An intelligent financial data processing and analysis system

Abstract

The present invention provides an intelligent financial data processing and analysis system, which belongs to the technical field of financial data processing and analysis. The intelligent financial data processing and analysis system includes a data acquisition module, which can accurately collect and verify various types of financial data through multiple interfaces; a data processing module, which uses algorithms to clean, classify, and integrate data to establish logical associations; an analysis module, which performs multi-dimensional analysis, such as financial ratio analysis and cost structure analysis, and generates intuitive charts and reports; an early warning module, which monitors key data based on preset thresholds, and promptly pops up windows and sound warnings when abnormalities occur, prompting teaching knowledge points. The system solves many problems existing in traditional financial data processing in accounting teaching, improves teaching quality and effect, promotes students' understanding and mastery of financial knowledge, and provides efficient and intelligent data processing and analysis tools for accounting teaching.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial data processing and analysis, and particularly relates to an intelligent financial data processing and analysis system. Background Art

[0002] In the field of accounting teaching, financial data processing and analysis are key links, but the existing methods have many problems. Traditional financial data processing relies heavily on manual operations, the data collection process is cumbersome and error-prone, the data source connection method is single, and it is difficult to obtain comprehensive and accurate financial data. For example, the reading compatibility of financial statements in different formats is poor, resulting in missing or incomplete data. The data processing process lacks standardization and intelligence, and the cleaning and classification integration work is inefficient. It is impossible to establish effective data logical relationships based on the specific requirements of accounting teaching, making it difficult for data to maximize its value in teaching applications. The analysis process is not in-depth and flexible enough, and usually only simple financial ratio calculations can be performed. It cannot be adjusted according to the characteristics of different industries and the needs of teaching cases. The generated analysis charts and reports are single in form and the content is not intuitive enough, which is not conducive to teachers' teaching and students' understanding and mastery. In addition, there is a lack of effective early warning mechanism. It is difficult for teachers to promptly discover deviations in teaching focus data or financial anomalies, and they cannot adjust teaching strategies in time to guide students to deeply explore the root causes of the problems, which seriously affects the quality and effectiveness of accounting teaching.

[0003] To solve the above problems, the present invention proposes an intelligent financial data processing and analysis system. The system realizes the accurate collection of various financial data by designing a data acquisition module with multiple data interfaces, and uses the data verification function to ensure data quality. The data processing module uses advanced algorithms to perform efficient data cleaning, classification and integration, and establishes data logical associations that meet the needs of accounting teaching. The analysis module provides multi-dimensional analysis functions based on accounting teaching principles and practical needs, including financial ratio analysis and cost structure analysis with flexible adjustment parameters, and generates intuitive analysis charts and reports. At the same time, the early warning module promptly issues early warning prompts for abnormal data based on preset thresholds, assists teachers in adjusting teaching strategies, and guides students to explore problems, thereby effectively improving the quality and effectiveness of accounting teaching and promoting students' understanding and mastery of financial knowledge. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent financial data processing and analysis system, which aims to solve the problems in the existing technology that traditional financial data processing relies on manual labor, the collection is cumbersome and error-prone, the data source connection is single, the report reading compatibility is poor and the data is incomplete, the processing lacks standardized intelligence, the logical relationship is poorly constructed, the analysis is simple and inflexible, the chart report is not conducive to teaching, and there is no effective early warning mechanism that affects the quality and effectiveness of accounting teaching.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent financial data processing and analysis system, characterized by comprising:

[0007] A data acquisition module, which is used to connect to various financial data sources and collect financial data. The data acquisition module includes multiple data interfaces and has a data verification function, which can verify the integrity and accuracy of the collected data. The financial data includes at least asset data, liability data, income data, and cost data;

[0008] a data processing module connected to the data acquisition module and configured to receive data that has undergone preliminary verification; wherein the data processing module includes a built-in algorithm program for cleaning the data, removing duplicate data, noise data, and data fields irrelevant to accounting teaching; and classifying the cleaned data into asset, liability, equity, income, and expense categories in accordance with accounting teaching data specifications; and integrating the classified data to establish logical associations between the data;

[0009] An analysis module, connected to the data processing module, performs multi-dimensional analysis on the processed data based on accounting teaching principles and practical needs. The analysis module includes:

