A tunnel excavation rock burst data analysis report automatic generation method

Abstract

The present application relates to the technical field of geological engineering, in particular to a kind of tunnel excavation rock burst data analysis report automatic generation method;The present application is arranged in tunnel by optical fiber acceleration sensor group real-time monitoring stress release event in rock stratum;The occurrence position of stress release event is calculated using multi-difference positioning equation, to provide accurate spatial information for subsequent rock burst risk assessment;The stress release event monitored is analyzed in time domain and frequency domain, key characteristic parameters are extracted, analysis eigenvectors are constructed, and scientific basis is provided for rock burst risk assessment;The potential harm degree of rock burst event is evaluated by calculating clustering energy index;The present application establishes the fitting surface equation of the coordinate of the occurrence position of the discrete event in microseismic event clustering, obtains the rock burst early warning line, and displays the rock burst early warning line through three-dimensional model, which intuitively presents the rock burst risk area and provides intuitive visual reference for construction personnel.

Description

Technical Field

[0001] The present invention relates to the field of geological engineering technology, and in particular to a method for automatically generating a tunnel excavation rock burst data analysis report. Background Art

[0002] Rockburst, also known as rock burst, is a sudden destructive phenomenon that occurs in open-air rock masses deep underground or in areas of high tectonic stress. Rockburst occurs when the strain energy accumulated in the open-air rock mass is suddenly and violently released, causing a brittle fracture similar to an explosion. When the high elastic strain energy accumulated in the rock mass exceeds the energy consumed by the rock's failure, the structural equilibrium of the rock mass is disrupted. The excess energy causes the rock to explode, causing rock fragments to break away and break out of the rock mass.

[0003] Rockburst is a common geological disaster in tunneling projects, causing severe damage to tunnel structures and construction equipment, and even threatening the lives of construction workers. To effectively address rockburst, the engineering and academic communities have been exploring and researching rockburst prediction, monitoring, and prevention measures.

[0004] However, existing methods for analyzing tunneling rockburst data mostly rely on manual operation and judgment, which presents numerous shortcomings. First, manual data analysis is not only time-consuming and labor-intensive, but also easily influenced by personal experience and subjective judgment, leading to inaccurate and inconsistent analysis results. Second, with the continuous advancement of tunneling projects, the amount of rockburst data generated is becoming increasingly massive, and manual analysis can no longer meet the timeliness and accuracy requirements of data processing. Furthermore, existing rockburst analysis software or platforms still have limitations in terms of functionality and usability, such as insufficient flexibility in data import and processing, a single method for presenting analysis results, and a lack of user customization and scalability.

[0005] In view of the above problems, it is necessary to propose an automatic generation method for tunnel excavation rockburst data analysis report. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems existing in the background technology and to propose a method for automatically generating a tunnel excavation rock burst data analysis report.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] Step 1: Record stress release events;

[0009] Taking the tunnel line starting stake number K0 as the starting point, a sensor group is arranged at every preset monitoring distance along the tunnel forward direction.

[0010] The sensor group is arranged on the same tunnel cross section and includes at least two optical fiber acceleration sensors respectively located at the arch waist positions on both sides.

[0011] The optical fiber acceleration sensor is placed in a steel sleeve and screwed into a whole with the sleeve by screws. The sleeve is placed in a borehole at a preset distance from the rock mass and coupled to the surrounding rock by an anchoring agent.

[0012] All fiber optic acceleration sensors arranged in the tunnel are numbered with a number symbol i, i=1, 2, 3, ..., n; n is the total number of fiber optic acceleration sensors.

[0013] A three-dimensional coordinate system is established with the starting pile number K0 of the monitored tunnel line as the origin o, the horizontal plane as the XoY plane, and the vertical direction as the Z axis, and the coordinates of each fiber optic acceleration sensor in the three-dimensional coordinate system are obtained (xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn).

[0014] Each fiber optic accelerometer indirectly monitors stress release events in nearby rock formations by recording the waveform of the received optical signal.

[0015] Every time a preset window time T passes, each fiber optic acceleration sensor i converts the recorded optical signal waveform into a time domain signal Si(t), where 0≤t≤T. The time domain signal reflects the change of the vibration acceleration detected by the sensor over time.

