A digital financial service system for business financial financing

Abstract

The application relates to the technical field of business and finance, in particular to a digital financial service system for business and finance. The application collects business basic data through establishment of a business management subsystem, automatically accounts by a financial management subsystem, fuses and analyzes the business and finance data by a decision analysis subsystem, and finally implements whole-process monitoring by an internal control management subsystem, so that a closed-loop data circulation chain is formed. Not only is the data barrier of business and finance broken, automatic data circulation and intelligent analysis are realized, but also a risk prevention and control mechanism based on data is established.

Description

Technical Field

[0001] The present application relates to the technical field of business-finance financing, and in particular to a digital financial service system for business-finance financing. Background Art

[0002] With the deepening digital transformation of finance, financial management is gradually moving towards intelligent and information-based approaches. National and local financial departments are faced with the need to process vast amounts of financial data across multiple processes, including budget preparation, execution, final accounting, and oversight. Leveraging modern information technology to achieve efficient integration and collaborative management of fiscal operations and financial data, and to improve both financial management and capital utilization efficiency, has become a key research area in the financial field.

[0003] Current financial management systems often utilize a decentralized architecture, with business management systems, financial accounting systems, and risk control systems operating independently. Data exchange between these systems occurs through manual operations or simple data interfaces. While this approach can support daily financial management to a certain extent, it lacks deep integration between systems and cannot meet the demands for real-time and interconnected financial data, as well as dynamic decision-making and analysis.

[0004] Therefore, there is an urgent need to establish a comprehensive service system that integrates business and finance to improve financial management and operational efficiency. This situation needs further improvement. Summary of the Invention

[0005] To address the existing problem of business data and financial data being separated from each other and unable to meet the needs of real-time and interconnected financial data and dynamic decision-making analysis, this application provides a digital financial service system for business and financial integration, which adopts the following technical solutions, including:

[0006] The business management subsystem is used to perform business activities and obtain basic business data;

[0007] The financial management subsystem is used to perform financial accounting and management based on the basic business data to obtain financial accounting data;

[0008] A decision analysis subsystem, configured to generate an analysis report based on the business basic data and the financial accounting data to obtain decision support data;

[0009] The internal control management subsystem is used to implement risk control and business tracing based on the decision support data to obtain internal control management data.

[0010] By adopting the above technical solution, this application proposes a digital financial service system for business and financial integration. By establishing a business management subsystem to collect basic business data, the financial management subsystem automatically calculates the data, and then uses the decision analysis subsystem to integrate and analyze the business and financial data. Finally, the internal control management subsystem implements full monitoring, forming a closed-loop data flow chain; it not only breaks through the business and financial data barriers, realizes the automatic flow and intelligent analysis of data, but also establishes a data-based risk prevention and control mechanism.

[0011] Optionally, the business management subsystem includes a group standard management module, which is used to:

[0012] According to the requirements of standard setting, technical experts are matched from member units to obtain expert recommendation data;

[0013] Based on the expert recommendation data, an online collaborative compilation platform is established to obtain the draft standard data;

[0014] Based on the draft standard data, blockchain technology is used to record modification opinions and version evolution to obtain standard formulation process data;

[0015] The standard formulation process data is analyzed, a standard development report is automatically generated, and standard management data is obtained.

[0016] By adopting the above technical solution, this application sets up a group standard management module in the business management subsystem, combines expert intelligent matching, online collaborative platform and blockchain technology, forms recommendation data through intelligent matching of experts from member units, uses the online collaborative platform to support real-time editing by multiple people, uses blockchain technology to record modification opinions and version evolution, and finally automatically generates a development report; it not only realizes the digital management of the entire process of standard setting, but also ensures the traceability and non-tamperability of the process through blockchain technology, thereby improving the efficiency and quality of standard setting.

[0017] Optionally, the financial management subsystem includes:

[0018] The project management module is used to classify and grade projects and manage the entire process to obtain basic project data;

[0019] A budget management module is used to perform intelligent budget compilation and dynamic adjustment based on the basic project data to obtain budget control data;

[0020] The revenue and expenditure management module is used to implement revenue and expenditure management based on the budget control data and obtain revenue and expenditure execution data.

[0021] By adopting the above technical solution, this application establishes a classified and graded project life cycle management system through the project management module, and the budget management module performs intelligent budget preparation and dynamic adjustment based on the basic project data, and finally realizes linkage control with the budget through the income and expenditure management module; it realizes data-driven management of projects-budgets-income and expenditures, transforming static management into dynamic management, which not only solves the information island problem in traditional financial management, but also establishes a data-based intelligent management and control mechanism.

[0022] Optionally, the project management module includes:

[0023] Project classification unit, used to intelligently classify projects according to their nature and funding sources to obtain classified data;

[0024] A project evaluation unit, configured to perform a feasibility evaluation based on the classified data to obtain evaluation data;

[0025] A project monitoring unit, configured to set milestone nodes based on the evaluation data and obtain monitoring data;

[0026] The project analysis unit is used to generate a project portrait based on the monitoring data to obtain basic project data.

