Data processing method and device, electronic equipment, storage medium and computer program product

Abstract

Embodiments of the present application provide a data processing method and device, electronic equipment, storage medium and computer program product, which are applied to a data server and include: receiving log data of a target object sent by an application server; determining feature data of the target object at a current time based on pre-stored first intermediate data of the target object and the log data of the target object; the first intermediate data is calculated based on log data of the target object at a historical time; mapping and storing an object identifier of the target object and the feature data at the current time into a feature database of the data server; and in response to a feature acquisition request of the target object sent by the application server, sending the feature data of the target object acquired from the feature database to the application server. The present application determines the feature data of the target object by combining the first intermediate data at the historical time and the log data at the current time, which can improve the accuracy of the feature data of the target object.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, and includes, but is not limited to, a data processing method, apparatus, electronic device, storage medium, and computer program product. Background Technology

[0002] With the development of internet technology, various types of games have emerged, making games an integral part of people's lives. To improve user experience, it's necessary to recommend content that users are interested in in real time. In related technologies, game servers can only use certain user actions as feature data for information recommendations. However, directly using these user actions as feature data reduces the accuracy of the feature data, thereby reducing the accuracy of information recommendations. Summary of the Invention

[0003] This application provides a data processing method, apparatus, electronic device, storage medium, and computer program product, which can be applied to at least the field of artificial intelligence. This application stores the feature data of a target object at the current moment through a feature database. The feature data is determined by combining data from historical moments and log data at the current moment. Therefore, accurate feature data can be obtained from the feature database.

[0004] The technical solution of this application embodiment is implemented as follows:

[0005] This application provides a data processing method applied to a data server, comprising: receiving log data of a target object sent by an application server; determining feature data of the target object at the current time based on pre-stored first intermediate data of the target object and the log data of the target object; wherein the first intermediate data is calculated based on log data of the target object at a historical time, the historical time being earlier than the current time; mapping and storing the object identifier of the target object and the feature data at the current time in a feature database of the data server; and, in response to a feature acquisition request of the target object sent by the application server, sending the feature data of the target object obtained from the feature database to the application server.

[0006] This application provides a data processing method applied to an application server, comprising: in response to receiving an interactive operation of a target object in a virtual interactive scene, generating log data of the target object; sending the log data to a data server, so that the data server determines the feature data of the target object at the current moment based on pre-stored first intermediate data of the target object and the log data of the target object, and storing the feature data at the current moment in the data server; the first intermediate data is calculated based on the log data of the target object at a historical moment, the historical moment being earlier than the current moment; in response to the virtual interactive scene being in a running state, obtaining the feature data of the target object from the data server; and recommending information to the target object based on the feature data.

[0007] This application provides a data processing apparatus, comprising: a receiving module for receiving log data of a target object sent by an application server; a determining module for determining feature data of the target object at the current time based on pre-stored first intermediate data of the target object and the log data of the target object; wherein the first intermediate data is calculated based on log data of the target object at a historical time, the historical time being earlier than the current time; a storage module for mapping and storing the object identifier of the target object and the feature data at the current time in a feature database of the data server; and a first sending module for sending the feature data of the target object obtained from the feature database to the application server in response to a feature acquisition request of the target object sent by the application server.

[0008] This application provides a data processing apparatus, comprising: a generation module, configured to generate log data of the target object in response to receiving an interactive operation of the target object in a virtual interactive scene; a second sending module, configured to send the log data to a data server, so that the data server determines the feature data of the target object at the current moment based on pre-stored first intermediate data of the target object and the log data of the target object, and stores the feature data at the current moment in the data server; the first intermediate data is calculated based on the log data of the target object at a historical moment, the historical moment being earlier than the current moment; an acquisition module, configured to acquire the feature data of the target object from the data server in response to the virtual interactive scene being in a running state; and an information recommendation module, configured to recommend information to the target object based on the feature data.

[0009] In the above scheme, the determining module is further configured to: parse the log data to obtain the behavior data of the target object at the current time; and determine the feature data of the target object at the current time based on the pre-stored first intermediate data of the target object and the behavior data at the current time.

[0010] In the above scheme, the data server includes a log consumption module; the determining module is further configured to: convert the format of the log data through the log consumption module to obtain structured log data; perform data cleaning on the structured log data to obtain standardized data; and determine the behavior data of the target object at the current time from the standardized data based on predefined mapping rules.

[0011] In the above scheme, the data server further includes a data caching module and a feature processing module; the determining module is further configured to: obtain first intermediate data of the target object in the data caching module; perform fusion processing on the first intermediate data and the behavior data at the current time to obtain fused data; and perform feature processing on the fused data through the feature processing module to obtain feature data of the target object at the current time.

[0012] In the above scheme, the device further includes a mapping storage module, used to: acquire second intermediate data generated during the feature processing module's feature processing of the fused data; and map and store the second intermediate data with the object identifier of the target object in the data cache module.

[0013] In the above scheme, the first sending module is further configured to: parse the object identifier of the target object from the feature acquisition request; query the feature database based on the object identifier to obtain the feature data of the target object; and send the feature data of the target object to the application server.

[0014] This application provides a data processing system, including: an application server and a data server; the application server is configured to generate log data of the target object in response to receiving an interactive operation of the target object in a virtual interactive scenario; and send the log data to the data server; the data server is configured to determine the feature data of the target object at the current time based on pre-stored first intermediate data of the target object and the log data of the target object in response to receiving the log data sent by the application server; the first intermediate data is calculated based on the log data of the target object at a historical time, the historical time being earlier than the current time; and map and store the object identifier of the target object and the feature data at the current time in the feature database of the data server; the data server is further configured to send the feature data of the target object obtained from the feature database to the application server in response to a feature acquisition request of the target object sent by the application server; the application server is further configured to recommend information to the target object based on the feature data.

[0015] This application provides an electronic device, including: a memory for storing computer-executable instructions or computer programs; and a processor for executing the computer-executable instructions or computer programs stored in the memory to implement the above-described data processing method.

[0016] This application provides a computer program product that stores computer-executable instructions or a computer program, which are used to cause a processor to execute the computer-executable instructions or the computer program to implement the above-described data processing method.

[0017] This application provides a computer-readable storage medium storing computer-executable instructions or computer programs, which are used to cause a processor to execute the computer-executable instructions or computer programs to implement the above-described data processing method.

