Data processing method, cross-domain aggregation view generation method, device and product

Abstract

The invention provides a data processing method, a cross-domain aggregation view generation method, a cross-domain aggregation view generation device and a product, and relates to the field of big data. The method comprises the following steps: acquiring a business data set corresponding to a target business field, and determining a data object set corresponding to the business data set; updating the cross-domain association aggregation view based on the data object set; performing data association processing on the business data set and the data object set to obtain a corresponding associated data set; and filling the plurality of associated data in the associated data set into the cross-domain associated aggregation view according to the corresponding association relationship. According to the method, through aggregation processing of cross-domain association data objects, a cross-domain association aggregation view is constructed, and full coverage of the data objects is achieved; besides, for the target business field, after the cross-domain association aggregation view is updated, the corresponding association data set is filled in the cross-domain association aggregation view; the single limitation of a traditional aggregation table is broken through, and cross-domain data association analysis is achieved.

Description

Technical Field

[0001] This application relates to the field of big data, and in particular to a data processing method, a method for generating cross-domain aggregated views, an apparatus, and a product. Background Technology

[0002] In banks, data generated by business systems is typically scattered across multiple heterogeneous systems, resulting in complex data structures and a lack of unified standards. A data platform integrates this scattered data into reusable and shareable high-value data assets by building a standardized, service-oriented data asset system to support core business needs such as business analysis, customer profiling, and regulatory reporting.

[0003] Currently, when processing data, the aggregation table generation in the data platform mainly involves changing the arrangement of fields to adapt to the needs of big data analysis. By rearranging and combining fields, the data structure becomes more rational, and querying and analysis become more efficient. However, in this process, the addition of new derived fields is relatively rare; the focus is primarily on processing based on the original data as much as possible. Furthermore, because each aggregation entity operates within a single subject domain, it lacks the ability to integrate cross-domain data. This necessitates additional multi-table joins for business analysis, increasing data complexity and hindering cross-domain data association and comprehensive analysis.

[0004] Therefore, a comprehensive and efficient data processing solution is urgently needed. Summary of the Invention

[0005] This application provides a data processing method, a method for generating cross-domain aggregated views, an apparatus, and a product to solve the technical problem that aggregated tables generated by existing data processing technologies are difficult to achieve cross-domain data association and analysis.

[0006] Firstly, this application provides a data processing method, including:

[0007] Obtain the set of business data corresponding to the target business domain, and determine the set of data objects corresponding to the set of business data;

[0008] Based on a collection of data objects, the cross-domain related aggregate view is updated. The cross-domain related aggregate view includes data objects corresponding to multiple business domains.

[0009] According to the preset association rules, the business data set and the data object set are processed to obtain the corresponding associated data set, which is used to indicate the association relationship between the data object and the business data.

[0010] Fill in the cross-domain association aggregation view with multiple related data in the related data set according to their corresponding relationships.

[0011] In one possible implementation, determining the set of data objects corresponding to the business data set includes:

[0012] Identify at least one business object corresponding to the business data set;

[0013] For any one of the at least one business objects, based on the business data set, determine at least one target data object from multiple candidate data objects corresponding to the business object, wherein the candidate objects corresponding to different business objects are different;

[0014] Generate a collection of data objects based on the target data object corresponding to at least one business object.

[0015] Secondly, this application provides a method for generating a cross-domain aggregated view, comprising:

[0016] Obtain historical business data sets corresponding to multiple business areas;

[0017] Based on the historical business data set, determine the initial data object set corresponding to each business domain;

[0018] Based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain, the cross-domain associated data object set is determined;

[0019] A cross-domain associated aggregated view is constructed based on a cross-domain associated data object set and a historical business data set.

[0020] In one possible implementation, a cross-domain associated aggregated view is constructed based on a cross-domain associated data object set and a historical business data set, including:

[0021] Based on historical business data sets, multiple business themes were identified;

[0022] Based on multiple business themes and a pre-defined theme aggregation framework, determine the theme aggregation table corresponding to each business theme;

[0023] Based on the framework of the full set of cross-domain related data objects, the topic aggregation table corresponding to each business entity, and the pre-defined cross-domain related aggregation view, a cross-domain related aggregation view is constructed.

