User behavior data management method and device, equipment and storage medium

Abstract

The invention provides a user behavior data management method and device, equipment and a storage medium, and the method comprises the steps: firstly obtaining burying point data of a client and a server, processing the burying point data of the client and the burying point data of the server, and pushing the processed burying point data to a message queue Kafka; then, hierarchically storing the user behavior data of the message queue Kafka in a warehouse-lake integrated platform according to logic; and finally, the Flink CDC is used to synchronize the service database data to the warehouse-lake integrated platform in real time so as to supplement the user behavior data in the warehouse-lake integrated platform, the service database data comprises increment information, dimension information and context information of the data, and the problem of low management efficiency of the user behavior data is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, equipment and storage medium for managing user behavior data. Background Art

[0002] In social applications, a large amount of user behavior data (such as clicks, page jumps, etc.) is generated, which is very important for analyzing user behavior, optimizing products, and providing personalized recommendations. However, in traditional data processing methods, real-time data and offline data need to be developed and maintained by two systems, and data storage and processing are scattered, and the data update frequency is low (such as daily or hourly), which cannot meet the real-time analysis needs.

[0003] In view of this, this application is filed. Summary of the invention

[0004] The present invention discloses a method, device, equipment and storage medium for managing user behavior data, aiming to solve the problem of low efficiency in user behavior data management.

[0005] A first aspect of the present invention provides a method for managing user behavior data, comprising: Obtain the buried data of the client and the server, and process the buried data of the client and the server respectively and push them to the message queue Kafka; The user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers; Use Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, where the business database data includes incremental information, dimension information, and context information of the data.

[0006] Preferably, the acquiring of the buried data of the client and the server, and processing the buried data of the client and the buried data of the server respectively and then pushing them to the message queue Kafka, specifically includes: Obtain the user's operation at the client embedding point, generate log data according to the operation, and push the log data to the message queue Kafka after standardization; The interaction information at the server-side embedding point is obtained, and standardized user behavior data is directly generated and pushed to the message queue Kafka, where the interaction information includes interface calls and database operations.

[0007] Preferably, the user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers, specifically: The original log data in the user behavior data of the message queue Kafka and the incremental data synchronized from the business database through Flink CDC are archived and stored in the ODS layer, wherein the ODS layer is configured to support offline analysis and data supplementation tasks to provide original data; The detailed data of the data stored in the ODS layer is cleaned and normalized and stored in the DWD layer, wherein the DWD layer is configured to support real-time query and feature calculation; The data stored in the DWD layer is aggregated and calculated in real time through Flink SQL, and the aggregated calculation results are saved in the DWS layer, wherein the DWS layer is configured to provide query data to BI reports and recommendation systems.

[0008] Preferably, it also includes: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

[0009] A second aspect of the present invention provides a user behavior data management device, comprising: The point-of-sale data acquisition unit is used to acquire the point-of-sale data of the client and the server, and process the point-of-sale data of the client and the server respectively and push them to the message queue Kafka; A hierarchical storage unit, used to store the user behavior data of the message queue Kafka in the Canghu integrated platform according to logical hierarchies; The data supplement unit is used to use Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information and context information of the data.

[0010] Preferably, the buried point data acquisition unit is specifically used for: Obtain the user's operation at the client embedding point, generate log data according to the operation, and push the log data to the message queue Kafka after standardization; The interaction information at the server-side embedding point is obtained, and standardized user behavior data is directly generated and pushed to the message queue Kafka, where the interaction information includes interface calls and database operations.

[0011] Preferably, the hierarchical storage unit is specifically used for: The original log data in the user behavior data of the message queue Kafka and the incremental data synchronized from the business database through Flink CDC are archived and stored in the ODS layer, wherein the ODS layer is configured to support offline analysis and data supplementation tasks to provide original data; The detailed data of the data stored in the ODS layer is cleaned and normalized and stored in the DWD layer, wherein the DWD layer is configured to support real-time query and feature calculation; The data stored in the DWD layer is aggregated and calculated in real time through Flink SQL, and the aggregated calculation results are saved in the DWS layer, wherein the DWS layer is configured to provide query data to BI reports and recommendation systems.

[0012] Preferably, the hierarchical storage unit is further used for: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

[0013] A third aspect of the present invention provides a user behavior data management device, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a user behavior data management method as described in any one of the above.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for managing user behavior data as described in any one of the above items.

[0015] Based on a user behavior data management method, device, equipment and storage medium provided by the present invention, the embedded data of the client and the server are first obtained, and the embedded data of the client and the server are processed respectively and pushed to the message queue Kafka; then, the user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers; finally, Flink CDC is used to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information and context information of the data, thereby solving the problem of low efficiency in user behavior data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a method for managing user behavior data provided by the first embodiment of the present invention; Figure 2It is a module diagram of a user behavior data management device provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0019] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0021] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0022] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0023] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] The present invention discloses a method, device, equipment and storage medium for managing user behavior data, aiming to solve the problem of low efficiency in user behavior data management.