[0010] The financial ratio analysis unit is used to automatically calculate the solvency ratio, profitability ratio, and operating capacity ratio. The solvency ratio includes at least the current ratio and quick ratio, calculated using the following formulas: Current ratio = Current assets ÷ Current liabilities, Quick ratio = (Current assets - Inventory) ÷ Current liabilities; Profitability ratio includes at least the gross profit margin and net profit margin, calculated using the following formulas: Gross profit margin = (Sales revenue - Cost of sales) ÷ Sales revenue × 100%, Net profit margin = Net profit ÷ Sales revenue × 100%; Operating capacity ratio includes at least the inventory turnover rate and accounts receivable turnover rate, calculated using the following formulas: Inventory turnover rate = Cost of sales ÷ Average inventory balance, Accounts receivable turnover rate = Net credit sales ÷ Average accounts receivable balance;

[0011] Cost structure analysis unit is used to analyze the components of enterprise costs, including the proportion of direct material costs, direct labor costs, and manufacturing expenses in total costs and their changing trends;

[0012] A report generation unit, used to generate intuitive analysis charts and reports, the analysis charts include bar charts, line charts, pie charts, and the reports include text analysis and data tables;

[0013] The early warning module is connected to the analysis module and is used to receive key data indicators from the analysis module. The early warning module allows the preset thresholds of key accounting indicators. The key indicator thresholds at least include the asset-liability ratio threshold range and the gross profit margin warning lower limit. When the data indicators exceed the preset thresholds, early warning prompts are issued in time through pop-up windows, sounds, etc.

[0014] As a preferred solution of the present invention, the data verification function of the data acquisition module uses data format verification, data range verification and data logic verification methods to perform preliminary verification on the collected data.

[0015] As a preferred solution of the present invention, during the integration processing of the data processing module, the establishment of data logical association is achieved by using an algorithm based on the accounting subject coding system and the cross-reference relationship of financial data.

[0016] As a preferred solution of the present invention, the financial ratio analysis unit of the analysis module can adjust the parameter value range in the financial ratio calculation formula according to the characteristics of different industries and the requirements of accounting teaching cases.

[0017] As a preferred solution of the present invention, the pop-up prompt of the early warning module includes displaying the name of the abnormal data indicator, the current value, the preset threshold range and the possible corresponding accounting teaching knowledge point prompt information.

[0018] As a preferred solution of the present invention, the report generated by the analysis module can be exported in multiple formats, including PDF format, WORD format, and EXCEL format.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. This solution, through multiple data interfaces, can be widely adapted to different types of financial data sources, ensuring the comprehensiveness of data sources and avoiding data loss. For example, it can successfully connect to various financial software databases and financial report file formats to obtain rich financial data, providing sufficient materials for subsequent analysis;

[0021] The data validation function effectively filters out erroneous or abnormal data to ensure the quality of data entering the system. For example, it accurately identifies data format errors (such as inconsistent date formats), abnormal data ranges (such as excessively high or low salary data, negative asset values, etc.), and data logic mismatches (such as inconsistent balance sheet data relationships). This improves data accuracy and completeness, laying the foundation for precise teaching analysis.

[0022] The early warning module monitors key data indicators based on preset thresholds, promptly identifying financial anomalies and deviations from key teaching data. For example, it sets reasonable asset-liability ratio thresholds and gross profit margin warning lower limits based on different industries. When data exceeds the range, it quickly issues an alert, helping teachers to promptly identify key issues in their teaching.

[0023] Pop-up prompts contain rich information (abnormal data indicator name, current value, threshold range and teaching knowledge point prompts), which makes it easier for teachers to locate problems and guide students to explore. For example, when the debt-to-asset ratio is abnormal, teachers are prompted to guide students to analyze capital structure optimization. When there is a gross profit margin warning, students are guided to explore the cost management link. This enhances teaching interactivity and students' problem-solving ability, and effectively improves the quality of accounting teaching.

[0024] 2. In this solution, built-in algorithms clean, classify, and integrate data according to accounting teaching data standards, improving data processing efficiency and standardization. For example, data is accurately classified into various accounting categories, and logical associations are established based on the accounting category coding system and the cross-reference relationship between financial data, forming an organic whole of data. This facilitates related queries and comprehensive analysis during teaching, and improves the usability and logic of teaching data.

[0025] The data cleaning function removes duplicate, noisy, and irrelevant data fields, improving data purity and enhancing the reliability of analysis results. For example, it removes duplicate transaction records from different data sources, outliers generated by collection interference, and special internal enterprise data that is not relevant to teaching, making the processed data more in line with accounting teaching needs and helping students accurately understand the logic of financial data.

