Method for determining user's operational characterization data with respect to an application

Abstract

Embodiments of the present application relate to a method, device and medium for determining user's operation characteristic data about an application. The method comprises, in response to determining a user's request about operation characteristic data, generating change information about the user's operation characteristic data in a memory database through verification; the memory database synchronously sends the change information about the user's operation characteristic data to a message queue; in response to detecting a confirmation message about the message queue receiving the change information, synchronously updating the user's operation characteristic data stored in the memory database; and through the message queue, sending the change information about the user's operation characteristic data to a disk database so that the user's operation characteristic data stored in the disk database is asynchronously updated; the message queue is deployed in a separate server different from the server where the memory database or the disk database is located. Thus, the update synchronization and efficiency of the operation characteristic data can be effectively improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application generally relate to the technical field of data processing, and more particularly to a method, a computing device and a storage medium for determining operation characterization data of a user about an application. BACKGROUND

[0002] With the development of Internet technology, various applications have emerged, and various functions have been added to many applications. Users operate operation characterization data about the application by participating in interaction. The operation characterization data of the application includes, for example, points, virtual currency, exchange coupons, etc. For example, the points of a user increase through the user's consumption behavior, browsing behavior, and interactive behavior such as participating in activities, and the user can exchange relevant benefits by consuming points. In this way, the activity of the user can be maintained.

[0003] A traditional method for determining operation characterization data of a user about an application, taking points as an example, stores each point obtained by the user in a database in a points updating stage. When the user consumes points, only the points that have been stored in the database can be consumed. When the points state of the user is in a concurrent scenario of obtaining and consuming, concurrent operation of the point change of the user cannot be performed, and the account needs to be locked so that only one point change of the same account can occur at the same time. In addition, in the points obtaining stage, the traditional method recommends to the user by extracting questions or information in a fixed question bank or a fixed database. The mode is relatively single and the matching degree of the user preference is insufficient.

[0004] In summary, the traditional method for determining operation characterization data of a user about an application has the following disadvantages: in a multi-user and complex operation characterization data change scenario, the update synchronization of the operation characterization data is poor and the efficiency is low. SUMMARY

[0005] To solve the above problems, the present application provides a method, a computing device and a storage medium for determining operation characterization data of a user about an application, which can effectively improve the update synchronization and efficiency of the operation characterization data.

[0006] According to a first aspect of the present application, there is provided a method for determining operation characterization data of a user with respect to an application, comprising: in response to determining a request of the user for the operation characterization data, generating, by verification, change information of the operation characterization data of the user in an in-memory database; the in-memory database synchronously sending the change information of the operation characterization data of the user to a message queue, the change information at least indicating a change time, a change type and a change value of the operation characterization data; in response to detecting a confirmation message of the message queue receiving the change information, synchronously updating the operation characterization data of the user stored in the in-memory database; and sending, by the message queue, the change information of the operation characterization data of the user to a disk database, so that the operation characterization data of the user stored in the disk database is asynchronously updated; the operation characterization data of the user stored in the disk database being configured to be used to restore the operation characterization data stored in the in-memory database in case of failure of the in-memory database; the message queue being deployed on a separate server different from a server where the in-memory database or the disk database is located.

[0007] According to a second aspect of the present application, there is provided a computing device, comprising: at least one processing unit; at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform the steps of the method according to the first aspect.

[0008] According to a third aspect of the present application, there is provided a computer-readable storage medium having stored thereon a computer program, the computer program, when executed by a machine, implementing the method according to the first aspect.

[0009] According to a fourth aspect of the present application, there is also provided a computer program product comprising a computer program, the computer program, when executed by a machine, implementing the method of the first aspect of the present application.

[0010] In some embodiments, the method for determining operation characterization data of a user with respect to an application further comprises: based on a predetermined time, confirming whether the change information of the operation characterization data in the message queue within a predetermined time interval is consistent with the update of the operation characterization data in the in-memory database; in response to confirming that the operation characterization data corresponding to the change information of the operation characterization data in the message queue within the predetermined time interval has completed the update in the in-memory database, confirming that the change information of the operation characterization data is consistent with the update of the operation characterization data in the in-memory database; and deleting, in the in-memory database, the change information within the predetermined time interval related to the operation characterization data that has completed the update.

[0011] In some embodiments, wherein the user's operation characterization data stored in the disk database is configured to be used to restore the operation characterization data stored in the in-memory database in response to a failure of the in-memory database comprises: in response to determining that the in-memory database fails, determining whether there is change information of the operation characterization data in the message queue that has not been sent to the disk database; in response to determining that there is no change information of the operation characterization data in the message queue that has not been sent to the disk database, determining whether there is user's operation characterization data in the disk database that has not been updated completely; and in response to determining that there is no user's operation characterization data in the disk database that has not been updated completely, restoring the operation characterization data stored in the in-memory database based on the user's operation characterization data stored in the disk database.

[0012] In some embodiments, the operation characterization data is a credit, and the user's operation characterization data stored in the disk database is further configured to be used to offset a payment amount of an order on the application, and determining that the request of the user about the operation characterization data is verified comprises: in response to determining that the user completes an interactive operation about the application, receiving an obtaining request about the operation characterization data from the user, the interactive operation at least including browsing a page, answering a question, and purchasing a product; based on the received obtaining request, performing risk verification on the user corresponding to the obtaining request, the risk verification including offline risk verification and real-time risk verification; and in response to determining that the user passes the offline risk verification and the real-time risk verification at the same time, determining that the obtaining request of the user is verified.