[0024] In one possible implementation, based on preset cross-domain aggregation rules and an initial set of data objects corresponding to each business domain, a cross-domain associated data object set is determined, including:

[0025] Based on historical business data sets and preset information classification rules, determine business attribute information and operational analysis attribute information;

[0026] According to the preset cross-domain aggregation rules, business attribute information, operational analysis attribute information, and the initial data object set are aggregated to obtain a cross-domain related data object set.

[0027] In one possible implementation, the method further includes:

[0028] Obtain new business data sets corresponding to multiple business domains; based on the new business data sets, determine the data object set corresponding to each business domain;

[0029] If it is determined that the data object corresponding to any business domain has been updated, the cross-domain associated aggregation view is updated based on the newly added business data set and the updated data object.

[0030] Thirdly, this application provides a data processing apparatus, comprising:

[0031] The acquisition module is used to acquire the set of business data corresponding to the target business domain and determine the set of data objects corresponding to the set of business data.

[0032] The update module is used to update cross-domain related aggregated views based on a collection of data objects. The cross-domain related aggregated views include data objects corresponding to multiple business domains.

[0033] The processing module is used to perform data association processing on the business data set and the data object set according to the preset association rules, so as to obtain the corresponding associated data set. The associated data set is used to indicate the association relationship between the data object and the business data.

[0034] The processing module is also used to fill multiple related data in the related data set into the cross-domain related aggregation view according to their corresponding relationships.

[0035] In one possible implementation, the acquisition module is further configured to:

[0036] Identify at least one business object corresponding to the business data set;

[0037] For any one of the at least one business objects, based on the business data set, determine at least one target data object from multiple candidate data objects corresponding to the business object, wherein the candidate objects corresponding to different business objects are different;

[0038] Generate a collection of data objects based on the target data object corresponding to at least one business object.

[0039] Fourthly, this application provides an apparatus for generating a cross-domain aggregated view, comprising:

[0040] The acquisition module is used to acquire historical business data sets corresponding to multiple business areas;

[0041] The processing module is used to determine the initial set of data objects for each business domain based on the historical business data set;

[0042] The processing module is also used to determine the cross-domain associated data object set based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain;

[0043] The building module is used to construct cross-domain associated aggregated views based on a collection of cross-domain associated data objects and a collection of historical business data.

[0044] In one possible implementation, the building module is also used for:

[0045] Based on historical business data sets, multiple business themes were identified;

[0046] Based on multiple business themes and a pre-defined theme aggregation framework, determine the theme aggregation table corresponding to each business theme;

[0047] Based on the framework of the full set of cross-domain related data objects, the topic aggregation table corresponding to each business entity, and the pre-defined cross-domain related aggregation view, a cross-domain related aggregation view is constructed.

[0048] In one possible implementation, the processing module is further configured to:

[0049] Based on historical business data sets and preset information classification rules, determine business attribute information and operational analysis attribute information;

[0050] According to the preset cross-domain aggregation rules, business attribute information, operational analysis attribute information, and the initial data object set are aggregated to obtain a cross-domain related data object set.

[0051] In one possible implementation, the apparatus for generating cross-domain aggregated views further includes an update module, which is used to:

[0052] Obtain new business data sets corresponding to multiple business domains; based on the new business data sets, determine the data object set corresponding to each business domain;

[0053] If it is determined that the data object corresponding to any business domain has been updated, the cross-domain associated aggregation view is updated based on the newly added business data set and the updated data object.

[0054] Fifthly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the first aspect and / or various possible implementations of the first aspect, or the second aspect and / or various possible implementations of the second aspect.

[0055] Sixthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect, or the second aspect and / or various possible implementations of the second aspect.

[0056] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect, or the second aspect and / or various possible implementations of the second aspect.

