Automatic construction system for financial data management

Abstract

The invention discloses an automatic construction system for financial data management, and relates to the related technical field of financial data management, and the system comprises a user interface module which is used for providing an intuitive graphical interface, and enabling a user to construct an application process in a mode of dragging a component; the business logic module is used for calculating the production cost of the product and automatically generating an asset balance sheet, a profit sheet and a cash flow sheet; and the financial trend prediction module needs historical financial data as input, the user inputs financial data and related characteristics, and the model outputs a prediction result. An automatic data processing and report generating system can be rapidly constructed through an automatic tool, the labor burden is relieved, a low-code platform system allows a user to customize and adjust the financial process in a visual mode, the individual requirement can be met without compiling a large number of codes, and the user experience is improved. The LLM technology can provide natural language understanding and intelligent analysis ability, and assists non-professionals in deep financial insight and strategic decision making.

Description

Technical Field

[0001] The present invention relates to the technical field related to financial data management, and in particular to an automatic construction system for financial data management. Background Art

[0002] Financial data management refers to a management method that standardizes, integrates, manages, optimizes and controls various financial data of an enterprise, ensures the accuracy and consistency of financial data, improves the quality and value of data, and provides strong support for the business decisions of the enterprise.

[0003] Traditional financial data management systems usually rely on manual operations and manual data entry, which is not only inefficient but also prone to errors. As the scale of enterprises expands and the complexity of business increases, this management model can no longer meet the needs of modern enterprises. The cumbersomeness of manual operations leads to high time and labor costs, and also increases the risk of data inconsistency and errors.

[0004] In addition, traditional systems are less flexible and difficult to quickly adapt to market changes and adjustments to business needs. Against the backdrop of intensified global competition, companies need more agile and efficient financial management tools to support decision-making processes. However, many traditional systems lack the necessary integration capabilities and real-time data analysis functions, resulting in management being unable to obtain timely and accurate information, which in turn affects the quality of strategic decision-making.

[0005] Therefore, it is necessary to propose an automated construction system for financial data management to solve the above problems. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the deficiencies in the prior art, the present invention provides an automated construction system for financial data management, which solves the problem that the existing financial data management requires manual operations, which is time-consuming and labor-intensive, while also increasing the risk of data inconsistency and errors, as well as making it difficult to support decision-making for corporate financial management and the inability of management to obtain timely and accurate information, thereby affecting strategic decision-making.

[0008] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: An automated construction system for financial data management, the automated construction system comprising: User interface module (UI layer): provides an intuitive graphical interface, allowing users to build application processes by dragging and dropping components. Users do not need to write code and configure parameters to complete the construction of the financial application system; Business logic module (Logic layer): calculates the production cost of products, helps enterprises make cost control and pricing decisions, helps enterprises formulate and manage budgets, ensures the rational allocation and use of financial resources, and automatically generates balance sheets, income statements and cash flow statements, automatically records the financial transactions of enterprises, generates accounting vouchers, and checks the financial accounts of enterprises to ensure that the accounts are consistent with the actual situation, manages and settles the accounts receivable and accounts payable of enterprises, manages the accounts receivable and accounts payable of enterprises, improves the efficiency of capital use, and automatically calculates and declares the taxes of enterprises; Financial trend prediction module: requires historical financial data as input, cleans and extracts features from the collected data, selects the LLM model architecture, and uses historical financial data to train the model. During the training process, the model will learn how to predict future financial trends based on the input features. The user inputs financial data and related features, and the model outputs the prediction results. Data storage module (Data layer): responsible for storing financial data and supporting multiple database systems; Integration module (Integration layer): interacts with other business systems to ensure data consistency and integrity; LLM engine module: interacts with the system by inputting natural language, inquiring about the financial status of a specific time period, parsing and entering financial terms, and supporting users to test and adjust financial processes through natural language descriptions.

[0009] Optionally, the user interface module includes a front-end framework, a drag component, a canvas, a property panel, a preview function unit, and an export and deployment unit; The front-end framework uses React or Vue modern front-end framework to build the user interface. The framework provides a rich component library and flexible state management mechanism, which helps to quickly develop and maintain the UI layer; The drag component provides various finance-related drag components, including data source components, data processing components, and report components. Components are added to the canvas by dragging and are connected to form a complete application process. The canvas is the main area where users build application processes. Users can freely drag components on the canvas and define data flows between components by connecting lines. The property panel is distributed in each component, and the user configures the parameters of the component through the property panel; The preview function unit provides a real-time preview function, and the user can check the actual running effect of the application process on the canvas, which helps the user to find problems and make corrections in time; The export and deployment unit is used to export the constructed application process into a file in JSON or XML format, and re-import and deploy it when needed, which makes the application process easy to migrate and reuse in different environments.

[0010] Optionally, the architecture of the user interface module and its mathematical formula are as follows: Grid layout layer: Grid layout is a common layout method that divides the canvas into several rows and columns. Each component occupies one or more cells. The mathematical formula of grid layout is: position = (row index, column index), where row index and column index are both non-negative integers; Absolute positioning layer: Absolute positioning allows components to be placed anywhere on the canvas. The mathematical formula for absolute positioning is: position = (x coordinate, y coordinate), where x coordinate and y coordinate are both real numbers; Relative positioning layer: Relative positioning refers to the positioning of a component relative to other components. The mathematical formula for relative positioning is: Position = (reference component position + Δx, reference component position + Δy), where Δx and Δy are the offsets relative to the reference component. Adaptive layout layer: Adaptive layout means automatically adjusting the size and position of components according to the size of the canvas. The mathematical formula for adaptive layout is: component size = f (canvas size), where f is a function used to calculate the size of the component.