[0016] Step 2: Time domain analysis:

[0017] Obtain the maximum amplitude MaxSi(t) and minimum amplitude MinSi(t) of the time domain signal Si(t). Obtain the time when the maximum amplitude MaxSi(t) occurs, which is recorded as the event characteristic time ti of the optical fiber acceleration sensor i.

[0018] By formula Calculate the amplitude fluctuation parameter p1i and the root mean square parameter p2i of the time domain signal Si(t).

[0019] After completing the domain analysis, proceed to step 3.

[0020] Step 3: Frequency domain analysis:

[0021] First, the time domain signal Si(t) is transformed from the time domain to the frequency domain through Fourier transform. The transformation formula is:

[0022] Wherein Si(f) is the frequency domain signal obtained after transformation, f is the frequency; wherein j is the imaginary unit and j×j=-1.

[0023] Subsequently, a proportional signal analysis is performed to obtain the maximum value of the frequency domain signal e0i=MaxSi(f), the maximum value of the frequency domain signal e1i at the first characteristic frequency f1Min to f1Max, and the maximum value of the frequency domain signal e2i at the second characteristic frequency f2Min to f2Max, that is:

[0024]

[0025] By formula Calculate the proportional anomaly factor q1i.

[0026] Finally, perform differential scale signal analysis to obtain the maximum value of the first-order derivative of the frequency domain signal Si(f) with respect to frequency

[0027] Generate the analysis feature vector (p1i, p2i, q1i, q2i) of the optical fiber acceleration sensor i, then end the frequency domain analysis and proceed to step 4.

[0028] Step 4: Comparison of analysis results and signal output;

[0029] By weighted Euclidean distance formula

[0030] Calculate the analytical eigenvector (p1i, p2i, q1i, q2i) and the reference eigenvector of the fiber optic acceleration sensor i The weighted Euclidean distance Wed(i) is calculated, where k1, k2, k3 and k4 are all preset weight factors.

[0031] When the weighted Euclidean distance Wed(i) exceeds the minimum threshold Wed_Min, a stress release event is determined to have occurred. Subsequently, an event signal is generated and the fiber optic acceleration sensor number i, event feature time ti, and sensor coordinates (xi, yi, zi) are output. Simultaneously, if an event signal is generated, the output status symbol signal(ti, i) is set to 1; otherwise, the output status symbol signal(ti, i) is set to 0.

[0032] Step 5: Energy analysis and rockburst risk assessment;

[0033] S501, stress release event positioning;

[0034] Every time a window time T passes, a status symbol check is performed. If a stress release event is detected within the previous window time T, that is, the values ​​of all status symbols output by each fiber optic acceleration sensor are not all 0, the coordinate positioning operation is entered;

[0035] Coordinate positioning operation:

[0036] Assuming that the coordinates of the stress release event are (x, y, z), the multi-difference positioning equation is established: Where εi is the actual arrival time tolerance of each fiber optic acceleration sensor i, and ti correct is the actual arrival time after error correction; Vi is the preset basic wave velocity; ri is the arrival time residual, that is, the difference between the theoretical arrival time and the actual arrival time.

[0037] The multi-difference positioning equation is traversed over all fiber optic acceleration sensor number symbols i to obtain the arrival time residual ri corresponding to each fiber optic acceleration sensor i.

[0038] Set the constraints of the multi-difference positioning equation: Under the constraints, x, y, and z are solved to obtain the occurrence position coordinates (x, y, z) of the stress release event in each window time T.

[0039] If no stress release event is detected within a certain window time T, that is, the values ​​of all status symbols output by each fiber optic acceleration sensor are 0, the coordinate positioning operation will not be entered and the position coordinates will not be output.

[0040] S502, location clustering occurs;

[0041] The occurrence position coordinates of the stress release events generated in each window time T are spatially merged, and a distance threshold d_threshold is preset.

[0042] All stress release events whose distance between occurrence locations is less than the threshold d_threshold are classified into the same event cluster group.

[0043] Specifically, a unique identifier group_id is set up for each event cluster group to distinguish different cluster groups; the group_id number starts from 1 and increases in sequence, that is, group_id = 1, 2, 3, ..., m; where m represents the total number of event cluster groups.

[0044] Each event cluster group j includes several stress release event location coordinates (x, y, z). Each stress release event location coordinate in the same event cluster group is assigned a unique number u: u = 1, 2, 3, ..., u(j). Where u(j) is the total number of stress release event location coordinates included in event cluster group j.

