Tunnel rock burst sensor layout optimization method based on TBM method

Abstract

The invention relates to the technical field of tunneling, in particular to a tunnel rock burst sensor layout optimization method based on a TBM method. According to the method, the corresponding tunnel layout pre-optimization is obtained according to the tunnel condition information, the corresponding engineering layout pre-optimization is obtained according to the engineering condition information, and then the tunnel layout pre-optimization information and the engineering layout pre-optimization information are comprehensively analyzed to obtain the sensor layout scheme. Analysis is carried out according to the specific condition of the tunnel and the current actual use condition of the tunnel, so that sensor layout optimization better meets the actual requirement, and the aiming range is wider. In addition, the level change information of the total risk level is obtained by monitoring the total risk level corresponding to each tunnel management section in real time, the corresponding dynamic adjustment information is obtained according to the level change information, and then the arrangement of the sensors can be monitored and adjusted in real time according to the dynamic adjustment information. And the installation position of the sensor is accurate and stable.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel excavation, and in particular to a tunnel rockburst sensor layout optimization method based on a TBM method. Background Art

[0002] Rockburst is a sudden and violent rupture phenomenon of hard and brittle rocks due to excavation unloading during underground engineering excavation under high ground stress conditions. It can cause serious harm to construction personnel and equipment, and rockburst may also cause damage to tunnel support structures. Severe rockburst may cause anchor bolts to break and shotcrete layer to peel off. Therefore, it is necessary to deploy sensors on the inner wall of the tunnel after tunnel excavation to collect the stress of the rock wall in order to facilitate the analysis of rockburst.

[0003] The TMB method is based on drilling data and can comprehensively consider the physical and mechanical properties of rocks, the ground stress state and geological structure characteristics, which are factors closely related to rock bursts. By analyzing the core samples obtained from drilling in detail and the corresponding stress test data, a more accurate prediction can be made on the possibility, intensity and location of rock bursts.

[0004] Existing tunnel excavation sensors first generate an estimated layout plan based on specific geographic data, and then collect actual data on the characteristics of the initial excavation tunnel and use it for analysis to obtain the actual layout plan; therefore, the existing sensor layout optimization is mainly targeted at analyzing the geographic data of the excavation tunnel, and is unable to perform multi-range layout optimization based on actual engineering conditions such as areas with important equipment or frequent personnel activities. Summary of the invention

[0005] The present invention provides a tunnel rockburst sensor deployment optimization method based on the TBM method, which is used to solve the above technical problems.

[0006] The first aspect of the present invention provides a tunnel rockburst sensor deployment optimization method based on the TBM method, comprising the following steps:

[0007] Step 1: Acquire the excavation data and engineering data of each tunnel management section corresponding to the excavation tunnel through the acquisition equipment, which is specifically as follows: divide the excavation tunnel into multiple tunnel management sections according to the preset standard length, and obtain the excavation data corresponding to each tunnel management section through the acquisition equipment, the excavation data includes excavation construction parameters and rock property parameters; obtain the engineering data corresponding to each tunnel management section through the sensor, the engineering data includes equipment installation information, personnel information and tunnel key component information.

[0008] Step 2: Obtain tunnel condition information based on tunnel excavation data, and obtain corresponding tunnel layout pre-optimization based on the tunnel condition information; obtain tunnel excavation data of each tunnel management section and analyze the excavation data to obtain tunnel condition information, and analyze the tunnel adjustment information to obtain tunnel layout pre-optimization information corresponding to each tunnel management section.

[0009] As a further improvement of the present invention, the excavation data is analyzed, and the specific analysis method is as follows:

[0010] The tunneling data includes tunneling construction parameters and rock mass property parameters; tunneling thrust, cutterhead vibration and tunneling speed are obtained by identifying tunneling construction parameters; the torque of the cutterhead per unit time corresponding to tunneling is obtained, and the tunneling thrust corresponding to each unit time is obtained according to the cutterhead torque; the pre-set tunneling thrust threshold is obtained, and the tunneling thrust corresponding to each unit time is compared with the tunneling thrust threshold; when the tunneling thrust is greater than the tunneling thrust threshold, the corresponding tunneling thrust is marked as risk thrust, and the duration corresponding to the risk thrust per unit time is obtained, and the risk thrust is multiplied by the duration to obtain the thrust impact value, which is recorded as N; when the cutterhead thrust suddenly increases, it may mean that the hardness of the rock mass ahead increases or there is a geological anomaly, for example, encountering a hard rock interlayer or an unbroken boulder; in this case, the stress state of the rock mass will change dramatically, which is easy to cause rock burst.

