Tunnel rock burst sensor layout optimization method based on TBM method

Abstract

The present application relates to the technical field of tunnel excavation, in particular to a tunnel rock burst sensor layout optimization method based on TBM method. According to the tunnel condition information, the corresponding tunnel layout pre-optimization is obtained, and according to the engineering condition information, the corresponding engineering layout pre-optimization is obtained. Then, the tunnel layout pre-optimization information and the engineering layout pre-optimization information are comprehensively analyzed to obtain a sensor layout scheme. According to the specific situation of the tunnel and the current actual use of the tunnel, the analysis is carried out, so that the sensor layout optimization is more in line with the actual demand and more widely targeted. The present application also obtains the grade change information of the total risk grade of each tunnel management section through real-time monitoring of the total risk grade of each tunnel management section, and obtains the corresponding dynamic adjustment information according to the grade change information. Then, according to the dynamic adjustment information, the layout of the sensor can be monitored and adjusted in real time, so as to ensure that the installation position of the sensor is accurate and stable.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring technology, specifically to an optimized method for the deployment of tunnel rockburst sensors based on the TBM method. Background Technology

[0002] Rockburst is a sudden and violent fracturing phenomenon that occurs in hard and brittle rocks during underground engineering excavation under high ground stress conditions due to the unloading caused by excavation. It can cause serious harm to construction personnel and equipment, and may also damage the tunnel support structure. Severe rockbursts may lead to the breakage of anchor bolts, the peeling of shotcrete layers, and other situations. Therefore, it is necessary to deploy sensors on the inner wall of the tunnel after tunnel excavation to collect stress data on the rock wall in order to facilitate the analysis of rockbursts.

[0003] The TMB method, based on borehole data, can comprehensively consider factors closely related to rockburst, such as the physical and mechanical properties of the rock, the state of in-situ stress, and the geological structure characteristics. By analyzing the core samples obtained from the borehole in detail, as well as the corresponding stress test data, it can make relatively accurate predictions of the probability, intensity, and location of rockbursts.

[0004] Existing tunnel excavation sensors first generate a predicted deployment plan based on specific geographical data, and then collect actual data on the characteristics of the initial tunnel excavation and use it for analysis to obtain the actual deployment plan. Therefore, the existing sensor deployment optimization is mainly focused on analyzing the geographical data of the tunnel excavation, and cannot perform multi-range deployment optimization based on actual engineering conditions such as areas with important equipment or frequent personnel activities. Summary of the Invention

[0005] This invention provides an optimized method for the deployment of tunnel rockburst sensors based on the TBM method, which is used to solve the above-mentioned technical problems.

[0006] The first aspect of this invention provides a method for optimizing the deployment of tunnel rockburst sensors based on the TBM method, comprising the following steps:

[0007] Step 1: Acquire the rock mass parameter data and engineering resource information data of each tunnel management section corresponding to the tunnel being excavated using data acquisition equipment. Specifically, the tunnel is divided into multiple tunnel management sections according to a preset standard length. The rock mass parameter data corresponding to each tunnel management section is acquired using data acquisition equipment. The rock mass parameter data includes tunneling construction parameters and rock mass property parameters. The engineering resource information data corresponding to each tunnel management section is acquired using sensors. The engineering resource information data includes equipment installation information, personnel information, and information on key tunnel components.

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

[0009] As a further improvement to the present invention, the tunneling rock mass parameter data is analyzed, and the specific analysis method is as follows:

[0010] The tunneling rock mass parameter data includes tunneling construction parameters and rock mass property parameters. Tunneling thrust, cutterhead vibration, and tunneling speed are obtained by identifying the tunneling construction parameters. The cutterhead torque is acquired for each unit of time during tunnel excavation. Based on the cutterhead torque, the tunneling thrust corresponding to each unit of time is obtained. A pre-set tunneling thrust threshold is acquired, and the tunneling thrust corresponding to each unit of time is compared with the tunneling thrust threshold. When the tunneling thrust exceeds the threshold, the corresponding tunneling thrust is marked as a risk thrust. The duration corresponding to the risk thrust per unit time is acquired, and the risk thrust is multiplied by the duration to calculate the thrust impact value, which is denoted as N. When the cutterhead thrust suddenly increases, it may indicate an increase in the hardness of the rock mass ahead or the presence of geological anomalies, such as encountering hard rock interlayers or unbroken boulders. In this case, the stress state of the rock mass will change drastically, easily triggering a rockburst.

