A bee colony status monitoring method and system for bee breeding

Abstract

The present invention discloses a bee colony status monitoring method and system for bee breeding, the method comprising: rejoining the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and clustering the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set; aligning each first target sound data sequence in the first target sound data set, and using a preset sliding window to slide on each first target sound data sequence according to a preset sliding rule; after each sliding, any first target sound data in the sliding window is input into a pre-constructed state classification recognition model. In this way, the amount of data input into the subsequent state classification recognition model can be reduced, and by obtaining the state results in different sliding windows, the user can obtain the bee colony status in different time periods.

Description

Technical Field

[0001] The invention belongs to the technical field of bee breeding, and in particular relates to a bee colony status monitoring method and system for bee breeding. Background Art

[0002] Manual inspection of beehives often disrupts the life cycle of the bee colony and affects honey production. Therefore, the use of automated monitoring to monitor the status of the bee colony can obtain a large amount of key information about bee behavior without interrupting the life cycle of the bee colony and reducing human resources, which helps us judge the health of the bee colony.

[0003] However, existing bee colony status monitoring methods often require the input of a large amount of data for identification. Especially when there are many beehives in large beekeeping sites, after collecting the sound data of the bee colonies in all the beehives, they all need to be input into the identification system, which can easily cause the identification system to crash and even lead to poor monitoring efficiency. Summary of the invention

[0004] The present invention provides a bee colony status monitoring method and system for bee breeding, which are used to solve the technical problem that the sound data input of a large number of bee colonies can easily cause the recognition system to crash and even lead to poor monitoring efficiency.

[0005] In a first aspect, the present invention provides a bee colony status monitoring method for bee breeding, comprising:

[0006] Acquire a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence;

[0007] Rejoining the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and clustering the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set;

[0008] Aligning each first target sound data sequence in a first target sound data set, and sliding a preset sliding window on each first target sound data sequence according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set;

[0009] After each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model, and the state classification and recognition model outputs a state result associated with the any first target sound data;

[0010] The status of the bee colony corresponding to each first target sound data sequence in the first target sound data set is determined according to each status result.

[0011] In a second aspect, the present invention provides a bee colony status monitoring system for bee breeding, comprising:

[0012] An interception module is configured to obtain a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence;

[0013] a splicing module configured to re-splice the at least one sound sub-data sequence based on a time sequence to obtain at least one target sound data sequence, and cluster the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set;

[0014] a sliding module configured to align each first target sound data sequence in a first target sound data set, and slide on each first target sound data sequence using a preset sliding window according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set;

[0015] an output module, configured to input any first target sound data in the sliding window into a pre-built state classification recognition model after each sliding, and the state classification recognition model outputs a state result associated with the any first target sound data;

[0016] The determination module is configured to determine the state of the bee colony corresponding to each first target sound data sequence in the first target sound data set according to each state result.

[0017] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for monitoring the status of a bee colony for bee breeding according to any embodiment of the present invention.

[0018] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of a method for monitoring the status of a bee colony for bee breeding according to any embodiment of the present invention.

[0019] The bee colony status monitoring method and system for bee breeding of the present application align each first target sound data sequence in the first target sound data set, and use a preset sliding window to slide on each first target sound data sequence according to a preset sliding rule. After each sliding, any first target sound data in the sliding window is input into a pre-constructed state classification and recognition model. In this way, the amount of data input into the subsequent state classification and recognition model can be reduced, and by obtaining the state results in different sliding windows, the user can obtain the bee colony status in different time periods, so that the user can subsequently manage the bee colony. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a bee colony status monitoring method for bee breeding provided in one embodiment of the present invention;

[0022] Figure 2 A structural block diagram of a bee colony status monitoring system for bee breeding provided by one embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 , which shows a flow chart of a bee colony status monitoring method for bee breeding in the present application.

[0026] like Figure 1 As shown, the bee colony status monitoring method for bee breeding specifically includes the following steps:

[0027] Step S101, obtaining a sound data sequence of at least one bee colony within a preset time period, and using a preset extraction rule to intercept each sound data sequence at least once to obtain at least one sound sub-data sequence corresponding to each sound data sequence.

[0028] In this step, a bee recording monitoring device composed of solar panels, batteries, five omnidirectional microphones, an RPi2 controller, and a Dspic with a digital-to-analog converter (ADC) is installed at the bottom of each beehive to collect the sound signals of the bee colony in the beehive.

