An abnormal seismic wave data correction method, device, equipment, medium and program product

Abstract

The present application belongs to the technical field of seismic wave acquisition, and particularly relates to an abnormal seismic wave data correction method, device, equipment, medium and program product. The present application provides an abnormal seismic wave data correction method, which comprises the following steps: acquiring seismic wave data sets collected by each geophone in a target geophone group; determining abnormal data in each seismic wave data set by using an isolated forest algorithm; determining the position of an interference source causing data abnormality according to each abnormal data; and correcting the abnormal data according to the position of the interference source. The present application solves the problem that a large amount of computing resources are wasted to calculate interpolation when correcting abnormal data in seismic wave data in the prior art, realizes fast correction of abnormal data in seismic wave data, saves computing resources and computing time, and can quickly locate the position of an interference source, thereby providing technical support for solving the problem that seismic wave acquisition is easily disturbed.

Description

A method, apparatus, equipment, medium, and program product for correcting anomalous seismic wave data. Technical Field

[0001] This invention belongs to the technical field of seismic wave acquisition, and specifically relates to a method, apparatus, equipment, medium, and program product for correcting anomalous seismic wave data. Background Technology

[0002] Seismic wave data is commonly used for geological structure analysis. During seismic wave data acquisition, seismic waves are generated by blasting at the shot point. These waves propagate through different underground strata and are received by geophones, thus obtaining seismic wave data. To ensure the accuracy of the acquired data, multiple geophones are arranged in an array in the seismic wave acquisition area, forming a geophone array. However, this arrangement still cannot completely eliminate the influence of external interference on the seismic wave data. For example, someone walking near the acquisition device may cause an additional spike in the seismic wave data. Therefore, the seismic wave data acquired by the acquisition device contains a lot of interference, which cannot be completely shielded. Current technology can only remove the interference data and then interpolate the location of the removed interference data. However, the data interpolation process is based on fitting the overall trend of seismic wave changes, but this method wastes a lot of computational resources, and the accuracy is difficult to guarantee. Summary of the Invention

[0003] This invention provides a method for correcting anomalous seismic wave data, comprising: acquiring seismic wave datasets collected by each detector in a target detector group; using an isolated forest algorithm to determine anomalous data in each of the seismic wave datasets; determining the location of interference sources causing the data anomalies based on each of the anomalous data; and correcting the anomalous data based on the location of the interference sources. This method solves the problem in existing technologies where correcting anomalous data in seismic wave data requires significant computational resources for interpolation calculations. It achieves rapid correction of anomalous data in seismic wave data, saving computational resources and time, and also enables rapid location of interference sources, providing technical support for addressing the problem of easy interference during seismic wave acquisition.

[0004] To address the aforementioned technical problems, this application proposes five aspects.

[0005] In a first aspect, this application provides a method for correcting anomalous seismic wave data, comprising: acquiring seismic wave datasets collected by each detector in a target detector group; determining anomalous data in each of the seismic wave datasets using an isolated forest algorithm; determining the location of interference sources causing the data anomalousness based on each of the anomalous data; and correcting the anomalous data based on the location of the interference sources.

[0006] In some embodiments, determining the location of the interference source causing the data anomaly based on each of the abnormal data includes: acquiring data features of each abnormal data, wherein the data features include the data acquisition time; arranging the abnormal data from the same seismic wave dataset according to the data acquisition time to form a second dataset; dividing the abnormal data in the second dataset into multiple first datasets according to the data features; and determining the location of the interference source of each interference source based on the data features of each abnormal data in each of the first datasets.

[0007] In some embodiments, dividing the abnormal data into multiple first datasets based on the data characteristics of the second dataset includes: generating each first dataset through the following steps; extracting the top-ranked abnormal data from each second dataset as first target data; sorting the first target data from earliest to latest according to the collection time to form a first candidate dataset; calculating the collection time difference between two adjacent first target data in the first candidate dataset; determining the first target data before the first collection time difference greater than or equal to a preset duration as second target data; deleting the second target data from each second dataset, and forming the first dataset based on the second target data.

[0008] In some embodiments, the data features further include: acquisition location; determining the location of the interference source causing the abnormal data based on each of the abnormal data includes: drawing a device distribution diagram of the interfered detectors based on the acquisition locations of each of the second target data in the first dataset; drawing equidistant lines on the device distribution diagram based on the acquisition time of each of the second target data; and determining the location of the interference source on the device distribution diagram based on the equidistant lines.