[0027] By adopting the above technical solution, this application establishes an intelligent classification system based on nature and funding sources through the project classification unit, uses the project evaluation unit to conduct feasibility evaluation, adopts milestone nodes to monitor the project, and finally generates a digital project portrait through the project analysis unit; it not only realizes the standardization and visualization of project management, but also forms a panoramic view of the project through data-driven, solving the problems of low efficiency and insufficient risk warning in existing project management.

[0028] Optionally, the budget management module includes:

[0029] Budget estimation unit, used to perform intelligent estimation based on historical data and project profiles to obtain estimation data;

[0030] A budget preparation unit, configured to form an initial budget based on the estimated data to obtain preparation data;

[0031] A budget adjustment unit, configured to perform dynamic adjustments based on the compilation data to obtain adjustment data;

[0032] The budget control unit is used to set the control node according to the adjustment data to obtain budget control data.

[0033] By adopting the above technical solution, this application uses the budget estimation unit to perform intelligent estimation based on historical data and project portraits, uses the budget preparation unit to form a data-driven initial budget, sets up a budget adjustment unit to achieve dynamic response, and finally establishes a node-based management and control system through the budget control unit; it not only achieves the accuracy and scientific nature of budget preparation, but also improves the flexibility of budget execution through the dynamic adjustment mechanism, while ensuring the effectiveness of budget control.

[0034] Optionally, the decision analysis subsystem includes:

[0035] A correlation determination module is used to obtain basic business data and financial accounting data, and determine the business-finance correlation based on the basic business data and the financial accounting data;

[0036] A correlation adjustment module is used to query the historical database to obtain historical business-finance correlation information, and adjust the business-finance correlation based on the historical business-finance correlation information;

[0037] The decision generation module is used to set analysis indicators based on the adjusted business-finance correlation and generate decision support data.

[0038] By adopting the above technical solution, this application realizes the automatic matching of business and financial data through the correlation judgment module, introduces the correlation adjustment module to optimize using historical experience, and finally transforms the correlation analysis into decision support through the decision generation module; it not only realizes the quantitative evaluation of the correlation of business and financial data, but also improves the analysis accuracy through the intelligent application of historical experience, providing reliable data support for management decisions.

[0039] Optionally, determine the business-finance correlation, specifically including the following steps:

[0040] Based on the correspondence between the business basic data and the financial accounting data, obtaining data matching and time series consistency;

[0041] A business-finance correlation is generated based on the data matching degree and the time series consistency.

[0042] By adopting the above technical solution, the business-finance correlation includes evaluation indicators of two dimensions: data matching and time consistency. Data matching reflects the degree of correspondence between business data and financial data in content, and time consistency reflects the synchronization between business occurrence and financial records in time. Combining the quantitative indicators of these two dimensions, starting from the content dimension and time dimension of data correlation, the correlation degree between business data and financial data can be scientifically evaluated, which will help to adopt differentiated analysis strategies and set corresponding decision indicators for data with different correlation degrees in the future; data with higher correlation can be directly used for decision analysis; data with lower correlation will be further cleaned and correlation optimized; this dual-dimensional correlation evaluation mechanism not only improves the accuracy of business-finance integration analysis, but also provides direction guidance for continuous optimization of data quality, and realizes more accurate decision support.

[0043] Optionally, obtain historical business and financial related information, including the following steps:

[0044] Query the historical database and mark the historical analysis data of the same type of business;

[0045] According to the tag, historical business-finance correlation information corresponding to the historical analysis data is obtained.

[0046] By adopting the above technical solution, the historical database contains historical data accumulated by the organization from business development to financial accounting, including the correlation evaluation information of each business-financial analysis; by querying the historical database and marking the same type of business, the business-financial correlation in similar business scenarios in history can be effectively identified and extracted, thereby obtaining the corresponding historical business-financial correlation information; this analysis method based on historical experience can discover the evolution laws and characteristic patterns of business-financial correlation from historical data, which helps to more accurately judge the correlation characteristics of current business and financial data; for example, by analyzing the changing trends of business-financial correlation of a certain type of business in different periods, it is possible to predict possible problems in data correlation and adjust the correlation evaluation parameters in time to generate correlation evaluation results that are more in line with actual conditions; it not only improves the accuracy of business-financial correlation analysis, but also realizes the effective reuse of experience data, providing more reliable data support for business-financial integration decision-making.

[0047] Optionally, adjusting the business-finance correlation may include the following steps:

[0048] Obtain historical business-finance correlation data and historical analysis data based on historical business-finance correlation information;

[0049] Compare current business-finance correlation data with historical business-finance correlation data, and mark businesses with the same comparison results as stable businesses;

[0050] Adjust the data matching degree of the corresponding business based on the historical analysis data of the stable business;

[0051] Based on the adjusted data matching degree, update the business-financial correlation of the corresponding business.

