Vibration wave classification method, electronic equipment and storage medium

Abstract

The invention provides a vibration wave classification method, electronic equipment and a storage medium, and relates to the technical field of vibration wave classification, and the method comprises the steps: converting an original vibration wave into a frequency domain feature matrix corresponding to the original vibration wave through short-time Fourier transform STFT; acquiring corresponding characteristic parameters of the original vibration waves in each preset direction; obtaining an n-dimensional first feature vector corresponding to the frequency domain feature matrix, an n-dimensional second feature vector corresponding to the original vibration wave and an n-dimensional third feature vector corresponding to the original vibration wave; performing feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector; inputting the fusion feature vector into a decision-making layer to obtain a category corresponding to the original vibration wave; according to the method, the final vibration wave classification result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of shock wave classification, and in particular to a shock wave classification method, electronic equipment and storage medium. Background Art

[0002] Seismic wave classification is of great significance to earthquake hazard assessment, regional tectonic research and the seismogenic mechanism of induced earthquakes. In traditional earthquake monitoring, technicians identify the type of seismic event through parameters such as P-wave polarity, spectral characteristics and moment tensor. This identification method relies on manual experience and is difficult to automate. In order to solve the above problems, the existing technology extracts features from seismic waves and then inputs them into a trained model to automatically identify the type of seismic waves. Since this method is based on a waveform of a single property, the method has poor adaptability and the accuracy of seismic wave classification in different regions varies greatly, resulting in low accuracy in the classification results of seismic waves. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is: According to a first aspect of the present application, a shock wave classification method is provided, the method comprising the following steps: S100, obtaining an original shock wave; wherein the original shock wave is a time series signal; S200, using short-time Fourier transform (STFT) to convert the original vibration wave into a corresponding frequency domain feature matrix; wherein the frequency domain feature matrix includes a plurality of rows and a plurality of columns, each row corresponds to a frequency, and each column corresponds to a time period; the element value of the frequency domain feature matrix is ​​the modulus of the vibration wave amplitude corresponding to the time period and the corresponding frequency; S300, obtaining characteristic parameters corresponding to the original shock wave in each preset direction; wherein the characteristic parameters include the maximum vibration velocity and the average vibration velocity of the original shock wave; S400, inputting the frequency domain feature matrix corresponding to the original shock wave into the first encoder of the preset shock wave classification model to obtain the n-dimensional first feature vector corresponding to the frequency domain feature matrix; S500, inputting the original shock wave into a second encoder of a preset shock wave classification model to obtain an n-dimensional second feature vector corresponding to the original shock wave; S600, inputting the characteristic parameters corresponding to the original shock wave in each preset direction into a third encoder of a preset shock wave classification model to obtain an n-dimensional third characteristic vector corresponding to the original shock wave; S700, performing feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector; S800, inputting the fused feature vector into the decision layer to obtain the category corresponding to the original shock wave.

[0004] According to another aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned shock wave classification method.

[0005] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0006] The present invention has at least the following beneficial effects: The shock wave classification method of the present invention uses short-time Fourier transform (STFT) to convert the original shock wave into a frequency domain feature matrix corresponding to the original shock wave; obtains the feature parameters corresponding to the original shock wave in each preset direction; inputs the frequency domain feature matrix corresponding to the original shock wave into a first encoder of a preset shock wave classification model to obtain an n-dimensional first feature vector corresponding to the frequency domain feature matrix; inputs the original shock wave into a second encoder of the preset shock wave classification model to obtain an n-dimensional second feature vector corresponding to the original shock wave; inputs the feature parameters corresponding to the original shock wave in each preset direction into a third encoder of the preset shock wave classification model to obtain an n-dimensional third feature vector corresponding to the original shock wave; performs feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector, and then outputs the category corresponding to the original shock wave through a decision layer; in the process of classifying the shock wave, since the time domain features, frequency domain features and feature parameters of the original shock wave are respectively obtained, and the frequency domain features, time domain features and feature parameters of the original shock wave are fused, the features of the original shock wave are enriched, and the fused fused feature vector can more accurately reflect the characteristics of the original shock wave, so that the final classification result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0008] Figure 1 A flow chart of a method for classifying shock waves provided by an embodiment of the present invention; Figure 2 A schematic structural diagram of a preset shock wave classification model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0010] It should be noted that, based on the present disclosure, those skilled in the art should understand that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0011] The following will refer to Figure 1 The flowchart of the shock wave classification method shown in the figure introduces a shock wave classification method.