[0011] Optionally, the business logic module includes a cost accounting unit, a budget management unit, and a financial report generation unit;

[0012] The cost accounting unit is used to accurately calculate the production cost of products and help enterprises make cost control and pricing decisions. The structure is as follows: Cost data collection layer: collect direct material, direct labor and manufacturing overhead data; Cost allocation layer: Allocate manufacturing costs to each product according to reasonable standards; Cost calculation layer: calculate the total cost and unit cost of the product; Cost analysis layer: Provide cost composition analysis to find out the key points of cost control;

[0013] The budget management unit is used to help enterprises formulate and manage budgets to ensure the rational allocation and use of financial resources. The structure is as follows: Budgeting layer: prepare budgets based on historical data and future forecasts; Budget approval layer: multi-level approval process ensures the rationality of the budget; Budget execution layer: monitor the difference between actual expenditure and budget; Budget adjustment layer: adjust the budget according to actual conditions; Budget analysis layer: providing budget execution analysis reports;

[0014] The financial statement generation unit is used to automatically generate a balance sheet, an income statement and a cash flow statement, and has the following structure: Data collection layer: obtain original financial data from various business systems; Data processing layer: cleans, transforms and summarizes the raw data; Report template layer: predefines templates for various financial reports; Data filling layer: fill the processed data into the report template; Output layer: Generate final financial statements (PDF, Excel format).

[0015] Optionally, the business logic module further includes an accounting unit, a reconciliation unit, a settlement unit, and a tax reporting unit;

[0016] The accounting unit is used to automatically record the financial transactions of the enterprise and generate accounting documents. The structure is as follows: Data collection layer: obtains original transaction data from various business systems; Data processing layer: cleans, transforms and summarizes the raw data; Accounting rule matching layer: maps transaction data to corresponding accounting subjects according to predefined accounting rules; Voucher generation layer: automatically generate accounting vouchers; Output layer: save accounting documents into the financial system;

[0017] The reconciliation unit is used to check the financial accounts of the enterprise to ensure that the accounts are consistent with the actual situation. The structure is as follows: Data import layer: import account data from financial systems and banking systems; Data matching layer: matches account data according to preset rules; Variance analysis layer: Analyze unmatched accounts and find out the reasons for the differences; Adjustment suggestion layer: provides adjustment suggestions to help users correct their accounts; Output layer: generate reconciliation report;

[0018] The settlement unit is used to manage and settle accounts receivable and accounts payable of the enterprise, effectively manage the accounts receivable and accounts payable of the enterprise, and improve the efficiency of capital use. The structure is as follows: Billing management: record all sales invoices and purchase invoices and their status; Collection tracking layer: Tracking the customer's payment status; Aging analysis layer: Analyze the age structure of accounts receivable; Payment plan layer: formulate payment plans and execute them; Settlement processing layer: handles settlement transactions and updates account status; Output layer: generate settlement report;

[0019] The tax reporting unit is used to automatically calculate and report the tax of the enterprise, and its structure is as follows: Tax source data collection layer: collects the financial data of enterprises as tax source data; Tax rate matching layer: matching the corresponding tax rates according to tax laws; Tax calculation layer: automatically calculate the tax payable; Tax declaration layer: Generate tax declaration forms and submit them to tax authorities; Output layer: save tax declaration records.

[0020] Optionally, the financial trend prediction module includes a data collection unit, a data cleaning unit, a feature engineering unit, a model training unit, and a prediction evaluation unit; The data collection unit: The LLM model requires a large amount of historical financial data as input, and collects the company's financial statements, market data, and macroeconomic indicators; the data cleaning unit cleans the collected data, removes noise and outliers, and ensures the accuracy and reliability of the data; the feature engineering unit extracts useful features from the original data, including financial ratios, growth rates, and market shares, which will be used for subsequent model training and prediction; the model training unit: selects the LLM model architecture, Transformer, BERT, or its variants. These models have powerful text processing capabilities and can effectively extract information from financial texts. The model is trained using historical financial data. During the training process, the model will learn how to predict future financial trends based on the input features, which usually involves optimizing the model's parameters to minimize prediction errors; the prediction evaluation unit: The trained model is used to predict the company's future financial trends. The user inputs the latest financial data and related features, and the model outputs the prediction results. In order to evaluate the performance of the model, cross-validation or other evaluation methods are used to test the accuracy and reliability of the model. In addition, the model is adjusted and optimized based on actual financial results.

[0021] Optionally, the LLM engine module parses financial rules and terms to help users understand key information in contracts and policy documents, which involves named entity recognition (NER) and relationship extraction technology in natural language processing. Named entity recognition (NER) is used to identify proper nouns and specific terms in text, such as company names, product names, and time expressions. Relationship extraction technology is used to extract relationships between entities from text, such as ownership relationships and contract terms.

[0022] The LLM engine module uses deep learning algorithms and large-scale text data training, and uses pre-training and fine-tuning processes to understand natural language instructions and perform corresponding tasks; pre-training: using unlabeled large-scale text datasets for self-supervised learning to learn the intrinsic structure and language rules of the text; fine-tuning: further training on labeled datasets for specific tasks to adapt to specific task requirements, such as question answering and sentiment analysis.

[0023] Optionally, the LLM engine module also includes a language model, which consists of a self-attention mechanism and position encoding, is used to capture long-distance dependencies in the input sequence, and has high parallel processing capabilities; the self-attention mechanism is used to calculate the relationship between each word and other words in the input sequence, and captures the long-distance dependencies in the sequence by calculating the weight and product between each word and other words; the position encoding is used to capture the position information of the words in the sequence, and the position encoding is usually a fixed one-dimensional convolutional neural network (CNN) used to map words to specific position vectors.

