Soft rock tunnel aging deformation dynamic early warning method and device based on micro-seismic monitoring

Abstract

The invention relates to a soft rock tunnel aging deformation dynamic early warning method and device based on micro-seismic monitoring. The method comprises the following steps: acquiring micro-seismic monitoring data of a surrounding rock area of the soft rock tunnel; analyzing and processing the micro-seismic monitoring data, and determining an energy release rate ratio, a dominant frequency offset ratio and a spatial distribution fractal dimension; comparing the energy release rate ratio, the dominant frequency offset ratio and the spatial distribution fractal dimension with a preset energy release rate ratio threshold value, a preset dominant frequency offset ratio threshold value and a preset spatial distribution fractal dimension threshold value respectively, and determining a deformation stage; when the spatial distribution fractal dimension of the target time window is smaller than a target spatial distribution fractal dimension threshold value and the energy release rate ratio of a continuous preset number of time windows behind the target window is larger than a target energy release rate ratio threshold value, determining the energy release rate ratio of the target time window; and outputting the early warning deformation risk grade of the soft rock tunnel surrounding rock area after the target time window, thereby realizing rheological grading early warning of the soft rock tunnel surrounding rock area.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel engineering safety monitoring, and in particular to a method and device for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring. Background Art

[0002] Traditional tunnel safety monitoring mainly relies on contact monitoring methods such as manual observation, convergence displacement meters, and anchor stress gauges. Although these methods can reflect structural deformation trends, they often have shortcomings such as delayed response, low spatial resolution, and sparse data. These shortcomings make it difficult to timely capture the "micro-disturbance" characteristics before surrounding rock failure, making it impossible to provide early warning. Summary of the Invention

[0003] Based on this, it is necessary to provide a dynamic early warning method, device, computer equipment, computer-readable storage medium and computer program product for soft rock tunnel deformation based on microseismic monitoring, which can provide early warning of soft rock tunnel surrounding rock deformation in response to the above technical problems.

[0004] In a first aspect, the present application provides a dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring, comprising:

[0005] Acquire microseismic monitoring data of the surrounding rock area of ​​soft rock tunnels; microseismic monitoring data includes waveform data of microseismic events in multiple time windows;

[0006] The microseismic monitoring data is analyzed and processed to determine the energy release rate ratio, main frequency deviation ratio, and spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation ratio is used to characterize the main frequency variation trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial location distribution of the microseismic event.

[0007] The energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension within each time window are compared with the preset energy release rate ratio threshold, the preset main frequency deviation rate threshold, and the preset spatial distribution fractal dimension threshold, respectively. Based on the comparison results, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within each time window is determined;

[0008] When the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output.

[0009] In one embodiment, determining the deformation stage of the surrounding rock region of a soft rock tunnel within each time window includes:

[0010] When the spatial distribution fractal dimension of the time window is greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the second main frequency offset rate threshold, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the initial rheological stage;

[0011] If the spatial distribution fractal dimension of the time window is greater than the second spatial distribution fractal dimension threshold and not greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the first energy release rate ratio threshold and not less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the first main frequency offset rate threshold and greater than the second main frequency offset rate threshold, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the stable rheological stage;

[0012] When the spatial distribution fractal dimension of the time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratio is not less than the first energy release rate ratio threshold, and the main frequency offset rate is greater than the first main frequency offset rate threshold, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the accelerated rheological stage;

[0013] Among them, the first energy release rate ratio threshold is greater than the second energy release rate ratio threshold; the first spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold; and the first main frequency offset rate threshold is greater than the second main frequency offset rate threshold.

[0014] In one embodiment, outputting the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window includes:

[0015] When the spatial distribution fractal dimension of the target time window is not greater than the third spatial distribution fractal dimension threshold and the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the third energy release rate ratio threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the first warning deformation risk level;

[0016] When the spatial distribution fractal dimension of the target time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the first energy release rate ratio threshold, and the main frequency deviation rate of at least one time window in the preset number of consecutive time windows after the target window is greater than the first main frequency deviation rate threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the second warning deformation risk level;

[0017] Among them, the third spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold, the third energy release rate ratio threshold is greater than the second energy release rate ratio threshold, and the risk level represented by the first warning deformation risk level is lower than the risk level represented by the second warning deformation risk level.

[0018] In one embodiment, determining the energy release rate ratio, the main frequency offset rate, and the spatial distribution fractal dimension in each time window based on microseismic monitoring data includes:

[0019] Obtain the uniaxial compressive strength of the tunnel surrounding rock; divide the total energy released by the microseismic events in each time window by the product of the uniaxial compressive strength and the time length corresponding to the time window to obtain the energy release rate ratio in each time window;

[0020] The main frequency of the last microseismic event in each time window is subtracted from the main frequency of the microseismic event preceding the last microseismic event to obtain the main frequency difference; the main frequency difference is divided by the main frequency of the microseismic event preceding the last microseismic event to obtain the main frequency offset rate in each time window;

[0021] According to the spatial position of the microseismic events in each time window, the fractal dimension of the spatial distribution in each time window is obtained.

