Multi-source dynamic disturbance frequency-reduction amplitude-reduction regulation and control method and system for deep tunnel

Abstract

The invention relates to the field of tunnel excavation construction, and relates to a deep tunnel multi-source dynamic disturbance frequency and amplitude reduction regulation and control method and system, and the method comprises the steps: obtaining first information and a wave-absorbing material data set; the installation position of the sensor is determined according to the first information, second information is obtained, and the second information comprises disturbance wave data collected by the sensor; constructing a first feature map according to the second information; determining third information according to the first feature map, wherein the third information comprises the type of the disturbance wave; dividing the wave-absorbing material data set according to the third information to obtain at least one divided wave-absorbing material data set; a neural network is trained on the basis of the divided wave-absorbing material data set, at least one trained neural network model is obtained, and the trained neural network model is used for outputting wave-absorbing material ratios corresponding to different types of disturbance waves. And the blank of a dynamic disturbance triggering type damage prevention and control technology is filled.

Description

Technical Field

[0001] The present invention relates to the field of tunnel excavation construction, and in particular to a method and system for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel. Background Art

[0002] As tunnel construction gradually shifts to the deep earth, the high ground stress environment caused by deep burial depth is one of the important challenges facing tunnel engineering. At present, the main tunnel excavation methods are drilling and blasting and TBM. Regardless of the excavation method, the surrounding rock will inevitably be affected by the dynamic disturbance of blasting waves and TBM vibration waves. Under the superposition of the dual unfavorable factors of high ground stress and severe excavation disturbance, repeated disturbances from multiple sources of power induce surrounding rock fractures and even rock bursts, which seriously affect the safe construction of deep buried tunnels. At present, the prevention and control technology for dynamic disturbance-triggered damage is relatively scarce. Disturbance control technologies such as shock-absorbing grooves commonly used in ground engineering are difficult to implement in deep high-stress tunnels. The lack of technology leads to frequent dynamic disturbance-triggered damage on site. Summary of the invention

[0003] The object of the present invention is to provide a method and system for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel, so as to improve the above-mentioned problems.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions: On the one hand, an embodiment of the present application provides a method for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep tunnel, the method comprising: Acquire first information and a data set of absorbing materials, wherein the first information includes a measuring point position of a sensor; Determine the installation position of the sensor according to the first information, and obtain second information, wherein the second information includes disturbance wave data collected by the sensor; Constructing a first feature map according to the second information, wherein the first feature map includes a time domain map and a frequency domain map; Determine third information according to the first characteristic graph, wherein the third information includes a type of disturbance wave; Dividing the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; A neural network is trained based on the divided absorbing material data set to obtain at least one trained neural network model, and the trained neural network model is used to output absorbing material ratios corresponding to different types of disturbance waves.

[0005] In a second aspect, an embodiment of the present application provides a frequency reduction and amplitude reduction control system for multi-source power disturbance in a deep-buried tunnel, the system comprising: An acquisition module, used to acquire first information and a data set of absorbing materials, wherein the first information includes a measurement point position of a sensor; a first processing module, configured to determine an installation position of a sensor according to the first information, and obtain second information, wherein the second information includes disturbance wave data collected by the sensor; A second processing module, configured to construct a first feature map according to the second information, wherein the first feature map includes a time domain map and a frequency domain map; A third processing module, configured to determine third information according to the first characteristic graph, wherein the third information includes a type of disturbance wave; a fourth processing module, configured to divide the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; The fifth processing module is used to train the neural network based on the divided absorbing material data set to obtain at least one trained neural network model, and the trained neural network model is used to output the absorbing material ratios corresponding to different types of disturbance waves.

[0006] In a third aspect, an embodiment of the present application provides a device, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the above-mentioned method for reducing the frequency and amplitude of multi-source power disturbance in a deep tunnel when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a 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 method for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel are implemented.

[0008] The beneficial effects of the present invention are: The present invention determines the installation position of the sensor through the first information, collects disturbance wave data using the sensor to construct a first characteristic graph, determines the type of disturbance wave according to the first characteristic graph, divides the absorbing material data set according to the type of disturbance wave, and then uses the divided absorbing material data set to train the neural network to obtain at least one trained neural network model. The present invention divides the absorbing material data set according to the type of disturbance wave, improves the quality of the data set, and thus improves the accuracy of the output absorbing material ratio, so as to achieve the purpose of improving the frequency reduction and amplitude reduction effect of the material.

