CN119149649A - User tag generation method, system, electronic device and storage medium - Google Patents

Abstract

The application provides a user tag generation method, a system, electronic equipment and a storage medium, wherein the user tag generation method comprises the steps of presetting contacts according to an access interface, and acquiring a plurality of target behavior data of a user in real time; and determining a user tag of the user according to a tag generation rule and the plurality of target behavior data, wherein the tag generation rule comprises a combination of the plurality of target behavior data and/or a sequence of generation of the plurality of target behavior data. The method is used for improving the accuracy of the determined user tag when user behavior analysis is carried out.

Description

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

Technical Field

The present application relates to the field of user behavior analysis, and in particular, to a method, a system, an electronic device, and a storage medium for generating a user tag.

Background

With the rapid development of internet applications, user behavior analysis is increasingly important in business decisions. And analyzing the demands of the user by acquiring the user behaviors, and recommending services or commodities for the user according to the analysis result. For example, the commodity vending platform pushes various new commodities to the user according to the behavior data of the user after releasing various new commodities.

Currently, in user behavior analysis, a user tag of a user is generally determined based on a user static attribute (such as age, gender, occupation, etc.) tag or historical behavior data of the user (such as forming a behavior tag, recommending according to the behavior tag), and then a product is pushed to the user according to the user tag, etc. The existing user behavior analysis is generally to match static attribute tags or historical behavior data of a user with a preset tag set to determine a user tag of the user, and the static attribute tag or the historical behavior data of the user and the preset tag set are generally in one-to-one relationship. However, in practical applications, the combination of behaviors of the user or the sequence of the behaviors has a great influence on the formation of the user tag, so that the existing determination mode of the user tag has the problem of lower accuracy.

Disclosure of Invention

The embodiment of the application aims to provide a user tag generation method, a system, electronic equipment and a storage medium, which are used for improving the accuracy of a determined user tag when user behavior analysis is carried out.

The method comprises the steps of acquiring a plurality of target behavior data of a user in real time according to access interface preset contacts, and determining a user tag of the user according to a tag generation rule and the target behavior data, wherein the tag generation rule comprises combination of the target behavior data and/or sequence of generation of the target behavior data.

In the embodiment of the application, all target behavior data generated by the user are acquired in real time, and the generation of the user tag is critical in consideration of the behavior combination and the occurrence sequence of the behaviors of the user. Therefore, the tag generation rule comprises the combination of multiple target behavior data and/or the sequence of generation of the multiple target behavior data, and when user behavior analysis is performed, the user tag of the user is determined according to the tag generation rule and the multiple target behavior data, so that real-time analysis of the user behavior is realized, and the accuracy of the determined user tag is improved.

In an alternative embodiment, after the multiple target behavior data of the user are acquired in real time, the method further comprises the steps of converting the multiple target behavior data of the user into json format and pushing the multiple target behavior data in the json format to the kafka message middleware.

In an optional embodiment, the determining the user tag of the user according to the tag generation rule and the plurality of target behavior data includes analyzing, by a Flink real-time computing component, the plurality of target behavior data in the kafka message middleware, determining behavior types corresponding to the plurality of target behavior data, and determining the user tag in real time according to the plurality of behavior types and the tag generation rule.

In an alternative embodiment, after the user tag of the user is determined according to the tag generation rule and the target behavior data, the method further comprises the steps of storing the user tag of the user in an HBase database, updating and confirming the user requirement in real time according to the user tag in the HBase database, and providing corresponding services.

The application provides a user tag generation system, which comprises a real-time acquisition unit and a Flink real-time calculation component, wherein the real-time acquisition unit is used for acquiring a plurality of target behavior data of a user in real time according to an access interface preset contact, and the Flink real-time calculation component is used for determining a user tag of the user according to a tag generation rule and the plurality of target behavior data, and the tag generation rule comprises a combination of the plurality of target behavior data and/or a sequence generated by the plurality of target behavior data.

In an optional implementation manner, the user tag generation system further comprises kafka message middleware, and the real-time acquisition unit is specifically used for converting the multiple target behavior data of the user into json format after acquiring the multiple target behavior data of the user in real time and pushing the multiple target behavior data of the json format to the kafka message middleware.

In an optional implementation manner, the link real-time computing component is specifically configured to receive the plurality of target behavior data from the kafka message middleware, parse the plurality of target behavior data, determine behavior types corresponding to the plurality of target behavior data, and determine the user tag in real time according to the plurality of behavior types and the tag generation rule.