[0022] In one embodiment, obtaining microseismic monitoring data of a surrounding rock area of ​​a soft rock tunnel includes:

[0023] Multiple microseismic sensors are used to monitor microseismic events in the surrounding rock area of ​​soft rock tunnels and obtain microseismic monitoring data of the surrounding rock area of ​​soft rock tunnels. The surrounding rock area of ​​soft rock tunnels is the area from the tunnel face to the front of the secondary lining. The microseismic sensors are distributed on the left and right side walls of the three sections behind the tunnel support face.

[0024] In one embodiment, outputting the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window includes:

[0025] When the warning deformation risk level is greater than the preset risk level threshold, the support strength of the surrounding rock area of ​​the soft rock tunnel is increased.

[0026] Secondly, the present application also provides a dynamic early warning device for time-dependent deformation of soft rock tunnels based on microseismic monitoring, comprising:

[0027] A monitoring data acquisition module is used to obtain microseismic monitoring data of the surrounding rock area of ​​a soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows;

[0028] The monitoring data processing module is used to analyze and process microseismic monitoring data to determine the energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation rate is used to characterize the main frequency variation trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial location distribution of the microseismic event.

[0029] A deformation stage determination module is used to compare the energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension within each time window with a preset energy release rate ratio threshold, a preset main frequency deviation rate threshold, and a preset spatial distribution fractal dimension threshold, and determine the deformation stage of the surrounding rock area of ​​the soft rock tunnel within each time window based on the comparison results;

[0030] The risk level warning module is used to output the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window when the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold.

[0031] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method steps of the first aspect when executing the computer program.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the method steps of the first aspect when the computer program is executed by a processor.

[0033] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the method steps of the first aspect when executed by a processor.

[0034] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows; the microseismic monitoring data is analyzed and processed to determine the energy release rate ratio, main frequency deviation rate and spatial distribution fractal dimension in each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation rate is used to characterize the main frequency change trend of the microseismic event; the spatial distribution fractal dimension is used to characterize the spatial position distribution of the microseismic event The energy release rate ratio, main frequency offset rate and spatial distribution fractal dimension in each time window are compared with the preset energy release rate ratio threshold, the preset main frequency offset rate threshold and the preset spatial distribution fractal dimension threshold respectively, and the deformation stage of the soft rock tunnel surrounding rock area in each time window is determined according to the comparison results; when the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of time windows after the target window is greater than the target energy release rate ratio threshold, the warning deformation risk level of the soft rock tunnel surrounding rock area after the target time window is output. According to the above content, it can be seen that the present application can determine the rheological deformation stage of the soft rock tunnel surrounding rock area in each time window based on the energy release rate ratio, main frequency offset rate and spatial distribution fractal dimension in each time window, thereby realizing the identification of the rheological deformation stage of the soft rock tunnel surrounding rock area. After determining the rheological deformation stage, the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel is determined based on the spatial distribution fractal dimension and the energy release rate ratio of multiple time windows. This realizes the rheological graded early warning of the surrounding rock area of ​​the soft rock tunnel and improves the safety of tunnel operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is a diagram illustrating an application environment of a dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring in one embodiment;

[0037] Figure 2 Schematic diagram of a flow chart of a method for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring in one embodiment;

[0038] Figure 3This is a system processing flow chart of a dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring in one embodiment;

[0039] Figure 4 A schematic diagram of a dynamic arrangement scheme of microseismic sensors and a topological structure of a microseismic monitoring network in one embodiment;

[0040] Figure 5 1. It is a structural block diagram of a dynamic early warning device for time-dependent deformation of soft rock tunnels based on microseismic monitoring in one embodiment;

[0041] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] It should be noted that the terms "first", "second", etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including" and "having" used in this application and any variations thereof are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions or any combination of multiple solutions.

[0044] The dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other network servers. Terminal 102 obtains microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows; the microseismic monitoring data is analyzed and processed to determine the energy release rate ratio, main frequency deviation rate and spatial distribution fractal dimension in each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation rate is used to characterize the main frequency change trend of the microseismic event; the spatial distribution fractal dimension is used to characterize the spatial position distribution of the microseismic event; the energy release rate ratio, main frequency deviation rate and spatial distribution fractal dimension in each time window are compared with a preset energy release rate ratio threshold, a preset main frequency deviation rate threshold and a preset spatial distribution fractal dimension threshold, respectively, and the deformation stage of the surrounding rock area of ​​the soft rock tunnel in each time window is determined based on the comparison results; when the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output. Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices may include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like. Server 104 may be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0045] In one embodiment, Figure 2 As shown, a dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment of the application, the method includes the following steps:

[0046] Step S210: Acquire microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows.