[0009] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 Schematic diagram of the process of reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel described in an embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of the structure of the frequency and amplitude reduction control system for multi-source dynamic disturbance in a deep-buried tunnel described in an embodiment of the present invention.

[0013] Figure 3 It is a schematic structural diagram of the deep-buried tunnel multi-source dynamic disturbance frequency and amplitude reduction control equipment described in an embodiment of the present invention.

[0014] Figure 4 This is the fourth characteristic diagram described in the embodiment of the present invention.

[0015] Figure 5 This is a graph showing the relationship between the fiber ratio and the peak vibration velocity attenuation ratio in an embodiment of the present invention.

[0016] Labels in the figure: 901, acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, fourth processing module; 906, fifth processing module; 800, deep-buried tunnel multi-source dynamic disturbance frequency and amplitude reduction control equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0019] Embodiment 1: This embodiment provides a method for reducing the frequency and amplitude of multi-source dynamic disturbances in a deep-buried tunnel. It can be understood that a scenario can be laid out in this embodiment, for example: when blasting and excavating a deep-buried tunnel, there is a scenario where disturbance waves induce surrounding rock fractures and it is difficult to continue construction.

[0020] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, step S4, step S5 and step S6.

[0021] Step S1, obtaining first information and a data set of absorbing materials, wherein the first information includes a measuring point position of a sensor; In this step, the absorbing material data set includes the ratios of different absorbing materials, including but not limited to cement mortar, foaming agent, basalt fiber, ceramic fiber, gypsum, rubber powder and polyacrylonitrile fiber.

[0022] The step S1 also includes step S11, step S12, step S13 and step S14, which specifically include: Step S11, obtaining microseismic monitoring and early warning data; Step S12: establishing a fourth characteristic diagram according to the microseismic monitoring and early warning data, wherein the fourth characteristic diagram includes a relationship between the distance from the excavation to the tunnel face and the proportion of microseismic events; In this step, the fourth feature map is as follows Figure 4 As shown in the figure, when the distance between the excavation and the tunnel face is close, there are a large number of microseismic events, and the proportion of microseismic events is high. Therefore, the grouting area of ​​the absorbing material should not be set too close, otherwise it will cause damage to the grouting area of ​​the absorbing material and affect the absorbing effect.

[0023] Step S13, determining a grouting area of ​​the absorbing material according to the fourth characteristic graph; In this step, a specific implementation method is that a large number of microseismic events are prone to occur within 2-3 times the tunnel diameter from the excavation point to the tunnel face, and stress adjustment mainly occurs in this range. Therefore, the wave-absorbing grouting area is set at three times the tunnel diameter, which can ensure that the grouting area of ​​the wave-absorbing material will not be damaged, and can avoid the natural attenuation of the disturbance wave with increasing distance, so that the wave-absorbing grouting area has a relatively weak attenuation effect on the disturbance wave.

[0024] Step S14: determining the measuring point position of the sensor according to the grouting area of ​​the absorbing material.

[0025] Step S2, determining the installation position of the sensor according to the first information, and acquiring second information, wherein the second information includes disturbance wave data collected by the sensor; In this step, after determining the grouting area of ​​the absorbing material, a monitoring hole with a diameter of 10 cm and a depth of 10 cm is arranged 10 cm before and after the grouting hole, and the sensor is placed in the monitoring hole and fixed with high-strength quick-setting gypsum.

[0026] Step S3: construct a first feature map according to the second information, where the first feature map includes a time domain map and a frequency domain map; Step S4, determining third information according to the first characteristic graph, wherein the third information includes a type of disturbance wave; The step S4 also includes step S41, step S42 and step S43, which specifically include: Step S41, determining the vibration frequency of the disturbance wave according to the frequency domain diagram; Step S42, extracting the amplitude of the disturbance wave at each moment in the time domain diagram to obtain the amplitude distribution range of the disturbance wave; Step S43: determining the type of the disturbance wave according to the vibration frequency of the disturbance wave and the amplitude distribution range of the disturbance wave.