[0024] Optionally, the automated construction system further includes a cost-saving strategy model, which includes a data collection and processing unit, a model training unit, a fine-tuning and optimization unit, an application unit, and an output unit; The data collection and processing unit is used to collect historical financial data, market data, and macroeconomic indicators, and to perform cleaning, standardization, and feature engineering. This step ensures the quality and consistency of the input data and provides a reliable foundation for subsequent model training. The model training unit selects a suitable LLM model for training and a pre-trained model for fine-tuning according to the specific needs of financial analysis; The fine-tuning and optimization unit fine-tunes the model for specific tasks to improve its performance in specific fields, and further improves the accuracy and efficiency of the model by adjusting model parameters and optimizing algorithm means; The application unit: applies the trained model to actual financial scenarios to perform present value calculation, compound interest calculation, and risk assessment on finance; The output unit provides financial decision makers with intuitive and easy-to-understand analysis reports and suggestions based on the output results of the model, thereby helping the decision makers make more informed financial decisions.

[0025] The present value calculation in the cost-saving strategy model is used to evaluate the value of an investment project or the repayment amount of a loan; the compound interest calculation in the cost-saving strategy model is used to calculate the value of an investment growing over time; and the risk assessment in the cost-saving strategy model is used to quantify risk using probability theory and statistical methods.

[0026] (III) Beneficial effects

[0027] The present invention provides an automated construction system for financial data management, which has the following beneficial effects:

[0028] 1. The business logic layer of the present invention can quickly process and analyze a large amount of financial data, including cost accounting, budget management, financial report generation, bookkeeping, reconciliation, settlement, and tax reporting. The business logic layer can automate many tedious financial tasks, and through automated processing of financial data, reduce manual operations, and significantly improve financial management efficiency. The automated system also avoids the problem of human calculation errors.

[0029] 2. Through the automation system of the present invention, enterprises can quickly collect and analyze historical financial data and use advanced forecasting models to predict future financial trends. This high efficiency enables management to quickly obtain key financial information and make more timely and accurate decisions. Based on accurate financial forecasts and cost analysis, the automation system can provide scientific decision-making support for enterprises and assist non-professionals in making in-depth financial insights and strategic decisions.

[0030] 3. The present invention provides a graphical interface, allowing users to intuitively operate and understand the application, reducing the user's learning cost and difficulty of use; the user interface layer provides rich components and tools, supports flexible business logic processing, and can quickly build an automated data processing and report generation system through automation tools, reducing the manual burden and allowing users to customize and adjust financial processes in a visual way to meet personalized needs without writing a lot of code. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of a module framework of the automatic construction system of the present invention;

[0032] Figure 2 It is a schematic diagram of the framework of the financial trend prediction module of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Example 1

[0035] Please refer to FIG1 , an automated construction system for financial data management, the automated construction system comprising: User interface module (UI layer): provides an intuitive graphical interface, allowing users to build application processes by dragging and dropping components. Users do not need to write code and configure parameters to complete the construction of the financial application system; Business logic module (Logic layer): calculates the production cost of products, helps enterprises make cost control and pricing decisions, helps enterprises formulate and manage budgets, ensures the rational allocation and use of financial resources, and automatically generates balance sheets, income statements and cash flow statements, automatically records the financial transactions of enterprises, generates accounting vouchers, and checks the financial accounts of enterprises to ensure that the accounts are consistent with the actual situation, manages and settles the accounts receivable and accounts payable of enterprises, manages the accounts receivable and accounts payable of enterprises, improves the efficiency of capital use, and automatically calculates and declares the taxes of enterprises; Financial trend prediction module: requires historical financial data as input, cleans and extracts features from the collected data, selects the LLM model architecture, and uses historical financial data to train the model. During the training process, the model will learn how to predict future financial trends based on the input features. The user inputs financial data and related features, and the model outputs the prediction results. Data storage module (Data layer): responsible for storing financial data and supporting multiple database systems, such as MySQL and PostgreSQL; Integration module (Integration layer): interacts with other business systems to ensure data consistency and integrity; LLM engine module: interacts with the system by inputting natural language, inquiring about the financial status of a specific time period, parsing and entering financial terms, and supporting users to test and adjust financial processes through natural language descriptions;

[0036] The user interface module includes the front-end framework, drag-and-drop components, canvas, property panel, preview function unit, export and deployment unit; The front-end framework uses React or Vue modern front-end framework to build the user interface. The framework provides a rich component library and flexible state management mechanism, which helps to quickly develop and maintain the UI layer; Drag-and-drop components provide various financial-related drag-and-drop components, including data source components, data processing components, and report components. Components are added to the canvas by dragging and dropping, and connected to form a complete application process; The canvas is the main area where users build application processes. Users can freely drag components on the canvas and define the data flow between components by connecting lines. The property panel is distributed in each component. Users configure the parameters of the component through the property panel. For example, the data source component can configure the database connection information, and the report component can configure the format and content of the report. The preview function unit provides a real-time preview function, allowing users to view the actual running effect of the application process on the canvas, which helps users to find problems and make modifications in time; The export and deployment unit is used to export the built application process into a JSON or XML format file, and re-import and deploy it when needed, which makes the application process easy to migrate and reuse in different environments;

