User tag generation method and system, electronic equipment and storage medium

Abstract

The invention provides a user tag generation method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of target behavior data of a user in real time according to a preset contact of an access interface; and determining a user tag of the user according to a tag generation rule and the multiple pieces of target behavior data, wherein the tag generation rule comprises a combination of the multiple pieces of target behavior data and / or a generation sequence of the multiple pieces of target behavior data. The method is used for improving the accuracy of the determined user tag during user behavior analysis.

Description

Technical Field

[0001] The present application relates to the field of user behavior analysis, and more specifically, to a user tag generation method, system, electronic device, and storage medium. Background Art

[0002] With the rapid development of Internet applications, user behavior analysis is becoming increasingly important in business decision-making. By obtaining user behavior, we analyze user needs and recommend services or products to users based on the analysis results. For example, after a product sales platform releases a variety of new products, it pushes various new products to users based on user behavior data.

[0003] At present, when analyzing user behavior, the user tag is usually determined based on the user's static attribute tags (such as age, gender, occupation, etc.) or the user's historical behavior data (such as forming a behavior tag and making recommendations based on the behavior tag), and then product push is performed to the user based on the user tag. Existing user behavior analysis usually matches the user's static attribute tags or historical behavior data with a preset tag set to determine the user's user tag, and the two are usually in a one-to-one relationship. However, in actual applications, the combination of user behaviors or the order in which the behaviors occur have a great influence on the formation of user tags, resulting in the problem of low accuracy in the existing method of determining user tags. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a user tag generation method, system, electronic device and storage medium, so as to improve the accuracy of the determined user tags when performing user behavior analysis.

[0005] In a first aspect, the present application provides a user tag generation method, the method comprising: obtaining multiple target behavior data of a user in real time according to preset contacts on an access interface; determining a user tag of the user according to a tag generation rule and the multiple target behavior data, the tag generation rule comprising a combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

[0006] In the embodiment of the present application, all target behavior data generated by the user are obtained in real time, considering that the user's behavior combination and the order in which the behavior occurs are critical to the generation of user tags. Therefore, the tag generation rule includes a combination of multiple target behavior data and / or the order in which multiple target behavior data are generated. When performing user behavior analysis, the user tag of the user is determined based on the tag generation rule and multiple target behavior data, so as to realize real-time analysis of user behavior and improve the accuracy of the determined user tag.

[0007] In an optional implementation, after acquiring the user's multiple target behavior data in real time, it also includes: converting the user's multiple target behavior data into JSON format, and pushing the multiple target behavior data in JSON format to Kafka message middleware.

[0008] In an optional implementation, determining the user tag of the user based on the tag generation rules and the multiple target behavior data includes: parsing the multiple target behavior data in the Kafka message middleware through the Flink real-time computing component to determine the behavior types corresponding to the multiple target behavior data; determining the user tag in real time based on the multiple behavior types and the tag generation rules.

[0009] In an optional embodiment, after determining the user tag of the user according to the tag generation rules and the multiple target behavior data, the method further includes: saving the user tag of the user in an HBase database, and updating and confirming user needs in real time according to the user tag in the HBase database to provide corresponding services.

[0010] In a second aspect, the present application provides a user tag generation system, including: a real-time acquisition unit, used to obtain multiple target behavior data of the user in real time according to preset contacts on the access interface; a Flink real-time computing component, used to determine the user tag of the user according to a tag generation rule and the multiple target behavior data, wherein the tag generation rule includes a combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

[0011] In an optional embodiment, the user tag generation system also includes: kafka message middleware; the real-time collection unit is specifically used to obtain the user's multiple target behavior data in real time, convert the user's multiple target behavior data into json format, and push the multiple target behavior data in json format to the kafka message middleware.

[0012] In an optional implementation, the Flink real-time computing component is specifically used to receive the multiple target behavior data from the kafka message middleware; parse the multiple target behavior data to determine the behavior types corresponding to the multiple target behavior data; and determine the user tag in real time according to the multiple behavior types and the tag generation rules.

[0013] In an optional implementation, the user tag generation system further includes: an HBase database for storing the user tags of the users, and updating and confirming user needs in real time according to the user tags in the HBase database to provide corresponding services.

