Task processing method, device, computer equipment, and storage medium

Abstract

The present application relates to a task processing method, apparatus, computer equipment, and storage medium. The method includes: receiving a task to be processed, wherein the task to be processed includes task data to be processed; obtaining a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data; matching the prediction data list with the task to be processed to obtain task data to be processed that matches the target prediction data; replacing the task data to be processed that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated task to be processed; and sending the updated task to be processed to an edge host to instruct the edge host to process the task. This method can improve task processing efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of big data processing, and in particular to a task processing method, apparatus, computer equipment, and storage medium. Background Art

[0002] With the continuous development of the Internet, the business scenarios using Content Delivery Networks (CDNs) are becoming increasingly diverse. The number of resource locators that content management platforms need to send to edge machines for processing has reached hundreds of millions.

[0003] In related technologies, the content management platform directly sends the resource locators that need to be processed to the edge machine. When the number of tasks is large, the processing of the resource locators involves a large amount of data transmission, occupies a large amount of bandwidth, and increases resource consumption. Summary of the Invention

[0004] Based on this, it is necessary to provide a task processing method, device, computer equipment, and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides a task processing method. The method comprises:

[0006] receiving a task to be processed, wherein the task to be processed includes task data to be processed;

[0007] Obtaining a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0008] Matching the predicted data list with the pending tasks to obtain pending task data that matches the target predicted data;

[0009] Replacing the pending task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated pending task;

[0010] The updated to-be-processed tasks are sent to the edge host to instruct the edge host to process them.

[0011] In one embodiment, sending the updated pending task to the edge host includes:

[0012] The updated pending task is sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0013] In one embodiment, the process of constructing the prediction data list includes:

[0014] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0015] Obtaining forecast data based on a preset forecast model and the historical task data;

[0016] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0017] In one embodiment, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0018] In one embodiment, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0019] In one embodiment, the forecast data list is constructed by forecast data generated periodically and / or in real time.

[0020] In a second aspect, the present application further provides a task processing device, comprising:

[0021] A receiving module, configured to receive a task to be processed, wherein the task to be processed includes task data to be processed;

[0022] An acquisition module, configured to acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0023] A matching module, configured to match the predicted data list with the tasks to be processed to obtain task data to be processed that matches the predicted data;

[0024] An updating module, configured to replace the to-be-processed task data that matches the predicted data with the predicted data identifier corresponding to the predicted data, to obtain an updated to-be-processed task;

[0025] The processing module is used to send the updated to-be-processed tasks to the edge host for processing.

[0026] In one embodiment, sending the updated pending task to the edge host includes:

[0027] The updated pending task is sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0028] In one embodiment, the process of constructing the prediction data list includes:

[0029] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0030] Obtaining forecast data based on a preset forecast model and the historical task data;

[0031] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0032] In one embodiment, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0033] In one embodiment, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0034] In one embodiment, the forecast data list is constructed by forecast data generated periodically and / or in real time.

[0035] In a third aspect, the present disclosure further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the task processing method when executing the computer program.

[0036] In a fourth aspect, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the task processing method when executed by a processor.

[0037] In a fifth aspect, the present disclosure further provides a computer program product, comprising a computer program that implements the steps of the task processing method when executed by a processor.

[0038] The above task processing method has at least the following beneficial effects:

[0039] The embodiments provided herein use a big data platform as a data center to generate a list of predicted data. This data analysis allows for further model analysis of user behavior and expectations, helping to continuously optimize prediction algorithms and, based on this data, to anticipate and mitigate data peaks. When the predicted data list matches a task to be processed, the predicted data identifier replaces longer strings of repeated keywords, minimizing processor resources and improving task processing efficiency.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the traditional technology, the following briefly introduces the drawings required for use in the embodiments or the description of the traditional technology. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 An application environment diagram of a task processing method in one embodiment;

[0043] Figure 2 1 is a flowchart of a task processing method in one embodiment;

[0044] Figure 3 A schematic diagram of a big data center pushing a prediction data list in one embodiment;

[0045] Figure 4 A schematic diagram of a process for using a prediction data list replacement in one embodiment;

[0046] Figure 5 is a schematic diagram of task processing in one embodiment;

[0047] Figure 6 A schematic diagram of constructing a prediction data list in one embodiment;

[0048] Figure 7 is a schematic diagram of task processing in one embodiment;

[0049] Figure 8 is a structural block diagram of a task processing device in one embodiment;

[0050] Figure 9 is a diagram of the internal structure of a computer device in one embodiment;

[0051] Figure 10 The figure is a diagram of the internal structure of a server in one embodiment. DETAILED DESCRIPTION

[0052] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0053] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the accompanying drawings are used to distinguish similar items and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims. The terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, product, or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, this does not preclude the presence of additional identical or equivalent elements in the process, method, product, or apparatus comprising the elements. For example, the use of terms such as "first," "second," and the like are intended to indicate names and do not imply any specific order.