[0013] In some embodiments, the offline risk verification comprises: based on a predetermined period, obtaining the change information of the user's interactive operation data, login data, and operation characterization data, so as to obtain the historical behavior data of the user; based on the historical behavior data of the user within a predetermined analysis period, generating a historical risk control prompt about the user; inputting the generated historical risk control prompt about the user into a predetermined risk control prediction model, so as to obtain a historical risk label of the user; and in response to determining that the historical risk label of the user indicates high risk, determining that the user fails the offline risk verification.

[0014] In some embodiments, the real-time risk verification comprises: based on the change information of the user's current interactive activity operation, login data, and operation characterization data, obtaining real-time behavior data of the user; based on the real-time behavior data of the user, generating a real-time risk control prompt about the user; inputting the generated real-time risk control prompt of the user into a predetermined risk control prediction model, so as to obtain a real-time risk label of the user; and in response to determining that the real-time risk label of the user indicates high risk, determining that the user fails the real-time risk verification.

[0015] In some embodiments, the method for determining the operation characterization data of the user about the application further comprises: based on a predetermined period, obtaining the login data of the user and the change information of the operation characterization data, generating an activity information prompt about the user, inputting the activity information prompt about the user into a predetermined activity prediction model to obtain predicted activity information of the user; based on the predicted activity information of the user, obtaining predicted login time and predicted operation characterization data of the user; and in response to determining that the predicted login time of the user is about to arrive, pulling the operation characterization data of the user from the disk database to pre-load the change information of the operation characterization data of the user in the in-memory database.

[0016] In some embodiments, the method for determining the operation characterization data of the user about the application further comprises: based on the basic information and the historical interaction activity information of the user, generating a prompt about the behavior preference of the user; wherein the historical interaction activity information at least includes the answer information, the browsing information and the commodity purchase information of the user; inputting the generated behavior preference prompt into a predetermined large language model for multi-round dialogue to generate a user portrait; based on the generated user portrait, the historical recommendation information of the user and the historical interaction activity information of the user, generating again a prompt about the behavior preference of the user; inputting the generated behavior preference prompt again into the predetermined large language model for multi-round dialogue to generate a user portrait in the information system; and based on the user portrait in the information system, recommending question information, commodity information and information information to the user.

[0017] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features, advantages and aspects of embodiments of the present application will become more apparent by describing in detail preferred embodiments thereof with reference to the attached drawings in which:

[0019] Figure 1 A schematic diagram of a system for implementing a method for determining operation characterization data of a user about an application according to an embodiment of the present application is shown.

[0020] Figure 2 A flowchart of a method for determining operation characterization data of a user about an application according to an embodiment of the present application is shown.

[0021] Figure 3 A flowchart of a method for determining that a request for operation characterization data of a user is verified according to an embodiment of the present application is shown.

[0022] Figure 4 A flowchart of a method for preloading operation characterization data of a user according to an embodiment of the present application is shown.

[0023] Figure 5 A flowchart of a method for recommending interaction activity information to a user according to an embodiment of the present application is shown.

[0024] Figure 6 A schematic diagram of a manner of generating a user portrait in an information system according to an embodiment of the present application is shown.

[0025] Figure 7 A block diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] Exemplary embodiments of the present application are described herein with reference to the accompanying drawings, in which various details are set forth to assist in an understanding of the present application. It will be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the spirit and scope of the present application. Thus, the present application is not intended to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the claims.

[0027] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," "contains," "containing," or variations thereof, are intended to be open-ended, i.e., to mean including, but not limited to. Unless otherwise noted, the terms "or" and "and" are used in their inclusive sense (and not in their exclusive sense). The term "based on" is used in the sense of "based, at least in part, on." The terms "one example embodiment" and "an example embodiment" are used interchangeably. The term "another embodiment" is used interchangeably with the term "at least one additional embodiment." The terms "first," "second," and the like are used merely to distinguish one element from another and are not intended to be limiting. Other terms can also be used, and capitalization is in several instances merely intended to emphasize certain portions of the descriptions.

[0028] As described previously, the conventional method for determining operation characterization data of a user with respect to an application program has the disadvantage that in a multi-user, complex operation characterization data change scenario, the updating of the operation characterization data is poor in synchronization and low in efficiency.

[0029] To at least partially solve one or more of the above problems and other potential problems, example embodiments of the present application propose a scheme for determining user's operation characterization data about an application, in which, in response to a request of the user for the operation characterization data, the change information of the operation characterization data about the user is generated in a memory database by verification; the memory database synchronously sends the change information of the operation characterization data of the user to a message queue, the change information at least indicating the change time, change type and change value of the operation characterization data; in response to detecting a confirmation message about the message queue receiving the change information, the operation characterization data stored in the memory database by the user is synchronously updated; thus, the scheme can make the change information of the operation characterization data in the message queue and the update of the operation characterization data in the memory database synchronous.