[0045] The coordinates of the event occurrence locations in event cluster group j are marked as (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)).

[0046] Define the center coordinates of each event cluster group

[0047] S503, stress release event frequency analysis;

[0048] Evaluate the frequency and time series concentration of stress release events in each event cluster group in the recent period. Get the current time t0, for each event cluster group j, through the formula Calculate the frequency index Count(j), where τ is the preset attenuation base.

[0049] S504, event aftereffect analysis;

[0050] By formula Calculate the clustering energy index E(j) of each event cluster group.

[0051] The cluster energy index corresponding to each event cluster is marked in the three-dimensional space diagram, and the event cluster whose cluster energy index E exceeds the preset threshold is marked as a high-energy rockburst event cluster and highlighted.

[0052] Event clusters whose cluster energy index E is less than or equal to a preset threshold are marked as microseismic event clusters.

[0053] S505, microseismic event prediction;

[0054] Obtain the coordinates of all discrete event locations in the microseismic event cluster (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)).

[0055] The fitting surface equation for all discrete event location coordinate points is z = ax + by + c, where a, b, and c are preset fitting surface parameters.

[0056] Establish the loss function:

[0057] Solving a system of equations Get the fitting surface parameters a, b and c.

[0058] Furthermore, the intersection line between the fitting surface z=ax+by+c and the tunnel surrounding rock surface is solved and recorded as the rock burst warning line.

[0059] The rockburst warning line is highlighted through the three-dimensional model display, and the mileage pile number where the rockburst warning line is located is output.

[0060] Step 6: Generate rockburst warning and analysis report;

[0061] Using natural language processing technology combined with chart generation algorithms, data analysis results are automatically combined with preset report templates and converted into natural language, automatically filling in analysis results and generating standardized rockburst data analysis reports.

[0062] The report content includes but is not limited to: event clustering 3D model, 3D model of rockburst warning line, and mileage pile number where the rockburst warning line is located.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] (1) The present invention can monitor stress release events in rock formations in real time and realize instant perception of rock bursts by arranging a group of fiber optic acceleration sensors in the tunnel. By using the multi-difference positioning equation, the location of the stress release event can be accurately calculated, providing accurate spatial information for subsequent rock burst risk assessment; the monitored stress release events are analyzed in the time domain and frequency domain, key characteristic parameters are extracted, and analysis characteristic vectors are constructed to provide a scientific basis for rock burst risk assessment. By comparing the weighted Euclidean distance formula with the benchmark characteristic vector, it is possible to accurately determine whether the stress release event constitutes a rock burst threat and output a corresponding warning signal. Comprehensive analysis and intelligent warning of rock bursts are achieved;

[0065] (2) The present invention spatially aggregates the locations of stress release events to form event clusters, facilitating analysis of the distribution patterns and trends of rockburst events. By calculating the cluster energy index, the potential hazard level of rockburst events can be assessed, providing a basis for developing targeted prevention and control measures.

[0066] (3) The present invention establishes a fitting surface equation for the coordinates of the locations where discrete events occur in microseismic event clustering, and solves it to obtain the rockburst warning line, thereby achieving accurate prediction of potential rockburst areas; the rockburst warning line is displayed through a three-dimensional model, which intuitively presents the rockburst risk area and provides an intuitive visual reference for construction personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0068] Figure 1 is a flow chart of the method of the present invention;

[0069] Figure 2 It is a schematic diagram of the arrangement of the sensor group of the present invention;

[0070] Figure 3 This is a schematic diagram of the time domain signal of the fiber optic acceleration sensor;

[0071] Figure 4 This is a schematic diagram of event clustering results;

[0072] Figure 5Schematic diagram of the fitting surface and rockburst warning line. DETAILED DESCRIPTION

[0073] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0074] See also Figure 1 As shown, a method for automatically generating a tunnel excavation rock burst data analysis report includes the following steps:

[0075] Step 1: Record stress release events;

[0076] See also Figure 2 As shown, starting from the tunnel line starting pile number K0, a sensor group is arranged at every preset monitoring distance along the tunnel forward direction.

[0077] The sensor group is arranged on the same tunnel cross section and includes at least two optical fiber acceleration sensors respectively located at the arch waist positions on both sides.

