Digital financial service system for business finance fusion

Abstract

The invention relates to the technical field of business financial financing, in particular to a digital financial service system for business financial financing. According to the application, the business management subsystem is established to collect business basic data, then the financial management subsystem performs automatic accounting, then the decision analysis subsystem performs fusion analysis on business and financial data, and finally the internal control management subsystem performs whole-process monitoring, so that a closed-loop data flow chain is formed; the financial data barrier is broken through, automatic circulation and intelligent analysis of the data are realized, and a risk prevention and control mechanism based on the data is also established.

Description

Technical Field

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

[0002] With the in-depth promotion of the digital transformation of finance, financial management is gradually developing towards intelligence and informatization. National and local financial departments need to process a large amount of financial data, covering multiple links such as budget preparation, execution, final accounts, and supervision. How to use modern information technology to achieve the efficient integration and collaborative management of financial business and financial data, and improve the level of financial management and the efficiency of fund use, has become an important research direction in the field of finance.

[0003] Currently, most financial management adopts a decentralized system architecture. The business management system, financial accounting system, and risk control system operate independently, and data exchange between systems is carried out through manual operations or simple data interfaces. Although this method can support the daily management of financial departments to a certain extent, due to the lack of deep integration between systems, it cannot meet the requirements of real-time, linkage, and dynamic decision-making analysis of financial data.

[0004] Therefore, there is an urgent need to establish an integrated service system that integrates business and finance to improve the level of financial management and operational efficiency, and this situation needs to be further improved. Summary of the Invention

[0005] In order to solve the problem that the existing business data and financial data are separated from each other and cannot meet the requirements of real-time, linkage, and dynamic decision-making analysis of financial data, this application provides a digital finance service system for business-finance integration, which adopts the following technical solutions, including: A business management subsystem for performing business activities operations to obtain business basic data; A financial management subsystem for performing financial accounting and management based on the business basic data to obtain financial accounting data; A decision-making analysis subsystem for generating an analysis report based on the business basic data and the financial accounting data to obtain decision-making support data; An internal control management subsystem for implementing risk control and business traceability based on the decision-making support data to obtain internal control management data.

[0006] By adopting the above technical solution, this application proposes a digital finance service system for business and finance integration. By establishing a business management subsystem to collect basic business data, then automatically accounting by the financial management subsystem, then using the decision analysis subsystem to conduct integrated analysis of business and financial data, and finally implementing full-process monitoring by the internal control management subsystem, a closed-loop data flow chain is formed; not only breaking through the business and financial data barriers, realizing the automatic flow and intelligent analysis of data, but also establishing a risk prevention and control mechanism based on data.

[0007] Optionally, the business management subsystem includes a group standard management module for: According to the standard formulation requirements, matching technical experts from member units to obtain expert recommendation data; Based on the expert recommendation data, establishing an online collaborative editing platform to obtain draft standard data; According to the draft standard data, using blockchain technology to record modification opinions and version evolution to obtain standard formulation process data; Analyzing the standard formulation process data and automatically generating a standard research report to obtain standard management data.

[0008] By adopting the above technical solution, this application sets a group standard management module in the business management subsystem, combines expert intelligent matching, online collaborative platform and blockchain technology, forms recommendation data by intelligent matching of experts in member units, supports multi-person real-time editing using the online collaborative platform, uses blockchain technology to record modification opinions and version evolution, and finally automatically generates a research report; not only realizes the digital management of the entire process of standard formulation, but also ensures traceability and immutability of the process through blockchain technology, improving the efficiency and quality of standard formulation.

[0009] Optionally, the financial management subsystem includes: A project management module for classifying, grading and full-process managing projects to obtain basic project data; A budget management module for intelligent budget preparation and dynamic adjustment according to the basic project data to obtain budget control data; An income and expenditure management module for implementing income and expenditure management based on the budget control data to obtain income and expenditure execution data.

[0010] By adopting the above technical solutions, this application establishes a classified and hierarchical project full-life cycle management system through the project management module. Based on the project basic data, the budget management module conducts intelligent budget preparation and dynamic adjustment, and finally realizes the linkage control with the budget through the revenue and expenditure management module; it realizes the data-driven management of project-budget-revenue and expenditure, transforms static management into dynamic management, not only solves the problem of information silos in traditional financial management, but also establishes an intelligent control mechanism based on data.

[0011] Optionally, the project management module includes: A project classification unit for intelligently classifying according to the project nature and funding source to obtain classification data; A project evaluation unit for conducting feasibility evaluation according to the classification data to obtain evaluation data; A project monitoring unit for setting milestone nodes based on the evaluation data to obtain monitoring data; A project analysis unit for generating a project portrait according to the monitoring data to obtain project basic data.