[0012] The shock wave classification method may include the following steps: S100, obtaining an original shock wave; wherein the original shock wave is a time series signal.

[0013] In this embodiment, the original shock wave can be obtained when a natural earthquake, mine tremor or collapse occurs; it can be understood that the original shock wave is not necessarily generated by a natural earthquake, but may also be generated by a mine tremor or collapse; usually, the original shock wave obtained is a time series signal.

[0014] S200, using short-time Fourier transform STFT to convert the original vibration wave into a corresponding frequency domain feature matrix; wherein the frequency domain feature matrix includes a plurality of rows and a plurality of columns, each row corresponds to a frequency, and each column corresponds to a time period; the element value of the frequency domain feature matrix is ​​the modulus of the vibration wave amplitude corresponding to the time period and the corresponding frequency.

[0015] In this embodiment, short-time Fourier transform can be used to process the original vibration wave, divide the original vibration wave into multiple time periods, and perform Fourier transform on each time period to obtain the spectral characteristics of the original vibration wave changing with time; the modulus of the vibration wave amplitude at each frequency is used as an element value, so as to obtain the modulus of the vibration wave amplitude at different frequencies corresponding to each time period, and then obtain the frequency domain feature matrix.

[0016] S300, obtaining characteristic parameters corresponding to the original shock wave in each preset direction; wherein the characteristic parameters include the maximum vibration velocity and the average vibration velocity of the original shock wave.

[0017] In this embodiment, the preset directions include vertical downward direction, north-south direction and east-west direction; the characteristic parameters include the maximum vibration speed P V and average vibration velocity IAV; the characteristic parameter calculation formula is as follows: ; ; Among them, v(t) is the time function of the original shock wave; pt is the preset time point.

[0018] For example: the original vibration wave is 120s of waveform data, and the input dimension is (3,12000); the frequency domain feature matrix reflects the change characteristics in the frequency domain, and the input dimension is (3,200,1201); the characteristic parameters corresponding to the original vibration wave in each preset direction provide additional background information for the model, and the dimension is (1,6).

[0019] Through the above formula, the maximum vibration velocity P of the original shock wave in the vertical downward direction, north-south direction and east-west direction can be obtained: v，UD , P v，NS and P v，EW ; and the average vibration velocity IAV of the original shock wave in the vertical downward direction, north-south direction, and east-west direction UD , IAV NS and IAV EW .

[0020] S400, inputting the frequency domain feature matrix corresponding to the original shock wave into the first encoder of the preset shock wave classification model to obtain the n-dimensional first feature vector corresponding to the frequency domain feature matrix.

[0021] In this embodiment, in order to process two sets of features in the time domain and the frequency domain, a multimodal neural network can be constructed to process these two sets of features respectively: Further, such as Figure 2 As shown, the first encoder of the preset shock wave classification model includes: a plurality of one-dimensional CNN convolutional layers and encoding layers; wherein, the plurality of one-dimensional CNN convolutional layers Conv1D are used to extract features from the frequency domain feature matrix, and the encoding layer is used to perform feature encoding on the features of the extracted frequency domain feature matrix.

[0022] S500: Inputting the original shock wave into a second encoder of a preset shock wave classification model to obtain an n-dimensional second feature vector corresponding to the original shock wave.

[0023] In this embodiment, the second encoder of the preset shock wave classification model includes: a plurality of two-dimensional convolutional layers Conv2D; wherein the plurality of two-dimensional convolutional layers are used to extract time domain and frequency domain features of the original shock wave.

[0024] S600, inputting the characteristic parameters corresponding to the original shock wave in each preset direction into the third encoder of the preset shock wave classification model to obtain the n-dimensional third characteristic vector corresponding to the original shock wave.

[0025] In this embodiment, the third encoder of the preset shock wave classification model includes: a plurality of linear layers FC.

[0026] The original shock wave input Waveform input first extracts information through a one-dimensional CNN convolution, and then passes through the Transformer encoding layer for feature encoding. The Transformer structure is widely used in seismology due to its information processing capabilities on time series signals. The frequency domain feature matrix input, that is, the time-frequency graph input STFT input, extracts time-frequency domain features through two-dimensional convolution. The convolution layers are connected through the ReLU activation function to introduce nonlinearity and improve computational efficiency and network expression capabilities. The feature parameter input Feature input extracts features through the linear layer FC.