[0018] The above scheme has the following beneficial effects:

[0019] The data processing method in this embodiment calculates the feature data of the target object by combining first intermediate data from historical moments and log data from the current moment. The first intermediate data from historical moments provides reference and background data for the log data from the current moment, so that the generated feature data not only depends on real-time data but also reflects the historical change trend of the target object, thereby improving the accuracy of the feature data. Furthermore, when further data processing using historical data is required, only the pre-stored first intermediate data needs to be called for data processing. This reduces the burden of repeated calculations on the data server, reduces resource consumption, and improves processing efficiency. Attached Figure Description

[0020] Figure 1 This is a diagram of a game server framework in related technologies;

[0021] Figure 2 This is an optional architecture diagram of the data processing system provided in the embodiments of this application;

[0022] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0023] Figure 4 This is an optional flowchart illustrating the data processing method provided in an embodiment of this application;

[0024] Figure 5 This is another optional flowchart illustrating the data processing method provided in the embodiments of this application;

[0025] Figure 6 This is another optional flowchart illustrating the data processing method provided in the embodiments of this application;

[0026] Figure 7 This is a schematic diagram of the process by which a data server determines the feature data of a target object at the current moment, as provided in an embodiment of this application.

[0027] Figure 8 This is a schematic diagram illustrating the personalized skin recommendation feature in the game store provided in this application embodiment;

[0028] Figure 9 This is a data processing system framework diagram provided in the embodiments of this application;

[0029] Figure 10 This is a schematic diagram of the data processing flow of the data processing system provided in the embodiments of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit the application.

[0032] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0033] Before describing the data processing method provided in the embodiments of this application, the technical terms involved in the embodiments of this application will be explained first:

[0034] (1) Operation log: refers to the operation log of game players on the game server.

[0035] (2) Kafka: Kafka is a distributed stream processing platform. Kafka is a high-throughput, distributed publish-subscribe messaging system, mainly used for processing real-time data streams.

[0036] To better understand the data processing method provided in the embodiments of this application, the data processing methods in related technologies will be described below.

[0037] In related technologies, traditional game servers typically focus on basic functions such as game logic processing, player connection response, and game scene management. In traditional game servers, player characteristic management is often decentralized, recording certain data as player characteristics while responding to player actions. See also... Figure 1 , Figure 1 This is a traditional game server framework diagram in related technologies. The terminal side 101 includes multiple terminals. Each terminal connects to server 102 through the access layer in server 102. The terminal transmits player operation data to server 102 through the access layer. Server 102 classifies the player operation data into lobby server data and room server data, and then stores the classified data in a unified database.

[0038] However, the aforementioned data processing methods suffer from at least the following problems: Traditional game servers lack a unified and efficient data source for data statistics and analysis. For example, it is difficult to fully capture and accurately convert complex player operation sequences and multi-module interaction behaviors into analyzable data features. Furthermore, traditional game servers have significant limitations in data update processing. When server upgrades, data structure adjustments, or algorithm optimizations are required, it often involves large-scale restarts or complex migration processes, which have a significant negative impact on the online player experience and may even lead to serious problems such as player disconnections. Additionally, directly using user actions as feature data results in low accuracy of the feature data.

[0039] To address the problems existing in related technologies, this application provides a data processing method. This method calculates feature data of a target object by combining first intermediate data from historical moments with log data from the current moment. The first intermediate data from historical moments provides a reference and background for the log data from the current moment, ensuring that the generated feature data not only relies on real-time data but also reflects the historical trends of the target object, thereby improving the accuracy of the feature data. Furthermore, when further data processing using historical data is required, only the pre-stored first intermediate data needs to be called for data processing. This reduces the burden of repetitive calculations on the data server, reduces resource consumption, and improves processing efficiency.

[0040] Specifically, this application provides a data processing method that involves receiving log data of a target object from an application server via a data server; then, determining the target object's feature data at the current moment based on pre-stored first intermediate data of the target object and the target object's log data; the first intermediate data is calculated based on the target object's log data at historical moments, where the historical moments are earlier than the current moment; next, mapping and storing the target object's object identifier and the feature data at the current moment in the data server's feature database; and finally, in response to a feature acquisition request from the application server, sending the target object's feature data obtained from the feature database to the application server.

[0041] Here, we first describe an exemplary application of the data processing device according to the embodiments of this application. This data processing device is an electronic device used to implement a data processing method. In one implementation, the data processing device (i.e., electronic device) provided in the embodiments of this application can be implemented as a terminal or as a server. When the electronic device is implemented as a server, it can be implemented as a data server or an application server. In one implementation, the data processing device provided in the embodiments of this application can be implemented as any terminal with data processing capabilities, such as a laptop, tablet, desktop computer, or intelligent robot. In another implementation, the data processing device provided in the embodiments of this application can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the embodiments of this application. Below, we will describe an exemplary application when the data processing device is implemented as a server.

[0042] See Figure 2 , Figure 2 This is an optional architecture diagram of the data processing system provided in this application embodiment. The data processing system 10 in this application embodiment includes at least a terminal 100, a network 200, an application server 300, and a data server 400. A data processing application is deployed on the terminal 100, and the application server 300 can be a backend server for the data processing application. Both the application server 300 and the data server 400 can constitute the data processing device of this application embodiment, that is, the data processing method of this application embodiment is jointly implemented through the application server 300 and the data server 400. The terminal 100 is connected to the application server 300 through the network 200, and the application server 300 is connected to the data server 400 through the network 200. The network 200 can be a wide area network (WAN), a local area network (LAN), or a combination of both.

[0043] See Figure 2Terminal 100 detects the target object's interactive operation in the virtual interactive scene and generates interactive information. This interactive information characterizes the target object's interactive operation in the virtual interactive scene. Terminal 100 sends the interactive information to application server 300 via network 200. In response to the received interactive information, application server 300 generates log data for the target object. Then, application server 300 sends the log data to data server 400 via network 200. Data server 400 determines the target object's feature data at the current moment based on pre-stored first intermediate data and the target object's log data, and stores the feature data at the current moment in the data server. The first intermediate data is calculated based on the target object's log data at historical moments, which are earlier than the current moment. Next, in response to the virtual interactive scene being in operation, application server 300 retrieves the target object's feature data from data server 400 via network 200 and retrieves recommendation information from the database based on the feature data. Then, application server 400 sends the recommendation information to terminal 100 via network 200. Finally, the recommendation information is displayed on the terminal 100's display interface.

[0044] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Figure 3 The illustrated electronic device may be a data processing device, such as a data server or an application server. The data processing device includes at least one processor 210, a memory 250, at least one network interface 220, and a user interface 230. The various components in the data processing device are coupled together via a bus system 240. It is understood that the bus system 240 is used to implement communication between these components. In addition to a data bus, the bus system 240 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 3 The general labeled all buses as Bus System 240.

[0045] Processor 210 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0046] User interface 230 includes one or more output devices 231 that enable the presentation of media content, and one or more input devices 232.

