Method for detecting high-time-phase SAR (Synthetic Aperture Radar) echo data specific-speed moving target

Abstract

The invention discloses a high-time-phase SAR echo data specific speed moving target detection method. The method comprises the steps of segmenting to-be-detected high-time-phase SAR echo data into a plurality of sub-data according to a time sequence based on a predetermined specific speed; performing imaging processing on each piece of sub-data to obtain a plurality of sub-images; performing high-dimensional mapping on a sequential image formed by each sub-image to obtain a sequential mapping image; determining a binary feature map of the sequential mapping image based on the kernel distance of each pixel in the sequential mapping image; and performing morphological closed operation on the binary feature map to obtain a detection result of the moving target at the specific speed. According to the method and the device, the moving target with the specific speed in the SAR echo data can be accurately detected, and directional detection is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of target detection, and particularly to a method for detecting moving targets with a specific speed in high-temporal-phase SAR echo data. Background Art

[0002] Synthetic Aperture Radar (SAR), as an all-weather and all-time remote sensing technology, has been widely applied in the fields of traffic control, environmental monitoring, military reconnaissance, etc. As a new imaging mode, high-temporal-phase SAR images further improve the temporal resolution and have stronger capabilities in moving target detection.

[0003] Currently, the existing methods for detecting moving targets in SAR images mainly utilize the Doppler characteristics and spatio-temporal joint distribution characteristics of moving targets to separate moving targets from background interference. Although these methods can suppress clutter to a certain extent and detect moving targets, they cannot accurately screen out moving targets with a specific speed alone, resulting in the existence of interfering moving targets that are not of interest in the detection results. Summary of the Invention

[0004] The present invention provides a method for detecting moving targets with a specific speed in high-temporal-phase SAR echo data, which can accurately detect moving targets with a specific speed in SAR echo data and achieve directional detection. The technical solution is as follows:

[0005] On the one hand, a method for detecting moving targets with a specific speed in high-temporal-phase SAR echo data is provided, and the method includes:

[0006] Based on a pre-determined specific speed, the high-temporal-phase SAR echo data to be detected is segmented into multiple sub-data in chronological order;

[0007] Each of the sub-data is respectively subjected to imaging processing to obtain multiple sub-images;

[0008] The sequential image composed of each sub-image is subjected to high-dimensional mapping to obtain a sequential mapping image;

[0009] Based on the kernel distances of the pixels in the sequential mapping image, a binary feature map of the sequential mapping image is determined;

[0010] The binary feature map is subjected to morphological closing operation to obtain the detection result of the moving targets with the specific speed.

[0011] On the other hand, a device for detecting moving targets with a specific speed in high-temporal-phase SAR echo data is provided, and the device includes:

[0012] A segmentation unit, configured to segment high-temporal-phase SAR echo data to be detected into a plurality of sub-data in chronological order based on a pre-determined specific speed;

[0013] An imaging unit, configured to perform imaging processing on each of the sub-data to obtain a plurality of sub-images;

[0014] A mapping unit, configured to perform high-dimensional mapping on a sequential image composed of each of the sub-images to obtain a sequential mapping image;

[0015] A feature map determination unit, configured to determine a binary feature map of the sequential mapping image based on the kernel distances of the pixels in the sequential mapping image;

[0016] A result determination unit, configured to perform morphological closing operation on the binary feature map to obtain a detection result of the moving target with the specific speed.

[0017] On the other hand, a computer device is provided, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the method for detecting a moving target with a specific speed in the above-mentioned high-temporal-phase SAR echo data.

[0018] On the other hand, a computer-readable storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for detecting a moving target with a specific speed in the above-mentioned high-temporal-phase SAR echo data are implemented.

[0019] On the other hand, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of the method for detecting a moving target with a specific speed in the above-mentioned high-temporal-phase SAR echo data are implemented.

[0020] An embodiment of the present invention provides a method for detecting a moving target with a specific speed in high-temporal-phase SAR echo data. First, using the high-temporal-phase SAR echo data as input, and then segmenting the echo data into several sub-data according to the specific speed, which can make the time-domain features of each sub-data more obvious. In this way, when performing high-dimensional mapping on the sequential image composed of sub-images, the high-dimensional mapping result of the kernel function can be maximized, excluding the interference of other moving targets, clutter, and noise. Finally, by mapping the sequential mapping image into a binary feature map and performing morphological closing operation on the binary feature map, a moving target with a specific speed can be detected, and other moving targets and noise can be removed, improving the accuracy of the detection result. It can be seen that the present application can accurately detect a moving target with a specific speed in SAR echo data and achieve directional detection. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of a method for detecting moving targets with a specific speed in high-temporal-phase SAR echo data provided by an embodiment of the present invention;