[0052] By adopting the above technical solution, historical business-financial correlation data and historical analysis data are extracted from historical business-financial correlation information. By comparing the current business-financial correlation data with the historical business-financial correlation data, businesses with the same business type and stable data characteristics are marked as stable businesses. Since these stable businesses have continuity and regularity within a certain period, but there may be changes in the business environment and evolution of data quality over time, it is necessary to dynamically adjust their data matching based on historical analysis data. For example, although the type and characteristics of a certain business remain stable, its historical analysis data shows that the matching accuracy of business-financial data is gradually improving, or it shows obvious regular changes in a specific period. These historical experiences should be used to optimize the current data matching assessment. Through this dynamic adjustment mechanism based on historical data, not only the accuracy of the business-financial correlation assessment is improved, but also the self-optimization of the assessment standards is achieved, so that the business-financial integration analysis can better adapt to the actual needs of business development, and ultimately provide more reliable data support for management decisions.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. This application proposes a digital fiscal service system for business and financial integration. This system establishes a business management subsystem to collect basic business data, which is then automatically calculated by the financial management subsystem. A decision analysis subsystem then integrates and analyzes the business and financial data. Finally, an internal control management subsystem implements full monitoring throughout the process, forming a closed-loop data flow chain. This system not only breaks down barriers to business and financial data, enabling automatic data flow and intelligent analysis, but also establishes a data-based risk prevention and control mechanism.

[0055] 2. This application establishes a categorized and hierarchical project lifecycle management system through the project management module. Based on basic project data, the budget management module performs intelligent budget compilation and dynamic adjustments. Finally, the revenue and expenditure management module implements coordinated control with the budget. This achieves data-driven management of projects, budgets, and revenue and expenditures, transforming static management into dynamic management. This not only solves the information silos problem in traditional financial management, but also establishes a data-based intelligent management and control mechanism.

[0056] 3. This application realizes the automatic matching of business and financial data through the correlation judgment module, introduces the correlation adjustment module to optimize using historical experience, and finally transforms the correlation analysis into decision support through the decision generation module; it not only realizes the quantitative evaluation of the correlation of business and financial data, but also improves the accuracy of analysis through the intelligent application of historical experience, providing reliable data support for management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the structure of a digital financial service system for business and financial integration according to an embodiment of the present application;

[0058] Figure 2 This is a schematic diagram of the structure of the business management subsystem in the digital financial service system of the embodiment of the present application;

[0059] Figure 3 This is a schematic diagram of the structure of the financial management subsystem in the digital financial service system of the embodiment of the present application;

[0060] Figure 4 This is a schematic diagram of the structure of the project management module in the financial management subsystem of the embodiment of the present application;

[0061] Figure 5 This is a schematic diagram of the structure of the budget management module in the financial management subsystem of the embodiment of the present application;

[0062] Figure 6 This is a schematic diagram of the structure of the decision analysis subsystem in the digital financial service system of the embodiment of the present application;

[0063] Figure 7 This is a flow chart of determining the degree of business-finance correlation in an embodiment of the present application;

[0064] Figure 8 This is a flowchart of obtaining historical business and financial related information in an embodiment of the present application;

[0065] Figure 9 It is a flowchart of adjusting the business-finance correlation in an embodiment of the present application. DETAILED DESCRIPTION

[0066] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0067] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0068] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0069] First, this application provides a digital financial service system for business financial financing, referring to Figure 1 The digital financial service system includes business management subsystem, financial management subsystem, decision analysis subsystem and internal control management subsystem.

[0070] Among them, the business management subsystem is used to perform business activity operations and obtain basic business data.

[0071] In this embodiment, the business management subsystem implements standardized management of various business data by establishing a unified business activity classification system. This subsystem pre-establishes a business foundation database, which includes a business type code table and a data standard specification table. The business type code table codes and categorizes common business activities, such as conferences, training programs, and membership services. The data standard specification table defines the collection elements, format specifications, and validation rules for various types of business data, ensuring data consistency and availability.

[0072] Specifically, the business management subsystem uses web forms for data collection. For example, in conference event management, the system automatically generates an event ID, and the operator fills in basic event information, attendees, and budget. The system ensures data integrity through field-level validation, such as checking for required fields and verifying the format of amounts. The collected data is stored in real time in the business database and made available to other subsystems through pre-defined data interfaces.

[0073] The financial management subsystem is used to perform financial accounting and management based on basic business data and obtain financial accounting data.

[0074] In this embodiment, the financial management subsystem converts basic business data into financial accounting data based on a pre-established business-finance mapping table. This subsystem establishes a financial accounting rule library that defines accounting treatments for different business types. It also pre-configures a standard chart of accounts and auxiliary accounting tables, and sets key accounting items such as special funds and project costs based on the characteristics of financial business. The system automatically identifies business types based on the business-finance mapping relationship and generates accounting entries according to the accounting rules.