[0029] It should be noted that, at least one sound data sequence of a bee colony within a preset time period is obtained, and each sound data sequence is intercepted at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence. In this way, some scene noise can be directly removed, and the possibility of partial loss of bee colony sound data caused by subsequent noise filtering of sound data can be reduced.

[0030] Specifically, at least one abnormal sound data in a sound data sequence whose sound frequency is not within a preset frequency range is obtained; each at least one abnormal sound data in the sound data sequence is removed to obtain at least one sound sub-data sequence corresponding to the sound data sequence.

[0031] For example, through the spectrum analysis of bee colony sound signals, it is known that the sound frequency of healthy bee colonies is mainly concentrated around 400Hz, and the main frequency of cricket calls in the environment is concentrated around 3kHz. Therefore, the above method can directly remove the calls of crickets in the environment.

[0032] Step S102, rejoining the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and clustering the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set.

[0033] In this step, at least one first sound sub-data sequence belonging to a certain sound data sequence is obtained; at least one first sound sub-data sequence is spliced ​​based on the time sequence to obtain a certain target sound data sequence. At least one amplitude range is set; each target sound data sequence belonging to a certain amplitude range is clustered to obtain a certain target sound data set corresponding to a certain amplitude range.

[0034] Step S103, aligning each first target sound data sequence in the first target sound data set, and using a preset sliding window to slide on each of the first target sound data sequences according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set.

[0035] In this step, the sliding distance of each sliding rule is the time length difference between two first target sound data sequences of adjacent distance lengths.

[0036] Specifically, using a preset sliding window to slide on each first target sound data sequence according to a preset sliding rule includes: setting the sliding window at an initial position of each first target sound data sequence, the initial position being the position of the first first target sound data of a certain first target sound data sequence, wherein the certain first target sound data sequence is the first target sound data sequence with the shortest time length among the first target sound data sequences; obtaining a first time length difference between another first target sound data sequence and a certain first target sound data sequence, and sliding the sliding window on each first target sound data sequence with the first time length difference as a first sliding distance, wherein the other first target sound data sequence is a first target sound data sequence with a distance length adjacent to the certain first target sound data sequence; obtaining a second time length difference between another first target sound data sequence and a certain first target sound data sequence, and sliding the sliding window on each first target sound data sequence with the second time length difference as a second sliding distance, wherein the another first target sound data sequence is a first target sound data sequence with a distance length adjacent to the other first target sound data sequence.

[0037] For example, the time length of the first target sound data sequence A is 5, the time length of the first target sound data sequence B is 6, the time length of the first target sound data sequence C is 9, the time length of the first target sound data sequence D is 11, the time length of the first target sound data sequence E is 15, and the time length of the first target sound data sequence F is 16.

[0038] Then, after aligning the above-mentioned first target sound data sequences side by side, the initial position of the sliding window is at the position of the first target sound data in the first target sound data sequence A, the first sliding distance of the sliding window is 6-5=1, the second sliding distance is 9-6=3, the third sliding distance is 11-9=2, the fourth sliding distance is 15-11=4, and the last sliding distance is 16-15=1.

[0039] In this embodiment, by identifying one target sound data in the sliding window each time, obtaining the state result of the target sound data, and assigning the state result of the target sound data to other target sound data, the amount of data input into the subsequent state classification recognition model can be reduced.

[0040] Step S104: after each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model, and the state classification and recognition model outputs a state result associated with the any first target sound data.

[0041] In this step, after each sliding, there is at least one first target sound data of the first target sound data sequence in the sliding window, and the first target sound data of a first target sound data sequence is selected and input into the pre-constructed state classification recognition model.

[0042] It should be noted that the SVDD model is constructed to extract the audio signal of the abnormal state of the bee colony, and then the SVM classifier is used to classify and identify the effective signal extracted from any first target sound data. The effective signal can be a characteristic parameter such as LPCC coefficients and MFCC feature parameters (Mel-Frequency Cepstrum Coefficients).

[0043] Step S105: determining the state of the bee colony corresponding to each first target sound data sequence in the first target sound data set according to each state result.

[0044] In this step, a status result associated with any first target sound data in the sliding window is obtained, and the status result is associated with other first target sound data in the sliding window, that is, at least one status result is associated with a first target sound data sequence; it is determined whether there is an abnormal status result in at least one status result associated with a first target sound data sequence; if there is no abnormal status result, the bee colony corresponding to the first target sound data sequence is in a normal state; if there is an abnormal status result, the bee colony corresponding to the first target sound data sequence is in an abnormal state.

