Data processing method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of big data, and relates to a data processing method, which comprises the following steps: receiving a submitted data processing instruction, and extracting a submitted item index from the submitted data processing instruction; obtaining a table model, an index asset document and a processing link corresponding to the submission item index; obtaining index data of the submission item index from the table model based on an index definition in the index asset document; and performing step-by-step processing on the index data according to the processing link to obtain an application layer table for submission. The invention further provides a data processing device, computer equipment and a storage medium. According to the invention, the accuracy and efficiency of submitted data processing are improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a data processing method, device, computer equipment and storage medium. Background Art

[0002] In the financial and insurance industries, as regulatory requirements become increasingly stringent, the accuracy, completeness and timeliness of data reporting have become particularly important. However, the existing regulatory reporting system generally has some defects and deficiencies.

[0003] First, many institutions rely on traditional database management systems, such as Oracle, for data processing. When these systems process complex business logic, they often lead to redundant and chaotic data processing logic, which brings great challenges to data management. Secondly, the lack of uniformity in data model design leads to different data processing methods in different business scenarios and low data standardization, which affects the reliability and comparability of data. All these reduce the accuracy and efficiency of reported data processing. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a data processing method, apparatus, computer equipment and storage medium to solve the problem of low accuracy and efficiency in processing reported data.

[0005] In order to solve the above technical problems, the present application provides a data processing method, which adopts the following technical solution:

[0006] Receiving a reporting data processing instruction, and extracting a reporting item indicator from the reporting data processing instruction;

[0007] Obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator;

[0008] Based on the indicator definition in the indicator asset document, obtaining the indicator data of the reporting item indicator from the table model;

[0009] The indicator data is processed step by step according to the processing link to obtain an application layer table for reporting.

[0010] In order to solve the above technical problems, the embodiment of the present application further provides a data processing device, which adopts the following technical solution:

[0011] An indicator extraction module, used for receiving a report data processing instruction and extracting a report item indicator from the report data processing instruction;

[0012] A reporting and extraction module, used to obtain a table model, an indicator asset document and a processing link corresponding to the reporting item indicator;

[0013] A data extraction module, used for acquiring the indicator data of the reporting item indicator from the table model based on the indicator definition in the indicator asset document;

[0014] The data processing module is used to process the indicator data step by step according to the processing link to obtain an application layer table for reporting.

[0015] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0016] Receiving a reporting data processing instruction, and extracting a reporting item indicator from the reporting data processing instruction;

[0017] Obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator;

[0018] Based on the indicator definition in the indicator asset document, obtaining the indicator data of the reporting item indicator from the table model;

[0019] The indicator data is processed step by step according to the processing link to obtain an application layer table for reporting.

[0020] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0021] Receiving a reporting data processing instruction, and extracting a reporting item indicator from the reporting data processing instruction;

[0022] Obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator;

[0023] Based on the indicator definition in the indicator asset document, obtaining the indicator data of the reporting item indicator from the table model;

[0024] The indicator data is processed step by step according to the processing link to obtain an application layer table for reporting.

[0025] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects: automatically receiving and parsing reporting data processing instructions, reducing manual intervention and reducing the possibility of human error; obtaining table models, indicator asset documents and processing links corresponding to the reporting item indicators, standardized table models and indicator asset documents ensure the consistency and comparability of the data, so that the data of various types of reporting item indicators can be efficiently and accurately extracted and processed; the indicator data is processed step by step according to the processing link, and the data link makes the data processing process clearer, which can effectively reduce redundant operations and complex logic, and improve the data processing speed. The present application improves the timeliness of data reporting, reduces resource consumption, and improves the accuracy and efficiency of reporting data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0028] Figure 2 is a flow chart of an embodiment of a data processing method according to the present application;

[0029] Figure 3 is a schematic structural diagram of an embodiment of a data processing device according to the present application;

[0030] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0034] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0035] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0036] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0037] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0038] It should be noted that the data processing method provided in the embodiment of the present application is generally executed by a server, and accordingly, the data processing device is generally set in the server.

