Rockburst dynamic early warning method and system based on micro-seismic information

Abstract

The embodiment of the invention provides a rockburst dynamic early warning method and system based on micro-seismic information, and can solve the problem that in engineering scenes such as mining and tunneling, continuous vibration of equipment such as a drilling machine and a heading machine can generate a large amount of background noise, so that the difficulty of effectively extracting a micro-seismic fracture event in a rock mass is increased. The method comprises the following steps: acquiring operation disturbance information of vibration of an operation machine in a typical strong rockburst hole section rock mass construction process on surrounding rock masses, so as to extract operation disturbance characteristics; determining a continuous influence period of operation disturbance in an acquisition point range during operation and after the operation is stopped according to the acquisition point range of micro-seismic monitoring; and based on the operation disturbance characteristics, the continuous influence period and the working state information of the operation equipment, performing dynamic denoising on the acquired micro-seismic information so as to perform rockburst early warning through the denoised micro-seismic information.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a rockburst dynamic early warning method and system based on microseismic information. Background Art

[0002] When using microseismic information for rockburst monitoring, the vibration of mechanical operating equipment will have a significant impact on the acquisition of fracture information. This interference is mainly reflected in the high-frequency vibration generated by mechanical equipment, which is close to the frequency of the microseismic signal generated by the real fracture inside the rock mass, which can easily mask or confuse the real fracture signal, resulting in misjudgment of monitoring data or noise enhancement. Especially in engineering scenarios such as mining and tunneling, the continuous vibration of equipment such as drilling rigs and tunneling machines will generate a large amount of background noise, making it more difficult to effectively extract microseismic fracture events in the rock mass. Summary of the invention

[0003] The embodiments of the present application provide a dynamic rockburst warning method and system based on microseismic information, which can solve the problem that in engineering scenarios such as mining and tunneling, the continuous vibration of equipment such as drilling rigs and tunneling machines will generate a large amount of background noise, making it more difficult to effectively extract microseismic rupture events in the rock mass.

[0004] A first aspect of an embodiment of the present application provides a rockburst dynamic early warning method based on microseismic information, comprising:

[0005] Obtain the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics;

[0006] Determine the continuous impact period of the operation disturbance in the collection point range during and after the operation is stopped according to the collection point range of microseismic monitoring;

[0007] Based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, the collected microseismic information is dynamically denoised, so as to provide rock burst warning through the denoised microseismic information.

[0008] Optionally, the step of obtaining the operation disturbance information of the operating machinery vibration on the surrounding rock mass during the typical strong rockburst tunnel section rock mass construction process to extract the operation disturbance characteristics includes:

[0009] By collecting vibration data generated during the operation of the operating machinery, the vibration characteristics of different operating equipment are analyzed and extracted;

[0010] The operation disturbance features are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

[0011] Optionally, extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment includes:

[0012] By combining wavelet transform and short-time Fourier transform, the operation disturbance characteristics are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

[0013] Optionally, extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment by combining wavelet transform and short-time Fourier transform includes:

[0014] The characteristic energy can be calculated and classified by the following formula:

[0015]

[0016] Among them, i and j are the vibration frequency set ranges of different devices, x is the vibration data, SFTF x (t, f) is the frequency spectrum distribution of the vibration data of the equipment, W x (a, t) is the energy distribution of the vibration data of the equipment.

[0017] Optionally, the dynamically denoising the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the operation equipment working status information to provide rock burst warning through the denoised microseismic information includes:

[0018] The filter parameters are automatically adjusted based on the working status of the mechanical working equipment through an adaptive noise cancellation algorithm.

[0019] Optionally, the method of automatically adjusting filter parameters based on the working state of the mechanical operating equipment by using an adaptive noise elimination algorithm includes:

[0020] Noise characteristics generated by the device in a specific state: d(t) = f(device type, state parameters, frequency characteristics);

[0021] According to the working status of the device, an adaptive filtering algorithm is used to dynamically adjust the filter parameters to minimize the noise component:

[0022] w n+1 =w n +2μe(n)d(n);

[0023] Among them, w n is the filter weight at the nth iteration, μ is the learning rate, which is used to control the step size of weight update, e(n) = x(n)-w n d(n) is the filtering error, which is the difference between the actual signal and the noise estimate.

[0024] Optionally, also include:

[0025] The system regularly updates the vibration signature library of the operating equipment, or automatically recalculates and updates it when abnormal vibration signatures are detected.