[0075] Specifically, when the business system generates new business data, the financial system automatically extracts key elements and generates vouchers. For example, when membership dues are received, the system automatically generates a bank deposit debit and membership dues income credit entry based on the member information and payment amount.

[0076] The decision analysis subsystem is used to generate analysis reports based on business basic data and financial accounting data to obtain decision support data.

[0077] In this embodiment, the decision analysis subsystem establishes a multidimensional analysis model to integrate and analyze business and financial data. The system pre-configures analysis dimension tables, including time, business, and organizational dimensions, and establishes a rule library for indicator calculations. Through data cleaning and standardization, data from various sources is unified into the analysis framework, enabling multidimensional data analysis.

[0078] Specifically, the system generates various analytical reports based on pre-set analysis templates. For example, the fund utilization analysis report compares budget execution data with actual expenditure data to calculate fund utilization rates and structural ratios. Another example is the member service analysis report, which measures member activity participation and service satisfaction to evaluate service effectiveness. The analysis results are displayed in charts and automatically generate analysis explanations.

[0079] The internal control management subsystem is used to implement risk control and business tracing based on decision support data to obtain internal control management data.

[0080] In this embodiment, the internal control management subsystem implements risk monitoring and business tracking by establishing a risk warning indicator library and a business traceability index table. The risk warning indicator library contains both quantitative and qualitative indicators, such as fund utilization rate and business compliance. The business traceability index table records key nodes and responsible individuals in business activities, supporting both upward and downward tracing.

[0081] Specifically, the system regularly monitors early warning indicators and automatically issues alerts when they exceed thresholds. For example, if a project's fund utilization rate is too low, the system generates an alert prompting attention. Regarding business tracing, the system supports querying the entire business process by transaction number, person in charge, and other criteria, enabling traceable management of business activities. Furthermore, the system maintains a log of operations to ensure traceability of important operations.

[0082] In one embodiment, referring to Figure 2 ,The business management subsystem includes conference management module, membership ,management module, journal management module and group standard management module.

[0083] The conference management module pre-installs a library of conference types and conference resources, enabling meeting preparation and organization through task list templates. The membership management module establishes a membership classification table and a service project library to support member information management and service tracking. The journal management module provides a manuscript processing workflow library and a review expert library to achieve full manuscript management. The group standard management module constructs an expert resource library and a standards development specification database, uses a keyword index table to achieve intelligent expert matching, and uses blockchain technology to record the standards development process. These modules use unified data interface specifications to transmit business data to other subsystems in real time for processing.

[0084] Specifically, the group standard management module is used to:

[0085] According to the requirements of standard setting, technical experts are matched from member units to obtain expert recommendation data;

[0086] Based on expert recommendation data, an online collaborative compilation platform was established to obtain standard draft data;

[0087] Based on the draft standard data, blockchain technology is used to record modification opinions and version evolution to obtain standard formulation process data;

[0088] Analyze the data of the standard formulation process, automatically generate the standard development report, and obtain the standard management data.

[0089] In this embodiment, the group standard management module pre-establishes an expert-technical field mapping table and a standard compilation specification library. The expert-technical field mapping table includes fields such as the expert's basic information, affiliation, and technical expertise, and uses keyword tags to identify the expert's technical areas of expertise. The standard compilation specification library stores regulatory documents such as standard writing templates, compilation processes, and review rules. To improve retrieval efficiency, the system establishes an inverted index table that maps technical field keywords to expert information.

[0090] Specifically, when a standard formulation requirement is received, the system first extracts technical field keywords from the requirement description and quickly locates relevant experts through an inverted index table. For the preparation of draft standards, the system provides an online document collaboration environment and uses a document version control mechanism to record the modification history. Each time a modification is made, the system writes information such as the modified content, modifier, and modification time into the blockchain to ensure that the modification process is traceable. The system automatically generates a standard development report according to a preset report template by statistically analyzing basic data such as the frequency of modifications and the number of opinions. For example, for the "Safety Requirements for Industrial Robots" standard, the system records the complete expert recommendation process, the modification history of each chapter, expert opinions and adoption status, etc., providing an objective basis for standard quality assessment.

[0091] In one embodiment, referring to Figure 3The financial management subsystem includes project management module, budget management module, income and expenditure management module, contract management module, procurement management module, accounting management module, asset management module, salary management module, performance management module, bill management module and foreign investment management module.

[0092] Among them, the project management module is used to classify and grade projects and manage the entire process to obtain basic project data.

[0093] In this embodiment, the project management module pre-establishes a project classification and grading table and a project process library. The project classification and grading table includes fields for vertical projects, horizontal projects, and self-funded projects, and sets up a three-level management hierarchy (ABC) based on factors such as project amount and cycle. The project process library defines standardized process nodes such as project application, process management, and project completion acceptance, and sets differentiated approval paths for different project types and levels.