[0039] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0040] Continue to refer Figure 2 , shows a flow chart of an embodiment of a data processing method according to the present application. The data processing method comprises the following steps:

[0041] Step S201, receiving a reporting data processing instruction, and extracting reporting item indicators from the reporting data processing instruction.

[0042] In this embodiment, the electronic device (eg, Figure 1 The server shown in the figure) can communicate with the terminal device through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0043] Specifically, the reporting data processing instructions are received, and the reporting item indicators are extracted from the reporting data processing instructions. The reporting data processing instructions can be reporting instructions from financial regulatory agencies or internal business departments, and contain specific indicator requirements. These indicators are the data that data reporting focuses on, and may include various financial data, insurance claims data, etc., with the purpose of helping regulatory agencies monitor financial risks and ensure industry stability. For example, an insurance company may need to report data on the number of policies, claims amounts, etc. every month so that regulatory agencies can assess its financial health and compliance.

[0044] Step S202, obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator.

[0045] Specifically, obtain the table model, indicator asset document and processing link corresponding to the reported indicator. The table model refers to a set of standardized data structures that define how to store and organize data related to the reported indicator. The indicator asset document contains a detailed description of each indicator, including its definition, calculation method and related properties. The processing link refers to the process of data processing, including the various processing steps from the original state to the final usable state of the data.

[0046] Step S203, based on the indicator definition in the indicator asset document, obtain the indicator data of the reporting item indicator from the table model.

[0047] Specifically, based on the indicator definition in the indicator asset document, the indicator data of the reported item indicator is obtained from the table model. In this process, the corresponding data is extracted from the pre-defined table model according to the standards specified in the indicator asset document, ensuring the accuracy and consistency of the data, so that the reported data meets the regulatory requirements.

[0048] Step S204, processing the indicator data step by step according to the processing link to obtain an application layer table for reporting.

[0049] Specifically, the indicator data is processed step by step according to the processing link to obtain the application layer table for reporting. The system processes the extracted indicator data step by step according to the defined processing link, and finally generates an application layer table that meets the reporting requirements. The application layer table is the processed data result and can be directly used for reporting to the regulatory agency.

[0050] In this embodiment, the reported data processing instructions are automatically received and parsed, which reduces manual intervention and the possibility of human error; the table model, indicator asset document and processing link corresponding to the reported item indicator are obtained, and the standardized table model and indicator asset document ensure the consistency and comparability of the data, so that the data of various types of reported item indicators can be extracted and processed efficiently and accurately; the indicator data is processed step by step according to the processing link, and the data link makes the data processing process clearer, which can effectively reduce redundant operations and complex logic, and improve the data processing speed. This application improves the timeliness of data reporting, reduces resource consumption, and improves the accuracy and efficiency of reported data processing.

[0051] Furthermore, before the above-mentioned step of receiving and submitting data processing instructions, it may also include: obtaining initial indicator data; organizing and classifying the initial indicator data according to preset business scenarios and levels to obtain multiple table models, wherein the table model organizes the data into a structure of dimensions and metrics.

[0052] Specifically, we first obtain the initial indicator data, which can be extracted from multiple data sources (such as databases, data lakes, or external data interfaces) to extract business-related raw data. These initial indicator data may include financial statements, insurance claims information, underwriting data, claims data, underwriting data, etc., which constitute the basic data source for subsequent processing.

[0053] According to the preset business scenarios and levels, the initial indicator data is organized and classified to obtain multiple table models. Business scenarios refer to different business needs and application scenarios, such as claims processing, policy management, etc. Levels can be different levels of data in the organizational structure, such as fine-grained data (specific single claims) and coarse-grained data (overall claims statistics).

[0054] Dimensions are attributes used to describe the background of data, such as time, region, product category, etc., which can provide contextual information for the data; while metrics are numerical values ​​that can be measured, such as amount, quantity, etc., which reflect the specific indicators of the business. Organizing the initial indicator data in a structured way of dimensions and metrics can present data relationships more clearly and facilitate subsequent data processing and analysis. For example, a sales table model may contain fields such as time, region, product, and sales.