[0014] In a third aspect, the present application provides a user tag generating device, which includes: an acquisition module, used to acquire multiple target behavior data of a user in real time according to preset contacts on an access interface; a determination module, used to determine the user tag of the user according to a tag generation rule and the multiple target behavior data, wherein the tag generation rule includes a combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

[0015] In an optional implementation, at every second preset time interval, the acquisition module executes the step of acquiring multiple target behavior data of the user in real time according to preset contacts on the access interface.

[0016] In an optional implementation, the determination module is specifically used to parse the multiple target behavior data to determine the behavior types corresponding to the multiple target behavior data; and determine the user tag according to the multiple behavior types and the tag generation rule.

[0017] In an optional implementation, the device further includes a saving module, configured to save the user tag of the user in an HBase database.

[0018] In a fourth aspect, the present application provides an electronic device, comprising: a processor, a memory and a bus; the processor and the memory communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method as described in any of the foregoing embodiments.

[0019] In a fifth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are read and executed by a computer, the method as described in any one of the aforementioned embodiments is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a method for generating user tags provided in an embodiment of the present application;

[0022] Figure 2 A structural block diagram of a user tag generation system provided in an embodiment of the present application;

[0023] Figure 3A structural block diagram of a user tag generating device provided in an embodiment of the present application;

[0024] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0025] Icons: 200-user tag generation system; 201-real-time acquisition unit; 202-Flink real-time computing component; 203-kafka message middleware; 204-HBase database; 300-user tag generation device; 301-acquisition module; 302-determination module; 303-saving module; 400-electronic device; 401-processor; 402-communication interface; 403-memory; 404-bus. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0027] The embodiments of the present application provide a user tag generation method, system, electronic device and storage medium, which are used to improve the accuracy of determined user tags when performing user behavior analysis.

[0028] See also Figure 1 , Figure 1 A flowchart of a method for generating a user tag is provided in an embodiment of the present application. The method for generating a user tag may include the following steps:

[0029] Step 101: Acquire multiple target behavior data of the user in real time according to preset touch points of the access interface.

[0030] In an embodiment of the present application, the target behavior data is the data generated after the user performs a behavioral operation. The user behavior data is generated by the preset touch points of the user access interface. When the user performs behavioral actions such as browsing, searching, collecting, adding to shopping carts, checking out the checkout page, submitting an order, etc., the preset touch points of the access interface are triggered and identified as target behaviors.

[0031] For example, in a shopping app, the target behavior data includes the user's behavior operations, keywords, product identifiers, and behavior time. The user's behavior operations are: when using a shopping app, the user performs operations such as searching, browsing, collecting, adding to a shopping cart, submitting an order, and checking out. After the user performs a certain behavior operation, the corresponding data is generated, and this data is the target behavior data. Keywords are keywords corresponding to the products that the user searches for. Product identifiers are product identifiers corresponding to user operations such as browsing, adding to a shopping cart, and submitting an order. Behavior time is the time corresponding to various behavior operations.

[0032] For another example, in a short video APP, the target behavior data includes the user's behavior operations, keywords, short video type identifiers, and behavior time. The user's behavior operations are: when using the short video APP, the user performs behavior operations such as searching, playing, and collecting. The keyword is the keyword corresponding to the short video searched by the user when searching. The short video type identifier is the short video type identifier corresponding to the user's behavior operations such as playing and collecting. The behavior time is the time corresponding to various behavior operations.

[0033] It is understandable that for different application scenarios, the target behavior data that is concerned in user behavior analysis is different. Therefore, the types of target behavior data in various application scenarios can be predefined. In different application scenarios, the corresponding target behavior data is obtained.

[0034] The user tag generation method provided in the embodiment of the present application is to analyze the user behavior generated by the user in real time, and then generate the corresponding user tag. In order to realize the real-time analysis of user behavior, it is necessary to obtain the user's target behavior data in real time. The real-time acquisition in the present application can be specifically periodic acquisition or setting a first preset duration, and performing user behavior analysis based on all target behavior data generated by the user in the first preset duration.

[0035] It should be noted that when a user uses an APP, target behavior data will be collected all the time. The first preset duration can be understood as a time window. Each time a user tag is generated, all target behavior data within the first preset duration is obtained for user behavior analysis. The first preset duration can be 5 minutes, 10 minutes, 15 minutes, etc., and this application does not make specific restrictions on this.