[0011] The cutterhead vibration value is obtained based on the cutterhead vibration, and a pre-set detection time period is acquired. The cutterhead vibration value is divided into multiple vibration value intervals, and a vibration risk value is assigned to each interval. The cutterhead vibration values ​​corresponding to multiple unit times within 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, which is denoted as zdf. The cutterhead vibration will be transmitted to the surrounding rock mass, and this vibration may excite the propagation of potential fractures within the rock mass. If the vibration frequency is close to the natural frequency of some fractures in the rock mass, then... Resonance can occur, leading to rapid fissure expansion. This expansion weakens the integrity of the rock mass, making it more susceptible to rockbursts. The number of rockbursts per unit time corresponding to the current tunneling speed is obtained, along with a pre-set rockburst reference number. The number of rockbursts per unit time is compared with this reference number. If the number of rockbursts exceeds the reference number, the portion exceeding the reference number is marked as the tunneling speed shadow value and denoted as JZ. When the tunneling speed is high, the stress in the rock mass ahead of the tunnel cannot be fully adjusted and released, resulting in stronger stress concentration around the excavation face, which easily increases the number of rockbursts.

[0012] Geological structure information is obtained by identifying rock mass property parameters, and the geological structure information corresponding to each tunneling management section is obtained. The pre-set influence area is also obtained. Based on the geological structure information, the special geological structures within the influence area corresponding to each tunneling management section are obtained. The special geological structures include, but are 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 tunneling management section is counted, and the number of special geological structures is marked as the geological influence value and denoted as DZ.

[0013] The thrust impact value, total vibration risk value, tunneling speed and shadow value, and geological impact value are normalized and their values ​​are taken. Then, the formula is used to... The tunnel risk value SD was calculated; where, The number is represented as the allowable number; h1, h2, h3, and h4 are preset weighting 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.

[0014] As a further improvement of the present invention, a corresponding tunnel layout pre-optimization is obtained based on tunnel condition information, and the specific analysis method is as follows:

[0015] Obtain tunnel condition information corresponding to each tunnel management section, obtain tunnel risk value corresponding to each tunnel management section based on tunnel condition information, divide 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 multiple tunnel risk value intervals to obtain the corresponding tunnel risk level, and generate corresponding tunnel layout pre-optimization information based on tunnel risk level.

[0016] Step 3: Obtain engineering condition information based on engineering resource information data, and obtain corresponding engineering layout pre-optimization based on engineering condition information; acquire engineering resource information data corresponding to each tunnel management section and analyze the engineering resource information data to obtain engineering condition information, and analyze the engineering condition information to obtain engineering layout pre-optimization information corresponding to each tunnel management section.

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

[0018] The engineering resource information data includes equipment installation information, personnel information, and information on key tunnel components. Equipment installation information for each tunnel management section is obtained; the equipment installation information is identified to obtain equipment value and usage time; a pre-set benchmark value is obtained; the equipment value for each piece of 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; the price difference is calculated by comparing the equipment value of key equipment with the benchmark value; the price differences for each piece of equipment in the tunnel management section are summed to obtain the value impact index; the daily usage time for each piece of equipment in the tunnel management section is obtained; the ratio of each piece of equipment's usage time to the total daily usage time is calculated; and the total equipment usage time is summed.

[0019] Based on the personnel information corresponding to each tunnel management section, the corresponding work types and personnel location information are obtained; the work types of each tunnel management section are identified to obtain risky work tasks, which include but are not limited to excavation operations, support operations, and muck removal operations; the number of risky work tasks corresponding to each tunnel management section is counted and marked as the risk task number; 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 with a corresponding flow impact value. The current personnel flow is matched with multiple personnel flow intervals to obtain the corresponding flow impact value; a pre-set activity reference duration is obtained, and the activity time of each person corresponding to the current tunnel management section is compared with the activity reference duration. When the activity time of a person is greater than the activity reference duration, the corresponding person is marked as a resident person, and the number of resident persons corresponding to each tunnel management section is counted. The number of resident persons is added to the flow impact value to obtain the personnel impact index.