[0030] The present application also sends the change information of the operation characterization data of the user to a disk database through the message queue, so that the operation characterization data of the user stored in the disk database is asynchronously updated; thus, the operation characterization data in the memory database can be permanently stored to the disk database through the message queue; the present application can also configure the operation characterization data of the user stored in the disk database to be used to restore the operation characterization data stored in the memory database when the memory database fails; in addition, in the scheme, the message queue is deployed in a separate server different from the server where the memory database or the disk database is located; thus, when the memory database fails, the data recovery of the memory database can be realized through the operation characterization data, the change information of the operation characterization data and other data stored in the message queue and the disk database, and since the message queue, the disk database and the memory database are respectively deployed in different servers, and the change information of the operation characterization data in the message queue and the update of the operation characterization data in the memory database are synchronous, the integrity of the operation characterization data when being restored is also ensured, so that when the memory database crashes, the operation characterization data that has not been asynchronously updated to the disk database can be stored in the message queue, so that this part of the operation characterization data does not lose; and the operation characterization data that has not been asynchronously updated to the disk database can also only need to be synchronously updated in the memory database and the message queue, and can continue to be updated according to the user's request without being permanently stored to the disk database.

[0031] Figure 1 A schematic diagram of a system 100 for implementing a method for determining user's operation characterization data about an application according to an embodiment of the present application is shown. As Figure 1As shown in the figure, the system 100 at least includes a memory database 110, a message queue 120, a disk-based database 130 and a network 140. A user 150, for example, on the user's terminal, the interactive page of the application program, or other interactive devices related to the application program, can interact with the system 100, the memory database 110 in the system 100 through the network 140; the memory database 110 and the message queue 120 are communicatively connected (for example, through the Internet, a local area network) for data interaction, and the message queue 120 and the disk-based database 130 are communicatively connected (for example, through the Internet, a local area network) for data interaction. Among them, the memory database 110, the message queue 120 and the disk-based database 130 are respectively deployed on different servers.

[0032] Regarding the memory database 110 (Memory Database), the memory database 110 is a type of database system that stores data in memory, with the characteristics of high speed and low latency; it may be, for example, a Reids database, a Memcached database, an Apache Ignite database, etc. The memory database 110 may be deployed, for example, on a single server, distributed on a cluster server, deployed on a physical server, deployed on a virtual machine, deployed on an embedded device, and also deployed on a cloud environment.

[0033] Regarding the message queue 120 (Message Queue), it may be, for example, a Kafka message system, a RabbitMQ message system, a RocketMQ message system, etc. The message queue 120 is separately deployed on a server or a server cluster that is different from the server where the memory database or the disk-based database is located.

[0034] Regarding the disk-based database 130 (Disk-based Database), also known as a persistent database, is a type of database system that stores data on a hard disk. Because the data is stored on a hard disk, the disk-based database has higher data capacity and stability; the disk-based database 130 may be, for example, a MySQL database, a PostgreSQL database, an Oracle database, etc.

[0035] As to the in-memory database 110, the computing device (e.g., a server, a virtual machine, a cloud server, and an embedded device) where the in-memory database 110 is deployed can have one or more processing units and memories. The one or more processing units include, for example, special-purpose processing units such as a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or a General-purpose computing on graphics processing units (GPGPU), and general-purpose processing units such as a CPU. In addition, the one or more processing units where the in-memory database 110 is deployed also run a plurality of software modules. In some embodiments, the software modules of the in-memory database 110 include, for example, a request receiving and verifying module 112, a data operation module 114, a message sending module 116, and a message receiving module 118.

[0036] As to the request receiving and verifying module 112, it is configured to receive and verify a request from a user to operate the characterization data.

[0037] As to the data operation module 114, it is configured to, in response to determining that the request from the user to operate the characterization data is verified, generate change information of the characterization data of the user in the in-memory database; and it is also configured to, in response to detecting a confirmation message from the message queue that the change information is received, synchronously update the characterization data of the user stored in the in-memory database.

[0038] As to the message sending module 116, it is configured to synchronously send the change information of the characterization data of the user to the message queue, the change information at least indicating a change time, a change type, and a change value of the characterization data.

[0039] As to the message receiving module 118, it is configured to receive the confirmation message from the message queue that the change information is received.

[0040] As to the message queue 120, it is configured to send the change information of the characterization data of the user to the disk database; the message queue is deployed in a separate server different from the server where the in-memory database or the disk database is deployed.

[0041] As to the disk database 130, it is configured to receive the change information of the user's operation characteristic data sent from the message queue 120, so that the user's operation characteristic data stored in the disk database is updated asynchronously; the user's operation characteristic data stored in the disk database is configured to restore the operation characteristic data stored in the in-memory database in case of failure of the in-memory database.

[0042] Figure 2 A flow chart of a method 200 for is shown according to an embodiment of the present application. The method 200 can be performed by the system 100 (at least comprising the in-memory database 110, the message queue 120 and the disk database 130) as shown, or can be performed at the electronic device 700 as shown. Figure 1 Figure 7 It should be understood that the method 200 can further comprise additional steps not shown and / or can omit the steps shown, and the scope of the present application is not limited in this respect.

[0043] At step 202, the in-memory database 110 generates change information of the user's operation characteristic data in the in-memory database if it is determined that the user's request for operation characteristic data is verified.

[0044] In some embodiments, the operation characteristic data is points, and the user's operation characteristic data stored in the disk database is further configured to offset the payment amount of the order on the application, and it is determined that the user's request for operation characteristic data is verified.

[0045] In some embodiments, the user's request for operation characteristic data is, for example, the user's request for consumption of points, the user's request for obtaining points. The method for determining that the user's request for operation characteristic data is verified will be described in detail below, and will not be described herein again. Figure 3

[0046] At step 204, the in-memory database 110 synchronously sends the change information of the user's operation characteristic data to the message queue, and the change information at least indicates the change time, the change type and the change value of the operation characteristic data.