[0011] The cutterhead vibration value is obtained according to the cutterhead vibration, and a pre-set detection time period is obtained, the cutterhead vibration value is divided into multiple vibration value intervals, a vibration risk value is set for each vibration value interval, the cutterhead vibration values ​​corresponding to multiple unit times in the current detection time period are matched with multiple vibration value intervals to obtain the corresponding vibration risk values, the vibration risk values ​​corresponding to each unit time are calculated and summed to obtain the total vibration risk value, and recorded as zdf; the cutterhead vibration will be transmitted to the surrounding rock mass, and this vibration may stimulate the potential crack expansion inside the rock mass; if the vibration frequency is close to the natural frequency of some cracks in the rock mass, then Resonance will occur, causing the cracks to expand rapidly; the expansion of the cracks will weaken the integrity of the rock mass and make it more susceptible to rockbursts; obtain the number of rockbursts corresponding to the current excavation speed per unit time, obtain the pre-set rockburst reference number, compare the number of rockbursts corresponding to the current unit time with the rockburst reference number, and when the number of rockbursts is greater than the rockburst reference number, mark the part of the rockburst number that exceeds the rockburst reference number as the excavation speed shadow value and record it as JZ; when the excavation speed is faster, the stress of the rock mass in front of the tunnel has no time to fully adjust and release, resulting in a stronger stress concentration around the excavation face, which is likely to increase the number of rockbursts.

[0012] The geological structure information is obtained by identifying the rock mass property parameters, the geological structure information corresponding to each excavation management section is obtained, and the pre-set influence area is obtained. The special geological structure in the influence area corresponding to each excavation management section is obtained according to the geological structure information. The special geological structure includes but is not limited to folds, faults, joints, and bedding that can affect the stability of the tunnel; the number of special geological structures corresponding to each excavation management section is counted, and the number of special geological structures is marked as a geological influence value, and recorded as DZ.

[0013] The thrust impact value, total vibration risk value, tunneling speed shadow value and geological impact value are normalized and their values ​​are taken. The formula is used to The tunnel risk value SD is calculated; DZ' represents the construction allowable number; h1, h2, h3, and h4 are preset weight factors, and their values ​​are 2.254, 1.705, 2.027, and 1.053, respectively; the tunnel risk value corresponding to each tunnel management section is used as the corresponding tunnel condition information.

[0014] As a further improvement of the present invention, the tunnel condition information is analyzed, and the specific analysis method is as follows:

[0015] Acquire tunnel condition information corresponding to each tunnel management section, obtain the tunnel risk value corresponding to each tunnel management section according to the tunnel condition information, divide the tunnel risk value into multiple tunnel risk value intervals, set a tunnel risk level for each tunnel risk value interval, match the tunnel risk value corresponding to each tunnel management section with the multiple tunnel risk value intervals to obtain the corresponding tunnel risk level, and generate the corresponding tunnel layout pre-optimization information according to the tunnel risk level.

[0016] Step three: obtain engineering condition information based on engineering data, and obtain corresponding engineering layout pre-optimization based on the engineering condition information; obtain engineering data corresponding to each tunnel management section and analyze the engineering data to obtain engineering condition information, and analyze the engineering condition information to obtain corresponding engineering layout pre-optimization information for each tunnel management section.

[0017] As a further improvement of the present invention, the engineering data is analyzed, and the specific analysis method is as follows:

[0018] The engineering data includes equipment installation information, personnel information and key tunnel component information; the equipment installation information corresponding to each tunnel management section is obtained, the equipment installation information is identified to obtain the equipment value and equipment usage time, the pre-set benchmark value is obtained, the equipment value corresponding to each equipment in the tunnel management section is compared with the benchmark value, when the equipment value is greater than the benchmark value, the corresponding equipment is marked as key equipment, and the equipment value corresponding to the key equipment and the benchmark value are calculated to obtain the price difference, and the price difference corresponding to each equipment in the tunnel management section is calculated and summed to obtain the value impact index; the daily usage time of each equipment in the tunnel management section is obtained, the usage time of each equipment is calculated to be the ratio of the usage time of each equipment to the length of one day to obtain the equipment usage, and the usage of each equipment is calculated and summed to obtain the total equipment usage.