[0020] Based on the information on key components of the tunnel, the number of key components corresponding to each tunnel management section is obtained. Key components include, but are not limited to, the distribution of steel arch frames at locations such as the arch crown, arch waist, and sidewalls, and the connection between anchor bolts and rock mass and steel arch frames. A pre-set benchmark number of key components is obtained. When the current number of key components is greater than the benchmark number, the difference between the number of key components and the benchmark number is calculated to obtain the component impact value.

[0021] Using the values ​​of the value impact index and total equipment utilization as the upper and lower bases of a right trapezoid, construct a right trapezoid. The value of the personnel impact index is equal to the right leg of the right trapezoid. Then, using the centroid of the right trapezoid as the starting point, draw a straight line perpendicular to the right trapezoid. The length of the straight line is equal to the value of the component impact. Then, construct a quadrangular pyramid using the right trapezoid and the straight line. Calculate the volume of the quadrangular pyramid and mark the volume value as the engineering risk value. Use the engineering risk value corresponding to each tunnel management section as the corresponding engineering condition information.

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

[0023] Obtain engineering condition information corresponding to each tunnel management section, obtain engineering risk value corresponding to each tunnel management section based on the engineering condition information, divide the engineering risk value into multiple engineering risk value intervals, set an engineering risk level for each engineering risk value interval, match the engineering risk value corresponding to each tunnel management section with multiple engineering risk value intervals to obtain the corresponding engineering risk level, and generate corresponding engineering layout pre-optimization information based on the engineering risk level.

[0024] Step 4: A comprehensive analysis of the tunnel layout pre-optimization information and the engineering layout pre-optimization information is conducted to obtain the sensor deployment scheme, which is as follows:

[0025] Based on the pre-optimized information of tunnel layout and engineering layout, the corresponding tunnel risk level and engineering risk level are obtained. The tunnel risk level and engineering risk level are calculated and summed to obtain the total risk level. Based on the total risk level, the corresponding sensor layout scheme is obtained.

[0026] Step 5: Dynamic adjustment analysis, which specifically involves: obtaining information on changes in the overall risk level by real-time monitoring of the overall risk level corresponding to each tunnel management section, acquiring a pre-set initial risk level change range, and generating corresponding dynamic adjustment information to increase or decrease the monitoring frequency when the risk level change information falls within the initial risk level change range; and generating corresponding dynamic adjustment information to increase or decrease the number of sensors when the risk level change information exceeds the initial risk level change range.

[0027] Step Six: Implementation and Monitoring Phase, which specifically involves: installing sensors based on the sensor deployment plan, monitoring stress data of the tunnel inner wall through the sensors, predicting rockbursts based on the collected stress data using the TMB method, identifying corresponding adjustment actions by analyzing dynamic adjustment information, and pushing out adjustments based on the adjustment actions.

[0028] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows:

[0029] 1. This invention obtains tunnel condition information based on tunnel excavation rock mass parameter data, and obtains corresponding tunnel layout pre-optimization based on the tunnel condition information. It also obtains engineering condition information based on engineering resource information data, and obtains corresponding engineering layout pre-optimization based on the engineering condition information. Then, it comprehensively analyzes the tunnel layout pre-optimization information and the engineering layout pre-optimization information to obtain a sensor deployment scheme. The analysis is based on the specific conditions of the tunnel and the current actual use of the tunnel, making the sensor deployment optimization more in line with actual needs and applicable to a wider range.

[0030] 2. This invention obtains information on the changes in the overall risk level of each tunnel management section by real-time monitoring, and obtains corresponding dynamic adjustment information based on the risk level changes. Then, based on the dynamic adjustment information, the deployment of sensors can be monitored and adjusted in real time to ensure that the sensor installation position is accurate and stable. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not deliberately drawn to scale according to the actual size, but are intended to show the main idea of ​​this application.

[0032] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0033] 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.

[0034] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 In one embodiment of the tunnel rockburst sensor deployment optimization method based on the TBM method, the following steps are included:

[0035] Step 1: Acquire the rock mass parameter data and engineering resource information data of each tunnel management section corresponding to the tunnel being excavated using data acquisition equipment. Specifically, the tunnel is divided into multiple tunnel management sections according to a preset standard length. The rock mass parameter data corresponding to each tunnel management section is acquired using data acquisition equipment. The rock mass parameter data includes tunneling construction parameters and rock mass property parameters. The engineering resource information data corresponding to each tunnel management section is acquired using sensors. The engineering resource information data includes equipment installation information, personnel information, and information on key tunnel components.