In an optional embodiment, the user tag generation system further comprises an HBase database, wherein the HBase database is used for storing user tags of the users, and updating and confirming user requirements in real time according to the user tags in the HBase database to provide corresponding services.

The application provides a user tag generating device, which comprises an acquiring module and a determining module, wherein the acquiring module is used for acquiring a plurality of target behavior data of a user in real time according to an access interface preset contact, and the determining module is used for determining a user tag of the user according to a tag generating rule and the target behavior data, and the tag generating rule comprises a combination of the target behavior data and/or a sequence generated by the target behavior data.

In an optional embodiment, each time the second preset duration is set, the obtaining module executes the step of obtaining, in real time, a plurality of target behavior data of the user according to the preset contact of the access interface.

In an optional embodiment, the determining module is specifically configured to parse the plurality of target behavior data, determine a behavior type corresponding to the plurality of target behavior data, and determine the user tag according to the plurality of behavior types and the tag generation rule.

In an alternative embodiment, the apparatus further comprises a storage module, configured to store the user tag of the user in an HBase database.

In a fourth aspect, the application provides an electronic device comprising a processor, a memory and a bus, the processor and the memory completing communication with each other via the bus, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform a method as in any of the previous embodiments.

In a fifth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform a method according to any of the preceding embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a user tag generating method according to an embodiment of the present application;

fig. 2 is a block diagram of a user tag generating system according to an embodiment of the present application;

fig. 3 is a block diagram of a user tag generating apparatus according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

The icons comprise a 200-user tag generating system, a 201-real-time acquisition unit, a 202-Flink real-time calculation component, a 203-kafka message middleware, a 204-HBase database, a 300-user tag generating device, a 301-acquisition module, a 302-determination module, a 303-storage module, a 400-electronic device, a 401-processor, a 402-communication interface, a 403-memory and a 404-bus.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

The embodiment of the application provides a user tag generation method, a system, electronic equipment and a storage medium, which are used for improving the accuracy of a determined user tag when user behavior analysis is performed.

Referring to fig. 1, fig. 1 is a flowchart of a user tag generating method according to an embodiment of the present application, where the user tag generating method may include the following steps:

and 101, presetting a contact according to an access interface, and acquiring a plurality of target behavior data of a user in real time.

In the embodiment of the application, the target behavior data is the data correspondingly generated after the user performs the behavior operation, the user behavior data is generated by the preset contact point of the user access interface, and when the user generates the behavior actions such as browsing, searching, collecting, adding shopping carts, settling pages, submitting orders and the like, the preset contact point of the access interface is triggered and identified as the target behavior.

For example, in shopping class APP, target behavior data includes a user's behavior operation, keywords, commodity identification, and behavior time. The behavior operation of the user is that the user searches, browses, collects, adds shopping carts, submits orders, settles and the like when using the shopping class APP. After a user performs a certain behavior operation, corresponding data is generated, and the data is target behavior data. The keywords are keywords corresponding to the searched commodities when the user searches. The commodity identification is corresponding to the behavior operations of browsing, adding shopping carts, submitting orders and the like of the user. The behavior time is the time corresponding to various behavior operations.

As another example, in short video APP, the target behavior data includes a user's behavior operation, keywords, short video type identification, and behavior time. The behavior operation of the user is that the user performs searching, playing, collecting and other behavior operations when using the short video APP. The keywords are keywords corresponding to short videos searched when the user searches. The short video type identifier is a short video type identifier corresponding to the behavior operations such as playing collection and the like of the user. The behavior time is the time corresponding to various behavior operations.

It will be appreciated that there are differences in the target behavior data for which user behavior analysis is a concern for unused application scenarios. Therefore, the kinds of the target behavior data in various application scenarios can be predefined. And acquiring corresponding target behavior data in different application scenes.

The user tag generation method provided by the embodiment of the application analyzes the user behavior generated by the user in real time, and further generates the corresponding user tag. In order to implement real-time analysis of user behavior, it is necessary to acquire target behavior data of the user in real time. The real-time acquisition in the application can be specifically that a first preset duration is periodically acquired or set, and the user behavior analysis is performed according to all target behavior data generated by the user in the first preset duration.

It should be noted that, in the process that a user uses a certain APP, target behavior data is collected all the time, the first preset duration may be understood as a time window, and when user tag generation is performed each time, all the target behavior data in the first preset duration is obtained to perform user behavior analysis. The first preset time period may be 5 minutes, 10 minutes, 15 minutes, etc., which is not particularly limited in the present application.