[0019] According to the personnel information corresponding to each tunnel management section, the corresponding work type and personnel location information are obtained; the work type of each tunnel management section is identified to obtain risky work tasks, which include but are not limited to excavation work, support work, and slag discharge work; the number of risky work tasks corresponding to the tunnel management section is counted and marked as the number of risk tasks; the personnel location information is identified to obtain the personnel flow and activity time corresponding to each tunnel management section, and the personnel flow is divided into multiple personnel flow intervals, each personnel flow interval corresponds to a flow impact value, and the current corresponding personnel flow is matched with multiple personnel flow intervals to obtain the corresponding flow impact value; the pre-set activity reference duration is obtained, and the activity time of each personnel corresponding to the current tunnel management section is compared with the activity reference duration. When the corresponding activity time of the personnel is greater than the activity reference duration, the corresponding personnel is marked as a resident personnel, and the number of resident personnel corresponding to each tunnel management section is obtained by counting, and the number of resident personnel and the flow impact value are added together to obtain the personnel impact index.

[0020] According to the information of tunnel key components, the number of key components corresponding to each tunnel management section is obtained. The key components include but are not limited to the distribution of steel arch frames at the arch crown, arch waist, side wall and other positions, and the connection between anchor rods and rock mass, and steel arch frames; the pre-set key component benchmark number is obtained, and when the current key component number is greater than the key component benchmark number, the key component number and the key component benchmark number are subtracted to obtain the component impact value.

[0021] A right-angled trapezoid is constructed with the values ​​of the value impact index and the total equipment usage as the upper and lower bases of the right-angled trapezoid. The value of the personnel impact index is equal to the right-angled waist of the right-angled trapezoid. Then, with the center of gravity of the right-angled trapezoid as the starting point, a straight line perpendicular to the right-angled trapezoid is drawn. The length of the straight line is equal to the value of the component impact value. Then, a tetrahedron is constructed with the right-angled trapezoid and the straight line. The volume of the tetrahedron is calculated and the value of the volume is marked as the engineering risk value. The engineering risk value corresponding to each tunnel management section is used as the corresponding engineering condition information.

[0022] As a further improvement of the present invention, the engineering condition information is analyzed, and the specific analysis method is as follows:

[0023] The engineering condition information corresponding to each tunnel management section is obtained, and the engineering risk value corresponding to each tunnel management section is obtained according to the engineering condition information. The engineering risk value is divided into multiple engineering risk value intervals, and an engineering risk level is set for each engineering risk value interval. The engineering risk value corresponding to each tunnel management section is matched with multiple engineering risk value intervals to obtain the corresponding engineering risk level, and the corresponding engineering layout pre-optimization information is generated according to the engineering risk level.

[0024] Step 4: Comprehensively analyze the tunnel layout pre-optimization information and engineering layout pre-optimization information to obtain the sensor layout plan, which is as follows:

[0025] The corresponding tunnel risk level and engineering risk level are obtained according to the tunnel layout pre-optimization information and the engineering layout pre-optimization information. The tunnel risk level and the engineering risk level are calculated and summed to obtain the total risk level. According to the total risk level, the corresponding sensor layout plan is obtained.

[0026] Step 5: Dynamic adjustment analysis, which is specifically as follows: by real-time monitoring of the total risk level corresponding to each tunnel management section, the level change information of the total risk level is obtained, and a pre-set initial level change interval is obtained. When the level change information corresponds to the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the monitoring frequency; and when the level change information corresponds to exceeding the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the number of sensors.

[0027] Step 6: Implementation and monitoring phase, which specifically includes: installing sensors based on the sensor layout plan, monitoring the stress data of the inner wall of the tunnel through sensors, predicting rock bursts through the TMB method based on the collected stress data; identifying the dynamic adjustment analysis information to obtain the corresponding adjustment actions, and pushing them according to the adjustment actions.