[0027] In this embodiment, the disturbance wave waveform is Fourier transformed by MATLAB, and the frequency and amplitude distribution ranges of different types of disturbance waves are obtained as follows: the frequency of seismic waves is between 5-20 Hz, and the amplitude is between 10-100 MPa; the frequency of face blasting waves is between 100-500 Hz, and the amplitude is between 10-100 MPa; the frequency of TBM mechanical vibration waves is between 800-1200 Hz, and the amplitude is between 1-10 MPa; the frequency of rock burst stress waves is between 100-500 Hz, and the amplitude is between 10-200 MPa, so the type of disturbance wave can be quickly determined according to the corresponding time domain diagram.

[0028] Step S5, dividing the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; In this step, since different absorbing materials have different frequency reduction and amplitude reduction effects on different types of disturbance waves, the absorbing material data set needs to be divided to improve the quality of the absorbing material data set, thereby improving the frequency reduction and amplitude reduction effects of the absorbing materials.

[0029] The step S5 also includes step S51, step S52, step S53 and step S54, which specifically include: Step S51, preprocessing is performed according to the time domain graph corresponding to the third information to obtain a preprocessed waveform graph; In this step, the preprocessing mainly includes removing outliers, waveform noise reduction, and averaging the parameters of the waveform.

[0030] Step S52, extracting disturbance wave characteristic parameters from the pre-processed waveform diagram; In this step, the characteristic parameters of the disturbance wave include but are not limited to peak vibration velocity and frequency.

[0031] Step S53, determining the weakening effect of each absorbing material in the absorbing material data set on the characteristic parameters of the disturbance wave to obtain fourth information; In this step, for high-amplitude disturbance waves, it is necessary to select an absorbing material with the most obvious amplitude weakening effect to regulate them, and for high-frequency disturbance waves, it is necessary to select an absorbing material with the most obvious frequency weakening effect to regulate them. Therefore, the absorbing material data sets of different types of disturbance waves can be screened through the weakening effect of each absorbing material on the characteristic parameters of the disturbance wave.

[0032] The step S53 also includes step S531, step S532, step S533 and step S534, which specifically include: Step S531, obtaining a second characteristic graph and a third characteristic graph, wherein the second characteristic graph includes a waveform graph of the disturbance wave before the absorbing material is injected, and the third characteristic graph includes a waveform graph of the disturbance wave after the absorbing material is injected; Step S532, calculating the difference between the vibration speed at each moment in the second characteristic graph and the third characteristic graph to obtain a first calculation result; Step S533, comparing the first calculation result at each moment with the vibration speed at each corresponding moment in the second characteristic graph to obtain a ratio result; In this step, the ratio result represents the attenuation ratio of the disturbance wave amplitude at each moment before and after the wave absorption.

[0033] Step S534, calculating the average value of each ratio result to obtain the amplitude weakening effect of the disturbance wave.

[0034] In this step, by calculating the mean of the attenuation ratio of the disturbance wave amplitude at each moment, the amplitude weakening effect of the absorbing material on the disturbance wave can be quantified, thereby providing a data basis for the subsequent data set division. It should be noted that the frequency weakening effect of the disturbance wave can also be determined by calculating the difference in the attenuation of the frequency amplitude of the disturbance wave at each moment.

[0035] Step S54: divide the absorbing material data set according to the fourth information and the third information.

[0036] Step S6: training a neural network based on the divided absorbing material data set to obtain at least one trained neural network model, wherein the trained neural network model is used to output absorbing material ratios corresponding to different types of disturbance waves.

[0037] A sample set is constructed based on the divided absorbing material data set, which includes the mapping relationship between different types of materials and disturbance wave characteristic parameters, such as Figure 5 As shown, it represents the relationship between the fiber ratio and the peak vibration velocity attenuation ratio. At the same time, in this step, since the divided absorbing material data set is obtained by dividing by absorbing type, a large number of materials with poor control effects on this type of disturbance wave have been screened out. It is only necessary to establish a mapping relationship between the materials with good control effects on this type of disturbance wave and the disturbance wave characteristic parameters in the divided absorbing material data set, which greatly improves the speed of establishing the sample set, thereby reducing the training time of the model. In addition, multiple sample sets can be constructed through the divided absorbing material data set, each sample set is adapted to a type of disturbance wave, thereby obtaining multiple trained neural network models, improving the accuracy of the absorbing material ratio, and achieving the purpose of improving the frequency reduction and amplitude reduction effect of the disturbance wave.