[0047] Memory 250 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. Memory 250 may optionally include one or more storage devices physically located remote from processor 210. Memory 250 may include volatile memory or non-volatile memory, or both. Non-volatile memory may be read-only memory (ROM), and volatile memory may be random access memory (RAM). The memory 250 described in this application embodiment is intended to include any suitable type of memory. In some embodiments, memory 250 is capable of storing data to support various operations, examples of which include programs, modules, and data structures, or subsets or supersets thereof, as illustrated below.

[0048] Operating system 251 includes system programs for handling various basic system services and performing hardware-related tasks, such as framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks; network communication module 252 is used to reach other computing devices via one or more (wired or wireless) network interfaces 220, exemplary network interfaces 220 include: Bluetooth, WiFi, and Universal Serial Bus (USB), etc.; input processing module 253 is used to detect and translate one or more user inputs or interactions from one or more input devices 232.

[0049] In some embodiments, the apparatus provided in this application may be implemented in software. Figure 3 A data processing device 254 stored in memory 250 is shown. This data processing device 254 can be a data processing device in an electronic device, and can be software in the form of programs and plug-ins. It includes the following software modules: a receiving module 2541, a determining module 2542, a storage module 2543, and a first transmitting module 2544. These modules are logically connected and can therefore be arbitrarily combined or further divided according to the functions they implement. The functions of each module will be described below.

[0050] In some embodiments, the data processing apparatus of this application further includes the following software modules ( Figure 3 (Not shown in the image): Generation module, second sending module, acquisition module, and information recommendation module.

[0051] In some embodiments, the apparatus provided in this application can be implemented in hardware. For example, the apparatus provided in this application can be a processor in the form of a hardware decoding processor, which is programmed to execute the data processing method provided in this application. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0052] The following examples illustrate the application scenarios of the data processing method provided in this application. This application can be applied to at least the following exemplary scenarios: In a game scenario, it is necessary to provide real-time information recommendations to players (target objects). Thus, the data processing method provided in this application can be used. First, the terminal detects the player's interactive operations in the game (virtual interactive scenario), generates interactive information, and sends the interactive information to the application server. Then, in response to the received interactive information, the application server generates the player's log data and sends the log data to the data server. Next, in response to receiving the log data sent by the application server, the data server determines the player's feature data at the current moment based on pre-stored first intermediate data of the player and the player's log data, and maps and stores the player's object identifier and the feature data at the current moment in the feature database of the data server. Then, in response to the game being in a running state, the application server generates a player feature acquisition request and sends the feature acquisition request to the data server. Next, in response to the player's feature acquisition request sent by the application server, the data server sends the player's feature data obtained from the feature database to the application server. Finally, the application server obtains recommendation information based on the feature data and sends the recommendation information to the terminal.

[0053] The data processing methods provided in the embodiments of this application can be executed by an electronic device, which can be a server or a terminal. That is, the data processing methods in the embodiments of this application can be executed by a server, by a terminal, or by interaction between a server and a terminal.

[0054] Figure 4 This is an optional flowchart illustrating the data processing method provided in the embodiments of this application. The following will be combined with... Figure 4The steps shown are explained as follows: Figure 4 As shown, taking a data server as the execution subject of the data processing method as an example, the method includes the following steps S101 to S104:

[0055] Step S101: Receive log data of the target object sent by the application server.

[0056] Here, receiving refers to the process by which a system or component obtains data from an external source. In this embodiment, receiving refers to the process by which a data server obtains log data of a target object sent by an application server. An application server is a software framework responsible for hosting and managing the execution of applications. An application server can be a standalone physical server or a virtual server hosted in a cloud service. In this embodiment, the application server can be a game server used to handle functions such as user interaction, execution of game logic, data storage, and transmission.

[0057] The target object refers to the user; in a game scenario, the target object refers to the game player. Log data refers to text or binary files generated by the system or application during operation. Log data records information such as the target object's operations, events, and errors.

[0058] Step S102: Based on the pre-stored first intermediate data of the target object and the log data of the target object, determine the characteristic data of the target object at the current moment; the first intermediate data is calculated based on the log data of the target object at historical moments, and the historical moments are earlier than the current moment.

[0059] Here, "current moment" refers to the current time point, such as 10:00, while "historical moment" refers to any time point before the current moment, such as 9:58. Feature data is a quantitative description of the state of the target object. Feature data may include the behavioral characteristics of the target object (such as activity level, consumption preferences, operational preferences, etc.), system state characteristics (such as response time, etc.), etc. The specific content included in the feature data is not limited in this application.

[0060] The first intermediate data refers to the intermediate data generated during the process of performing feature calculations on the log data of the target object at a historical time to obtain the feature data at that historical time. For example, if feature extraction is performed on the log data A of the target object at a historical time to obtain data B, and further feature extraction is performed on data B to obtain data C, and finally feature data D is obtained, then data B and data C are the first intermediate data.

[0061] In some embodiments, the data server includes at least a log consumption module, a data caching module, and a feature processing module; wherein, the log consumption module refers to a system component for receiving, parsing, and processing log data; the data caching module refers to a system component for temporarily storing intermediate data or calculation result data; and the feature processing module refers to a system component for processing data and extracting features. In this case, step S102 can be implemented as follows: First, the received log data is standardized by the log consumption module. Standardization refers to converting the log data into data that can be processed by a computer. Standardization includes format conversion and data cleaning. Format conversion refers to converting log data stored in Lightweight Data Interchange Format (JSON, JavaScript Object Notation) or other data formats into the form of a dictionary or object. Data cleaning refers to filtering the format-converted data to remove incomplete or irrelevant data. Then, relevant data is extracted from the standardized data according to predefined mapping rules. The predefined mapping rules refer to the pre-defined correspondence between fields of the relevant data and fields of the standardized data, such as the "action_type" field in the relevant data corresponding to the "action" field in the standardized data. Next, based on the object identifier of the target object, intermediate data of the target object is retrieved from the data caching module. This intermediate data is then fused with data extracted from the log data according to predefined mapping rules to obtain fused data. Finally, the feature processing module extracts features from the fused data to obtain the feature data of the target object at the current moment.

[0062] Step S103: Map and store the object identifier of the target object and the feature data at the current moment into the feature database of the data server.

[0063] Here, the object identifier of a target object refers to an identifier used to uniquely identify a target object. Mapping storage refers to establishing a mapping relationship between the target object identifier and the corresponding feature data, and saving the mapping relationship to the feature database.

[0064] A feature database is a database that stores feature data of target objects, typically used for large-scale data storage and query optimization. A data server is a computer system or software component specifically designed to provide data services, including data computation, data storage, management, and querying. A data server can be a standalone physical server or a virtual server hosted in a cloud service.

[0065] In some embodiments, step S103 can be implemented by the following method: First, after determining the feature data of the target object at the current time, a mapping relationship is established based on the object identifier of the target object and the feature data of the target object at the current time; then, the constructed mapping relationship is stored in the feature database of the data server.