[0047] ​​For example, when the operation characteristic data is points, the change information of the operation characteristic data is change information of points, and the change information of points of the user indicates the time of change of points of the user, the type of change of points (such as obtaining, consumption, increase, decrease), and the value of change of points; for example, the change information A1 of points of the user A (2024-08-15, 18:00; increase; 20) indicates that the user A requests to increase the value of points by 20 at 18:00 on August 15, 2024. For example, the change information of points can also indicate the way of obtaining points, such as indicating that the increase of points of the user is due to the user answering the questions in the page, such as the increase of points of the user is due to the user exchanging goods, such as the increase of points of the user is due to the user browsing information, such as the decrease of points of the user is due to the user exchanging certain goods, such as the decrease of points of the user is due to the user exchanging certain coupons, and such as the decrease of points of the user is due to the user exchanging certain services. It should be understood that the above examples of the change information of the operation characteristic data are only illustrative, and the format and content of the change information of the operation characteristic data can be adjusted according to the actual format requirements of the in-memory database and the specific content of the change information of the operation characteristic data.

[0048] In step 206, the in-memory database 110 synchronously updates the operation characteristic data of the user stored in the in-memory database if the in-memory database 110 detects the confirmation message about the message queue receiving the change information.

[0049] For example, when the operation characteristic data is points, the change information of the operation characteristic data is change information of points, and the change information of points of the user indicates the time of change of points of the user, the type of change of points (such as obtaining, consumption, increase, decrease), and the value of change of points; for example, the change information A1 of points of the user A (2024-08-15, 18:00; increase; 20) indicates that the user A requests to increase the value of points by 20 at 18:00 on August 15, 2024. For example, the change information of points can also indicate the way of obtaining points, such as indicating that the increase of points of the user is due to the user answering the questions in the page, such as the increase of points of the user is due to the user exchanging goods, such as the increase of points of the user is due to the user browsing information, such as the decrease of points of the user is due to the user exchanging certain goods, such as the decrease of points of the user is due to the user exchanging certain coupons, and such as the decrease of points of the user is due to the user exchanging certain services. It should be understood that the above examples of the change information of the operation characteristic data are only illustrative, and the format and content of the change information of the operation characteristic data can be adjusted according to the actual format requirements of the in-memory database and the specific content of the change information of the operation characteristic data.

[0050] Thus, the update of the operation characteristic data of the user stored in the in-memory database is synchronized with the change message of the operation characteristic data of the user received by the message queue, so that when the in-memory database fails (for example, crashes), the real-time operation characteristic data of the user is not lost, and the latest change message (i.e., update) of the operation characteristic data of the user is stored in the message queue; the in-memory database can restore the operation characteristic data based on the change message of the operation characteristic data in the message queue and the historical data of the operation characteristic data in the disk database. It should be understood that the synchronization update can be performed in the message queue and the in-memory database based on different operation characteristic data.

[0051] In addition, the scheme provided by the above technical means can realize real-time parallel operation of the operation characteristic data. Taking the points as an example, the real-time acquisition and consumption of the points by the same user or multiple users can be supported. In the traditional technology, the points in the disk database need to be completed and stored in the disk database before the points in the disk database are consumed. In the present scheme, the update of the operation characteristic data is synchronized with the change information of the operation characteristic data received by the message queue, so that the consumption of the operation characteristic data, for example, the consumption of the points, can be performed, thereby providing a more efficient user experience.

[0052] In step 208, the change information of the operation characteristic data of the user is sent to the disk database through the message queue 120, so that the operation characteristic data of the user stored in the disk database is updated asynchronously; the operation characteristic data of the user stored in the disk database is configured to restore the operation characteristic data stored in the in-memory database when the in-memory database fails; the message queue is deployed in a separate server different from the server where the in-memory database or the disk database is located.

[0053] Continuing the above example, after the in-memory database 110 completes the update of the points of user A about the change information A1, the in-memory database 110 can send a message about the completion of the update of the points of user A about the change information A1 to the message queue 120; after the message queue 120 returns the confirmation message of receiving the change information A1 to the in-memory database 110, the message queue 120 pushes the change information A1 of the points of user A to the disk database 130. It should be understood that the message sending between the message queue 120 and the disk database 130 can be asynchronous. In the above scheme, the operation characteristic data of the user (including the current value of the operation characteristic data and the change information of the operation characteristic data) between the message queue 120 and the in-memory database 110 is synchronized, while the operation characteristic data of the user between the message queue 120 and the disk database 130 is asynchronous, that is, the operation characteristic data of the user between the in-memory database 110 and the disk database 130 is asynchronous.

[0054] Thus, the application can realize the operation characteristic data synchronization between the memory database 110 and the message queue 120, and the asynchronous operation characteristic data permanent storage between the memory database 110 and the disk database 130.

[0055] In some embodiments, the operation characteristic data of the user stored in the disk database is configured to be used for recovering the operation characteristic data stored in the memory database in case of failure of the memory database, which includes: if it is determined that the memory database 110 fails, determining whether there is change information of the operation characteristic data in the message queue 120 which has not been sent to the disk database 130; if it is determined that there is no change information of the operation characteristic data in the message queue 120 which has not been sent to the disk database 130, determining whether there is operation characteristic data of the user in the disk database 130 which has not been updated completely; and if it is determined that there is no operation characteristic data of the user in the disk database 130 which has not been updated completely, recovering the operation characteristic data stored in the memory database 110 based on the operation characteristic data of the user stored in the disk database 130.

