Business data processing method, warehouse system, and electronic device

Abstract

The application discloses a kind of processing method of service data and warehouse system, electronic equipment, wherein the processing method includes: receiving the data processing request of target organization, based on data source identification, obtain the data to be processed, and based on organization attribute and business type, analyze the data to be processed, generate multiple dimension files, determine the change data in each dimension file, and based on change data, generate change file, based on multiple dimension files and change file, using preset data architecture, construct the data model of target organization.The present application solves the technical problem that the amount of information is too large in related art, which cannot efficiently analyze the change information directly, resulting in low work efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method for processing business data, a warehouse system, and electronic equipment. Background Technology

[0002] A data model is a tool and method for abstractly describing the real world. It represents the relationships between transactions in the real world through abstract entities and the connections between them. A data warehouse model is a specific data model for a particular data warehouse application system. Among data models, the star schema is a commonly used dimensional modeling method. In a star schema, the fact table is central, and all dimension tables are directly connected to the fact table. Dimensional modeling in a star schema consists of a fact table and a set of dimension tables. The snowflake schema is an extension of the star schema. In a snowflake schema, dimension tables can have other dimension tables. Although this model is more standardized than the star schema, it is less easy to understand, has higher maintenance costs, and requires joining multiple layers of dimension tables, resulting in lower performance compared to the star schema.

[0003] In related technologies, Figure 1 This is a schematic diagram of an alternative data warehouse model based on existing technologies, such as... Figure 1 As shown, the system includes: Department Table, Sales Table, Product Table, Region Table, Time Table, Country Table, Province Table, and City Table. Each table adds fields such as surrogate key, operation time, operation type, previous record field, and automatic end time of status to the existing business fields. It is specifically designed to be applied to various data warehouse data usage scenarios, unifying the data organization format, being compatible with more storage engines, providing more direct single-table analysis capabilities and more flexible data association logic, and enabling more cohesive data integration. This makes continuous integration of new data sources more convenient and provides a more effective continuous integration modeling approach. Based on a predefined state machine, it facilitates the design and development of state transition checking functions, real-time checks on data accuracy, and timely alerts.

[0004] However, for the above data warehouse model, since each model table in the model architecture is relatively independent and needs to be continuously designed according to requirements, it will result in an excessive number of tables, which will not be intuitive enough. In addition, when applying it, it will lead to low data mining efficiency, difficulty in aggregation analysis and application, and when obtaining data from each single table dimension, different departments provide data for different purposes, making it difficult to verify the correctness of the data.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides a method for processing business data, a warehouse system, and electronic equipment to at least solve the technical problem in related technologies where excessively large amounts of data make it impossible to efficiently analyze changing information, resulting in low work efficiency.

[0007] According to one aspect of the present invention, a method for processing business data is provided, comprising: receiving a data processing request from a target organization, wherein the data processing request includes at least: a data source identifier and an organization attribute; acquiring data to be processed based on the data source identifier, and analyzing the data to be processed based on the organization attribute and business type to generate multiple dimension files; determining the variable data in each of the dimension files, and generating a change file based on the variable data; and constructing a data model of the target organization based on the multiple dimension files and the change file, using a preset data architecture.

[0008] Optionally, before analyzing the data to be processed and generating multiple dimension files based on the organizational attributes and business types, the method further includes: refining the department information carried by the organizational attributes based on the business types to obtain a target department set; determining the number of employees in each target department in the target department set based on the employee information carried by the organizational attributes; and recording the joining time of each employee and the establishment time of each target department based on the department information and the employee information.

[0009] Optionally, the step of analyzing the data to be processed and generating multiple dimension files based on the organizational attributes and business types includes: classifying the data to be processed based on the business types to obtain multiple business data sets, wherein each business data set is associated with a target department; processing the business data sets associated with each target department to obtain multiple dimension files, wherein each dimension file represents the record information of a business data set corresponding to the target department, and the record information includes at least one of the following: the number of employees in the target department, the joining time of each employee in the target department, and the establishment time of the target department.