[0026] A second aspect of the embodiment of the present application provides a rockburst dynamic early warning device based on microseismic information, comprising:

[0027] An acquisition unit is used to acquire the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics;

[0028] A determination unit, used to determine the continuous influence period of the operation disturbance in the collection point range during the operation and after the operation is stopped according to the collection point range of the microseismic monitoring;

[0029] The analysis unit is used to dynamically denoise the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, so as to provide rock burst warning through the denoised microseismic information.

[0030] A third aspect of an embodiment of the present application provides an electronic system, including a memory and a processor, wherein the processor is configured to implement the steps of the above-mentioned rockburst dynamic early warning method based on microseismic information when executing a computer program stored in the memory.

[0031] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned rockburst dynamic early warning method based on microseismic information are implemented.

[0032] In summary, the rockburst dynamic early warning method based on microseismic information provided in the embodiment of the present application obtains the operation disturbance information of the operating mechanical vibration on the surrounding rock mass during the construction process of the typical strong rockburst tunnel section rock mass to extract the operation disturbance characteristics; determines the continuous impact period of the operation disturbance in the collection point range during the operation and after the operation stops according to the collection point range of the microseismic monitoring; dynamically denoises the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operating equipment, so as to provide rockburst early warning through the denoised microseismic information. Thus, after removing the mechanical vibration, the real microseismic events in the monitoring signal are highlighted, and the reliability and clarity of the signal are enhanced. Dynamic denoising based on the working status of the equipment can adapt to complex on-site conditions, and automatically adjust the denoising parameters when the mechanical equipment is turned on and off and the vibration characteristics change, to ensure timely response to the real rock mass fracture. The denoised signal is closer to the real stress state of the rock mass, helping the monitoring system to accurately judge the potential area and time of rockburst occurrence, and provide effective early warning.

[0033] Correspondingly, the rockburst dynamic warning device based on microseismic information, the electronic system and the computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a possible rockburst dynamic early warning method based on microseismic information provided in an embodiment of the present application;

[0035] Figure 2 A schematic structural block diagram of a possible rockburst dynamic early warning device based on microseismic information provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the hardware structure of a possible rockburst dynamic early warning device based on microseismic information provided in an embodiment of the present application;

[0037] Figure 4 A schematic structural block diagram of a possible electronic system provided in an embodiment of the present application;

[0038] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided for an embodiment of the present application. DETAILED DESCRIPTION

[0039] The embodiments of the present application provide a dynamic rockburst warning method and system based on microseismic information, which can solve the problem that in engineering scenarios such as mining and tunneling, the continuous vibration of equipment such as drilling rigs and tunneling machines will generate a large amount of background noise, making it more difficult to effectively extract microseismic rupture events in the rock mass.

[0040] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0041] See also Figure 1 , which is a flow chart of a rockburst dynamic early warning method based on microseismic information provided in an embodiment of the present application, and may specifically include: S110-S130.

[0042] S110, obtaining the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the typical strong rock burst tunnel section rock mass construction process, so as to extract the operation disturbance characteristics.

[0043] S120, determining the continuous impact period of the operation disturbance in the collection point range during the operation and after the operation is stopped according to the collection point range of the microseismic monitoring.

[0044] S130, dynamically denoising the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the operation equipment working status information, so as to provide rock burst warning through the denoised microseismic information.

[0045] It is understandable that by collecting vibration data generated during mechanical operation, the vibration characteristics of different operating equipment (such as drilling rigs and tunneling machines), such as frequency and amplitude, are analyzed and extracted. These characteristics can be used to distinguish mechanical disturbances from real rock fracture microseismic signals. After the mechanical operation stops, the impact of vibration will not disappear immediately, but will gradually decay over time. By analyzing the attenuation curve of the vibration at each monitoring point after the mechanical operation stops, the continuous influence period of different collection points, that is, the natural decay time of the vibration, is determined. This information helps determine the distinction criteria between mechanical vibration and natural microseismic events. Based on the collected disturbance characteristics, continuous influence period and real-time working status of the equipment, the collected microseismic information is dynamically denoised. Dynamic denoising refers to dynamically adjusting the data processing algorithm according to the real-time operating status of the equipment to remove mechanical interference, thereby obtaining more realistic rock fracture signals. The processed microseismic signals are then used to analyze the stress changes and potential fracture risks of the rock mass.