[0045] In summary, the method of the present application aligns each first target sound data sequence in the first target sound data set, and uses a preset sliding window to slide on each first target sound data sequence according to a preset sliding rule. After each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model. In this way, the amount of data input into the subsequent state classification and recognition model can be reduced, and by obtaining the state results in different sliding windows, the user can obtain the state of the bee colony in different time periods, so that the user can subsequently manage the bee colony.

[0046] See also Figure 2 , which shows a structural block diagram of a bee colony status monitoring system for bee breeding in the present application.

[0047] like Figure 2 As shown, the bee colony status monitoring system 200 includes a capture module 210 , a splicing module 220 , a sliding module 230 , an output module 240 and a determination module 250 .

[0048] The interception module 210 is configured to obtain a sound data sequence of at least one bee colony within a preset time period, and to intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence; the splicing module 220 is configured to re-splice the at least one sound sub-data sequence based on a time sequence to obtain at least one target sound data sequence, and to cluster the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set; the sliding module 230 is configured to cluster each first target sound data sequence in the first target sound data set into a clustering rule; The first target sound data set is aligned in rows, and a preset sliding window is used to slide on each first target sound data sequence according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set; an output module 240 is configured to input any first target sound data in the sliding window into a pre-constructed state classification recognition model after each sliding, and the state classification recognition model outputs a state result associated with any first target sound data; a determination module 250 is configured to determine the state of the swarm corresponding to each first target sound data sequence in the first target sound data set according to each state result.

[0049] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0050] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the bee colony status monitoring method for bee breeding in any of the above method embodiments;

[0051] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0052] Acquire a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence;

[0053] Rejoining the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and clustering the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set;

[0054] Aligning each first target sound data sequence in a first target sound data set, and sliding a preset sliding window on each first target sound data sequence according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set;

[0055] After each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model, and the state classification and recognition model outputs a state result associated with the any first target sound data;

[0056] The status of the bee colony corresponding to each first target sound data sequence in the first target sound data set is determined according to each status result.

[0057] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the bee colony state monitoring system for bee breeding, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the bee colony state monitoring system for bee breeding via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0058] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the colony status monitoring method for bee breeding in the above-mentioned method embodiment is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the colony status monitoring system for bee breeding. The output device 340 may include display devices such as display screens.

[0059] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0060] As an embodiment, the electronic device is applied to a bee colony status monitoring system for bee breeding, and is used for a client, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0061] Acquire a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence;

[0062] Rejoining the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and clustering the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set;

[0063] Aligning each first target sound data sequence in a first target sound data set, and sliding a preset sliding window on each first target sound data sequence according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set;

[0064] After each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model, and the state classification and recognition model outputs a state result associated with the any first target sound data;

[0065] The status of the bee colony corresponding to each first target sound data sequence in the first target sound data set is determined according to each status result.

[0066] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bee colony status monitoring method for bee breeding, characterized in that: include: Acquire a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence, wherein the sound data includes the frequency and amplitude of the sound; The method of using a preset extraction rule to intercept each sound data sequence at least once to obtain at least one sound sub-data sequence corresponding to each sound data sequence includes: Acquire at least one abnormal sound data in a sound data sequence, in which the frequency of the sound is not within a preset frequency range; Removing each of the at least one abnormal sound data from the certain sound data sequence, thereby obtaining at least one sound sub-data sequence corresponding to the certain sound data sequence; The at least one sound sub-data sequence is rejoined based on the time sequence to obtain at least one target sound data sequence, and the at least one target sound data sequence is clustered using a pre-constructed clustering rule to obtain at least one target sound data set, wherein the at least one target sound data sequence is clustered using a pre-constructed clustering rule to obtain at least one target sound data set comprises: Setting at least one amplitude range; Clustering target sound data sequences within a certain amplitude range to obtain a target sound data set corresponding to the certain amplitude range; Aligning each first target sound data sequence in the first target sound data set, and sliding on each first target sound data sequence using a preset sliding window according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set, and wherein each sliding distance in the sliding rule is a time length difference between two first target sound data sequences of adjacent distance lengths; The adopting a preset sliding window to slide on each of the first target sound data sequences according to a preset sliding rule comprises: Setting the sliding window at an initial position of each of the first target sound data sequences, wherein the initial position is a position of the first first target sound data of a first target sound data sequence, wherein the first target sound data sequence is a first target sound data sequence with the shortest time length among the first target sound data sequences; Acquire a first time length difference between another first target sound data sequence and the certain first target sound data sequence, and slide the sliding window on each of the first target sound data sequences with the first time length difference as a first sliding distance, wherein the another first target sound data sequence is a first target sound data sequence having a distance length adjacent to the certain first target sound data sequence; Acquire a second time length difference between another first target sound data sequence and the certain first target sound data sequence, and slide the sliding window on each of the first target sound data sequences with the second time length difference as a second sliding distance, wherein the further first target sound data sequence is a first target sound data sequence having a distance length adjacent to the another first target sound data sequence; After each sliding, any first target sound data in the sliding window is input into a pre-built state classification and recognition model, and the state classification and recognition model outputs a state result associated with the any first target sound data; The status of the bee colony corresponding to each first target sound data sequence in the first target sound data set is determined according to each status result.