[0057] The data processing method, cross-domain aggregation view generation method, apparatus, and product provided in this application acquire a set of business data corresponding to a target business domain and determine a set of data objects corresponding to the business data set; update the cross-domain association aggregation view based on the set of data objects; perform data association processing on the business data set and the set of data objects according to preset association rules to obtain a corresponding association data set, which indicates the association relationship between data objects and business data; and fill multiple association data in the association data set into the cross-domain association aggregation view according to their corresponding association relationships. This method constructs an initial multi-dimensional cross-domain association aggregation view through the aggregation processing of cross-domain association data objects, achieving full coverage of data objects; furthermore, when generating the cross-domain association set view corresponding to the target business domain, the cross-domain association aggregation view is updated based on the set of data objects corresponding to the target domain; and the obtained corresponding association data set is filled into the cross-domain association aggregation view; overcoming the single limitation of traditional aggregation tables, this method achieves cross-domain data association analysis, reduces the complexity of multi-table associations, and improves the efficiency of business analysis and data query. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] Figure 1 A flowchart illustrating a data processing method provided in this application;

[0060] Figure 2A schematic diagram of a cross-domain associated aggregated view provided in this application;

[0061] Figure 3 A flowchart illustrating a method for generating a cross-domain aggregated view provided in this application;

[0062] Figure 4 This application provides an intention to represent a topic aggregation.

[0063] Figure 5 A schematic diagram of the structure of a data processing device provided in this application;

[0064] Figure 6 A schematic diagram of the structure of a cross-domain aggregated view generation device provided in this application;

[0065] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this application.

[0066] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0067] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application 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 the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0069] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0070] It should be noted that the data processing method, cross-domain aggregation view generation method, apparatus and product provided in this application can be used in the field of big data, or in any field other than big data. The application fields of the data processing method, cross-domain aggregation view generation method, apparatus and product in this application are not limited.

[0071] First, the terms used in this application will be explained:

[0072] Cross-domain: refers to an aggregated view that includes multiple thematic domains, such as channel themes, product themes, customer themes, etc.

[0073] Cross-domain association: refers to associating data objects corresponding to each subject domain across multiple subject domains.

[0074] Cross-domain aggregated view: refers to a view with a unified standard that links data objects from multiple subject areas across domains.

[0075] In banks, data generated by business systems (such as core systems, credit systems, and customer relationship management systems) is typically scattered across multiple heterogeneous systems, resulting in complex data structures and a lack of unified standards. A data platform integrates this scattered data into reusable and shareable high-value data assets by building a standardized, service-oriented data asset system to support core business needs such as business analysis, customer profiling, and regulatory reporting.

[0076] Currently, when processing data, the aggregation table generation in the data platform mainly involves changing the arrangement of fields to adapt to the needs of big data analysis. By rearranging and combining fields, the data structure becomes more rational, and querying and analysis become more efficient. However, in this process, the addition of new derived fields is relatively rare; the focus is primarily on processing based on the original data as much as possible. Furthermore, because each aggregation entity operates within a single subject domain, it lacks the ability to integrate cross-domain data. This necessitates additional multi-table joins for business analysis, increasing data complexity and hindering cross-domain data association and comprehensive analysis.

[0077] Specifically, aggregated tables generated based on existing technologies have the following drawbacks: First, they lack an overall view that reflects the actual coverage of business operations, only providing a coarse-grained view of business object coverage. This makes it difficult to clarify the actual business coverage and to answer what has already been built and what still needs to be built. Second, aggregated tables suffer from insufficient data object aggregation: current aggregated tables are built on a physical table basis, resulting in low data object coverage and a lack of a unified aggregation unit standard, leading to asset fragmentation and redundant construction. Finally, aggregated tables lack operational analysis elements: because they are mainly based on transaction records and account balances, they do not embed the tag information required for operational analysis, such as costs and profits, limiting their application in business decision-making.

[0078] Therefore, a comprehensive and efficient data processing solution is urgently needed.