[0028] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0029] 1. The present invention obtains tunnel condition information based on tunnel excavation data, and obtains corresponding tunnel layout pre-optimization based on the tunnel condition information, and obtains engineering condition information based on engineering data, and obtains corresponding engineering layout pre-optimization based on the engineering condition information, and then comprehensively analyzes the tunnel layout pre-optimization information and the engineering layout pre-optimization information to obtain a sensor layout plan, and analyzes the specific conditions of the tunnel and the current actual use of the tunnel, so that the sensor layout optimization is more in line with actual needs and targets a wider range.

[0030] 2. The present invention obtains the level change information of the total risk level by real-time monitoring of the total risk level corresponding to each tunnel management section, and obtains the corresponding dynamic adjustment information based on the level change information. Then, the layout of the sensors can be monitored and adjusted in real time based on the dynamic adjustment information to ensure that the installation position of the sensors is accurate and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0032] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0034] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In an embodiment of the present invention, an embodiment of a tunnel rockburst sensor deployment optimization method based on a TBM method includes the following steps:

[0035] Step 1: Acquire the excavation data and engineering data of each tunnel management section corresponding to the excavation tunnel through the acquisition equipment, which is specifically as follows: divide the excavation tunnel into multiple tunnel management sections according to the preset standard length, and obtain the excavation data corresponding to each tunnel management section through the acquisition equipment, the excavation data includes excavation construction parameters and rock property parameters; obtain the engineering data corresponding to each tunnel management section through the sensor, the engineering data includes equipment installation information, personnel information and tunnel key component information.

[0036] Step 2: Obtain tunnel condition information based on tunnel excavation data, and obtain corresponding tunnel layout pre-optimization based on the tunnel condition information; obtain tunnel excavation data of each tunnel management section and analyze the excavation data to obtain tunnel condition information, and analyze the tunnel adjustment information to obtain tunnel layout pre-optimization information corresponding to each tunnel management section.

[0037] The excavation data is analyzed in the following specific ways:

[0038] The tunneling data includes tunneling construction parameters and rock mass property parameters; tunneling thrust, cutterhead vibration and tunneling speed are obtained by identifying tunneling construction parameters; the torque of the cutterhead per unit time corresponding to tunneling is obtained, and the tunneling thrust corresponding to each unit time is obtained according to the cutterhead torque; the pre-set tunneling thrust threshold is obtained, and the tunneling thrust corresponding to each unit time is compared with the tunneling thrust threshold; when the tunneling thrust is greater than the tunneling thrust threshold, the corresponding tunneling thrust is marked as risk thrust, and the duration corresponding to the risk thrust per unit time is obtained, and the risk thrust is multiplied by the duration to obtain the thrust impact value; when the cutterhead thrust suddenly increases, it may mean that the hardness of the rock mass ahead increases or there are geological anomalies, for example, encountering hard rock interlayers or unbroken boulders; in this case, the stress state of the rock mass will change dramatically, which is easy to cause rock burst.

[0039] The cutterhead vibration value is obtained according to the cutterhead vibration, and a pre-set detection time period is obtained, the cutterhead vibration value is divided into multiple vibration value intervals, a vibration risk value is set for each vibration value interval, the cutterhead vibration values ​​corresponding to multiple unit times in the current detection time period are matched with multiple vibration value intervals to obtain the corresponding vibration risk values, and the vibration risk values ​​corresponding to each unit time are calculated and summed to obtain the total vibration risk value; the cutterhead vibration will be transmitted to the surrounding rock mass, and this vibration may stimulate the potential expansion of cracks inside the rock mass; if the vibration frequency is close to the natural frequency of some cracks in the rock mass, it will Resonance phenomenon is generated, resulting in rapid expansion of cracks; the expansion of cracks will weaken the integrity of the rock mass and make the rock mass more susceptible to rockbursts; the number of rockbursts corresponding to the current excavation speed per unit time is obtained, the pre-set rockburst reference number is obtained, the number of rockbursts corresponding to the current unit time is compared with the rockburst reference number, and when the number of rockbursts is greater than the rockburst reference number, the part of the rockburst number that exceeds the rockburst reference number is marked as the excavation speed shadow value; when the excavation speed is faster, the stress of the rock mass in front of the tunnel has no time to fully adjust and release, resulting in a stronger stress concentration around the excavation face, which is likely to increase the number of rockbursts.

