Data processing method, data processing apparatus, electronic device, medium, and product
By splitting and aggregating the data to be stored according to attribute dimensions, the problem of low data query efficiency in multi-database clusters is solved, and fast data access is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INDUSTRIAL AND COMMERCIAL BANK OF CHINA
- Filing Date
- 2026-03-06
- Publication Date
- 2026-07-03
AI Technical Summary
In systems such as bank-securities transfer, existing technologies store data in multiple independent database clusters, resulting in low data query efficiency.
By splitting the data to be stored, identifying attribute dimensions and attribute parameters, generating attribute aggregate data, and storing it for subsequent querying.
It improves data query efficiency, reduces the need to extract data from different databases, and increases query speed.
Smart Images
Figure CN122332376A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of computer and big data technology, and more specifically to a data processing method, data processing device, electronic device, medium, and product. Background Technology
[0002] In systems such as bank-securities transfer, numerous users generate corresponding data when purchasing products through banks and other institutions. This data is usually stored in multiple independent database clusters to achieve data isolation and balance of transaction volume.
[0003] However, this data storage method may require reading data corresponding to the data query operation from different database clusters when facing subsequent data queries, resulting in low data query efficiency. Summary of the Invention
[0004] In view of the above problems, embodiments of this application provide a data processing method, a data processing apparatus, an electronic device, a medium, and a product.
[0005] According to a first aspect of this application, a data processing method is provided, comprising: responding to a data storage instruction, splitting the data to be stored for each piece of data to be stored to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents a user's resource purchase records in historical time; for any one of the attribute dimensions, identifying the attribute parameters of each piece of data under the attribute dimension and the user's resource purchase information; for any one attribute parameter, generating attribute aggregate data based on multiple pieces of data related to the attribute parameter and their respective resource purchase information, and storing the attribute aggregate data.
[0006] According to an embodiment of this application, the above-mentioned attribute aggregation data has attribute identifiers; wherein, the above method further includes: in response to a data query instruction, determining a query attribute corresponding to the data query instruction; determining a target attribute identifier matching the query attribute from the attribute identifiers of the plurality of above-mentioned attribute aggregation data, and displaying the attribute aggregation data corresponding to the target attribute identifier.
[0007] According to an embodiment of this application, splitting the data to be stored to obtain split data corresponding to at least one attribute dimension includes: identifying multiple first attribute information of the data to be stored, wherein the attribute dimension includes address attributes representing different storage addresses; and dividing the data to be stored into split data corresponding to each of the multiple first attribute information.
[0008] According to an embodiment of this application, the method further includes: identifying multiple second attribute information of the split data, wherein the attribute dimensions include attribute types characterizing different resource providers; and dividing the split data into new split data corresponding to each of the multiple second attribute information.
[0009] According to an embodiment of this application, the first attribute information or the second attribute information is identified in the following manner: target keyword identification is performed on the data to be identified to obtain an identification result, wherein the target keyword is related to the first attribute information or the second attribute information, and the data to be identified includes the data to be stored or the split data; when the identification result indicates that the data to be identified includes the target keyword, the data to be identified is split into fields based on the position of the target keyword to obtain split fields, wherein the target keyword in the split fields represents the first attribute information or the second attribute information.
[0010] According to an embodiment of this application, identifying the attribute parameters and user resource purchase information of each split data under the above attribute dimensions includes: for any of the above split data, using a target recognition algorithm to extract the above attribute parameters and the above resource purchase information from the above split data.
[0011] According to an embodiment of this application, attribute aggregate data is generated based on multiple split data related to the above attribute parameters and their respective resource purchase information, including: combining multiple split data related to the above attribute parameters to obtain initial aggregate data; for each split data in the initial aggregate data, adding the file attribute identifier of the split data to the initial aggregate data based on the file attribute of the data to be stored to which the split data belongs, thereby generating the attribute aggregate data.