[0079] This application provides a data processing method, a method, apparatus, and product for generating cross-domain aggregated views. The method involves acquiring a set of business data corresponding to a target business domain and determining a set of data objects corresponding to that set. Based on the set of data objects, the cross-domain associated aggregated view is updated. According to preset association rules, the business data set and the set of data objects are associated to obtain a corresponding associated data set, which indicates the relationship between data objects and business data. Multiple associated data within the associated data set are then filled into the cross-domain associated aggregated view according to their corresponding relationships. This method constructs an initial multi-dimensional cross-domain associated aggregated view by aggregating cross-domain associated data objects, achieving full coverage of data objects. Furthermore, when generating a cross-domain associated set view corresponding to the target business domain, the cross-domain associated aggregated view is updated based on the set of data objects corresponding to the target domain. The resulting associated data set is then filled into the cross-domain associated aggregated view. This overcomes the limitations of traditional single-domain aggregated tables, enabling cross-domain data association analysis, reducing the complexity of multi-table associations, and improving the efficiency of business analysis and data querying.

[0080] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0081] Figure 1 A flowchart illustrating a data processing method provided in this application is shown below. Figure 1 As shown, the data processing method provided in this application includes:

[0082] S101. Obtain the set of business data corresponding to the target business domain, and determine the set of data objects corresponding to the set of business data.

[0083] Here, the business data set refers to the related business data generated when handling the target business area; the data object represents the sub-branch corresponding to each business data. For example, if the target business area is the personal loan business area, then the obtained business data set includes the business data generated when handling personal loan business; the data object set consists of the subdivided business data categories corresponding to each business data when handling personal loan business.

[0084] In one possible implementation, the set of data objects corresponding to the business data set is determined, and the specific process is as follows:

[0085] Identify at least one business object corresponding to the business data set;

[0086] For any one of the at least one business objects, based on the business data set, determine at least one target data object from multiple candidate data objects corresponding to the business object, wherein the candidate objects corresponding to different business objects are different;

[0087] Generate a collection of data objects based on the target data object corresponding to at least one business object.

[0088] Based on the acquired set of business data, the business objects contained in the set of business data are determined. For example, in the field of personal loan business, based on the obtained set of business data, the business objects corresponding to the handling of personal loan business are determined to include user information, business agreements, and business management.

[0089] Specifically, for each business object, based on the business data set, at least one target data object is determined from multiple candidate data objects corresponding to the business object; for example, for loan business, the data objects corresponding to user information include personal loan information, corporate loan information, departmental loan information, etc. Based on the business data set in the personal loan business field, the corresponding target data object can be determined to be personal loan information.

[0090] Similarly, the data object sets corresponding to the loan business agreement and post-loan management are as follows: the data object set corresponding to the loan business agreement includes personal loan agreements, personal loan product agreements, personal loan agreement applications, personal loan participant agreements, etc., and the data object set corresponding to post-loan management includes repayment strategies, personal loan execution evidence, etc.

[0091] This step involves identifying the business object and its corresponding set of data objects, ensuring that the key object data for building the corresponding view of the target business domain is not missing, reducing redundant data, lowering costs, and improving the generation efficiency of aggregated views.

[0092] S102. Based on the data object set, update the cross-domain related aggregation view, which includes data objects corresponding to multiple business domains.

[0093] The cross-domain related aggregation view is guided by the business domain of the business architecture model. It inherits data objects from the Online Transaction Processing (OLTP) system of the product domain in the banking business architecture model. It uses a selected range of data objects as the main data object and performs aggregation and reorganization within or between these data objects to generate the cross-domain related aggregation view. This view contains data objects from multiple business domains, such as those corresponding to personal loan business, credit card processing business, repayment business, and withdrawal business. When a cross-domain related aggregation view for a specific target business domain needs to be generated, the view must be updated to remove irrelevant or conflicting data objects.