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

[0037] The specific analysis method for the tunneling rock mass parameter data is as follows:

[0038] The tunneling rock mass parameter data includes tunneling construction parameters and rock mass property parameters. Tunneling thrust, cutterhead vibration, and tunneling speed are obtained by identifying the tunneling construction parameters. The cutterhead torque is acquired for each unit of time during tunnel excavation. Based on the cutterhead torque, the tunneling thrust corresponding to each unit of time is obtained. A pre-set tunneling thrust threshold is acquired, and the tunneling thrust corresponding to each unit of time is compared with the tunneling thrust threshold. When the tunneling thrust exceeds the threshold, the corresponding tunneling thrust is marked as a risk thrust. The duration corresponding to the risk thrust per unit time is acquired, and the thrust impact value is calculated by multiplying the risk thrust by the duration. When the cutterhead thrust suddenly increases, it may indicate an increase in the hardness of the rock mass ahead or the presence of geological anomalies, such as encountering hard rock interlayers or unbroken boulders. In this case, the stress state of the rock mass will change drastically, easily triggering a rockburst.

[0039] The cutterhead vibration value is obtained based on the cutterhead vibration, and a pre-set detection time period is acquired. The cutterhead vibration value is divided into multiple vibration value intervals, and a vibration risk value is assigned to each interval. The cutterhead vibration values ​​corresponding to multiple unit times within 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. The cutterhead vibration will be transmitted to the surrounding rock mass, and this vibration may excite the propagation of potential fractures within the rock mass. If the vibration frequency is close to the natural frequency of some fractures in the rock mass, it will... Resonance occurs, causing rapid expansion of cracks. This expansion weakens the integrity of the rock mass, making it more susceptible to rockbursts. The number of rockbursts per unit time corresponding to the current tunneling speed is obtained, along with a pre-set rockburst reference number. The number of rockbursts per unit time is compared with this reference number. When the number of rockbursts exceeds the reference number, the portion exceeding the reference number is marked as the tunneling speed shadow value. When the tunneling speed is high, the stress in the rock mass ahead of the tunnel cannot be fully adjusted and released, leading to stronger stress concentration around the excavation face and increasing the frequency of rockbursts.

[0040] Geological structure information is obtained by identifying rock mass property parameters, and the geological structure information corresponding to each tunneling management section is obtained. The pre-set influence area is also obtained. Based on the geological structure information, the special geological structures within the influence area corresponding to each tunneling management section are obtained. The special geological structures include, but are 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 tunneling management section is counted, and the number of special geological structures is marked as the geological influence value.

[0041] The thrust impact value, total vibration risk value, tunneling speed and shadow value, and geological impact value are normalized and their values ​​are taken. Then, the formula is used to... The tunnel risk value SD was calculated; where N, zdf, JZ, and DZ represent the thrust impact value, total vibration risk value, tunneling speed impact value, and geological impact value, respectively. The number is represented as the allowable number; h1, h2, h3, and h4 are preset weighting 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] Based on tunnel condition information, the corresponding tunnel layout pre-optimization is obtained, and the specific analysis method is as follows:

[0043] Obtain tunnel condition information corresponding to each tunnel management section, obtain tunnel risk value corresponding to each tunnel management section based on tunnel condition information, divide 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 multiple tunnel risk value intervals to obtain the corresponding tunnel risk level, and generate corresponding tunnel layout pre-optimization information based on tunnel risk level.

[0044] Step 3: Obtain engineering condition information based on engineering resource information data, and obtain corresponding engineering layout pre-optimization based on engineering condition information; acquire engineering resource information data corresponding to each tunnel management section and analyze the engineering resource information data to obtain engineering condition information, and analyze the engineering condition information to obtain engineering layout pre-optimization information corresponding to each tunnel management section.

[0045] The specific analysis methods for engineering resource information data are as follows:

[0046] The engineering resource information data includes equipment installation information, personnel information, and information on key tunnel components. Equipment installation information for each tunnel management section is obtained; the equipment installation information is identified to obtain equipment value and usage time; a pre-set benchmark value is obtained; the equipment value for each piece of 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; the price difference is calculated by comparing the equipment value of key equipment with the benchmark value; the price differences for each piece of equipment in the tunnel management section are summed to obtain the value impact index; the daily usage time for each piece of equipment in the tunnel management section is obtained; the ratio of each piece of equipment's usage time to the total daily usage time is calculated; and the total equipment usage time is summed.