[0012] According to a second aspect of this application, a data processing apparatus is provided, comprising: a splitting module, configured to, in response to a data storage instruction, split the data to be stored for each piece of data to be stored to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents a user's resource purchase records in historical time; an identification module, configured to, for any one of the attribute dimensions, identify the attribute parameters of each split data under the attribute dimension and the user's resource purchase information; and an aggregation storage module, configured to, for any attribute parameter, generate attribute aggregation data based on multiple split data related to the attribute parameter and their respective resource purchase information, and store the attribute aggregation data.
[0013] According to a third aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.
[0014] According to a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program or instructions are stored, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.
[0015] According to a fifth aspect of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.
[0016] According to embodiments of this application, by splitting the data to be stored, split data corresponding to at least one attribute dimension is obtained. For any attribute dimension, the attribute parameters and user resource purchase information of each split data under the attribute dimension are identified. For any attribute parameter, attribute aggregate data is generated and stored based on multiple split data related to the attribute parameter and their respective resource purchase information. Since the data is divided based on attribute dimensions, and the split data is aggregated according to the attribute dimensions and the identified attribute parameters, the resulting attribute aggregate data is stored in a way that facilitates quick retrieval of all data for that attribute parameter during subsequent queries, thus improving data query efficiency. Attached Figure Description
[0017] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:
[0018] Figure 1 This diagram illustrates an application scenario of the data processing method according to an embodiment of this application.
[0019] Figure 2 A flowchart illustrating a data processing method according to an embodiment of this application is shown schematically.
[0020] Figure 3 A flowchart illustrating a data processing method according to another embodiment of this application is shown schematically;
[0021] Figure 4 This schematic diagram illustrates a structural block diagram of a data processing apparatus according to an embodiment of the present application;
[0022] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a data processing method according to an embodiment of this application. Detailed Implementation
[0023] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.
[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.
[0026] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).
[0027] This application provides a data processing method, data processing apparatus, electronic device, medium, and product. The method includes, in response to a data storage instruction, splitting the data to be stored for each piece of data to be stored to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents a user's resource purchase records in historical time; for any attribute dimension, identifying the attribute parameters of each piece of data under the attribute dimension and the user's resource purchase information; for any attribute parameter, generating attribute aggregate data based on multiple pieces of data related to the attribute parameter and their respective resource purchase information, and storing the attribute aggregate data.
[0028] It should be noted that the data processing method and data processing device provided in this application can be used in the financial technology field, such as financial institutions like banks, and can also be used in any field other than the financial technology field, such as the health field like hospitals. Therefore, the application field of the data processing method and data processing device provided in this application is not limited.
[0029] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.
[0030] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided in this application all provide users with corresponding operation entry points for users to choose to agree to or reject the automated decision results; if the user chooses to reject, the process enters the expert decision-making process.
[0031] Figure 1 The diagram illustrates an application scenario of the data processing method according to an embodiment of this application.
[0032] like Figure 1 As shown, application scenario 100 according to an embodiment of this application may include a first terminal device 101, a second terminal device 102, a database 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the database 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. For example, a user can use the first terminal device 101 and the second terminal device 102 to interact with the server 105 and / or the database 103 through the network 104 to receive or send information, etc.
[0033] The first terminal device 101 and the second terminal device 102 can be electronic devices such as smartphones, wearable devices, personal computers, intelligent voice interaction devices, smart home appliances, intelligent vehicles, in-vehicle terminals, aircraft, unmanned vending terminals, and extended reality devices. Extended reality devices can include virtual reality devices, augmented reality devices, and mixed reality devices. A client application for the target application can be installed and run on the terminal device. This target application can include, but is not limited to, financial transaction applications, payment applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, and social media platform software (these are just examples). Furthermore, this application embodiment does not limit the form of the target application, including but not limited to applications, mini-programs, etc., installed on the terminal device, and can also be in web page form.