[0012] By adopting the above technical solutions, this application establishes an intelligent classification system based on nature and funding source through the project classification unit, conducts feasibility evaluation using the project evaluation unit, monitors the project using milestone nodes, 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 driving, solving the problems of low efficiency and insufficient risk warning in existing project management.

[0013] Optionally, the budget management module includes: A budget calculation unit for intelligently calculating according to historical data and the project portrait to obtain calculation data; A budget preparation unit for forming an initial budget according to the calculation data to obtain preparation data; A budget adjustment unit for dynamically adjusting based on the preparation data to obtain adjustment data; A budget control unit for setting control nodes according to the adjustment data to obtain budget control data.

[0014] By adopting the above technical solutions, this application conducts intelligent calculation based on historical data and the project portrait through the budget calculation unit, forms a data-driven initial budget using the budget preparation unit, sets up a budget adjustment unit to achieve dynamic response, and finally establishes a node-based control system through the budget control unit; it not only realizes the precision 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.

[0015] Optionally, the decision analysis subsystem includes: A relevance judgment module, configured to obtain business basic data and financial accounting data, and judge the business-finance relevance based on the business basic data and the financial accounting data; A relevance adjustment module, configured to query the historical database, obtain historical business-finance relevance information, and adjust the business-finance relevance based on the historical business-finance relevance information; A decision generation module, configured to set analysis indicators based on the adjusted business-finance relevance and generate decision support data.

[0016] By adopting the above technical solution, the present application realizes the automatic matching of business and finance data through the relevance judgment module, introduces the relevance adjustment module to optimize using historical experience, and finally converts the correlation analysis into decision support through the decision generation module; not only realizes the quantitative evaluation of the business-finance data relevance, but also improves the analysis accuracy through the intelligent application of historical experience, providing reliable data support for management decisions.

[0017] Optionally, judging the business-finance relevance specifically includes the following steps: Based on the corresponding relationship between the business basic data and the financial accounting data, obtain the data matching degree and the timing consistency; Generate the business-finance relevance according to the data matching degree and the timing consistency.

[0018] By adopting the above technical solution, the business-finance relevance includes evaluation indicators of two dimensions: data matching degree and timing consistency. The data matching degree reflects the corresponding degree of business data and financial data in terms of content, and the timing consistency reflects the synchronization of business occurrence and financial records in terms of time. Combining the quantitative indicators of these two dimensions, starting from the content dimension and time dimension of data correlation, the correlation degree of business data and financial data can be scientifically evaluated, which helps to adopt differentiated analysis strategies and set corresponding decision indicators for data with different relevance degrees in the follow-up; for data with a higher relevance degree, it can be directly used for decision analysis; for data with a lower relevance degree, further data cleaning and correlation optimization are carried out; this two-dimensional based relevance evaluation mechanism not only improves the accuracy of business-finance integration analysis, but also provides a direction for continuously optimizing data quality, realizing more accurate decision support.

[0019] Optionally, obtaining the historical business-finance relevance information specifically includes the following steps: Query the historical database and mark the historical analysis data of the same type of business; According to the mark, obtain the historical business-finance relevance information corresponding to the historical analysis data.

[0020] By adopting the above technical solution, the historical database contains historical data accumulated during the process of an organization's business operation to financial accounting, including the correlation evaluation information of each business and finance analysis; by querying the historical database and marking the same type of business, the business and finance correlation in similar historical business scenarios can be effectively identified and extracted, so as to obtain the corresponding historical business and finance correlation information; this analysis method based on historical experience can discover the evolution law and characteristic pattern of business and finance correlation from historical data, which helps to more accurately judge the correlation characteristics of current business and financial data; for example, by analyzing the business and finance correlation change trend of a certain type of business in different periods, potential problems in data correlation can be predicted, and the correlation evaluation parameters can be adjusted in time, so as to generate a more realistic correlation evaluation result; it not only improves the accuracy of business and finance correlation analysis, but also realizes the effective reuse of empirical data, providing more reliable data support for business and finance integration decision-making.

[0021] Optionally, adjusting the business and finance correlation degree specifically includes the following steps: Obtain historical business and finance correlation data and historical analysis data according to the historical business and finance correlation information; Compare the current business and finance correlation data with the historical business and finance correlation data, and mark the business with the same comparison result as a stable business; Adjust the data matching degree of the corresponding business according to the historical analysis data of the stable business; Update the business and finance correlation degree of the corresponding business according to the adjusted data matching degree.