[0094] Understandably, different target business domains face different business objects and data objects, but the framework for cross-domain aggregated views remains consistent. Figure 2 This application provides a schematic diagram of a cross-domain associated aggregation view, such as... Figure 2 As shown, in the updated cross-domain related aggregation view, the dashed line between the initial topic aggregation table and the updated topic aggregation table indicates the generation method of the updated topic aggregation table. For example, the updated topic aggregation table 1 is generated from the initial topic aggregation table 1 and the initial topic aggregation table 2. Taking the personal loan business domain as an example, the framework of the cross-domain related aggregation view, i.e., the cross-domain related aggregation view before the update, involves 3 business objects, 13 data objects, and 43 topic aggregation tables. Based on the data object set corresponding to the personal loan business domain, the cross-domain related aggregation view is updated, and the resulting updated cross-domain related aggregation view contains 3 business objects, 9 data objects, and 13 topic aggregation tables.

[0095] S103. According to the preset association rules, perform data association processing on the business data set and the data object set to obtain the corresponding associated data set. The associated data set is used to indicate the association relationship between the data object and the business data.

[0096] Understandably, different types of business data are scattered. By using pre-defined association rules, these scattered business data are linked to determine which data object each business data belongs to. For example, a data object set contains data object 1, data object 2, and data object 3; the obtained business data set contains business data 1, business data 2, business data 3, business data 4, business data 5, and business data 6. Following the pre-defined association rules, the business data set and the data object set are associated, resulting in the following associated data sets: "Data object 1 - Business data 2", "Data object 2 - Business data 1, business data 3, business data 5", and "Data object 3 - Business data 4, business data 6".

[0097] S104. Fill in the cross-domain association aggregation view with multiple related data in the related data set according to their corresponding association relationships.

[0098] Based on the obtained associated data, fill in the updated cross-domain association aggregation view according to the corresponding association relationship to obtain the cross-domain association aggregation view corresponding to the target domain.

[0099] This embodiment provides a data processing method that involves obtaining a set of business data corresponding to a target business domain and determining a set of data objects corresponding to that set; updating a cross-domain association aggregation view based on the set of data objects; performing data association processing on the set of business data and the set of data objects according to preset association rules to obtain a corresponding association data set, which indicates the association relationship between data objects and business data; and filling multiple association data sets within the association data set into the cross-domain association aggregation view according to their corresponding association relationships. This method updates the cross-domain association aggregation view based on the set of data objects corresponding to the target domain and fills the obtained corresponding association data set into the cross-domain association aggregation view; it overcomes the limitations of traditional single aggregation tables, realizes cross-domain data association analysis, reduces the complexity of multi-table associations, and improves the efficiency of business analysis and data querying.

[0100] Figure 3 A flowchart illustrating a method for generating a cross-domain aggregated view provided in this application is shown below. Figure 3 As shown, the method for generating a cross-domain aggregated view provided in this application includes:

[0101] S301. Obtain historical business data sets corresponding to multiple business areas.

[0102] S302. Based on the historical business data set, determine the initial data object set corresponding to each business domain.

[0103] Understandably, in financial transactions, historical business data sets corresponding to business domain 1, business domain 2, and business domain 3 are obtained as historical business data set 1, historical business data set 2, and historical business data set 3, respectively. The obtained historical business data sets are then processed to determine the initial data object set corresponding to each business domain.

[0104] S303. Based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain, determine the cross-domain associated data object set.

[0105] Cross-domain aggregation rules are a set of rules that define how to aggregate data from different domains and of different types. These rules are typically formulated based on data attributes, relationships, and business needs, and are used to guide how to associate scattered data objects to obtain data objects with related relationships.

[0106] In one possible implementation, the cross-domain associated data object set is determined based on preset cross-domain aggregation rules and the initial data object set corresponding to each business domain. The specific process is as follows:

[0107] Based on historical business data sets and preset information classification rules, determine business attribute information and operational analysis attribute information;

[0108] According to the preset cross-domain aggregation rules, business attribute information, operational analysis attribute information, and the initial data object set are aggregated to obtain a cross-domain related data object set.