[0023] Figure 2 It is a structural diagram of a device for detecting moving targets with a specific speed in high-temporal-phase SAR echo data provided by an embodiment of the present invention;

[0024] Figure 3 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of each moving target and its speed included in high-temporal-phase SAR echo data provided by an embodiment of the present invention;

[0026] Figures 5 to 7 They are respectively for using the method of the present application and different specific speeds to Figure 4 detect the moving targets in it, and it is a schematic diagram of the results. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0028] As described above, the inventor found in the work that the existing SAR moving target detection mainly has the following defects: on the one hand, although the traditional method can suppress clutter to a certain extent and detect moving targets, it is difficult to accurately screen moving targets with a specific speed. On the other hand, the deep learning method is too dependent on a large amount of labeled data and consumes a large amount of computing resources, making it difficult to meet the real-time requirements for detecting moving targets with a specific speed in high-temporal-phase SAR.

[0029] Based on the above problems, the inventor proposed that the high temporal resolution characteristics of high-temporal-phase SAR echo data can be utilized to change the time-domain characteristics of moving targets with a specific speed, maximize the high-dimensional mapping result of the kernel function, exclude interference from other moving targets, clutter, and noise, etc., and achieve directional detection.

[0030] The following describes the specific implementation manners of the above concepts.

[0031] Please refer to Figure 1 , a method for detecting a moving target with a specific speed in high-temporal-phase SAR echo data provided by an embodiment of the present invention, the method includes:

[0032] Step 100, based on a pre-determined specific speed, sequentially divide the high-temporal-phase SAR echo data to be detected into multiple sub-data according to the time sequence;

[0033] Step 102, perform imaging processing on each sub-data respectively to obtain multiple sub-images;

[0034] Step 104, perform high-dimensional mapping on the sequential image composed of each sub-image to obtain a sequential mapping image;

[0035] Step 106, determine the binary feature map of the sequential mapping image based on the kernel distances of the pixels in the sequential mapping image;

[0036] Step 108, perform morphological closing operation on the binary feature map to obtain the detection result of the moving target with the specific speed.

[0037] In the embodiment of the present invention, first, the high-temporal-phase SAR echo data is used as the input, and then the echo data is divided into several sub-data according to the specific speed, which can make the time-domain features of each sub-data more obvious. In this way, when performing high-dimensional mapping on the sequential image composed of sub-images, the high-dimensional mapping result of the kernel function can be maximized, and interferences such as other moving targets, clutter, and noise can be excluded. Finally, by mapping the sequential mapping image into a binary feature map and performing morphological closing operation on the binary feature map, the moving target with the specific speed can be detected, and other moving targets and noises can be removed, improving the accuracy of the detection result. It can be seen that this application can accurately detect the moving target with the specific speed in the SAR echo data and achieve directional detection.

[0038] The following describes Figure 1 the execution manners of the respective steps shown.

[0039] First, for step 100, based on a pre-determined specific speed, sequentially divide the high-temporal-phase SAR echo data to be detected into multiple sub-data according to the time sequence.

[0040] In this step, the specific speed is a speed range, which can be determined according to user needs, such as 60 - 80 km / h, 120 - 130 km / h, etc. Since the moving speeds of different moving targets are different, different speeds can roughly characterize different target features. For example, the speed ranges of cars are significantly different from those of high-speed rails and airplanes.

[0041] In some embodiments, the number of sub-data is determined based on a specific speed, the speed of the satellite for acquiring echo data, the Doppler frequency modulation slope of the echo data, and the synthetic aperture time.

[0042] In this embodiment, the number of segmented sub-data has a matching relationship with the speed of the moving target, such that a larger mapping value can be obtained for the high-dimensional mapping of the subsequent sequential images, thereby facilitating the distinction of noise.

[0043] Therefore, it is preferable to determine the segmentation number, i.e., the number of sub-data, using the following formula:

[0044]

[0045] In the formula, n is the number of sub-data; V t is the specific speed; V s is the speed of the satellite for acquiring echo data; K a is the Doppler frequency modulation slope of the echo data; T a is the synthetic aperture time.

[0046] For step 102, imaging processing is performed on each sub-data respectively to obtain a plurality of sub-images.