[0022] By adopting the above technical solution, historical business and finance correlation data and historical analysis data are extracted from the historical business and finance correlation information. By comparing the current business and finance correlation data with the historical business and finance correlation data, the business with the same business type and stable data characteristics is marked as a stable business; 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 degree 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 and finance data is gradually improving, or shows obvious regular changes in a specific period, and these historical experiences should be used to optimize the current data matching degree evaluation; through this dynamic adjustment mechanism based on historical data, not only the accuracy of business and finance correlation degree evaluation is improved, but also the self-optimization of evaluation criteria is realized, enabling business and finance integration analysis to better adapt to the actual needs of business development and ultimately providing more reliable data support for management decision-making.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application proposes a digital fiscal service system for business - finance integration. By establishing a business management subsystem to collect basic business data, then automatically calculating by the financial management subsystem, then using the decision - analysis subsystem to conduct integrated analysis on business - finance data, and finally implementing full - process monitoring by the internal control management subsystem, a closed - loop data - flow chain is formed; it not only breaks through the business - finance data barrier, realizes automatic data flow and intelligent analysis, but also establishes a risk prevention and control mechanism based on data; 2. This application establishes a classification - and - grading full - life - cycle management system for projects through the project management module. Based on project basic data, the budget management module conducts intelligent budget preparation and dynamic adjustment, and finally realizes linkage control with the budget through the revenue - expenditure management module; it realizes data - driven management of project - budget - revenue - expenditure, transforms static management into dynamic management, not only solves the information - island problem in traditional financial management, but also establishes an intelligent control mechanism based on data; 3. This application realizes automatic matching of business - finance data through the correlation - degree judgment module, introduces the correlation - degree adjustment module to optimize using historical experience, and finally converts the correlation analysis into decision support through the decision - generation module; it not only realizes the quantitative evaluation of the correlation degree of business - finance data, but also improves the analysis accuracy through the intelligent application of historical experience, providing reliable data support for management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic structural diagram of a digital fiscal service system for business - finance integration according to an embodiment of this application; Figure 2 is a schematic structural diagram of the business management subsystem in the digital fiscal service system according to an embodiment of this application; Figure 3 is a schematic structural diagram of the financial management subsystem in the digital fiscal service system according to an embodiment of this application; Figure 4 is a schematic structural diagram of the project management module in the financial management subsystem according to an embodiment of this application; Figure 5 is a schematic structural diagram of the budget management module in the financial management subsystem according to an embodiment of this application; Figure 6 is a schematic structural diagram of the decision - analysis subsystem in the digital fiscal service system according to an embodiment of this application; Figure 7 is a schematic flow diagram for judging the business - finance correlation degree in an embodiment of this application; Figure 8 is a schematic flow diagram for obtaining historical business - finance correlation information in an embodiment of this application; Figure 9 is a schematic flow diagram for adjusting the business - finance correlation degree in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The terms used in the following embodiments 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 the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0026] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0027] The following further describes the embodiments of the present application in conjunction with the accompanying drawings of the specification.

[0028] In a first aspect, the present application provides a digital fiscal service system for business and financial financing. Referring to Figure 1 , the digital fiscal service system includes a business management subsystem, a financial management subsystem, a decision-making analysis subsystem, and an internal control management subsystem.

[0029] Among them, the business management subsystem is used to perform business activity operations to obtain business basic data.

[0030] In this embodiment, the business management subsystem realizes the standardized management of various business data by establishing a unified business activity classification system. This subsystem has pre-established a business basic database, which includes a business type code table and a data standard specification table. The business type code table encodes and classifies common business activities, such as conference activities, training programs, membership services, etc. The data standard specification table defines the collection elements, format specifications, and verification rules of various business data to ensure the consistency and availability of the data.

[0031] Specifically, the business management subsystem uses the Web form method for data collection. For example, in conference activity management, the system automatically generates an activity number, and the operator fills in the basic information of the activity, participants, revenue and expenditure budget, etc. The system ensures data integrity through field-level verification, such as mandatory item checks, amount format verification, etc. The collected data is stored in the business basic database in real time and provided to other subsystems for calling through a preset data interface.

[0032] The financial management subsystem is used to perform financial accounting and management based on the business basic data to obtain financial accounting data.

[0033] In this embodiment, the financial management subsystem converts business basic data into financial accounting data based on a pre-established business-finance mapping relationship table. This subsystem has established a financial accounting rule library, which defines the accounting treatment methods for different business types. At the same time, a standard accounting subject table and an auxiliary accounting table are preset, and key accounting items such as special funds and project costs are set according to the characteristics of fiscal business. The system automatically identifies the business type through the business-finance mapping relationship and generates accounting entries according to the accounting rules.

[0034] Specifically, when the business system generates new business data, the financial system automatically extracts key elements and generates vouchers. For example, when receiving membership fees, the system automatically generates an entry with a debit to bank deposits and a credit to membership fee income based on member information and the payment amount.