[0009] In some embodiments, the data features further include: abnormal values; the correction of the abnormal data based on the location of the interference source includes: determining the attenuation rate of the interference signal generated by the interference source during propagation based on the abnormal values ​​of each of the second target data; determining the maximum propagation distance of the interference signal based on the device distribution map and the location of the interference source; determining the maximum interference value of the interference signal based on the maximum propagation distance and the attenuation rate; determining the interference value in each of the second target data based on the distance between each affected device and the location of the interference source in the device distribution map; and correcting the abnormal values ​​based on the interference values.

[0010] In some embodiments, determining the maximum propagation distance of the interference signal based on the device distribution map and the location of the interference source includes: determining the target acquisition location that is not subject to interference in the target detector group based on the device distribution map; calculating the straight-line distance between each target acquisition location and the location of the interference source; and determining the shortest straight-line distance as the maximum propagation distance.

[0011] Secondly, this application proposes an anomalous seismic wave data correction device, comprising: a first acquisition module for acquiring seismic wave datasets collected by each detector in a target detector group; a first determination module for determining anomalous data in each of the seismic wave datasets using an isolated forest algorithm; a second determination module for determining the location of the interference source causing the anomalous data based on each of the anomalous data; and a first execution module for correcting the anomalous data based on the location of the interference source.

[0012] Thirdly, this application proposes a computer electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in any of the first aspects.

[0013] Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects of the claims.

[0014] Fifthly, this application proposes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0015] This invention provides a method for correcting anomalous seismic wave data, comprising: acquiring seismic wave datasets collected by each detector in a target detector group; using an isolated forest algorithm to determine anomalous data in each of the seismic wave datasets; determining the location of interference sources causing the data anomalies based on each of the anomalous data; and correcting the anomalous data based on the location of the interference sources. This method solves the problem in existing technologies where correcting anomalous data in seismic wave data requires significant computational resources for interpolation calculations. It achieves rapid correction of anomalous data in seismic wave data, saving computational resources and time, and also enables rapid location of interference sources, providing technical support for addressing the problem of easy interference during seismic wave acquisition. Attached Figure Description

[0016] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0017] Figure 1 is a main flowchart of a method for correcting anomalous seismic wave data provided in an embodiment of this application;

[0018] Figure 2 is a main structural block diagram of an anomalous seismic wave data correction device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0020] Seismic wave data is commonly used for analyzing the geological structure of a target site. During seismic wave data acquisition, seismic waves are typically generated by blasting at the shot point. These waves propagate through different underground strata and are received by geophones, thus obtaining seismic wave data. To ensure the accuracy of the acquired data, multiple geophones are arranged in an array in the seismic wave acquisition area, forming a geophone array. However, even this arrangement cannot completely eliminate the influence of external interference on the seismic wave data. For example, someone walking near the acquisition device might cause an additional spike in the seismic wave data. Therefore, the seismic wave data acquired by the acquisition device contains a lot of interference, which cannot be completely shielded. Current technology can only remove the interference data and then interpolate the locations of the removed interference data. However, the data interpolation process is based on fitting the overall trend of seismic wave changes, but this method wastes a lot of computational resources, and the accuracy is difficult to guarantee.

[0021] Although existing technologies propose that the acquired seismic waves can be corrected by the interference released at the known location of the interference source, some unnoticed interference sources often appear briefly during the seismic wave acquisition process. The existence of these unknown interference sources makes it impossible to correct the abnormal data using existing technologies.

[0022] To address the aforementioned technical problems, this invention proposes a method for correcting anomalous seismic wave data. The implementation details of the bandwidth determination method in this embodiment are described below. The following content is provided for ease of understanding and is not essential for implementing this solution.

[0023] Example 1:

[0024] As shown in Figure 1, this application provides a method for correcting anomalous seismic wave data. This method is applicable to electronic production equipment, which can be a server, mobile terminal, computer, cloud platform, etc. The data processing functionality provided in this embodiment can be implemented by the processor of the electronic production equipment calling program code, wherein the program code can be stored in a computer storage medium. The method for correcting anomalous seismic wave data includes:

[0025] Step S1: Obtain the seismic wave dataset collected by each detector in the target detector group.

[0026] The seismic wave data collected by the geophones are generated by blasting at the shot point. Each geophone will detect one seismic wave data, that is, the seismic wave data collected by each geophone will form a seismic wave dataset.