[0054] The present disclosure provides a task processing method that can be applied to Figure 1 In the application environment shown. Among them, the components of the content distribution network may include a content management platform 100, a task distribution center 102, an edge host agent 104, an edge gateway 106, a big data center 108, and a statistical analysis center 110. The content management platform 100 can send pending tasks to the big data center 108. The statistical analysis center 110 regularly judges and predicts the data of the big data center 108, obtains a prediction data list, and sends it to the content management platform 100, so that the content management platform 100 replaces the pending task data in the pending task with the prediction data in the prediction data list, obtains an updated pending task, and sends it to the task distribution center 102. The task distribution center 102 then pushes the updated pending task to the edge host agent 104, restores the data in the updated pending task, and sends it to the edge gateway 106 for processing.

[0055] In some embodiments of the present disclosure, Figure 2 As shown, a task processing method is provided, which is applied to Figure 1The content control platform in the example of processing the pending task is used to illustrate. In a specific embodiment, the method may include the following steps:

[0056] S202: Receive a task to be processed, where the task to be processed includes task data to be processed.

[0057] The content management platform receives pending tasks, which may include content updates, cache refreshes, content preheating, etc. The pending task data may include task type, task identifier, resource locator (URL, Uniform Resource Locator) list, cache strategy, execution time, etc.

[0058] S204: Acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data.

[0059] The big data center uses data analysis and machine learning models to query the latest analysis results and generate prediction data. The prediction data can include user behavior predictions, content access predictions, network traffic predictions, etc., and generates a unique identifier for each prediction data. The prediction data and the prediction data identifier corresponding to the prediction data constitute the prediction data list. Figure 3 This diagram illustrates a big data center pushing a prediction data list in one embodiment. The big data center pushes the prediction data list to the content management platform and edge host proxy. The edge host proxy receives the prediction data list and performs corresponding processing based on the prediction data, such as content preheating and caching policy adjustment. Using a big data platform as a data center, data analysis enables subsequent model analysis of user behavior and expectations, helping to continuously optimize prediction algorithms. This data can also be used to predict and prevent data peaks, improving task processing efficiency.

[0060] S206: Matching the predicted data list with the tasks to be processed to obtain task data to be processed that matches the target predicted data.

[0061] The predicted data list includes the predicted data and the predicted data identifier corresponding to the predicted data. The pending tasks include the pending task data. A matching algorithm is designed to match the predicted data with the pending data to find the pending task data that matches the target predicted data.

[0062] S208: replacing the to-be-processed task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated to-be-processed task.

[0063] S210: Send the updated to-be-processed task to the edge host to instruct the edge host to process it.

[0064] The pending task data that matches the target prediction data is replaced with the prediction data identifier corresponding to the target prediction data to obtain an updated pending task. The updated pending task includes the prediction data identifier corresponding to the target prediction data and the unreplaced pending task data. Figure 4 This is a flow chart of using a prediction data list replacement in one embodiment. After receiving a refresh URL task, it is determined whether there is matching prediction data in the current time period. If there is a match, the URL is quickly replaced with the prediction data and sent to the edge host agent. If there is no match, the original data is sent to the edge host agent.

[0065] The above task processing method uses a big data platform as a data center to generate a list of predicted data. This data analysis allows for further model analysis of user behavior and expectations, helping to continuously optimize the prediction algorithm. This data can also be used to predict and mitigate data peaks. When the predicted data list matches the task to be processed, the predicted data identifier is used to replace long strings of repeated keywords, reducing processor resources and improving task processing efficiency.

[0066] In some embodiments of the present disclosure, sending the updated to-be-processed task to the edge host includes:

[0067] The updated pending task is sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0068] Figure 5 This is a schematic diagram of task processing in an embodiment. When a content management platform receives a pending task and determines that it is in prediction mode, it can perform a process of matching the prediction data list with the pending task. After performing relevant business processing internally, the pending task is pushed to the task distribution center, which then pushes it to the agent of the edge host for processing. The agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0069] In some embodiments of the present disclosure, the process of constructing the prediction data list includes:

[0070] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0071] Obtaining forecast data based on a preset forecast model and the historical task data;

[0072] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0073] Figure 6 Figure 1 shows a schematic diagram of building a prediction data list in one embodiment. When the content management platform receives a refresh request containing a list of URLs to be refreshed, if the big data prediction function is enabled, it pushes this URL data to the big data center. The statistical analysis center then periodically analyzes and predicts the data in the big data center. Predicted data can be generated based on historical task data and a pre-set prediction model. Historical task data can include the latest hot news, keywords previously visited by users, and so on.