[0037] The architecture of the user interface module and its mathematical formula are as follows: Grid layout layer: Grid layout is a common layout method that divides the canvas into several rows and columns. Each component occupies one or more cells. The mathematical formula of grid layout is: position = (row index, column index), where row index and column index are both non-negative integers; Absolute positioning layer: Absolute positioning allows components to be placed anywhere on the canvas. The mathematical formula for absolute positioning is: position = (x coordinate, y coordinate), where x coordinate and y coordinate are both real numbers; Relative positioning layer: Relative positioning refers to the positioning of a component relative to other components. The mathematical formula for relative positioning is: Position = (reference component position + Δx, reference component position + Δy), where Δx and Δy are the offsets relative to the reference component. Adaptive layout layer: Adaptive layout means automatically adjusting the size and position of components according to the size of the canvas. The mathematical formula for adaptive layout is: component size = f (canvas size), where f is a function used to calculate the size of the component;

[0038] Implementation Front-end framework selection: Choose a suitable front-end framework according to project requirements. For example, React is suitable for large single-page applications, while Vue is more suitable for small and medium-sized projects. Drag component implementation: Use third-party libraries (such as ReactDnD or VueDraggable) to implement the drag function. These libraries provide rich APIs to help developers quickly implement the drag component function; Canvas design: Design a flexible canvas system that supports multiple layout methods (such as grid layout, absolute positioning), and use CSSGrid or Flexbox to implement layout; Property panel development: Develop a general property panel component, display different parameter options according to different component types, and use form elements (such as input boxes and drop-down menus) to implement parameter configuration; Preview function implementation: Implement a real-time preview function. When the user modifies the application process, the preview effect is updated immediately, and virtual DOM technology is used to improve performance; Export and Deployment: Provides an export button to export the application process as a JSON or XML format file. At the same time, provides an import button to re-import the previously exported file into the canvas.

[0039] In this embodiment, the UI layer provides a graphical interface so that users can intuitively operate and understand the application. This interface usually includes icons, menus, and button visual elements, which are presented to users in an intuitive manner, reducing the user's learning cost and difficulty of use. Good UI design can improve the aesthetics and usability of the product, thereby enhancing the user experience. Users can easily use the product, understand the product's functions and operating procedures, and improve user satisfaction and loyalty. The user interface layer provides rich components and tools, supports flexible business logic processing, and facilitates data interaction and storage. These frameworks simplify the UI development process and save developers time and energy.

[0040] Example 2

[0041] This embodiment is based on Example 1 and is optimized as follows. Specifically, the business logic module includes a cost accounting unit, a budget management unit, and a financial report generation unit; The cost accounting unit is used to accurately calculate the production cost of products and help enterprises make cost control and pricing decisions. The structure is as follows: Cost data collection layer: collect direct material, direct labor and manufacturing overhead data; Cost allocation layer: Allocate manufacturing costs to each product according to reasonable standards; Cost calculation layer: calculate the total cost and unit cost of the product; Cost analysis layer: Provide cost composition analysis to find out the key points of cost control; Mathematical formula: Total cost: Total cost = direct materials + direct labor + manufacturing overhead; Unit cost: Unit cost = total cost / production quantity;

[0042] The budget management unit is used to help enterprises formulate and manage budgets to ensure the rational allocation and use of financial resources. The structure is as follows: Budgeting layer: prepare budgets based on historical data and future forecasts; Budget approval layer: multi-level approval process ensures the rationality of the budget; Budget execution layer: monitor the difference between actual expenditure and budget; Budget adjustment layer: adjust the budget according to actual conditions; Budget analysis layer: providing budget execution analysis reports; Mathematical formula: Budget deviation rate: Budget deviation rate = (actual expenditure - budget) / pre-approval * 100%; Budget execution rate: Budget execution rate = actual expenditure / budget*100%;

[0043] The financial statement generation unit is used to automatically generate the balance sheet, income statement and cash flow statement. The structure is as follows: Data collection layer: obtain original financial data from various business systems; Data processing layer: cleans, transforms and summarizes the raw data; Report template layer: predefines templates for various financial reports; Data filling layer: fill the processed data into the report template; Output layer: Generate final financial statements (PDF, Excel format); Balance sheet: Assets = Liabilities + Owners' Equity, Current assets + non-current assets = current liabilities + non-current liabilities + owners' equity; Income statement: Profit = Revenue − Expenses, Net profit = operating income - operating costs - business taxes and surcharges - sales expenses - administrative expenses - financial expenses; Cash flow statement: ΔCash balance = cash flow from operating activities + cash flow from investing activities + cash flow from financing activities;

[0044] The business logic module also includes accounting unit, reconciliation unit, settlement unit, and tax reporting unit;

[0045] The accounting unit is used to automatically record the financial transactions of the enterprise and generate accounting documents. The structure is as follows: Data collection layer: obtains original transaction data from various business systems; Data processing layer: cleans, transforms and summarizes the raw data; Accounting rule matching layer: maps transaction data to corresponding accounting subjects according to predefined accounting rules; Voucher generation layer: automatically generate accounting vouchers; Output layer: save accounting documents into the financial system; Accounting balance formula: Assets = Liabilities + Owners’ Equity, Current assets + non-current assets = current liabilities + non-current liabilities + owners' equity;

[0046] The reconciliation unit is used to check the financial accounts of the enterprise to ensure that the accounts are consistent with the actual situation. The structure is as follows: Data import layer: import account data from financial systems and banking systems; Data matching layer: matches account data according to preset rules; Variance analysis layer: Analyze unmatched accounts and find out the reasons for the differences; Adjustment suggestion layer: provides adjustment suggestions to help users correct their accounts; Output layer: generate reconciliation report; Reconciliation accuracy formula: Reconciliation accuracy = number of matched accounts / total number of accounts*100%;