[0047] Among them, the surrounding rock area of ​​a soft rock tunnel refers to an area within a certain range around the tunnel, which is composed of rock and soil with low bearing capacity, weak rock quality, developed joints and fissures, and broken structure. Its stability is affected by multiple factors such as geological conditions, groundwater, and construction methods. Targeted support measures must be taken to ensure construction safety.

[0048] A microseismic event is the release of low-energy elastic waves due to tiny fractures or dislocations in a rock mass or medium when it is subjected to internal stress changes or external disturbances. Waveform data includes the frequency and amplitude of the elastic waves released by microseismic events.

[0049] In the embodiment of the present application, the surrounding rock area of ​​the soft rock tunnel can be monitored by using a microseismic sensor to obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel.

[0050] In step S220, the microseismic monitoring data is analyzed and processed to determine the energy release rate ratio, the main frequency deviation ratio, and the spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation ratio is used to characterize the main frequency change trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial position distribution of the microseismic event.

[0051] The Energy Release Rate Ratio (ERR) reflects the energy density released by microseismic activity in the surrounding rock of a soft rock tunnel per unit time. A higher ERR indicates a greater release of energy per unit strength in a shorter period of time, typically indicating more intense or concentrated structural activity and possessing significant predictive power for time-sensitive deformation.

[0052] To capture the dynamic trends of microseismic event dominant frequencies, the Frequency Offset Ratio (FOR) was constructed to quantify the fluctuation characteristics of the dominant frequency of microseismic events. Significant frequency offsets are often associated with structural changes, crack evolution, or acoustic emission spectrum reconstruction, and are highly sensitive to unstable processes.

[0053] The fractal dimension of the spatial distribution of microseismic events is a key parameter that quantitatively describes the complexity and self-similarity of their spatial distribution. Its value, calculated by the slope of the relationship between the number of microseismic events and the coverage scale in a double logarithmic coordinate system, can reveal the evolution of surrounding rock fractures and precursors to instability. A high fractal dimension indicates that microseismic events are uniformly distributed in space, resulting in disordered rock fractures. A gradually decreasing fractal dimension indicates that microseismic events are clustered in specific areas, and rock fractures have entered a stage of orderly concentration.

[0054] In an embodiment of the present application, the energy release rate ratio within each time window can be determined based on the total energy released by multiple microseismic events within each time window and the duration of the time window. A short-time Fourier transform can be performed on the waveform data of the microseismic events within each time window to obtain the dominant frequency of the microseismic events, and thus the dominant frequency offset rate within each time window. The spatial distribution fractal dimension within each time window can be obtained based on the spatial position of the microseismic events within each time window.

[0055] In step S230, the energy release rate ratio, the main frequency offset rate, and the spatial distribution fractal dimension in each time window are compared with a preset energy release rate ratio threshold, a preset main frequency offset rate threshold, and a preset spatial distribution fractal dimension threshold, respectively. Based on the comparison results, the deformation stage of the surrounding rock area of ​​the soft rock tunnel in each time window is determined.

[0056] Among them, the preset energy release rate ratio threshold, the preset main frequency offset rate threshold, and the preset spatial distribution fractal dimension threshold can be set according to actual needs.

[0057] The deformation phase of the surrounding rock region of a soft rock tunnel is a dynamic evolutionary process, influenced by multiple factors, including geological conditions, ground stress, construction methods, and support measures. The deformation phases of the surrounding rock region of a soft rock tunnel include the initial rheological phase, the stable rheological phase, and the accelerated rheological phase.

[0058] In this embodiment of the present application, based on a preset energy release rate ratio threshold, a preset primary frequency offset ratio threshold, and a preset spatial distribution fractal dimension threshold, the energy release rate ratio range, primary frequency offset ratio range, and spatial distribution fractal dimension range corresponding to each deformation stage can be divided. When the energy release rate ratio, primary frequency offset ratio, and spatial distribution fractal dimension of the surrounding rock region of a soft rock tunnel all fall within the corresponding energy release rate ratio range, primary frequency offset ratio range, and spatial distribution fractal dimension range, the deformation stage of the surrounding rock region of the soft rock tunnel is determined to be the corresponding deformation stage.

[0059] Step S240: When the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output.

[0060] The target time window is any time window among the time windows.

[0061] Among them, the target spatial distribution fractal dimension threshold and the target energy release rate ratio threshold can be set according to actual needs.

[0062] Specifically, given the nonlinear mutation characteristics of "latency-acceleration-instability" associated with the time-dependent rheological behavior of soft rock tunnel surrounding rock, a dynamic dual-threshold early warning mechanism was proposed to achieve risk level classification based on multi-parameter fusion and multi-period superposition. Specifically, the early warning deformation risk levels include the first and second risk levels. The first risk level indicates the end of stable rheological stage, with a trend toward accelerated rheological stage. The second risk level indicates the entry into the accelerated rheological stage or even the approach of instability.