[0094] Specifically, when a project is established, the system automatically determines the project type and level based on basic project information. For example, a 5 million yuan Ministry of Science and Technology project is automatically identified as a "Vertical-A" project and the corresponding management process is initiated. Pre-set task templates are used to generate project milestones, such as the project opening report, mid-term review, and final acceptance. The system employs a simple progress tracking mechanism, comparing planned and actual completion times to calculate project progress deviations and issuing warnings when the deviation exceeds a threshold.

[0095] The budget management module is used to prepare and dynamically adjust the budget intelligently based on the basic project data to obtain budget control data.

[0096] In this embodiment, the budget management module establishes a budget account library and a budget standard table. The budget account library categorizes expenditure items, including common items such as equipment expenses, material expenses, and travel expenses. The budget standard table sets limits for various expenditures, such as the daily limit per person for conference expenses. The system also establishes a database of historical project budgets to provide a reference for preparing new project budgets.

[0097] Specifically, the system automatically loads budget templates based on the project type. For example, for conference projects, the system automatically calculates budgets for conference fees, travel expenses, and other expenses based on the number of attendees and the duration of the meeting. During budget execution, the system compares actual expenditures with the budgeted amount to calculate the budget execution rate. When expenditures for a specific item approach the budget ceiling, the system automatically prompts budget adjustment suggestions. For example, if a trend of overspending on materials is detected, the system recommends adjusting budget amounts from items with larger surpluses.

[0098] The revenue and expenditure management module is used to implement revenue and expenditure management based on budget control data and obtain revenue and expenditure execution data.

[0099] In this embodiment, the income and expenditure management module pre-configures an income type table and an expenditure approval process library. The income type table categorizes membership dues, project funds, and service income. The expenditure approval process library establishes hierarchical approval paths based on expenditure amount and purpose. The system establishes an income and expenditure accounting rule table to standardize the recording and statistics of income and expenditure data.

[0100] Specifically, in terms of revenue management, the system supports automatic matching of payment records from multiple channels. For example, when funds arrive for a project, the system automatically links them to the corresponding project based on the payer information and amount. Regarding expenditure management, the system audits expenditures using pre-set quota control rules. Furthermore, the system calculates project fund balances in real time and automatically issues alerts when the balance is insufficient.

[0101] In one embodiment, referring to Figure 4 , the project management module includes:

[0102] Project classification unit, used to intelligently classify projects according to their nature and funding sources to obtain classified data;

[0103] A project evaluation unit is used to conduct feasibility evaluation based on the classified data and obtain evaluation data;

[0104] Project monitoring unit, used to set milestone nodes based on assessment data and obtain monitoring data;

[0105] The project analysis unit is used to generate a project portrait based on monitoring data and obtain basic project data.

[0106] In this embodiment, the project management module pre-establishes a project feature library and an evaluation rule library. The project feature library contains a table of project classification elements, defining the classification criteria for project nature (e.g., scientific research projects, technical service projects, etc.) and funding sources (e.g., government grants, corporate commissions, etc.). The evaluation rule library establishes an evaluation indicator system covering dimensions such as technical feasibility, economic feasibility, and risk level. By establishing a project profile template, the system provides a standardized description of key information throughout the project lifecycle, enabling refined project management.

[0107] Specifically, when a new project is applied for, the system first performs intelligent classification through preset feature matching rules. For example, for a technology development project commissioned by a certain enterprise, the system classifies it as a "horizontal-technology development-general risk" type based on the contract amount and research content. During the project evaluation phase, the system loads the corresponding evaluation indicators and uses a scoring mechanism to conduct a feasibility assessment. For example, when evaluating the technical feasibility, the system scores based on factors such as technology maturity and R&D team configuration. Through the preset milestone node template, the system automatically generates a project schedule, such as key nodes such as technical solution confirmation, prototype development, and test acceptance. During the project execution process, the system uses visual methods such as Gantt charts to display the project progress, and through statistical analysis of project execution data, forms a project portrait containing basic information, progress status, risk warnings and other dimensions. For abnormal situations such as delays in important nodes or cost overruns, the system automatically generates early warning information.

[0108] Relevant project information is transmitted to the budget management module through a standardized data interface for use in project budget compilation and adjustment. Simultaneously, basic project data supports decision-making analysis, helping management understand the overall project status.

[0109] In one embodiment, referring to Figure 5 , the budget management module includes:

[0110] Budget estimation unit, used to perform intelligent estimation based on historical data and project profiles to obtain estimation data;

[0111] The budget preparation unit is used to form an initial budget based on the estimated data and obtain the preparation data;

[0112] A budget adjustment unit is used to make dynamic adjustments based on the compilation data to obtain adjustment data;

[0113] The budget control unit is used to set the control node according to the adjustment data to obtain the budget control data.