[0040] The geological structure information is obtained by identifying the rock mass property parameters, the geological structure information corresponding to each excavation management section is obtained, and the pre-set influence area is obtained. The special geological structure in the influence area corresponding to each excavation management section is obtained according to the geological structure information. The special geological structure includes but is not limited to folds, faults, joints, and bedding that can affect the stability of the tunnel; the number of special geological structures corresponding to each excavation management section is counted, and the number of special geological structures is marked as a geological impact value.

[0041] The thrust impact value, total vibration risk value, tunneling speed shadow value and geological impact value are normalized and their values ​​are taken. The formula is used to The tunnel risk value SD is calculated; N, zdf, JZ, and DZ represent the thrust influence value, the total vibration risk value, the tunneling speed shadow value, and the geological influence value, respectively; DZ' represents the structural allowable number; h1, h2, h3, and h4 are all preset weight factors, with values ​​of 2.254, 1.705, 2.027, and 1.053, respectively; the tunnel risk value corresponding to each tunnel management section is used as the corresponding tunnel condition information.

[0042] The tunnel condition information is analyzed in the following specific ways:

[0043] Acquire tunnel condition information corresponding to each tunnel management section, obtain the tunnel risk value corresponding to each tunnel management section according to the tunnel condition information, divide the tunnel risk value into multiple tunnel risk value intervals, set a tunnel risk level for each tunnel risk value interval, match the tunnel risk value corresponding to each tunnel management section with the multiple tunnel risk value intervals to obtain the corresponding tunnel risk level, and generate the corresponding tunnel layout pre-optimization information according to the tunnel risk level.

[0044] Step three: obtain engineering condition information based on engineering data, and obtain corresponding engineering layout pre-optimization based on the engineering condition information; obtain engineering data corresponding to each tunnel management section and analyze the engineering data to obtain engineering condition information, and analyze the engineering condition information to obtain corresponding engineering layout pre-optimization information for each tunnel management section.

[0045] The specific analysis method for analyzing engineering data is as follows:

[0046] The engineering data includes equipment installation information, personnel information and key tunnel component information; the equipment installation information corresponding to each tunnel management section is obtained, the equipment installation information is identified to obtain the equipment value and equipment usage time, the pre-set benchmark value is obtained, the equipment value corresponding to each equipment in the tunnel management section is compared with the benchmark value, when the equipment value is greater than the benchmark value, the corresponding equipment is marked as key equipment, and the equipment value corresponding to the key equipment and the benchmark value are calculated to obtain the price difference, and the price difference corresponding to each equipment in the tunnel management section is calculated and summed to obtain the value impact index; the daily usage time of each equipment in the tunnel management section is obtained, the usage time of each equipment is calculated to be the ratio of the usage time of each equipment to the length of one day to obtain the equipment usage, and the usage of each equipment is calculated and summed to obtain the total equipment usage.

[0047] According to the personnel information corresponding to each tunnel management section, the corresponding work type and personnel location information are obtained; the work type of each tunnel management section is identified to obtain risky work tasks, which include but are not limited to excavation work, support work, and slag discharge work; the number of risky work tasks corresponding to the tunnel management section is counted and marked as the number of risk tasks; the personnel location information is identified to obtain the personnel flow and activity time corresponding to each tunnel management section, and the personnel flow is divided into multiple personnel flow intervals, each personnel flow interval corresponds to a flow impact value, and the current corresponding personnel flow is matched with multiple personnel flow intervals to obtain the corresponding flow impact value; the pre-set activity reference duration is obtained, and the activity time of each personnel corresponding to the current tunnel management section is compared with the activity reference duration. When the corresponding activity time of the personnel is greater than the activity reference duration, the corresponding personnel is marked as a resident personnel, and the number of resident personnel corresponding to each tunnel management section is obtained by counting, and the number of resident personnel and the flow impact value are added together to obtain the personnel impact index.