Further, as an optional implementation manner, the method for generating the user tag provided by the embodiment of the application includes:

and executing the step of acquiring a plurality of target behavior data of the user in real time according to the preset contact point of the access interface every second preset time length.

In the embodiment of the present application, the second preset duration may be understood as a step length, that is, the step 101 is executed once every time the second preset duration passes, a plurality of target behavior data in the first preset duration are obtained, and user behavior analysis is performed according to all the target behavior data generated by the user in the first preset duration. The user tag is updated once every second preset time period.

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 comprises a combination of the plurality of target behavior data and/or a sequence of generation of the plurality of target behavior data.

After a plurality of target behavior data in a first preset time period are obtained, user behavior analysis is carried out on the plurality of target behavior data according to a tag generation rule, and a user tag is determined.

When analyzing the behavior of the user, the generation of the user label is critical in consideration of the behavior combination and the occurrence sequence of the behaviors of the user. Therefore, the tag generation rule comprises the combination of multiple target behavior data and/or the sequence of the generation of the multiple target behavior data, the multiple target behavior data in the first preset duration are matched with the combination mode of the multiple target behavior data and the sequence of the generation of the multiple target behavior data in the tag generation rule, and the user tag is determined.

As an alternative embodiment, the step 102 may include the following:

Analyzing the multiple target behavior data in the kafka message middleware through the Flink real-time computing component, determining behavior types corresponding to the multiple target behavior data, and determining the user labels according to the multiple behavior types and label generation rules.

In the embodiment of the application, the behavior type characterizes the types of all behavior operations performed by the user within a first preset duration.

For example, the plurality of target behavior data in the first preset duration are search, browse, collection, keywords, commodity identification and time corresponding to three behavior operations of search, browse and collection, and the behavior types of the user are determined to be search, browse and collection.

For example, the target behavior data in the first preset duration are the time corresponding to three behavior operations of searching, browsing, adding shopping carts, submitting orders, keywords, commodity identification and searching, browsing and adding shopping carts, and the behavior types of the user are determined to be searching, browsing and adding shopping carts.

For example, the target behavior data in the first preset duration are the time corresponding to four behavior operations of searching, browsing, adding shopping carts, submitting orders, keywords, commodity identification, searching, browsing, adding shopping carts and submitting orders, and the behavior types of the user are determined to be searching, browsing, adding shopping carts and submitting orders.

The tag generation rule is a combination of various behavior types and a corresponding relation of user tags.

For example, the tag generation rule 1 is that the behavior types are search, browse and collection, and the corresponding user tags are collection not purchased. And the label generation rule 2 is that the behavior types are searching, browsing and shopping cart adding, and the corresponding user labels are shopping cart adding and not purchasing. And 3, generating a rule 3, wherein the behavior types are searching, browsing, shopping cart adding and order submitting, and the corresponding user labels are purchased.

When the behavior type of the user is determined to be searching, browsing and collecting, the behavior type is matched with the tag generation rule 1, and the user tag is determined to be not purchased in collecting. And when the behavior type of the user is determined to be searching, browsing and shopping cart, and the behavior type is matched with the tag generation rule 2, determining that the user tag is not purchased for shopping cart. When the behavior type of the user is determined to be searching, browsing, shopping cart adding and order submitting, the behavior type is matched with the tag generation rule 3, and the user tag is determined to be purchased.

By the method, the behavior analysis is carried out on the user according to the target behavior data of the user within a period of time, the user tag is generated in real time, and the accuracy of the user tag is improved.

Further, as an optional implementation manner, after step 102, the method for generating a user tag provided by the embodiment of the present application further includes:

and storing the user label of the user in an HBase database.

The HBase database has the following characteristics:

large-capacity-billions of single tables can be stored with a certain column defined, and can itself periodically merge smaller files into a large file to reduce access pressure to the disk.

Column store-data is stored in columns.

Sparsity-for columns characterized as empty, no storage space is occupied, and the table can be designed to be sparser.

Extensibility-support of lateral extension, providing storage space and performance by continually adding servers to the cluster.

When the user labels are stored, the user labels corresponding to different users are different, the different users cannot correspond to all the user labels, and the labels of the users are continuously changed and increased according to the target behavior data of the users in different time periods. Thus, the characteristics of the HBase database are adapted to the presence of user tags.

