A method, system, electronic device, and storage medium for intelligent processing of business and financial data.

Abstract

This invention relates to the field of enterprise data processing technology, specifically disclosing a method, system, electronic device, and storage medium for intelligent processing of business and financial data. The method includes: generating scenario definition information based on multiple business and financial data application scenarios; collecting metadata from multiple heterogeneous data sources according to the scenario definition information and constructing a data asset catalog based on the metadata; constructing a data computing capability library containing multiple parameterized computing units; configuring selected parameterized computing units, predefined rules, and external functions through a logic orchestration interface to generate customized logic for processing specific business and financial fields; and associating and processing business and financial data from multiple heterogeneous data sources based on the data asset catalog, data computing capability library, and customized logic to form a unified business and financial integrated data asset. This invention solves the problems of inconsistent data standards, difficulties in cross-domain integration, and reliance on manual intervention, thereby improving the efficiency of automated integration of business and financial data and unified asset construction.

Description

Technical Field

[0001] This invention relates to the field of enterprise data processing technology, and in particular to an intelligent processing method, system, electronic device, and storage medium for business and financial data. Background Technology

[0002] As enterprises deepen their digital transformation, the integrated analysis of business and financial data has become a crucial element in supporting management decision-making. Currently, business and financial data are generally characterized by large scale, multiple dimensions, and dispersed sources. Business systems and financial systems are often built independently, with inconsistent data standards and heterogeneous storage structures. This makes it difficult for frontline staff to quickly integrate cross-domain data, let alone effectively identify the inherent relationships between data. This fragmented state prevents massive amounts of data from being transformed into effective guidance for business development, severely limiting its decision support role and making it difficult to construct an accurate profile of the organization's operating status.

[0003] In terms of data sharing and analytical depth, the existing system faces dual constraints of security and flexibility. Limited by system functional boundaries and data confidentiality requirements, significant barriers exist in data flow between different departments and levels, hindering both efficient sharing and in-depth, penetrating analysis. Organizations cannot promptly grasp their position and ranking within the entire system, lacking reference points for horizontal comparison and gap-filling, impacting benchmarking and continuous improvement capabilities. Simultaneously, data processing heavily relies on manual operations, resulting in lengthy cycles from anomaly detection to strategy implementation and significant delays in feedback from lower levels. The lack of professional automated tools for report data analysis not only limits efficiency but also compromises accuracy, significantly inhibiting the release of data value.

[0004] A deeper problem lies in the lack of quantification in resource allocation and performance evaluation mechanisms. Resource allocation has long relied on experience-based judgment, lacking scientific data models and quantitative support, resulting in insufficient precision in resource allocation and difficulty in matching actual business development needs. Similarly, the lack of a quantifiable indicator system in performance management hinders objective evaluation of operational performance, creating a bottleneck in management efficiency improvement. In traditional data processing workflows, developers spend significant time repeatedly communicating with business departments to explore the definitions, relationships, and processing logic of various data tables in the database. This model heavily relies on personal experience and cross-departmental coordination, resulting in long information acquisition paths, high costs, and substantial investment of manpower and time. With the rapid increase in data scale and complexity, the efficiency of traditional models is declining, making it difficult to support flexible and ever-changing data demands, and has become a core bottleneck in business response and data delivery.

[0005] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method, system, electronic device, and storage medium for intelligent processing of business and financial data.

[0007] Firstly, the present invention provides a method for intelligent processing of business and financial data, the technical solution of which is as follows: Based on multiple business and financial data application scenarios, scenario definition information is generated, which includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario. Based on the scenario definition information, metadata is collected from multiple heterogeneous data sources, and a data asset catalog is constructed based on the metadata. The data asset catalog is used to record the business semantics, financial semantics, and data lineage of the metadata. Construct a data computing capability library containing multiple parameterized computing units, each of which encapsulates a standard computing logic; Configure the selected parameterized calculation units, predefined rules, and external functions through the logic orchestration interface to generate customized logic for processing specific business and financial fields; Based on the data asset catalog, the data computing capability library, and the customized logic, business data and financial data from multiple heterogeneous data sources are associated and processed to form a unified business and financial integrated data asset.

[0008] The beneficial effects of the intelligent processing method for business and financial data of the present invention are as follows: The method of this invention defines business and financial indicator requirements for multiple business and financial application scenarios, collects metadata from heterogeneous data sources to construct a data asset catalog that records business semantics, financial semantics, and data lineage, establishes a parameterized computing unit library that encapsulates standard computing logic, and generates customized logic by configuring parameterized computing units, predefined rules, and external functions based on the logic orchestration interface. Based on the data asset catalog and computing power library, heterogeneous business and financial data are correlated and processed, solving the problems of inconsistent data standards, difficulties in cross-domain integration, and reliance on manual intervention, thereby improving the efficiency of automated integration of business and financial data and unified asset construction.

[0009] Based on the above solution, the intelligent processing method for business and financial data of the present invention can be further improved as follows.

[0010] In one alternative approach, the data requirements include: data type, data dimension, and data granularity; the step of generating scenario definition information based on multiple business and financial data application scenarios includes: Obtain the business objectives and expected output data format for each business and financial data application scenario; Based on the business objectives and expected output data format of each business and financial data application scenario, the associated business indicators and financial indicators are determined for each business and financial data application scenario. Based on the business and financial indicators of each business and financial data application scenario, determine the data types, data dimensions, and data granularity required for each business and financial data application scenario. The business indicators, financial indicators, data types, data dimensions, and data granularity of the multiple business and financial data application scenarios are integrated to generate the scenario definition information.