[0027] S700, performing feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector.

[0028] Furthermore, step S700 includes the following steps: S710, obtain the first eigenvector XA=(XA1, XA2, ..., XA i , …, XA n ), i=1, 2,...,n; among them, XA i is the i-th frequency domain feature corresponding to the frequency domain feature matrix.

[0029] S711, obtain the second eigenvector XB=(XB1, XB2, ..., XB i , …, XB n ); where XB i is the i-th time-frequency domain feature corresponding to the original shock wave.

[0030] S712, obtain the third eigenvector XC=(XC1, XC2, ..., XC i , …, XC n ); where XC i is the i-th characteristic parameter corresponding to the original shock wave.

[0031] S713, concatenate XA, XB and XC to obtain a fused feature vector XD = (XA1, XA2, ..., XA i , …, XA n , XB1, XB2, …, XB i , …, XB n , XC1, XC2, …, XCi , …, XC n ).

[0032] In this embodiment, after obtaining the first eigenvector corresponding to the frequency domain feature matrix, the second eigenvector and the third eigenvector corresponding to the original vibration wave, the first eigenvector, the second eigenvector and the third eigenvector can be quickly fused by splicing to obtain a fused feature vector. The fused feature vector contains the frequency domain characteristics, time domain characteristics and characteristic parameters of the original vibration wave, and can better reflect the characteristics of the original vibration wave.

[0033] Furthermore, after step S712, the method further includes the following steps: S720, obtaining each preset geographical feature of the target area for generating the original shock wave, so as to obtain a geographical feature list D=(D1, D2, ..., D j , …, D m ), j = 1, 2, …, m; where D j The jth preset geographical feature of the target area for generating the original shock wave, m is the number of the preset geographical features; the preset geographical features include altitude, terrain type, landform, underground rock thickness and soil type.

[0034] In the present embodiment, it is understandable that the generation of the original shock wave will correspond to a region; however, the geographical features of different regions are also different. For example, the first region and the second region, which are far apart, have large differences in altitude, terrain type, landform, underground rock thickness and soil type. It should be noted that the characteristics of the shock waves generated by the same event in different regions may also vary greatly.

[0035] S730, convert each geographic feature in D into a corresponding feature value to obtain a geographic feature vector D'=(D'1, D'2, ..., D' j , …, D' m ); where D' j D j The corresponding eigenvalues.

[0036] Furthermore, D' j It can be obtained by following the steps below: S731, if D j is the preset numerical feature, then determine D' j =D j .

[0037] In this embodiment, the numerical feature can be understood as a feature whose parameter is a specific numerical value, such as altitude, thickness of underground rock layer, etc.

[0038] S732, if D j If it is a preset type feature, then get D j Corresponding eigenvalue mapping table YD j Among them, YD j It includes several rows, each row corresponds to a type feature and the feature value corresponding to the type feature.

[0039] S733, according to YD j , determine D j The corresponding eigenvalue D' j .

[0040] In this embodiment, the type feature can be understood as a feature whose parameters are not specific numerical values, for example: the terrain type is plain, the soil type is clay type, etc.; all types under each feature can be listed in an exhaustive manner, and then each type is uniquely encoded to obtain the corresponding feature value mapping table; in subsequent use, you only need to traverse the feature mapping table to determine the corresponding feature value.

[0041] S740 , performing feature fusion on the first eigenvector, the second eigenvector, and the third eigenvector according to D′.

[0042] Further, step S740 may include the following steps: S741, input D' into a preset fusion weight prediction model to obtain a fusion weight list W=(W1, W2, ..., W) corresponding to the first eigenvector, the second eigenvector, and the third eigenvector. k , …, W 3n ), k=1, 2, …, 3n; where W k is the kth weight obtained; 3n is the sum of the elements in the first eigenvector, the second eigenvector and the third eigenvector.

[0043] In this embodiment, a fusion weight prediction model is preset, and the fusion weight prediction model is used to output a fusion weight list corresponding to different geographical features.