[0034] Server 105 can be a server providing various services, such as a backend management server supporting websites browsed by users using the first terminal device 101 and the second terminal device 102 (this is just an example). The backend management server can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data. The server can be a backend server for the aforementioned target application, used to provide backend services to the clients of the target application.
[0035] Database 103 is a professional storage system for storing and managing data. It can store various types of data related to the target application, such as user account information, business transaction records, and content resource data. It supports structured, semi-structured, or unstructured data storage and has management capabilities such as adding, deleting, modifying, querying, backing up, and restoring data. In this application scenario, database 103 can be connected to server 105 via a communication link. Server 104 can retrieve the required data from database 103 for processing based on requests from the first terminal device 101 and the second terminal device 102. It can also synchronously store new data generated by the operations of the first terminal device 101 and the second terminal device 102 into database 103, thereby achieving data persistence and efficient retrieval.
[0036] It should be noted that the data processing method provided in the embodiments of this application can generally be executed by server 105 and / or terminal devices 101-102. Accordingly, the data processing device provided in the embodiments of this application can generally be disposed in server 105 and / or terminal devices 101-102.
[0037] It should be understood that Figure 1 The number of terminal devices, networks, databases, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, databases, and servers can be included.
[0038] Figure 2 A flowchart illustrating a data processing method according to an embodiment of this application is shown.
[0039] like Figure 2 As shown, the data processing method 200 according to the embodiments of this application may include steps S210 to S230.
[0040] In step S210, in response to the data storage instruction, for each piece of data to be stored, the data to be stored is split to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents the user's resource purchase records in historical time.
[0041] In step S220, for any attribute dimension, identify the attribute parameters of each split data and the user's resource purchase information under the attribute dimension.
[0042] In step S230, for any attribute parameter, attribute aggregate data is generated and stored based on multiple split data related to the attribute parameter and their respective resource purchase information.
[0043] In embodiments of this application, user consent or authorization can be obtained before identifying user resource purchase information. For example, a request to obtain user information can be sent to the user before step S220. If the user consents or authorizes the acquisition of user information, step S220 is executed.
[0044] According to embodiments of this application, data storage instructions can be generated automatically by staff inputting corresponding operations on electronic devices, and the electronic devices responding to the operations to automatically generate the instructions, or the electronic devices can automatically generate the instructions every preset time interval (e.g., 5 minutes).
[0045] According to an embodiment of this application, the data to be stored can be data from financial transfer transactions. This data includes purchase and payment records of resources (such as vehicles, computers, mobile phones, etc.) purchased by users. The attribute dimensions can be divided according to storage address, partner, or customer.
[0046] According to the embodiments of this application, in financial transfer business, the bank's bank-securities transfer system will record the data to be stored for the purchase of products by different users in real time. For each data to be stored, it can be split according to different attribute dimensions, such as splitting it into split data corresponding to different partners according to the partner dimension.
[0047] According to embodiments of this application, for each piece of split data obtained from the splitting process, attribute parameters of each piece of split data can be identified. For example, at the partner dimension, multiple user information contained in each piece of split data can be identified, including but not limited to user ID, resource type, and resource quantity. A Transformer-based detector can be used for this identification.
[0048] According to embodiments of this application, for each attribute parameter, multiple split data belonging to that attribute parameter and the resource purchase information corresponding to each split data can be aggregated to form attribute aggregate data for that attribute parameter. During aggregation, split data from different data to be stored can be aggregated. After aggregation is complete, the attribute aggregate data can be stored in a corresponding database for convenient unified querying of subsequent data. Attribute aggregate data for one type of attribute dimension can be stored in one database; for example, data categorized by partners can be stored in one database.
[0049] According to embodiments of this application, by splitting the data to be stored, split data corresponding to at least one attribute dimension is obtained. For any attribute dimension, the attribute parameters and user resource purchase information of each split data under the attribute dimension are identified. For any attribute parameter, attribute aggregate data is generated and stored based on multiple split data related to the attribute parameter and their respective resource purchase information. Since the data is divided based on attribute dimensions, and the split data is aggregated according to the attribute dimensions and the identified attribute parameters, the resulting attribute aggregate data is stored in a way that facilitates quick retrieval of all data for that attribute parameter during subsequent queries, thus improving data query efficiency.