[0109] The preset information classification rules are used to standardize the classification of scattered aggregated information. For example, a historical business data set contains data information 1, data information 2, data information 3, data information 4, data information 5, data information 6, data information 7, and data information 8. Using the preset information classification rules, the historical business data is divided into business attribute information and operational analysis attribute information. Specifically, business attribute information includes data information 1 and data information 2; operational analysis attribute information includes operational information 1, operational information 2, and operational information 3. The data information corresponding to each operational information is as follows: "Operational Information 1 - Data Information 3, Data Information 4, Data Information 5", "Operational Information 2 - Data Information 7", and "Operational Information 3 - Data Information 6, Data Information 8". Then, according to the preset cross-domain aggregation rules, the business attribute information, operational analysis attribute information, and the initial data object set are aggregated to obtain a cross-domain related data object set.

[0110] This step aggregates and reorganizes data objects from multiple domains using pre-defined cross-domain aggregation rules, achieving full coverage of core data objects. Simultaneously, based on operational needs, it adds business attribute information and operational analysis attribute information, improving the business adaptability of data assets and enhancing the application of aggregated views in operational decision-making.

[0111] S304. Construct a cross-domain associated aggregated view based on the cross-domain associated data object set and the historical business data set.

[0112] In one possible implementation, a cross-domain associated aggregated view is constructed based on a cross-domain associated data object set and a historical business data set. The specific process is as follows:

[0113] Based on historical business data sets, multiple business themes were identified;

[0114] Based on multiple business themes and a pre-defined theme aggregation framework, determine the theme aggregation table corresponding to each business theme;

[0115] Based on the framework of the full set of cross-domain related data objects, the topic aggregation table corresponding to each business entity, and the pre-defined cross-domain related aggregation view, a cross-domain related aggregation view is constructed.

[0116] Understandably, different thematic domain aggregations target different business and data objects, but the framework for cross-domain related aggregation views remains consistent. Within the business domain, thematic aggregations are broadly divided into six major themes—customers, institutions, employees, channels, and finance—based on the characteristics of each theme. Furthermore, the key points for constructing thematic aggregation tables under each theme are clarified.

[0117] For example, the subject aggregation of parties involved: This type of subject aggregation is general information and needs to be extracted and integrated from data objects under the subject of business objects such as participants in various business areas. Its construction granularity is personnel ID or organization ID, and it mainly revolves around the information of parties involved and their relationships. Among them, customers include individual customers, partners, etc., organizations include mainframe organizations, branches, and various business system organizations derived from branches, and employees include bank employees and employees in various business positions, such as tellers, business consultants, and account managers.

[0118] Financial Themes: These themes are aggregated into general information, and they are mainly built around data objects (such as general ledger, in-balance sheet accounts, etc.) under business object themes such as accounts / agreements and events.

[0119] Product Theme: Based on the business architecture modeling of product and management domains, combined with the key points of business analysis and the historical construction of theme aggregation, the main data objects under the business object themes of protocol / account / medium, event and product are used as the main data objects for coverage construction. Other data objects can be associated as auxiliary data objects, but are not included in the coverage assessment, to meet various analysis needs.

[0120] Channel Theme: Focusing on data objects related to bank channel touchpoints, and combining key business analysis points with the historical development of theme aggregation, the coverage construction will focus on data objects under business objects such as agreements and events as master data objects to meet various analysis needs.

[0121] Figure 4 This application provides an intention to represent a topic aggregation, such as Figure 4 As shown; for example, based on historical business data sets, multiple business themes are identified; based on multiple business themes and a preset theme aggregation framework, the theme aggregation table corresponding to each business theme is determined; specifically, the theme aggregation tables corresponding to each business theme are determined as follows: Product theme "Intermediate Business, Management, Funds", Customer theme "Individual Customers, Partners", Institution theme "Maintenance Institutions, Branch Institutions", Employee theme "Employees, Position Employees", Financial theme "General Ledger", Channel theme "Mobile Banking, Online Banking, Self-Service Terminal".

[0122] This step involves identifying multiple business themes, defining the key points for building aggregation tables for each theme based on their characteristics, improving the coverage and standardization of cross-domain related aggregation views, and achieving unified data management.