[0056] For example, when recovering the operation characteristic data stored in the memory database 110, the operation characteristic data of the last update (at least including the value of the operation characteristic data, and also including the change information of the operation characteristic data of the last update) is recovered, since when the memory database is recovered, all the operation characteristic data of the memory database at the time of failure is stored in the disk database through the message queue, and includes the historical data of the operation characteristic data; therefore, only recovering the operation characteristic data of the last update will not affect the operation experience of the user, and can greatly improve the data recovery efficiency of the memory database.

[0057] Thus, the application can make the message queue 120 send the change information of the operation characteristic data to the disk database 130 in case of failure of the memory database 110, so that the disk database 130 can obtain the same operation characteristic data (including the value of the operation characteristic data, and the change information of the operation characteristic data) as that at the time of failure of the memory database 110, thereby making the memory database call the operation characteristic data of the user stored in the disk database for data recovery.

[0058] In the above scheme, the application determines the user's request for operating the characteristic data through verification, generates change information of the user's operating characteristic data in the in-memory database; the in-memory database synchronously sends the change information of the user's operating characteristic data to the message queue; if the confirmation message of receiving the change information by the message queue is detected, the user's operating characteristic data stored in the in-memory database is synchronously updated; thereby, the scheme can make the change information of the operating characteristic data in the message queue and the update of the operating characteristic data in the in-memory database synchronous, so that the operating characteristic data in the in-memory database does not lose when the in-memory database fails. Then, through the message queue, the change information of the user's operating characteristic data is sent to the disk database, so that the user's operating characteristic data stored in the disk database is asynchronously updated; thereby, the operating characteristic data in the in-memory database can be permanently stored to the disk database through the message queue; the application can also configure the user's operating characteristic data stored in the disk database to restore the operating characteristic data stored in the in-memory database when the in-memory database fails.

[0059] The above technical means is different from the existing in-memory database recovery technology. The existing in-memory database usually adopts a timing data snapshot; or an operation log is established in the in-memory database to synchronize the operation log every predetermined time; the above two existing methods have problems, for example, the data between two data snapshots cannot be recovered, the data between two operation log updates cannot be recovered, and the real-time operating characteristic data of the in-memory database when the in-memory database fails is lost. The above technical means provided in the application solves these problems, and the recovery efficiency does not need to recover the data based on the operation log step by step, and the efficiency is much higher than the existing technology.

[0060] In some embodiments, the system 100 confirms whether the change information of the operating characteristic data in the message queue 120 within a predetermined time interval is consistent with the update of the operating characteristic data in the in-memory database 110 based on the predetermined time; if it is confirmed that the operating characteristic data corresponding to the change information of the operating characteristic data of the message queue 120 within the predetermined time interval has been updated in the in-memory database 110, it is confirmed that the change information of the operating characteristic data is consistent with the update of the operating characteristic data in the in-memory database 110; and in the in-memory database 110, the change information related to the operating characteristic data which has been updated within the predetermined time interval is deleted.

[0061] By adopting the technical means, the application further reduces the storage space occupation of the memory database, so that when the multi-threaded and parallel operation representation data change operation is performed, the user's account does not need to be locked, and the operation representation data does not need to be waited to complete the database, and the real-time change operation of the operation representation data can be supported. And because the operation representation data that has been synchronized with the message queue is periodically deleted, more memory of the memory database can be used for data interaction, the data reading efficiency is improved, and the user's interactive experience can also be improved.

[0062] Figure 3 A flowchart of a method 300 for determining that a request of a user about operation representation data is verified according to an embodiment of the application is shown. The method 300 can be performed by the system 100 (at least including the system memory database 110, the message queue 120 and the disk database 130) as shown, or can be performed at the electronic device 700 as shown. Figure 1 Figure 7 It should be understood that the method 300 can also include additional steps not shown and / or can omit the steps shown, and the scope of the application is not limited in this regard.

[0063] In some embodiments, the operation representation data is a credit, and the operation representation data of the user stored in the disk database is also configured to be used for offsetting the payment amount of an order on an application, and determining that the request of the user about the operation representation data is verified includes steps 302 to 306; it should be understood that the application understood in this regard can be an application deployed on a webpage, or an application deployed on an interactive terminal, a user terminal, etc.

[0064] In step 302, the memory database 110 receives a request for obtaining operation representation data from the user if it is determined that the user completes an interactive operation on the application, and the interactive operation at least includes browsing a page, answering a question and purchasing a product.

[0065] In step 304, the memory database 110 performs risk verification on the user corresponding to the request for obtaining based on the received request for obtaining, and the risk verification includes offline risk verification and real-time risk verification.

[0066] In step 306, the memory database 110 determines that the request of the user is verified if it is determined that the user passes the offline risk verification and the real-time risk verification at the same time.

[0067] ​In some embodiments, the offline risk verification comprises: based on a predetermined period, obtaining the change information of the user's interactive operation data, login data, and operation characterization data from the disk database 130 to obtain the user's historical behavior data; based on the user's historical behavior data within a predetermined analysis period, generating a historical risk control prompt about the user; inputting the generated historical risk control prompt about the user into a predetermined risk prediction model to obtain the user's historical risk label; and if it is determined that the user's historical risk label indicates high risk, determining that the user fails the offline risk verification.