[0050] Figure 3 A flowchart illustrating a data processing method according to another embodiment of this application is shown.
[0051] According to embodiments of this application, attribute aggregation data has attribute identifiers.
[0052] According to an embodiment of this application, the data processing method further includes: responding to a data query instruction, determining a query attribute corresponding to the data query instruction; determining a target attribute identifier matching the query attribute from attribute identifiers of multiple attribute aggregate data, and displaying the attribute aggregate data corresponding to the target attribute identifier.
[0053] According to embodiments of this application, the attribute identifier can represent the category to which the attribute aggregated data belongs. The category can represent a partner category, a user category, or a resource category.
[0054] According to embodiments of this application, such as Figure 3 As shown, after a staff member enters a query operation on an electronic device, in response to the data query instruction corresponding to the query operation, the instruction can be parsed to determine the query attribute corresponding to the data query instruction. The query instruction is then matched with the attribute identifiers of multiple attribute aggregate data stored in the database. The attribute aggregate data corresponding to one or more successfully matched target attribute identifiers is extracted and displayed through a webpage or software page.
[0055] According to the embodiments of this application, when performing data queries, the query attributes are matched with the attribute identifiers of the attribute aggregation data, thereby quickly determining the data to be queried. This allows all the data to be obtained at once, eliminating the need to extract query-related data from different databases as in related technologies, thus improving query efficiency.
[0056] According to an embodiment of this application, splitting the data to be stored to obtain split data corresponding to at least one attribute dimension includes: identifying multiple first attribute information of the data to be stored, wherein the attribute dimension includes address attributes representing different storage addresses; and dividing the data to be stored into split data corresponding to each first attribute information based on the multiple first attribute information.
[0057] According to an embodiment of this application, the first attribute information may refer to a storage address stored in a different database.
[0058] According to the embodiments of this application, since the data to be stored is data for a time period, it includes a lot of data belonging to different users or partners. In order to reduce the storage pressure of the database, multiple databases can be used for storage, for example, one database can store the data of one or more partners (or users).
[0059] According to embodiments of this application, when splitting data, it is first necessary to determine which categories the data to be stored belongs to, that is, to determine the first attribute information of each category. After determining the first attribute information, the data to be stored can be divided into separate split data based on each first attribute information.
[0060] According to the embodiments of this application, dividing the data to be stored into different split data by the address attribute of the storage address helps to store different databases based on the address attribute when storing the attribute aggregation data in the subsequent process, thereby reducing the storage pressure of a single database. Furthermore, since each database stores fixed attribute aggregation data, the data reading efficiency can be improved when reading the data in the subsequent process.
[0061] According to an embodiment of this application, the data processing method further includes: identifying multiple second attribute information of the split data, wherein the attribute dimension includes attribute types that characterize different resource providers; and dividing the split data into new split data corresponding to each second attribute information based on the multiple second attribute information.
[0062] According to the embodiments of this application, since the split data is split based on address attributes, these split data do not explicitly indicate which partner or user the split data belongs to. In subsequent data queries, the data obtained may contain data unrelated to the query operation. Therefore, after obtaining the split data, further splitting can be performed. At this time, the data splitting can be based on the attribute type of the resource provider (i.e., partner, such as merchant) or the resource receiver (i.e. user).
[0063] According to an embodiment of this application, during the secondary splitting, it is first necessary to identify multiple second attribute information contained in each split data. Based on the multiple second attribute information, the split data can be divided into new split data corresponding to each second attribute information. For example, each new split data corresponds to transaction data between a partner and a user. The transaction data includes transaction time, amount, product type, product quantity, etc.