[0046] For example, tests are conducted in typical strong rockburst tunnel sections to collect vibration information generated by mechanical equipment under different working conditions, such as vibration signals during excavation and when stopped. Through the accumulation of a large amount of data, a disturbance feature library of the operating machinery is formed. Microseismic signals are collected in different time periods after the mechanical equipment stops working, and the vibration attenuation time of different monitoring points is analyzed. Through model analysis, the impact period of mechanical vibration on each collection point is estimated and used as the time parameter for denoising. In actual construction, the working status of the machinery is monitored in real time, and the collected microseismic data is dynamically denoised in combination with the disturbance characteristics and impact period obtained in the early stage. Specifically, signal processing methods such as bandpass filtering can be used to remove the mechanical vibration components in a specific frequency band to ensure that the remaining signal is closer to the actual rock fracture information.

[0047] In summary, the rockburst dynamic early warning method based on microseismic information provided in the above embodiment obtains the operation disturbance information of the operating machinery vibration on the surrounding rock mass during the construction process of the typical strong rockburst tunnel section rock mass to extract the operation disturbance characteristics; determines the continuous impact period of the operation disturbance in the collection point range during the operation and after the operation stops according to the collection point range of the microseismic monitoring; based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operating equipment, the collected microseismic information is dynamically denoised to provide rockburst early warning through the denoised microseismic information. Thus, after removing the mechanical vibration, the real microseismic events in the monitoring signal are highlighted, and the reliability and clarity of the signal are enhanced. Dynamic denoising based on the working status of the equipment can adapt to complex on-site conditions, automatically adjust the denoising parameters when the mechanical equipment is turned on and off, and the vibration characteristics change, to ensure timely response to the real rock mass fracture. The denoised signal is closer to the real stress state of the rock mass, helping the monitoring system to accurately judge the potential area and time of rockburst, and provide effective early warning.

[0048] According to some embodiments, obtaining the operation disturbance information of the operating machinery vibration on the surrounding rock mass during the typical strong rockburst tunnel section rock mass construction process to extract the operation disturbance characteristics includes:

[0049] By collecting vibration data generated during the operation of the operating machinery, the vibration characteristics of different operating equipment are analyzed and extracted;

[0050] The operation disturbance features are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

[0051] For example, in a typical strong rockburst tunnel section environment, accelerometers and seismic wave sensors installed on rock masses and equipment are used to collect vibration data of operating machinery (such as drilling rigs, tunneling machines, scrapers, etc.) in different operating modes (such as starting, stopping, and working at different powers). First, establish an operating equipment feature library, perform frequency domain and time domain analysis on the collected data, and extract the vibration characteristics of each device in typical operations, including: frequency range: For example, the main vibration frequency of the tunneling machine may be concentrated in 200-500Hz, while the frequency of the drilling rig is 300-600Hz; amplitude characteristics: measure the amplitude range, such as the amplitude changes of different devices under different power states; time domain characteristics: extract the time characteristics of the signal, and record the changes in the vibration curve of the equipment at the moment of starting and stopping. A vibration feature library of different equipment is formed for subsequent disturbance identification and denoising. During the construction process, the vibration data collected in real time is matched with the established feature library. Use pattern recognition algorithms (such as template matching, frequency domain similarity analysis, etc.) to determine which device the current vibration signal belongs to. By comparison, the characteristics of the current equipment under real-time working conditions, such as the main vibration frequency, amplitude intensity, etc., are extracted as the disturbance characteristics of the current operating equipment.

[0052] In one embodiment, extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment includes:

[0053] By combining wavelet transform and short-time Fourier transform, the operation disturbance characteristics are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