[0068] Regarding the historical risk behavior that may lead to risk, for example, the same user consumes or obtains points at multiple different login IP locations within a predetermined period of time, the same user consumes or obtains points for multiple different users within a predetermined period of time, multiple users consume or obtain points for the same user within a predetermined period of time, etc.

[0069] In some embodiments, the real-time risk verification comprises: based on the user's current interactive activity operation, login data, and change information of operation characterization data, obtaining the user's real-time behavior data; based on the user's real-time behavior data, generating a real-time risk control prompt about the user; inputting the generated real-time risk control prompt of the user into a predetermined risk prediction model to obtain the user's real-time risk label; and if it is determined that the user's real-time risk label indicates high risk, determining that the user fails the real-time risk verification.

[0070] Regarding the real-time risk behavior that may lead to risk, for example, the amount of points obtained by the user in this request is greater than the upper limit within a predetermined period of time, for example, the amount of points consumed by the user in this request is greater than the threshold of points consumed on the same day.

[0071] In some embodiments, the predetermined risk prediction model is, for example, an AIGC (Artificial Intelligence Generated Content) model, which can also be a large language model, based on the system 100 through multiple rounds of interaction between the historical risk control prompt and the predetermined risk prediction model to determine the user's historical risk label, real-time risk label.

[0072] By adopting the above technical means, the present application enables the user to obtain points only after passing the real-time and offline risk verification, thereby ensuring the legitimacy of the user identity and the security of the system, greatly avoiding black production users and illegal users, and improving the quality user rate and the enthusiasm of ordinary users in participating in interaction. At the same time, when determining the risk label, the AIGC model, the large language model, etc. are combined, so that the present application can adapt to the uncertainty of each user and the diversity of risk types, avoid missing new risk behaviors or risk behaviors outside the risk rules, and based on the historical behavior data and real-time behavior data of the user, the risk label obtained also has higher credibility.

[0073] Figure 4 A flowchart of a method 400 for preloading operation characterization data of a user according to an embodiment of the present application is shown. The method 400 can be performed by a system 100 as shown (at least including a system memory database 110, a message queue 120 and a disk database 130), or can be performed at an electronic device 700 as shown. Figure 1 Figure 7 It should be understood that the method 400 can also include additional steps not shown and / or can omit the steps shown, and the scope of the present application is not limited in this respect.

[0074] At step 402, the system 100 obtains the login data of the user and the change information of the operation characterization data based on a predetermined period, generates an activity information prompt about the user, and inputs the activity information prompt of the user into a predetermined activity prediction model to obtain the predicted activity information of the user.

[0075] The predetermined activity prediction model is, for example, an AIGC model or a large language model, which interacts with the activity prediction model through the activity information prompt of the user to determine the predicted activity information of the user.

[0076] For example, through the activity information prompt of the user A in the last 2 months, it is predicted that the user A usually logs in between 9:00 and 10:00 in the morning and obtains points by answering questions, browsing information, etc., and logs in between 10:00 and 11:00 at night and usually consumes points.

[0077] At step 404, the system 100 obtains the predicted login time and the predicted operation characterization data of the user based on the predicted activity information of the user.

[0078] For another example, continuing the above example, after predicting the activity information of the user A, it is predicted that the user A will log in at 9:00 am every day and obtain points, with an average of 20 points; and log in at 10:00 pm and consume points, for example, an average of 10 points. ​

[0079] At step 406, the system 100 pulls the user's operational characterization data from the disk database if it determines that the predicted login time of the user is approaching, so as to preload the change information of the user's operational characterization data in the in-memory database.

[0080] For another example, continuing the above example, it is determined that the predicted login time of the user A is approaching, i.e. 9:00 am, for example, 8:55 am, the operational characterization data of the user A (e.g. the latest score value of the user A, 1000) is pulled from the disk database 130, based on which the latest score value 1000 of the user is preloaded in the in-memory database 110, and the possible score change information (e.g. increase, 20) that the user can obtain is preloaded.

[0081] By adopting the above technical means, the present application can predict the user who is about to log in, pull the operational characterization data of the user from the disk database in advance before the user logs in, preload the change information of the operational characterization data of the user in the in-memory database based on the predicted user activity information, so as to make data writing operation in advance in the in-memory database, and improve system running efficiency; and the AIGC model is combined to predict the user activity information, which can improve the prediction efficiency and intuitiveness of the predicted data.

[0082] Figure 5 A flowchart of a method 500 for recommending interactive activity information to a user according to an embodiment of the present application is shown. The method 500 can be performed by the system 100 (at least including the in-memory database 110, the message queue 120 and the disk database 130) as shown, or can be performed at the electronic device 700 as shown. Figure 1 The method 500 can also include additional steps not shown and / or can omit steps shown, without limitation in this respect. Figure 7

[0083] At step 502, the system 100 generates a prompt about the behavior preference of the user based on the basic information and the historical interactive activity information of the user; wherein the historical interactive activity information at least includes the answer information, the browsing information and the commodity purchase information of the user.