2. A bee colony status monitoring method for bee breeding according to claim 1, characterized in that: The rejoining of the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence comprises: Acquire at least one first sound sub-data sequence belonging to a certain sound data sequence; The at least one first sound sub-data sequence is spliced ​​based on the time sequence to obtain a target sound data sequence.

3. A bee colony status monitoring method for bee breeding according to claim 1, characterized in that: The step of inputting any first target sound data in the sliding window into a pre-built state classification recognition model after each sliding includes: After each sliding, there is at least one first target sound data of the first target sound data sequence in the sliding window, and the first target sound data of a first target sound data sequence is selected and input into the pre-constructed state classification recognition model.

4. A bee colony status monitoring method for bee breeding according to claim 1, characterized in that: Determining the state of the bee colony corresponding to each first target sound data sequence in the first target sound data set according to each state result includes: Acquire a state result associated with any first target sound data in the sliding window, and associate the state result with other first target sound data in the sliding window, that is, obtain at least one state result associated with a certain first target sound data sequence; Determining whether there is an abnormal status result in at least one status result associated with the first target sound data sequence; If there is no abnormal state result, the bee colony corresponding to the first target sound data sequence is in a normal state; If there is an abnormal state result, the bee colony corresponding to the first target sound data sequence is in an abnormal state.

5. A bee colony status monitoring system for bee breeding, characterized in that: include: An interception module is configured to obtain a sound data sequence of at least one bee colony within a preset time period, and intercept each sound data sequence at least once using a preset extraction rule to obtain at least one sound sub-data sequence corresponding to each sound data sequence, wherein the sound data includes the frequency and amplitude of the sound; The method of using a preset extraction rule to intercept each sound data sequence at least once to obtain at least one sound sub-data sequence corresponding to each sound data sequence includes: Acquire at least one abnormal sound data in a sound data sequence, in which the frequency of the sound is not within a preset frequency range; Removing each of the at least one abnormal sound data from the certain sound data sequence, thereby obtaining at least one sound sub-data sequence corresponding to the certain sound data sequence; The splicing module is configured to re-splice the at least one sound sub-data sequence based on the time sequence to obtain at least one target sound data sequence, and cluster the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set, wherein the clustering of the at least one target sound data sequence using a pre-constructed clustering rule to obtain at least one target sound data set includes: Setting at least one amplitude range; Clustering target sound data sequences within a certain amplitude range to obtain a target sound data set corresponding to the certain amplitude range; a sliding module configured to align each first target sound data sequence in the first target sound data set, and slide on each first target sound data sequence using a preset sliding window according to a preset sliding rule, wherein the first target sound data set is any one of the at least one target sound data set, and wherein the sliding distance each time in the sliding rule is the time length difference between two first target sound data sequences of adjacent distance lengths; The adopting a preset sliding window to slide on each of the first target sound data sequences according to a preset sliding rule comprises: Setting the sliding window at an initial position of each of the first target sound data sequences, wherein the initial position is a position of the first first target sound data of a first target sound data sequence, wherein the first target sound data sequence is a first target sound data sequence with the shortest time length among the first target sound data sequences; Acquire a first time length difference between another first target sound data sequence and the certain first target sound data sequence, and slide the sliding window on each of the first target sound data sequences with the first time length difference as a first sliding distance, wherein the another first target sound data sequence is a first target sound data sequence having a distance length adjacent to the certain first target sound data sequence; Acquire a second time length difference between another first target sound data sequence and the certain first target sound data sequence, and slide the sliding window on each of the first target sound data sequences with the second time length difference as a second sliding distance, wherein the further first target sound data sequence is a first target sound data sequence having a distance length adjacent to the another first target sound data sequence; an output module, configured to input any first target sound data in the sliding window into a pre-built state classification recognition model after each sliding, and the state classification recognition model outputs a state result associated with the any first target sound data; The determination module is configured to determine the state of the bee colony corresponding to each first target sound data sequence in the first target sound data set according to each state result.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

Images