[0047] Based on the personnel information corresponding to each tunnel management section, the corresponding work types and personnel location information are obtained; the work types of each tunnel management section are identified to obtain risky work tasks, which include but are not limited to excavation operations, support operations, and muck removal operations; the number of risky work tasks corresponding to each tunnel management section is counted and marked as the risk task number; 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 with a corresponding flow impact value. The current personnel flow is matched with multiple personnel flow intervals to obtain the corresponding flow impact value; a pre-set activity reference duration is obtained, and the activity time of each person corresponding to the current tunnel management section is compared with the activity reference duration. When the activity time of a person is greater than the activity reference duration, the corresponding person is marked as a resident person, and the number of resident persons corresponding to each tunnel management section is counted. The number of resident persons is added to the flow impact value to obtain the personnel impact index.

[0048] Based on the information on key components of the tunnel, the number of key components corresponding to each tunnel management section is obtained. Key components include, but are not limited to, the distribution of steel arch frames at locations such as the arch crown, arch waist, and sidewalls, and the connection between anchor bolts and rock mass and steel arch frames. A pre-set benchmark number of key components is obtained. When the current number of key components is greater than the benchmark number, the difference between the number of key components and the benchmark number is calculated to obtain the component impact value.

[0049] Using the values ​​of the value impact index and total equipment utilization as the upper and lower bases of a right trapezoid, construct a right trapezoid. The value of the personnel impact index is equal to the right leg of the right trapezoid. Then, using the centroid of the right trapezoid as the starting point, draw a straight line perpendicular to the right trapezoid. The length of the straight line is equal to the value of the component impact. Then, construct a quadrangular pyramid using the right trapezoid and the straight line. Calculate the volume of the quadrangular pyramid and mark the volume value as the engineering risk value. Use the engineering risk value corresponding to each tunnel management section as the corresponding engineering condition information.

[0050] The engineering condition information is analyzed using the following specific methods:

[0051] Obtain engineering condition information corresponding to each tunnel management section, obtain engineering risk value corresponding to each tunnel management section based on the engineering condition information, divide the engineering risk value into multiple engineering risk value intervals, set an engineering risk level for each engineering risk value interval, match the engineering risk value corresponding to each tunnel management section with multiple engineering risk value intervals to obtain the corresponding engineering risk level, and generate corresponding engineering layout pre-optimization information based on the engineering risk level.

[0052] Step 4: Conduct a comprehensive analysis of the pre-optimized information on tunnel layout and engineering layout to obtain a sensor layout plan; based on the pre-optimized information on tunnel layout and engineering layout, obtain the corresponding tunnel risk level and engineering risk level; calculate and sum the tunnel risk level and 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. By real-time monitoring of the overall risk level corresponding to each tunnel management section, the risk level change information is obtained, and a pre-set initial level change range is acquired. When the level change information is within the initial level change range, corresponding dynamic adjustment information is generated to increase or decrease the monitoring frequency. Furthermore, when the level change information exceeds the initial level change range, corresponding dynamic adjustment information is generated to increase or decrease the number of sensors.

[0054] Step Six: Implementation and Monitoring Phase. Based on the sensor deployment plan, sensors are installed and stress data of the tunnel inner wall is monitored. Based on the collected stress data, rockburst prediction is performed using the TMB method. Corresponding adjustment actions are obtained by identifying dynamic adjustment analysis information and the adjustments are pushed out accordingly.