[0035] The decision-making and analysis subsystem is used to generate analysis reports based on business basic data and financial accounting data to obtain decision support data.

[0036] In this embodiment, the decision-making and analysis subsystem has established a multi-dimensional analysis model to integrate and analyze business data and financial data. The system has preset an analysis dimension table, including time dimension, business dimension, organizational dimension, etc., and has established an index calculation rule library. Through data cleaning and standardization processing, data from different sources are unified into the analysis framework to achieve multi-dimensional data analysis.

[0037] Specifically, the system generates various analysis reports according to the preset analysis templates. For example, for the fund usage analysis report, the system calculates the fund usage rate and structural ratio by comparing the budget execution data and the actual expenditure data. Another example is the member service analysis report, where the system evaluates the service effect by counting the participation rate of member activities and the service satisfaction. The analysis results are presented in the form of charts and an analysis description is automatically generated.

[0038] The internal control management subsystem is used to implement risk control and business traceability based on the decision support data to obtain internal control management data.

[0039] In this embodiment, the internal control management subsystem realizes risk monitoring and business tracking by establishing a risk warning index library and a business traceability index table. The risk warning index library contains quantitative indicators and qualitative indicators, such as fund usage rate, business compliance, etc. The business traceability index table records the key nodes and responsible persons of business activities, supporting upward traceability and downward extension.

[0040] Specifically, the system regularly monitors the warning indicators and automatically issues a warning when the indicators exceed the threshold. For example, when the capital utilization rate of a certain project is too low, the system generates a warning message to prompt attention. In terms of business traceability, the system supports querying the full-process records of business activities according to conditions such as business numbers and handlers, realizing traceable management of business activities. At the same time, the system records operation logs to achieve trace management of important operations.

[0041] In one embodiment, referring to Figure 2 , the business management subsystem includes a meeting management module, a member management module, a journal management module, and a group standard management module.

[0042] Among them, the meeting management module pre-sets a meeting type library and a conference service resource library, and realizes meeting preparation and organization through a task list template; the member management module establishes a member classification table and a service item library, supporting member information management and service tracking; the journal management module sets up a manuscript processing process library and a reviewer database, realizing full-process management of manuscripts; the group standard management module constructs an expert resource library and a standard formulation specification database, realizes intelligent matching of experts through a keyword index table, and uses blockchain technology to record the standard formulation process. These modules adopt a unified data interface specification to transmit business data to other subsystems for processing in real time.

[0043] Specifically, the group standard management module is used for: According to the standard formulation requirements, match technical experts from member units to obtain expert recommendation data; Based on the expert recommendation data, establish an online collaborative compilation platform to obtain draft standard data; According to the draft standard data, use blockchain technology to record modification opinions and version evolution to obtain standard formulation process data; Analyze the standard formulation process data and automatically generate a standard research report to obtain standard management data.

[0044] 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 contains fields such as expert basic information, affiliated unit, and technical expertise, and labels the technical directions that experts are good at through keyword tags. The standard compilation specification library stores normative documents such as standard writing templates, compilation processes, and review rules. To improve the retrieval efficiency, the system establishes an inverted index table to establish a correspondence between technical field keywords and expert information.

[0045] Specifically, when receiving a standard formulation requirement, the system first extracts technical field keywords from the requirement description and quickly locates relevant experts through the 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 modification content, the modifier, and the 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, etc., to provide an objective basis for standard quality assessment.

[0046] In one embodiment, referring to Figure 3 The 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.

[0047] Among them, the project management module is used to classify and grade projects and manage the entire process to obtain basic project data.

[0048] 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 contains type fields such as vertical projects, horizontal projects, and self-raised projects, and sets the ABC three-level management level according to 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 projects of different types and levels.

[0049] Specifically, when a project is established, the system automatically determines the project type and level based on the basic information of the project. For example, for a 5 million yuan project of the Ministry of Science and Technology, the system automatically identifies it as a "vertical-A level" project and starts the corresponding management process. Project milestone nodes are generated through preset task templates, such as opening report, mid-term inspection, and final acceptance. The system uses a simple progress tracking mechanism to calculate the project progress deviation by comparing the planned time and the actual completion time, and issue an early warning prompt when the deviation exceeds the threshold.

[0050] 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.

[0051] In this embodiment, the budget management module has established a budget item library and a budget standard table. The budget item library classifies the expenditure items of funds, including common items such as equipment fees, material fees, and travel expenses. The budget standard table sets the limit standards for various types of expenditures, such as the upper limit per person per day for conference fees. The system provides a reference basis for the preparation of the new project budget by establishing a historical project budget database.