[0036] Further, as an optional implementation, the user tag generation method provided in the embodiment of the present application includes:

[0037] At intervals of a second preset time length, the step of obtaining multiple target behavior data of the user in real time according to preset contacts on the access interface is executed.

[0038] In the embodiment of the present application, the second preset duration can be understood as a step length, that is, each time the second preset duration passes, the above step 101 is performed once, a plurality of target behavior data within the first preset duration is obtained, and user behavior analysis is performed based on all target behavior data generated by the user in the first preset duration. The user tag is updated once each time the second preset duration passes.

[0039] Step 102: determining a user tag of the user according to a tag generation rule and a plurality of target behavior data, wherein the tag generation rule includes a combination of the plurality of target behavior data and / or a sequence in which the plurality of target behavior data are generated.

[0040] After acquiring a plurality of target behavior data within a first preset time period, user behavior analysis is performed on the plurality of target behavior data according to a tag generation rule to determine a user tag.

[0041] When analyzing user behaviors, it is considered that the user's behavior combination and the order in which the behaviors occur are critical to the generation of user tags. Therefore, the tag generation rule includes the combination of multiple target behavior data and / or the order in which multiple target behavior data are generated. The multiple target behavior data within the first preset time period are matched with the combination of multiple target behavior data and the order in which multiple target behavior data are generated in the tag generation rule to determine the user tag.

[0042] As an optional implementation, the above step 102 may include the following contents:

[0043] The Flink real-time computing component is used to parse multiple target behavior data in the Kafka message middleware to determine the behavior types corresponding to the multiple target behavior data; the user tags are determined based on multiple behavior types and tag generation rules.

[0044] In the embodiment of the present application, the behavior type represents the types of all behavior operations performed by the user within the first preset time period.

[0045] For example, the multiple target behavior data within the first preset time period are: search, browse, collect, keyword, product identification and the time corresponding to the three behavior operations of search, browse and collect, then the user's behavior type is determined to be search, browse and collect.

[0046] For another example, the multiple target behavior data within the first preset time period are: searching, browsing, adding to shopping cart, submitting an order, keywords, product identification, and the time corresponding to the three behavior operations of searching, browsing, and adding to shopping cart, then the user's behavior type is determined to be searching, browsing, and adding to shopping cart.

[0047] For another example, the multiple target behavior data within the first preset time period are: searching, browsing, adding to shopping cart, submitting order, keywords, product identification and the time corresponding to the four behavior operations of searching, browsing, adding to shopping cart and submitting order, then the user's behavior type is determined to be searching, browsing, adding to shopping cart and submitting order.

[0048] The label generation rule is the corresponding relationship between the combination of various behavior types and user labels.

[0049] For example, label generation rule 1: the behavior types are search, browse and favorite, and the corresponding user label is favorite but not purchased. Label generation rule 2: the behavior types are search, browse and add to cart, and the corresponding user label is add to cart but not purchased. Label generation rule 3: the behavior types are search, browse, add to cart and submit order, and the corresponding user label is purchased.

[0050] When it is determined that the user's behavior type is search, browse, and favorite, the behavior type matches label generation rule 1, and the user label is determined to be favorited but not purchased. When it is determined that the user's behavior type is search, browse, and add to cart, the behavior type matches label generation rule 2, and the user label is determined to be added to cart but not purchased. When it is determined that the user's behavior type is search, browse, add to cart, and submit order, the behavior type matches label generation rule 3, and the user label is determined to be purchased.

[0051] Through the above method, the user's behavior analysis is performed based on the user's target behavior data within a period of time, and user tags are generated in real time to improve the accuracy of user tags.

[0052] Further, as an optional implementation, after step 102, the user tag generation method provided in the embodiment of the present application further includes:

[0053] The user tag is saved in the HBase database.

[0054] The HBase database has the following features:

[0055] Large capacity: It can store tens of billions of single tables by limiting a certain column, and can periodically merge smaller files into large files to reduce the pressure on disk access.

[0056] Column storage: Stores data in columns.

[0057] Sparsity: Columns whose representation is empty will not occupy storage space, so the table can be designed to be relatively sparse.

[0058] Scalability: Supports horizontal expansion by continuously adding servers to the cluster to provide storage space and performance.

[0059] When saving user tags, different users have different corresponding user tags. Different users do not correspond to all user tags, and user tags will continue to change and increase according to the user's target behavior data in different time periods. Therefore, the characteristics of the HBase database are suitable for the existence of user tags.