[0047] In this step, the purpose of the imaging processing is to convert the echo data into image data that can be used for subsequent processing. The user can adopt the following imaging processing methods: Range-Doppler algorithm (RDA), Chirp Scaling algorithm (CSA), wavenumber domain algorithm (ω-K algorithm), etc. Of course, other methods can also be adopted, and this application is not limited thereto.

[0048] For step 104, high-dimensional mapping is performed on the sequential image composed of each sub-image to obtain a sequential mapping image.

[0049] In this step, through high-dimensional mapping, it is beneficial to extract features of the moving target.

[0050] In some embodiments, the following kernel function can be used to perform high-dimensional mapping on the sequential image to obtain a sequential mapping image:

[0051]

[0052] In the formula, ker p is the kernel function; x q is an element in the previous time series segment; y q is an element in the subsequent time series segment; η is the correlation modulation coefficient; L is the total length of the time series; W is the sliding window length; I is the sliding window interval; p represents the time series position along the high-dimensional mapping result; L - 2W - I + 1 is the total length of the mapped time series.

[0053] In the above mapping process, according to the mapping relationship, for the amplitude time series of each pixel, two sliding windows with a length of L and an interval of I are set from the starting position, and the amplitude segments within the windows are sorted by intensity. The sliding windows are slid in time sequence with a preset step size to calculate the similarity between the two amplitude segments in the window, and this process is repeated until all images are traversed to obtain a sequential mapping image. Through the above calculation, the sequential image with a length of L can be mapped into a sequential mapping image with a length of L - 2W - I + 1, which is conducive to extracting features of moving targets.

[0054] For step 106, based on the kernel distances of the pixels in the sequential mapping image, determining the binary feature map of the sequential mapping image includes:

[0055] Traverse each pixel in the sequential mapping image, and for each traversed pixel, perform the following:

[0056] Perform normalization processing on the kernel distance time series corresponding to the current pixel to obtain a normalized kernel distance time series; determine whether the maximum value in the normalized kernel distance time series is not less than a pre-determined hard threshold; if so, set the value of the current pixel to 1, and if not, set the value of the current pixel to 0;

[0057] And so on until each pixel is traversed to obtain the binary feature map of the sequential mapping image.

[0058] In the above calculation process, being less than the hard threshold is considered a negative class, that is, no moving target is detected for this pixel; being greater than or equal to the hard threshold is considered a positive class, that is, a moving target is detected for this pixel. Traverse all pixels to obtain a binary feature image.

[0059] In some embodiments, the following formula is used to perform normalization processing on the kernel distance time series corresponding to each pixel:

[0060]

[0061] where D i '(0, 1, …, L - 2W - I + 1) is the normalized kernel distance time series of the i-th pixel, i = 1, 2, …… K, and K is the total number of pixels in the mapping image; D i (0, 1, …, L - 2W - I + 1) is the kernel distance time series of the i-th pixel before normalization; μ pi is the expectation of the kernel distance time series of the i-th pixel; σ pi is the standard deviation of the kernel distance time series of the i-th pixel.

[0062] In some embodiments, the following decision formula is used to determine the value of each pixel:

[0063]

[0064] In the formula, thr is the hard threshold; C0 represents the negative class, that is, the pixel value is 0; C1 represents the positive class, that is, the pixel value is 1.

[0065] Finally, for step 108, the following formula is used to perform morphological closing operation on the binary feature map:

[0066]

[0067] In the formula, b is the binary image; t is the template function; · represents the closing operation; represents the erosion operation; represents the dilation operation.

[0068] The detection image of the moving target with a specific speed obtained in this step can be used for subsequent applications such as target tracking and trajectory extraction.

[0069] To prove the effectiveness of the method of this application, the inventor uses Figure 4 the high-temporal-phase SAR echo data shown containing 5 moving targets with different speeds as the input, and uses the method of this application to perform three types of screening and detection on the moving targets with different speeds existing in the data respectively. The detection results are as follows:

[0070] (1) When the inventor sets the specific speed to 20 - 40 m / s, the detection result is as Figure 5 shown. It can be seen from the figure that the method of this application can not only screen out the targets with speeds in the range of 20 - 40 m / s, but also eliminate the targets with speeds of 60 m / s and 80 m / s, meeting the detection requirements.

[0071] (2) When the inventor sets the specific speed to 60 - 80 m / s, the detection result is as Figure 6 shown. It can be seen from the figure that the method of this application can not only screen out the targets with speeds in the range of 60 - 80 m / s, but also eliminate the targets with speeds of 20 m / s, 27 m / s and 40 m / s, meeting the detection requirements.