[0084] For example, the user C often browses related content such as yoga mat, tennis racket, tennis, diet, etc., and recently has purchased coconut flour, bulletproof coffee and dumbbell, etc.; a prompt about the behavior preference of the user C is generated, such as ketogenic diet, fat loss and sugar reduction, tennis and yoga, muscle gain and fat loss, etc. As for the answer information, for example, it includes the preference of the user's answer, the correctness rate of the questions in each field, so as to infer the field that the user is good at, the field that the user pays attention to, etc.

[0085] ​At step 504, the system 100 inputs the generated behavior preference prompt into the predetermined large language model for multi-round dialogue to generate a user portrait.

[0086] At step 506, the system 100 generates again the prompt of the user's behavior preference based on the generated user portrait, the user's historical recommendation information and the user's historical interaction activity information.

[0087] At step 508, the system 100 inputs the generated behavior preference prompt into the predetermined large language model for multi-round dialogue to generate a user portrait in the information system.

[0088] At step 510, the system 100 recommends question information, commodity information and information information to the user based on the user portrait in the information system.

[0089] Please refer to Figure 6 The generation of the user portrait in the information system of the embodiment of the present application is shown in the schematic diagram, the behavior preference prompt of the user is generated based on the user basic information 604 and the user historical interaction activity information 602; the generated behavior preference prompt of the user 606 is input into the large language model 608 to obtain the user portrait 610; the behavior preference prompt of the user 606 is generated again based on the user portrait 610, the user historical interaction activity information 614 and the user historical recommendation information 612, so as to generate the user portrait 616 in the information system based on the large language model 608. It should be noted that the user historical interaction activity information 614 and the user historical interaction activity information 606 can select the same or different activity information content to generate various preference prompts for the same user; the behavior preference prompt 606 can be generated multiple times to facilitate the large language model to interact multiple times.

[0090] By using the above technical means, the present application can solve the problem that the traditional recommendation system cannot integrate external knowledge or update knowledge in real time. The present application is based on the user historical activity information data and the user basic information, combined with the large language model, and the content about the user's preference such as knowledge, consultation and commodity obtained from the user behavior portrait reasoning; the user's behavior is converted into a prompt, and the user's preference is learned through the large language model; then the user's recommendation history and the candidate set generated by the recommendation system are input into the large language model, so that the large language model performs filtering, sorting and other operations, and completes multi-round recommendation, repeatedly cycles and improves the user's preference; based on the user's preference, through the prediction function of the large language model, the user's portrait in the information system is finally generated.

[0091] In addition, by adopting the above technical means, the pre-trained large language model can be converted into a recommendation model to replace the traditional recommendation system, so that the historical interaction activity information of the user can be used in the recommendation system to infer and introduce the user preference information outside the predetermined content of the recommendation system, so as to obtain more accurate recommendation of the user to the problem information, commodity information and information information. By connecting the large language model and the recommendation module of the consultation system through the prompt, the user behavior is analyzed, the user and the large language model are simulated to carry out multi-round dialogue, the recommendation candidate range is continuously narrowed, and finally the accurate recommendation result and the recommendation reason are given; so as to obtain higher efficiency and accuracy of recommendation, so that the user stickiness and activity are improved, and the operation frequency of the user to the operation representation data is improved.

[0092] Figure 7 A schematic step diagram of an example electronic device 700 that can be used to implement embodiments of the present specification is shown. For example, the memory database 110 can be implemented by the electronic device 700. As shown, the electronic device 700 includes a central processing unit (CPU) 701 that can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 702 or loaded from a storage unit 708 into a random access memory (RAM) 703. In the random access memory 703, various programs and data required for the operation of the electronic device 700 can also be stored. The central processing unit 701, the read-only memory 702 and the random access memory 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704. Figure 1

[0093] Various components in the electronic device 700 are connected to the input / output interface 705, including: an input unit 706, such as a keyboard, a mouse, a microphone, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0094] ​The various processes and processes described above, such as the methods 200-500, can be performed by the central processing unit 701. For example, in some embodiments, the methods 200-500 can be implemented as a computer software program tangibly embodied in a machine readable medium, such as the storage unit 708. In some embodiments, portions or all of the computer program can be loaded and / or installed onto the device 700 via the read only memory 702 and / or the communication unit 709. When the computer program is loaded onto the random access memory 703 and executed by the central processing unit 701, one or more acts of the methods 200-500 described above can be performed.

[0095] The present disclosure relates to methods, apparatus, systems, electronic devices, computer readable storage media, and / or computer program products. The computer program product can include computer readable program instructions for executing various aspects of the present disclosure.

[0096] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0097] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0098] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0099] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0100] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or nonvolatile memory, or a suitable combination of the different types of computer readable storage media. The computer readable program instructions can also be downloaded to a computer, other programmable data processing apparatus, or other device from a computer readable storage medium or to an external computer or external storage device via a data signal that can include a telecommunication, a modem, and / or a network connection, to cause the computer, other programmable data processing apparatus, or other device to function in a manner described herein.

[0101] programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0102] The flow and step diagrams in the drawings show the architectural, functional and operational views of possible implementations of systems, methods and computer program products according to the embodiments of the present application. In this regard, each block in the flow and step diagrams can represent a module, a procedure, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may also be executed in reverse order, depending on the functionality involved. It will also be noted that each block in the step and / or flow diagrams and combinations of blocks in the step and / or flow diagrams can be implemented by special purpose hardware-based systems which perform the specified functions or acts or combinations of special purpose hardware and computer instructions.