[0052] Specifically, the system automatically loads the budget preparation template according to the project type. For example, for a conference project, the system automatically calculates the expenditure budgets such as conference fees and travel expenses based on the number of participants and the duration of the conference. During the budget execution process, the system calculates the budget execution rate by comparing the actual expenditure with the budget amount. When the expenditure of a certain item is close to the budget upper limit, the system automatically prompts budget adjustment suggestions. For example, when it is found that there is a trend of overspending on material fees, the system suggests adjusting the budget amount from the item with more balance.

[0053] The revenue and expenditure management module is used to implement revenue and expenditure management based on the budget control data to obtain the revenue and expenditure execution data.

[0054] In this embodiment, the revenue and expenditure management module has preset a revenue type table and an expenditure approval process library. The revenue type table classifies and manages membership fees income, project funds, service income, etc. The expenditure approval process library sets the hierarchical approval path according to the expenditure amount and purpose. The system standardizes the recording and statistics of revenue and expenditure data by establishing a revenue and expenditure accounting rule table.

[0055] Specifically, in terms of revenue management, the system supports the automatic matching of multi-channel collection records. For example, when a certain project fund arrives, the system automatically associates it with the corresponding project according to the payer information and amount. In terms of expenditure management, the system conducts expenditure audits through the preset amount control rules. At the same time, the system calculates the balance of project funds in real time and automatically issues a warning when the balance is insufficient.

[0056] In one embodiment, referring to Figure 4 , the project management module includes: The project classification unit is used to perform intelligent classification according to the project nature and funding source to obtain classification data; The project evaluation unit is used to conduct a feasibility evaluation according to the classification data to obtain evaluation data; The project monitoring unit is used to set milestone nodes based on the evaluation data to obtain monitoring data; The project analysis unit is used to generate a project portrait according to the monitoring data to obtain project basic data.

[0057] In this embodiment, the project management module pre - establishes a project feature library and an evaluation rule library. The project feature library contains a project classification element table, which defines the classification criteria for project nature (such as scientific research projects, technical service projects, etc.) and funding sources (such as government appropriations, enterprise commissions, etc.). The evaluation rule library sets up an evaluation index system including dimensions such as technical feasibility, economic feasibility, and risk level. The system standardizes the description of the key information in the entire project life cycle by establishing a project portrait template, realizing the refined management of the project.

[0058] Specifically, when a new project is applied for, the system first conducts intelligent classification through preset feature matching rules. For example, for a technology development project commissioned by an enterprise, the system classifies it as the type of "horizontal - technology development - general risk" according to the contract amount and research content. In the project evaluation stage, the system loads the corresponding evaluation indicators and uses a scoring mechanism for feasibility evaluation. For example, when evaluating technical feasibility, the system scores according to elements 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 like 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 forms a project portrait including dimensions such as basic information, progress status, and risk warning by statistically analyzing the project execution data. For abnormal situations such as delays in important nodes or cost overruns, the system automatically generates warning information.

[0059] The relevant project information is transmitted to the budget management module through a standardized data interface for the preparation and adjustment of the project budget. At the same time, the project basic data also provides support for decision - making analysis, helping the management understand the overall operation status of the project.

[0060] In one embodiment, referring to Figure 5 , the budget management module includes: A budget calculation unit for intelligently calculating based on historical data and the project portrait to obtain calculation data; A budget preparation unit for forming an initial budget based on the calculation data to obtain preparation data; A budget adjustment unit for dynamically adjusting based on the preparation data to obtain adjustment data; A budget control unit for setting control nodes according to the adjustment data to obtain budget control data.

[0061] In this embodiment, the budget management module pre - establishes a subject standard library and a budget model library. The subject standard library includes a budget subject classification table and a quota standard table, which standardize the budget preparation standards for various types of expenditures. The budget model library stores budget preparation templates and measurement parameters for different types of projects, such as typical budget models for scientific research projects, conference projects, etc. The system extracts the reference basis for budget preparation by establishing a historical project budget execution database and using simple statistical analysis methods. At the same time, a budget adjustment rule table is set up to define the budget adjustment authorities and approval processes at different levels.

[0062] Specifically, the system first extracts budget - related elements based on the project portrait, such as project scale, execution cycle, etc., and conducts intelligent measurement in combination with the budget execution data of historical similar projects. For example, for a two - year technology development project, the system generates a preliminary measurement result by analyzing the expenditure composition such as personnel expenses and equipment expenses of historical projects. In the budget preparation link, the system automatically loads the budget template according to the project type and allocates the amount according to the subject standard. Such as control requirements that the equipment cost does not exceed 30% of the total budget. During the execution process, the system monitors the budget execution situation in real - time. When there is a trend of over - expenditure in a subject, dynamic adjustment is carried out through the preset adjustment rules. For example, the allowed amount adjustment between subjects does not exceed 20% of the original budget. The system sets budget control points at key expenditure nodes. For example, large - value equipment purchases require budget approval to ensure the standardization of budget execution. Expenditure applications exceeding the threshold automatically enter the budget review process to avoid unbudgeted expenditures.