[0027] Because seismic waves have high energy and can travel through many strata, they can propagate over long distances in various strata. Therefore, although there is a certain distance between the detectors in the detector group, this distance is negligible compared to the propagation distance of the seismic waves. As a result, the differences between the seismic wave data collected by the different target detectors in the same group are small.

[0028] Some external disturbances have lower energy and act on shallower strata, generally propagating in a single bottom layer. Therefore, the propagation distance of external disturbances is shorter, and the resulting disturbance attenuation is faster.

[0029] Step S2: Use the isolated forest algorithm to identify anomalous data in each of the seismic wave datasets.

[0030] During seismic wave propagation, the energy attenuation changes with distance and time, and this change follows a trend. However, if unknown interference affects the detector during acquisition, outliers will appear in the dataset. The Isolation Forest algorithm can segment the data in the dataset using a binary tree structure, separating out abrupt changes. In this application, the Isolation Forest algorithm can be used to filter out outliers from various seismic wave datasets by setting the segmentation depth and segmentation value.

[0031] Step S3: Determine the location of the interference source causing the data anomaly based on each of the abnormal data.

[0032] In some embodiments, step S3, "determining the location of the interference source causing the data anomaly based on each of the abnormal data", includes:

[0033] Step S31: Obtain the data characteristics of each abnormal data, wherein the data characteristics include the data collection time.

[0034] Step S32: Arrange the anomalous data from the same seismic wave dataset according to the data acquisition time to form a second dataset.

[0035] Step S33: Divide the abnormal data in the second dataset into multiple first datasets according to the data characteristics.

[0036] In some embodiments, step S33, "dividing the abnormal data in the second dataset into multiple first datasets according to the data characteristics," includes:

[0037] Step S331: Extract the top-ranked outlier data from each of the second datasets as the first target data.

[0038] Step S332: Sort the first target data from morning to night according to the collection time to form the first candidate dataset.

[0039] Step S333: Calculate the acquisition time difference between two adjacent first target data in the first candidate dataset.

[0040] Step S334: The first target data before the first acquisition time difference that is greater than or equal to the preset time is determined as the second target data.

[0041] Step S335: Delete the second target data in each of the second datasets, and form the first dataset based on the second target data.

[0042] Step S34: Determine the location of each interference source based on the data characteristics of each abnormal data in each of the first datasets.

[0043] In some embodiments, step S34, "determining the location of each interference source based on the data characteristics of each abnormal data in each of the first datasets," includes:

[0044] Step S341: Draw a device distribution diagram of the interfered detectors based on the acquisition locations of each of the second target data in the first dataset.

[0045] Step S342: Draw equidistant lines on the device distribution map according to the acquisition time of each of the second target data.

[0046] Step S343: Determine the location of the interference source in the device distribution diagram based on the equidistant lines.

[0047] In this application, anomalous data in each seismic wave dataset are sorted from earliest to latest according to the acquisition time, which is a sorting of the time of interference occurrence. This allows anomalous data with different sequence numbers to characterize the batches of interference experienced by each detector. When interference occurs, it inevitably affects the acquisition results of multiple detectors. Therefore, anomalous data with the same sequence number are used to determine which anomalous data are caused by the same interference source. The data characteristics in this application include: the acquisition time, the acquisition location, and the data value, i.e., the anomalous value. If two adjacent anomalous data acquisition times are caused by the same interference source, then the acquisition time difference between these two anomalous data must be less than a preset time. Conversely, if two adjacent anomalous data acquisition times are caused by different interferences, then the acquisition time difference between them will be greater than the preset time. Therefore, in this application, the position in the first candidate dataset where the acquisition time difference is greater than the preset time is used as the dividing line between different batches of anomalous data. The anomalous data before the first acquisition time difference greater than or equal to the preset time is identified as anomalous data affected by the same interference source, thus forming the first dataset.

[0048] During seismic wave acquisition, various interference sources are encountered, some of which are persistent. These sources, due to their well-defined locations and clear fluctuations, are easily eliminated from seismic waves. However, some interference of unknown origin is also encountered. This interference is fleeting, and sometimes its occurrence cannot be determined. Therefore, for interference caused by these sources, it is necessary to first determine the location of the interference source and the energy of the interference signal before correcting the abnormal data. Since the interference signal generated by the interference source spreads outward from the source, the location of the interference source can be determined by the acquisition time and location of each abnormal data point in the first dataset. For example, even if two abnormal data points are far apart, if their acquisition times are close, it indicates that the two detectors are close to the interference source. Therefore, in this application, to determine the location of the interference source, the equipment distribution diagram of the affected detectors is first determined based on the acquisition locations of each abnormal data point. Then, equidistant lines are drawn on the equipment distribution diagram according to the acquisition times of each abnormal data point to represent the distance between each detector and the interference source. The location of the interference source is then determined using these equidistant lines on the equipment distribution diagram.