[0010] Optionally, after processing the business data sets associated with each of the target departments to obtain multiple dimension files, the method further includes: dividing a preset time period into multiple sub-time periods; determining the employee allocation status and business status for processing each of the business data sets within each sub-time period based on the multiple dimension files; and determining the duration of the employee allocation status and business status for each of the business data sets.

[0011] Optionally, the step of determining the variable data in each of the dimension files and generating a change file based on the variable data includes: comparing the employee allocation status and the business status in the current sub-time period with those in the previous sub-time period to obtain a comparison result; determining the employee change information and business change information in each of the dimension files based on the comparison result; and extracting the employee change information and business change information from each of the dimension files to generate the change file.

[0012] Optionally, after constructing the data model of the target organization based on the multiple dimension files and the variable files using a preset data architecture, the method further includes: cleaning incomplete and duplicate data information in the data model; determining preset indicator values ​​for each dimension file based on the cleaned data model to obtain a set of preset indicator values ​​for each dimension file; and classifying the dimension files in the data model based on business type to obtain multiple target files.

[0013] Optionally, after classifying the dimension files in the data model based on business type to obtain multiple target files, the method further includes: receiving a data analysis request from the target organization, wherein the data analysis request includes at least: a business type identifier; determining the corresponding target file based on the business type identifier; analyzing the business data set indicated by the target file to obtain analysis results; and, if the analysis results are encrypted, transmitting the encrypted analysis results to the target organization.

[0014] According to another aspect of the present invention, a warehouse system is also provided, comprising: a data warehouse, configured to receive data processing requests from a target organization; obtain data to be processed from a data source based on a data source identifier carried in the data processing request; analyze the data to be processed based on a business type and an organization attribute carried in the data processing request; generate multiple dimension files; determine variable data in each dimension file; generate a change file based on the variable data; construct a data model of the target organization based on the multiple dimension files and the change file using a preset data architecture; and store the data model in a data storage layer; and a data application layer, configured to receive data analysis requests from the target organization; obtain corresponding dimension files from the data storage layer based on a business type identifier carried in the data analysis request; analyze the business data set indicated by the dimension files to obtain analysis results; and, if the analysis results are encrypted, transmit the encrypted analysis results to the target organization.

[0015] Optionally, the data warehouse includes: a data source for storing source data from various institutions, wherein each source data is associated with a data source identifier; a data mart layer for constructing a data model of the target institution based on the data to be processed, according to the business type and the institution attributes carried in the data processing request; a data storage layer for storing the data model in a storage repository; a cleaning module for cleaning incomplete and duplicate data information in the data model; a statistics module for determining preset indicator values ​​for each dimension file based on the cleaned data model, thereby obtaining a set of preset indicator values ​​for each dimension file; and a classification module for classifying the dimension files in the data model based on the business type, thereby obtaining multiple target files.

[0016] Optionally, the data mart layer includes: a business department segmentation unit, used to refine the department information carried by the organization attribute based on the business type using a department segmentation module to obtain a target department set; to determine the number of employees in each target department in the target department set using a personnel segmentation module based on the employee information carried by the organization attribute; to record the joining time of each employee and the establishment time of each target department using a time recording module based on the department information and the employee information; to determine the changed data in each dimension file using a change module, and to update the record information in the dimension file based on the changed data; and a business unit, used to classify the data to be processed based on the business type using a business classification module to obtain multiple business data sets; to divide a preset time period into multiple sub-time periods using a business personnel allocation module, and to determine the employee allocation status and business status for processing each business data set in each sub-time period based on multiple dimension files; to determine the duration of the employee allocation status and business status of each business data set using a duration module; and to change the employee information and business information in each dimension file using a change module.

[0017] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the business data processing method described above.