[0011] The advantages of adopting the above optional approach are: it further clarifies the constituent elements and generation process of scenario definition information, determines indicators driven by business objectives, and then derives data type, dimension and granularity requirements, thereby achieving standardization and refinement of scenario definition generation and improving the pertinence of subsequent data processing.

[0012] In one alternative approach, the step of collecting metadata from multiple heterogeneous data sources based on the scenario definition information includes: Based on the data type, data dimension, and data granularity in the scenario definition information, the types of metadata to be collected and the corresponding multiple heterogeneous data sources are determined; wherein, the multiple heterogeneous data sources include: business database, financial database, and external data interface; Based on the metadata type, table-level metadata and field-level metadata are automatically collected from the business database, the financial database, and the external data interface; The table-level metadata is associated with the field-level metadata to form the associated metadata; Establish a mapping relationship between the metadata and the multiple business and financial data application scenarios.

[0013] The advantages of adopting the above optional methods are: it further realizes the accurate collection of metadata on demand, obtains table-level and field-level metadata from multi-source heterogeneous data sources in an automated manner, and establishes a mapping relationship with application scenarios, providing a complete and accurate metadata foundation for the construction of data asset catalog.

[0014] In one alternative approach, the step of constructing a data asset catalog based on the metadata includes: Based on the mapping relationship, add corresponding business semantics and financial semantics to the metadata; Based on the source and processing flow of the metadata, record the data lineage relationship between the metadata; The business semantics, financial semantics, and data lineage are structurally integrated to generate the data asset catalog.

[0015] The advantages of adopting the above-mentioned optional approach are: by giving metadata both business and financial semantics and recording data lineage, a structured data asset catalog is formed, which enhances data understandability and traceability, and supports the accuracy of subsequent data processing.

[0016] In one alternative approach, the step of constructing a data computing capability library containing multiple parameterized computing units includes: Multiple standard computation logics are defined, including: numerical aggregation computation logic, time-based window computation logic, and text-based feature extraction computation logic. Each standard computation logic is encapsulated into an independent computation unit with a configurable parameter interface to obtain the plurality of parameterized computation units; The multiple parameterized calculation units are aggregated to form the data calculation capability library.

[0017] The advantages of adopting the above optional approach are: further encapsulating various standard computing logics into configurable parameterized computing units, forming a modular and reusable computing capability library, improving the flexibility and scalability of computing logic, and reducing the cost of customized development.

[0018] In an alternative approach, the step of configuring the selected parameterized calculation unit, predefined rules, and external functions through a logic orchestration interface to generate customized logic for processing specific business fields includes: The logical orchestration interface displays multiple parameterized calculation units, predefined rules, and callable external functions in the data calculation capability library; In response to the user's configuration operation in the logical orchestration interface, a target parameterized calculation unit is selected from the plurality of parameterized calculation units, and the target parameterized calculation unit is logically connected with the specified predefined rule and the external function; Based on the processing order and dependencies determined by the logical connections, configure the calculation parameters of the target parameterized calculation unit, the judgment conditions of the predefined rules, and the calling parameters of the external function; Based on the configured calculation parameters, judgment conditions, and calling parameters, the customized logic for processing the specific business financial field is generated.

[0019] The advantages of adopting the above optional approach are: it further provides a visual logic orchestration interface, supports users to flexibly combine computing units, rules and external functions through configuration operations, generates customized processing logic, reduces the technical threshold, and improves logic configuration efficiency.

[0020] In one alternative approach, the step of associating and processing business data and financial data from multiple heterogeneous data sources to form a unified business-finance integrated data asset, based on the data asset catalog, the data computing capability library, and the customized logic, includes: Based on the business semantics, financial semantics, and data lineage recorded in the data asset catalog, corresponding business data and financial data are extracted from the multiple heterogeneous data sources. The business data and the financial data are processed in a standardized manner according to the standard calculation logic encapsulated by the target parameterized calculation unit. Based on the calculation parameters, judgment conditions, and calling parameters configured in the customized logic, customized rule processing is performed on specific business and financial fields in the standardized processed data; The business and financial data, after being processed by customized rules, are semantically aligned and structurally integrated according to the business and financial data application scenarios in the scenario definition information to form the unified business and financial fusion data asset.

[0021] The advantages of adopting the above-mentioned optional methods are: to further automate the entire process from data extraction, standardized processing, customized processing to semantic alignment; to achieve deep integration of heterogeneous data based on the data asset catalog and computing power library, forming a unified asset and improving the quality and consistency of data processing.

[0022] Secondly, this invention provides an intelligent processing system for business and financial data, the technical solution of which is as follows: The scenario generation module is used to generate scenario definition information based on multiple business and financial data application scenarios. The scenario definition information includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario. The catalog building module is used to collect metadata from multiple heterogeneous data sources according to the scenario definition information, and build a data asset catalog based on the metadata. The data asset catalog is used to record the business semantics, financial semantics and data lineage of the metadata. Encapsulate building blocks to build a data computing capability library containing multiple parameterized computing units, each of which encapsulates a standard computing logic; The logic generation module is used to configure the selected parameterized calculation units, predefined rules and external functions through the logic orchestration interface to generate customized logic for processing specific business and financial fields. The association and processing module is used to associate and process business data and financial data from multiple heterogeneous data sources based on the data asset catalog, the data computing capability library and the customized logic, to form a unified business and financial integrated data asset.