[0060] Further, as an optional implementation, after step 102, the user tag generation method provided in the embodiment of the present application also includes: updating and confirming user needs in real time according to user tags in the HBase database, and providing corresponding services, for example: pushing information according to user tags and target behavior data.

[0061] In the embodiment of the present application, information push may include related product recommendations, promotional event recommendations, etc.

[0062] For example, when a user's label is a collection but not purchased, it is considered that the user has a certain interest in the collected items. The type of items the user is interested in is determined based on the item identifier in the target behavior data, and item information of the same type as the item identifier in the target behavior data is pushed to the user.

[0063] For another example, when the user tag is added to the shopping cart but not purchased, it is considered that the user has a certain purchase intention for the collected items. The corresponding promotional information of the item is determined based on the item identifier in the target behavior data, which can be a coupon or a discount for a certain amount, and the corresponding promotional information of the item is pushed to the user.

[0064] Based on the same inventive concept, the present application also provides a user tag generation system. Figure 2 , Figure 2 A structural block diagram of a user tag generation system provided in an embodiment of the present application, the user tag generation system 200 may include:

[0065] A real-time acquisition unit 201 is used to obtain multiple target behavior data of the user in real time according to preset touch points on the access interface;

[0066] The Flink real-time computing component 202 is used to determine the user tag of the user in real time according to the tag generation rule and the multiple target behavior data. The tag generation rule includes the combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

[0067] In an optional implementation, the user tag generation system also includes: a kafka message middleware 203; the real-time collection unit 201 is specifically used to obtain multiple target behavior data of the user in real time, convert the multiple target behavior data of the user into a json format, and push the multiple target behavior data in the json format to the kafka message middleware 203.

[0068] In an optional implementation, the Flink real-time computing component 202 is specifically used to receive the multiple target behavior data from the kafka message middleware 203; parse the multiple target behavior data to determine the behavior types corresponding to the multiple target behavior data; and determine the user tag in real time according to the multiple behavior types and the tag generation rules.

[0069] In an optional implementation, the user tag generation system further includes: an HBase database 204 for storing the user tags of the users, and updating and confirming user needs in real time according to the user tags in the HBase database to provide corresponding services.

[0070] The user tag generation system provided in the embodiment of the present application can execute the above-mentioned user tag generation method.

[0071] Since the number of users is very large, and in order to realize real-time calculation of user tags for each user, the embodiment of the present application adopts the Flink processing framework to form a Flink real-time computing component 202. The Flink real-time computing component 202 performs real-time calculation through a sliding window according to the time window period (first preset duration), step size (second preset duration), tag generation rules and multiple target behavior data, and generates user tags in real time.

[0072] Flink is an open source stream processing framework developed by the Apache Software Foundation. Its core is a distributed stream data flow engine written in Java and Scala. Flink executes any stream data program in a data-parallel and pipelined manner. Flink's pipeline runtime system can execute batch and stream processing programs. In addition, Flink's runtime itself also supports the execution of iterative algorithms.

[0073] It can be understood that the user tag generation system 200 corresponds to the aforementioned user tag generation method, and each functional module corresponds to each step of the aforementioned user tag generation method. Therefore, the implementation method of each functional module refers to the implementation method of the user tag generation method in the aforementioned embodiment, and will not be repeated here.

[0074] Based on the same inventive concept, the present application also provides a user tag generation device. Figure 3 , Figure 3 This is a structural block diagram of a user tag generation device provided in an embodiment of the present application. The user tag generation device 300 may include:

[0075] The acquisition module 301 is used to acquire multiple target behavior data of the user in real time according to preset touch points of the access interface;

[0076] The determination module 302 is used to determine the user tag of the user in real time according to the tag generation rule and the multiple target behavior data. The tag generation rule includes the combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

[0077] In an optional implementation, at every second preset time interval, the acquisition module 301 executes the step of acquiring multiple target behavior data of the user in real time according to preset contacts on the access interface.

[0078] In an optional implementation, the determination module 302 is specifically used to parse the multiple target behavior data to determine the behavior types corresponding to the multiple target behavior data; and determine the user tag in real time according to the multiple behavior types and the tag generation rule.

[0079] In an optional implementation, the user tag generating device further includes a saving module 303 for saving the user tag of the user in an HBase database, and updating and confirming user needs in real time according to the user tag in the HBase database to provide corresponding services.