[0047] The settlement unit is used to manage and settle the accounts receivable and accounts payable of the enterprise, effectively manage the accounts receivable and accounts payable of the enterprise, and improve the efficiency of capital use. The structure is as follows: Billing management: record all sales invoices and purchase invoices and their status; Collection tracking layer: Tracking the customer's payment status; Aging analysis layer: Analyze the age structure of accounts receivable; Payment plan layer: formulate payment plans and execute them; Settlement processing layer: handles settlement transactions and updates account status; Output layer: generate settlement report; Mathematical formula: Accounts receivable turnover rate: Accounts receivable turnover rate = annual sales revenue / average accounts receivable balance; Accounts payable turnover ratio: Accounts payable turnover ratio = annual purchase cost / average accounts payable balance;

[0048] The tax reporting unit is used to automatically calculate and report the company's taxes. The structure is as follows: Tax source data collection layer: collects the financial data of enterprises as tax source data; Tax rate matching layer: matching the corresponding tax rates according to tax laws; Tax calculation layer: automatically calculate the tax payable; Tax declaration layer: Generate tax declaration forms and submit them to tax authorities; Output layer: save tax declaration records; Mathematical formula: Corporate income tax calculation formula: Taxable income = total income - amount of deductions allowed - tax-exempt income; Income tax payable = taxable income × applicable tax rate − tax exemption − tax credit.

[0049] In this embodiment, the business logic layer can quickly process and analyze large amounts of financial data, including cost accounting, budget management, financial report generation, bookkeeping, reconciliation, settlement, and tax reporting. The business logic layer can automate many tedious financial tasks, such as data cleaning, classification, and aggregation. The business logic layer is the core part of the system architecture. It is located between the presentation layer and the data access layer, and plays a connecting role. By automating the processing of financial data, it reduces manual operations and significantly improves financial management efficiency. For example, the automated cost accounting function can quickly and accurately calculate costs without the need for manual entry and calculation one by one. The automated system also avoids the problem of human calculation errors.

[0050] Example 3

[0051] Please refer to FIG. 2 . This embodiment is based on Example 1 or Example 2 and is optimized as follows. Specifically, the financial trend prediction module includes a data acquisition unit, a data cleaning unit, a feature engineering unit, a model training unit, and a prediction evaluation unit. Data collection unit: The LLM model requires a large amount of historical financial data as input, collecting the company's financial statements (such as balance sheets, income statements, cash flow statements), market data (such as stock prices, trading volumes), and macroeconomic indicators (such as GDP growth rate, inflation rate); the data cleaning unit cleans the collected data, removes noise and outliers, and ensures the accuracy and reliability of the data; the feature engineering unit extracts useful features from the raw data, including financial ratios (such as current ratios, debt-to-asset ratios), growth rates, and market shares. These features will be used for subsequent model training and prediction; model training unit: select the LLM model architecture, Transformer, BERT, or its variants, which have powerful text processing capabilities , which can effectively extract information from financial texts and use historical financial data to train the model. During the training process, the model will learn how to predict future financial trends based on the input features, which usually involves optimizing the model's parameters to minimize the prediction error; Prediction and evaluation unit: The trained model is used to predict the company's future financial trends. The user inputs the latest financial data and related features, and the model outputs the prediction results. In order to evaluate the performance of the model, cross-validation or other evaluation methods are used to test the accuracy and reliability of the model. In addition, the model is adjusted and optimized based on the actual financial results. The data storage module is used to store the information collected by the data acquisition unit and the results of the prediction and evaluation unit, and transmit the information to the user interface module so that the user can view it;

[0052] In the LLM model, the mathematical formula for predicting future financial trends mainly involves regression analysis, which is used to illustrate how to make predictions based on historical data: Linear regression model: used to predict continuous variables, such as a company's future revenue or profit. The formula is as follows; , where y is the dependent variable (e.g., income), and x 1 ,x 2 ,...,xn is the independent variable (such as historical revenue, market growth rate), β 0 ,β 1 ,...,β n is the regression coefficient, ϵ is the error term;

[0053] Logistic regression model: used for classification tasks, such as predicting whether a company will experience financial crisis. The formula is as follows: , Among them, P(y=1|x) is the probability that the dependent variable y takes the value of 1 given the independent variable x; In practical applications, LLM models may use more complex nonlinear functions and multiple feature variables for prediction, but the basic principles are similar.

[0054] In this embodiment, through the automation system, the enterprise can quickly collect and analyze historical financial data, and use advanced forecasting models to predict future financial trends. This high efficiency enables the management to quickly obtain key financial information, so as to make more timely and accurate decisions. Through automation, the reliance on a large number of manual labor is reduced, and labor costs are reduced. Optimizing processes can reduce unnecessary waste and further reduce operating costs. Based on accurate financial forecasts and cost analysis, the automation system can provide scientific decision-making support for enterprises. Through data analysis, enterprises can discover financial trends and problems and provide a basis for formulating strategic plans. At the same time, the system can also simulate the financial impact of different scenarios to help enterprises evaluate the risks and benefits of different decision-making plans. LLM can not only understand existing data, but also make intelligent inferences and predictions based on these data. For example, it can predict key financial indicators such as future revenue, profit and cash flow based on the company's historical financial data and market trends. This predictive ability is of great significance for enterprises to formulate strategic plans and decisions.