[0123] In one possible implementation, the method further includes:

[0124] Obtain new business data sets corresponding to multiple business domains; based on the new business data sets, determine the data object set corresponding to each business domain;

[0125] If it is determined that the data object corresponding to any business domain has been updated, the cross-domain associated aggregation view is updated based on the newly added business data set and the updated data object.

[0126] Understandably, the cross-domain associated aggregation view can be adjusted based on the real-time changes in the newly added business data set and / or business needs; ensuring the real-time performance of the cross-domain aggregation view to meet the data management requirements of new business domains. Specifically, the process involves obtaining new business data sets corresponding to multiple business domains; determining the data object set corresponding to each business domain based on the new business data set; and updating the cross-domain associated aggregation view based on the new business data set and the updated data object if it is determined that the data object corresponding to any business domain has been updated.

[0127] For example, the current cross-domain aggregated view contains 43 data objects. When analyzing and processing newly added business data sets, and obtaining data objects not included in the cross-domain aggregated view, the cross-domain aggregated view can be dynamically adjusted based on the newly added business data sets and the corresponding newly added data objects, without the need for manual modification of the table structure, thus enabling rapid response to changes in business needs.

[0128] Figure 5 A schematic diagram of the structure of a data processing device provided in this application is shown below. Figure 5 As shown, the data processing apparatus 500 provided in this application includes:

[0129] The acquisition module 501 is used to acquire the set of business data corresponding to the target business domain and determine the set of data objects corresponding to the set of business data.

[0130] Update module 502 is used to update cross-domain associated aggregated views based on a collection of data objects. The cross-domain associated aggregated views include data objects corresponding to multiple business domains.

[0131] The processing module 503 is used to perform data association processing on the business data set and the data object set according to the preset association rules to obtain the corresponding associated data set, which is used to indicate the association relationship between the data object and the business data.

[0132] The processing module 503 is also used to fill multiple related data in the related data set into the cross-domain related aggregation view according to the corresponding relationship.

[0133] In one possible implementation, the acquisition module 501 is further configured to:

[0134] Identify at least one business object corresponding to the business data set;

[0135] For any one of the at least one business objects, based on the business data set, determine at least one target data object from multiple candidate data objects corresponding to the business object, wherein the candidate objects corresponding to different business objects are different;

[0136] Generate a collection of data objects based on the target data object corresponding to at least one business object.

[0137] The data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0138] Figure 6 A schematic diagram of the structure of a cross-domain aggregated view generation device provided in this application is shown below. Figure 6As shown, the cross-domain aggregated view generation apparatus 600 provided in this application includes:

[0139] The acquisition module 601 is used to acquire historical business data sets corresponding to multiple business domains;

[0140] Processing module 602 is used to determine the initial data object set corresponding to each business domain based on the historical business data set;

[0141] The processing module 602 is also used to determine the cross-domain associated data object set based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain;

[0142] Module 603 is used to build a cross-domain associated aggregated view based on a cross-domain associated data object set and a historical business data set.

[0143] In one possible implementation, the building module 603 is further configured to:

[0144] Based on historical business data sets, multiple business themes were identified;

[0145] Based on multiple business themes and a pre-defined theme aggregation framework, determine the theme aggregation table corresponding to each business theme;

[0146] Based on the framework of the full set of cross-domain related data objects, the topic aggregation table corresponding to each business entity, and the pre-defined cross-domain related aggregation view, a cross-domain related aggregation view is constructed.

[0147] In one possible implementation, the processing module 602 is further configured to:

[0148] Based on historical business data sets and preset information classification rules, determine business attribute information and operational analysis attribute information;

[0149] According to the preset cross-domain aggregation rules, business attribute information, operational analysis attribute information, and the initial data object set are aggregated to obtain a cross-domain related data object set.

[0150] In one possible implementation, the apparatus for generating cross-domain aggregated views further includes an update module 604, which is configured to:

[0151] Obtain new business data sets corresponding to multiple business domains; based on the new business data sets, determine the data object set corresponding to each business domain;

[0152] If it is determined that the data object corresponding to any business domain has been updated, the cross-domain associated aggregation view is updated based on the newly added business data set and the updated data object.