[0054] Exemplarily, wavelet transform is suitable for the analysis of non-stationary signals, and can provide the multi-scale decomposition capability of signals at different frequencies and times. Through wavelet transform, the vibration signal of the equipment is decomposed into different scales, and the low-frequency and high-frequency components are extracted, so as to capture the transient vibration characteristics of the equipment. Short-time Fourier transform (STFT) is suitable for processing stable and periodic signals, and has a fixed resolution in time and frequency. Through STFT, the spectrum information of the equipment in a specific time window can be obtained, the main frequency components of different equipment (such as the specific frequency bands of drilling rigs and tunneling machines) can be identified, and their main vibration frequencies can be extracted. Then the collected vibration signal of the operating equipment can be subjected to wavelet transform, and it can be decomposed into sub-signals of different frequency bands, corresponding to the low-frequency and high-frequency components in the vibration signal of the equipment. For example, the low-frequency component may correspond to the structural vibration of the equipment, while the high-frequency component may correspond to the vibration frequency during mechanical operation. STFT is used on the sub-signals of each frequency band obtained by wavelet decomposition to obtain its spectrum characteristics in different time windows. By analyzing these spectra, the main frequency distribution of each device in a specific time period can be clearly observed. The vibration characteristics of different equipment are classified through cluster analysis, and the vibration mode of each equipment is individually identified according to the frequency distribution and amplitude characteristics to form a disturbance feature set for each operating equipment. For example, suppose that there are vibration signal interferences from drilling rigs and tunnel boring machines at the same time during tunnel construction: Drilling rig: After wavelet-STFT analysis, it is found that its main frequency is between 300-600Hz, and the characteristic signal shows intermittent high-frequency vibration. Tunnel boring machine: The main frequency distribution is between 200-500Hz, and it has continuous low-frequency vibration characteristics. Through wavelet-STFT decomposition and spectrum analysis, the vibration characteristics of the two equipment can be extracted separately, and they can be classified and identified based on their main frequency bands and amplitude characteristics. In the subsequent denoising process, directional filtering is performed for each characteristic frequency band to effectively eliminate the corresponding mechanical vibration interference.

[0055] It is understandable that wavelet transform is used to capture non-stationary features, and STFT is used for high-time resolution analysis to ensure that the vibration characteristics of equipment in different frequency bands and times can be clearly separated. In scenarios where multiple devices are running, the precise extraction and classification of characteristic frequency bands can more effectively distinguish the interference sources of different devices, providing an accurate basis for denoising. Since the vibration characteristics of each device are extracted separately, mechanical vibration interference can be identified and filtered out in real time, improving the clarity and real-time performance of rock burst warning signals.

[0056] In one embodiment, extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment by combining wavelet transform and short-time Fourier transform includes:

[0057] The characteristic energy can be calculated and classified by the following formula:

[0058]

[0059] Among them, i and j are the vibration frequency set ranges of different devices, x is the vibration data, SFTF x (t, f) is the frequency spectrum distribution of the vibration data of the equipment, W x (a, t) is the energy distribution of the vibration data of the equipment.

[0060] Assume that the vibration frequency band generated by the drilling rig is 300-600Hz. Wavelet transform extracts the scales with higher energy coefficients in this frequency band to form the equipment characteristic frequency band for subsequent classification. Assume that the main vibration frequency of the tunnel boring machine is concentrated in 200-500Hz. In STFT, the energy of the 200-500Hz frequency band in each time window is accumulated to form the time-frequency characteristic curve of the equipment.

[0061] After combining wavelet transform and STFT, a disturbance feature set can be constructed based on the frequency characteristics and time-frequency domain characteristics of equipment vibration. For specific equipment (such as drilling rigs and tunneling machines), their characteristic energy can be comprehensively calculated and classified using the following formula:

[0062]

[0063] Where: E drill (t) represents the characteristic energy of the drilling rig at time t, E TBM (t) represents the characteristic energy of the tunnel boring machine at time t. By accumulating the energy, the main source of equipment disturbance at that moment can be identified. Based on the above calculation results, the threshold T can be set drill and T TBM Used for classification:

[0064] If E drill (t)>T drill , then it is judged as drilling rig disturbance, if E TBM (t)>T TBM , it is determined to be a tunnel boring machine disturbance. By combining wavelet transform and STFT, the vibration characteristics of different operating equipment can be effectively extracted, thereby achieving accurate equipment disturbance feature extraction and denoising in a multi-equipment operating environment. This process achieves a balance between frequency and time resolution through formulas and algorithms, providing higher accuracy and real-time performance for rockburst monitoring.

[0065] In one embodiment, the dynamically denoising the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the operation equipment working status information to provide rock burst warning through the denoised microseismic information includes:

[0066] The filter parameters are automatically adjusted based on the working status of the mechanical working equipment through an adaptive noise cancellation algorithm.