[0023] The beneficial effects of the intelligent processing system for business and financial data of the present invention are as follows: The system of this invention defines business and financial indicator requirements for multiple business and financial application scenarios, collects metadata from heterogeneous data sources to construct a data asset catalog that records business semantics, financial semantics, and data lineage, establishes a parameterized computing unit library that encapsulates standard computing logic, and generates customized logic by configuring parameterized computing units, predefined rules, and external functions based on the logic orchestration interface. Based on the data asset catalog and computing capability library, it performs correlation processing on heterogeneous business and financial data, solving the problems of inconsistent data standards, difficulties in cross-domain integration, and reliance on manual intervention, thereby improving the efficiency of automated integration of business and financial data and unified asset construction.

[0024] Thirdly, the technical solution of an electronic device according to the present invention is as follows: The invention includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the business data intelligent processing method of the present invention.

[0025] Fourthly, the technical solution of a computer-readable storage medium provided by the present invention is as follows: The computer-readable storage medium stores instructions that, when read, cause the computer-readable storage medium to perform the steps of the business data intelligent processing method of the present invention.

[0026] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating an embodiment of the intelligent processing method for business and financial data according to the present invention. Figure 2 This is a schematic diagram of the structure of an embodiment of the intelligent processing system for business and financial data according to the present invention; Figure 3 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0028] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0029] Figure 1 This diagram illustrates a flowchart of an embodiment of an intelligent business data processing method provided by the present invention. This method can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the intelligent business data processing method by having its processor call computer-readable instructions stored in its memory. Figure 1 As shown, it includes the following steps: S1. Based on multiple business and financial data application scenarios, generate scenario definition information, which includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario.

[0030] Among them, business and financial data application scenarios refer to specific situations in enterprise operation and management where business data and financial data need to be integrated and analyzed simultaneously to support specific decisions or operational activities. For example, in a life insurance company, "individual long-term life insurance surrender rate analysis" is a business and financial data application scenario, which requires analysis combining policy business data and financial revenue and payment data. Scenario definition information refers to a standardized set of information formed after a structured description of one or more business and financial data application scenarios. For example, for the "individual long-term life insurance surrender rate analysis" scenario, its scenario definition information includes the business indicator "monthly surrender rate," the financial indicator "surrender payment ratio," and the data requirement "requires policy details and accounting entries data from the past two years." Business indicators refer to quantifiable metrics used to measure and evaluate the status, process, or results of business activities. For example, in the individual long-term life insurance surrender rate analysis scenario, the business indicator is the "monthly surrender rate," calculated by dividing the number of surrenders in the current month by the number of valid policies at the beginning of the month. Financial metrics refer to quantifiable measures used to measure and evaluate a company's financial condition, operating results, or cash flow. For example, in the same scenario, a financial metric might be "the ratio of surrender payments to premium income," used to assess the impact of surrenders on financial cash flow. Data requirements refer to the specific characteristics and specifications that the data must meet to achieve the analytical goals of a particular business and financial data application scenario. For example, to achieve surrender rate analysis, data requirements include data type "structured table data," data dimensions "product type, sales channel, and insured age," and data granularity "transaction records at the policy level."

[0031] S2. Based on the scenario definition information, collect metadata from multiple heterogeneous data sources, and construct a data asset catalog based on the metadata. The data asset catalog is used to record the business semantics, financial semantics, and data lineage of the metadata.

[0032] Heterogeneous data sources refer to multiple independent data sources that differ in technical architecture, data format, storage method, or management system; for example, data supporting surrender rate analysis may originate from business databases, financial databases, and external credit data interfaces. Metadata refers to information describing data attributes, structure, relationships, sources, and management rules; for example, describing the name of the "Insurance Date" field in the "Policy Table," its data type as "DATE," and its relationship with the "Customer Information Table"—this information constitutes metadata. A data asset catalog refers to a centralized catalog system for storing and managing data asset description information; for example, the data asset catalog records the business semantics of the "Policy Table.Insurance Date" field as "insurance contract effective date," its financial semantics as "premium income recognition start point," and records its data lineage. Business semantics refers to the specific meaning and interpretation given to data or data fields from a business perspective; for example, the business semantics of the field "policy_status" is "policy status," and its code "1" represents "valid." Financial semantics refers to the specific meaning and interpretation given to data or data fields from a financial accounting and management perspective; for example, when the "policy_status" field changes to represent the status of "surrender," it is semantically associated with the "surrender payment" account. Data lineage refers to the complete flow path and dependencies of data from its generation, processing, to its final use; for example, the data lineage of the "monthly surrender amount" indicator in the report can be traced back to the "accounting entries" in the financial system, and is generated after being aggregated and linked with the "surrender application form."

[0033] S3. Construct a data computing capability library containing multiple parameterized computing units, each of which encapsulates a standard computing logic.

[0034] Among them, a parameterized calculation unit refers to a reusable software component that encapsulates specific calculation logic, and its calculation behavior can be configured through input parameters; for example, a parameterized calculation unit that calculates the "month-on-month growth rate" accepts "current period value" and "previous period value" as input parameters. A data calculation capability library refers to a resource collection formed by aggregating multiple pre-built, reusable parameterized calculation units; for example, a data calculation capability library includes multiple parameterized calculation units such as "summation," "average," and "year-on-year calculation." Standard calculation logic refers to general rules or algorithms that have been abstracted and standardized to handle a certain type of common data calculation needs; for example, "time window calculation logic" defines how to perform rolling aggregation of data according to a time window of the past N months.

[0035] S4. Configure the selected parameterized calculation unit, predefined rules and external functions through the logic orchestration interface to generate customized logic for processing specific business fields.