[0026] See also Figure 1 In a first aspect, the present invention provides a method for managing user behavior data, which can be executed by a user behavior data management device (hereinafter referred to as a management device), and in particular, executed by one or more processors in the management device to at least implement the following steps: S101, obtaining the buried data of the client and the server, and respectively processing the buried data of the client and the buried data of the server and pushing them to the message queue Kafka; In this embodiment, the management device may be a terminal with data processing capabilities such as a server, a desktop computer, or a laptop computer. The management device may be installed with a corresponding operating system and application software, and the functions required by this embodiment may be implemented through the combination of the operating system and application software.

[0027] Specifically, in this embodiment, during the collection of user behavior data, the management device first deploys code modules at key locations on the client and server through the tracking technology to capture user operations and manage device interaction information. Taking client tracking as an example, when the user clicks a button, jumps to a page, or interacts with an application on a mobile or web page, the tracking module will capture these behavior operations and record relevant information, including event name, trigger time, device type, operation path, etc. The captured information is organized into semi-structured log data files, and then the data is formatted through built-in standardized components, such as converting the non-uniform log formats generated by different clients into a unified JSON format to ensure the consistency of the field structure. The standardized log data is pushed to the Kafka message queue in real time through the client's upload interface, thereby achieving rapid data transmission and storage.

[0028] In the scenario of server-side tracking, the management device not only captures the request behavior directly triggered by the user (such as API calls), but also records the interaction process with the background database, such as data writing, updating or deleting operations. The server-side tracking module is directly embedded in the core processing logic of the server. When the user operation triggers a specific service, the management device will generate a detailed interaction record, including the interface name, request parameters, response status, and the database operations involved. These interaction information is converted into structured data through the standardization processing module on the server, and is also stored in JSON format. Since the server-side data source itself has a high degree of structuring, the standardization process is relatively simplified, and the processed data will be pushed directly to Kafka.

[0029] Through both client and server tracking, management devices can achieve comprehensive coverage of user behavior data. Specifically, client tracking can capture real-time operations on the user side in a fine-grained manner, reflecting the user's direct behavior path; while server tracking records key interactions at the management device level, making up for the lack of client data.

[0030] S102, storing the user behavior data of the message queue Kafka in the Canghu integrated platform according to logical layers; Specifically, in this embodiment, when the message queue Kafka receives the user behavior data pushed by the client and the server, these data are first archived and stored in the ODS (raw data layer) of the Canghu integrated platform. The ODS layer is designed to carry unprocessed raw data, and its core function is to retain the integrity and authenticity of the data in order to support offline analysis tasks and scenarios that require recalculation or supplementation of historical data. For example, after the log data generated by the client's buried points and the interaction data recorded by the server enter the ODS layer through the Kafka message queue, the management device will organize the data directory by hour or day, which can provide a stable data source for subsequent data flow. At the same time, the incremental data of the business database synchronized in real time by Flink CDC will also be stored in the ODS layer, forming a complete set of original data together with the data from the client and the server to ensure data consistency and timeliness.

[0031] Next, the data stored in the ODS layer will be gradually loaded into the DWD (detailed data layer), which is achieved through cleaning and normalization operations. Cleaning operations include data deduplication, format conversion, and field mapping, while normalization is to align data from multiple sources into a unified structure. For example, for the client's log data, the management device will remove duplicate event records and add necessary field labels; while the interactive data synchronized from the server will extract key attributes according to predefined standard fields. The processed data is stored in the DWD layer to form a standardized detailed data set that can be used directly. The main function of the DWD layer is to support real-time query and feature calculation. Therefore, the wide table built at this layer combines multi-dimensional data to improve query efficiency and response speed.

[0032] Finally, the detailed data stored in the DWD layer will be aggregated in real time through Flink SQL, and the results will be stored in DWS (aggregate data layer). Aggregate calculations include statistics and indicator generation of user behavior data, such as page views (PV), number of unique visitors (UV), etc. The data in the DWS layer is optimized into an efficient query structure, which can directly serve business scenarios such as BI reports and recommended management devices. For example, the feature data prepared in the DWS layer for recommended management devices can be quickly responded through the real-time query interface, and the statistical charts in the BI report can be directly generated from the pre-calculated aggregate data. Hierarchical storage and step-by-step processing realize the full-link optimization from data collection to efficient application, while significantly reducing the computing cost of real-time queries.

[0033] S103, using Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information, and context information of the data.