[0038] The step S6 also includes step S61, step S62, step S63 and step S64, which specifically include: Step S61, using the least squares method to calculate the connection weights between the hidden layer and the output layer of the neural network; Step S62, determining a preset activation function of a neural network according to the connection weights; Step S63, obtaining disturbance wave characteristic parameters according to the time domain graph and the frequency domain graph; Step S64: normalize the disturbance wave characteristic parameters to obtain input samples of the neural network activation function.

[0039] In this embodiment, an RBF neural network is used to complete the establishment of a deep learning artificial network identification model; the RBF neural network is a three-layer neural network, which includes an input layer, a hidden layer, and an output layer; the transformation from the input layer to the hidden layer is nonlinear, while the transformation from the hidden layer to the output layer is linear, wherein the preset activation function is specifically: ; In the above formula, R represents the preset activation function of the RBF neural network. is the pth input sample, and the sample content includes the vibration speed and frequency of the disturbance wave; is the center vector of the radial basis function of the pth hidden layer node, m is the total number of samples, is the Euclidean norm; is the Gaussian function variance, which is determined by solving the weights between the hidden layer and the output layer, that is, ; ,in, is the maximum distance between the selected cluster centers, is the total number of cluster centers.

[0040] The RBF neural network is trained through the sample set until the grid converges and reaches the set error standard, and the trained deep learning artificial network identification model, that is, the trained neural network model, is obtained. The monitored disturbance wave characteristic parameters (peak vibration velocity, frequency) are uploaded to the terminal device. The trained neural network model can output the optimal absorbing material and ratio, thereby realizing the frequency reduction and amplitude reduction control of the disturbance wave.

[0041] Embodiment 2: like Figure 2 As shown, this embodiment provides a frequency reduction and amplitude reduction control system for multi-source dynamic disturbance in a deep buried tunnel, the system includes an acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, a fourth processing module 905 and a fifth processing module 906, which specifically include: An acquisition module 901 is used to acquire first information and a data set of absorbing materials, wherein the first information includes a measurement point position of a sensor; A first processing module 902, configured to determine an installation position of a sensor according to the first information, and obtain second information, wherein the second information includes disturbance wave data collected by the sensor; A second processing module 903, configured to construct a first feature map according to the second information, where the first feature map includes a time domain map and a frequency domain map; A third processing module 904 is used to determine third information according to the first characteristic map, where the third information includes a type of disturbance wave; A fourth processing module 905 is used to divide the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; The fifth processing module 906 is used to train the neural network based on the divided absorbing material data set to obtain at least one trained neural network model, and the trained neural network model is used to output the absorbing material ratios corresponding to different types of disturbance waves.

[0042] In a specific embodiment of the present disclosure, the third processing module further includes a first processing unit, a second processing unit and a third processing unit, which specifically include: A first processing unit, configured to determine a vibration frequency of the disturbance wave according to the frequency domain diagram; A second processing unit is used to extract the amplitude of the disturbance wave at each moment in the time domain diagram to obtain the amplitude distribution range of the disturbance wave; The third processing unit is used to determine the type of the disturbance wave according to the vibration frequency of the disturbance wave and the amplitude distribution range of the disturbance wave.

[0043] In a specific embodiment of the present disclosure, the fourth processing module further includes a preprocessing unit, a fourth processing unit, a fifth processing unit and a sixth processing unit, which specifically include: A preprocessing unit, configured to perform preprocessing according to the time domain graph corresponding to the third information to obtain a preprocessed waveform graph; A fourth processing unit, used for extracting disturbance wave characteristic parameters from the pre-processed waveform diagram; a fifth processing unit, configured to determine a weakening effect of each absorbing material in the absorbing material data set on the characteristic parameter of the disturbance wave, and obtain fourth information; The sixth processing unit is configured to divide the absorbing material data set according to the fourth information and the third information.

[0044] In a specific implementation of the present disclosure, the fifth processing unit further includes a first acquisition unit, a seventh processing unit, an eighth processing unit and a ninth processing unit, which specifically include: A first acquisition unit is used to acquire a second characteristic graph and a third characteristic graph, wherein the second characteristic graph includes a waveform graph of the disturbance wave before the absorbing material is injected, and the third characteristic graph includes a waveform graph of the disturbance wave after the absorbing material is injected; a seventh processing unit, configured to calculate a difference between the vibration speeds at each moment in the second characteristic graph and the third characteristic graph, and obtain a first calculation result; An eighth processing unit, configured to compare the first calculation result at each moment with the vibration speed at each corresponding moment in the second characteristic graph to obtain a ratio result; The ninth processing unit is used to calculate the average value of each of the ratio results to obtain the amplitude weakening effect of the disturbance wave.