[0026] 3. In this solution, the financial ratio analysis unit can flexibly adjust the parameter value range according to the characteristics of different industries and the needs of teaching cases, providing in-depth customized analysis. For example, the parameters in the calculation formulas of current ratio, quick ratio, gross profit margin, etc. can be adjusted for the retail and manufacturing industries to make the analysis results more in line with the actual industry situation, enrich the teaching case content, and help students deeply understand the financial characteristics and analysis methods of different industries;

[0027] The multi-dimensional analysis function combines cost structure analysis with rich and intuitive analytical charts and report generation to help improve teaching effectiveness. For example, it provides a detailed analysis of the components of corporate costs and their changing trends, and intuitively displays financial data comparisons, trends, and proportions through bar charts, line charts, pie charts, etc. The text analysis and data tables in the report provide precise details, making it easier for teachers to explain vividly and for students to effectively learn and master financial knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 This is a flow chart of the intelligent financial data processing and analysis system of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0031] Example 1

[0032] See also Figure 1 , the present invention provides the following technical solutions:

[0033] An intelligent financial data processing and analysis system, characterized by comprising:

[0034] A data acquisition module, which is used to connect to various financial data sources and collect financial data. The data acquisition module includes multiple data interfaces and has a data verification function, which can verify the integrity and accuracy of the collected data. The financial data includes at least asset data, liability data, income data, and cost data;

[0035] a data processing module connected to the data acquisition module and configured to receive data that has undergone preliminary verification; wherein the data processing module includes a built-in algorithm program for cleaning the data, removing duplicate data, noise data, and data fields irrelevant to accounting teaching; and classifying the cleaned data into asset, liability, equity, income, and expense categories in accordance with accounting teaching data specifications; and integrating the classified data to establish logical associations between the data;

[0036] An analysis module, connected to the data processing module, performs multi-dimensional analysis on the processed data based on accounting teaching principles and practical needs. The analysis module includes:

[0037] The financial ratio analysis unit is used to automatically calculate the solvency ratio, profitability ratio, and operating capacity ratio. The solvency ratio includes at least the current ratio and quick ratio, calculated using the following formulas: Current ratio = Current assets ÷ Current liabilities, Quick ratio = (Current assets - Inventory) ÷ Current liabilities; Profitability ratio includes at least the gross profit margin and net profit margin, calculated using the following formulas: Gross profit margin = (Sales revenue - Cost of sales) ÷ Sales revenue × 100%, Net profit margin = Net profit ÷ Sales revenue × 100%; Operating capacity ratio includes at least the inventory turnover rate and accounts receivable turnover rate, calculated using the following formulas: Inventory turnover rate = Cost of sales ÷ Average inventory balance, Accounts receivable turnover rate = Net credit sales ÷ Average accounts receivable balance;

[0038] Cost structure analysis unit is used to analyze the components of enterprise costs, including the proportion of direct material costs, direct labor costs, and manufacturing expenses in total costs and their changing trends;

[0039] A report generation unit, used to generate intuitive analysis charts and reports, the analysis charts include bar charts, line charts, pie charts, and the reports include text analysis and data tables;

[0040] The early warning module is connected to the analysis module and is used to receive key data indicators from the analysis module. The early warning module allows the preset thresholds of key accounting indicators. The key indicator thresholds at least include the asset-liability ratio threshold range and the gross profit margin warning lower limit. When the data indicators exceed the preset thresholds, early warning prompts are issued in time through pop-up windows, sounds, etc.

[0041] In a specific embodiment of the present invention, first, a data acquisition module:

[0042] Multiple data interfaces: Designed to accommodate different types of financial data sources, these interfaces may include common enterprise financial software database interfaces (e.g., database connection interfaces for software like UFIDA and Kingdee), and interfaces for reading various financial report files (e.g., interfaces for reading financial reports in Excel and PDF formats). These diverse interfaces enable data to be obtained from a wide range of financial information storage and presentation formats, ensuring comprehensive data sources and avoiding missing or incomplete data due to data source limitations.