[0049] Step S4: Correct the abnormal data according to the location of the interference source.

[0050] In some embodiments, step S4, "correcting the abnormal data according to the location of the interference source," includes:

[0051] Step S41: Determine the attenuation rate of the interference signal generated by the interference source during propagation based on the abnormal values ​​of each of the second target data.

[0052] Step S42: Determine the maximum propagation distance of the interference signal based on the device distribution map and the location of the interference source.

[0053] In some embodiments, step S42, "determining the maximum propagation distance of the interference signal based on the device distribution map and the location of the interference source," includes:

[0054] Step S421: Determine the target acquisition location that is not subject to interference in the target detector group according to the equipment distribution diagram.

[0055] Step S422: Calculate the straight-line distance between each of the target acquisition locations and the location of the interference source.

[0056] Step S423: Determine the shortest straight-line distance as the maximum propagation distance.

[0057] Step S43: Determine the maximum interference value of the interference signal based on the maximum propagation distance and the attenuation rate.

[0058] Step S44: Determine the interference value in each of the second target data based on the distance between each affected device and the location of the interference source in the device distribution map.

[0059] Step S45: Correct the abnormal value according to the interference value.

[0060] After identifying the location of the interference source, it is still not possible to remove the abnormal data generated by the interference source from the abnormal data. This is because the energy of the interference signal emitted by the interference source affects the proportion of interference signals in each abnormal data set. Therefore, this application also needs to determine the energy of the interference signal emitted by the interference source. Since the energy of the interference signal gradually decreases as it propagates, eventually becoming so small as to be undetectable or easily dispersed by other energies, the rate at which energy decreases with distance during propagation is called the attenuation rate. For seismic wave data, the energy is sufficiently large and the propagation distance is sufficiently long. However, the distance between the detectors in the same detector group is very small compared to the propagation distance of the seismic wave. Therefore, the acquisition results of the same seismic wave data in the same detector group are similar, especially the seismic wave data acquired by two adjacent detectors. Therefore, if there is a significant difference in the numerical values ​​of the abnormal data acquired by two adjacent detectors, this difference must be caused by the attenuation of the interference signal during propagation. Therefore, in this application, the attenuation rate curve of the interference signal during propagation can be fitted based on the collection location and abnormal value of each abnormal data in the first dataset, and thus the attenuation rate of the interference signal during propagation can be obtained.

[0061] Although the attenuation rate is obtained, the abnormal data is formed by the superposition of seismic wave data and interference signals. Since the value of the seismic wave data is uncertain, the attenuation rate alone is insufficient for correction. The energy at which the interference signal is generated is also needed. However, the energy at which the interference signal is generated cannot be directly calculated from the abnormal data in the first dataset. Considering that other detectors besides those involved in the first dataset are not significantly affected by the interference signal, it can be assumed that the interference signal is undetectable or negligible before reaching detectors other than those involved in the first dataset. Therefore, the location of the nearest unaffected detector to the interference source can be determined as the boundary position of the interference signal propagation. Then, the propagation distance of the interference signal, i.e., the maximum propagation distance in this application, can be determined based on the boundary position and the location of the interference source. After obtaining the propagation distance, the energy of the interference signal generated at the interference source can be determined based on the attenuation rate, and thus the interference value of the interference signal collected at each affected acquisition location can be determined. Finally, the abnormal data can be corrected based on the interference value.

[0062] This invention provides a method for correcting anomalous seismic wave data, comprising: acquiring seismic wave datasets collected by each detector in a target detector group; using an isolated forest algorithm to determine anomalous data in each of the seismic wave datasets; determining the location of interference sources causing the data anomalies based on each of the anomalous data; and correcting the anomalous data based on the location of the interference sources. This method solves the problem in existing technologies where correcting anomalous data in seismic wave data requires significant computational resources for interpolation calculations. It achieves rapid correction of anomalous data in seismic wave data, saving computational resources and time, and also enables rapid location of interference sources, providing technical support for addressing the problem of easy interference during seismic wave acquisition.

[0063] Example 2:

[0064] Based on the foregoing embodiments, this application provides a device for correcting anomalous seismic wave data. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0065] As shown in Figure 2, a device for correcting abnormal seismic wave data includes: a first acquisition module 1, a first determination module 2, a second determination module 3, and a first execution module 4.