[0045] In a specific implementation of the present disclosure, the fifth processing module further includes a tenth processing unit, an eleventh processing unit, a twelfth processing unit and a thirteenth processing unit, which specifically include: a tenth processing unit, for calculating the connection weights between the hidden layer and the output layer of the neural network using the least square method; an eleventh processing unit, configured to determine a preset activation function of a neural network according to the connection weights; A twelfth processing unit, configured to obtain a disturbance wave characteristic parameter according to the time domain graph and the frequency domain graph; The thirteenth processing unit is used to normalize the disturbance wave characteristic parameters to obtain input samples of the neural network activation function.

[0046] In a specific implementation of the present disclosure, the acquisition module further includes a second acquisition unit, a fourteenth processing unit, a fifteenth processing unit, and a sixteenth processing unit, which specifically include: The second acquisition unit is used to acquire microseismic monitoring and early warning data; A fourteenth processing unit is used to establish a fourth characteristic map according to the microseismic monitoring and early warning data, wherein the fourth characteristic map includes a relationship between the distance of the excavation from the tunnel face and the proportion of microseismic events; A fifteenth processing unit, configured to determine a grouting area of ​​the absorbing material according to the fourth characteristic map; The sixteenth processing unit is used to determine the measuring point position of the sensor according to the grouting area of ​​the absorbing material.

[0047] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0048] Embodiment 3: Corresponding to the above method embodiment, this embodiment also provides a deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device. The deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device described below and the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control method described above can be referenced to each other.

[0049] Figure 3 FIG. 8 is a block diagram of a deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 according to an exemplary embodiment. Figure 3 As shown, the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 may include: a processor 801, a memory 802. The deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0050] The processor 801 is used to control the overall operation of the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 to complete all or part of the steps in the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control method. The memory 802 is used to store various types of data to support the operation of the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800, and these data may include, for example, instructions for any application or method operating on the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0051] In an exemplary embodiment, the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control method.

[0052] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control method are implemented. For example, the computer-readable storage medium may be the memory 802 including the program instructions, and the program instructions may be executed by the processor 801 of the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control device 800 to complete the deep-buried tunnel multi-source power disturbance frequency reduction and amplitude reduction control method.

[0053] Embodiment 4: Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the method for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel described above can refer to each other.

[0054] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep-buried tunnel according to the above method embodiment.

[0055] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0057] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for reducing the frequency and amplitude of multi-source dynamic disturbance in a deep buried tunnel, characterized in that: include: Acquire first information and a data set of absorbing materials, wherein the first information includes a measuring point position of a sensor; Determine the installation position of the sensor according to the first information, and obtain second information, wherein the second information includes disturbance wave data collected by the sensor; Constructing a first feature map according to the second information, wherein the first feature map includes a time domain map and a frequency domain map; Determine third information according to the first characteristic graph, wherein the third information includes a type of disturbance wave; Dividing the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; Training a neural network based on the divided absorbing material data set to obtain at least one trained neural network model, wherein the trained neural network model is used to output absorbing material ratios corresponding to different types of disturbance waves; The step of dividing the absorbing material data set according to the third information includes: Performing preprocessing according to the time domain graph corresponding to the third information to obtain a preprocessed waveform graph; Extracting disturbance wave characteristic parameters from the preprocessed waveform diagram; Determine the weakening effect of each absorbing material in the absorbing material data set on the characteristic parameter of the disturbance wave to obtain fourth information; dividing the absorbing material data set according to the fourth information and the third information; Wherein, determining the weakening effect of each absorbing material in the absorbing material data set on the characteristic parameters of the disturbance wave includes: Acquire a second characteristic graph and a third characteristic graph, wherein the second characteristic graph includes a waveform graph of the disturbance wave before the absorbing material is injected, and the third characteristic graph includes a waveform graph of the disturbance wave after the absorbing material is injected; Calculating the difference between the vibration speed at each moment in the second characteristic graph and the third characteristic graph to obtain a first calculation result; Compare the first calculation result at each moment with the vibration speed at each corresponding moment in the second characteristic graph to obtain a ratio result; The average value of each ratio result is calculated to obtain the amplitude weakening effect of the disturbance wave.