[0043] Data Validation: Completeness checks verify the existence of required financial data items, such as the missing key data such as total assets and total liabilities in the balance sheet. Accuracy checks assess the rationality of data, such as whether revenue data falls within a reasonable range (based on industry averages, historical company data, or preset empirical values). This prevents significantly abnormal data caused by data entry or transmission errors from entering subsequent processing, providing a reliable data starting point for the entire system.

[0044] The types of financial data collected include asset data (such as the original value of fixed assets, accumulated depreciation, cash and cash equivalents in current assets, accounts receivable, etc.), liability data (short-term loans, long-term loans, accounts payable, etc.), income data (main business income, other business income, etc.), and cost data (production cost, sales cost, etc.). These data are the basic materials for comprehensive financial analysis and accounting teaching.

[0045] 2. Data Processing Module

[0046] Built-in algorithm program: The data cleaning algorithm improves data quality by identifying duplicate records in the data (for example, the same transaction appears repeatedly in different data sources), noise data (such as outliers caused by interference during the data collection process), and data fields that are not relevant to the specific scenarios of accounting teaching (such as special data tags or non-financial core data used for internal management of some enterprises).

[0047] The classification algorithm accurately categorizes data according to the accounting subject system, based on accounting teaching data standards. For example, asset data is further subdivided into current assets (such as cash and inventory) and non-current assets (such as long-term equity investments and fixed assets) based on the liquidity and nature of the assets; liabilities are divided into current liabilities (such as short-term loans and notes payable) and non-current liabilities (such as long-term loans and bonds payable). This allows subsequent analysis modules to process different data categories in accordance with accounting teaching logic.

[0048] The integration processing algorithm is based on the accounting subject coding system and the cross-reference relationship of financial data, such as the cross-reference relationship that the total assets in the balance sheet are equal to the sum of the total liabilities and the total owner's equity. The classified data are logically associated to form an organic whole, which facilitates data association query and comprehensive analysis during the analysis process, and meets the requirements for the internal logical relationship of data in accounting teaching.

[0049] 3. Analysis Module

[0050] Financial Ratio Analysis Unit:

[0051] The current ratio and quick ratio, used in the solvency ratio calculation, are key indicators of a company's short-term solvency. The current ratio reflects the ability of a company's current assets to be converted into cash to repay short-term debts before they mature. A reasonable current ratio is generally considered to be no less than a certain empirical value (such as 2), but this may vary across industries. The quick ratio further removes the impact of inventory, which is relatively slow to convert into cash, from the current ratio, providing a more accurate reflection of a company's short-term solvency.

[0052] The gross profit margin, a profitability ratio, reflects the initial profitability of a company's products or services. Calculated by comparing sales revenue with sales costs, it is a key indicator for assessing a company's product competitiveness and cost control. The net profit margin, on the other hand, takes into account the company's ultimate profitability after all costs and expenses, reflecting its overall profitability and operating efficiency.

[0053] The inventory turnover ratio, part of the operating capacity ratio, reflects a company's inventory turnover rate. Faster inventory turnover indicates more efficient inventory management and lower capital tied-up costs. The accounts receivable turnover ratio reflects the speed at which a company collects receivables. A higher turnover ratio indicates faster collection, lower risk of bad debt losses, and stronger asset liquidity. These ratios are calculated based on different financial data items and have different reference values ​​across different industries and company sizes. The analysis unit can flexibly adjust the parameter value range based on the needs of the teaching case to accommodate diverse teaching scenarios.

[0054] Cost Structure Analysis Unit: Direct material costs, direct labor costs, and manufacturing overhead are the primary components of a company's production costs. Analyze their contribution to total costs and their changing trends. For example, in a manufacturing company, a high proportion of direct material costs may indicate a need to optimize procurement strategies or seek out more cost-effective raw material suppliers. An unusually high proportion of manufacturing overhead could indicate issues with equipment maintenance or workshop management. This analysis, in accounting education, can help students gain a deeper understanding of the key points of cost control and the impact of cost structure on business operations.

[0055] Report Generation Unit: Generated bar charts can be used to visually compare the size of different financial data items, such as revenue comparisons across different years. Line charts are suitable for displaying data trends over time, such as the curve of cost changes over multiple accounting periods. Pie charts can clearly show the proportion of each data component within the overall picture, such as the proportion of each cost within the total cost. The text analysis in the report provides a detailed interpretation of the analysis results, and the data tables provide precise data details, making it easier for teachers to explain the content in detail in class and for students to review and conduct in-depth research after class.