[0063] Relevant budget control data are transmitted to the revenue and expenditure management module through a standard interface as the basis for handling specific revenue and expenditure operations. At the same time, the budget execution data is fed back to the project management module for project cost control. This circular feedback mechanism ensures the real - time and accuracy of budget management.

[0064] In one embodiment, referring to Figure 6 , the decision - making analysis subsystem includes: The correlation degree judgment module is used to obtain business basic data and financial accounting data, and judge the business - finance correlation degree based on the business basic data and financial accounting data.

[0065] In this embodiment, the correlation degree judgment module pre - establishes a business - finance mapping table and a correlation rule library. The business - finance mapping table defines the corresponding relationship between business activities and financial subjects, including fields such as business type, accounting subject, and accounting dimension. The correlation rule library sets the basic rules for judging the business - finance correlation degree, such as judgment criteria in dimensions such as time matching degree and amount consistency. The system conducts a quantitative evaluation of the matching situation between business data and financial data by establishing a correlation degree calculation method.

[0066] Specifically, the system first obtains business data such as project approval and contract signing from the business management subsystem, and at the same time obtains financial data such as revenue and expenditure accounting from the financial accounting subsystem. The relevance is judged through preset matching rules, such as the time difference and amount consistency between project receipts and financial receipt records. For example, for a contract receipt of 2 million yuan for a technical service project, the system queries the financial receipt record through the contract number, calculates the time matching degree and amount matching degree of business and financial data, and generates a relevance score according to the preset weight.

[0067] The relevance adjustment module is used to query the historical database, obtain historical business and financial relevance information, and adjust the business and financial relevance based on the historical business and financial relevance information.

[0068] In this embodiment, the relevance adjustment module establishes a historical data analysis database and an adjustment parameter table. The historical data analysis database stores the relevance situations of past business and financial data, including successful and failed cases of relevance. The adjustment parameter table sets adjustment coefficients for different business types to optimize the relevance judgment result. The system uses statistical methods to analyze the relevance rules in historical data and provides a correction basis for the current relevance judgment.

[0069] Specifically, after obtaining the preliminary relevance judgment result, the system automatically queries the historical database and extracts the relevance characteristics of similar businesses. For example, for conference-type businesses, the system analyzes the business and financial relevance patterns of historical conference projects, including characteristics such as the timing of receipts and payments and accounting subjects. Based on the historical relevance rules, the system corrects the initial relevance through a preset adjustment algorithm. For example, if it is found that there is a fixed receipt delay for a certain type of business, the calculation standard of the time matching degree is adjusted accordingly. The system records the adjusted relevance result in the database and continuously optimizes the adjustment parameters.

[0070] The decision-making generation module is used to set analysis indicators based on the adjusted business and financial relevance and generate decision support data.

[0071] In this embodiment, the decision-making generation module pre-sets an analysis indicator library and a decision template library. The analysis indicator library contains quantitative indicators in 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 converts business and financial relevance data into management decision-making information by establishing an index calculation rule table.

[0072] Specifically, the system selects applicable analysis indicators for calculation based on the adjusted business-finance correlation. For example, if a project has a high business-finance correlation, the system automatically calculates indicators such as the project's capital utilization efficiency and cost control situation. Through a preset decision analysis template, the system integrates the calculation results to form a decision report. For example, in the analysis of a scientific research project group, the system generates analysis charts including dimensions such as the progress of funds utilization and the deviation of budget execution, providing data support for management decisions. The system regularly updates decision support data and predicts future development trends through trend analysis methods.

[0073] In one embodiment, referring to Figure 7 , to determine the business-finance correlation, the following steps are specifically included: S710. Based on the correspondence between business basic data and financial accounting data, obtain the data matching degree and time sequence consistency.

[0074] In this embodiment, the data matching degree refers to the matching degree of business data and financial data in key elements such as amount and subject. The time sequence consistency refers to the degree of coincidence between the occurrence time of business activities and the recording time of financial records.

[0075] Specifically, based on the business basic data and financial accounting data, determine the type of each current business data, match the complete information index table of the corresponding business type from the preset business-finance correlation database, and correspondingly input the financial accounting data corresponding to each current business data into the complete information index table, so as to obtain the data matching degree of each business; in addition, based on the information indicating the business occurrence time in each current business data, combined with the accounting time indicated by the financial accounting data, judge the time interval between the business occurrence and the financial record, and then obtain the time sequence consistency corresponding to each business.