[0048] According to the information of tunnel key components, the number of key components corresponding to each tunnel management section is obtained. The key components include but are not limited to the distribution of steel arch frames at the arch crown, arch waist, side wall and other positions, and the connection between anchor rods and rock mass, and steel arch frames; the pre-set key component benchmark number is obtained, and when the current key component number is greater than the key component benchmark number, the key component number and the key component benchmark number are subtracted to obtain the component impact value.

[0049] A right-angled trapezoid is constructed with the values ​​of the value impact index and the total equipment usage as the upper and lower bases of the right-angled trapezoid. The value of the personnel impact index is equal to the right-angled waist of the right-angled trapezoid. Then, with the center of gravity of the right-angled trapezoid as the starting point, a straight line perpendicular to the right-angled trapezoid is drawn. The length of the straight line is equal to the value of the component impact value. Then, a tetrahedron is constructed with the right-angled trapezoid and the straight line. The volume of the tetrahedron is calculated and the value of the volume is marked as the engineering risk value. The engineering risk value corresponding to each tunnel management section is used as the corresponding engineering condition information.

[0050] The engineering condition information is analyzed in the following specific ways:

[0051] The engineering condition information corresponding to each tunnel management section is obtained, and the engineering risk value corresponding to each tunnel management section is obtained according to the engineering condition information. The engineering risk value is divided into multiple engineering risk value intervals, and an engineering risk level is set for each engineering risk value interval. The engineering risk value corresponding to each tunnel management section is matched with multiple engineering risk value intervals to obtain the corresponding engineering risk level, and the corresponding engineering layout pre-optimization information is generated according to the engineering risk level.

[0052] Step 4: Comprehensively analyze the tunnel layout pre-optimization information and the engineering layout pre-optimization information to obtain the sensor layout plan; obtain the corresponding tunnel risk level and engineering risk level based on the tunnel layout pre-optimization information and the engineering layout pre-optimization information, calculate and sum the tunnel risk level and the engineering risk level to obtain the total risk level, and obtain the corresponding sensor layout plan based on the total risk level.

[0053] Step 5: Dynamic adjustment analysis obtains the level change information of the total risk level through real-time monitoring of the total risk level corresponding to each tunnel management section, and obtains the pre-set initial level change interval. When the level change information corresponds to the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the monitoring frequency; and when the level change information corresponds to exceeding the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the number of sensors.

[0054] Step 6: Implementation and monitoring phase: Sensors are installed based on the sensor deployment plan, and the stress data of the inner wall of the tunnel is monitored through the sensors. Rock burst prediction is performed based on the collected stress data using the TMB method. The corresponding adjustment actions are obtained by identifying the dynamic adjustment analysis information, and the adjustment actions are pushed accordingly.

[0055] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tunnel rockburst sensor deployment optimization method based on TBM method, characterized in that: The steps include: Step 1: Acquire the excavation data and engineering data of each tunnel management section corresponding to the excavation tunnel through the acquisition equipment; Step 2: obtaining tunnel condition information according to tunnel excavation data, and obtaining corresponding tunnel layout pre-optimization according to the tunnel condition information; Step 3: Obtain engineering condition information according to engineering data, and obtain corresponding engineering layout pre-optimization according to the engineering condition information; Step 4: Comprehensively analyze the tunnel layout pre-optimization information and engineering layout pre-optimization information to obtain a sensor layout plan; Step 5: Dynamic adjustment analysis; Step 6: Implementation and monitoring phase.

2. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 1 is characterized in that: The excavation data and engineering data of each tunnel management section corresponding to the excavation tunnel are obtained by collecting equipment, which is specifically as follows: the excavation tunnel is divided into multiple tunnel management sections according to a preset standard length, and the excavation data corresponding to each tunnel management section is obtained by collecting equipment, and the excavation data includes excavation construction parameters and rock property parameters; the engineering data corresponding to each tunnel management section is obtained by collecting sensors, and the engineering data includes equipment installation information, personnel information and tunnel key component information.

3. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 1 is characterized in that: The tunnel condition information is obtained according to the tunnel excavation data, and the corresponding tunnel layout pre-optimization is obtained according to the tunnel condition information, which specifically includes: taking the tunnel excavation data of each tunnel management section and analyzing the excavation data to obtain the tunnel condition information, and analyzing the tunnel adjustment information to obtain the tunnel layout pre-optimization information corresponding to each tunnel management section; The excavation data is analyzed in the following specific analysis method: the excavation data includes excavation construction parameters and rock mass property parameters; the excavation thrust, cutterhead vibration and excavation speed are obtained by identifying the excavation construction parameters; the torque of the cutterhead per unit time corresponding to the tunnel excavation is obtained, the excavation thrust corresponding to each unit time is obtained according to the cutterhead torque, a pre-set excavation thrust threshold is obtained, the excavation thrust corresponding to each unit time is compared with the excavation thrust threshold, when the excavation thrust is greater than the excavation thrust threshold, the corresponding excavation thrust is marked as risk thrust, the duration corresponding to the risk thrust per unit time is obtained, and the risk thrust is multiplied by the duration to obtain the thrust impact value; A cutter disc vibration value is obtained according to the cutter disc vibration, and a pre-set detection time period is obtained, the cutter disc vibration value is divided into a plurality of vibration value intervals, a vibration risk value is set for each vibration value interval, the cutter disc vibration values ​​corresponding to a plurality of unit times in the current detection time period are matched with the plurality of vibration value intervals to obtain corresponding vibration risk values, and the vibration risk values ​​corresponding to each unit time are calculated and summed to obtain a total vibration risk value; Obtain the number of rock bursts corresponding to the tunneling speed in the current unit time, obtain a preset rock burst reference number, compare the number of rock bursts corresponding to the current unit time with the rock burst reference number, and when the number of rock bursts is greater than the rock burst reference number, mark the part of the rock burst number that exceeds the rock burst reference number as the tunneling speed shadow value; The geological structure information is obtained by identifying the rock mass property parameters, the geological structure information corresponding to each tunneling management section is obtained, and the pre-set influence area is obtained. The special geological structure in the influence area corresponding to each tunneling management section is obtained according to the geological structure information; the thrust impact value, the total vibration risk value, the tunneling speed shadow value and the geological impact value are comprehensively calculated to obtain the tunnel risk value; the tunnel risk value corresponding to each tunnel management section is used as the corresponding tunnel condition information.

4. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 3 is characterized in that: The thrust impact value, the total vibration risk value, the tunneling speed value and the geological impact value are comprehensively calculated as follows: The thrust impact value, total vibration risk value, tunneling speed shadow value and geological impact value are normalized and their values ​​are taken. The formula is used to The tunnel risk value SD is calculated; among them, N, zdf, JZ, DZ represent the thrust impact value, the total vibration risk value, the tunneling speed shadow value and the geological impact value respectively, and DZ' represents the structural allowable number; h1, h2, h3, and h4 are all preset weight factors.

5. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 3 is characterized in that: The tunnel condition information is analyzed in the following specific manner: Acquire tunnel condition information corresponding to each tunnel management section, obtain the tunnel risk value corresponding to each tunnel management section according to the tunnel condition information, divide the tunnel risk value into multiple tunnel risk value intervals, set a tunnel risk level for each tunnel risk value interval, match the tunnel risk value corresponding to each tunnel management section with the multiple tunnel risk value intervals to obtain the corresponding tunnel risk level, and generate the corresponding tunnel layout pre-optimization information according to the tunnel risk level.

6. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 1 is characterized in that: The engineering condition information is obtained according to the engineering data, and the corresponding engineering layout pre-optimization is obtained according to the engineering condition information, which specifically includes: obtaining the engineering data corresponding to each tunnel management section and analyzing the engineering data to obtain the engineering condition information, and analyzing the engineering condition information to obtain the engineering layout pre-optimization information corresponding to each tunnel management section; The engineering data is analyzed in the following specific manner: the engineering data includes equipment installation information, personnel information and tunnel key component information; the equipment installation information corresponding to each tunnel management section is obtained, the equipment installation information is identified to obtain the equipment value and equipment usage time, a preset benchmark value is obtained, the equipment value corresponding to each equipment in the tunnel management section is compared with the benchmark value, when the equipment value is greater than the benchmark value, the corresponding equipment is marked as key equipment, and the equipment value corresponding to the key equipment is calculated with the benchmark value to obtain the price difference, and the price difference corresponding to each equipment in the tunnel management section is calculated and summed to obtain the value impact index; Obtain the daily usage time of each device in the tunnel management section, calculate the ratio of the usage time of each device to the duration of a day to obtain the device usage, and calculate and sum the usage of each device to obtain the total device usage; According to the personnel information corresponding to each tunnel management section, the corresponding work type and personnel location information are obtained; Identify the work type of each tunnel management section to obtain risky work tasks, count the number of risky work tasks corresponding to the tunnel management section and mark them as the number of risky tasks; Identify the personnel location information to obtain the personnel flow and activity time corresponding to each tunnel management section, divide the personnel flow into multiple personnel flow intervals, each personnel flow interval corresponds to a flow impact value, match the current corresponding personnel flow with multiple personnel flow intervals to obtain the corresponding flow impact value; obtain the pre-set activity reference duration, compare the activity time of each person corresponding to the current tunnel management section with the activity reference duration, when the activity time corresponding to the person is greater than the activity reference duration, mark the corresponding person as a permanent person, and obtain the number of permanent persons corresponding to each tunnel management section by statistics, and add the number of permanent persons and the flow impact value to obtain the personnel impact index; According to the tunnel key component information, the key component quantity corresponding to each tunnel management section is obtained, and the pre-set key component benchmark quantity is obtained. When the current key component quantity is greater than the key component benchmark quantity, the key component quantity and the key component benchmark quantity are calculated to obtain the component impact value. A right-angled trapezoid is constructed with the values ​​of the value impact index and the total equipment usage as the upper and lower bases of the right-angled trapezoid. The value of the personnel impact index is equal to the right-angled waist of the right-angled trapezoid. Then, with the center of gravity of the right-angled trapezoid as the starting point, a straight line perpendicular to the right-angled trapezoid is drawn. The length of the straight line is equal to the value of the component impact value. Then, a tetrahedron is constructed with the right-angled trapezoid and the straight line. The volume of the tetrahedron is calculated and the value of the volume is marked as the engineering risk value. The engineering risk value corresponding to each tunnel management section is used as the corresponding engineering condition information.

7. The method for optimizing the deployment of rockburst sensors in tunnels based on TBM method according to claim 6, characterized in that: The specific analysis method of analyzing the engineering condition information is as follows: The engineering condition information corresponding to each tunnel management section is obtained, and the engineering risk value corresponding to each tunnel management section is obtained according to the engineering condition information. The engineering risk value is divided into multiple engineering risk value intervals, and an engineering risk level is set for each engineering risk value interval. The engineering risk value corresponding to each tunnel management section is matched with multiple engineering risk value intervals to obtain the corresponding engineering risk level, and the corresponding engineering layout pre-optimization information is generated according to the engineering risk level.

8. The method for optimizing the deployment of tunnel rockburst sensors based on TBM method according to claim 1, characterized in that: The sensor deployment plan is obtained by comprehensively analyzing the tunnel layout pre-optimization information and the engineering layout pre-optimization information, which is specifically: the corresponding tunnel risk level and engineering risk level are obtained according to the tunnel layout pre-optimization information and the engineering layout pre-optimization information, the tunnel risk level and the engineering risk level are calculated and summed to obtain the total risk level, and the corresponding sensor deployment plan is obtained according to the total risk level.

9. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 1, characterized in that: The dynamic adjustment analysis is specifically as follows: By real-time monitoring of the total risk level corresponding to each tunnel management section, the level change information of the total risk level is obtained, and a pre-set initial level change interval is obtained. When the level change information corresponds to the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the monitoring frequency; and when the level change information corresponds to exceeding the initial level change interval, the corresponding dynamic adjustment information is generated to increase and decrease the number of sensors.

10. The tunnel rockburst sensor deployment optimization method based on TBM method according to claim 9, characterized in that: The implementation and monitoring phases are as follows: Sensors are installed based on the sensor layout plan, and the stress data of the inner wall of the tunnel is monitored by the sensors. The corresponding adjustment actions are obtained by identifying the dynamic adjustment analysis information, and pushed according to the adjustment actions.

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