[0067] In one embodiment, the method of automatically adjusting the filter parameters based on the working state of the mechanical working equipment by an adaptive noise elimination algorithm includes:

[0068] Noise characteristics generated by the device in a specific state: d(t) = f(device type, state parameters, frequency characteristics);

[0069] According to the working status of the device, an adaptive filtering algorithm is used to dynamically adjust the filter parameters to minimize the noise component:

[0070] w n+1 =w n +2μe(n)d(n);

[0071] Among them, w n is the filter weight at the nth iteration, μ is the learning rate, which is used to control the step size of weight update, e(n) = x(n)-w n d(n) is the filtering error, that is, the difference between the actual signal and the noise estimate. Through continuous iteration, the filter weight w n It will gradually converge, thus accurately eliminating noise. As a result, the denoising frequency band and amplitude can be automatically adjusted according to the device status, effectively removing the noise generated by different devices. The adaptive filter can be adjusted at the moment of device status change to adapt to the rapid changes in frequency and amplitude, thereby ensuring real-time performance. After removing mechanical interference, the effective components of the microseismic signal are retained, providing an accurate monitoring basis for rock burst warning and reducing false alarms and missed alarms.

[0072] In one embodiment, it also includes:

[0073] The system regularly updates the vibration feature library of the operating equipment, or automatically recalculates and updates it when abnormal vibration features are detected. Therefore, in complex working conditions with multiple equipment construction and equipment status changes, the system can dynamically update the feature library and adaptively adjust the denoising parameters, thereby improving the adaptability and stability of the system in actual engineering.

[0074] The rockburst dynamic early warning method based on microseismic information in the embodiment of the present application is described above. The rockburst dynamic early warning device based on microseismic information in the embodiment of the present application is described below.

[0075] See also Figure 2 An embodiment of a rockburst dynamic early warning device based on microseismic information in the embodiment of the present application may include:

[0076] The acquisition unit 201 is used to acquire the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the typical strong rock burst tunnel section rock mass construction process, so as to extract the operation disturbance characteristics;

[0077] A determination unit 202 is used to determine the continuous impact period of the operation disturbance in the collection point range during the operation and after the operation is stopped according to the collection point range of the microseismic monitoring;

[0078] The analysis unit 203 is used to dynamically denoise the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, so as to provide rock burst warning through the denoised microseismic information.

[0079] In summary, the rockburst dynamic early warning device based on microseismic information provided in the above embodiment obtains the operation disturbance information of the operating mechanical vibration on the surrounding rock mass during the construction process of the typical strong rockburst tunnel section rock mass to extract the operation disturbance characteristics; determines the continuous impact period of the operation disturbance in the collection point range during the operation and after the operation stops according to the collection point range of the microseismic monitoring; dynamically denoises the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operating equipment, so as to provide rockburst early warning through the denoised microseismic information. As a result, after removing the mechanical vibration, the real microseismic events in the monitoring signal are highlighted, and the reliability and clarity of the signal are enhanced. Dynamic denoising based on the working status of the equipment can adapt to complex on-site conditions, and automatically adjust the denoising parameters when the mechanical equipment is turned on and off and the vibration characteristics change, ensuring timely response to the real rock mass fracture. The denoised signal is closer to the real stress state of the rock mass, helping the monitoring system to accurately judge the potential area and time of rockburst occurrence, and provide effective early warning.

[0080] above Figure 2 The rockburst dynamic early warning device based on microseismic information in the embodiment of the present application is described from the perspective of modular functional entities. The rockburst dynamic early warning device based on microseismic information in the embodiment of the present application is described in detail from the perspective of hardware processing. Please refer to Figure 3 , an embodiment of a rockburst dynamic early warning device 300 based on microseismic information in an embodiment of the present application includes:

[0081] An input device 301, an output device 302, a processor 303 and a memory 304, wherein the number of the processor 303 can be one or more. Figure 3In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a bus or other means, wherein: Figure 3 The example of connecting through bus is taken in the following.

[0082] Wherein, by calling the operation instruction stored in the memory 304, the processor 303 is used to perform the following steps:

[0083] Obtain the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics;

[0084] Determine the continuous impact period of the operation disturbance in the collection point range during and after the operation is stopped according to the collection point range of microseismic monitoring;

[0085] Based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, the collected microseismic information is dynamically denoised, so as to provide rock burst warning through the denoised microseismic information.

[0086] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0087] See also Figure 4 , Figure 4 A schematic diagram of an electronic system according to an embodiment of the present application.