[0063] In an embodiment of the present application, the spatial distribution fractal dimension of each time window is compared with the target spatial distribution fractal dimension threshold. When the spatial distribution fractal dimension is less than the target spatial distribution fractal dimension threshold, the energy release rate ratios of multiple consecutive time windows are statistically analyzed to determine whether they are all greater than the target energy release rate ratio threshold to determine the warning deformation risk level.

[0064] In order to facilitate understanding of steps S210 to S240, Figure 3 As shown, a system processing flow chart corresponding to the dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring is provided. The system includes a microseismic sensor, a signal preprocessing module, a characteristic parameter calculation module, a threshold judgment engine, an early warning release interface, and a support strength increase module. The microseismic sensor is used to obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel. The signal preprocessing module is used to preprocess the microseismic monitoring data, including data denoising, data normalization, time synchronization and alignment, signal interception and segmentation, etc. The characteristic parameter calculation module is used to analyze and process the microseismic monitoring data to determine the energy release rate ratio, main frequency offset rate, and spatial distribution fractal dimension in each time window. The threshold judgment engine is used to execute step S230, the early warning release interface is used to execute S240, and the support strength increase module is used to increase the support strength when the early warning risk level is greater than the preset risk level threshold.

[0065] The above-mentioned dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring obtains microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel. The microseismic monitoring data includes waveform data of microseismic events in multiple time windows. The microseismic monitoring data is analyzed and processed to determine the energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension in each time window. The energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation rate is used to characterize the main frequency variation trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial location distribution of the microseismic event. The energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension in each time window are compared with preset energy release rate ratio thresholds, preset main frequency deviation rate thresholds, and preset spatial distribution fractal dimension thresholds, respectively. Based on the comparison results, the deformation stage of the surrounding rock area of ​​the soft rock tunnel in each time window is determined. If the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratios of a preset number of consecutive time windows after the target window are greater than the target energy release rate ratio threshold, the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output. Based on the above, this application can determine the rheological deformation stage of the surrounding rock area of ​​a soft rock tunnel within each time window based on the energy release rate ratio, main frequency offset rate, and spatial distribution fractal dimension within each time window, thereby realizing the identification of the rheological deformation stage of the surrounding rock area of ​​a soft rock tunnel. After determining the rheological deformation stage, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel is determined based on the spatial distribution fractal dimension and the energy release rate ratio of multiple time windows, realizing the rheological graded warning of the surrounding rock area of ​​the soft rock tunnel and improving the safety of tunnel operation.

[0066] In one embodiment, obtaining microseismic monitoring data of a surrounding rock area of ​​a soft rock tunnel includes:

[0067] In step S212, a plurality of microseismic sensors are used to monitor microseismic events in the surrounding rock area of ​​the soft rock tunnel to obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the surrounding rock area of ​​the soft rock tunnel is the area from the tunnel face to the front of the secondary lining, and the microseismic sensors are distributed on the left and right side walls of the three sections behind the tunnel support face.

[0068] The tunnel face is the working face during tunnel excavation, i.e., the front section of the tunnel currently being blasted, excavated, or mechanically excavated. It is the "front line" of tunnel construction, directly exposed to the unsupported surrounding rock environment.

[0069] The pre-secondary lining refers to the period after the initial tunnel support is completed and before the secondary lining is applied. The secondary lining is the permanent tunnel support structure, usually made of reinforced concrete or plain concrete.

[0070] Among them, the two sections behind the tunnel face refer to the three monitoring or support sections arranged in sequence from the tunnel face to the rear (i.e. the excavation direction).

[0071] In the embodiments of this application, Figure 4 As shown, a dynamic arrangement scheme of microseismic sensors and a schematic diagram of the topology of a microseismic monitoring network are provided. The above-mentioned microseismic monitoring network topology includes 6-channel microseismic acceleration sensors, which are located on 3 sections behind the tunnel face. Two sensors are installed on the left and right side walls of each section (the left section is provided with a first acceleration sensor 402, a second acceleration sensor 403 and a third acceleration sensor 404, and the right section is provided with a fourth acceleration sensor 405, a fifth acceleration sensor 406 and a sixth acceleration sensor 407). In order to ensure the monitoring effect and the sensors are not damaged, the distance between the tunneling head (401) and the acceleration sensor closest to the tunnel face is set to 25 meters, and the distance between adjacent sensor sections is set to 25 meters. When the tunnel face continues to be excavated for 25 meters, the two farthest acceleration sensors will be recovered and moved to a new section close to the tunnel face (see Figure 4 The obtained microseismic monitoring data is then transmitted to a signal processing host (408) for analysis and processing of the microseismic monitoring data, and the processed analysis data is transmitted to a computer (409), which then transmits the data to a data analysis center (410) via the computer (409).

[0072] In one embodiment, determining the energy release rate ratio, the main frequency offset rate, and the spatial distribution fractal dimension in each time window based on microseismic monitoring data includes:

[0073] Step S222, obtaining the uniaxial compressive strength of the tunnel surrounding rock; dividing the total energy released by the microseismic events in each time window by the product of the uniaxial compressive strength and the time length corresponding to the time window, to obtain the energy release rate ratio in each time window.