[0066] Step S104: In response to the feature acquisition request of the target object sent by the application server, the feature data of the target object obtained from the feature database is sent to the application server.

[0067] Here, a feature acquisition request refers to a request initiated by the application server. The purpose of a feature acquisition request is to collect feature data of the target object from the data server.

[0068] In some embodiments, step S104 can be implemented by the following method: First, the data server receives a feature acquisition request sent by the application server; then, the data server parses the feature acquisition request to obtain the object identifier of the target object encapsulated in the feature acquisition request; next, based on the object identifier of the target object, the data server traverses the feature database to obtain the feature data corresponding to the object identifier; finally, the data server sends the feature data of the target object extracted from the feature database to the application server.

[0069] The data processing method in this application calculates feature data of a target object by combining first intermediate data from historical moments with log data from the current moment. The first intermediate data from historical moments provides a reference and background for the log data from the current moment, so that the generated feature data not only depends on real-time data but also reflects the historical change trend of the target object, thereby improving the accuracy of the feature data. In addition, when historical data needs to be used, calling the pre-stored first intermediate data can reduce the burden of repeated calculations on the data server, reduce resource consumption, and improve processing efficiency.

[0070] Figure 5 This is another optional flowchart illustrating the data processing method provided in the embodiments of this application. The following will be combined with... Figure 5 The steps shown are explained as follows: Figure 5 As shown, taking the application server as the execution subject of the data processing method as an example, the method includes the following steps S201 to S204:

[0071] Step S201: In response to receiving the interactive operation of the target object in relation to the virtual interactive scene, generate log data of the target object.

[0072] Here, a virtual interactive scene refers to a simulated digital environment, typically found in applications such as games, virtual reality, and augmented reality. Interactive operations refer to the specific behaviors performed by the target object within the virtual interactive scene, such as clicking, dragging, or purchasing goods.

[0073] In some embodiments, step S201 can be implemented as follows: After detecting that the target object has performed an interaction in the virtual interactive environment, the application server generates log data of the target object based on the specific interaction. For example, if the target object clicks button 1 in the virtual interactive scene, the application server will generate log data about the target object clicking button 1 after detecting the click operation. The generated log data can be stored in JSON format.

[0074] Step S202: The log data is sent to the data server so that the data server can determine the characteristic data of the target object at the current time based on the first intermediate data of the target object and the log data of the target object, and store the characteristic data at the current time in the data server; the first intermediate data is calculated based on the log data of the target object at historical times, and the historical times are earlier than the current times.

[0075] Here, after generating the log data for the target object, the application server actively sends the log data to the data server so that the data server can perform feature processing on the log data to obtain the feature data of the target object at the current moment. The data server's processing of the log data is described in step S102 above, and will not be repeated here.

[0076] Step S203: In response to the virtual interactive scene being in operation, obtain the feature data of the target object from the data server.

[0077] Here, "running state" refers to the virtual interactive scene being in an active or active state. When the virtual interactive scene is in an active or active state, the application server retrieves the characteristic data of the target object from the data server, which can be divided into the following two cases:

[0078] Scenario 1: When the target object enters the virtual interaction scene, the application server directly obtains the target object's feature data from the data server based on the target object's object identifier.

[0079] Scenario 2: After the target object enters the virtual interaction scene, the application server generates a feature data acquisition request and sends the feature data acquisition request to the data server. The data server retrieves the feature data of the target object from the feature database based on the object identifier encapsulated in the feature data acquisition request and sends the feature data to the application server.

[0080] Step S204: Based on feature data, recommend information to the target object.

[0081] Here, information recommendation refers to recommending content related to the characteristic data of a target object. The recommended content can be articles, products, services, etc., and the specific content is not limited in this application.

[0082] In some embodiments, step S204 can be implemented by the following method: determining recommendation information in the database of the application server based on feature data, and recommending the recommendation information to the target object. For example, if the feature data is "the target object's commonly used virtual character is virtual character A", then content related to virtual character A, such as skins and virtual equipment of virtual character A, is retrieved from the database based on the feature data, and the retrieved skins and virtual equipment of virtual character A are recommended to the target object as recommendation information.

[0083] The data processing method in this application embodiment generates log data for the target object after the application server receives the target object's interactive operation, and sends the log data to a data server. The data server processes the data to obtain the target object's feature data, thereby ensuring the real-time updating of the target object's feature data. Furthermore, recommending information to the target object based on its feature data ensures personalized recommendations, improving the user experience and increasing the target object's acceptance of the recommended content, thus enhancing the effectiveness of the interaction.

[0084] The data processing method of the embodiments of this application will be described below. Figure 6 This is another optional flowchart illustrating the data processing method provided in the embodiments of this application, such as... Figure 6 As shown, the method includes the following steps S301 to S314:

[0085] Step S301: The terminal detects the interactive operation of the target object in response to the virtual interactive scene.

[0086] Here, "terminal" refers to the device or system through which the target object interacts with the virtual interactive scene. The terminal can be a personal computer, smartphone, or virtual reality headset, etc., and this application does not limit this. "Virtual interactive scene" refers to a virtual environment generated by a computer. "Interactive operation" refers to the behavior of the target object interacting with elements in the virtual interactive scene. Interactive operations can be any form of input, such as clicking, swiping, or pressing buttons.

[0087] In some embodiments, sensors may be deployed on the terminal to detect gestures of the target object or changes in pressure on the terminal screen, thereby determining that the target object has performed an interactive operation in the virtual interactive scene. The interactive operation detection method in this application embodiment is merely illustrative and does not limit the method for detecting interactive operations.

[0088] In step S302, the terminal responds to the interactive operation and generates interactive information.

[0089] Here, interaction information refers to the text information generated by the terminal after detecting the interaction operation of the target object. The interaction information is used to characterize the interaction operation of the target object in response to the virtual scene. The interaction information may include operation type (such as click, swipe, etc.), operation parameters (such as touch position, voice command content, etc.), and timestamp, etc. For example, if the target object clicks button 1, the generated interaction information may be "The target object clicked button 1 at X hour X minute X second, and the coordinates of button 1 are (3, 5)". The specific interaction information is not limited in this application.

[0090] In step S303, the terminal sends the interaction information to the application server.

[0091] In step S304, the application server responds to the received interaction information and generates log data for the target object.

[0092] In some embodiments, step S304 can be implemented by the following method: First, after receiving the interaction information, the application server parses the interaction information to obtain key content in the interaction information, such as operation type, operation object, timestamp, etc.; then, the application server formats the parsed key content according to the format of log data to obtain the log data of the target object.

[0093] In step S305, the application server sends the log data to the data server.