[0036] The logical orchestration interface refers to a software interface that allows users to visually combine calculation units, rules, and functions in a graphical way to define complex data processing flows. For example, a user can connect the icons "Data Filtering," "Amount Summary," and "Month-on-Month Calculation" with arrows on a canvas interface to form a flow. Predefined rules refer to business conditions or logical statements configured in advance within the system for data verification, judgment, or classification. For example, a predefined rule might be: "If the policy's effective days are less than 30 days and the reason for cancellation is 'misunderstanding of the terms,' then mark it as 'cancellation during the cooling-off period.'" External functions refer to program functions or algorithm services provided externally to this system but callable through agreed-upon interfaces. For example, calling a "fraud risk scoring function" provided by an external model team. Specific business and financial fields refer to fields that require processing using special, non-standardized rules during the business and financial data fusion process. For example, the "Suspected Abnormal Cancellation Identifier" field is a specific business and financial field. Customized logic refers to a complete data processing logic sequence configured and generated through a logic orchestration interface to meet the unique needs of a specific business scenario; for example, a logic sequence configured to generate a "high-risk channel insurance cancellation report" that includes filtering, calculation, and comparison steps.

[0037] S5. Based on the data asset catalog, the data computing capability library, and the customized logic, business data and financial data from the multiple heterogeneous data sources are associated and processed to form a unified business and financial integrated data asset.

[0038] Among them, unified business and financial data assets refer to a set of data that integrates business data and financial data from different sources after cleaning, correlation, processing and fusion, forming a data set with consistent standards that can be directly used for analysis and decision-making; for example, the "Panoramic View of Institutional Insurance Withdrawal Health Status" dataset is a unified business and financial data asset.

[0039] The technical solution of this embodiment defines business and financial indicator requirements for multiple business and financial application scenarios, collects metadata from heterogeneous data sources to build a data asset catalog that records business semantics, financial semantics and data lineage, establishes a parameterized computing unit library that encapsulates standard computing logic, and generates customized logic by configuring parameterized computing units, predefined rules and external functions based on the logic orchestration interface. Based on the data asset catalog and computing power library, heterogeneous business and financial data are correlated and processed, which solves the problems of inconsistent data standards, difficulties in cross-domain integration and reliance on manual labor, and improves the efficiency of automated integration of business and financial data and unified asset construction.

[0040] In one alternative approach, the data requirements include: data type, data dimension, and data granularity; S1 specifically includes: Obtain the business objectives and expected output data format for each business and financial data application scenario.

[0041] Here, "business objective" refers to the specific and measurable operational or management goals that an enterprise expects to achieve in a particular business scenario. For example, the business objective of the "individual long-term life insurance surrender rate analysis" scenario is "to reduce the 13-month surrender rate of new policies to below the industry average within the next six months." "Expected output data format" refers to the specific form or structure of the final data output required by the business scenario. For example, the expected output of this scenario is a "monthly institutional surrender analysis report," including columns such as institutional name, month, and surrender rate.

[0042] Based on the business objectives and expected output data format of each business and financial data application scenario, relevant business indicators and financial indicators are determined for each business and financial data application scenario.

[0043] Based on the business and financial metrics for each business and financial data application scenario, determine the required data types, data dimensions, and data granularity for each scenario.

[0044] Data type refers to the classification of the basic properties of the data itself; for example, in the required data, "insurance date" belongs to the DATE type, "premium amount" belongs to the DECIMAL type, and "product name" belongs to the VARCHAR type. Data dimension refers to the analytical perspective or angle used to observe, segment, and aggregate data; for example, when analyzing surrender rates, data dimensions include "time," "product," and "channel." Data granularity refers to the level of detail or refinement of data in a specific dimension; for example, the granularity of surrender data can be refined to "every surrender operation record for every policy."

[0045] The business indicators, financial indicators, data types, data dimensions, and data granularity of the multiple business and financial data application scenarios are integrated to generate the scenario definition information.

[0046] Among the above-mentioned optional methods, the constituent elements and generation process of scenario definition information are further clarified. By determining the indicators driven by business objectives, the data type, dimension and granularity requirements are derived, thereby achieving the standardization and refinement of scenario definition generation and improving the pertinence of subsequent data processing.

[0047] In one alternative approach, the step of collecting metadata from multiple heterogeneous data sources based on the scenario definition information includes: Based on the data type, data dimension, and data granularity in the scenario definition information, the types of metadata to be collected and the corresponding multiple heterogeneous data sources are determined; wherein, the multiple heterogeneous data sources include: business database, financial database, and external data interface.

[0048] Metadata types refer to the classification of metadata based on the different objects being described; for example, metadata types include "table-level metadata" and "field-level metadata." Business databases refer to databases that primarily store and process data related to a company's core business processes and operational activities; for example, the core business system database of a life insurance company stores data such as policies and policy maintenance records. Financial databases refer to databases that primarily store and process data related to a company's financial accounting and financial management; for example, the financial system database of a life insurance company stores data such as accounting entries and financial statements. External data interfaces refer to standardized communication channels or program interfaces through which a company obtains data from external partners or data platforms; for example, an API interface used to obtain customer credit report data.

[0049] Based on the metadata type, table-level metadata and field-level metadata are automatically collected from the business database, the financial database, and the external data interface.

[0050] Table-level metadata refers to metadata that describes the overall attributes of a data table in the database; for example, the table-level metadata describing the "Policy Information Table" includes the table name, the number of records, and the business system to which it belongs. Field-level metadata refers to metadata that describes the attributes of a single field in a data table; for example, the field-level metadata describing the "Annual Premium" field includes the field name, the data type DECIMAL(16,2), and its business meaning.

[0051] The table-level metadata is associated with the field-level metadata to form the associated metadata.