[0078] The optical fiber acceleration sensor is placed in a steel sleeve and screwed into a whole with the sleeve by screws. The sleeve is placed in a borehole at a preset distance from the rock mass and coupled to the surrounding rock by an anchoring agent.

[0079] All fiber optic acceleration sensors arranged in the tunnel are numbered with a number symbol i, i=1, 2, 3, ..., n; n is the total number of fiber optic acceleration sensors.

[0080] A three-dimensional coordinate system is established with the starting pile number K0 of the monitored tunnel line as the origin o, the horizontal plane as the XoY plane, and the vertical direction as the Z axis, and the coordinates of each fiber optic acceleration sensor in the three-dimensional coordinate system are obtained (xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn).

[0081] Each fiber optic accelerometer indirectly monitors stress release events in nearby rock formations by recording the waveform of the received optical signal.

[0082] It's important to note that stress-release events, typified by rockbursts, not only involve rock fractures during their development but also trigger seismic waves. When these seismic waves travel through rock formations and act on fiber optic sensors, they induce minute strains in the fibers. These microstrains cause subtle changes in the frequency and phase of the optical signal transmitted within them. By recording the optical signal waveform, we can indirectly record information about the frequency, intensity, and location of the vibration points, enabling monitoring and analysis of rock stress-release events.

[0083] See also Figure 3 As shown, every time a preset window time T passes, each fiber optic acceleration sensor i converts the recorded optical signal waveform into a time domain signal Si(t), where 0≤t≤T. The time domain signal reflects the change of the vibration acceleration detected by the sensor over time.

[0084] Step 2: Time domain analysis:

[0085] Obtain the maximum amplitude MaxSi(t) and minimum amplitude MinSi(t) of the time domain signal Si(t). Obtain the time when the maximum amplitude MaxSi(t) occurs, which is recorded as the event characteristic time ti of the optical fiber acceleration sensor i.

[0086] By formula Calculate the amplitude fluctuation parameter p1i and the root mean square parameter p2i of the time domain signal Si(t).

[0087] After completing the domain analysis, proceed to step 3.

[0088] Step 3: Frequency domain analysis:

[0089] First, the time domain signal Si(t) is transformed from the time domain to the frequency domain through Fourier transform. The transformation formula is:

[0090] Wherein Si(f) is the frequency domain signal obtained after transformation, f is the frequency; wherein j is the imaginary unit and j×j=-1.

[0091] Subsequently, a proportional signal analysis is performed to obtain the maximum value of the frequency domain signal e0i=MaxSi(f), the maximum value of the frequency domain signal e1i at the first characteristic frequency f1Min to f1Max, and the maximum value of the frequency domain signal e2i at the second characteristic frequency f2Min to f2Max, that is:

[0092]

[0093] By formula Calculate the proportional anomaly factor q1i.

[0094] Finally, perform differential scale signal analysis to obtain the maximum value of the first-order derivative of the frequency domain signal Si(f) with respect to frequency

[0095] Generate the analysis feature vector (p1i, p2i, q1i, q2i) of the optical fiber acceleration sensor i, then end the frequency domain analysis and proceed to step 4.

[0096] Step 4: Comparison of analysis results and signal output;

[0097] By weighted Euclidean distance formula

[0098] Calculate the analytical eigenvector (p1i, p2i, q1i, q2i) and the reference eigenvector of the fiber optic acceleration sensor i The weighted Euclidean distance Wed(i) is calculated, where k1, k2, k3 and k4 are all preset weight factors.

[0099] When the weighted Euclidean distance Wed(i) exceeds the minimum threshold Wed_Min, a stress release event is determined to have occurred. Subsequently, an event signal is generated and the fiber optic acceleration sensor number i, event feature time ti, and sensor coordinates (xi, yi, zi) are output. Simultaneously, if an event signal is generated, the output status symbol signal(ti, i) is set to 1; otherwise, the output status symbol signal(ti, i) is set to 0.

[0100] Step 5: Energy analysis and rockburst risk assessment;

[0101] S501, stress release event positioning;

[0102] Every time a window time T passes, a status symbol check is performed. If a stress release event is detected within the previous window time T, that is, the values ​​of all status symbols output by each fiber optic acceleration sensor are not all 0, the coordinate positioning operation is entered;

[0103] Coordinate positioning operation:

[0104] Assuming that the coordinates of the stress release event are (x, y, z), the multi-difference positioning equation is established: Where εi is the actual arrival time tolerance of each fiber optic acceleration sensor i, and ti correct is the actual arrival time after error correction; Vi is the preset basic wave velocity; ri is the arrival time residual, that is, the difference between the theoretical arrival time and the actual arrival time.