Further, as an optional implementation manner, after step 102, the method for generating a user tag according to the embodiment of the present application further includes updating and confirming the user requirement in real time according to the user tag in the HBase database, and providing a corresponding service, for example, pushing information according to the user tag and the target behavior data.

In the embodiment of the application, the information pushing can comprise related product recommendation, preferential activity recommendation and the like.

For example, when the user tag is that the collection is not purchased, the user is considered to have a certain interest in the collected goods, the type of the goods interested by the user is determined by combining the goods identification in the target behavior data, and the goods information similar to the goods identification in the target behavior data is pushed to the user.

For example, when the user tag is not purchased by adding the shopping cart, the user is considered to have a certain purchase intention on the collected commodity, and the preferential activity information corresponding to the commodity is determined by combining the commodity identification in the target behavior data, which can be a coupon, a full-reduced order, and the like, and the preferential activity information corresponding to the commodity is pushed to the user.

Based on the same inventive concept, the embodiment of the application also provides a user label generation system. Referring to fig. 2, fig. 2 is a block diagram illustrating a user tag generating system according to an embodiment of the present application, where the user tag generating system 200 may include:

The real-time acquisition unit 201 is configured to preset a contact according to an access interface, and acquire multiple target behavior data of a user in real time;

the link real-time calculation component 202 is configured to determine, in real-time, a user tag of the user according to a tag generation rule and the plurality of target behavior data, where 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.

In an alternative embodiment, the user tag generation system further includes a kafka message middleware 203, and the real-time acquisition unit 201 is specifically configured to convert the multiple target behavior data of the user into json format after acquiring the multiple target behavior data of the user in real time, and push the multiple target behavior data in json format to the kafka message middleware 203.

In an alternative embodiment, the link real-time computing component 202 is specifically configured to receive the plurality of target behavior data from the kafka message middleware 203, parse the plurality of target behavior data, determine behavior types corresponding to the plurality of target behavior data, and determine the user tag in real time according to the plurality of behavior types and the tag generation rule.

In an alternative embodiment, the user tag generation system further includes an HBase database 204, configured to store the user tag of the user, update and confirm the user requirement in real time according to the user tag in the HBase database, and provide a corresponding service.

The user tag generation system provided by the embodiment of the application can execute the user tag generation method.

Because the number of users is very huge, and in order to realize real-time calculation of user labels of all users, the embodiment of the application adopts a Flink processing framework to form a Flink real-time calculation component 202, and the Flink real-time calculation component 202 performs real-time calculation through a sliding window according to a time window period (first preset duration), a step length (second preset duration), a label generation rule and a plurality of target behavior data to generate the user labels in real time.

Flink is an open source stream processing framework developed by the Apache software Foundation, the core of which is a distributed stream data stream engine written in Java and Scala. The Flink executes any stream data program in a data parallel and pipeline manner, and the pipeline runtime system of the Flink can execute batch processing and stream processing programs. Furthermore, the runtime itself of the flank also supports the execution of the iterative algorithm.

It will be appreciated that the user tag generation system 200 corresponds to the foregoing user tag generation method, and each functional module corresponds to each step of the foregoing user tag generation method, and therefore, the implementation of each functional module refers to the implementation of the user tag generation method in the foregoing embodiment, and will not be described herein again.

Based on the same inventive concept, the embodiment of the application also provides a user label generating device. Referring to fig. 3, fig. 3 is a block diagram illustrating a configuration of a user tag generating apparatus according to an embodiment of the present application, the user tag generating apparatus 300 may include:

the acquiring module 301 is configured to preset a contact according to an access interface, and acquire multiple target behavior data of a user in real time;

the determining module 302 is configured to determine, in real time, a user tag of the user according to a tag generation rule and the plurality of target behavior data, where the tag generation rule includes a combination of a plurality of target behavior data and/or a sequence in which the plurality of target behavior data are generated.

In an alternative embodiment, the obtaining module 301 performs the step of obtaining, in real time, a plurality of target behavior data of the user according to the access interface preset contacts at each interval for a second preset duration.

In an optional embodiment, the determining module 302 is specifically configured to parse the plurality of target behavior data, determine a behavior type corresponding to the plurality of target behavior data, and determine the user tag in real time according to the plurality of behavior types and the tag generation rule.

In an optional embodiment, the user tag generating device further includes a storage module 303, configured to store the user tag of the user in an HBase database, update and confirm the user requirement in real time according to the user tag in the HBase database, and provide a corresponding service.