[0055] In this embodiment, the systematization and efficiency of data processing are improved by effectively acquiring and structuring the initial indicator data; after classifying and organizing the initial data, a table model that meets the specific business scenario can be quickly generated, reducing the complexity of subsequent data processing. By clarifying the separation of dimensions and metrics, relevant data can be efficiently extracted and utilized. This organizational method also lays the foundation for subsequent reporting and processing, making the flow of data in the processing process smoother, reducing errors and redundant operations caused by chaotic data structure, improving the speed of data processing, and enhancing the accuracy and consistency of reported data.

[0056] Furthermore, the above-mentioned step of organizing and classifying the initial indicator data according to preset business scenarios and levels to obtain multiple table models may also include: classifying the initial indicator data according to preset business scenarios in the data mart to obtain initial indicator data under multiple business scenarios; organizing the initial indicator data under each business scenario according to preset dimensions and metrics to obtain table models under each business scenario.

[0057] Specifically, the initial indicator data is classified according to the preset business scenarios in the data mart. This process involves segmenting the original data according to specific business needs, such as dividing the data into different scenarios such as claims, underwriting, customer service, finance, and subject matter. A data mart is a subset that provides data storage and access for a specific business or department, which can better support decision-making and analysis.

[0058] Next, the initial indicator data for each business scenario is organized according to the preset dimensions and metrics to generate table models for each business scenario. The preset dimensions and metrics refer to the structure and format predetermined when designing the table model. Dimensions may include time (such as month, quarter), region (such as city, country), etc., while metrics are quantifiable values, such as claim amount, number of policies, etc.

[0059] Through this structured organization method, the table model in each business scenario can clearly show the relationship between related data, making subsequent data processing and analysis more efficient, facilitating data storage and management, and making data query and use more flexible and convenient.

[0060] In this embodiment, the initial indicator data is classified in the data mart and organized according to preset dimensions and metrics, which improves the manageability and analysis efficiency of the data; classification by business scenario makes data access more in line with actual needs, and users can quickly find data related to them, improving work efficiency; the use of a structured table model makes the data processing process more consistent, avoids data duplication and redundancy, reduces unnecessary computing burden, and improves data visualization, making it easier for business personnel to understand and analyze; through dimension and metric division, data query and report generation can be performed more quickly.

[0061] Furthermore, before the above step S202, it may also include: obtaining indicators of each reporting item; obtaining the indicator caliber and indicator attributes corresponding to each reporting item indicator from the configuration page, the attributes including the dimension, measurement, business limitation and frequency of the reporting item indicator; generating an indicator asset document according to each reporting item and its corresponding indicator caliber and indicator attributes.

[0062] Specifically, obtain each reporting item indicator. Reporting item indicators are key data indicators that need to be reported, usually important statistical data in the financial and insurance industries, such as the number of insurance policies, claim amounts, number of customers, etc. Each reporting item indicator corresponds to a specific business requirement.

[0063] Get the indicator scope and indicator attributes corresponding to each reporting item indicator from the configuration page. Indicator scope refers to the definition and explanation of the reporting item indicator, including the calculation method and scope of the data; while indicator attributes include dimensions (such as time, region), metrics (such as claim amount), business limitations (such as only auto insurance), time limitations (such as January 2023 to December 2023), and frequency (such as monthly reporting). Specifically:

[0064] Dimensions are descriptive attributes of data, such as time (by month, by year), geography (city, region), etc.

[0065] Metrics are quantifiable values, such as amounts, quantities, etc.;

[0066] Business limitation refers to the scope or conditions under which data is applicable in business, such as only applicable to specific products or customer groups;

[0067] Frequency indicates how often data is updated, such as daily, weekly, monthly, etc.