[0018] This disclosure involves receiving a data processing request from a target organization, acquiring the data to be processed based on a data source identifier, analyzing the data based on the organization's attributes and business type, generating multiple dimension files, identifying the variable data in each dimension file, generating a change file based on the variable data, and constructing a data model for the target organization based on the multiple dimension files and the change file, using a pre-defined data architecture. In this application, the data to be processed can be analyzed according to the business type and the target organization's attributes to obtain multiple dimension files, identify the variable data in each dimension file to obtain a change file, and then construct a data model for the target organization based on the multiple dimension files and the change file. This enables clear data integration and classification, intuitive display, reduced table size, and efficient analysis of change information through the change file, improving work efficiency. This solves the technical problem in related technologies where excessive table size prevents efficient direct analysis of change information, leading to low work efficiency. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a schematic diagram of an alternative data warehouse model based on existing technology;

[0021] Figure 2 This is a flowchart of an optional business data processing method according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of an optional warehouse system according to an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of an optional data mart layer according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of an optional data application layer according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent from the aforementioned user or organization.

[0028] The following embodiments of the present invention can be applied to various systems / applications / devices that process business data. The present invention, by collecting and classifying the required data, achieves clear data integration and categorization. By identifying change files, it enables efficient and intuitive analysis of change information. Simultaneously, based on received analytical requirements, it can summarize, analyze, and provide feedback on the categorized and statistically processed data in the data storage layer, effectively reducing unnecessary data redundancy and resulting in high processing efficiency for data aggregation applications, enabling rapid retrieval of required data. Furthermore, the present invention provides data through a unified data source, improving the consistency of data statistical standards and facilitating data accuracy verification and subsequent traceability.

[0029] The present invention will now be described in detail with reference to various embodiments.

[0030] Example 1

[0031] According to an embodiment of the present invention, a method for processing business data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 2 This is a flowchart of an optional business data processing method according to an embodiment of the present invention, such as... Figure 2As shown, the method includes the following steps:

[0033] Step S201: Receive a data processing request from the target organization, wherein the data processing request includes at least: data source identifier and organization attributes.

[0034] Step S202: Based on the data source identifier, obtain the data to be processed, and based on the organization attributes and business type, analyze the data to be processed to generate multi-dimensional files.

[0035] Step S203: Determine the variable data in each dimension file and generate a variable file based on the variable data.

[0036] Step S204: Based on multiple dimension files and change files, and using a preset data architecture, construct a data model of the target organization.

[0037] Through the above steps, a data processing request from the target organization can be received. Based on the data source identifier, the data to be processed can be obtained. Based on the organization's attributes and business type, the data to be processed can be analyzed to generate multiple dimension files. The variable data in each dimension file can be determined, and a change file can be generated based on the variable data. Based on the multiple dimension files and the change file, a data model of the target organization can be constructed using a preset data architecture. In this embodiment of the invention, the data to be processed can be analyzed according to the business type and the organization's attributes to obtain multiple dimension files. The variable data in each dimension file can be determined to obtain a change file. Then, based on the multiple dimension files and the change file, a data model of the target organization can be constructed. This makes data integration and classification clear, the display intuitive, and reduces the number of tables. Furthermore, the change file allows for efficient analysis of change information, improving work efficiency. This solves the technical problem in related technologies where excessive table information makes it impossible to efficiently analyze change information directly, resulting in low work efficiency.

[0038] The embodiments of the present invention will now be described in detail with reference to the steps described above.

[0039] Step S201: Receive a data processing request from the target organization, wherein the data processing request includes at least: data source identifier and organization attributes.

[0040] In this embodiment of the invention, the target institution can be a financial institution (e.g., a bank) or other institutions. The system can first receive a data processing request from the target institution (the data processing request includes: data source identifier, institution attributes, etc.), then, based on the data source identifier, retrieve the data to be processed required by the target institution from the corresponding data source, and finally, create a data model that meets the needs of the target institution according to its design requirements.

[0041] Optionally, before analyzing the data to be processed based on organizational attributes and business types and generating multi-dimensional files, the process may include: refining the department information carried by the organizational attributes based on business types to obtain a set of target departments; determining the number of employees in each target department in the set of target departments based on the employee information carried by the organizational attributes; and recording the joining time of each employee and the establishment time of each target department based on the department information and employee information.

[0042] In this embodiment of the invention, the department information carried by the institutional attributes can be refined according to the business type (e.g., bill type, fund type, etc.) to obtain a set of target departments. Such a target department can handle one type of business. The number of employees in each target department in the set of target departments can be determined according to the employee information carried by the institutional attributes. The joining time of each employee and the establishment time of each target department can be recorded according to the department information and employee information.