[0074] Among them, the uniaxial compressive strength of the tunnel surrounding rock refers to the maximum compressive stress value when the surrounding rock specimen reaches failure under uniaxial pressure under standard test conditions.

[0075] In the embodiment of the present application, the energy release rate ratio is calculated as follows:

[0076]

[0077] in, is the total energy of microseismic events in the selected time window (J), is the time length corresponding to the time window (h), is the uniaxial compressive strength of the tunnel surrounding rock (MPa).

[0078] In step S224, the main frequency of the last microseismic event in each time window is subtracted from the main frequency of the microseismic event before the last microseismic event to obtain the main frequency difference; the main frequency difference is divided by the main frequency of the microseismic event before the last microseismic event to obtain the main frequency offset rate in each time window.

[0079] In the embodiment of the present application, the calculation formula of the main frequency offset rate is:

[0080]

[0081] in, represents the dominant frequency of the i-th microseismic event, represents the dominant frequency of the i-1th microseismic event.

[0082] Considering that there are multiple microseismic events in each time window, the dominant frequency offset ratio for each time window is calculated based on the dominant frequency of the last microseismic event in each time window and the dominant frequency of the microseismic event immediately preceding the last microseismic event. For example, if there are three microseismic events in a time window, the dominant frequency offset ratio for that time window is the ratio of the dominant frequency difference between the third and second microseismic events, in chronological order, to the dominant frequency of the second microseismic event.

[0083] Step S226 , obtaining the spatial distribution fractal dimension in each time window according to the spatial position of the microseismic event in each time window.

[0084] In the embodiment of the present application, the box dimension method or the correlation dimension method can be used to process the spatial position of the microseismic event in each time window to obtain the fractal dimension of the spatial distribution in each time window.

[0085] In one embodiment, the preset energy release rate ratio threshold includes a first energy release rate ratio threshold and a second energy release rate ratio threshold; the preset main frequency offset rate threshold includes a first main frequency offset rate threshold and a second main frequency offset rate threshold; the preset spatial distribution fractal dimension threshold includes a first spatial distribution fractal dimension threshold and a second spatial distribution fractal dimension threshold;

[0086] Determine the deformation stage of the surrounding rock area of ​​a soft rock tunnel within various time windows, including:

[0087] In step S232, if the spatial distribution fractal dimension of the time window is greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the second main frequency offset rate threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the initial rheological stage.

[0088] The first energy release rate ratio threshold and the second energy release rate ratio threshold can be set according to actual needs. Specifically, the first energy release rate ratio threshold and the second energy release rate ratio threshold are 0.15 and 0.05 respectively.

[0089] The first main frequency offset rate threshold and the second main frequency offset rate threshold can be set according to actual needs. Specifically, the first main frequency offset rate threshold and the second main frequency offset rate threshold are 30% and 10%, respectively.

[0090] The first spatial distribution fractal dimension threshold and the second spatial distribution fractal dimension threshold can be set according to actual needs. Specifically, the first spatial distribution fractal dimension threshold and the second spatial distribution fractal dimension threshold are 2 and 1.5 respectively.

[0091] In the embodiment of the present application, when the spatial distribution fractal dimension of the time window , energy release rate ratio , main frequency deviation rate , the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the initial rheological stage.

[0092] In step S234, if the spatial distribution fractal dimension of the time window is greater than the second spatial distribution fractal dimension threshold and not greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the first energy release rate ratio threshold and not less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the first main frequency offset rate threshold and greater than the second main frequency offset rate threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the stable rheological stage.

[0093] In the embodiment of the present application, when the spatial distribution fractal dimension of the time window , energy release rate ratio , main frequency deviation rate , it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the stable rheological stage.

[0094] Step S236: If the spatial distribution fractal dimension of the time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratio is not less than the first energy release rate ratio threshold, and the main frequency offset ratio is greater than the first main frequency offset ratio threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the accelerated rheological stage;

[0095] Among them, the first energy release rate ratio threshold is greater than the second energy release rate ratio threshold; the first spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold; and the first main frequency offset rate threshold is greater than the second main frequency offset rate threshold.

[0096] In the embodiment of the present application, when the spatial distribution fractal dimension of the time window , energy release rate ratio , main frequency deviation rate , it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the accelerated rheological stage.

[0097] The embodiment of the present application constructs a three-dimensional coupled indicator system of "intensity-frequency-space" based on the energy release rate ratio (ERR), the main frequency offset ratio (FOR) and the spatial distribution fractal dimension (D). This overcomes the limitations of traditional monitoring of a single energy or frequency parameter and significantly improves the comprehensiveness and sensitivity of the identification of time-dependent deformation of the surrounding rock of soft rock tunnels.