The user tag generating apparatus 300 corresponds to the user tag generating method described above, and each functional module corresponds to each step of the user tag generating method described above, and therefore, the implementation of each functional module refers to the implementation of the user tag generating method in the foregoing embodiment, and the description thereof will not be repeated.

Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device 400 according to an embodiment of the present application, where 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. Where bus 404 is used to enable direct connection communication of these components, communication interface 402 is used for communication of signaling or data with other node devices, and memory 403 stores machine readable instructions executable by processor 401. When the electronic device 400 is in operation, the processor 401 and the memory 403 communicate via the bus 404, and the machine readable instructions when invoked by the processor 401 perform the user tag generation method as in the previous embodiments.

The processor 401 may be an integrated circuit chip having signal processing capabilities. The processor 401 may be a general-purpose processor including a central Processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc., a digital signal processor (DIGITAL SIGNAL Processing, DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), a Field-Programmable gate array (fieldprogrammable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. Which may implement or perform the various methods, steps, and logical blocks disclosed in embodiments of the application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The Memory 403 may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.

It is to be understood that the configuration shown in fig. 4 is merely illustrative, and that electronic device 400 may also include more or fewer components than those shown in fig. 4, or have a different configuration than that shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof. In the embodiment of the present application, the electronic device 400 may be, but is not limited to, a physical device such as a desktop, a notebook, a smart phone, an intelligent wearable device, a vehicle-mounted device, or a virtual device such as a virtual machine. In addition, the electronic device 400 is not necessarily a single device, but may be a combination of a plurality of devices, such as a server cluster, or the like.

In addition, the embodiment of the present application further provides a computer storage medium, on which a computer program is stored, which when executed by a computer, performs the steps of the user tag generating method in the above embodiment.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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.

Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.

It should be noted that the functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A method of generating a user tag, the method comprising:

presetting a contact according to an access interface, and acquiring a plurality of target behavior data of a user in real time;

And determining a user tag of the user according to a tag generation rule and the plurality of target behavior data, wherein the tag generation rule comprises a combination of the plurality of target behavior data and/or a sequence of generation of the plurality of target behavior data.

2. The method of claim 1, wherein after the acquiring the plurality of target behavior data of the user in real time, further comprising:

The multiple target behavior data of the user are converted into json format, and the multiple target behavior data in the json format are pushed to the kafka message middleware.

3. The method of claim 2, wherein the determining the user tag of the user based on tag generation rules and the plurality of target behavior data comprises:

Analyzing the plurality of target behavior data in the kafka message middleware through a Flink real-time computing component to determine behavior types corresponding to the plurality of target behavior data;

and determining the user tag in real time according to the behavior types and the tag generation rule.

4. The method of claim 1, wherein after the determining the user tag of the user based on tag generation rules and the plurality of target behavior data, the method further comprises:

Storing the user label of the user in an HBase database;

and updating and confirming the user demand in real time according to the user label in the HBase database, and providing corresponding service.

5. A user tag generation system, comprising:

the real-time acquisition unit is used for presetting contacts according to an access interface and acquiring a plurality of target behavior data of a user in real time;

And the Flink real-time calculation component is used for determining the user tag of the user in real time according to a tag generation rule and the plurality of target behavior data, wherein the tag generation rule comprises the combination of the plurality of target behavior data and/or the sequence of the generation of the plurality of target behavior data.

6. The user tag generation system of claim 5, wherein the user tag generation system further comprises kafka message middleware;

The real-time acquisition unit is specifically configured to convert the multiple target behavior data of the user into json format after acquiring the multiple target behavior data of the user in real time, and push the multiple target behavior data of the json format to the kafka message middleware.

7. The user tag generation system of claim 6, wherein the Flink real-time computing component is specifically configured to receive the plurality of target behavior data from the kafka message middleware, parse the plurality of target behavior data to determine behavior types corresponding to the plurality of target behavior data, and determine the user tag in real-time according to the plurality of behavior types and the tag generation rule.

8. The user tag generation system of claim 5, wherein the user tag generation system further comprises:

And the HBase database is used for storing the user labels of the users, updating and confirming the user demands in real time according to the user labels in the HBase database, and providing corresponding services.

9. An electronic device comprising a processor, a memory and a bus, the processor and the memory completing communication with each other via the bus, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-4.

10. A computer readable storage medium, characterized in that it has stored thereon computer program instructions, which when read and run by a computer, perform the method according to any of claims 1-4.