[0043] Step S202: Based on the data source identifier, obtain the data to be processed, and based on the organization attributes and business type, analyze the data to be processed to generate multi-dimensional files.

[0044] Optionally, the step of analyzing the data to be processed and generating multiple dimension files based on organizational attributes and business types includes: classifying the data to be processed based on business types to obtain multiple business data sets, wherein each business data set is associated with a target department; processing the business data sets associated with each target department to obtain multiple dimension files, wherein each dimension file represents the record information of a business data set corresponding to a target department, and the record information includes at least one of the following: the number of employees in the target department, the joining time of each employee in the target department, and the establishment time of the target department.

[0045] In this embodiment of the invention, the data to be processed can be classified according to the business type to obtain multiple business data sets. Each business data set can be associated with a target department. Then, the business data sets associated with each target department can be processed separately, so that each business data set generates a dimension file (e.g., invoice file, fund file, employee information file, etc.), thereby obtaining multiple dimension files. Each dimension file can represent the record information of a business data set corresponding to a target department. The record information may include: the number of employees in the target department, the joining time of each employee in the target department, the establishment time of the target department, etc.

[0046] Optionally, after processing the business data sets associated with each target department to obtain multiple dimension files, the method further includes: dividing the preset time period into multiple sub-time periods; determining the employee allocation status and business status for processing each business data set within each sub-time period based on the multiple dimension files; and determining the duration of the employee allocation status and business status for each business data set.

[0047] In this embodiment of the invention, a preset time period (such as the past year) can be divided into multiple sub-time periods (such as dividing a year into 12 monthly sub-time periods). Then, based on multiple dimension files, the employee allocation status (i.e., the specific employee assigned to a certain business) and the business status (i.e., the status of a certain business, whether it is maintained normally or in a changing state) for processing each business data set in each sub-time period can be determined. The duration of the employee allocation status and the business status of the business data set in the current state can also be determined.

[0048] Step S203: Determine the variable data in each dimension file and generate a variable file based on the variable data.

[0049] Optionally, the steps of determining the changed data in each dimension file and generating a change file based on the changed data include: comparing the employee allocation status and business status in the current sub-time period with those in the previous sub-time period to obtain the comparison results; determining the employee change information and business change information in each dimension file based on the comparison results; and extracting the employee change information and business change information from each dimension file to generate a change file.

[0050] In this embodiment of the invention, the employee allocation status and business status of the current sub-time period (e.g., within the current month) can be compared with those of the previous sub-time period (within the previous month) to determine whether the status has changed and obtain the comparison results. If a change has occurred, the employee change information and business change information of each dimension file can be extracted to obtain the change file. This facilitates the analysis of change information within a certain time period without having to query each dimension file sequentially, thus improving work efficiency.

[0051] Step S204: Based on multiple dimension files and change files, and using a preset data architecture, construct a data model of the target organization.

[0052] In this embodiment of the invention, a data model required by the target organization can be constructed based on a pre-established data architecture and multiple dimension files and change files, so as to facilitate subsequent data analysis.

[0053] Optionally, after constructing the data model of the target organization based on multiple dimension files and variable files using a preset data architecture, the process also includes: cleaning incomplete and duplicate data information in the data model; determining preset indicator values ​​for each dimension file based on the cleaned data model to obtain a set of preset indicator values ​​for each dimension file; and classifying the dimension files in the data model based on business type to obtain multiple target files.

[0054] In this embodiment of the invention, the obtained data model can be saved to the data storage layer. Then, incomplete and duplicate data information in the data model can be cleaned. For the cleaned data model, preset indicator values ​​(e.g., transaction volume, improvement rate, etc.) of each dimension file can be calculated, thereby obtaining a set of preset indicator values ​​for each dimension file, which facilitates subsequent indicator analysis. Furthermore, the dimension files in the data model can be dimensionality-reduced and classified according to business type, that is, business data that can be correlated and are applicable to the same type of business can be integrated to obtain multiple target files.