[0055] Example 4

[0056] This embodiment is based on Example 1 or Example 2 and has been optimized as follows. Specifically, in the LLM engine module, financial rules and terms are parsed to help users understand key information in contracts and policy documents, involving named entity recognition (NER) and relationship extraction technology in natural language processing. Named entity recognition (NER) is used to identify proper nouns and specific terms in text, such as company names, product names, and time expressions. Relationship extraction technology is used to extract relationships between entities from text, such as ownership relationships and contract terms.

[0057] The LLM engine module uses deep learning algorithms and large-scale text data training to understand natural language instructions and perform corresponding tasks using pre-training and fine-tuning processes; pre-training: using unlabeled large-scale text datasets (such as Wikipedia, Internet corpora) for self-supervised learning to learn the intrinsic structure and language rules of text; fine-tuning: further training on labeled datasets for specific tasks to adapt to specific task requirements, such as question answering;

[0058] The LLM engine module also includes a language model, which consists of a self-attention mechanism and positional encoding, and is used to capture long-distance dependencies in the input sequence and has high parallel processing capabilities; the self-attention mechanism is used to calculate the relationship between each word in the input sequence and other words, and captures the long-distance dependencies in the sequence by calculating the weight and product between each word and other words; the positional encoding is used to capture the position information of the words in the sequence, and the positional encoding is usually a fixed one-dimensional convolutional neural network (CNN) used to map words to specific position vectors;

[0059] User interface layer: Provides a graphical interface or command line interface for users to enter natural language queries. For example, users can ask "Please tell me about the financial situation in the past year" or "Analyze the financial terms in this contract"; Natural language processing layer: Use LLM models (such as GPT-3 or ChatGPT) to understand and parse the user's natural language input. This layer converts natural language into an internal representation that can be understood by the machine; Data processing layer: extract relevant financial data from the database or data warehouse based on user queries, including income, expenditure, assets, and liabilities; Financial analysis layer: Analyze the extracted data to generate financial reports or answer specific questions from users, such as calculating total revenue, total expenditure, and net profit within a specific time period; Result presentation layer: The analysis results are presented to users in an easy-to-understand form (such as charts, tables, or text).

[0060] In this embodiment, NER technology can automatically identify entities with specific meanings from text, such as names of people, places, and institutions. In the analysis of financial rules and terms, NER can help quickly locate key information points, such as company names, contract amounts, and dates, greatly improving the efficiency of information extraction. Through automated information extraction, the workload of manual reading and annotation is reduced, and overall work efficiency is improved. NER technology uses algorithms such as deep learning to learn language representation from a large amount of unannotated text, and then completes the recognition of named entities under the guidance of a small amount of annotated data, which helps to improve data accuracy and reduce human errors.

[0061] Relationship extraction technology can automatically identify and extract the relationship between entities from text. In the analysis of financial rules and terms, relationship extraction can help understand the correlation between different entities, such as the rights and obligations of the two parties to the contract and the payment method of the transaction. Relationship extraction technology is one of the important steps in building a knowledge graph. By storing the entities and their relationships in financial rules and terms in a structured form, a knowledge graph in the financial field can be formed. The knowledge graph not only helps to quickly retrieve and integrate information, but also provides intelligent decision-making support for enterprises. In the question-answering system, relationship extraction technology can help identify the entities and relationships in the question, thereby improving the accuracy and efficiency of the question-answering system.

[0062] In the LLM (Large Language Model) engine, the self-attention mechanism allows the model to automatically learn the weight distribution between different positions when processing sequence data, thereby capturing global dependencies. This means that the model can better understand the context and capture complex semantic relationships, overcoming the limitations of traditional RNN and CNN in processing long-distance dependencies. The self-attention mechanism allows the model to process all elements in the sequence in parallel, which greatly speeds up the training process and improves the efficiency when processing large-scale data sets. This parallel processing capability makes the self-attention mechanism particularly suitable for modern hardware accelerators such as GPUs or TPUs;

[0063] The self-attention mechanism allows the model to flexibly adjust the focus of attention according to the needs of the current task and capture key information more accurately. This flexibility enables the self-attention mechanism to demonstrate excellent performance in a variety of natural language processing tasks. In text generation tasks, the self-attention mechanism ensures that the generated text is not only grammatically correct but also logically coherent, because the model can refer to the entire context information when generating each word.

[0064] Example 5

[0065] This embodiment is based on Example 1 or Example 2 and is optimized as follows. Specifically, the automated construction system also includes a cost-saving strategy model, which includes a data collection and processing unit, a model training unit, a fine-tuning and optimization unit, an application unit, and an output unit; The data collection and processing unit is used to collect historical financial data, market data, and macroeconomic indicators, and to perform cleaning, standardization, and feature engineering. This step ensures the quality and consistency of the input data, providing a reliable foundation for subsequent model training. The model training unit selects the appropriate LLM model for training and the pre-trained model for fine-tuning according to the specific needs of financial analysis; The fine-tuning and optimization unit fine-tunes the model for specific tasks (such as financial statement analysis and risk management) to improve its performance in specific areas. It further improves the accuracy and efficiency of the model by adjusting model parameters and optimizing algorithmic means. Application unit: Apply the trained model to actual financial scenarios to perform present value calculation, compound interest calculation, and risk assessment on finance; Output unit: Based on the output results of the model, it provides financial decision makers with intuitive and easy-to-understand analysis reports and suggestions to help them make more informed financial decisions;

[0066] The present value calculation in the cost saving strategy model is used to evaluate the value of an investment project or the repayment amount of a loan. The formula is: , where PV is present value, C is future cash flow, r is the discount rate, and n is the number of periods; The compound interest calculation in the cost saving strategy model is used to calculate the value of the investment over time. The formula is: , where A is the future value, P is the principal, r is the annual interest rate, n is the number of interest calculations per year, and t is time (years); Risk Assessment: Use probability theory and statistical methods to quantify risk. For example, standard deviation or variance can be used to measure the risk level of a portfolio.