[0105] The multi-difference positioning equation is traversed over all fiber optic acceleration sensor number symbols i to obtain the arrival time residual ri corresponding to each fiber optic acceleration sensor i.

[0106] Set the constraints of the multi-difference positioning equation: Under the constraints, x, y, and z are solved to obtain the occurrence position coordinates (x, y, z) of the stress release event in each window time T.

[0107] If no stress release event is detected within a certain window time T, that is, the values ​​of all status symbols output by each fiber optic acceleration sensor are 0, the coordinate positioning operation will not be entered and the position coordinates will not be output.

[0108] S502, location clustering occurs;

[0109] The occurrence position coordinates of the stress release events generated in each window time T are spatially merged, and a distance threshold d_threshold is preset.

[0110] All stress release events whose distance between occurrence locations is less than the threshold d_threshold are classified into the same event cluster group.

[0111] Specifically, a unique identifier group_id is set up for each event cluster group to distinguish different cluster groups; the group_id number starts from 1 and increases in sequence, that is, group_id = 1, 2, 3, ..., m; where m represents the total number of event cluster groups.

[0112] Each event cluster group j includes several stress release event location coordinates (x, y, z). Each stress release event location coordinate in the same event cluster group is assigned a unique number u: u = 1, 2, 3, ..., u(j). Where u(j) is the total number of stress release event location coordinates included in event cluster group j.

[0113] The coordinates of the event occurrence locations in event cluster group j are marked as (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)).

[0114] Define the center coordinates of each event cluster group

[0115] It should be noted that each event cluster represents a group of stress release events that occur consecutively in time and are close in space. The center coordinates of these event clusters provide the geometric center positions of these events in spatial distribution.

[0116] S503, stress release event frequency analysis;

[0117] Evaluate the frequency and time series concentration of stress release events in each event cluster group in the recent period. Get the current time t0, for each event cluster group j, through the formula Calculate the frequency index Count(j), where τ is the preset attenuation base.

[0118] It should be noted that the frequency index is proportional to the frequency, concentration, and temporal proximity of stress release events. A larger frequency index indicates that event cluster group j has recorded more stress release events in the recent past.

[0119] S504, event aftereffect analysis;

[0120] By formula Calculate the clustering energy index E(j) of each event cluster group.

[0121] See also Figure 4 As shown, the cluster energy index corresponding to each event cluster is marked in the three-dimensional space diagram, and the event cluster whose cluster energy index E exceeds the preset threshold is marked as a high-energy rock burst event cluster and highlighted.

[0122] Event clusters whose cluster energy index E is less than or equal to a preset threshold are marked as microseismic event clusters.

[0123] It should be noted that microseismic events in tunnels are often accompanied by crack expansion, joint development, and stress concentration. Just before a rockburst, microseismic events gradually increase, accompanied by high-energy microseismic events. The spatial distribution of microseismic events clearly corresponds to the location of a rockburst or stress concentration zone. Recording microseismic events can visualize stress transfer paths or concentration locations, providing early warning of rockbursts.

[0124] S505, microseismic event prediction;

[0125] Obtain the coordinates of all discrete event locations in the microseismic event cluster (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)).

[0126] The fitting surface equation for all discrete event location coordinate points is z = ax + by + c, where a, b, and c are preset fitting surface parameters.

[0127] Establish the loss function:

[0128] Solving a system of equations Get the fitting surface parameters a, b and c.

[0129] Furthermore, the intersection line between the fitting surface z=ax+by+c and the tunnel surrounding rock surface is solved and recorded as the rock burst warning line.

[0130] See also Figure 5 As shown, the rock burst warning line is highlighted through the three-dimensional model display, and the mileage pile number where the rock burst warning line is located is output.

[0131] Step 6: Generate rockburst warning and analysis report;

[0132] Using natural language processing technology combined with chart generation algorithms, data analysis results are automatically combined with preset report templates and converted into natural language, automatically filling in analysis results and generating standardized rockburst data analysis reports.

[0133] The report content includes but is not limited to: event clustering 3D model, 3D model of rockburst warning line, and mileage pile number where the rockburst warning line is located.