[0094] In step S306, the data server determines the characteristic data of the target object at the current moment based on the first intermediate data of the target object and the log data of the target object that are pre-stored; the first intermediate data is calculated based on the log data of the target object at historical moments, and the historical moments are earlier than the current moment.

[0095] Here, the data server performs data fusion and feature processing on the pre-stored first intermediate data of the target object and the log data of the target object to obtain the feature data of the target object at the current moment.

[0096] In some embodiments, see Figure 7 , Figure 7This is a schematic diagram illustrating the process by which a data server determines the feature data of a target object at the current moment, as provided in an embodiment of this application. Figure 7 The following shows that in step S306, the data server determines the characteristic data of the target object at the current moment based on the pre-stored first intermediate data and log data of the target object. This can be achieved through steps S3061 to S3062:

[0097] Step S3061: Parse the log data to obtain the target object's behavior data at the current moment.

[0098] Here, behavioral data is a detailed record of the actions performed by the target object, including information such as action type (e.g., clicking, swiping) and action content (e.g., inputting, purchasing, browsing). Behavioral data is often used to analyze the target object's operating habits and preferences.

[0099] In some embodiments, the data server includes a log consumption module. In this case, step S3061 can be implemented by the following method: First, the log data is format-converted by the log consumption module to obtain structured log data; then, the structured log data is cleaned to obtain standardized data; finally, based on predefined mapping rules, the behavioral data of the target object at the current moment is determined in the standardized data.

[0100] Here, the log consumption module refers to the component in the data server responsible for receiving, processing, and parsing log data. The log consumption module typically includes functions such as log reading, format conversion, and data cleaning.

[0101] Format conversion refers to transforming log data from a standardized format into a format suitable for subsequent processing, such as dictionary or object format. Structured log data refers to log data with a fixed structure after format conversion. Unlike the original log data, structured log data is easier for the log consumption module to process and analyze.

[0102] Data cleaning refers to the process of filtering, correcting, and removing invalid, erroneous, or duplicate data from structured log data. For example, data cleaning may include removing irrelevant data, filling in missing values, and deleting duplicate records. Data cleaned is more accurate and reliable. Standardized data refers to data with a uniform format obtained after data cleaning. Predefined mapping rules refer to the pre-defined correspondence between behavioral data fields and standardized data fields; for example, the "action_type" field in behavioral data corresponds to the "action" field in standardized data.

[0103] As an example of step S3061, assuming the log data is in JSON format, the log consumption module converts the JSON-formatted log data into a dictionary format by calling a library function; then, the dictionary-formatted data is cleaned, such as deleting one of the timestamp data if the log data in the dictionary format records timestamp data repeatedly, to ensure that there is no redundant data in the obtained standardized data; finally, based on the correspondence between the behavioral data fields and the standardized data fields, the behavioral data of the target object at the current moment is determined in the standardized data.

[0104] It should be noted that, in this embodiment, the log consumption module performs format conversion on the raw log data to ensure data consistency and structure. Then, data cleaning removes noise and corrects errors to improve data quality and facilitate subsequent analysis. Based on predefined mapping rules, the data server can accurately extract behavioral data of target objects from standardized data, providing reliable support for personalized recommendations and intelligent decision-making.

[0105] Step S3062: Based on the pre-stored first intermediate data of the target object and the behavior data at the current moment, determine the feature data of the target object at the current moment.

[0106] Here, the data server extracts features from the first intermediate data of the target object and the behavioral data at the current moment to obtain the feature data of the target object at the current moment.

[0107] In some embodiments, the data server further includes a data caching module and a feature processing module. In this case, step S3062 can be implemented by the following method: First, the first intermediate data of the target object is obtained in the data caching module; then, the first intermediate data and the behavior data at the current moment are fused to obtain fused data; finally, the feature processing module performs feature processing on the fused data to obtain the feature data of the target object at the current moment.

[0108] Here, the data caching module is used to temporarily store and manage data in order to quickly retrieve historical data. For example, if the target object is a player in an online game, the data caching module may store the player's activity data, consumption records, etc. over a period of time.

[0109] Fusion processing refers to combining a user's first intermediate data from a historical moment with their current behavioral data. The resulting data is called fused data. For example, combining a player's historical activity level with their current active time creates new activity data.

[0110] The feature processing module is used to further analyze, process, and transform the fused data. Its function is to extract feature data with practical analytical significance from the fused data. Feature processing refers to the operation of feature extraction or data analysis.

[0111] It should be noted that, in this embodiment of the application, the historical behavior data (first intermediate data) of the target object is fused with the behavior data at the current moment to generate more comprehensive and accurate fused data. The fused data is further processed by the feature processing module to extract the most valuable features, thereby improving the timeliness, accuracy and flexibility of the data.

[0112] In some embodiments, after obtaining the feature data of the target object at the current moment, the intermediate data generated during the feature processing can also be stored. This can be achieved by the following method: First, obtain the second intermediate data generated during the feature processing module's feature processing of the fused data; then, map and store the second intermediate data with the object identifier of the target object in the data cache module.

[0113] Here, the second intermediate result refers to the intermediate data generated during the feature processing of the fused data by the feature processing module. For example, the feature processing module extracts features from the fused data 1 to obtain fused data 4, where fused data 4 is the final feature data. The fused data 2 and fused data 3 generated during the processing are the second intermediate data.

[0114] It should be noted that, in this embodiment of the application, the second intermediate data is mapped and stored in the data cache module along with the object identifier of the target object. This can significantly reduce the computational overhead of the data server, save computational resources, and improve computational efficiency by avoiding redundant calculations.

[0115] In step S307, the data server maps and stores the object identifier of the target object and the feature data at the current moment into the feature database of the data server.

[0116] Here, a feature database is a database specifically designed to store the feature data of a target object. Feature databases typically contain a large amount of feature data of the target object and can support efficient data querying, updating, and retrieval.

[0117] In step S308, the application server responds to the fact that the virtual interactive scene is in a running state and generates a feature acquisition request for the target object.

[0118] Here, the application server can encapsulate the object identifier of the target object into the feature acquisition request.

[0119] In some embodiments, to ensure the security of feature acquisition requests, authentication parameters are added to the feature acquisition requests. These authentication parameters are used to verify the legitimacy of the feature acquisition requests.

[0120] In step S309, the application server sends a feature acquisition request for the target object to the data server.

[0121] In some embodiments, the application server sends the encapsulated feature acquisition request to the data server, typically using protocols such as HTTP or WebSocket.

[0122] In step S310, the data server responds to the feature acquisition request of the target object sent by the application server and retrieves the feature data of the target object from the feature database.