[0056] 4. Early Warning Module: Receives key data indicators and compares them with preset thresholds:

[0057] The debt-to-asset ratio threshold can be set based on the characteristics of different industries and teaching needs. For example, for traditional manufacturing, a reasonable debt-to-asset ratio range might be between 40% and 60%. When a company's debt-to-asset ratio exceeds this range, the early warning module will issue a warning. Setting a lower limit for gross profit margin warnings can help teachers focus on changes in a company's profitability during teaching. If the gross profit margin falls below the warning limit, it may indicate that the company is facing rising costs, falling prices, or weakening product competitiveness. This warning can guide students to deeply explore the underlying financial principles and possible response strategies.

[0058] Early warning prompts are provided in the form of pop-up windows and sounds. The pop-up prompts are rich in content, not only displaying the name of the abnormal data indicator (such as "abnormal debt-to-asset ratio"), the current value (such as 70%), and the preset threshold range (such as 40%-60%), but also providing corresponding accounting teaching knowledge point prompt information (such as "too high debt-to-asset ratio may indicate increased financial risks for the company, and attention should be paid to debt repayment ability and capital structure optimization"), which makes it convenient for teachers to quickly locate problems and guide students to think. The sound prompts can attract teachers' attention in time when they are busy with other teaching operations, ensuring that the early warning information can be effectively received and processed.

[0059] For details, please refer to Figure 1 The data verification function of the data acquisition module uses data format verification, data range verification and data logic verification methods to perform preliminary verification on the collected data.

[0060] In this embodiment, data format verification primarily checks for specific formats that different types of financial data should follow. For example, date data must follow the "YYYY-MM-DD" format, and monetary amounts must be numeric and may have a limited number of decimal places (e.g., to two decimal places for monetary amounts). If the collected date data is incorrectly formatted, such as "DD-MM-YYYY" or contains non-numeric characters, the system will flag the data as incorrectly formatted.

[0061] Data range validation is based on the general range of financial data or the empirical range of specific companies and industries. For example, if the average salary for a company's industry is between 3,000 and 10,000 yuan, and the collected salary data for an employee is 200,000 yuan with no special explanation (such as a high executive salary but no position information), the system will determine that the data is out of the reasonable range. For asset data, for example, the value of a company's fixed assets is generally not negative. If the collected original value of a fixed asset is -10,000 yuan, a data range validation exception will be triggered.

[0062] Data logic validation verifies the inherent logical relationships between financial data. For example, in a balance sheet, total assets should equal the sum of total liabilities and total equity. If the collected data shows total assets of 10 million yuan, total liabilities of 6 million yuan, and total equity of 3 million yuan, the system will detect a mismatch between the three relationships through data logic validation and flag the data as having a logical error. These three validation methods can effectively filter out errors or anomalies in the collected data, ensuring the quality of the data entering subsequent processing.

[0063] For details, please refer to Figure 1 During the integration process of the data processing module, the establishment of data logical association is achieved by using an algorithm based on the accounting subject coding system and the cross-reference relationship of financial data.

[0064] In this embodiment: When establishing logical associations of data based on the accounting subject coding system, the accounting subject coding specified in the enterprise accounting standards is used as the basis. For example, asset subject codes begin with "1", liabilities begin with "2", and so on. When processing data, the system classifies the data into corresponding categories such as assets and liabilities according to the accounting subject codes to which the data belongs, and further establishes data associations according to the hierarchical relationship of the codes. For example, if the fixed asset subject code is "1601" and its accumulated depreciation subject code is "1602", the system identifies the association between the two through the coding, and establishes a logical connection between the original value data of the fixed assets and the accumulated depreciation data during the integration processing, so that relevant data can be quickly and accurately obtained when performing analysis such as net asset value calculation.

[0065] In terms of establishing logical connections based on the cross-references of financial data, let's take the data from the income statement and balance sheet as an example. The net profit data in the income statement should be cross-referenced with the current period's increase or decrease in owner's equity in the balance sheet (excluding other equity change factors, net profit should be equal to the current period's increase in owner's equity minus the current period's newly invested capital, etc.). During the integration process, the system uses an algorithm to identify this cross-reference relationship and associate the net profit data in the income statement with the relevant data in the balance sheet. When conducting financial analysis or explaining teaching cases, the inherent connections between data changes can be easily discovered from the data associations, such as how the increase in net profit affects the change in owner's equity and how this change is reflected in the balance sheet, thus providing a clear data logic display for accounting teaching.