[0088] like Figure 4 As shown, an embodiment of the present application provides an electronic system, including a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0089] Use wireless communication to control unmanned operation equipment to perform mobile operation tasks in the tunnel;

[0090] When the unmanned operation equipment reaches the target location, ultra-remote installation of the microseismic monitoring equipment is performed by the unmanned operation equipment;

[0091] Remote monitoring of the rockburst section of the tunnel is carried out based on the installed microseismic monitoring equipment.

[0092] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0093] Since the electronic system introduced in this embodiment is a device used to implement a rockburst dynamic warning device based on microseismic information in an embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic system of this embodiment and its various variations. Therefore, how the electronic system implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0094] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.

[0095] like Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0096] Obtain the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics;

[0097] Determine the continuous impact period of the operation disturbance in the collection point range during and after the operation is stopped according to the collection point range of microseismic monitoring;

[0098] Based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, the collected microseismic information is dynamically denoised, so as to provide rock burst warning through the denoised microseismic information.

[0099] In the specific implementation process, when the computer program 511 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0100] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process in the rockburst dynamic early warning method based on microseismic information in the corresponding embodiment.

[0106] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0112] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dynamic early warning method for rockburst based on microseismic information, characterized in that: The method comprises: Obtain the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics; Determine the continuous impact period of the operation disturbance in the collection point range during and after the operation is stopped according to the collection point range of microseismic monitoring; Based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, the collected microseismic information is dynamically denoised, so as to provide rock burst warning through the denoised microseismic information.

2. The method according to claim 1, characterized in that The method of obtaining the operation disturbance information of the surrounding rock mass caused by the operation mechanical vibration during the construction process of the typical strong rock burst tunnel section to extract the operation disturbance characteristics includes: By collecting vibration data generated during the operation of the operating machinery, the vibration characteristics of different operating equipment are analyzed and extracted; The operation disturbance features are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

3. The method according to claim 2, characterized in that The step of extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment includes: By combining wavelet transform and short-time Fourier transform, the operation disturbance characteristics are extracted from the operation disturbance information based on the vibration characteristics of different operation equipment.

4. The method according to claim 3, characterized in that The method of extracting the operation disturbance feature from the operation disturbance information based on the vibration features of different operation equipment by combining wavelet transform and short-time Fourier transform includes: The characteristic energy can be calculated and classified by the following formula: Among them, i and j are the vibration frequency set ranges of different devices, x is the vibration data, and STFT x (t, f) is the frequency spectrum distribution of the vibration data of the equipment, W x (a, t) is the energy distribution of the vibration data of the equipment.

5. The method according to claim 1, characterized in that The dynamically denoising the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the operation equipment working status information to provide rock burst warning through the denoised microseismic information, including: The filter parameters are automatically adjusted based on the working status of the mechanical working equipment through an adaptive noise cancellation algorithm.

6. The method according to claim 5, characterized in that The method of automatically adjusting the filter parameters based on the working state of the mechanical operation equipment by using an adaptive noise elimination algorithm includes: Noise characteristics generated by the device in a specific state: d(t) = f(device type, state parameters, frequency characteristics); According to the working status of the device, an adaptive filtering algorithm is used to dynamically adjust the filter parameters to minimize the noise component: w n+1 =w n +2μe(n)d(n); Among them, w n is the filter weight at the nth iteration, μ is the learning rate, which is used to control the step size of weight update, e(n) = x(n)-w n d(n) is the filtering error, which is the difference between the actual signal and the noise estimate.

7. The method according to claim 2, characterized in that Also includes: The system regularly updates the vibration signature library of the operating equipment, or automatically recalculates and updates it when abnormal vibration signatures are detected.

8. A rockburst dynamic early warning device based on microseismic information, characterized in that: include: An acquisition unit is used to acquire the operation disturbance information of the surrounding rock mass caused by the operation machinery vibration during the construction process of the typical strong rock burst tunnel section, so as to extract the operation disturbance characteristics; A determination unit, used to determine the continuous influence period of the operation disturbance in the collection point range during the operation and after the operation is stopped according to the collection point range of the microseismic monitoring; The analysis unit is used to dynamically denoise the collected microseismic information based on the operation disturbance characteristics, the continuous impact period, and the working status information of the operation equipment, so as to provide rock burst warning through the denoised microseismic information.

9. An electronic system, comprising a memory and a processor, characterized in that: The processor is used to implement the steps of the rockburst dynamic early warning method based on microseismic information as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. 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 rockburst dynamic early warning method based on microseismic information are implemented as described in any one of claims 1 to 7.

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