[0068] According to the indicators of each reported item and their corresponding indicator caliber and indicator attributes, the system will generate an indicator asset document. The indicator asset document is a comprehensive indicator description document, which contains detailed information on each indicator and its calculation method, providing a clear basis for the use and analysis of data.

[0069] In this embodiment, by obtaining the indicators of each reporting item and its corresponding indicator caliber and attributes, the standardization and accuracy of data management are improved; the generated indicator asset document centrally integrates the definitions and attributes of each reporting item, so that business personnel can quickly understand and use data, avoiding misuse or misunderstanding due to unclear definitions, improving data consistency, and ensuring the quality of data reporting; through clear indicator attribute division, data analysis becomes more efficient and transparent, and business personnel can extract and analyze data based on clear dimensions and metrics, improving data availability and flexibility; systematic indicator management also provides a clear basis for subsequent data processing and reporting processes, reduces the complexity of manual operations, and reduces errors.

[0070] Furthermore, the above step S203 may include: determining dimension fields and metric fields in the table model based on the indicator definition of the reporting item indicator in the indicator asset document and the minimum data authority control principle; and obtaining data according to the determined dimension fields and metric fields to obtain indicator data of the reporting item indicator.

[0071] Specifically, based on the definition of the reporting item indicators in the indicator asset document and the principle of minimum data permission control, the system determines the relevant dimension fields and measurement fields. Dimension fields refer to the attributes used for classification and analysis in the data, such as time, region, and product type, while measurement fields can be used to calculate and count values, such as policy amounts, number of claims, etc.

[0072] By combining these definitions, the system can effectively identify and extract the required fields to ensure the completeness and accuracy of the data. For example, if the reporting item indicator is "Monthly Claim Amount", the corresponding dimension fields may be "Month" and "Region", and the measurement field is "Claim Amount".

[0073] After determining the dimension fields and metric fields, the system will extract data according to these determined fields to obtain the final reporting item indicator data. During the data extraction process, the system will follow the principle of minimum data permission control to ensure that only necessary data is obtained to avoid data redundancy and security risks.

[0074] The principle of least data privilege control is a security and privacy management principle, which means that each user or system should only have the minimum data access rights required to complete its tasks. This can reduce the risk of data leakage and ensure data security. For example, if an employee only needs to view policy information, he should not be authorized to access claims data.

[0075] In this embodiment, standardization and automation of the data extraction process are achieved; clear definitions of dimension fields and metric fields improve the accuracy of data extraction, avoid erroneous extraction due to ambiguous definitions, help improve the quality of reported data, and ensure that regulatory requirements are met; the principle of minimum data authority control ensures data security and compliance, and by extracting only the required data, reduces the risk of data leakage and abuse, and enhances the protection of sensitive information; a unified data extraction process simplifies the complexity of data extraction, allowing business personnel to focus more on data analysis and decision-making rather than dealing with tedious data extraction processes.

[0076] Furthermore, the above step S204 may include: according to the processing chain, performing first-level processing on the indicator data to obtain a light summary table; performing second-level processing on the light summary table to obtain an indicator summary table, the indicator summary table contains summary information of the reporting item indicators; performing third-level processing on the indicator summary table to obtain an application layer table, the application layer table is used for data reporting.

[0077] Specifically, the system performs the first-level processing on the extracted indicator data according to the preset processing link to obtain a light summary table. A light summary table refers to the preliminary statistics and summary of the original indicator data, which usually contains basic summary information of each dimension. For example, if the indicator data involves the claim amount of insurance policies in multiple regions, the light summary table may show the total claim amount of each region, or a table of the number of insurance policies and total premiums summarized by month.

[0078] Next, the system performs a second-level processing on the light summary table to generate an indicator summary table. This table contains more detailed summary information of the reported indicators and can provide summary statistics of the reported indicators, such as detailed analysis by month, region, etc. This step can effectively integrate information from different dimensions, making the data more readable and valuable for analysis.

[0079] Finally, the system will perform a third-level processing on the indicator summary table to obtain the application layer table. The application layer table is the final result table used for data reporting, integrating all necessary summary information to ensure that it meets regulatory requirements. The structure and content of this table are optimized for direct application to various reporting requirements.