[0066] For details, please refer to Figure 1 The financial ratio analysis unit of the analysis module can adjust the parameter value range in the financial ratio calculation formula according to the characteristics of different industries and the requirements of accounting teaching cases.

[0067] In this embodiment, the adjustment of the parameter value range in the financial ratio calculation formula is illustrated by taking the retail industry and the manufacturing industry as examples. In the calculation of the current ratio, for the retail industry, due to its fast inventory turnover and the relative ease of converting goods into cash, the inventory in the current assets can be relatively quickly converted into cash for debt repayment. Therefore, when calculating the current ratio, the weight of inventory can be appropriately reduced, that is, the parameter of the inventory deduction ratio in the current ratio calculation formula is adjusted. For example, the current ratio calculation formula for general enterprises is current assets ÷ current liabilities. For retail enterprises, it can be adjusted to (current assets - inventory × 0.5) ÷ current liabilities to more accurately reflect its short-term debt repayment ability.

[0068] For manufacturing companies, the collection cycle for accounts receivable is relatively long and carries a certain risk of bad debt. Therefore, when calculating the quick ratio, the discount percentage for accounts receivable can be appropriately increased. For example, the general quick ratio formula is (current assets - inventory) ÷ current liabilities. For manufacturing companies, this can be adjusted to (current assets - inventory - accounts receivable × 0.3) ÷ current liabilities. This more accurately measures a manufacturing company's short-term debt repayment capacity without relying on inventory and partial liquidation of accounts receivable. Regarding profitability ratios, for manufacturing companies, due to their complex production cost structure and significant fixed asset investment, fixed cost factors such as fixed asset depreciation can be factored into the gross profit margin calculation. Adjusting the parameters in the gross profit margin formula, such as gross profit margin = (sales revenue - cost of sales - fixed asset depreciation allocation) ÷ sales revenue × 100%, can more accurately reflect the actual profitability of manufacturing companies' products and meet the need for case studies tailored to the characteristics of different industries in accounting teaching.

[0069] For details, please refer to Figure 1 The pop-up window prompt of the early warning module includes displaying the name of the abnormal data indicator, the current value, the preset threshold range, and the possible corresponding accounting teaching knowledge point prompt information.

[0070] In this embodiment, when the early warning module detects an abnormal debt-to-asset ratio, a pop-up window displays the following information: The abnormal data indicator is named "Debt-to-asset ratio," the current value is assumed to be 75%, and the preset threshold range is 40%-60%. A possible corresponding accounting teaching knowledge point prompt is "An excessively high debt-to-asset ratio indicates that a company's long-term debt repayment ability may be weak and its debt burden is heavy. In accounting teaching, students can be guided to analyze whether the company's capital structure is reasonable, whether it is overly dependent on debt financing, and how to improve its financial situation by adjusting its asset and liability structure. For example, consideration can be given to increasing owner's equity investment, reducing debt size, or optimizing asset allocation and improving asset operating efficiency to enhance debt repayment ability."

[0071] If the gross profit margin warning is triggered, a pop-up window will display the name of the abnormal data indicator "gross profit margin", the current value is 15%, and the preset threshold lower limit is 20%. The corresponding accounting teaching knowledge point prompt information is "The gross profit margin is lower than the warning lower limit, which means that the profit margin of the company's products is squeezed. Possible reasons include rising raw material procurement costs, falling product sales prices, or poor cost control in the production process. In teaching, students can be guided to explore various aspects of cost management in depth, such as procurement cost control methods (finding better and lower-priced suppliers, optimizing procurement batches, etc.), production cost reduction strategies (improving production efficiency, reducing scrap rates, etc.) and the relationship between product pricing strategies and market competition environments, etc., to help students understand the close connection between corporate profitability and cost management." Through such detailed pop-up prompt information, teachers can quickly grasp the core of the problem and guide students to conduct in-depth financial analysis and learning based on the prompt information.

[0072] For details, please refer to Figure 1 The report generated by the analysis module can be exported in multiple formats, including PDF, WORD, and EXCEL.

[0073] In this embodiment, when teachers or students need to export reports generated by the analysis module, they can choose PDF format. This format is suitable for formal document distribution and printing. Its fixed page layout ensures consistent display across different devices and operating systems, and it provides good document security, preventing data from being tampered with. For example, when compiling teaching materials or students submitting assignments, PDF reports can ensure the integrity and standardization of the report content.