[0072] (3) When the inventor sets the specific speed to 20 - 80 m / s, the detection result is as Figure 7 shown. It can be seen from the figure that the method of this application can screen out all the targets within this speed range, meeting the detection requirements.

[0073] From the above results, it can be seen that by adjusting the specific speed, the number of echo segments, and the subsequent processing parameters, low-speed, high-speed or all moving targets can be screened out respectively. Thus, it can be seen that the method of this application can accurately detect the moving targets with a specific speed in the SAR echo data and achieve directional detection.

[0074] Such as Figure 2, Figure 3 As shown, an embodiment of the present invention provides a detection device for moving targets with a specific speed in high-temporal-phase SAR echo data. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. At the hardware level, as Figure 2 shown, it is a hardware architecture diagram of a computing device where the detection device for moving targets with a specific speed in high-temporal-phase SAR echo data provided by an embodiment of the present invention is located. In addition to Figure 2 the processor, memory, network interface, and non-volatile memory shown, the computing device where the device is located in the embodiment usually may also include other hardware, such as a forwarding chip responsible for processing packets, etc. Taking software implementation as an example, as Figure 3 shown, as a device in a logical sense, it is formed by reading the corresponding computer program in the non-volatile memory into the memory by the CPU of its computing device and running it.

[0075] Please refer to Figure 3 , an embodiment of the present application provides a detection device for moving targets with a specific speed in high-temporal-phase SAR echo data, including:

[0076] A segmentation unit 300, configured to segment the high-temporal-phase SAR echo data to be detected into multiple sub-data in chronological order based on a predetermined specific speed;

[0077] An imaging unit 302, configured to perform imaging processing on each sub-data respectively to obtain multiple sub-images;

[0078] A mapping unit 304, configured to perform high-dimensional mapping on the sequential image composed of each sub-image to obtain a sequential mapping image;

[0079] A feature map determination unit 306, configured to determine a binary feature map of the sequential mapping image based on the kernel distances of the pixels in the sequential mapping image;

[0080] A result determination unit 308, configured to perform morphological closing operation on the binary feature map to obtain the detection result of the moving target with a specific speed.

[0081] In some embodiments, the number of sub-data is determined based on the specific speed, the satellite speed for obtaining the echo data, the Doppler frequency modulation slope of the echo data, and the synthetic aperture time.

[0082] In some embodiments, the following kernel function is used to perform high-dimensional mapping on the sequential image to obtain a sequential mapping image:

[0083]

[0084] In the formula, ker p is the kernel function; x qis an element in the previous time series segment; y q is an element in the subsequent time series segment; η is the associated modulation coefficient; L is the total length of the time series; W is the sliding window length; I is the sliding window interval; p represents the temporal position along the high-dimensional mapping result; L - 2W - I + 1 is the total length of the mapped time series.

[0085] In some embodiments, the feature map determination unit 306 is configured to perform the following operations:

[0086] Traverse each pixel in the sequential mapping image, and for each traversed pixel, perform:

[0087] Normalize the kernel distance time series corresponding to the current pixel to obtain the normalized kernel distance time series; determine whether the maximum value in the normalized kernel distance time series is not less than a pre-determined hard threshold; if so, set the value of the current pixel to 1, and if not, set the value of the current pixel to 0;

[0088] And so on, until each pixel is traversed to obtain the binary feature map of the sequential mapping image.

[0089] In some embodiments, the following formula is used to normalize the kernel distance time series corresponding to each pixel:

[0090]

[0091] In the formula, D i '(0, 1, …, L - 2W - I + 1) is the normalized kernel distance time series of the i-th pixel, i = 1, 2, …… K, K is the total number of pixels in the mapping image; D i (0, 1, …, L - 2W - I + 1) is the kernel distance time series of the i-th pixel before normalization; μ pi is the expectation of the kernel distance time series of the i-th pixel; σ pi is the standard deviation of the kernel distance time series of the i-th pixel.

[0092] In some embodiments, the following formula is used to perform morphological closing operation on the binary feature map:

[0093]

[0094] In the formula, b is the binary image; t is the template function; · represents the closing operation; represents the erosion operation; represents the dilation operation.

[0095] It should be noted that: the detection device for moving targets with a specific speed in high-temporal-phase SAR echo data provided in the above embodiments is only illustrated by dividing the above-mentioned functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the detection device for moving targets with a specific speed in high-temporal-phase SAR echo data provided in the above embodiments and the embodiments of the detection method for moving targets with a specific speed in high-temporal-phase SAR echo data belong to the same concept. The specific implementation process can be seen in the method embodiments and will not be elaborated here.