[0076] S720. Generate the business-finance correlation based on the data matching degree and time sequence consistency.

[0077] Specifically, in the preset business-finance correlation database, in addition to including the complete information index table of each corresponding business type, it also includes the data matching degree weight value of the corresponding business type, that is, the weight value used to convert the data matching degree into the business-finance correlation. The data matching degree weight values corresponding to different types of businesses are different. For example, the data matching degree and time sequence consistency of contract receipt and prepayment reimbursement are the same, but due to different business natures, the corresponding data matching degree weight values are different, so the business-finance correlations corresponding to the two types of businesses are different; therefore, through the preset business-finance correlation database, judge the data matching degree weight of each business, and then combine the time sequence consistency corresponding to each business. For example, through the product of the data matching degree weight and the time sequence consistency, the business-finance correlation of each business is obtained.

[0078] In one embodiment, referring to Figure 8, obtain historical business - finance association information, specifically including the following steps: S810. Query the historical database and mark the historical analysis data of the same - type business.

[0079] In this embodiment, the historical analysis data refers to the association degree data evaluated during historical business - finance association analysis. The historical business - finance association information includes the association characteristics and association rules between historical business data and financial data.

[0080] Specifically, the historical database refers to the database that stores the historical association data in the process of business management and financial accounting. Therefore, the historical database includes the association degree evaluated for business data during historical business - finance association analysis, that is, the historical analysis data. The system marks the historical analysis data of the same - type business in the historical database through a preset business - type mapping table. For example, for technical service - type businesses, the system marks the historical analysis data of this type of business in the historical database, including information such as historical business - finance association degree and association characteristics.

[0081] S820. According to the mark, obtain the historical business - finance association information corresponding to the historical analysis data.

[0082] Specifically, according to the mark in the historical database, query and extract the corresponding historical business - finance association information with marks, that is, obtain the historical business - finance association information corresponding to the historical analysis data. Among them, the historical business - finance association information includes the association rules adopted during historical business - finance association analysis, the business - type characteristics and data dimensions analyzed, and the association - degree indicators used, etc. For example, when analyzing meeting - type businesses, the system extracts the association rules of meeting - type businesses in the historical database, including information such as typical business - finance time - series characteristics and common matching anomalies, to provide a reference basis for the judgment and adjustment of the current business - finance association degree. The system extracts key features from the historical business - finance association information through statistical analysis methods to optimize the current association - degree judgment result.

[0083] In one embodiment, refer to Figure 9 , adjust the business - finance association degree, specifically including the following steps: S910. According to the historical business - finance association information, obtain the historical business - finance association data and historical analysis data.

[0084] In this embodiment, the historical analysis data refers to the matching data and the corresponding evaluation - index data during each historical business - finance association analysis. A stable business refers to a business where the current business - finance association degree is the same as the previous historical business - finance association degree.

[0085] Specifically, each time a business-finance association analysis is performed historically, an evaluation of the degree of association is carried out. Therefore, extract the degree-of-association data evaluated each time a business-finance association analysis is performed historically from the historical business-finance association information, as well as the matching data and the corresponding evaluation index data, that is, the historical analysis data. For example, the system extracts the historical degree-of-association evaluation records of technology service-related businesses, including information such as the degree-of-association values, matching parameters, and evaluation criteria of each evaluation.

[0086] S920. Compare the current business-finance association data with the historical business-finance association data, and mark the businesses with the same comparison results as stable businesses.

[0087] Specifically, compare the previous historical business-finance association degree corresponding to each business with the currently evaluated business-finance association degree, that is, compare the current business-finance association data with the historical business-finance association data, and mark the businesses with the same comparison results as stable businesses, that is, the businesses with the same current business-finance association degree and the previous historical business-finance association degree. For example, for the contract receipt business of a certain project, if the current degree-of-association evaluation result is consistent with the previous evaluation result, it is marked as a stable business.

[0088] S930. Adjust the data matching degree of the corresponding business according to the historical analysis data of the stable business.

[0089] Specifically, a stable business indicates that there are no obvious changes in the data matching degree and temporal consistency corresponding to this business. However, since there is a certain time interval between the previous business-finance association analysis and the current time point, the business characteristics may have changed, so the corresponding matching rules and evaluation indicators should be adjusted to a certain extent. Therefore, analyze the historical analysis data of the stable business, judge the change trend of the historical analysis data of the stable business, and then analyze the matching deviation of the current business data. In this embodiment, statistical methods can be used to analyze the historical analysis data of the stable business. By analyzing the matching characteristics and the correlation between various parameters when a large number of corresponding stable businesses are executed normally, and then according to the correlation relationship between the various parameters, identify the matching anomalies existing in the current data of the stable business and judge whether the matching degree of the current stable business is reasonable. For example, for the advance payment reimbursement business, if it is found through analyzing the historical matching data that there is a fixed arrival delay, the evaluation criteria for temporal consistency are adjusted accordingly. Based on this, by analyzing the historical analysis data of the stable business, generate updated data matching degrees, and adjust the matching evaluation results of the corresponding stable businesses based on the updated data matching degrees.