[0153] The cross-domain aggregated view generation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0154] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 7 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0155] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0156] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0157] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method; its implementation principle and technical effect are similar and will not be described in detail here.

[0158] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the above method embodiments. Its implementation principle and technical effects are similar, and will not be repeated here.

[0159] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0160] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0161] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0162] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0163] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0164] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0165] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0166] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0167] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data processing method, characterized in that, include: Obtain the set of business data corresponding to the target business domain, and determine the set of data objects corresponding to the set of business data; Based on the data object set, the cross-domain related aggregate view is updated, and the cross-domain related aggregate view includes data objects corresponding to multiple business domains. According to preset association rules, data association processing is performed on the business data set and the data object set to obtain a corresponding associated data set, which is used to indicate the association relationship between the data object and the business data. Multiple related data within the aforementioned related data set are filled into the cross-domain related aggregation view according to their corresponding relationships.

2. The method according to claim 1, characterized in that, Determining the set of data objects corresponding to the business data set includes: Identify at least one business object corresponding to the business data set; For any one of the at least one business object, based on the business data set, at least one target data object is determined from multiple candidate data objects corresponding to the business object, wherein the candidate objects corresponding to different business objects are different; The data object set is generated based on the target data object corresponding to at least one of the business objects.

3. A method for generating a cross-domain aggregated view, characterized in that, include: Obtain historical business data sets corresponding to multiple business areas; Based on the aforementioned historical business data set, determine the initial data object set corresponding to each business domain; Based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain, the cross-domain associated data object set is determined; Based on the cross-domain associated data object set and the historical business data set, a cross-domain associated aggregated view is constructed.

4. The method according to claim 3, characterized in that, The construction of a cross-domain associated aggregated view based on the cross-domain associated data object set and the historical business data set includes: Based on the aforementioned historical business data set, multiple business themes are identified; Based on the multiple business themes and the preset theme aggregation framework, determine the theme aggregation table corresponding to each business theme; Based on the framework of the full set of cross-domain related data objects, the topic aggregation table corresponding to each business entity, and the preset cross-domain related aggregation view, a cross-domain related aggregation view is constructed.

5. The method according to claim 3, characterized in that, The determination of the cross-domain associated data object set based on preset cross-domain aggregation rules and the initial data object set corresponding to each business domain includes: Based on the historical business data set and the preset information classification rules, business attribute information and operational analysis attribute information are determined. According to the preset cross-domain aggregation rules, the business attribute information, the operational analysis attribute information, and the initial data object set are aggregated to obtain the cross-domain associated data object set.

6. The method according to claim 3, characterized in that, The method further includes: Obtain new business data sets corresponding to multiple business domains; based on the new business data sets, determine the data object set corresponding to each business domain; If it is determined that the data object corresponding to any business domain has been updated, the cross-domain association aggregation view is updated based on the newly added business data set and the updated data object.

7. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire a set of business data corresponding to the target business domain and determine a set of data objects corresponding to the set of business data. The update module is used to update the cross-domain associated aggregation view based on the data object set, wherein the cross-domain associated aggregation view includes data objects corresponding to multiple business domains; The processing module is used to perform data association processing on the business data set and the data object set according to preset association rules to obtain a corresponding associated data set, which is used to indicate the association relationship between the data object and the business data. The processing module is also used to fill multiple related data in the related data set into the cross-domain related aggregation view according to their corresponding relationships.

8. An apparatus for generating a cross-domain aggregated view, characterized in that, include: The acquisition module is used to acquire historical business data sets corresponding to multiple business areas; The processing module is used to determine the initial data object set corresponding to each business domain based on the historical business data set; The processing module is also used to determine the cross-domain associated data object set based on the preset cross-domain aggregation rules and the initial data object set corresponding to each business domain; The construction module is used to construct a cross-domain associated aggregated view based on the cross-domain associated data object set and the historical business data set.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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