[0044] S742, according to W, perform feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector XD = (W1×XA1, W2×XA2, …, W i ×XA i , …, W n ×XA n , W n+1 ×XB1,W n+2 ×XB2,…,W n+i ×XB i , …, W 2n ×XB n , W2n+1 ×XC1,W 2n+2 ×XC2,…,W 2n+i ×XC i , …, W 3n ×XC n ).

[0045] In this embodiment, through the above method, the geographical features of the area where the original shock wave is generated can be added to the first feature and the second feature, so that the obtained fusion feature not only includes the frequency domain features and time domain features of the original shock wave, but also includes the geographical features of the area where the original shock wave is generated, thereby improving the versatility of the shock wave classification model and making the final shock wave classification result more accurate.

[0046] Furthermore, the output layer of the preset shock wave classification model includes Dense and Softmax.

[0047] In this embodiment, for example, there are three types of original shock waves: natural earthquakes, mining earthquakes, and collapses. Then Dense outputs three results, which are normalized through Softmax to obtain the final classification result.

[0048] Further, n=64.

[0049] In this embodiment, the Adam optimizer and the schedule mechanism are used for model training to ensure that the learning rate can be adjusted efficiently during the optimization process. The Adam optimizer, with its adaptive learning rate and momentum update mechanism, can dynamically adjust the learning rate when different parameters are updated, thereby accelerating convergence and improving model performance. We set the maximum training rounds to 100, and use the early stopping mechanism, setting patience to 10 to implement the early stopping strategy and prevent overfitting. That is, if the verification loss does not improve in 10 consecutive rounds, the training will stop early.

[0050] During the training process, a multi-classification cross entropy loss function is used, which can effectively measure the difference between the model's predicted probability and the true label. By monitoring the loss value and learning curve during the training process, the convergence of the model and the performance change can be clearly observed. The training learning curve shows the changing trend of the loss function in each training round, so as to achieve better training results.

[0051] The vibration wave classification method of the present embodiment uses short-time Fourier transform (STFT) to convert the original vibration wave into a frequency domain feature matrix corresponding to the original vibration wave; obtains the feature parameters corresponding to the original vibration wave in each preset direction; inputs the frequency domain feature matrix corresponding to the original vibration wave into the first encoder of the preset vibration wave classification model to obtain the n-dimensional first feature vector corresponding to the frequency domain feature matrix; inputs the original vibration wave into the second encoder of the preset vibration wave classification model to obtain the n-dimensional second feature vector corresponding to the original vibration wave; inputs the feature parameters corresponding to the original vibration wave in each preset direction into the third encoder of the preset vibration wave classification model to obtain the n-dimensional third feature vector corresponding to the original vibration wave; performs feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector, and then outputs the category corresponding to the original vibration wave through the decision layer; in the process of classifying the vibration wave, since the time domain features, frequency domain features and feature parameters of the original vibration wave are respectively obtained, and the frequency domain features, time domain features and feature parameters of the original vibration wave are fused, the features of the original vibration wave are enriched, and the fused fused feature vector can more accurately reflect the characteristics of the original vibration wave, so that the final classification result is more accurate.

[0052] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0053] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0054] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0055] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0057] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0058] An embodiment of the present invention further provides an electronic device, comprising a processor and the aforementioned non-transitory computer-readable storage medium.

[0059] The electronic device is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0060] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one memory mentioned above, and a bus connecting different system components (including the memory and the processor).

[0061] The memory stores program codes, which can be executed by the processor, so that the processor executes the steps in various embodiments described in this specification.

[0062] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0063] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0064] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0065] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0066] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0067] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0068] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for classifying shock waves, characterized in that: The method comprises the following steps: S100, obtaining an original shock wave; wherein the original shock wave is a time series signal; S200, using short-time Fourier transform (STFT) to convert the original vibration wave into a corresponding frequency domain feature matrix; wherein the frequency domain feature matrix includes a plurality of rows and a plurality of columns, each row corresponds to a frequency, and each column corresponds to a time period; the element value of the frequency domain feature matrix is ​​the modulus of the vibration wave amplitude corresponding to the time period and the corresponding frequency; S300, obtaining characteristic parameters corresponding to the original shock wave in each preset direction; wherein the characteristic parameters include the maximum vibration velocity and the average vibration velocity of the original shock wave; S400, inputting the frequency domain feature matrix corresponding to the original shock wave into the first encoder of the preset shock wave classification model to obtain the n-dimensional first feature vector corresponding to the frequency domain feature matrix; S500, inputting the original shock wave into a second encoder of a preset shock wave classification model to obtain an n-dimensional second feature vector corresponding to the original shock wave; S600, inputting the characteristic parameters corresponding to the original shock wave in each preset direction into a third encoder of a preset shock wave classification model to obtain an n-dimensional third characteristic vector corresponding to the original shock wave; S700, performing feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector; S800, inputting the fused feature vector into the decision layer to obtain the category corresponding to the original shock wave.