[0067] In this embodiment, LLM can quickly process and analyze a large amount of financial data, including historical data, market data, and macroeconomic indicators, thereby helping financial personnel quickly identify potential cost savings points and risk warning signals. Through deep learning algorithms, LLM can perform complex reasoning and predictions. It can predict the company's future financial status and potential risks based on historical data and trends, providing strong support for decision-making;

[0068] By automating the processing of financial data and reducing manual operations, the efficiency of financial management can be significantly improved. For example, the automated cost accounting function can quickly and accurately calculate costs without the need for manual entry and calculation. The system can monitor and manage various expenses in real time, including office supplies, travel expenses, and employee benefits, effectively control expenses, and promptly discover and resolve overspending issues;

[0069] By optimizing procurement processes, streamlining production processes, optimizing inventory management and other measures, companies can reduce raw material procurement costs, production costs and inventory costs, thereby reducing overall operating costs. Optimization of human resource management, such as improving employee productivity and reducing labor costs, is also an important way to reduce costs. Effective cost control and cost reduction strategies can reduce unnecessary cost expenditures and directly improve the profitability of the company.

[0070] With the continuous advancement of AI technology, the system can continuously improve its performance and capabilities by introducing the latest algorithms and technologies.

[0071] In summary: LLM system can automate the processing and generation of financial reports, reduce manual operations, and improve work efficiency. The automated system reduces the need for manual data entry and processing, thereby reducing the possibility of human errors. Through standardized data management and processing processes, the consistency and accuracy of data can be ensured. The system can monitor and analyze financial data in real time, provide timely business insights to management, and support quick decision-making. Through advanced data analysis and predictive models, the system can help companies identify potential risks and opportunities and provide support for strategic planning. Automated processing reduces dependence on financial personnel and reduces labor costs. Through efficient data processing and analysis, companies can better allocate resources and avoid waste. In general, the LLM-based financial data management automation construction system brings significant value to enterprises through benefits such as improving efficiency, enhancing data accuracy, optimizing user experience, facilitating decision support, reducing costs, and supporting continuous improvement.

[0072] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An automated construction system for financial data management, characterized in that: The automated construction system includes: User interface module: provides an intuitive graphical interface, allowing users to build application processes by dragging and dropping components. Users do not need to write code and configure parameters to complete the construction of the financial application system; Business logic module: calculates the production cost of products and automatically generates balance sheets, income statements and cash flow statements, automatically records the company's financial transactions, generates accounting vouchers, verifies the company's financial accounts, manages and settles the company's accounts receivable and accounts payable, manages the company's accounts receivable and accounts payable, and automatically calculates and declares the company's taxes; Financial trend prediction module: requires historical financial data as input, cleans and extracts features from the collected data, selects the LLM model architecture, and uses historical financial data to train the model. During the training process, the model will learn how to predict future financial trends based on the input features. The user inputs financial data and related features, and the model outputs the prediction results. Data storage module: responsible for storing financial data and supporting multiple database systems; Integration module: data interaction with other business systems; LLM engine module: interacts with the system by inputting natural language, inquiring about the financial status of a specific time period, parsing and entering financial terms, and supporting users to test and adjust financial processes through natural language descriptions.

2. The automated construction system for financial data management according to claim 1, characterized in that: The user interface module includes a front-end framework, a drag component, a canvas, a property panel, a preview function unit, and an export and deployment unit; The front-end framework uses React or Vue modern front-end framework to build the user interface; The drag component provides various finance-related drag components, including data source components, data processing components, and report components. Components are added to the canvas by dragging and are connected to form a complete application process. The canvas is the main area where users build application processes. Users can freely drag components on the canvas and define data flows between components by connecting lines. The property panel is distributed in each component, and the user configures the parameters of the component through the property panel; The preview function unit provides a real-time preview function, and the user can check the actual running effect of the application process on the canvas; The export and deployment unit is used to export the constructed application process into a file in JSON or XML format, and re-import and deploy it when needed.

3. The automated construction system for financial data management according to claim 2, characterized in that: The architecture of the user interface module and its mathematical formula are as follows: Grid layout layer: divides the canvas into several rows and columns. Each component occupies one or more cells. The mathematical formula of grid layout is: position = (row index, column index), where row index and column index are both non-negative integers; Absolute positioning layer: Absolute positioning allows components to be placed anywhere on the canvas. The mathematical formula for absolute positioning is: position = (x coordinate, y coordinate), where x coordinate and y coordinate are both real numbers; Relative positioning layer: Relative positioning refers to the positioning of a component relative to other components. The mathematical formula for relative positioning is: Position = (reference component position + Δx, reference component position + Δy), where Δx and Δy are the offsets relative to the reference component. Adaptive layout layer: Adaptive layout means automatically adjusting the size and position of components according to the size of the canvas. The mathematical formula for adaptive layout is: component size = f (canvas size), where f is a function used to calculate the size of the component.

