S represents the relative pressure borne by the rock mass. The larger the value, the closer the rock mass is to the state of destruction, and a larger support casting volume is required; the frequency of microseismic activity f reflects the activity of rock mass fracture in the region. The higher the value, the more unstable the internal structure of the rock mass is, and the higher the support demand is; ω3 reflects the casting volume demand of foundation support. Even if the stress and microseismic activity in the region are low, a certain casting volume is required to provide foundation support;

[0047] The preventive processing analysis module is also used to analyze the generated processing signal 1 and the processing signal 2, specifically:

[0048] Analyze the processing signal 1: obtain the initial position data of the mark, select the controlled mobile support of appropriate specifications according to the initial position data, and calculate the distance between the mobile support and the position corresponding to the processing signal 1; the processing execution module moves the support system accurately to the specified position through the positioning module; when the support system is in place, apply initial pressure first; according to the pressure demand and balance analysis of each area of ​​the support, the hydraulic support system will automatically adjust the support force to ensure the balanced distribution of stress in each area. The specific adjustment process is: P 支护 =α×P 监测 , P 支护 is the support pressure, P 监测 is the monitored regional pressure, α is the control coefficient, and the support force is dynamically adjusted according to the preset threshold; if the pressure or displacement is found to exceed the target range during the support process, the support system controlled by the processing execution module will automatically adjust the support force;

[0049] Analyze the second processing signal: Similarly, move the support system precisely to the specified position; when the support system is in place, apply initial pressure first; according to the pressure demand and balance analysis of each support area, the hydraulic support system will automatically adjust the support force;

[0050] The processing execution module is also used to calculate the actual pouring volume V of the pouring area, and a comprehensive calculation is performed based on the pouring volume coefficient JZZ, the cross-sectional area A of the tunnel, and the pouring pressure P1 of the pouring area, through the formula: Output the actual pouring volume V of the pouring area, where P1=β0×P2, P1 is the required pouring pressure, P2 is the actual pressure of the area collected, β0 is the pouring adjustment coefficient, which is determined according to the geological conditions of the area, rock strength and other factors; P0 is the reference pressure, which is used to convert the pouring pressure into a relative proportion (the reference pressure can be the on-site safety standard pressure or an empirical value); the processing execution module then fills the material according to the calculated actual pouring volume V. During the pouring process, the pressure, volume filling amount and support effect are monitored in real time. If deviations are found, the pouring pressure or material filling amount is automatically adjusted to ensure that the support effect meets the requirements.

[0051] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A support safety monitoring system for rock burst in tunnel excavation, comprising a tunnel survey module, a rock burst analysis module and a prevention and treatment analysis module; characterized in that: The tunnel survey module is used to collect data from tunnels that are about to be mined and tunnels that have been opened, and to distinguish the data, marking the data corresponding to the tunnels that are about to be mined as T1, and marking the data corresponding to the opened tunnels as T2; The rockburst analysis module is used to perform rockburst analysis on tunnels before and after mining, specifically: Analysis of T1: Standardization of stress, rock mass strength and microseismic activity data to make their values ​​on a uniform scale; feature extraction from the standardized data; Extract features from stress data: peak stress and stress concentration coefficient; extract from microseismic data: microseismic event frequency and energy release; then divide the extracted stress features and microseismic features into high stress areas and microseismic high activity areas; perform correlation analysis on stress features and microseismic features to obtain the Pearson correlation coefficient r; Analyze T2: Statistically calculate the changes in displacement data in the time series, observe whether there is an abnormal increase trend in displacement, calculate the average rate of displacement change, fit the displacement data series into a straight line, and use linear regression to calculate the trend coefficient of displacement data; then set a safety threshold for the displacement change rate. If the set displacement change rate exceeds the corresponding threshold, the displacement change is considered abnormal, and a processing signal 1 is generated; then analyze the change trend of stress and strain data to identify concentrated areas of high stress or high strain; calculate the stress change, perform linear regression on the stress data to obtain the trend coefficient of the strain structure data, compare the safety threshold for the stress change rate with the stress change rate, and if the stress change rate exceeds the corresponding safety threshold, it indicates the risk of stress concentration or sudden increase, and a processing signal 2 is generated; The preventive processing and analysis module is used to process and analyze the marked area, specifically: The initial stress, rock strength and microseismic activity data collected in each area are obtained for comprehensive analysis to obtain the casting volume coefficient; the generated processing signal 1 and processing signal 2 are analyzed to obtain the support pressure.