[0055] Optionally, after classifying the dimension files in the data model based on business type to obtain multiple target files, the method further includes: receiving a data analysis request from the target organization, wherein the data analysis request includes at least: a business type identifier; determining the corresponding target file based on the business type identifier; analyzing the business data set indicated by the target file to obtain analysis results; and, if the analysis results are encrypted, transmitting the encrypted analysis results to the target organization.

[0056] In this embodiment of the invention, a data analysis request from a target organization can be received. The data analysis request includes a business type identifier, etc. Then, based on the business type identifier, the corresponding target file is determined. According to the analysis requirements in the data analysis request, the business data set indicated by the target file is analyzed to obtain the analysis results. Afterward, the analysis results can be encrypted and transmitted to the target organization.

[0057] In this embodiment of the invention, by collecting and classifying the required data, the data integration and classification are clear. Furthermore, based on the received analysis requirements, the classified and statistically analyzed data in the data storage layer can be summarized, analyzed, and fed back. This reduces unnecessary data redundancy, improves the processing efficiency of data aggregation applications, and enables quick retrieval of required data. At the same time, by providing data through a unified data source, the inconsistency of data statistical standards can be effectively improved, facilitating data accuracy verification and subsequent traceability.

[0058] The following is a detailed description with reference to another embodiment.

[0059] Example 2

[0060] Figure 3This is a schematic diagram of an optional warehouse system according to an embodiment of the present invention, such as... Figure 3 As shown, it includes: a data warehouse 1 and a data application layer 2. Data warehouse 1 includes: a data source 11, a data mart layer 12, and a data storage layer 13. The output of data source 11 is connected to the input of data mart layer 12, and the output of data mart layer 12 is connected to the input of data storage layer 13. Data warehouse 1 and data application layer 2 are bidirectionally connected. Data source 11 includes: a database and working logs. Data storage layer 13 includes: a storage database, a cleaning module, a statistics module, and a classification module. Specific functions are as follows:

[0061] The data warehouse is used to receive data processing requests from target organizations, obtain data to be processed from the data source based on the data source identifier carried in the data processing request, analyze the data to be processed based on the business type and the organization attributes carried in the data processing request, and generate multiple dimension files; identify the changed data in each dimension file, and generate change files based on the changed data; construct the data model of the target organization based on the multiple dimension files and change files, using a preset data architecture; and store the data model in the data storage layer.

[0062] The data application layer is used to receive data analysis requests from the target organization, retrieve the corresponding dimension file from the data storage layer based on the business type identifier carried in the data analysis request, analyze the business data set indicated by the dimension file to obtain the analysis results, and transmit the encrypted analysis results to the target organization if the analysis results are encrypted.

[0063] In one optional embodiment, the data warehouse includes: a data source for storing source data from various institutions, wherein each source data is associated with a data source identifier; and a data mart layer for constructing a data model of the target institution based on the data to be processed, according to the business type and the institutional attributes carried in the data processing request.

[0064] Figure 4 This is a schematic diagram of an optional data mart layer according to an embodiment of the present invention, such as... Figure 4 As shown, it includes: business department division unit 121 and business unit 122. The business department division unit 121 includes: personnel division module, department division module, time recording module and change module. The business unit 122 includes: business classification module, business personnel allocation module, duration module and change module.

[0065] In one optional embodiment, the data mart layer includes: a business department segmentation unit, used to refine the department information carried by the organization attribute based on the business type through the department segmentation module to obtain a target department set; to determine the number of employees in each target department in the target department set based on the employee information carried by the organization attribute through the personnel segmentation module; to record the joining time of each employee and the establishment time of each target department through the time recording module based on the department information and employee information; to determine the changed data in each dimension file through the change module, and to update the record information in the dimension file based on the changed data; a business unit, used to classify the data to be processed based on the business type through the business classification module to obtain multiple business data sets; to divide a preset time period into multiple sub-time periods through the business personnel allocation module, and to determine the employee allocation status and business status of each business data set within each sub-time period based on multiple dimension files; to determine the duration of the employee allocation status and business status of each business data set through the duration module; and to modify the employee information and business information in each dimension file through the modification module.