[0034] Specifically, in this embodiment, Flink CDC is used to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform. The data in the business database includes incremental information, dimension information, and context information, which can be efficiently synchronized in real time through the Flink CDC tool. It should be noted that Flink CDC captures new, updated, or deleted data operations based on the database change log (such as MySQL's binlog or PostgreSQL's WAL), and converts these operations into streaming data events and pushes them to the Canghu integrated platform.

[0035] Incremental information is the core part of real-time synchronization. By capturing the change data of the business database, it can ensure that the user behavior data in the Canghu integrated platform is always up to date. It effectively avoids the inefficiency of the traditional full synchronization method. For example, when a user updates his personal information or completes an order in the business management device, this information will be immediately captured and synchronized to Canghu, supplemented with the user's behavior data, thus forming a complete data link.

[0036] Dimension information is another important part of the synchronization process, which is mainly used to enhance the analysis capabilities of data within the Canghu integrated platform. For example, the user information table and product classification table in the business database contain rich attribute information. After being synchronized to Canghu, these dimensional data are stored in the DIM layer and effectively associated with the behavioral data. Through dimensional information, data analysis can be extended from behavioral details to a broader context, such as a user's geographic location, purchase preferences, or product categories.

[0037] In addition, after the context information is synchronized to the Canghu integrated platform through Flink CDC, it can provide the necessary background support for complex analysis. For example, the context information of the order details table includes product prices, promotions, or delivery methods. When this information is combined with user behavior data, more in-depth behavior analysis can be achieved, such as user purchase conversion rate analysis or promotion effect evaluation. By associating the context data with the wide table of the DWD layer, it can directly provide rich additional information during data query, greatly improving query efficiency.

[0038] In a possible implementation of the present invention, the present invention further includes: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

[0039] Specifically, during the data processing process, the DIM layer, as a public dimension layer in the Canghu integrated platform, is mainly used to store stable contextual data such as user information and product classification. Such data usually comes from static dimension information stored in the business database, such as basic attributes of users (gender, age, region, etc.) or classification labels of content (category, keyword, hierarchical structure, etc.). These data are synchronized to the DIM layer in real time through the Flink CDC tool to ensure that the data can be reflected in the management device in a timely manner after being updated. In addition, for some dimensional data that does not change frequently or requires refined management, it can be regularly imported into the DIM layer through manual maintenance.

[0040] In terms of storage structure, data in the DIM layer is usually organized using primary key indexes to ensure that each record can be quickly retrieved. Each record represents a unique dimension entity, such as a detailed description of a specific user or a type of content. This provides efficient support for the subsequent construction of wide tables. When the data in the DWD layer is processed into a wide table, the detailed data is associated with the contextual information in the DIM layer through FlinkSQL, such as using the user ID or content ID as the primary key for a connection operation. The associated wide table not only contains the details of the behavioral event, but also supplements rich contextual information, such as the age distribution of a user, purchase preferences, or the category level to which a certain content belongs.

[0041] The context information of the DIM layer is combined with the data of the DWD layer to improve the flexibility of data query and enhance the depth of data analysis. For example, in the recommendation management device, by querying the wide table, the behavior path and preference characteristics of a specific user can be quickly obtained to provide data support for real-time recommendation; in the BI report, the combination of the dimension information of the DIM layer can more clearly show the user portrait and behavior analysis results. The dimension association mechanism reduces the direct query dependence on the business database and improves the overall performance and response speed of the management device.

[0042] See also Figure 2 The second aspect of the present invention provides a user behavior data management device, comprising: The buried point data acquisition unit 201 is used to acquire the buried point data of the client and the server, and process the buried point data of the client and the buried point data of the server respectively and push them to the message queue Kafka; A hierarchical storage unit 202, used to store the user behavior data of the message queue Kafka in the Canghu integrated platform according to logical hierarchies; The data supplement unit 203 is used to use Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information and context information of the data.

[0043] Preferably, the buried point data acquisition unit is specifically used for: Obtain the user's operation at the client embedding point, generate log data according to the operation, and push the log data to the message queue Kafka after standardization; The interaction information at the server-side embedding point is obtained, and standardized user behavior data is directly generated and pushed to the message queue Kafka, where the interaction information includes interface calls and database operations.

[0044] Preferably, the hierarchical storage unit is specifically used for: The original log data in the user behavior data of the message queue Kafka and the incremental data synchronized from the business database through Flink CDC are archived and stored in the ODS layer, wherein the ODS layer is configured to support offline analysis and data supplementation tasks to provide original data; The detailed data of the data stored in the ODS layer is cleaned and normalized and stored in the DWD layer, wherein the DWD layer is configured to support real-time query and feature calculation; The data stored in the DWD layer is aggregated and calculated in real time through Flink SQL, and the aggregated calculation results are saved in the DWS layer, wherein the DWS layer is configured to provide query data to BI reports and recommendation systems.