[0098] In one embodiment, the target spatial distribution fractal dimension threshold includes a second spatial distribution fractal dimension threshold and a third spatial distribution fractal dimension threshold, the target energy release rate ratio threshold includes a first energy release rate ratio threshold and a third energy release rate ratio threshold, and outputting a warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window includes:

[0099] In step S242, when the spatial distribution fractal dimension of the target time window is not greater than the third spatial distribution fractal dimension threshold and the energy release rate ratios of a preset number of consecutive time windows after the target window are greater than the third energy release rate ratio threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the first warning deformation risk level.

[0100] The third spatial distribution fractal dimension threshold can be set according to actual needs. Specifically, the third spatial distribution fractal dimension threshold is 1.8. The third energy release rate ratio threshold can be set according to actual needs. Specifically, the third energy release rate ratio threshold is 0.1. The number of consecutive presets can be set according to actual needs.

[0101] In the embodiment of the present application, when the spatial distribution fractal dimension of the target time window is and 4 consecutive hours The warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the first warning deformation risk level. Specifically, different colors can be used to represent different warning deformation risk levels. For example, the first warning deformation risk level is marked as a yellow warning.

[0102] Step S244: If the spatial distribution fractal dimension of the target time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the first energy release rate ratio threshold, and the dominant frequency deviation rate of at least one time window among the preset number of consecutive time windows after the target window is greater than the first dominant frequency deviation rate threshold, then the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the second warning deformation risk level.

[0103] Among them, the third spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold, the third energy release rate ratio threshold is greater than the second energy release rate ratio threshold, and the risk level represented by the first warning deformation risk level is lower than the risk level represented by the second warning deformation risk level.

[0104] In the embodiment of the present application, when the spatial distribution fractal dimension of the target time window is and 4 consecutive hours , and there is at least one time window within 4 consecutive hours , outputting the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window as the second warning deformation risk level. For example, the second warning deformation risk level is marked as a red warning.

[0105] The embodiment of the present application proposes a dynamic warning mechanism with dual thresholds and multi-time period integration, which distinguishes between the first-level (yellow) and second-level (red) warning deformation risk levels, significantly improving the accuracy of the warning deformation.

[0106] In one embodiment, outputting the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window includes:

[0107] Step S250: When the early warning deformation risk level is greater than a preset risk level threshold, the support strength of the surrounding rock area of ​​the soft rock tunnel is increased.

[0108] Among them, the preset risk level threshold is the first-level risk level.

[0109] Among them, in tunnel construction, improving support strength is an important means to cope with complex geological conditions (such as weak surrounding rock, high ground stress, fault fracture zone, etc.) or control excessive deformation of surrounding rock.

[0110] In an embodiment of the present application, when the warning deformation risk level is greater than the first-level risk level (i.e., the warning deformation risk level is the second warning deformation risk level), the support strength is improved by optimizing the support structure design, selecting high-strength materials or adopting a composite support system, thereby improving the safety of tunnel operation.

[0111] It should be understood that, although the various steps in the flowcharts involved in the above embodiments are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0112] Based on the same inventive concept, the embodiments of the present application also provide a device for dynamically warning the time-dependent deformation of soft rock tunnels based on microseismic monitoring, which is used to implement the aforementioned method for dynamically warning the time-dependent deformation of soft rock tunnels based on microseismic monitoring. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for dynamically warning the time-dependent deformation of soft rock tunnels based on microseismic monitoring provided below can be found in the limitations of the method for dynamically warning the time-dependent deformation of soft rock tunnels based on microseismic monitoring above, and will not be repeated here.

[0113] In an exemplary embodiment, see Figure 5 , provides a dynamic early warning device for time-dependent deformation of soft rock tunnels based on microseismic monitoring, including:

[0114] The monitoring data acquisition module 510 is used to acquire microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows;

[0115] Monitoring data processing module 520 is used to analyze and process microseismic monitoring data to determine the energy release rate ratio, main frequency deviation ratio, and spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation ratio is used to characterize the main frequency variation trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial location distribution of the microseismic event.

[0116] The deformation stage determination module 530 is configured to compare the energy release rate ratio, the main frequency deviation rate, and the spatial distribution fractal dimension within each time window with a preset energy release rate ratio threshold, a preset main frequency deviation rate threshold, and a preset spatial distribution fractal dimension threshold, respectively, and determine the deformation stage of the surrounding rock region of the soft rock tunnel within each time window based on the comparison results;

[0117] The risk level warning module 540 is used to output the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window when the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold.

[0118] In one embodiment, the monitoring data acquisition module 510 is used to use multiple microseismic sensors to monitor microseismic events in the surrounding rock area of ​​the soft rock tunnel and obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the surrounding rock area of ​​the soft rock tunnel is the area from the tunnel face to the front of the second lining, and the microseismic sensors are distributed on the left and right side walls of the three sections behind the tunnel support face.