[0055] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the layout of a tunnel rock burst sensor based on the TBM method, characterized in that, The method comprises the following steps: Step one: obtaining tunneling rock mass parameter data and engineering resource information data of each tunnel management section corresponding to the tunneling tunnel through a collection device; Step two: obtaining tunnel condition information according to the tunneling rock mass parameter data, and obtaining corresponding tunnel layout pre-optimization according to the tunnel condition information, which specifically comprises: obtaining tunneling rock mass parameter data of each tunnel management section and analyzing the tunneling rock mass parameter data to obtain tunnel condition information, and analyzing the tunnel adjustment information to obtain tunnel layout pre-optimization information corresponding to each tunnel management section; The analysis of the tunneling rock mass parameter data specifically comprises: the tunneling rock mass parameter data comprises tunneling construction parameters and rock mass property parameters; the tunneling thrust, cutterhead vibration and tunneling speed are obtained by identifying the tunneling construction parameters; the torque of the cutterhead per unit time corresponding to the tunneling is obtained, the tunneling thrust corresponding to each unit time is obtained according to the cutterhead torque, a pre-set tunneling thrust threshold is obtained, 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 a risk thrust, the duration corresponding to the risk thrust within a unit time is obtained, the risk thrust is multiplied by the duration to obtain a thrust influence value; The cutterhead vibration value is obtained according to the cutterhead vibration, a pre-set detection time period is obtained, the cutterhead vibration value is divided into a plurality of vibration value intervals, each vibration value interval is set with a vibration risk value, the cutterhead vibration value corresponding to a plurality of unit times within the current detection time period is matched with the plurality of vibration value intervals to obtain the corresponding vibration risk value, and the vibration risk values corresponding to each unit time are summed to obtain a vibration risk total value; The rock burst frequency corresponding to the tunneling speed within the current unit time is obtained, a pre-set rock burst reference number is obtained, the rock burst frequency corresponding to the current unit time is compared with the rock burst reference number, when the rock burst frequency is greater than the rock burst reference number, the part of the rock burst frequency exceeding the rock burst reference number is marked as a tunneling speed impact value; The geological structure information is obtained by identifying the rock mass property parameters, the geological structure information corresponding to each tunnel management section is obtained, a pre-set influence area is obtained, the special geological structure within the influence area corresponding to each tunnel management section is obtained according to the geological structure information; the thrust influence value, the vibration risk total value, the tunneling speed impact value and the geological influence value are comprehensively calculated to obtain a tunnel risk value; The tunnel risk value corresponding to each tunnel management section is taken as the corresponding tunnel condition information; The specific analysis method of obtaining the corresponding tunnel layout pre-optimization according to the tunnel condition information is as follows: The tunnel condition information corresponding to each tunnel management section is obtained, the tunnel risk value corresponding to each tunnel management section is obtained according to the tunnel condition information, the tunnel risk value is divided into a plurality of tunnel risk value intervals, each tunnel risk value interval is set with a tunnel risk level, the tunnel risk value corresponding to each tunnel management section is matched with the plurality of tunnel risk value intervals to obtain the corresponding tunnel risk level, and the corresponding tunnel layout pre-optimization information is generated according to the tunnel risk level; Step three: obtaining engineering condition information according to the engineering resource information data, and obtaining corresponding engineering layout pre-optimization according to the engineering condition information; Step four: obtaining the sensor layout scheme by comprehensively analyzing the tunnel layout pre-optimization information and the engineering layout pre-optimization information; Step five: dynamic adjustment analysis, which is specifically: obtaining the grade change information of the total risk grade of each tunnel management section by real-time monitoring of the total risk grade of each tunnel management section, obtaining the preset primary grade change interval, when the grade change information corresponds to the primary grade change interval, generating corresponding dynamic adjustment information for increasing and decreasing the monitoring frequency; and when the grade change information corresponds to more than the primary grade change interval, generating corresponding dynamic adjustment information for increasing and decreasing the number of sensors; Step six: implementation and monitoring stage. 2.The TBM method based tunnel rock burst sensor layout optimization method according to claim 1, wherein, The tunnel management section corresponding to the tunneling tunnel is divided into multiple tunnel management sections according to a preset standard length, and the tunneling rock mass parameter data corresponding to each tunnel management section is obtained by a collection device, wherein the tunneling rock mass parameter data includes tunneling construction parameters and rock mass property parameters; the engineering resource information data corresponding to each tunnel management section is obtained by an inductor, and the engineering resource information data includes device installation information, personnel information, and tunnel key component information. 3.The TBM method based tunnel rock burst sensor layout optimization method according to claim 1, wherein, The thrust influence value, vibration total risk value, tunneling speed shadow value, and geological influence value are comprehensively calculated, which is specifically: The thrust influence value, the vibration risk total value, the tunneling speed shadow value and the geological influence value are normalized and the values are taken, and a formula is used to calculate the tunnel risk value SD; wherein N, zdf, JZ and DZ represent the thrust influence value, the vibration risk total value, the tunneling speed shadow value and the geological influence value respectively, and the construction allowance number is represented; h1, h2, h3 and h4 are all preset weight factors. 