[0045] Preferably, the hierarchical storage unit is further used for: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

[0046] A third aspect of the present invention provides a user behavior data management device, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a user behavior data management method as described in any one of the above.

[0047] A fourth aspect of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for managing user behavior data as described in any one of the above items.

[0048] Based on a user behavior data management method, device, equipment and storage medium provided by the present invention, the embedded data of the client and the server are first obtained, and the embedded data of the client and the server are processed respectively and pushed to the message queue Kafka; then, the user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers; finally, Flink CDC is used to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information and context information of the data, thereby solving the problem of low efficiency in user behavior data management.

[0049] Exemplarily, the computer program described in the third and fourth embodiments of the present invention may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device for implementing a user behavior data management. For example, the device described in the second embodiment of the present invention.

[0050] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the user behavior data management method, and uses various interfaces and lines to connect the various parts of the user behavior data management method.

[0051] The memory can be used to store the computer program and / or module, and the processor realizes various functions of a method for managing user behavior data by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0052] Wherein, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0053] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0054] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for managing user behavior data, characterized in that: include: Obtain the buried data of the client and the server, and process the buried data of the client and the server respectively and push them to the message queue Kafka; The user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers; Use Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, where the business database data includes incremental information, dimension information, and context information of the data.

2. A method for managing user behavior data according to claim 1, characterized in that: The method of obtaining the buried data of the client and the server, and processing the buried data of the client and the buried data of the server respectively and pushing them to the message queue Kafka is specifically as follows: Obtain the user's operation at the client embedding point, generate log data according to the operation, and push the log data to the message queue Kafka after standardization; The interaction information at the server-side embedding point is obtained, and standardized user behavior data is directly generated and pushed to the message queue Kafka, where the interaction information includes interface calls and database operations.

3. A method for managing user behavior data according to claim 1, characterized in that: The user behavior data of the message queue Kafka is stored in the Canghu integrated platform according to logical layers, specifically: The original log data in the user behavior data of the message queue Kafka and the incremental data synchronized from the business database through Flink CDC are archived and stored in the ODS layer, wherein the ODS layer is configured to support offline analysis and data supplementation tasks to provide original data; The detailed data of the data stored in the ODS layer is cleaned and normalized and stored in the DWD layer, wherein the DWD layer is configured to support real-time query and feature calculation; The data stored in the DWD layer is aggregated and calculated in real time through Flink SQL, and the aggregated calculation results are saved in the DWS layer, wherein the DWS layer is configured to provide query data to BI reports and recommendation systems.

4. A method for managing user behavior data according to claim 3, characterized in that: Also includes: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

5. A user behavior data management device, characterized in that: include: The point-of-sale data acquisition unit is used to acquire the point-of-sale data of the client and the server, and process the point-of-sale data of the client and the server respectively and push them to the message queue Kafka; A hierarchical storage unit, used to store the user behavior data of the message queue Kafka in the Canghu integrated platform according to logical hierarchies; The data supplement unit is used to use Flink CDC to synchronize business database data to the Canghu integrated platform in real time to supplement the user behavior data in the Canghu integrated platform, wherein the business database data includes incremental information, dimension information and context information of the data.

6. A user behavior data management device according to claim 5, characterized in that: The buried point data acquisition unit is specifically used for: Obtain the user's operation at the client embedding point, generate log data according to the operation, and push the log data to the message queue Kafka after standardization; The interaction information at the server-side embedding point is obtained, and standardized user behavior data is directly generated and pushed to the message queue Kafka, where the interaction information includes interface calls and database operations.

7. A user behavior data management device according to claim 5, characterized in that: The hierarchical storage unit is specifically used for: The original log data in the user behavior data of the message queue Kafka and the incremental data synchronized from the business database through Flink CDC are archived and stored in the ODS layer, wherein the ODS layer is configured to support offline analysis and data supplementation tasks to provide original data; The detailed data of the data stored in the ODS layer is cleaned and normalized and stored in the DWD layer, wherein the DWD layer is configured to support real-time query and feature calculation; The data stored in the DWD layer is aggregated and calculated in real time through Flink SQL, and the aggregated calculation results are saved in the DWS layer, wherein the DWS layer is configured to provide query data to BI reports and recommendation systems.

8. A user behavior data management device according to claim 7, characterized in that: The hierarchical storage unit is specifically used for: The DIM layer is used to receive dimension data from a business database or manually maintained, and to provide context information, which can be associated with a wide table constructed based on the data in the DWD layer.

9. A user behavior data management device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a user behavior data management method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for managing user behavior data as described in any one of claims 1 to 4.

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