2. The method for reducing frequency and amplitude of multi-source dynamic disturbance in deep buried tunnels according to claim 1 is characterized in that: Determining third information according to the first characteristic graph, the third information including the type of the disturbance wave, including: determining the vibration frequency of the disturbance wave according to the frequency domain diagram; Extracting the amplitude of the disturbance wave at each moment in the time domain diagram to obtain the amplitude distribution range of the disturbance wave; The type of the disturbance wave is determined according to the vibration frequency of the disturbance wave and the amplitude distribution range of the disturbance wave.

3. The method for reducing frequency and amplitude of multi-source dynamic disturbance in deep tunnel according to claim 1 is characterized in that: The neural network is trained based on the divided absorbing material data set to obtain at least one trained neural network model, including: The connection weights between the hidden layer and the output layer of the neural network are calculated using the least squares method; Determine a preset activation function of a neural network according to the connection weights; Acquire disturbance wave characteristic parameters according to the time domain graph and the frequency domain graph; The disturbance wave characteristic parameters are normalized to obtain input samples of the neural network activation function.

4. A frequency reduction and amplitude reduction control system for multi-source dynamic disturbance in deep buried tunnels, characterized in that: include: An acquisition module, used to acquire first information and a data set of absorbing materials, wherein the first information includes a measurement point position of a sensor; a first processing module, configured to determine an installation position of a sensor according to the first information, and obtain second information, wherein the second information includes disturbance wave data collected by the sensor; A second processing module, configured to construct a first feature map according to the second information, wherein the first feature map includes a time domain map and a frequency domain map; A third processing module, configured to determine third information according to the first characteristic graph, wherein the third information includes a type of disturbance wave; a fourth processing module, configured to divide the absorbing material data set according to the third information to obtain at least one divided absorbing material data set; A fifth processing module, used for training a neural network based on the divided absorbing material data set to obtain at least one trained neural network model, wherein the trained neural network model is used for outputting absorbing material ratios corresponding to different types of disturbance waves; Wherein, the fourth processing module includes: A preprocessing unit, configured to perform preprocessing according to the time domain graph corresponding to the third information to obtain a preprocessed waveform graph; A fourth processing unit, used for extracting disturbance wave characteristic parameters from the pre-processed waveform diagram; a fifth processing unit, configured to determine a weakening effect of each absorbing material in the absorbing material data set on the characteristic parameter of the disturbance wave, and obtain fourth information; a sixth processing unit, configured to divide the absorbing material data set according to the fourth information and the third information; Wherein, the fifth processing unit comprises: A first acquisition unit is used to acquire a second characteristic graph and a third characteristic graph, wherein the second characteristic graph includes a waveform graph of the disturbance wave before the absorbing material is injected, and the third characteristic graph includes a waveform graph of the disturbance wave after the absorbing material is injected; a seventh processing unit, configured to calculate a difference between the vibration speeds at each moment in the second characteristic graph and the third characteristic graph, and obtain a first calculation result; An eighth processing unit, configured to compare the first calculation result at each moment with the vibration speed at each corresponding moment in the second characteristic graph to obtain a ratio result; The ninth processing unit is used to calculate the average value of each of the ratio results to obtain the amplitude weakening effect of the disturbance wave.

5. The frequency reduction and amplitude reduction control system for multi-source dynamic disturbance in deep buried tunnels according to claim 4 is characterized in that: The third processing module comprises: A first processing unit, configured to determine a vibration frequency of the disturbance wave according to the frequency domain diagram; A second processing unit is used to extract the amplitude of the disturbance wave at each moment in the time domain diagram to obtain the amplitude distribution range of the disturbance wave; The third processing unit is used to determine the type of the disturbance wave according to the vibration frequency of the disturbance wave and the amplitude distribution range of the disturbance wave.

6. The frequency reduction and amplitude reduction control system for multi-source dynamic disturbance in deep buried tunnels according to claim 4 is characterized in that: The fifth processing module comprises: a tenth processing unit, for calculating the connection weights between the hidden layer and the output layer of the neural network using the least square method; an eleventh processing unit, configured to determine a preset activation function of a neural network according to the connection weights; A twelfth processing unit, configured to obtain a disturbance wave characteristic parameter according to the time domain graph and the frequency domain graph; The thirteenth processing unit is used to normalize the disturbance wave characteristic parameters to obtain input samples of the neural network activation function.

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