[0074] When exporting in Word format, the report is presented as an editable document. Teachers can easily annotate, modify, and supplement the report content within the Word document, making it ideal for personalizing reports during lesson preparation, adding teaching annotations, or simplifying or expanding the report content based on the learning progress and comprehension abilities of different students. Students can also record and organize their study notes within the Word report, facilitating in-depth study and summary of the financial knowledge contained in the report.

[0075] EXCEL reports offer powerful data processing and analysis capabilities. Teachers can leverage Excel's functions and tools like pivot tables to further explore and analyze the data in reports, such as conducting comparative analysis of multi-year financial data or categorizing and summarizing data by department or project. Students can also utilize Excel's calculation capabilities to verify and independently analyze report data during their studies, deepening their understanding of financial data processing and analysis methods. By providing report export options in multiple formats, the system meets the needs of different users for financial analysis reports in diverse scenarios, enhancing its practicality and flexibility.

[0076] The working principle and use process of the present invention:

[0077] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent financial data processing and analysis system, characterized in that: include: A data acquisition module is used to connect to various financial data sources and collect financial data. The data acquisition module includes multiple data interfaces and has a data verification function, which can verify the integrity and accuracy of the collected data. The financial data includes at least asset data, liability data, income data, and cost data; A data processing module is connected to the data acquisition module and is used to receive the data that has been preliminarily verified. The data processing module has a built-in algorithm program, which is used to clean the data, remove duplicate data, noise data, and data fields that are irrelevant to accounting teaching, and classify the cleaned data into assets, liabilities, equity, income, and expenses according to the accounting teaching data specification requirements, and integrate the classified data to establish logical associations between the data; The analysis module is connected to the data processing module and performs multi-dimensional analysis on the processed data based on accounting teaching principles and practical needs. The analysis module includes: The financial ratio analysis unit is used to automatically calculate the solvency ratio, profitability ratio, and operating capacity ratio, wherein the solvency ratio includes at least the current ratio and the quick ratio, and the calculation formulas are: current ratio = current assets ÷ current liabilities, quick ratio = (current assets - inventory) ÷ current liabilities; the profitability ratio includes at least the gross profit margin and the net profit margin, and the calculation formulas are: gross profit margin = (sales revenue - sales cost) ÷ sales revenue × 100%, net profit margin = net profit ÷ sales revenue × 100%; the operating capacity ratio includes at least the inventory turnover rate and the accounts receivable turnover rate, and the calculation formulas are: inventory turnover rate = sales cost ÷ average inventory balance, accounts receivable turnover rate = net credit sales ÷ average accounts receivable balance; The cost structure analysis unit is used to analyze the components of enterprise costs, including the proportion of direct material costs, direct labor costs, and manufacturing expenses in total costs and their changing trends; A report generation unit, used to generate intuitive analysis charts and reports, the analysis charts include bar charts, line charts, pie charts, and the reports include text analysis and data tables; The early warning module is connected to the analysis module and is used to receive key data indicators from the analysis module. The early warning module allows the preset thresholds of key accounting indicators. The key indicator thresholds at least include the asset-liability ratio threshold range and the gross profit margin warning lower limit. When the data indicator exceeds the preset threshold, an early warning prompt is issued in time through pop-up windows, sounds, etc.

2. An intelligent financial data processing and analysis system according to claim 1, wherein the data verification function of the data acquisition module uses data format verification, data range verification and data logic verification methods to perform preliminary verification on the collected data.

3. According to the intelligent financial data processing and analysis system of claim 1, during the integration processing of the data processing module, the establishment of data logical association is implemented by an algorithm based on the accounting subject coding system and the cross-reference relationship of financial data.

4. According to the intelligent financial data processing and analysis system of claim 1, the financial ratio analysis unit of the analysis module can adjust the parameter value range in the financial ratio calculation formula according to the characteristics of different industries and the requirements of accounting teaching cases.

5. According to an intelligent financial data processing and analysis system as described in claim 1, the pop-up prompt of the early warning module includes displaying the name of the abnormal data indicator, the current value, the preset threshold range and the possible corresponding accounting teaching knowledge point prompt information.

6. According to the intelligent financial data processing and analysis system of claim 1, the report generated by the analysis module can be exported in multiple formats, including PDF format, WORD format, and EXCEL format.

Images