[0123] Here, after receiving a feature acquisition request, the data server parses the request. For example, for an HTTP request, the server can parse the request header and request body. Parsing the request header retrieves relevant request information; parsing the request body retrieves the main data of the request, i.e., the object identifier of the target object. Specific fields or parameters in the request body, which contain the object identifier of the target object, are parsed. A specific data format, such as JSON or XML, is extracted from the request body, and then this data format is parsed to obtain the object identifier of the target object.

[0124] In some embodiments, step S310 can be implemented by the following method: first, parsing the object identifier of the target object from the feature acquisition request; then, based on the object identifier, querying the feature database to obtain the feature data of the target object.

[0125] As an example of step S310, after receiving a feature acquisition request, the data server parses the feature acquisition request to obtain the object identifier of the target object encapsulated in the feature acquisition request; then, it traverses the feature database in the data server according to the object identifier of the target object to obtain the feature data of the target object.

[0126] In step S311, the data server sends the feature data of the target object to the application server.

[0127] In step S312, the application server retrieves recommendation information from a preset database based on the feature data.

[0128] Here, the preset database refers to a pre-defined database. The preset database usually contains a large amount of information to be recommended, which may include products, services, videos, etc. This application does not limit this.

[0129] In some embodiments, step S312 can be implemented by the following method: after the application server obtains the feature data, it extracts keywords from the feature data to obtain the key content in the feature data; then, it matches the extracted key content with the information to be recommended stored in the preset database, and determines the information to be recommended with a matching result greater than a preset threshold as the recommended information.

[0130] In step S313, the application server sends the recommendation information to the terminal.

[0131] Step S314: The terminal displays recommended information on the current interface.

[0132] Based on the above data processing method, this application embodiment also provides a data processing system, which includes an application server and a data server; wherein, the application server is used to generate log data of the target object in response to receiving an interactive operation of the target object in a virtual interactive scene; and send the log data to the data server; the data server is used to determine the feature data of the target object at the current time based on the pre-stored first intermediate data of the target object and the log data of the target object in response to receiving the log data sent by the application server; the first intermediate data is calculated based on the log data of the target object at a historical time, the historical time being earlier than the current time; and map and store the object identifier of the target object and the feature data at the current time in the feature database of the data server; the data server is also used to send the feature data of the target object obtained from the feature database to the application server in response to the feature acquisition request of the target object sent by the application server; the application server is also used to recommend information to the target object based on the feature data.

[0133] The specific processing method of the data processing system is described in steps S301 to S315 above, and will not be repeated here.

[0134] The data processing method provided in this application generates accurate and timely feature data by combining first intermediate data from historical moments of the target object with log data from the current moment. Historical data provides context for the current log data, reflecting the historical trends of the target object, thereby improving the accuracy of the feature data. Furthermore, pre-stored first intermediate data reduces redundant calculations on the data server, lowers resource consumption, and improves processing efficiency. In addition, during data processing, the log consumption module can convert the format of the raw log data to ensure data consistency, and then perform data cleaning to remove noise and correct errors, thereby improving data quality. Simultaneously, storing the second intermediate data generated during feature data generation along with the object identifier of the target object in the data cache module effectively avoids redundant calculations, further improving computational efficiency. Furthermore, the application server can perform personalized information recommendations based on the feature data of the target object, thereby enhancing the user experience.

[0135] The following will describe an exemplary application of the embodiments of this application in a real-world application scenario.

[0136] This application provides a design scheme for a feature server framework for game players (i.e., the target object) based on performance logs (i.e., the log data mentioned above). The feature data server (i.e., the data server mentioned above) is deployed and updated independently of the game server (i.e., the application server mentioned above), facilitating the updating of player characteristics. Using performance logs (i.e., the log data mentioned above) as input, the feature data server can record various player operations, from logging into the game, interactions in different game scenarios, task completion status to social interactions with other players—all behaviors can be recorded in detail, improving the completeness and comprehensiveness of the data and providing rich material for in-depth analysis of player behavior. Furthermore, the feature data server based on performance logs is more independent than traditional game servers. Traditional servers have high functional integration, making it difficult to avoid impacting online players during updates and maintenance. However, the feature data server framework in this application embodiment will not affect the online player's game process during restarts or updates, thus enabling more flexible and faster development and deployment, meeting the ever-growing demand for agile development in online games.

[0137] See Figure 8 , Figure 8This is a schematic diagram illustrating personalized skin recommendations in the game store provided in this application embodiment. Taking the recommendation of virtual character skins in the game store as an example, each player has unique purchasing intentions. By leveraging big data statistics, personalized recommendations can be made based on players' gameplay preferences and spending levels, resulting in better purchasing outcomes. Furthermore, in-game functions such as game matchmaking, promotion of special matches, and game difficulty adjustment have all been integrated into the data processing platform, enabling personalized configuration and optimized targeting.

[0138] It should be noted that personalized recommendations require the feature data server to perform statistical analysis on the player's (the target audience) past game and spending records in advance to generate feature data. Through interaction between the game server and the feature data server, the game server initiates a request, obtains the feature data for the specific player from the feature data server, and then performs personalized recommendations.

[0139] See Figure 9 , Figure 9 This is a data processing system framework diagram provided in an embodiment of this application. The data processing system includes a terminal 801, a game server 802, a distributed stream processing platform 803, and a feature data server 804. The feature data server 804 includes a feature consumption module (i.e., the log consumption module mentioned above), a caching module (i.e., the data caching module mentioned above), a feature calculation module (i.e., the feature processing module mentioned above), and a database (i.e., the feature database mentioned above).

[0140] The message consumption module is the data entry point for the feature data server 804. The distributed stream processing platform 803 (Kafka) features high throughput and low latency. After receiving interactive operations from players on terminal 801, the game server 802 generates message logs and sends them to the distributed stream processing platform 803. The message consumption module is responsible for parsing the message log data from the distributed stream processing platform 803 and ensuring that each message log is consumed exactly once. It uses the offset management mechanism of the distributed stream processing platform 803 to achieve precise message consumption control, avoiding data loss or duplicate processing.

[0141] The caching module is a high-performance key-value store database primarily used to cache intermediate results during the calculation process. During feature calculation, some player features (i.e., the feature data mentioned above) need to be calculated jointly from multiple data analysis logs. This joint calculation process generates some temporary intermediate values ​​that need to be reused in subsequent calculations (i.e., the first intermediate result mentioned above). The caching module caches these intermediate calculated values, allowing for quick retrieval of relevant data when needed, significantly improving computational efficiency.

[0142] The feature calculation module is the core analysis component of the feature data server 804. It obtains data from the data consumption module's logs. During calculations, if multiple logs are involved in jointly calculating features, the module retrieves previously cached intermediate values ​​from the caching module. Then, it processes and calculates the acquired data according to pre-defined algorithms and rules, transforming various player actions into features with practical analytical significance (i.e., the aforementioned feature data), such as player activity characteristics, skill usage habits, and spending tendencies. The database is the software system used to store this feature data.