2. A support safety monitoring system for rock burst in tunnel excavation according to claim 1, characterized in that: It also includes a processing execution module; during the support process, the processing execution module automatically adjusts the support force according to the real-time monitored pressure and displacement to maintain the balanced stress distribution in each area; the processing execution module is also responsible for the dynamic adjustment of material filling and pouring pressure; during the pouring process, the processing execution module is also used to calculate the actual pouring volume V of the pouring area, and performs a comprehensive calculation based on the pouring volume coefficient JZZ, the cross-sectional area A of the tunnel, and the pouring pressure P1 of the pouring area, through the formula: Output the actual pouring volume V of the pouring area, where P1=β0×P2, P1 is the required pouring pressure, P2 is the collected actual pressure of the area, β0 is the pouring adjustment coefficient, and P0 is the reference pressure.

3. A support safety monitoring system for rock burst in tunnel excavation according to claim 1, characterized in that: The specific process of standardizing the stress, rock mass strength and microseismic activity data to make their values ​​on a unified scale is as follows: The mean μ and standard deviation σ of each data point are calculated by the formula: Where x is a data point, N is the total number of data points, i = 1, 2, ..., N; then each data point is standardized: Each data point x i Normalized to z i .

4. A support safety monitoring system for rock burst in tunnel excavation according to claim 3, characterized in that: The feature extraction from stress data is: Peak stress and stress concentration factor are extracted from microseismic data: The specific process of microseismic event frequency and energy release is: The peak stress is calculated by finding the maximum value c in the stress data set c. max =max(c), the stress concentration factor is calculated by the ratio of the maximum principal stress to the compressive strength of the material, and the formula Output stress concentration factor SCF,c max is the calculated peak stress, C is the compressive strength of the material; the frequency of earthquake events is calculated by counting the number of microseismic events occurring within a certain period of time, using the formula: Output the microseismic event frequency WPL, H is the total number of microseismic events within the time interval Δt; energy release is calculated by calculating the energy release E of each microseismic event and summing it within a certain time period: From the formula: Output energy release ESF, s = 1, 2, ..., H, H is the total number of microseismic events.

5. A support safety monitoring system for rock burst in tunnel excavation according to claim 1, characterized in that: The specific process of dividing the extracted stress characteristics and microseismic characteristics into high stress areas and microseismic high activity areas is as follows: In the model, the stress distribution map is determined to identify the area with high stress, which is divided into a high stress area; the frequency and energy release of microseismic events are counted and compared to obtain the area with frequent microseismic activities, which is divided into a microseismic high activity area; the high stress area and the microseismic high activity area are superimposed; if the two areas overlap highly, it indicates that the rock burst risk in this area is relatively high, and it is divided into a key attention activity area, a general attention activity area, or an ultra-key attention activity area. The division is judged by the correlation between the stress characteristics and the microseismic characteristics, and the correlation is calculated to obtain the Pearson correlation coefficient r. When r=1, it means a complete positive correlation, and it is divided into an ultra-key attention activity area, indicating that the stress characteristics and microseismic characteristics increase and decrease synchronously under certain circumstances; when r=-1, it means a complete negative correlation, and it is divided into a general attention activity area, indicating that when one feature increases, the other will decrease; when r=0, it means no correlation, and it is divided into a key attention activity area, indicating that there is no obvious relationship between the two.

6. A support safety monitoring system for rock burst in tunnel excavation according to claim 1, characterized in that: The specific process of obtaining the initial collected stress, rock mass strength and microseismic activity data in each area and conducting a comprehensive analysis to obtain the casting volume coefficient is as follows: By formula: The output calculation results in the casting volume coefficient JZZ, c' is the initial stress value, S is the acquired rock mass strength, f is the frequency of microseismic activity, E is the energy release, ω1, ω2 and ω3 are the preset weight coefficients, Indicates the relative pressure on the rock mass.

7. A support safety monitoring system for rock burst in tunnel excavation according to claim 6, characterized in that: The specific process of analyzing the generated processing signal 1 and the processing signal 2 to obtain the support pressure is as follows: Analyze the processing signal 1: obtain the initial position data of the mark, calculate the distance between the mobile support and the position corresponding to the processing signal 1; the processing execution module moves the support system to the specified position through the positioning module; when the support system is in place, apply initial pressure, according to the pressure demand and balance analysis of each area of ​​the support, by the formula: P 支护 =α×P 监测 , output support pressure P 支护 , P 监测 is the monitored regional pressure, and α is the control coefficient; if the pressure or displacement is found to exceed the target range during the support process, the support system controlled by the processing execution module will adjust the support force; Analyze processing signal 2: Same as processing signal 1, move the support system to the specified position; when the support system is in place, apply initial pressure first; based on the pressure demand and balance analysis of each support area, the hydraulic support system will adjust the support force.

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