[0103] Embodiments of the application have been described above with the understanding that the explanations are exemplary, are not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations of the described embodiments will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms herein is intended to best describe the principles of the embodiments, practical application, or improvement to the art in the marketplace, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining user operation characterization data about an application, comprising: generating, in a memory database, change information about the user's operation characterization data in response to determining that a request by the user for the operation characterization data is authenticated; the operation characterization data is a score; the memory database synchronously sends the change information of the user's operation characterization data to a message queue, the change information at least indicating a change time, a change type, and a change value of the operation characterization data; in response to detecting a confirmation message about the message queue receiving the change information, synchronously updating the user's operation characterization data stored in the memory database; and through the message queue, sending the change information of the user's operation characterization data to a disk database so that the user's operation characterization data stored in the disk database is asynchronously updated; the user's operation characterization data stored in the disk database is configured to restore the operation characterization data stored in the memory database in the event of a failure of the memory database; the message queue is deployed on a separate server from the server where the memory database or the disk database is located; in the memory database, deleting the change information related to the completed updated operation characterization data within a predetermined time interval; determining that the request by the user for the operation characterization data is authenticated comprises: based on the received request for obtaining, performing risk verification for the user corresponding to the request for obtaining, the risk verification comprising offline risk verification and real-time risk verification; and the risk verification further comprises: based on a predetermined risk control prediction model and a risk control prompt, obtaining a historical risk label and a real-time risk label of the user, the risk control prediction model being a generative artificial intelligence model or a large language model.

2. The method of claim 1, further comprising: based on a predetermined time, confirming whether the change information of the operation characterization data in the message queue within a predetermined time interval is consistent with the update of the operation characterization data in the memory database; in response to confirming that the operation characterization data corresponding to the change information of the operation characterization data of the message queue within the predetermined time interval has been updated in the memory database, confirming that the change information of the operation characterization data is consistent with the update of the operation characterization data in the memory database. the user's operation characterization data stored in the disk database is configured to restore the operation characterization data stored in the memory database in the event of a failure of the memory database comprises:

3. The method of claim 1, wherein, in response to determining that the memory database fails, determining whether there is change information of the operation characterization data in the message queue that has not been sent to the disk database; in response to determining that there is no change information of the operation characterization data in the message queue that has not been sent to the disk database, determining whether there is user's operation characterization data that has not been updated completely in the disk database; and in response to determining that there is no user's operation characterization data that has not been updated completely in the disk database, restoring the operation characterization data stored in the memory database based on the user's operation characterization data stored in the disk database. ​ 4. The method of claim 1, wherein, The operation characteristic data of the user stored in the disk database is also configured to offset the payment amount of the order on the application, and the determination that the request of the user about the operation characteristic data is verified includes: in response to determining that the user completes the interactive operation about the application, receiving a request for obtaining operation characteristic data from the user, the interactive operation at least includes browsing pages, answering questions and purchasing goods; and in response to determining that the user passes the offline risk check and the real-time risk check at the same time, it is determined that the user's request for obtaining is verified.

5. The method of claim 4, wherein, The offline risk check includes: based on a predetermined period, obtaining the user's interactive operation data, login data, and change information of operation characteristic data, so as to obtain the user's historical behavior data; based on the user's historical behavior data in a predetermined analysis period, generate a historical risk control prompt about the user; input the generated historical risk control prompt about the user into a predetermined risk prediction model to obtain the user's historical risk label; and in response to determining that the user's historical risk label indicates high risk, it is determined that the user does not pass the offline risk check.

6. The method of claim 4, wherein, The real-time risk check includes: based on the user's current interactive activity operation, login data, and change information of operation characteristic data, obtaining the user's real-time behavior data; based on the user's real-time behavior data, generate a real-time risk control prompt about the user; input the generated real-time risk control prompt of the user into a predetermined risk prediction model to obtain the real-time risk label of the user; and in response to determining that the user's real-time risk label indicates high risk, it is determined that the user does not pass the real-time risk check.

7. The method of claim 1, further comprising: based on a predetermined period, obtaining the user's login data and change information of operation characteristic data, generating activity information prompt about the user, so as to input the user's activity information prompt into a predetermined activity prediction model to obtain the user's predicted activity information; based on the user's predicted activity information, obtaining the user's predicted login time and predicted operation characteristic data; and in response to determining that the user's predicted login time is about to arrive, pulling the user's operation characteristic data from the disk database to preload the change information of the user's operation characteristic data in the in-memory database.

8. The method of claim 4, further comprising: based on the user's basic information and historical interactive activity information, generate a prompt about the user's behavior preference; wherein the historical interactive activity information at least includes the user's answer information, browsing information and commodity purchase information; input the generated behavior preference prompt into a predetermined large language model for multi-round dialogue to generate a user portrait; based on the generated user portrait, the user's historical recommendation information and the user's historical interactive activity information, generate the behavior preference prompt about the user again; input the generated behavior preference prompt again into a predetermined large language model for multi-round dialogue to generate a user portrait in the information system; and based on the user portrait in the information system, recommend question information, commodity information and information information to the user.

9. A computing device comprising: at least one processing unit; at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, cause the device to perform steps of the method according to any one of claims 1 to 8.

10. A computer readable storage medium having stored thereon a computer program, the computer program, when executed by a machine, implementing the method according to any one of claims 1 to 8.

11. A computer program product, characterised in that, comprising a computer program, the computer program, when executed by a machine, performing the method according to any one of claims 1-8.

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