[0096] Embodiments of the present application also provide a computer device. Please refer to Figure 3 , the computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the detection method for moving targets with a specific speed in high-temporal-phase SAR echo data provided in the above method embodiments.

[0097] Embodiments of the present application also provide a computer-readable storage medium. At least one instruction, at least one program, a code set or an instruction set is stored on the computer-readable storage medium. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the detection method for moving targets with a specific speed in high-temporal-phase SAR echo data provided in the above method embodiments.

[0098] Embodiments of the present application also provide a computer program product. The computer program product includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the detection method for moving targets with a specific speed in high-temporal-phase SAR echo data described in any one of the above embodiments.

[0099] For the convenience of description, when describing the above system or device, it is divided into various modules or units according to functions for description. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0100] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0101] Finally, it should also be noted that in this article, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0102] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A detection method for moving targets with a specific velocity in high-temporal-phase SAR echo data, characterized in that, The method includes: Based on a pre-determined specific velocity, the high-temporal-phase SAR echo data to be detected is sliced into multiple sub-data in chronological order; Performing imaging processing on each of the sub-data respectively to obtain multiple sub-images; Performing high-dimensional mapping on the sequential image composed of each of the sub-images to obtain a sequential mapping image; Based on the kernel distances of the pixels in the sequential mapping image, determining the binary feature map of the sequential mapping image; Performing morphological closing operation on the binary feature map to obtain the detection result of the moving target with the specific velocity.

2. The method according to claim 1, wherein The number of the sub-data is determined based on the specific velocity, the satellite velocity for obtaining the echo data, the Doppler frequency modulation slope of the echo data, and the synthetic aperture time.

3. The method according to claim 1, characterized in that, The following kernel function is used to perform high-dimensional mapping on the sequential image to obtain a sequential mapping image: where ker p is the kernel function; x q is an element in the previous time series segment; y q is an element in the subsequent time series segment; η is the correlation modulation coefficient; L is the total length of the time series; W is the sliding window length; I is the sliding window interval; p represents the chronological position of the high-dimensional mapping result; L - 2W - I + 1 is the total length of the time series after mapping.

4. The method according to claim 1, wherein The determining the binary feature map of the sequential mapping image based on the kernel distances of the pixels in the sequential mapping image includes: Traversing each pixel in the sequential mapping image, and for each traversed pixel, performing: Performing normalization processing on the kernel distance time series corresponding to the current pixel to obtain a normalized kernel distance time series; determining whether the maximum value in the normalized kernel distance time series is not less than a pre-determined hard threshold; if so, setting the value of the current pixel to 1, and if not, setting the value of the current pixel to 0; And so on until each pixel is traversed to obtain the binary feature map of the sequential mapping image.

5. The method according to claim 4, wherein The following formula is used to perform normalization processing on the kernel distance time series corresponding to each pixel: where D i '(0, 1, …, L - 2W - I + 1) is the normalized kernel distance time series of the i-th pixel, i = 1, 2, …… K, and K is the total number of pixels in the mapped image; D i (0, 1, …, L - 2W - I + 1) is the kernel distance time series of the i-th pixel before normalization; μ pi is the expectation of the kernel distance time series of the i-th pixel; σ pi is the standard deviation of the kernel distance time series of the i-th pixel.

6. The method according to claim 1, wherein The following formula is used to perform morphological closing operation on the binary feature map: Wherein, b is a binary image; t is a template function; · represents a closing operation; represents an erosion operation; represents a dilation operation.

7. A detection device for moving targets with a specific speed in high-temporal-phase SAR echo data, characterized in that, The device includes: A slicing unit, configured to slice the high-temporal-phase SAR echo data to be detected into multiple sub-data in chronological order based on a pre-determined specific velocity; An imaging unit, configured to perform imaging processing on each of the sub-data respectively to obtain multiple sub-images; A mapping unit, configured to perform high-dimensional mapping on the sequential image composed of each of the sub-images to obtain a sequential mapping image; A feature map determination unit, configured to determine the binary feature map of the sequential mapping image based on the kernel distances of the pixels in the sequential mapping image; A result determination unit, configured to perform morphological closing operation on the binary feature map to obtain the detection result of the moving target with the specific velocity.

8. A computer device, characterized in that, The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the method according to any one of claims 1 - 6 above.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 - 6.

10. A computer program product, characterized in that, Including a computer program, and when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 - 6.

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