[0066] The first acquisition module 1 is used to acquire the seismic wave datasets collected by each detector in the target detector group. The first determination module 2 is used to determine the anomalous data in each of the seismic wave datasets using the isolated forest algorithm. The second determination module 3 is used to determine the location of the interference source causing the anomalous data based on each of the anomalous data. The first execution module 4 is used to correct the anomalous data based on the location of the interference source.

[0067] The modules in the aforementioned anomalous seismic wave data correction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the device in hardware form or independently of it, or stored in the memory of the processing device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods.

[0068] Example 3:

[0069] Thirdly, this application provides a computer electronic production apparatus, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described in the first aspect.

[0070] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0071] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0072] Example 4:

[0073] Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0074] Example 5:

[0075] Fifthly, this application proposes a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in any of the first aspects.

[0076] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0077] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for correcting anomalous seismic wave data, characterized in that, include: Obtain the seismic wave dataset acquired by each detector in the target detector group; The isolated forest algorithm was used to identify anomalous data in each of the aforementioned seismic wave datasets; The location of the interference source causing the data anomaly is determined based on each of the aforementioned abnormal data. The step of determining the location of the interference source causing the data anomaly based on each of the abnormal data includes: acquiring the data characteristics of each abnormal data, wherein the data characteristics include the data acquisition time; arranging the abnormal data from the same seismic wave dataset in order of data acquisition time from earliest to latest to form a second dataset; dividing the abnormal data in the second dataset into multiple first datasets based on the data characteristics; determining the location of each interference source based on the data characteristics of each abnormal data in each of the first datasets; and correcting the abnormal data based on the location of the interference source.

2. The method according to claim 1, characterized in that, The step of dividing the abnormal data into multiple first datasets based on the data characteristics of the second dataset includes: generating each first dataset through the following steps; extracting the top-ranked abnormal data from each second dataset as first target data; sorting the first target data from earliest to latest according to the collection time to form a first candidate dataset; calculating the collection time difference between two adjacent first target data in the first candidate dataset; determining the first target data before the first collection time difference greater than or equal to a preset time as second target data; deleting the second target data from each second dataset, and forming the first dataset based on the second target data.

3. The method according to claim 2, characterized in that, The data features also include: acquisition location; determining the location of the interference source causing the abnormal data based on each of the abnormal data includes: drawing a device distribution diagram of the interfered detectors based on the acquisition location of each of the second target data in the first dataset; drawing equidistant lines on the device distribution diagram based on the acquisition time of each of the second target data; and determining the location of the interference source on the device distribution diagram based on the equidistant lines.

4. The method according to claim 3, characterized in that, The data features also include: abnormal values; the correction of the abnormal data based on the location of the interference source includes: determining the attenuation rate of the interference signal generated by the interference source during propagation based on the abnormal values ​​of each of the second target data; determining the maximum propagation distance of the interference signal based on the device distribution map and the location of the interference source; determining the maximum interference value of the interference signal based on the maximum propagation distance and the attenuation rate; determining the interference value in each of the second target data based on the distance between each affected device and the location of the interference source in the device distribution map; and correcting the abnormal values ​​based on the interference values.

5. The method according to claim 4, characterized in that, The step of determining the maximum propagation distance of the interference signal based on the equipment distribution map and the location of the interference source includes: determining the target acquisition location that is not subject to interference in the target detector group based on the equipment distribution map; calculating the straight-line distance between each target acquisition location and the location of the interference source; and determining the shortest straight-line distance as the maximum propagation distance.

6. A device for correcting anomalous seismic wave data, characterized in that, include: The first acquisition module is used to acquire the seismic wave dataset collected by each detector in the target detector group; The first determination module is used to determine the anomalous data in each of the seismic wave datasets using the isolated forest algorithm; The second determining module is used to determine the location of the interference source causing the abnormal data based on each of the abnormal data; The step of determining the location of the interference source causing the data anomaly based on each of the abnormal data includes: acquiring the data characteristics of each abnormal data, wherein the data characteristics include the data acquisition time; arranging the abnormal data from the same seismic wave dataset in order of data acquisition time from earliest to latest to form a second dataset; dividing the abnormal data in the second dataset into multiple first datasets based on the data characteristics; determining the location of each interference source based on the data characteristics of each abnormal data in each of the first datasets; and a first execution module for correcting the abnormal data based on the location of the interference source.

7. A computer electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

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