[0114] In this embodiment, the budget management module pre-establishes a subject standard library and a budget model library. The subject standard library contains a budget category classification table and a quota standard table, which standardize the budget compilation standards for various expenditure types. The budget model library stores budget compilation templates and calculation parameters for different types of projects, such as typical budget models for scientific research projects and conference projects. The system establishes a historical project budget execution database and uses simple statistical analysis methods to extract reference information for budget compilation. Furthermore, a budget adjustment rule table is set up to define different levels of budget adjustment permissions and approval processes.

[0115] Specifically, the system first extracts budget-related elements based on the project portrait, such as project scale and execution cycle, and then performs intelligent calculations based on the budget execution data of historical similar projects. For example, for a two-year technology development project, the system generates preliminary calculation results by analyzing the expenditure structure of historical projects such as personnel expenses and equipment expenses. During the budget preparation process, the system automatically loads the budget template according to the project type and allocates the quota according to the subject standards. For example, the equipment fee does not exceed 30% of the total budget and other control requirements. During the execution process, the system monitors the budget execution in real time. When there is a trend of overspending on a subject, it dynamically adjusts it according to the preset adjustment rules. For example, the inter-subject transfer quota is allowed to not exceed 20% of the original budget. The system sets budget control points at key expenditure nodes. For example, large-scale equipment purchases require budget approval to ensure the standardization of budget execution. Expenditure applications that exceed the threshold automatically enter the budget review process to avoid unbudgeted expenditures.

[0116] Relevant budget control data is transmitted to the revenue and expenditure management module via a standard interface, serving as the basis for specific revenue and expenditure transactions. Simultaneously, budget execution data is fed back to the project management module for project cost control. This feedback loop ensures the real-time and accuracy of budget management.

[0117] In one embodiment, referring to Figure 6 , the decision analysis subsystem includes:

[0118] The correlation judgment module is used to obtain basic business data and financial accounting data, and judge the business-finance correlation based on the basic business data and financial accounting data.

[0119] In this embodiment, the correlation determination module pre-establishes a business-finance mapping table and an association rule library. The business-finance mapping table defines the correspondence between business activities and financial accounts, including fields such as business type, accounting account, and accounting dimension. The association rule library provides basic rules for determining the degree of business-finance correlation, such as time matching and amount consistency. The system establishes a correlation calculation method to quantitatively evaluate the matching between business data and financial data.

[0120] Specifically, the system first obtains business data such as project approval and contract signing from the business management subsystem, and financial data such as income and expenditure records from the financial accounting subsystem. Pre-set matching rules are used to determine correlation, such as the time difference and amount consistency between project receipts and financial receipt records. For example, if a contract receipt for a technical service project is 2 million yuan, the system will query the financial receipt record by linking it to the contract number, calculate the time and amount matching of the business and financial data, and generate a correlation score based on pre-set weights.

[0121] The correlation adjustment module is used to query the historical database, obtain historical business-finance correlation information, and adjust the business-finance correlation based on the historical business-finance correlation information.

[0122] In this embodiment, the relevance adjustment module establishes a historical data analysis library and an adjustment parameter table. The historical data analysis library stores the correlation of historical business and financial data, including successful and failed correlation cases. The adjustment parameter table sets adjustment coefficients for different business types to optimize relevance determination results. The system uses statistical methods to analyze the correlation patterns in historical data and provide a basis for revising current relevance determinations.

[0123] Specifically, after obtaining a preliminary correlation determination, the system automatically queries the historical database to extract correlation features for similar businesses. For example, for conference-related businesses, the system analyzes the business-financial correlation patterns of historical conference projects, including characteristics such as the timing of receipts and payments and accounting subjects. Based on historical correlation patterns, the system uses a preset adjustment algorithm to modify the initial correlation. For example, if a certain type of business is found to have a fixed payment delay, the time matching calculation standard will be adjusted accordingly. The system records the adjusted correlation results in the database and continuously optimizes the adjustment parameters.

[0124] The decision generation module is used to set analysis indicators based on the adjusted business-finance correlation and generate decision support data.

[0125] In this embodiment, the decision generation module pre-installs an analysis indicator library and a decision template library. The analysis indicator library contains quantitative indicators for dimensions such as business efficiency, capital turnover, and cost-benefit. The decision template library stores analysis report templates for different topics, such as project analysis and budget execution analysis. The system establishes an indicator calculation rule table to transform business and financial data into management decision information.

[0126] Specifically, the system selects applicable analytical indicators for calculation based on the adjusted business-finance correlation. For example, if a project's business and finances are highly correlated, the system automatically calculates indicators such as project funding efficiency and cost control. Using pre-set decision analysis templates, the system integrates these calculation results into a decision-making report. For example, when analyzing a research project group, the system generates analytical charts covering dimensions such as funding progress and budget execution deviations, providing data support for management decisions. The system regularly updates decision-support data and uses trend analysis to predict future development trends.