[0090] S940. Update the business-finance association degree of the corresponding business according to the adjusted data matching degree.

[0091] Specifically, according to the data matching degree of the adjusted stable business and in combination with the corresponding timing consistency, the business-finance association degree of the corresponding stable business is updated. For example, the system recalculates the business-finance association degree of the stable business according to the evaluation result of the adjusted matching degree and in accordance with the preset association degree calculation rule to ensure the accuracy and timeliness of the association degree evaluation result.

[0092] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0093] The above are all preferred embodiments of the present application. Without restricting the protection scope of the present application accordingly, therefore: Any equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A digital fiscal service system for business and financial integration, characterized in that, Including: A business management subsystem for performing business activity operations to obtain business basic data; A financial management subsystem for performing financial accounting and management based on the business basic data to obtain financial accounting data; A decision-making analysis subsystem for generating an analysis report based on the business basic data and the financial accounting data to obtain decision support data; An internal control management subsystem for implementing risk control and business traceability based on the decision support data to obtain internal control management data.

2. The digital fiscal service system for business and financial integration according to claim 1, characterized in that, The business management subsystem includes a group standard management module for: Matching technical experts from member units according to standard formulation requirements to obtain expert recommendation data; Based on the expert recommendation data, establishing an online collaborative compilation platform to obtain draft standard data; According to the draft standard data, using blockchain technology to record modification opinions and version evolution to obtain standard formulation process data; Analyzing the standard formulation process data to automatically generate a standard development report to obtain standard management data.

3. The digital fiscal service system for business and financial integration according to claim 1, characterized in that, The financial management subsystem includes: A project management module for classifying, grading, and managing the whole process of projects to obtain project basic data; A budget management module for performing intelligent budget compilation and dynamic adjustment based on the project basic data to obtain budget control data; An income and expenditure management module for implementing income and expenditure management based on the budget control data to obtain income and expenditure execution data.

4. The digital fiscal service system for business and financial integration according to claim 3, characterized in that, The project management module includes: A project classification unit for performing intelligent classification according to project nature and funding sources to obtain classification data; A project evaluation unit for performing feasibility evaluation according to the classification data to obtain evaluation data; A project monitoring unit for setting milestone nodes based on the evaluation data to obtain monitoring data; A project analysis unit for generating a project portrait according to the monitoring data to obtain project basic data.

5. The digital fiscal service system for business and financial integration according to claim 4, wherein The budget management module includes: A budget calculation unit for performing intelligent calculation according to historical data and project portraits to obtain calculation data; A budget compilation unit for forming an initial budget according to the calculation data to obtain compilation data; A budget adjustment unit for performing dynamic adjustment based on the compilation data to obtain adjustment data; A budget control unit for setting control nodes according to the adjustment data to obtain budget control data.

6. The digital fiscal service system for business and financial integration according to claim 1, wherein The decision-making analysis subsystem includes: A correlation degree judgment module for obtaining business basic data and financial accounting data and judging the business-finance correlation degree based on the business basic data and the financial accounting data; A correlation degree adjustment module for querying the historical database to obtain historical business-finance correlation information and adjusting the business-finance correlation degree based on the historical business-finance correlation information; A decision generation module for setting analysis indicators based on the adjusted business-finance correlation degree and generating decision support data.

7. The digital fiscal service system for business financial financing according to claim 6, characterized in that, Judging the business-finance correlation degree specifically includes the following steps: Based on the correspondence between the business basic data and the financial accounting data, obtaining data matching degree and time series consistency; Generating the business-finance correlation degree according to the data matching degree and the time series consistency.

8. The digital fiscal service system for business and financial integration according to claim 6, characterized in that, Obtaining historical business-finance correlation information specifically includes the following steps: Query the historical database and mark the historical analysis data of the same type of business; According to the above marking, obtain the historical business-finance association information corresponding to the historical analysis data.

9. The digital fiscal service system for business and financial integration according to claim 8, wherein Adjust the business-finance association degree, which specifically includes the following steps: According to the historical business-finance association information, obtain the historical business-finance association data and the historical analysis data; Compare the current business-finance association data with the historical business-finance association data, and mark the business with the same comparison result as the stable business; According to the historical analysis data of the stable business, adjust the data matching degree of the corresponding business; According to the adjusted data matching degree, update the business-finance association degree of the corresponding business.

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