[0080] In this embodiment, through the processing link, the hierarchical and systematic data processing is realized, and the quality and efficiency of the reported data are improved; the hierarchical processing method makes the data aggregation process clearer and easier to track and manage; each level of processing can perform specific statistical analysis to ensure the gradual optimization and integration of data; the generation of light summary tables and indicator summary tables provides a basis for subsequent data analysis, reduces the burden on users during data analysis, and enables the final application layer table to quickly respond to regulatory needs. Structured processing improves the availability and transparency of data and promotes decision support; finally, the automation of the overall process reduces the need for manual intervention, reduces the risk of human errors, and improves the stability and consistency of data processing.

[0081] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned indicator asset documents, the above-mentioned indicator asset documents can also be stored in a node of a blockchain.

[0082] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0084] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0085] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a data processing device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0086] like Figure 3 As shown, the data processing device 300 described in this embodiment includes: an indicator extraction module 301, a reporting extraction module 302, a data extraction module 303 and a data processing module 304, wherein:

[0087] The indicator extraction module 301 is used to receive the reporting data processing instruction and extract the reporting item indicator from the reporting data processing instruction.

[0088] The reporting and extraction module 302 is used to obtain the table model, indicator asset document and processing link corresponding to the reporting item indicator.

[0089] The data extraction module 303 is used to obtain the indicator data of the reporting item indicator from the table model based on the indicator definition in the indicator asset document.

[0090] The data processing module 304 is used to process the indicator data step by step according to the processing link to obtain an application layer table for reporting.

[0091] In this embodiment, the reported data processing instructions are automatically received and parsed, which reduces manual intervention and the possibility of human error; the table model, indicator asset document and processing link corresponding to the reported item indicator are obtained, and the standardized table model and indicator asset document ensure the consistency and comparability of the data, so that the data of various types of reported item indicators can be extracted and processed efficiently and accurately; the indicator data is processed step by step according to the processing link, and the data link makes the data processing process clearer, which can effectively reduce redundant operations and complex logic, and improve the data processing speed. This application improves the timeliness of data reporting, reduces resource consumption, and improves the accuracy and efficiency of reported data processing.

[0092] In some optional implementations of this embodiment, the data processing device 300 may further include:

[0093] The initial acquisition module is used to obtain initial indicator data.

[0094] The organization and classification module is used to organize and classify the initial indicator data according to the preset business scenarios and levels to obtain multiple table models, wherein the table model organizes the data into a structure of dimensions and metrics.

[0095] In this embodiment, the systematization and efficiency of data processing are improved by effectively acquiring and structuring the initial indicator data; after classifying and organizing the initial data, a table model that meets the specific business scenario can be quickly generated, reducing the complexity of subsequent data processing. By clarifying the separation of dimensions and metrics, relevant data can be efficiently extracted and utilized. This organizational method also lays the foundation for subsequent reporting and processing, making the flow of data in the processing process smoother, reducing errors and redundant operations caused by chaotic data structure, improving the speed of data processing, and enhancing the accuracy and consistency of reported data.

[0096] In some optional implementations of this embodiment, the organization classification module may also be used to:

[0097] In the data mart, the initial indicator data is classified according to the preset business scenarios to obtain the initial indicator data under multiple business scenarios; the initial indicator data under each business scenario is organized according to the preset dimensions and metrics to obtain the table model under each business scenario.

[0098] In this embodiment, the initial indicator data is classified in the data mart and organized according to preset dimensions and metrics, which improves the manageability and analysis efficiency of the data; classification by business scenario makes data access more in line with actual needs, and users can quickly find data related to them, improving work efficiency; the use of a structured table model makes the data processing process more consistent, avoids data duplication and redundancy, reduces unnecessary computing burden, and improves data visualization, making it easier for business personnel to understand and analyze; through dimension and metric division, data query and report generation can be performed more quickly.