2. The shock wave classification method according to claim 1, characterized in that: Step S700 includes the following steps: S710, obtain the first eigenvector XA=(XA1, XA2, ..., XA i , …, XA n ), i=1, 2,...,n; among them, XA i is the i-th frequency domain feature corresponding to the frequency domain feature matrix; S711, obtain the second eigenvector XB=(XB1, XB2, ..., XB i , …, XB n ); where XB i is the i-th time-frequency domain feature corresponding to the original shock wave; S712, obtain the third eigenvector XC=(XC1, XC2, ..., XC i , …, XC n ); where XC i is the i-th characteristic parameter corresponding to the original shock wave; S713, concatenate XA, XB and XC to obtain a fused feature vector XD = (XA1, XA2, ..., XA i , …, XA n , XB1, XB2, …, XB i , …, XB n , XC1, XC2, …, XC i , …, XC n ).

3. The shock wave classification method according to claim 2, characterized in that: After step S712, the method further includes the following steps: S720, obtaining each preset geographical feature of the target area for generating the original shock wave, so as to obtain a geographical feature list D=(D1, D2, ..., D j , …, D m ), j = 1, 2, …, m; where D j The jth preset geographical feature of the target area for generating the original shock wave, m is the number of the preset geographical features; the preset geographical features include altitude, terrain type, landform, underground rock thickness and soil type; S730, convert each geographic feature in D into a corresponding feature value to obtain a geographic feature vector D'=(D'1, D'2, ..., D' j , …, D' m ); where D' j D j The corresponding eigenvalues; S740 , performing feature fusion on the first eigenvector, the second eigenvector, and the third eigenvector according to D′.

4. The shock wave classification method according to claim 3, characterized in that: Step S740 includes the following steps: S741, input D' into a preset fusion weight prediction model to obtain a fusion weight list W=(W1, W2, ..., W) corresponding to the first feature vector, the second feature vector, and the third feature vector. k , …, W 3n ), k=1, 2, …, 3n; where W k is the kth weight obtained; 3n is the sum of the elements in the first eigenvector, the second eigenvector and the third eigenvector; S742, according to W, perform feature fusion on the first feature vector, the second feature vector and the third feature vector to obtain a fused feature vector XD = (W1×XA1, W2×XA2, …, W i ×XA i , …, W n ×XA n , W n+1 ×XB1,W n+2 ×XB2,…,W n+i ×XB i , …, W 2n ×XB n , W 2n+1 ×XC1,W 2n+2 ×XC2,…,W 2n+i ×XC i , …, W 3n ×XC n ).

5. The shock wave classification method according to claim 3, characterized in that: D' j Obtained through the following steps: S731, if D j is the preset numerical feature, then determine D' j =D j ; S732, if D j If it is a preset type feature, then get D j Corresponding eigenvalue mapping table YD j Among them, YD j It includes several rows, each row corresponds to a type feature and the feature value corresponding to the type feature; S733, according to YD j , determine D j The corresponding eigenvalue D' j .

6. The shock wave classification method according to claim 1, characterized in that: The first encoder of the preset shock wave classification model includes: a plurality of one-dimensional CNN convolutional layers and an encoding layer; wherein the plurality of one-dimensional CNN convolutional layers are used to extract features from the frequency domain feature matrix, and the encoding layer is used to perform feature encoding on the features of the extracted frequency domain feature matrix; The second encoder of the preset shock wave classification model includes: a plurality of two-dimensional convolutional layers; wherein the plurality of two-dimensional convolutional layers are used to extract time domain and frequency domain features of the original shock wave; The third encoder of the preset shock wave classification model includes: a plurality of linear layers.

7. The shock wave classification method according to claim 1, characterized in that: n=64。 8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the shock wave classification method according to any one of claims 1 to 7.

9. An electronic device, characterized in that: Includes a processor and the non-transitory computer-readable storage medium of claim 8.

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