[0143] See Figure 10 , Figure 10 This is a schematic diagram of the data processing flow of the data processing system provided in this application embodiment. The data processing flow of the data processing system will be described below.

[0144] During the input phase: Game server 902 generates an operational log 903 based on player actions 901 in the game. The operational log 903 is then sent to the distributed stream processing platform as a data stream. For example, every click, movement direction adjustment, or item use by a player in the game generates a corresponding operational log record.

[0145] During the consumption and processing phase: The consumption module 904 reads messages from the distributed stream processing platform. For each log entry 903, the consumption module 904 parses key information from the log content, such as player ID, operation type, and operation time. Then, based on the log's unique identifier and the distributed stream processing platform's offset management mechanism, it ensures that each log entry is consumed accurately only once. The consumed log data is then sent to the feature calculation module 906 for further processing.

[0146] In the feature calculation and caching stage: The feature calculation module 906 first performs preliminary analysis and classification of the consumption data from the economic analysis consumption module 904. If calculating a player feature only requires a single economic analysis log entry, such as a player's login time or single click operation type, the feature can be directly calculated and stored. However, for features that require joint calculation from multiple economic analysis log entries, such as a player's skill release habits over a period of time, the feature calculation module needs to obtain previously relevant intermediate calculation values ​​(i.e., the first intermediate data mentioned above) from the caching module 905 and then perform joint calculation. According to the predefined feature calculation algorithm, the player's operation data is analyzed in depth. For example, features such as the player's game duration, skill usage frequency, and behavioral patterns in different scenarios are calculated, ultimately generating the player's player feature 908 (i.e., the feature data mentioned above). New intermediate results generated during the calculation process (i.e., the second intermediate results mentioned above) are also stored in the caching module 905 for subsequent calculations.

[0147] In the data storage and application phase: Player characteristics 908 are stored in database 907 for use in various game applications. Player characteristics 908 can be applied to various aspects of game operation, such as activity and task requirements, personalized recommendation systems (e.g., recommending new characters or game items that match players' operating habits), and game balance adjustments (analyzing players' use of different skills or game elements based on characteristics).

[0148] The data processing method provided in this application embodiment features a feature data server where each module (data analysis consumption module, caching module, and feature calculation module) has a clearly defined and relatively independent function. This design allows the feature data server to be expanded or optimized by operating on specific modules without requiring a large-scale reconstruction of the entire system. For example, if you want to optimize a certain algorithm in the feature calculation module, you only need to modify it without affecting the interface with other modules, and it will not interfere with modules that are consuming logs or managing the cache.

[0149] Furthermore, feature data servers based on performance analysis logs can be independently restarted and updated compared to traditional game servers. During game development, as game versions are continuously updated, new gameplay features are introduced, or the needs for player behavior analysis change, the module configurations or algorithm parameters within the framework can be quickly adjusted. For example, when a new character skill is introduced, the feature calculation module can quickly update its algorithm to adapt to the new data feature calculation requirements without affecting online players' gameplay.

[0150] It is understood that in the embodiments of this application, if data related to user information or enterprise information is involved, when the embodiments of this application are applied to specific products or technologies, it is necessary to obtain user permission or consent, or to obfuscate this information in order to eliminate the correspondence between this information and the user; and the collection and processing of related data should strictly comply with the requirements of relevant laws and regulations when applied in practice, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0151] The following continues to describe an exemplary structure of the data processing apparatus 254 provided in the embodiments of this application as a software module. In some embodiments, such as Figure 3As shown, the data processing device 254 includes: a receiving module 2541, used to receive log data of a target object sent by an application server; a determining module 2542, used to determine the feature data of the target object at the current time based on pre-stored first intermediate data of the target object and the log data of the target object; the first intermediate data is calculated based on the log data of the target object at a historical time, the historical time being earlier than the current time; a storage module 2543, used to map and store the object identifier of the target object and the feature data at the current time into the feature database of the data server; and a first sending module 2544, used to send the feature data of the target object obtained from the feature database to the application server in response to the feature acquisition request of the target object sent by the application server.

[0152] The data processing device 254 also includes ( Figure 3 (Not shown in the image): A generation module, used to generate log data of the target object in response to receiving an interactive operation of the target object in a virtual interactive scene; a second sending module, used to send the log data to a data server, so that the data server can determine the feature data of the target object at the current moment based on the pre-stored first intermediate data of the target object and the log data of the target object, and store the feature data at the current moment in the data server; the first intermediate data is calculated based on the log data of the target object at a historical moment, the historical moment being earlier than the current moment; an acquisition module, used to acquire the feature data of the target object from the data server in response to the virtual interactive scene being in a running state; and an information recommendation module, used to recommend information to the target object based on the feature data.

[0153] In some embodiments, the determining module is further configured to: parse the log data to obtain the behavior data of the target object at the current time; and determine the feature data of the target object at the current time based on the pre-stored first intermediate data of the target object and the behavior data at the current time.

[0154] In some embodiments, the data server includes a log consumption module; the determining module is further configured to: convert the log data into a format using the log consumption module to obtain structured log data; perform data cleaning on the structured log data to obtain standardized data; and determine the behavioral data of the target object at the current time from the standardized data based on predefined mapping rules.

[0155] In some embodiments, the data server further includes a data caching module and a feature processing module; the determining module is further configured to: obtain first intermediate data of the target object in the data caching module; perform fusion processing on the first intermediate data and the behavior data at the current time to obtain fused data; and perform feature processing on the fused data through the feature processing module to obtain feature data of the target object at the current time.

[0156] In some embodiments, the apparatus further includes a mapping storage module, configured to: acquire second intermediate data generated during the feature processing module's feature processing of the fused data; and map and store the second intermediate data with the object identifier of the target object in the data cache module.

[0157] In some embodiments, the first sending module is further configured to: parse the object identifier of the target object from the feature acquisition request; query the feature database based on the object identifier to obtain the feature data of the target object; and send the feature data of the target object to the application server.

[0158] This application provides a data processing system, including: an application server and a data server; the application server is configured to generate log data of the target object in response to receiving an interactive operation of the target object in a virtual interactive scenario; and send the log data to the data server; the data server is configured to determine the feature data of the target object at the current time based on pre-stored first intermediate data of the target object and the log data of the target object in response to receiving the log data sent by the application server; the first intermediate data is calculated based on the log data of the target object at a historical time, the historical time being earlier than the current time; and map and store the object identifier of the target object and the feature data at the current time in the feature database of the data server; the data server is further configured to send the feature data of the target object obtained from the feature database to the application server in response to a feature acquisition request of the target object sent by the application server; the application server is further configured to recommend information to the target object based on the feature data.