[0119] In one embodiment, the monitoring data processing module 520 is configured to obtain the uniaxial compressive strength of the tunnel surrounding rock; divide the total energy released by the microseismic events in each time window by the product of the uniaxial compressive strength and the time length corresponding to the time window to obtain the energy release rate ratio in each time window;

[0120] The main frequency of the last microseismic event in each time window is subtracted from the main frequency of the microseismic event preceding the last microseismic event to obtain the main frequency difference; the main frequency difference is divided by the main frequency of the microseismic event preceding the last microseismic event to obtain the main frequency offset rate in each time window;

[0121] According to the spatial position of the microseismic events in each time window, the fractal dimension of the spatial distribution in each time window is obtained.

[0122] In one embodiment, the deformation stage determination module 530 is configured to determine that the deformation stage of the surrounding rock region of the soft rock tunnel within the time window is the initial rheological stage if the spatial distribution fractal dimension of the time window is greater than a first spatial distribution fractal dimension threshold, the energy release rate ratio is less than a second energy release rate ratio threshold, and the main frequency offset rate is not greater than a second main frequency offset rate threshold;

[0123] If the spatial distribution fractal dimension of the time window is greater than the second spatial distribution fractal dimension threshold and not greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the first energy release rate ratio threshold and not less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the first main frequency offset rate threshold and greater than the second main frequency offset rate threshold, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the stable rheological stage;

[0124] When the spatial distribution fractal dimension of the time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratio is not less than the first energy release rate ratio threshold, and the main frequency offset rate is greater than the first main frequency offset rate threshold, the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is determined to be the accelerated rheological stage;

[0125] Among them, the first energy release rate ratio threshold is greater than the second energy release rate ratio threshold; the first spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold; and the first main frequency offset rate threshold is greater than the second main frequency offset rate threshold.

[0126] In one embodiment, the risk level warning module 540 is configured to output a warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window as a first warning deformation risk level when the spatial distribution fractal dimension of the target time window is not greater than a third spatial distribution fractal dimension threshold and the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the third energy release rate ratio threshold.

[0127] When the spatial distribution fractal dimension of the target time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the first energy release rate ratio threshold, and the main frequency deviation rate of at least one time window in the preset number of consecutive time windows after the target window is greater than the first main frequency deviation rate threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the second warning deformation risk level;

[0128] Among them, the third spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold and less than the first spatial distribution fractal dimension threshold, the third energy release rate ratio threshold is greater than the second energy release rate ratio threshold and less than the first energy release rate threshold, and the risk level represented by the first warning deformation risk level is lower than the risk level represented by the second warning deformation risk level.

[0129] Each module in the aforementioned dynamic early warning device for time-dependent deformation of soft rock tunnels based on microseismic monitoring can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0130] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which can be achieved via Wi-Fi, mobile cellular networks, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring. The display unit of the computer device is used to produce visual images and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0131] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components. In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for dynamic early warning of aging deformation of soft rock tunnels based on microseismic monitoring. The steps of the method for dynamic early warning of aging deformation of soft rock tunnels based on microseismic monitoring here may be the steps of the method for dynamic early warning of aging deformation of soft rock tunnels based on microseismic monitoring in each of the above-mentioned embodiments.

[0132] In one embodiment, a computer-readable storage medium is provided, storing a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the aforementioned method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring. The steps of the method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring can be the steps of the aforementioned method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring.

[0133] In one embodiment, a computer program product is provided, including a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the aforementioned method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring. The steps of the method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring may be the steps of the method for dynamic early warning of aging-related deformation of soft rock tunnels based on microseismic monitoring in each of the aforementioned embodiments.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0135] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0136] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0137] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring, characterized by: The method comprises: Acquiring microseismic monitoring data of a surrounding rock area of ​​a soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events within multiple time windows; Analyzing and processing the microseismic monitoring data to determine the energy release rate ratio, the main frequency deviation ratio, and the spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation ratio is used to characterize the main frequency change trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial position distribution of the microseismic event; comparing the energy release rate ratio, the main frequency offset rate, and the spatial distribution fractal dimension within each time window with a preset energy release rate ratio threshold, a preset main frequency offset rate threshold, and a preset spatial distribution fractal dimension threshold, respectively, and determining the deformation stage of the surrounding rock region of the soft rock tunnel within each time window based on the comparison results; When the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold, the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output.

2. According to the method for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring in claim 1, the preset energy release rate ratio threshold comprises a first energy release rate ratio threshold and a second energy release rate ratio threshold; the preset main frequency deviation rate threshold comprises a first main frequency deviation rate threshold and a second main frequency deviation rate threshold; The preset spatial distribution fractal dimension threshold includes a first spatial distribution fractal dimension threshold and a second spatial distribution fractal dimension threshold; The step of determining the deformation stage of the surrounding rock region of the soft rock tunnel within each time window is characterized by comprising: When the spatial distribution fractal dimension of the time window is greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the second main frequency offset rate threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel within the time window is the initial rheological stage; If the spatial distribution fractal dimension in the time window is greater than the second spatial distribution fractal dimension threshold and not greater than the first spatial distribution fractal dimension threshold, the energy release rate ratio is less than the first energy release rate ratio threshold and not less than the second energy release rate ratio threshold, and the main frequency offset rate is not greater than the first main frequency offset rate threshold and greater than the second main frequency offset rate threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel in the time window is the stable rheological stage; When the spatial distribution fractal dimension in the time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratio is not less than the first energy release rate ratio threshold, and the main frequency offset rate is greater than the first main frequency offset rate threshold, it is determined that the deformation stage of the surrounding rock area of ​​the soft rock tunnel in the time window is the accelerated rheology stage; Among them, the first energy release rate ratio threshold is greater than the second energy release rate ratio threshold; the first spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold; the first main frequency offset rate threshold is greater than the second main frequency offset rate threshold.