[0134] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0135] It should also be understood that the terms used in this disclosure are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations;

[0136] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for automatically generating a tunnel excavation rock burst data analysis report, characterized in that: The following steps are involved: Step 1: Recording stress release events: Arrange a fiber optic accelerometer group in the tunnel; Use the fiber optic accelerometer to monitor stress release events in the nearby rock formation by recording the waveform of the received optical signal, and obtain a time domain signal Si(t); Step 2: Time domain analysis: Convert the optical signal waveform recorded by each fiber optic acceleration sensor into a time domain signal, and obtain the maximum amplitude, minimum amplitude and their occurrence time in the time domain signal; calculate the amplitude fluctuation parameters and root mean square parameters of the time domain signal; Step 3: Frequency domain analysis: The time domain signal is converted into a frequency domain signal through Fourier transform; the frequency domain signal is subjected to proportional and differential scale signal analysis to calculate the proportional anomaly factor and generate the analysis feature vector of the fiber optic acceleration sensor; The specific process of frequency domain analysis is: First, the time domain signal Si(t) is transformed from the time domain to the frequency domain through Fourier transform. The transformation formula is: Where Si(f) is the frequency domain signal obtained after transformation, and f is the frequency; where j is the imaginary unit and j×j=-1; Subsequently, a proportional signal analysis is performed to obtain the maximum value of the frequency domain signal e0i=MaxSi(f), the maximum value of the frequency domain signal e1i at the first characteristic frequency f1Min to f1Max, and the maximum value of the frequency domain signal e2i at the second characteristic frequency f2Min to f2Max, that is: ; By formula Calculate the proportional anomaly factor q1i; Finally, perform differential scale signal analysis to obtain the maximum value of the first-order derivative of the frequency domain signal Si(f) with respect to frequency. ; Generate the analysis feature vector (p1i, p2i, q1i, q2i) of the fiber optic acceleration sensor i, and then end the frequency domain analysis; Step 4: Compare the analysis results and output the signal; calculate the weighted Euclidean distance between the analysis feature vector and the reference feature vector using the weighted Euclidean distance formula; when the weighted Euclidean distance is greater than the minimum threshold, determine that a stress release event has occurred, and output the corresponding sensor number, event feature time, sensor coordinates, and status symbol; Step 5: Energy analysis and rockburst risk assessment; S501, stress release event location; S502, occurrence location clustering; S503, stress release event frequency analysis; S504, event aftereffect analysis; S505, microseismic event prediction; Step 6: Generate rockburst warning and analysis report; Using natural language processing technology combined with a chart generation algorithm, the data analysis results are automatically combined with the preset report template and converted into natural language, the analysis results are automatically filled in, and a standardized rockburst data analysis report is generated; the report content includes but is not limited to: a 3D model of event clustering, a 3D model of the rockburst warning line, and the mileage pile number where the rockburst warning line is located.

2. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The arrangement method and monitoring process of the fiber optic acceleration sensor group described in step 1 are specifically as follows: Starting from the tunnel line starting stake number K0, a sensor group is arranged at every preset monitoring distance along the tunnel forward direction; The sensor group is arranged on the same tunnel cross section and includes at least two optical fiber acceleration sensors respectively located at the arch waist positions on both sides; The fiber optic acceleration sensor is placed in a steel sleeve and screwed into a whole with the sleeve by screws. The sleeve is placed in a borehole at a preset distance from the rock mass and coupled to the surrounding rock through an anchoring agent. All fiber optic acceleration sensors arranged in the tunnel are numbered i, where i=1, 2, 3, ..., n; n is the total number of fiber optic acceleration sensors; A three-dimensional coordinate system is established with the starting stake K0 of the monitored tunnel line as the origin o, the horizontal plane as the XoY plane, and the vertical direction as the Z axis. The coordinates of each fiber optic acceleration sensor in the three-dimensional coordinate system are obtained (xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn). Each fiber optic accelerometer indirectly monitors stress release events in nearby rock formations by recording the waveform of the received optical signal; Every time a preset window time T passes, each fiber optic acceleration sensor i converts the recorded optical signal waveform into a time domain signal Si(t), where 0≤t≤T. The time domain signal reflects how the vibration acceleration detected by the sensor changes over time.

3. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of time domain analysis described in step 2 is as follows: Obtain the maximum amplitude MaxSi(t) and the minimum amplitude MinSi(t) in the time domain signal Si(t); obtain the generation time of the maximum amplitude MaxSi(t), which is recorded as the event characteristic time ti of the optical fiber acceleration sensor i; By formula Calculate the amplitude fluctuation parameter p1i and the root mean square parameter p2i of the time domain signal Si(t).

4. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of calculating the weighted Euclidean distance described in step 4 is: By weighted Euclidean distance formula Calculate the analytical eigenvector (p1i, p2i, q1i, q2i) and the reference eigenvector ( , , , ), where k1, k2, k3 and k4 are preset weight factors; When the weighted Euclidean distance Wed(i) is greater than the minimum threshold Wed_Min, a stress release event is determined to have occurred. Subsequently, an event signal is generated and the fiber optic acceleration sensor number symbol i, event characteristic time ti, and sensor coordinates (xi, yi, zi) are output. At the same time, if an event signal is generated, the value of the output status symbol signal(ti, i) is 1, otherwise the value of the output status symbol signal(ti, i) is 0.

5. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of coordinate positioning operation in S501 is: Assuming that the coordinates of the stress release event are (x, y, z), the multi-difference positioning equation is established: ; where εi is the actual arrival time tolerance error of each fiber optic acceleration sensor i, is the actual arrival time after error correction; Where Vi is the preset basic wave velocity; ri is the arrival time residual, that is, the difference between the theoretical arrival time and the actual arrival time; Apply the multi-difference positioning equation to all fiber optic acceleration sensor number symbols i to obtain the arrival time residual ri corresponding to each fiber optic acceleration sensor i; Set the constraints of the multi-difference positioning equation: Solve x, y, and z under the constraints to obtain the coordinates (x, y, z) of the occurrence position of the stress release event in each window time T; If no stress release event is detected within a certain window time T, that is, the values ​​of all status symbols output by each fiber optic acceleration sensor are 0, the coordinate positioning operation will not be entered and the position coordinates will not be output.

6. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of location clustering described in S502 is as follows: The occurrence coordinates of the stress release events generated in each window time T are spatially merged, and a distance threshold d_threshold is preset; All stress release events whose distance between occurrence locations is less than the threshold d_threshold are classified into the same event cluster group; Specifically, a unique identifier group_id is set up for each event cluster group to distinguish different cluster groups; the group_id number starts from 1 and increases in sequence, that is, group_id = 1, 2, 3, ..., m; where m represents the total number of event cluster groups; Each event cluster group j includes several stress release event location coordinates (x, y, z), and a unique number u is matched to each stress release event location coordinate in the same event cluster group; u = 1, 2, 3, ..., u (j); Where u(j) is the total number of stress release event location coordinates contained in event cluster group j; The coordinates of the event locations in event cluster group j are marked as (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)); Define the center coordinates of each event cluster group (xj_0, yj_0, zj_0) = ( , , ).

7. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of stress release event frequency analysis described in S503 is: Get the current time t0, for each event cluster group j, through the formula Calculate the frequency index Count(j), where τ is the preset attenuation base.

8. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of the event after-effect analysis described in S504 is as follows: By formula Calculate the clustering energy index E(j) of each event cluster group; The cluster energy index corresponding to each event cluster is marked in the three-dimensional space graph, and the event cluster whose cluster energy index E exceeds the preset threshold is marked as a high-energy rockburst event cluster and highlighted; Event clusters whose cluster energy index E is less than or equal to a preset threshold are marked as microseismic event clusters.

9. The method for automatically generating a tunnel excavation rockburst data analysis report according to claim 1, characterized in that: The specific process of microseismic event prediction described in S505 is as follows: Obtain the coordinates of all discrete event locations in the microseismic event cluster (xu, yu, zu) = (x1, y1, z1), (x2, y2, z2), ..., (xu(j), yu(j), zu(j)); The fitting surface equation for all discrete event location coordinate points is established as z=ax+by+c; where a, b and c are the preset fitting surface parameters; Establish the loss function: ; Solving a system of equations Get the fitting surface parameters a, b and c; Calculate the intersection line between the fitting surface z=ax+by+c and the tunnel surrounding rock surface, and record it as the rockburst warning line; The rockburst warning line is highlighted through the three-dimensional model display, and the mileage pile number where the rockburst warning line is located is output.

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