[0127] In one embodiment, referring to Figure 7 , to determine the degree of business-finance correlation, specifically including the following steps:

[0128] S710. Based on the correspondence between the business basic data and the financial accounting data, obtain data matching and time series consistency.

[0129] In this embodiment, data matching refers to the degree of matching between business data and financial data in terms of key elements such as amounts and accounts. Time consistency refers to the degree of conformity between the time of business activities and financial records.

[0130] Specifically, based on the basic business data and financial accounting data, the type of each current business data is determined, and the complete information index table of the corresponding business type is matched from the preset business-finance association database, and the financial accounting data corresponding to each current business data is entered into the complete information index table to obtain the data matching degree of each business; in addition, based on the information indicating the time when the business occurred in each current business data, combined with the accounting time indicated by the financial accounting data, the time interval between the business occurrence and the financial record is determined, thereby obtaining the corresponding time series consistency for each business.

[0131] S720. Generate business-finance correlation based on data matching and time series consistency.

[0132] Specifically, the pre-set business-finance correlation database includes not only a complete information index table for each corresponding business type, but also a data matching weight value for the corresponding business type. This weight value is used to convert data matching into business-finance correlation. Different types of businesses have different data matching weight values. For example, the data matching and time series consistency for contract collection and prepayment reimbursement are the same, but due to the different nature of the business, the corresponding data matching weight values ​​are different, resulting in different business-finance correlations for the two types of business. Therefore, the data matching weight of each business is determined through the pre-set business-finance correlation database, and then combined with the corresponding time series consistency of each business, for example, by multiplying the data matching weight by the time series consistency, to obtain the business-finance correlation of each business.

[0133] In one embodiment, referring to Figure 8 , obtain historical business and financial related information, specifically including the following steps:

[0134] S810: Query the historical database and mark the historical analysis data of the same type of business.

[0135] In this embodiment, historical analysis data refers to the correlation data evaluated during the historical business-finance correlation analysis. Historical business-finance correlation information includes the correlation characteristics and correlation patterns between historical business data and financial data.

[0136] Specifically, a historical database stores historical correlation data from business management and financial accounting processes. Therefore, the historical database includes the correlation of business data assessed during historical business-financial correlation analysis, i.e., historical analysis data. The system uses a pre-set business type mapping table to tag historical analysis data for businesses of the same type within the historical database. For example, for technical service businesses, the system tags the historical analysis data for this type of business in the historical database, including information such as the historical business-financial correlation and correlation characteristics.

[0137] S820. Obtain historical business and financial correlation information corresponding to the historical analysis data according to the tag.

[0138] Specifically, based on the tags in the historical database, the system queries and extracts the corresponding historical business and financial correlation information with the tags, that is, obtains the historical business and financial correlation information corresponding to the historical analysis data, wherein the historical business and financial correlation information includes the association rules used in the historical business and financial correlation analysis, the business type characteristics and data dimensions analyzed, and the correlation indicators used. For example, when analyzing conference-related businesses, the system extracts the correlation patterns of conference-related businesses in the historical database, including typical business and financial time series characteristics, common matching anomalies, and other information, to provide a reference basis for the judgment and adjustment of the current business and financial correlation. The system uses statistical analysis methods to extract key features from historical business and financial correlation information to optimize the current correlation judgment results.

[0139] In one embodiment, referring to Figure 9 , adjust the business-finance correlation, specifically including the following steps:

[0140] S910. Obtain historical business-finance correlation data and historical analysis data based on historical business-finance correlation information.

[0141] In this embodiment, historical analysis data refers to the matching data and corresponding evaluation index data of each historical business-finance correlation analysis. Stable business refers to business with the same current business-finance correlation as the previous historical business-finance correlation.

[0142] Specifically, each time a business-finance correlation analysis is conducted, a correlation degree is assessed. Therefore, the correlation degree data assessed during each historical business-finance correlation analysis, along with matching data and corresponding evaluation indicator data, is extracted from the historical business-finance correlation information, thus forming the historical analysis data. For example, the system extracts historical correlation degree assessment records for technical service businesses, including information such as the correlation degree values, matching parameters, and evaluation criteria from each assessment.

[0143] S920: Compare the current business-finance correlation data with the historical business-finance correlation data, and mark businesses with the same comparison results as stable businesses.

[0144] Specifically, the previous historical business-financial correlation for each transaction is compared with the currently assessed business-financial correlation. Transactions with identical comparison results are marked as stable transactions, meaning they have the same current and previous historical correlations. For example, for a contract collection transaction for a project, if the current correlation result matches the previous one, it is marked as stable.

[0145] S930. Adjust the data matching degree of the corresponding business according to the historical analysis data of the stable business.