4. The automated construction system for financial data management according to claim 1, characterized in that: The business logic module includes a cost accounting unit, a budget management unit, and a financial report generation unit; The cost accounting unit is used to accurately calculate the production cost of the product, and its structure is as follows: Cost data collection layer: collect direct material, direct labor and manufacturing cost data; Cost allocation layer: allocate manufacturing costs to each product according to reasonable standards; Cost calculation layer: calculate the total cost and unit cost of the product; Cost analysis layer: provide cost composition analysis to find out the key points of cost control; The budget management unit is used to help enterprises formulate and manage budgets. The structure is as follows: budget preparation layer: prepare budgets based on historical data and future forecasts; budget approval layer: multi-level approval process to ensure the rationality of the budget; budget execution layer: monitor the difference between actual expenditures and budgets; budget adjustment layer: adjust the budget according to actual conditions; budget analysis layer: provide budget execution analysis reports; The financial statement generation unit is used to automatically generate a balance sheet, an income statement and a cash flow statement, and has the following structure: data collection layer: obtaining original financial data from various business systems; data processing layer: cleaning, converting and summarizing the original data; report template layer: predefining templates for various financial statements; Data filling layer: fill the processed data into the report template; Output layer: Generate final financial statements.

5. The automated construction system for financial data management according to claim 4, characterized in that: The business logic module also includes an accounting unit, a reconciliation unit, a settlement unit, and a tax reporting unit; The accounting unit is used to automatically record the financial transactions of the enterprise and generate accounting vouchers. The structure is as follows: data collection layer: obtain original transaction data from various business systems; data processing layer: clean, convert and summarize the original data; accounting rule matching layer: map transaction data to corresponding accounting subjects according to predefined accounting rules; voucher generation layer: automatically generate accounting vouchers; output layer: save accounting vouchers to the financial system; The reconciliation unit is used to check the financial accounts of the enterprise, and its structure is as follows: data import layer: import account data from the financial system and the bank system; data matching layer: match the account data according to the preset rules; difference analysis layer: analyze the unmatched accounts and find out the reasons for the differences; adjustment suggestion layer: provide adjustment suggestions to help users correct the accounts; Output layer: generate reconciliation report; The settlement unit is used to manage and settle accounts receivable and accounts payable of the enterprise, and the structure is as follows: Bill management layer: records all sales invoices and purchase invoices and their status; Collection tracking layer: tracking the payment status of customers; Aging analysis layer: analyzing the age structure of accounts receivable; Payment plan layer: formulate payment plans and execute them; Settlement processing layer: handles settlement transactions and updates account status; Output layer: generate settlement report; The tax reporting unit is used to automatically calculate and report the tax of the enterprise, and its structure is as follows: tax source data collection layer: collect the financial data of the enterprise as tax source data; tax rate matching layer: match the corresponding tax rate according to the tax law; tax calculation layer: automatically calculate the tax payable; tax reporting layer: generate tax return forms and submit them to the tax authorities; Output layer: save tax declaration records.

6. The automated construction system for financial data management according to claim 1, characterized in that: The financial trend prediction module includes a data acquisition unit, a data cleaning unit, a feature engineering unit, a model training unit, and a prediction evaluation unit; The data collection unit collects the company's financial statements, market data, and macroeconomic indicators; the data cleaning unit cleans the collected data to remove noise and outliers; the feature engineering unit extracts features from the original data, including financial ratios, growth rates, and market shares; the model training unit selects the LLM model architecture, Transformer, BERT, or its variants, and uses historical financial data to train the model. During the training process, the model will learn how to predict future financial trends based on the input features to minimize prediction errors; the prediction evaluation unit uses the trained model to predict the company's future financial trends. The user inputs the latest financial data and related features, and the model outputs prediction results.

7. The automated construction system for financial data management according to claim 1, characterized in that: In the LLM engine module, financial rules and terms are parsed, which involves named entity recognition and relationship extraction technology in natural language processing. Named entity recognition is used to identify proper nouns and specific terms in text, and relationship extraction technology is used to extract the relationship between entities from text; The LLM engine module uses deep learning algorithms and large-scale text data training to understand natural language instructions and perform corresponding tasks using pre-training and fine-tuning processes; Pre-training: Use unlabeled large-scale text datasets for self-supervised learning to learn the intrinsic structure and language rules of the text; fine-tuning: Further training on labeled datasets for specific tasks to adapt to specific task requirements.

8. An automated construction system for financial data management according to claim 7, characterized in that: The LLM engine module also includes a language model, which is composed of a self-attention mechanism and a positional encoding, and is used to capture long-distance dependencies in the input sequence; the self-attention mechanism is used to calculate the relationship between each word and other words in the input sequence, and the long-distance dependencies in the sequence are captured by calculating the weight and product between each word and other words; Position encoding is used to capture the position information of words in the sequence. Position encoding is usually a fixed one-dimensional convolutional neural network used to map words to specific position vectors.

9. The automated construction system for financial data management according to claim 1, characterized in that: The automated construction system also includes a cost-saving strategy model, which includes a data collection and processing unit, a model training unit, a fine-tuning and optimization unit, an application unit, and an output unit; The data collection and processing unit is used to collect historical financial data, market data, and macroeconomic indicators, and perform cleaning, standardization, and feature engineering processing; The model training unit selects a suitable LLM model for training and a pre-trained model for fine-tuning according to the specific needs of financial analysis; The fine-tuning and optimization unit fine-tunes the model for a specific task; The application unit: applies the trained model to actual financial scenarios to perform present value calculation, compound interest calculation, and risk assessment on finance; The output unit provides analysis reports and suggestions to financial decision makers based on the output results of the model.

10. An automated construction system for financial data management according to claim 9, characterized in that: The present value calculation in the cost saving strategy model is used to evaluate the value of an investment project or the repayment amount of a loan; the compound interest calculation is used to calculate the value of an investment growing over time; and the risk assessment is used to quantify risk using probability theory and statistical methods.

Images