[0052] Establish a mapping relationship between the metadata and the multiple business and financial data application scenarios.

[0053] In this context, mapping relationship refers to an explicit correspondence established between two different entities or sets; for example, the mapping relationship established between the "policy information table" and the "personal long-term life insurance surrender rate analysis" scenario.

[0054] Among the above optional methods, on-demand and accurate metadata collection is further realized. Table-level and field-level metadata are obtained from multi-source heterogeneous data sources in an automated manner, and a mapping relationship with application scenarios is established to provide a complete and accurate metadata foundation for the construction of data asset catalog.

[0055] In one alternative approach, the step of constructing a data asset catalog based on the metadata includes: Based on the mapping relationship, corresponding business semantics and financial semantics are added to the metadata.

[0056] Based on the source and processing flow of the metadata, the data lineage relationship between the metadata is recorded.

[0057] Among them, source and processing flow refer to the initial location where the data is generated and the path through which subsequent processing processes use and transform it; for example, the field "cumulative premiums paid" originates from the actual receipt records of the business system, flows to the financial system for confirmation, and is finally called by the analysis model.

[0058] The business semantics, financial semantics, and data lineage are structurally integrated to generate the data asset catalog.

[0059] Structured integration refers to the process of reorganizing, associating, and merging scattered and heterogeneous information into an orderly whole according to a predetermined data model and specifications; for example, business semantics, financial semantics, and data lineage information are organized and stored in a unified model to generate a data asset catalog.

[0060] Among the above-mentioned optional approaches, a structured data asset catalog is formed by assigning both business and financial semantics to metadata and recording data lineage, thereby enhancing data understandability and traceability and supporting the accuracy of subsequent data processing.

[0061] In one alternative approach, S3 specifically includes: Several standard computation logics are defined, including: numerical aggregation computation logic, time-based window computation logic, and text-based feature extraction computation logic.

[0062] Numerical aggregation calculation logic refers to standard calculation rules specifically designed for numerical data, used to summarize multiple data values ​​into a total or representative value; for example, the "summation" calculation logic adds up the "annual premium" values ​​of a group of insurance policies to obtain the total premium. Time-window calculation logic refers to standard calculation rules specifically designed for time-series data, used to calculate data indicators within a specific time interval; for example, the calculation of the "12-month moving average surrender rate" uses time-window calculation logic. Text-based feature extraction calculation logic refers to standard calculation rules specifically designed for text data, used to identify and extract key information from unstructured text; for example, the keyword matching algorithm used to extract the "unsatisfactory service" tag from the surrender reason description.

[0063] Each standard computation logic is encapsulated into an independent computation unit with a configurable parameter interface, resulting in the plurality of parameterized computation units.

[0064] In this context, an independent computing unit refers to a well-encapsulated, functionally cohesive computing module that can be independently deployed and invoked in software implementation; for example, encapsulating the "month-on-month growth rate calculation logic" into a Python function with explicit input and output interfaces.

[0065] The multiple parameterized calculation units are aggregated to form the data calculation capability library.

[0066] In the above-mentioned optional approaches, various standard computing logics are further encapsulated into configurable parameterized computing units to form a modular and reusable computing capability library, thereby improving the flexibility and scalability of computing logic and reducing the cost of customized development.

[0067] In one alternative approach, S4 specifically includes: The logical orchestration interface displays multiple parameterized calculation units, predefined rules, and callable external functions in the data calculation capability library.

[0068] In response to the user's configuration operation in the logic orchestration interface, a target parameterized calculation unit is selected from the plurality of parameterized calculation units, and the target parameterized calculation unit is logically connected with the specified predefined rule and the external function.

[0069] Configuration operations refer to the actions users take on the logic orchestration interface, such as clicking, dragging, connecting lines, and filling out forms, to select and set parameters for calculation units, rules, or functions. For example, a user might drag the "Month-on-Month Calculation" unit onto the canvas and set the "Comparison Period" to "Month." Target parameterized calculation units refer to the parameterized calculation units that users specifically select and prepare from the data calculation capability library during the logic orchestration process for building customized logic. For example, the "Summation" calculation unit that a user selects from the library and drags onto the canvas.

[0070] Based on the processing order and dependencies determined by the logical connections, configure the calculation parameters of the target parameterized calculation unit, the judgment conditions of the predefined rules, and the calling parameters of the external function.

[0071] In this context, processing order and dependencies refer to the execution sequence of each processing step and the dependency chain of data inputs within the customized logic. For example, the customized logic might stipulate that the "screening for policy cancellations during the cooling-off period" rule must be executed first, followed by the "calculating the percentage of policy cancellations during the cooling-off period." Judgment conditions refer to the logical expressions or thresholds that must be met in predefined rules to trigger specific actions or perform classifications. For example, a rule's judgment condition might be: "Policy cancellation application date - policy effective date < 30." Call parameters refer to the specific values ​​or variables that need to be passed to an external function to control its behavior or provide the data required for calculation. For example, when calling a "fraud risk scoring function," "customer ID number" and "transaction amount" are passed as call parameters.

[0072] Based on the configured calculation parameters, judgment conditions, and calling parameters, the customized logic for processing the specific business financial field is generated.

[0073] In addition to the above optional methods, a visual logic orchestration interface is further provided, which allows users to flexibly combine computing units, rules and external functions through configuration operations to generate customized processing logic, thereby reducing the technical threshold and improving the efficiency of logic configuration.

[0074] In one alternative approach, S5 specifically includes: Based on the business semantics, financial semantics, and data lineage recorded in the data asset catalog, corresponding business data and financial data are extracted from the multiple heterogeneous data sources.