[0064] According to the embodiments of this application, by further dividing the data based on the second attribute information on top of the first attribute information, the data can be stored based on the second attribute information in the subsequent storage of attribute aggregated data. This achieves classified storage of different split data, which helps to quickly query the data based on the attribute information in the subsequent data query.
[0065] According to an embodiment of this application, the first attribute information or the second attribute information is identified in the following manner: target keyword identification is performed on the data to be identified to obtain an identification result, wherein the target keyword is related to the first attribute information or the second attribute information, and the data to be identified includes data to be stored or split data; when the identification result indicates that the data to be identified includes the target keyword, the data to be identified is split into fields based on the position of the target keyword to obtain split fields, wherein the target keyword in the split fields represents the first attribute information or the second attribute information.
[0066] According to the embodiments of this application, when performing attribute recognition, the data to be recognized is first identified by keywords. For example, each partner name or partner code contained in the data (i.e., target keyword) can be identified. If the recognition result includes a target keyword, the field can be split based on the field where the target keyword is located and the context, so as to obtain the split field corresponding to the target keyword.
[0067] According to an embodiment of this application, during subsequent data splitting, multiple splitting fields belonging to the same target keyword can be merged to form splitting data belonging to that target keyword.
[0068] According to an embodiment of this application, identifying the attribute parameters and user resource purchase information of each split data under the attribute dimension includes: for any split data, using a target recognition algorithm to extract attribute parameters and resource purchase information from the split data.
[0069] According to embodiments of this application, target recognition algorithms may include, but are not limited to, word frequency-inverse document frequency algorithms and methods based on deep learning / neural networks.
[0070] In one specific embodiment, the split data can be input into a pre-trained large language model, which can then identify the attribute parameters contained in the split data and the resource purchase information corresponding to each attribute parameter. For example, when the attribute parameter is the code parameter of a partner, the resource purchase information can represent the quantity and type of products purchased by the user from that partner.
[0071] According to an embodiment of this application, attribute aggregate data is generated based on multiple split data related to attribute parameters and their respective resource purchase information, including: combining multiple split data related to attribute parameters to obtain initial aggregate data; for each split data in the initial aggregate data, adding a file attribute identifier of the split data to the initial aggregate data based on the file attribute of the data to be stored to which the split data belongs, thereby generating attribute aggregate data.
[0072] According to embodiments of this application, multiple split data with the same attribute parameters can be combined into initial aggregate data in the form of a tuple. The multiple split data within this tuple may belong to multiple data sets to be stored. Therefore, a file attribute identifier for the split data can be generated based on the file attributes (such as file name) of the data set to be stored. This file attribute identifier may also include records of the specific paragraph of the split data within the data set to be stored, the file directory of the data set to be stored, and the file's read permissions.
[0073] According to an embodiment of this application, file attribute identifiers can be represented in the form of file encoding. After the file attribute identifiers are formed, they can be added to the corresponding positions in the tuple to form attribute aggregate data.
[0074] In one specific embodiment, when querying data, the permissions of the querying user are first verified. If the querying user has low permissions (does not meet the conditions for querying important files), when displaying the attribute aggregation data corresponding to the data query, it can be checked whether the permissions of each split data in the attribute aggregation data can be read by the querying user. If not, the split data can be hidden during display.
[0075] According to an embodiment of this application, when the attribute parameter is a user ID, the corresponding attribute aggregate data can be sorted in different ways according to the frequency or quantity of purchased resources.
[0076] Based on the above data processing method, embodiments of this application also provide a data processing apparatus. The following will be combined with... Figure 4 The device is described in detail.
[0077] Figure 4 A schematic block diagram of a data processing apparatus according to an embodiment of this application is shown.
[0078] like Figure 4 As shown, the data processing device 400 in this embodiment includes a splitting module 410, an identification module 420, and an aggregation storage module 430.
[0079] The splitting module 410 is used to respond to data storage instructions and split the data to be stored for each piece of data to be stored, so as to obtain split data corresponding to at least one attribute dimension. The attribute dimension represents the data type to which the split data belongs, and the data to be stored represents the user's resource purchase records in historical time.