[0066] Another optional feature in this embodiment is a data storage layer, which is used to store the data model through a storage library; clean the incomplete and duplicate data information in the data model through a cleaning module; determine the preset indicator values ​​of each dimension file based on the cleaned data model through a statistics module, and obtain a set of preset indicator values ​​for each dimension file; and classify the dimension files in the data model based on the business type through a classification module to obtain multiple target files.

[0067] Figure 5 This is a schematic diagram of an optional data application layer according to an embodiment of the present invention, such as... Figure 5 As shown, it includes: a receiving module, an analysis module, an encryption module, a data aggregation module, and a feedback module.

[0068] In this embodiment, the receiving module of the data application layer collects data from the data storage library according to the received analysis requirements. The data aggregation module integrates and classifies the collected data, and the analysis module analyzes it. The analysis results are encrypted by the encryption module to ensure data security, and the analysis results are transmitted to the terminal of the target institution through the feedback module.

[0069] The following is combined with Figure 3 , Figure 4 , Figure 5 This section provides a detailed explanation of the application of the warehouse system.

[0070] (1) The source data is recorded in the database and work log. The business department division unit 121 and business unit 122 in the data mart layer 12 obtain the target data corresponding to the data source 11 according to the data source identifier, and create a data model that meets their own needs according to the design required by the target organization.

[0071] (2) The personnel division module is used to display the number of personnel in each department. The department division module is used to refine the departments according to the division of business. The time recording module is used to record the joining time of each person and the establishment time of the department. The change module is used to delete or change the dimension data of the changed data when the target data is changed. At the same time, business unit 122 can display the business events related to the current organization. The business classification module can classify specific businesses according to the nature of the business. The business personnel allocation module is used to display the specific personnel allocated in different specific times. The duration module is used to display the duration of the current state of the business. The change module can change the personnel flow and business changes in specific businesses.

[0072] (3) The data model obtained by the data mart layer 12 can be saved in the storage library in the data storage layer 13. The cleaning module is used to remove incomplete and duplicate data information in the storage library. The statistics module can perform detailed statistics on the data transmitted to the storage library. The classification module can classify the information according to the business type.

[0073] (4) The receiving module of the data application layer 2 can collect data from the data storage library according to the received analysis requirements. The data aggregation module can integrate and classify the collected data and analyze it through the analysis module. The analysis results are encrypted through the encryption module to ensure data security, and the analysis results are transmitted to the terminal of the target organization through the feedback module.

[0074] In this embodiment, data in the storage library can be automatically deleted if the retention time exceeds a preset period (such as one year), thereby expanding the storage space.

[0075] In this embodiment, the warehouse system can collect and classify the required data through the data mart layer, making the data integration and classification clear. At the same time, the data application layer can summarize, analyze and provide feedback on the classified and statistical data in the data storage layer according to the received analysis requirements, reducing unnecessary data redundancy and making the data aggregation application highly efficient, enabling quick querying of the required data. Furthermore, by providing data through a unified data source, the inconsistency of data statistical standards is improved, facilitating data accuracy and subsequent traceability.

[0076] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors cause the one or more processors to implement the above-described method for processing business data.

[0077] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0078] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0082] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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 storage medium 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0083] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing business data, characterized in that, include: Receive a data processing request from the target organization, wherein the data processing request includes at least: a data source identifier and organization attributes; Based on the data source identifier, the data to be processed is obtained, and based on the organization attributes and business type, the data to be processed is analyzed to generate multi-dimensional files; Identify the changed data in each of the dimension files, and generate a change file based on the changed data; Based on the multiple dimension files and the variable files, a data model of the target organization is constructed using a preset data architecture; Before analyzing the data to be processed and generating multiple dimension files based on the organizational attributes and business types, the process further includes: refining the department information carried by the organizational attributes based on the business types to obtain a target department set; determining the number of employees in each target department in the target department set based on the employee information carried by the organizational attributes; and recording the joining time of each employee and the establishment time of each target department based on the department information and the employee information. The steps of analyzing the data to be processed and generating multiple dimension files based on the organizational attributes and business types include: classifying the data to be processed based on the business types to obtain multiple business data sets, wherein each business data set is associated with a target department; processing the business data sets associated with each target department to obtain multiple dimension files, wherein each dimension file represents the record information of a business data set corresponding to the target department, and the record information includes at least one of the following: the number of employees in the target department, the joining time of each employee in the target department, and the establishment time of the target department; The steps of determining the changed data in each of the dimension files and generating a change file based on the changed data include: comparing the employee allocation status and business status in the current sub-time period with those in the previous sub-time period to obtain a comparison result; determining the employee change information and business change information in each of the dimension files based on the comparison result; and extracting the employee change information and business change information from each of the dimension files to generate the change file.