[0075] The business data and the financial data are processed in a standardized manner according to the standard calculation logic encapsulated in the target parameterized calculation unit.

[0076] Standardized processing refers to the standardized and generalized calculation and transformation operations performed on data using pre-built parameterized calculation units in the data calculation capability library; for example, using the "data cleaning" unit to handle null values ​​and using the "aggregation and summation" unit to calculate the total premium.

[0077] Based on the calculation parameters, judgment conditions, and calling parameters configured in the customized logic, customized rule processing is performed on specific business and financial fields in the standardized processed data.

[0078] Customized rule processing refers to the specialized processing of specific business and financial fields by applying complex rules and conditions configured in customized logic according to the unique needs of specific business and financial scenarios; for example, applying the configured customized logic to calculate and mark "suspected abnormal cancellation" for insurance cancellation records.

[0079] The business and financial data, after being processed by customized rules, are semantically aligned and structurally integrated according to the business and financial data application scenarios in the scenario definition information to form the unified business and financial fusion data asset.

[0080] Semantic alignment and structural integration refer to the process of matching and associating data from different sources according to business meaning during the data fusion stage, and organizing them into a unified format according to the target data model. For example, associating the "policy number" of the business system with the "policy number" of the financial system, and integrating the relevant amount fields into the corresponding columns of the final analysis table.

[0081] Among the above-mentioned optional methods, the entire process from data extraction, standardized processing, customized processing to semantic alignment is further automated. Based on the data asset catalog and computing power library, heterogeneous data is deeply integrated to form a unified asset, thereby improving the quality and consistency of data processing.

[0082] In this embodiment, it should be noted that: 1) Based on multiple business and financial data application scenarios, business scenario collection is performed. This step comprehensively reviews and solidifies business requirements, serving as the logical starting point for all subsequent data processing. The business scenario collection process involves in-depth collaboration with business departments to systematically collect, define, and structure various data application scenarios, such as precision marketing, risk management, and supply chain optimization. For each business and financial data application scenario, the business objectives, core analytical indicators, required data dimensions and granularity, and expected output data format are clearly defined. This process generates a standardized business scenario definition list, providing a clear and unified input and scope benchmark for subsequent field definitions and model construction.

[0083] 2) After clarifying the application scenarios for business and financial data, configure the metadata for table and field information. This step focuses on building a unified and standardized data asset catalog. It automatically collects and integrates metadata from various data sources, including business databases, log systems, and external data. The collected metadata includes physical name, business meaning, data type, value range, lineage, and data quality rules. Based on this, and according to the identified business and financial data application scenarios, fields are categorized and semantically annotated, clarifying their specific roles and calculation methods in each scenario. Through centralized, business- and technology-integrated metadata management, users can quickly understand and locate the required data assets.

[0084] 3) To meet the diverse computational needs of business and financial data application scenarios, a standardized and reusable data computation capability library is constructed by organizing different types of data computation methods. The process systematically summarizes and organizes various data computation methods. For example, for numerical fields, it defines aggregation operations, year-on-year and month-on-month comparisons, and ratio calculations; for time fields, it defines date difference, time period slicing, and time window calculations; and for text fields, it defines keyword extraction, classification, and sentiment analysis. Each computation method is encapsulated as an independent, parameterized computation unit, with clearly defined input / output specifications, computational logic, and applicable scenarios. This process standardizes and reuses computational logic.

[0085] 4) Considering that some complex and ever-changing business rules cannot be fully covered by the aforementioned standardized calculations, a special field logic configuration toolkit is provided. This toolkit includes a visual logic orchestration interface, allowing users to define and implement non-generic, highly customized field processing logic through various methods such as dragging and dropping components, configuring conditional branches, setting thresholds, and calling external functions or models. Examples include risk score calculation that integrates multiple model scores, business rules, and human experience. The special field logic configuration toolkit transforms complex code development into intuitive business logic configuration.

[0086] In another embodiment of the present invention, a method for intelligent processing of business and financial data includes the following steps: S10. Based on multiple business and financial data application scenarios, generate scenario definition information; collect metadata from multiple heterogeneous data sources according to the scenario definition information, and build a data asset catalog based on the metadata; build a data computing capability library containing multiple parameterized computing units; configure the selected parameterized computing units, predefined rules and external functions through the logic orchestration interface to generate customized logic for processing specific business and financial fields. S20. Based on the configured custom logic and the data lineage recorded in the data asset catalog, start the simulation test mode in the logic orchestration interface; in the simulation test mode, according to the processing order and dependency relationship defined in the custom logic, call the target parameterized calculation unit in the data calculation capability library, load the simulation input dataset associated with the specific business and financial fields, execute the simulation operation of the customized rule processing, and generate a test dataset containing the simulation results. S30. Display the test dataset in the logic orchestration interface and receive user evaluation feedback on the test dataset based on business objectives; respond to the evaluation feedback, adjust the calculation parameters of the target parameterized calculation unit, the judgment conditions of the predefined rules, or the calling parameters of the external function through the logic orchestration interface, and update the customized logic; based on the updated customized logic, repeat the simulation test steps until the test dataset meets the business objective requirements. S40. Based on the data asset catalog, data computing capability library, and updated and tested customized logic, business data and financial data from multiple heterogeneous data sources are associated and processed to form a unified business and financial integrated data asset.