[0080] The identification module 420 is used to identify the attribute parameters and user resource purchase information of each split data under any attribute dimension.
[0081] The aggregation storage module 430 is used to generate and store attribute aggregation data for any attribute parameter based on multiple split data related to the attribute parameter and their respective resource purchase information.
[0082] According to embodiments of this application, by splitting the data to be stored, split data corresponding to at least one attribute dimension is obtained. For any attribute dimension, the attribute parameters and user resource purchase information of each split data under the attribute dimension are identified. For any attribute parameter, attribute aggregate data is generated and stored based on multiple split data related to the attribute parameter and their respective resource purchase information. Since the data is divided based on attribute dimensions, and the split data is aggregated according to the attribute dimensions and the identified attribute parameters, the resulting attribute aggregate data is stored in a way that facilitates quick retrieval of all data for that attribute parameter during subsequent queries, thus improving data query efficiency.
[0083] According to embodiments of this application, attribute aggregation data has attribute identifiers.
[0084] According to an embodiment of this application, the data processing device 400 further includes a determination module and a determination display module.
[0085] The determination module is used to respond to data query commands and determine the query attributes corresponding to the data query commands.
[0086] The determination and display module is used to determine the target attribute identifier that matches the query attribute from the attribute identifiers of multiple attribute aggregate data, and to display the attribute aggregate data corresponding to the target attribute identifier.
[0087] According to an embodiment of this application, the splitting module 410 includes a first identification unit and a first division unit.
[0088] The first identification unit is used to identify multiple first attribute information of the data to be stored, wherein the attribute dimension includes address attributes that characterize different storage addresses;
[0089] The first partitioning unit is used to divide the data to be stored into split data corresponding to each first attribute information based on multiple first attribute information.
[0090] According to an embodiment of this application, the splitting module 410 further includes a second identification unit and a second division unit.
[0091] The second identification unit is used to identify multiple second attribute information of the split data, wherein the attribute dimensions include attribute types that characterize different resource providers.
[0092] The second partitioning unit is used to divide the split data into new split data corresponding to each second attribute information based on multiple second attribute information.
[0093] According to an embodiment of this application, the first attribute information or the second attribute information is identified by a third identification unit and a splitting unit.
[0094] The third identification unit is used to identify target keywords in the data to be identified and obtain identification results. The target keywords are related to the first attribute information or the second attribute information. The data to be identified includes data to be stored or split data.
[0095] The splitting unit is used to split the data to be identified based on the position of the target keyword when the identification result indicates that the data to be identified includes the target keyword, to obtain split fields, wherein the target keyword in the split fields represents the first attribute information or the second attribute information.
[0096] According to an embodiment of this application, the identification module 410 includes an extraction unit.
[0097] The extraction unit is used to extract attribute parameters and resource purchase information from any split data using a target recognition algorithm.
[0098] According to an embodiment of this application, the aggregated storage module 430 includes a combination unit and a tagging unit.
[0099] The combination unit is used to combine multiple split data related to attribute parameters to obtain initial aggregate data.
[0100] The tagging unit is used to add the file attribute identifier of each split data in the initial aggregate data based on the file attribute of the data to be stored to which the split data belongs, and generate attribute aggregate data.
[0101] According to embodiments of this application, any multiple modules among the splitting module 410, identification module 420, and aggregation storage module 430 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the splitting module 410, identification module 420, and aggregation storage module 430 can be at least partially implemented as a hardware circuit, such as a field-programmable gate array, a programmable logic array, a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit, or implemented by any other reasonable means of integrating or packaging the circuit, or implemented by any one of software, hardware, and firmware, or by a suitable combination of any of these. Alternatively, at least one of the splitting module 410, identification module 420, and aggregation storage module 430 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.
[0102] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a data processing method according to an embodiment of this application.