4.The TBM method based tunnel rock burst sensor layout optimization method of claim 1, wherein, The engineering condition information is obtained according to the engineering resource information data, and the corresponding engineering layout pre-optimization is obtained according to the engineering condition information, which is specifically: obtaining the engineering resource information data corresponding to each tunnel management section and analyzing the engineering resource information 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 resource information data is analyzed, and the specific analysis method is: the engineering resource information data includes device installation information, personnel information, and tunnel key component information; the device installation information corresponding to each tunnel management section is obtained, the device installation information is identified to obtain the device value and the device use time, a preset reference value is obtained, the device value corresponding to each device of the tunnel management section is compared with the reference value, when the device value is greater than the reference value, the corresponding device is marked as a key device, and the device value corresponding to the key device is compared with the reference value to obtain a price difference, the price difference corresponding to each device of the tunnel management section is calculated and summed to obtain a value influence index; The use time of each device of the tunnel management section per day is obtained, the use time of each device is calculated by ratio to obtain the device use degree, and the use degrees of each device are calculated and summed to obtain the total device use degree; The working type and personnel position information corresponding to each tunnel management section are obtained according to the personnel information corresponding to each tunnel management section; The working type of each tunnel management section is identified to obtain a risk work task, the number of risk work tasks corresponding to the tunnel management section is counted and marked as a risk task number; The personnel position information is identified to obtain the personnel flow and activity time corresponding to each tunnel management section, the personnel flow is divided into multiple personnel flow intervals, each personnel flow interval corresponds to a flow influence value, the current personnel flow is matched with the multiple personnel flow intervals to obtain the corresponding flow influence value, a pre-set activity reference duration is obtained, the activity time of each personnel corresponding to the current tunnel management section is compared with the activity reference duration, when the activity time of the personnel is greater than the activity reference duration, the corresponding personnel is marked as a resident personnel, the number of resident personnel corresponding to each tunnel management section is counted, and the resident personnel number and the flow influence value are added to obtain a personnel influence index; The number of key components corresponding to each tunnel management section is obtained according to the tunnel key component information, a pre-set key component reference number is obtained, when the current number of key components is greater than the key component reference number, the difference between the number of key components and the key component reference number is calculated to obtain a component influence value; A right triangle is constructed with the values of the value influence index and the total equipment usage degree as the upper base and the lower base, the value of the personnel influence index is equal to the right-angle waist of the right triangle, a straight line perpendicular to the right triangle is drawn from the center of gravity of the right triangle as the starting point, the length of the straight line is equal to the value of the component influence value, a four-pyramid is constructed with the right triangle and the straight line, the volume of the four-pyramid is calculated and the value of the volume is marked as an engineering risk value, and the engineering risk value corresponding to each tunnel management section is taken as the corresponding engineering condition information. 5.The TBM method based tunnel rock burst sensor layout optimization method according to claim 4, wherein, The engineering condition information is analyzed, and the specific analysis method is as follows: The engineering condition information corresponding to each tunnel management section is obtained, 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, an engineering risk grade is set for each engineering risk value interval, the engineering risk value corresponding to each tunnel management section is matched with the multiple engineering risk value intervals to obtain the corresponding engineering risk grade, and the corresponding engineering layout pre-optimization information is generated according to the engineering risk grade. 6.The TBM method based tunnel rock burst sensor layout optimization method according to claim 1, wherein, The tunnel layout pre-optimization information and the engineering layout pre-optimization information are comprehensively analyzed to obtain a sensor layout scheme, and the specific method is as follows: the tunnel risk grade and the engineering risk grade are obtained according to the tunnel layout pre-optimization information and the engineering layout pre-optimization information, the risk total grade is obtained by calculating and summing the tunnel risk grade and the engineering risk grade, and the corresponding sensor layout scheme is obtained according to the risk total grade. 7.The TBM method based tunnel rock burst sensor layout optimization method according to claim 1, wherein, The implementation and monitoring stage, and the specific method is as follows: The sensor installation is performed based on the sensor layout scheme, the stress data of the inner wall of the tunnel is monitored through the sensor, the adjustment action is obtained by identifying the dynamic adjustment analysis information, and the adjustment action is pushed according to the adjustment action.

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