[0087] This embodiment introduces a simulation test mode based on data lineage and a user interactive feedback iteration mechanism, adding a pre-verification and optimization step for customized logic in the intelligent processing flow of business and financial data. This solves the technical problems of high trial and error costs, large configuration deviations, and delayed response to business needs caused by the lack of effective verification methods for customized data processing logic in traditional methods. It improves the reliability of customized logic configuration, enhances the agility of responding to business needs, and strengthens the accuracy of business and financial data fusion processing.

[0088] Figure 2 This diagram illustrates the structure of an embodiment of the intelligent processing system 200 for business and financial data provided by the present invention. Figure 2 As shown, the business data intelligent processing system 200 includes: The scenario generation module 201 is used to generate scenario definition information based on multiple business and financial data application scenarios. The scenario definition information includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario. The directory construction module 202 is used to collect metadata from multiple heterogeneous data sources according to the scenario definition information, and construct a data asset directory based on the metadata. The data asset directory is used to record the business semantics, financial semantics and data lineage of the metadata. Encapsulation module 203 is used to build a data computing capability library containing multiple parameterized computing units, each parameterized computing unit encapsulating a standard computing logic; The logic generation module 204 is used to configure the selected parameterized calculation unit, predefined rules and external functions through the logic orchestration interface to generate customized logic for processing specific business fields. The association processing module 205 is used to associate and process business data and financial data from multiple heterogeneous data sources based on the data asset catalog, the data computing capability library and the customized logic, to form a unified business and financial integrated data asset.

[0089] In one alternative approach, the data requirements include: data type, data dimension, and data granularity; the scenario generation module 201 is specifically used for: Obtain the business objectives and expected output data format for each business and financial data application scenario; Based on the business objectives and expected output data format of each business and financial data application scenario, the associated business indicators and financial indicators are determined for each business and financial data application scenario. Based on the business and financial indicators of each business and financial data application scenario, determine the data types, data dimensions, and data granularity required for each business and financial data application scenario. The business indicators, financial indicators, data types, data dimensions, and data granularity of the multiple business and financial data application scenarios are integrated to generate the scenario definition information.

[0090] In an alternative embodiment, the directory building module 202 is specifically used for: Based on the data type, data dimension, and data granularity in the scenario definition information, the types of metadata to be collected and the corresponding multiple heterogeneous data sources are determined; wherein, the multiple heterogeneous data sources include: business database, financial database, and external data interface; Based on the metadata type, table-level metadata and field-level metadata are automatically collected from the business database, the financial database, and the external data interface; The table-level metadata is associated with the field-level metadata to form the associated metadata; Establish a mapping relationship between the metadata and the multiple business and financial data application scenarios.

[0091] In an alternative embodiment, the directory building module 202 is specifically used for: Based on the mapping relationship, add corresponding business semantics and financial semantics to the metadata; Based on the source and processing flow of the metadata, record the data lineage relationship between the metadata; The business semantics, financial semantics, and data lineage are structurally integrated to generate the data asset catalog.

[0092] In an alternative embodiment, the encapsulation building module 203 is specifically used for: Multiple standard computation logics are defined, including: numerical aggregation computation logic, time-based window computation logic, and text-based feature extraction computation logic. Each standard computation logic is encapsulated into an independent computation unit with a configurable parameter interface to obtain the plurality of parameterized computation units; The multiple parameterized calculation units are aggregated to form the data calculation capability library.

[0093] In an alternative embodiment, the logic generation module 204 is specifically used for: The logical orchestration interface displays multiple parameterized calculation units, predefined rules, and callable external functions in the data calculation capability library; In response to the user's configuration operation in the logical orchestration interface, a target parameterized calculation unit is selected from the plurality of parameterized calculation units, and the target parameterized calculation unit is logically connected with the specified predefined rule and the external function; Based on the processing order and dependencies determined by the logical connections, configure the calculation parameters of the target parameterized calculation unit, the judgment conditions of the predefined rules, and the calling parameters of the external function; Based on the configured calculation parameters, judgment conditions, and calling parameters, the customized logic for processing the specific business financial field is generated.

[0094] In one alternative embodiment, the associated processing module 205 is specifically used for: Based on the business semantics, financial semantics, and data lineage recorded in the data asset catalog, corresponding business data and financial data are extracted from the multiple heterogeneous data sources. The business data and the financial data are processed in a standardized manner according to the standard calculation logic encapsulated by the target parameterized calculation unit. Based on the calculation parameters, judgment conditions, and calling parameters configured in the customized logic, customized rule processing is performed on specific business and financial fields in the standardized processed data; The business and financial data, after being processed by customized rules, are semantically aligned and structurally integrated according to the business and financial data application scenarios in the scenario definition information to form the unified business and financial fusion data asset.

[0095] It should be noted that the beneficial effects of the business and financial data intelligent processing system 200 provided in the above embodiments are the same as those of the above-described business and financial data intelligent processing method, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0096] The business and financial data intelligent processing system 200 of the present invention can be a computer program (including program code) running on a computer device. For example, the business and financial data intelligent processing system 200 of the present invention is an application software that can be used to execute the corresponding steps in the business and financial data intelligent processing method of the present invention.

[0097] In some embodiments, the business data intelligent processing system 200 of the present invention can be implemented in a combination of hardware and software. As an example, the business data intelligent processing system 200 of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the business data intelligent processing method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0098] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0099] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned intelligent processing methods for business and financial data. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the intelligent processing method for business and financial data shown in any embodiment of the present invention by calling the computer program.

[0100] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0101] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0102] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0103] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0104] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0105] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0106] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0107] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described intelligent processing methods for business and financial data.

[0108] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0109] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned business data intelligent processing method.