[0080] The user tag generating device 300 corresponds to the aforementioned user tag generating method, and each functional module corresponds to each step of the aforementioned user tag generating method. Therefore, the implementation of each functional module refers to the implementation of the user tag generating method in the aforementioned embodiment, and will not be repeated here.

[0081] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device 400 of an embodiment of the present application, and the electronic device 400 includes: at least one processor 401, at least one communication interface 402, at least one memory 403 and at least one bus 404. Among them, the bus 404 is used to realize the direct connection and communication of these components, the communication interface 402 is used to communicate signaling or data with other node devices, and the memory 403 stores machine-readable instructions executable by the processor 401. When the electronic device 400 is running, the processor 401 communicates with the memory 403 through the bus 404, and the machine-readable instructions are called by the processor 401 to execute the user tag generation method in the aforementioned embodiment.

[0082] The processor 401 can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0083] The memory 403 may include but is not limited to random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0084] Understandably, Figure 4 The structure shown is for illustration only. The electronic device 400 may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown. Figure 4 Each component shown in can be implemented by hardware, software or a combination thereof. In the embodiment of the present application, the electronic device 400 can be, but not limited to, a physical device such as a desktop computer, a laptop computer, a smart phone, a smart wearable device, a vehicle-mounted device, etc., and can also be a virtual device such as a virtual machine. In addition, the electronic device 400 is not necessarily a single device, but can also be a combination of multiple devices, such as a server cluster, etc.

[0085] In addition, an embodiment of the present application further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a computer, the steps of the user tag generating method in the above embodiment are executed.

[0086] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0087] In addition, 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, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0089] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0090] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0091] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for generating a user tag, characterized in that: The method comprises: According to the preset touch points of the access interface, multiple target behavior data of users can be obtained in real time; The user tag of the user is determined according to a tag generation rule and the multiple target behavior data, wherein the tag generation rule includes a combination of multiple target behavior data and / or a sequence in which the multiple target behavior data are generated.

2. The method according to claim 1, characterized in that After the real-time acquisition of multiple target behavior data of the user, the method further includes: Convert multiple target behavior data of users into JSON format and push the multiple target behavior data in JSON format to Kafka message middleware.

3. The method according to claim 2, characterized in that The determining the user tag of the user according to the tag generation rule and the plurality of target behavior data includes: The multiple target behavior data in the Kafka message middleware are parsed through the Flink real-time computing component to determine the behavior types corresponding to the multiple target behavior data; The user tag is determined in real time according to the multiple behavior types and the tag generation rule.

4. The method according to claim 1, characterized in that: After determining the user tag of the user according to the tag generation rule and the plurality of target behavior data, the method further includes: The user tag of the user is stored in the HBase database; According to the user tags in the HBase database, real-time updates are made to confirm user needs and provide corresponding services.

5. A user tag generation system, characterized in that: include: A real-time acquisition unit, used to obtain multiple target behavior data of users in real time according to preset touch points on the access interface; The Flink real-time computing component is used to determine the user tag of the user in real time according to the tag generation rule and the multiple target behavior data, wherein the tag generation rule includes the combination of multiple target behavior data and / or the order in which the multiple target behavior data are generated.

6. The user tag generation system according to claim 5, characterized in that: The user tag generation system also includes: Kafka message middleware; The real-time acquisition unit is specifically used to obtain multiple target behavior data of the user in real time, convert the multiple target behavior data of the user into JSON format, and push the multiple target behavior data in JSON format to the Kafka message middleware.

7. The user tag generation system according to claim 6, characterized in that: The Flink real-time computing component is specifically used to receive the multiple target behavior data from the Kafka message middleware; the multiple target behavior data are parsed to determine the behavior types corresponding to the multiple target behavior data; The user tag is determined in real time according to the multiple behavior types and the tag generation rule.

8. The user tag generation system according to claim 5, characterized in that: The user tag generation system further includes: The HBase database is used to store the user tags of the users, and to update and confirm the user needs in real time according to the user tags in the HBase database, and to provide corresponding services.

9. An electronic device, characterized in that: include: processor, memory, and bus; The processor and the memory communicate with each other via the bus; The memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 4 by calling the program instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a computer, the method according to any one of claims 1 to 4 is executed.

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