2. The processing method according to claim 1, characterized in that, After processing the business data sets associated with each of the target departments to obtain multiple dimension files, the process further includes: Divide the preset time period into multiple sub-time periods; Based on the multiple dimension files, determine the employee allocation status and business status for processing each set of business data within each sub-time period; Determine the employee allocation status and the duration of the business status for each of the aforementioned business data sets.

3. The processing method according to claim 1, characterized in that, After constructing the data model of the target organization based on the multiple dimension files and the variable files, using a preset data architecture, the method further includes: Clean up incomplete and duplicate data in the data model; Based on the cleaned data model, the preset index values ​​of each dimension file are determined to obtain a set of preset index values ​​for each dimension file. Based on the business type, the dimensional files in the data model are classified to obtain multiple target files.

4. The processing method according to claim 3, characterized in that, After classifying the dimension files in the data model based on business type to obtain multiple target files, the process further includes: Receive a data analysis request from the target organization, wherein the data analysis request includes at least: a business type identifier; Based on the business type identifier, the corresponding target file is determined; The analysis results are obtained by analyzing the set of business data indicated by the target file; If the analysis results are encrypted, the encrypted analysis results are transmitted to the target organization.

5. A warehouse system, characterized in that, include: A data warehouse is used to receive data processing requests from target organizations, obtain data to be processed from the data source based on the data source identifier carried in the data processing request, analyze the data to be processed based on the business type and the organization attributes carried in the data processing request, generate multiple dimension files, determine the changed data in each dimension file, and generate change files based on the changed data. Based on the multiple dimension files and the variable files, a data model of the target organization is constructed using a preset data architecture; the data model is then stored in the data storage layer. The data application layer is used to receive data analysis requests from the target organization, retrieve the corresponding dimension file from the data storage layer based on the business type identifier carried in the data analysis request, analyze the business data set indicated by the dimension file, and obtain analysis results. If the analysis results are encrypted, the encrypted analysis results are transmitted to the target organization. The data warehouse includes a data mart layer, wherein the data mart layer includes: The business department segmentation unit is used to refine the department information carried by the organization attribute based on the business type through the department segmentation module to obtain a target department set; to determine the number of employees in each target department in the target department set through the personnel segmentation module based on the employee information carried by the organization attribute; to record the joining time of each employee and the establishment time of each target department through the time recording module based on the department information and the employee information; and to determine the changed data in each dimension file through the change module and update the record information in the dimension file based on the changed data. The business unit is used to classify the data to be processed based on the business type using a business classification module to obtain multiple business data sets; to divide a preset time period into multiple sub-time periods using a business personnel allocation module, and to determine the employee allocation status and business status for processing each business data set within each sub-time period based on multiple dimension files; to determine the duration of the employee allocation status and business status for each business data set using a duration module; and to modify the employee information and business information in each dimension file using a modification module.

6. The warehouse system according to claim 5, characterized in that, The data warehouse includes: The data source is used to store the source data of each institution, wherein each source data is associated with a data source identifier; The data mart layer is used to construct the data model of the target organization based on the data to be processed, according to the business type and the organization attributes carried by the data processing request; The data storage layer is used to store the data model in a storage library; the cleaning module cleans incomplete and duplicate data information in the data model; the statistics module determines the preset indicator values ​​of each dimension file based on the cleaned data model to obtain a set of preset indicator values ​​for each dimension file; and the classification module classifies the dimension files in the data model based on business type to obtain multiple target files.

7. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the business data processing method according to any one of claims 1 to 4.

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