[0110] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0113] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0114] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0115] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0116] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0117] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent processing of business and financial data, characterized in that, include: Based on multiple business and financial data application scenarios, scenario definition information is generated, which includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario. Based on the scenario definition information, metadata is collected from multiple heterogeneous data sources, and a data asset catalog is constructed based on the metadata. The data asset catalog is used to record the business semantics, financial semantics, and data lineage of the metadata. Construct a data computing capability library containing multiple parameterized computing units, each of which encapsulates a standard computing logic; Configure the selected parameterized calculation units, predefined rules, and external functions through the logic orchestration interface to generate customized logic for processing specific business and financial fields; Based on the data asset catalog, the data computing capability library, and the customized logic, business data and financial data from multiple heterogeneous data sources are associated and processed to form a unified business and financial integrated data asset.

2. The intelligent processing method for business and financial data according to claim 1, characterized in that, The data requirements include: data type, data dimension, and data granularity; the steps for generating scenario definition information based on multiple business and financial data application scenarios include: Obtain the business objectives and expected output data format for each business and financial data application scenario; Based on the business objectives and expected output data format of each business and financial data application scenario, the associated business indicators and financial indicators are determined for each business and financial data application scenario. Based on the business and financial indicators of each business and financial data application scenario, determine the data types, data dimensions, and data granularity required for each business and financial data application scenario. The business indicators, financial indicators, data types, data dimensions, and data granularity of the multiple business and financial data application scenarios are integrated to generate the scenario definition information.

3. The intelligent processing method for business and financial data according to claim 2, characterized in that, The step of collecting metadata from multiple heterogeneous data sources based on the scenario definition information includes: Based on the data type, data dimension, and data granularity in the scenario definition information, the types of metadata to be collected and the corresponding multiple heterogeneous data sources are determined; wherein, the multiple heterogeneous data sources include: business database, financial database, and external data interface; Based on the metadata type, table-level metadata and field-level metadata are automatically collected from the business database, the financial database, and the external data interface; The table-level metadata is associated with the field-level metadata to form the associated metadata; Establish a mapping relationship between the metadata and the multiple business and financial data application scenarios.

4. The intelligent processing method for business and financial data according to claim 3, characterized in that, The step of constructing a data asset catalog based on the metadata includes: Based on the mapping relationship, add corresponding business semantics and financial semantics to the metadata; Based on the source and processing flow of the metadata, record the data lineage relationship between the metadata; The business semantics, financial semantics, and data lineage are structurally integrated to generate the data asset catalog.

5. The intelligent processing method for business and financial data according to claim 4, characterized in that, The step of constructing a data computing capability library containing multiple parameterized computing units includes: Multiple standard computation logics are defined, including: numerical aggregation computation logic, time-based window computation logic, and text-based feature extraction computation logic. Each standard computation logic is encapsulated into an independent computation unit with a configurable parameter interface to obtain the plurality of parameterized computation units; The multiple parameterized calculation units are aggregated to form the data calculation capability library.

6. The intelligent processing method for business and financial data according to any one of claims 1 to 5, characterized in that, The step of configuring the selected parameterized calculation unit, predefined rules, and external functions through the logic orchestration interface to generate customized logic for processing specific business fields includes: The logical orchestration interface displays multiple parameterized calculation units, predefined rules, and callable external functions in the data calculation capability library; In response to the user's configuration operation in the logical orchestration interface, a target parameterized calculation unit is selected from the plurality of parameterized calculation units, and the target parameterized calculation unit is logically connected with the specified predefined rule and the external function; Based on the processing order and dependencies determined by the logical connections, configure the calculation parameters of the target parameterized calculation unit, the judgment conditions of the predefined rules, and the calling parameters of the external function; Based on the configured calculation parameters, judgment conditions, and calling parameters, the customized logic for processing the specific business financial field is generated.

7. The intelligent processing method for business and financial data according to claim 6, characterized in that, The step of associating and processing business and financial data from multiple heterogeneous data sources to form a unified business-finance integrated data asset, based on the data asset catalog, the data computing capability library, and the customized logic, includes: Based on the business semantics, financial semantics, and data lineage recorded in the data asset catalog, corresponding business data and financial data are extracted from the multiple heterogeneous data sources. The business data and the financial data are processed in a standardized manner according to the standard calculation logic encapsulated by the target parameterized calculation unit. Based on the calculation parameters, judgment conditions, and calling parameters configured in the customized logic, customized rule processing is performed on specific business and financial fields in the standardized processed data; The business and financial data, after being processed by customized rules, are semantically aligned and structurally integrated according to the business and financial data application scenarios in the scenario definition information to form the unified business and financial fusion data asset.

8. A business and financial data intelligent processing system, characterized in that, include: The scenario generation module is used to generate scenario definition information based on multiple business and financial data application scenarios. The scenario definition information includes business indicators, financial indicators and data requirements corresponding to each business and financial data application scenario. The catalog building module is used to collect metadata from multiple heterogeneous data sources according to the scenario definition information, and build a data asset catalog based on the metadata. The data asset catalog is used to record the business semantics, financial semantics and data lineage of the metadata. Encapsulate building blocks to build a data computing capability library containing multiple parameterized computing units, each of which encapsulates a standard computing logic; The logic generation module is used to configure the selected parameterized calculation units, predefined rules and external functions through the logic orchestration interface to generate customized logic for processing specific business and financial fields. The association and processing module is used to associate and process business data and financial data from multiple heterogeneous data sources based on the data asset catalog, the data computing capability library and the customized logic, to form a unified business and financial integrated data asset.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to implement the business data intelligent processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which, when executed by a processor, implements the business data intelligent processing method as described in any one of claims 1 to 7.

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