[0146] Specifically, a stable business indicates that the data matching and temporal consistency corresponding to that business have not changed significantly. However, since the last business-finance correlation analysis is somewhat different from the current time point, the business characteristics may have changed, and the corresponding matching rules and evaluation indicators should be adjusted. Therefore, the historical analysis data of the stable business is analyzed to determine the changing trends of the historical analysis data of the stable business, and then the matching deviations of the current business data are analyzed. In this embodiment, the historical analysis data of the stable business can be analyzed using statistical methods. By analyzing the matching characteristics of a large number of corresponding stable businesses during normal execution and the correlations between various parameters, matching anomalies in the current data of the stable business can be identified based on the correlations between the various parameters, and the matching degree of the current stable business can be determined to be reasonable. For example, for prepayment reimbursement business, if the historical matching data is analyzed to find a fixed arrival delay, the temporal consistency evaluation criteria can be adjusted accordingly. Based on this, an updated data matching degree is generated by analyzing the historical analysis data of the stable business, and the matching evaluation results of the corresponding stable business are adjusted based on the updated data matching degree.

[0147] S940. Update the business-finance correlation of the corresponding business based on the adjusted data matching degree.

[0148] Specifically, the business-finance correlation of the corresponding stable business is updated based on the adjusted data matching degree and the corresponding time series consistency. For example, based on the adjusted matching evaluation results, the system recalculates the business-finance correlation of the stable business according to the preset correlation calculation rules to ensure the accuracy and timeliness of the correlation evaluation results.

[0149] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0150] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A digital financial service system for business and financial integration, characterized by: include: The business management subsystem is used to perform business activities and obtain basic business data; The financial management subsystem is used to perform financial accounting and management based on the basic business data to obtain financial accounting data; A decision analysis subsystem, configured to generate an analysis report based on the business basic data and the financial accounting data to obtain decision support data; An internal control management subsystem, used to implement risk control and business tracing based on the decision support data, and obtain internal control management data; Wherein, the decision analysis subsystem includes: A correlation determination module is configured to obtain basic business data and financial accounting data, obtain data matching and time series consistency based on the basic business data and the financial accounting data, and generate a business-finance correlation based on the data matching and time series consistency. The correlation determination module pre-establishes a business-finance mapping table and an association rule library. The business-finance mapping table defines the correspondence between business activities and financial subjects, including business types, accounting subjects, and accounting dimensions. The association rule library sets basic rules for determining the degree of business-finance correlation. Different business types correspond to different data matching weight values. The data matching weight value refers to the weight value used to convert the data matching degree into the business-finance correlation degree. A correlation adjustment module is configured to query a historical database, mark historical analysis data of businesses of the same type, obtain historical business-finance correlation information corresponding to the historical analysis data based on the mark, obtain historical business-finance correlation data and historical analysis data based on the historical business-finance correlation information, compare current business-finance correlation data with historical business-finance correlation data, mark businesses with the same comparison results as stable businesses, adjust the data matching degree of the corresponding businesses based on the historical analysis data of the stable businesses, and update the business-finance correlation degree of the corresponding businesses based on the adjusted data matching degree, wherein a statistical method is used to identify matching features of the stable business during normal historical execution and the correlation between various parameters, identify matching anomalies in the current data, and generate an updated data matching degree; The decision generation module is used to set analysis indicators based on the adjusted business-finance correlation and generate decision support data.

2. The digital financial service system for business financial integration according to claim 1, characterized in that: The business management subsystem includes a group standard management module, which is used to: According to the requirements of standard setting, technical experts are matched from member units to obtain expert recommendation data; Based on the expert recommendation data, an online collaborative compilation platform is established to obtain the draft standard data; Based on the draft standard data, blockchain technology is used to record modification opinions and version evolution to obtain standard formulation process data; The standard formulation process data is analyzed, a standard development report is automatically generated, and standard management data is obtained.

3. The digital financial service system for business financial integration according to claim 1, characterized in that: The financial management subsystem includes: The project management module is used to classify and grade projects and manage the entire process to obtain basic project data; A budget management module is used to perform intelligent budget compilation and dynamic adjustment based on the basic project data to obtain budget control data; The revenue and expenditure management module is used to implement revenue and expenditure management based on the budget control data and obtain revenue and expenditure execution data.

4. The digital financial service system for business financial integration according to claim 3, characterized in that: The project management module includes: Project classification unit, used to intelligently classify projects according to their nature and funding sources to obtain classified data; A project evaluation unit, configured to perform a feasibility evaluation based on the classified data to obtain evaluation data; A project monitoring unit, configured to set milestone nodes based on the evaluation data and obtain monitoring data; The project analysis unit is used to generate a project portrait based on the monitoring data to obtain basic project data.

5. The digital financial service system for business financial integration according to claim 4, characterized in that: The budget management module includes: Budget estimation unit, used to perform intelligent estimation based on historical data and project profiles to obtain estimation data; A budget preparation unit, configured to form an initial budget based on the estimated data to obtain preparation data; A budget adjustment unit, configured to perform dynamic adjustments based on the compilation data to obtain adjustment data; The budget control unit is used to set the control node according to the adjustment data to obtain budget control data.

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