[0074] In some embodiments of the present disclosure, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0075] Figure 7 This is a diagram of task processing in one embodiment. After a pending task is updated, it can carry a task identifier. When the edge host agent receives the pending task, it determines whether it matches the predicted data based on the task identifier. If it matches, it quickly restores the predicted data to obtain the pending task data. If it doesn't, the data is the original data and can be used directly. The original data is then sent to the gateway for processing.

[0076] In some embodiments of the present disclosure, the prediction data list is constructed by periodically generated and / or real-time generated prediction data.

[0077] Using big data as a reference, a custom algorithm generates a list of predicted data based on the data likely to be accessed within the next timeframe and distributes it to edge node machines in advance. The content management platform can use this list of predicted data for replacement and distinguish between hit and miss pending task data. The hit rate can also be pushed to the big data platform for the next prediction calculation. This allows for automatic updates of predicted data, and even if the predicted data is incorrect, normal business processes are not affected. This ensures that the pending task data can be processed normally without replacement, minimizing computing resources and reducing network transmission overhead.

[0078] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0079] Based on the same inventive concept, the presently disclosed embodiments further provide a task processing device for implementing the aforementioned task processing method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the following embodiments of the task processing device can be found in the aforementioned limitations of the task processing method and will not be further elaborated here.

[0080] The device may include a system (including a distributed system), software (application), module, component, server, client, etc. that uses the method described in the embodiments of this specification and is combined with the necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided by the embodiments of the present disclosure is as described in the following embodiments. Since the implementation scheme of the device to solve the problem is similar to the method, the implementation of the specific device in the embodiments of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements the predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0081] In one embodiment, Figure 8 As shown, a task processing device 800 is provided. The device may be the aforementioned server, or a module, component, device, unit, etc. integrated in the server. The device 800 may include:

[0082] A receiving module 802 is configured to receive a task to be processed, wherein the task to be processed includes task data to be processed;

[0083] An acquisition module 804 is configured to acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0084] A matching module 806 is configured to match the predicted data list with the tasks to be processed to obtain task data to be processed that matches the predicted data;

[0085] An updating module 808 is configured to replace the to-be-processed task data that matches the predicted data with the predicted data identifier corresponding to the predicted data, thereby obtaining an updated to-be-processed task;

[0086] The processing module 810 is configured to send the updated pending tasks to the edge host for processing. In one embodiment, sending the updated pending tasks to the edge host includes:

[0087] The updated pending task is sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0088] In one embodiment, the process of constructing the prediction data list includes:

[0089] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0090] Obtaining forecast data based on a preset forecast model and the historical task data;

[0091] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0092] In one embodiment, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0093] In one embodiment, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0094] In one embodiment, the forecast data list is constructed by forecast data generated periodically and / or in real time.

[0095] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0096] Each module in the task processing device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0097] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store a list of prediction data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a task processing method is implemented.

[0098] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. The wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When the computer program is executed by the processor, the task processing method is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or keys, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0099] Those skilled in the art will understand that Figure 9 、 Figure 10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0100] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in any embodiment of the present disclosure is implemented.

[0101] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method described in any embodiment of the present disclosure is implemented.

[0102] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, and the like.

[0103] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above-described embodiments merely represent several implementation methods of the present disclosure. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present disclosure. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present disclosure, all of which fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be determined by the appended claims.

Claims

1. A task processing method, characterized in that: Applied to a content management platform, the method includes: receiving a task to be processed, wherein the task to be processed includes task data to be processed; Obtaining a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data; Matching the predicted data list with the pending tasks to obtain pending task data that matches the target predicted data; Replacing the pending task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated pending task; The updated to-be-processed tasks are sent to the edge host to instruct the edge host to process them.

2. The method according to claim 1, characterized in that The sending of the updated pending tasks to the edge host comprises: The updated pending task is sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending task to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

3. The method according to claim 1, characterized in that The process of constructing the prediction data list includes: Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency; Obtaining forecast data based on a preset forecast model and the historical task data; A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

4. The method according to claim 1, wherein The updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

5. The method according to claim 1, wherein In a case where the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

6. The method according to claim 1, characterized in that The prediction data list is constructed by periodically generated and / or real-time generated prediction data.

7. A task processing device, characterized in that: The device comprises: A receiving module, configured to receive a task to be processed, wherein the task to be processed includes task data to be processed; An acquisition module, configured to acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data; A matching module, configured to match the predicted data list with the tasks to be processed to obtain task data to be processed that matches the target predicted data; An updating module, configured to replace the to-be-processed task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data, to obtain an updated to-be-processed task; The processing module is used to send the updated to-be-processed tasks to the edge host for processing.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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