3. According to the dynamic early warning method for time-dependent deformation of soft rock tunnels based on microseismic monitoring in claim 1, the target spatial distribution fractal dimension threshold comprises a second spatial distribution fractal dimension threshold and a third spatial distribution fractal dimension threshold, the target energy release rate ratio threshold comprises a first energy release rate ratio threshold and a third energy release rate ratio threshold, and the output of the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is characterized in that: include: When the spatial distribution fractal dimension of the target time window is not greater than the third spatial distribution fractal dimension threshold and the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the third energy release rate ratio threshold, outputting the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window as the first warning deformation risk level; When the spatial distribution fractal dimension of the target time window is not greater than the second spatial distribution fractal dimension threshold, the energy release rate ratios of a preset number of consecutive time windows after the target window are all greater than the first energy release rate ratio threshold, and the main frequency offset rate of at least one time window among the preset number of consecutive time windows after the target window is greater than the first main frequency offset rate threshold, the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is output as the second warning deformation risk level; Among them, the third spatial distribution fractal dimension threshold is greater than the second spatial distribution fractal dimension threshold, the third energy release rate ratio threshold is greater than the second energy release rate ratio threshold, and the risk level represented by the first warning deformation risk level is lower than the risk level represented by the second warning deformation risk level.

4. The method for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring according to claim 1, wherein the energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension within each time window are determined based on the microseismic monitoring data, and the method is characterized in that: include: Obtain the uniaxial compressive strength of the tunnel surrounding rock; Dividing the total energy released by the microseismic events in each time window by the product of the uniaxial compressive strength and the time length corresponding to the time window to obtain the energy release rate ratio in each time window; Subtracting the main frequency of the last microseismic event in each time window from the main frequency of the microseismic event before the last microseismic event to obtain a main frequency difference; Dividing the main frequency difference by the main frequency of the microseismic event preceding the last microseismic event to obtain the main frequency offset rate in each time window; According to the spatial positions of the microseismic events in the respective time windows, the spatial distribution fractal dimensions in the respective time windows are obtained.

5. The method for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring according to any one of claims 1 to 4, wherein the microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel is obtained, include: Using multiple microseismic sensors, monitoring microseismic events in the surrounding rock area of ​​the soft rock tunnel to obtain microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; The surrounding rock area of ​​the soft rock tunnel is the area from the tunnel face to the front of the second lining, and the microseismic sensors are distributed on the left and right side walls of the three sections behind the tunnel support face.

6. The method for dynamic early warning of time-dependent deformation of soft rock tunnels based on microseismic monitoring according to any one of claims 1 to 4, wherein the outputting of the early warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window is characterized in that: include: When the early warning deformation risk level is greater than a preset risk level threshold, the support strength of the surrounding rock area of ​​the soft rock tunnel is increased.

7. A dynamic early warning device for time-dependent deformation of soft rock tunnels based on microseismic monitoring, characterized in that: The device comprises: A monitoring data acquisition module is used to acquire microseismic monitoring data of the surrounding rock area of ​​the soft rock tunnel; the microseismic monitoring data includes waveform data of microseismic events in multiple time windows; A monitoring data processing module is used to analyze and process the microseismic monitoring data to determine the energy release rate ratio, main frequency deviation rate, and spatial distribution fractal dimension within each time window; the energy release rate ratio is used to characterize the energy density released by the microseismic event; the main frequency deviation rate is used to characterize the main frequency change trend of the microseismic event; and the spatial distribution fractal dimension is used to characterize the spatial position distribution of the microseismic event; a deformation stage determination module, configured to compare the energy release rate ratio, the main frequency deviation rate, and the spatial distribution fractal dimension within each time window with a preset energy release rate ratio threshold, a preset main frequency deviation rate threshold, and a preset spatial distribution fractal dimension threshold, respectively, and determine the deformation stage of the surrounding rock region of the soft rock tunnel within each time window based on the comparison results; The risk level warning module is used to output the warning deformation risk level of the surrounding rock area of ​​the soft rock tunnel after the target time window when the spatial distribution fractal dimension of the target time window is less than the target spatial distribution fractal dimension threshold and the energy release rate ratio of a preset number of consecutive time windows after the target window is greater than the target energy release rate ratio threshold.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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