[0099] In some optional implementations of this embodiment, the data processing device 300 may further include:

[0100] The indicator acquisition module is used to obtain the indicators of each reporting item.

[0101] The configuration acquisition module is used to obtain the indicator caliber and indicator attributes corresponding to each reporting item indicator from the configuration page. The attributes include the dimension, measurement, business limitation and frequency of the reporting item indicator.

[0102] The document generation module is used to generate indicator asset documents based on each reporting item and its corresponding indicator caliber and indicator attributes.

[0103] In this embodiment, by obtaining the indicators of each reporting item and its corresponding indicator caliber and attributes, the standardization and accuracy of data management are improved; the generated indicator asset document centrally integrates the definitions and attributes of each reporting item, so that business personnel can quickly understand and use data, avoiding misuse or misunderstanding due to unclear definitions, improving data consistency, and ensuring the quality of data reporting; through clear indicator attribute division, data analysis becomes more efficient and transparent, and business personnel can extract and analyze data based on clear dimensions and metrics, improving data availability and flexibility; systematic indicator management also provides a clear basis for subsequent data processing and reporting processes, reduces the complexity of manual operations, and reduces errors.

[0104] In some optional implementations of this embodiment, the data extraction module 303 may also be used to:

[0105] Based on the indicator definition of the reporting item indicator in the indicator asset document and the minimum data permission control principle, the dimension field and the measurement field are determined in the table model; data is obtained according to the determined dimension field and measurement field to obtain the indicator data of the reporting item indicator.

[0106] In this embodiment, standardization and automation of the data extraction process are achieved; clear definitions of dimension fields and metric fields improve the accuracy of data extraction, avoid erroneous extraction due to ambiguous definitions, help improve the quality of reported data, and ensure that regulatory requirements are met; the principle of minimum data authority control ensures data security and compliance, and by extracting only the required data, reduces the risk of data leakage and abuse, and enhances the protection of sensitive information; a unified data extraction process simplifies the complexity of data extraction, allowing business personnel to focus more on data analysis and decision-making rather than dealing with tedious data extraction processes.

[0107] In some optional implementations of this embodiment, the data processing module 304 may also be used to:

[0108] According to the processing chain, the indicator data is processed at the first level to obtain a light summary table; the light summary table is processed at the second level to obtain an indicator summary table, which contains summary information of the indicators of the reporting items; the indicator summary table is processed at the third level to obtain an application layer table, which is used for data reporting.

[0109] In this embodiment, through the processing link, the hierarchical and systematic data processing is realized, and the quality and efficiency of the reported data are improved; the hierarchical processing method makes the data aggregation process clearer and easier to track and manage; each level of processing can perform specific statistical analysis to ensure the gradual optimization and integration of data; the generation of light summary tables and indicator summary tables provides a basis for subsequent data analysis, reduces the burden on users during data analysis, and enables the final application layer table to quickly respond to regulatory needs. Structured processing improves the availability and transparency of data and promotes decision support; finally, the automation of the overall process reduces the need for manual intervention, reduces the risk of human errors, and improves the stability and consistency of data processing.

[0110] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0111] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 having a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0112] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0113] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of data processing methods, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0114] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the data processing method.

[0115] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0116] The computer device provided in this embodiment can execute the above data processing method. The data processing method here can be the data processing method of each of the above embodiments.

[0117] In this embodiment, the reported data processing instructions are automatically received and parsed, which reduces manual intervention and the possibility of human error; the table model, indicator asset document and processing link corresponding to the reported item indicator are obtained, and the standardized table model and indicator asset document ensure the consistency and comparability of the data, so that the data of various types of reported item indicators can be extracted and processed efficiently and accurately; the indicator data is processed step by step according to the processing link, and the data link makes the data processing process clearer, which can effectively reduce redundant operations and complex logic, and improve the data processing speed. This application improves the timeliness of data reporting, reduces resource consumption, and improves the accuracy and efficiency of reported data processing.

[0118] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the data processing method as described above.