[0103] like Figure 5 As shown, an electronic device 500 according to an embodiment of this application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage portion 508 into a random access memory 503. The processor 501 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.
[0104] Random access memory 503 stores various programs and data required for the operation of electronic device 500. Processor 501, read-only memory 502, and random access memory 503 are interconnected via bus 504. Processor 501 executes various steps of the method flow according to embodiments of this application by executing programs in read-only memory 502 and / or random access memory 503. It should be noted that the programs may also be stored in one or more memories other than read-only memory 502 and random access memory 503. Processor 501 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.
[0105] According to embodiments of this application, the electronic device 500 may further include an input / output interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card, such as a local area network card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.
[0106] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.
[0107] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 502, and / or random access memory 503, and / or one or more memories other than read-only memory 502 and random access memory 503 described above.
[0108] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.
[0109] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.
[0110] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0111] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0113] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.
Claims
1. A data processing method, characterized in that, The method includes: In response to a data storage instruction, for each piece of data to be stored, the data to be stored is split into data to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents the user's resource purchase records in historical time. For any of the aforementioned attribute dimensions, identify the attribute parameters and user resource purchase information for each split data under that attribute dimension; For any attribute parameter, attribute aggregate data is generated and stored based on multiple split data related to the attribute parameter and their respective resource purchase information.
2. The method according to claim 1, characterized in that, The attribute aggregation data has attribute identifiers; The method further includes: In response to a data query command, determine the query attribute corresponding to the data query command; The target attribute identifier that matches the query attribute is determined from the attribute identifiers of the multiple attribute aggregation data, and the attribute aggregation data corresponding to the target attribute identifier is displayed.
3. The method according to claim 1, characterized in that, The data to be stored is split into data segments, resulting in split data corresponding to at least one attribute dimension, including: Identify multiple first attribute information of the data to be stored, wherein the attribute dimension includes address attributes that characterize different storage addresses; The data to be stored is divided into split data corresponding to each of the first attribute information based on multiple first attribute information.
4. The method according to claim 3, characterized in that, The method further includes: Identify multiple second attribute information of the split data, wherein the attribute dimensions include attribute types that characterize different resource providers; The split data is divided into new split data corresponding to each of the second attribute information based on multiple second attribute information.
5. The method according to claim 4, characterized in that, The first attribute information or the second attribute information is identified in the following way: The target keyword is identified in the data to be identified to obtain the identification result, wherein the target keyword is related to the first attribute information or the second attribute information, and the data to be identified includes the data to be stored or the split data; When the recognition result indicates that the data to be identified includes a target keyword, the data to be identified is split into fields based on the position of the target keyword to obtain split fields, wherein the target keyword in the split fields represents the first attribute information or the second attribute information.
6. The method according to claim 1, characterized in that, Identify the attribute parameters and user resource purchase information for each split data point under the aforementioned attribute dimension, including: For any of the aforementioned split data, a target recognition algorithm is used to extract the attribute parameters and the resource purchase information from the split data.
7. The method according to claim 1 or 6, characterized in that, Based on multiple split data related to the attribute parameters and their respective resource purchase information, attribute aggregate data is generated, including: The initial aggregated data is obtained by combining multiple split data related to the attribute parameters; For each split data in the initial aggregated data, based on the file attribute of the data to be stored to which the split data belongs, the file attribute identifier of the split data is added to the initial aggregated data to generate the attribute aggregated data.
8. A data processing apparatus, characterized in that, The device includes: The splitting module is used to respond to data storage instructions and split the data to be stored for each piece of data to be stored to obtain split data corresponding to at least one attribute dimension, wherein the attribute dimension represents the data type to which the split data belongs, and the data to be stored represents the user's resource purchase records in historical time. The identification module is used to identify the attribute parameters and user resource purchase information of each split data under any of the attribute dimensions. An aggregation storage module is used to generate and store attribute aggregation data for any attribute parameter based on multiple split data related to the attribute parameter and their respective resource purchase information.
9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.
11. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.