[0159] It should be noted that the description of the apparatus in this application embodiment is similar to the description of the method embodiment described above, and has similar beneficial effects as the method embodiment; therefore, it will not be repeated. For technical details not disclosed in this apparatus embodiment, please refer to the description of the method embodiment of this application for understanding.

[0160] This application provides an electronic device, including: a memory for storing computer-executable instructions or computer programs; and a processor for executing the computer-executable instructions or computer programs stored in the memory to implement the above-described data processing method.

[0161] This application provides a computer program product, which includes computer-executable instructions or a computer program, and the computer-executable instructions or computer program are stored in a computer-readable storage medium; wherein, when the processor of an electronic device reads the computer-executable instructions or computer program from the computer-readable storage medium and executes the computer-executable instructions or computer program, the above-mentioned data processing method is implemented.

[0162] This application provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will perform the aforementioned data processing method, for example, as... Figure 4 or Figure 5 The method shown.

[0163] In some embodiments, the storage medium may be a computer-readable storage medium, such as a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEROM), flash memory, magnetic surface memory, optical disk, or a compact disk-read-only memory (CD-ROM); or it may be a device that includes one or any combination of the above-mentioned memories.

[0164] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0165] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file containing other programs or data, for example, in one or more scripts within a Hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., files storing one or more modules, subroutines, or code sections). As an example, executable instructions may be deployed to execute on a single electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0166] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A data processing method, characterized in that, Applied to a data server, the method includes: Receive log data of the target object sent by the application server; Based on the pre-stored first intermediate data of the target object and the log data of the target object, the characteristic data of the target object at the current moment are determined; the first intermediate data is calculated based on the log data of the target object at a historical moment, which is earlier than the current moment; The object identifier of the target object and the feature data at the current moment are mapped and stored in the feature database of the data server; In response to the feature acquisition request of the target object sent by the application server, the feature data of the target object obtained from the feature database is sent to the application server.

2. The method according to claim 1, characterized in that, The step of determining the characteristic data of the target object at the current moment based on the pre-stored first intermediate data of the target object and the log data of the target object includes: The log data is parsed to obtain the behavior data of the target object at the current moment; Based on the pre-stored first intermediate data of the target object and the behavior data at the current moment, the feature data of the target object at the current moment are determined.

3. The method according to claim 2, characterized in that, The data server includes a log consumption module; parsing the log data to obtain the target object's behavior data at the current moment includes: The log consumption module performs format conversion on the log data to obtain structured log data. The structured log data is cleaned to obtain standardized data; Based on predefined mapping rules, the behavioral data of the target object at the current moment are determined from the standardized data.

4. The method according to claim 2, characterized in that, The data server also includes a data caching module and a feature processing module; The step of determining the feature data of the target object at the current moment based on the pre-stored first intermediate data of the target object and the behavior data at the current moment includes: The first intermediate data of the target object is obtained from the data caching module; The first intermediate data and the behavioral data at the current moment are fused together to obtain fused data. The feature processing module performs feature processing on the fused data to obtain the feature data of the target object at the current time.

5. The method according to claim 4, characterized in that, After performing feature processing on the fused data through the feature processing module to obtain the feature data of the target object at the current time, the method further includes: Acquire the second intermediate data generated during the feature processing of the fused data by the feature processing module; The second intermediate data is mapped and stored in the data cache module along with the object identifier of the target object.

6. The method according to any one of claims 1 to 5, characterized in that, The step of responding to the feature acquisition request of the target object sent by the application server and sending the feature data of the target object obtained from the feature database to the application server includes: The object identifier of the target object is obtained by parsing the feature acquisition request; Based on the object identifier, the feature data of the target object is obtained by querying the feature database; The feature data of the target object is sent to the application server.

7. A data processing method, characterized in that, Applied to an application server, the method includes: In response to receiving an interactive operation from the target object in relation to a virtual interactive scenario, log data of the target object is generated; The log data is sent to the data server so that the data server can determine the feature data of the target object at the current time based on the first intermediate data of the target object and the log data of the target object, and store the feature data at the current time in the data server; the first intermediate data is calculated based on the log data of the target object at a historical time, which is earlier than the current time; In response to the virtual interactive scene being in operation, the feature data of the target object is obtained from the data server; Based on the feature data, information is recommended for the target object.

8. A data processing system, characterized in that, include: Application servers and data servers; The application server is used to generate log data of the target object in response to the received interactive operation of the target object in the virtual interactive scenario; The log data is sent to the data server; The data server is configured to, in response to receiving the log data sent by the application server, determine the characteristic data of the target object at the current moment based on the first intermediate data of the target object and the log data of the target object that are pre-stored; The first intermediate data is calculated based on the log data of the target object at a historical time, where the historical time is earlier than the current time; the object identifier of the target object and the feature data at the current time are mapped and stored in the feature database of the data server; The data server is also configured to respond to the feature acquisition request of the target object sent by the application server, and send the feature data of the target object obtained from the feature database to the application server; The application server is also used to recommend information to the target object based on the feature data.

9. A data processing apparatus, characterized in that, The device includes: The receiving module is used to receive log data of the target object sent by the application server. The determination module is used to determine the feature data of the target object at the current time based on the first intermediate data of the target object and the log data of the target object that are pre-stored; the first intermediate data is calculated based on the log data of the target object at a historical time, and the historical time is earlier than the current time; The storage module is used to map and store the object identifier of the target object and the feature data at the current moment into the feature database of the data server; The first sending module is configured to, in response to a feature acquisition request for the target object sent by the application server, send the feature data of the target object obtained from the feature database to the application server.

10. A data processing apparatus, characterized in that, The device includes: The generation module is used to generate log data of the target object in response to the received interactive operation of the target object in response to the virtual interactive scene; The second sending module is used to send the log data to the data server, so that the data server can determine the feature data of the target object at the current time based on the first intermediate data of the target object and the log data of the target object, and store the feature data at the current time in the data server; the first intermediate data is calculated based on the log data of the target object at a historical time, and the historical time is earlier than the current time; The acquisition module is used to acquire feature data of the target object from the data server in response to the virtual interactive scene being in a running state; The information recommendation module is used to recommend information to the target object based on the feature data.

11. An electronic device, characterized in that, include: Memory is used to store executable instructions or computer programs. A processor, when executing computer-executable instructions or computer programs stored in the memory, implements the data processing method according to any one of claims 1 to 6, or implements the data processing method according to claim 7.

12. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions or computer programs for causing a processor to execute the computer-executable instructions or computer programs to implement the data processing method according to any one of claims 1 to 6, or to implement the data processing method according to claim 7.

13. A computer program product comprising computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the data processing method according to any one of claims 1 to 6, or implement the data processing method according to claim 7.

Images