[0119] In this embodiment, the reported data processing instructions are automatically received and parsed, which reduces manual intervention and the possibility of human error; the table model, indicator asset document and processing link corresponding to the reported item indicator are obtained, and the standardized table model and indicator asset document ensure the consistency and comparability of the data, so that the data of various types of reported item indicators can be extracted and processed efficiently and accurately; the indicator data is processed step by step according to the processing link, and the data link makes the data processing process clearer, which can effectively reduce redundant operations and complex logic, and improve the data processing speed. This application improves the timeliness of data reporting, reduces resource consumption, and improves the accuracy and efficiency of reported data processing.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0121] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data processing method, characterized in that: The steps include: Receiving a reporting data processing instruction, and extracting a reporting item indicator from the reporting data processing instruction; Obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator; Based on the indicator definition in the indicator asset document, obtaining the indicator data of the reporting item indicator from the table model; The indicator data is processed step by step according to the processing link to obtain an application layer table for reporting.

2. The data processing method according to claim 1, characterized in that: Before the step of receiving and submitting the data processing instruction, the method further includes: Get initial indicator data; According to preset business scenarios and levels, the initial indicator data is organized and classified to obtain multiple table models, wherein the table model organizes the data into a structure of dimensions and metrics.

3. The data processing method according to claim 2, characterized in that: The step of organizing and classifying the initial indicator data to obtain multiple table models according to the preset business scenarios and levels includes: Classifying the initial indicator data according to preset business scenarios in the data mart to obtain initial indicator data under multiple business scenarios; The initial indicator data under each business scenario is organized according to preset dimensions and metrics to obtain the table models under the business scenarios.

4. The data processing method according to claim 1, characterized in that: Before the step of obtaining the table model, indicator asset document and processing link corresponding to the reported item indicator, the method further includes: Obtain indicators for each reporting item; Obtain the indicator caliber and indicator attributes corresponding to each of the reporting item indicators from the configuration page, wherein the attributes include the dimension, measurement, business limitation and frequency of the reporting item indicator; Generate an indicator asset document based on the various reporting items and their corresponding indicator calibers and indicator attributes.

5. The data processing method according to claim 1, characterized in that: The step of acquiring the indicator data of the reporting item indicator from the table model based on the indicator definition in the indicator asset document comprises: Based on the indicator definition of the reporting item indicator in the indicator asset document and the minimum data authority control principle, determine the dimension field and the metric field in the table model; According to the determined dimension fields and metric fields, data is obtained to obtain the indicator data of the reporting item indicator.

6. The data processing method according to claim 1, characterized in that: The step of processing the indicator data level by level according to the processing link to obtain an application layer table for reporting includes: According to the processing link, the indicator data is first processed to obtain a light summary table; Performing a second-level processing on the light summary table to obtain an indicator summary table, wherein the indicator summary table contains summary information of the indicators of the reported items; The indicator summary table is subjected to third-level processing to obtain an application layer table, and the application layer table is used for data reporting.

7. A data processing device, characterized in that: include: An indicator extraction module, used for receiving a report data processing instruction and extracting a report item indicator from the report data processing instruction; A reporting and extraction module, used to obtain a table model, an indicator asset document and a processing link corresponding to the reporting item indicator; A data extraction module, used for acquiring the indicator data of the reporting item indicator from the table model based on the indicator definition in the indicator asset document; The data processing module is used to process the indicator data step by step according to the processing link to obtain an application layer table for reporting.

8. The data processing device according to claim 7, characterized in that: The device also includes: The indicator acquisition module is used to obtain the indicators of each reporting item; A configuration acquisition module, used to obtain the indicator caliber and indicator attributes corresponding to each of the reporting item indicators from the configuration page, wherein the attributes include the dimension, measurement, business limitation and frequency of the reporting item indicator; The document generation module is used to generate an indicator asset document based on the various reporting items and their corresponding indicator calibers and indicator attributes.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the data processing method according to any one of claims 1 to 6 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 6.

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