Financial data monthly settlement processing method and device based on intelligent analysis, and terminal

Abstract

The invention discloses a financial data monthly settlement processing method and device based on intelligent analysis and a terminal, and the method comprises the steps: automatically collecting and obtaining various types of data of an enterprise, carrying out the data integration and preprocessing, and converting the data into preprocessed integrated data; performing intelligent identification and classification on the preprocessed integrated data by using machine learning and deep learning algorithms, and automatically identifying and classifying financial data related to the financial vouchers; automatically executing account adjustment and accounting entry on the classified financial data related to the financial vouchers by adopting an accounting rule-based engine and event-driven architecture to generate entry financial data; and automatically executing a monthly settlement process for the entries of financial data through a workflow automatic control and report generation tool, performing cost calculation, and automatically generating a corresponding financial report for output. According to the method, intelligent processing of the monthly financial data settlement process can be realized, a large amount of manual operation is not needed any more, the labor cost is greatly saved, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data intelligent processing, and particularly relates to a monthly settlement processing method, device, intelligent terminal and storage medium for financial data based on intelligent analysis. Background Art

[0002] In the existing ERP system (Enterprise Resource Planning system), the financial monthly settlement process usually requires a large amount of manual operations, including the checking of financial accounts, the preparation of financial statements, the handling of taxes, etc. These operations are not only time-consuming and laborious, but also very error-prone. Especially when dealing with a large amount of data, as the enterprise scale expands and the business becomes more complex, the difficulty and complexity of monthly settlement are also increasing continuously.

[0003] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a monthly settlement processing method, device, intelligent terminal and storage medium for financial data based on intelligent analysis, aiming at the problems and defects that the monthly settlement processing of financial data in the existing technology requires a large amount of manual operations, which are not only time-consuming and laborious, but also very error-prone.

[0005] The technical solution adopted by the present invention to solve the problem is as follows: A monthly settlement processing method for financial data based on intelligent analysis, which includes: Automatically collect and obtain various types of enterprise data, perform data integration and preprocessing on the collected various types of data, and convert them into integrated preprocessed data; For the integrated preprocessed data, use machine learning and deep learning algorithms for intelligent identification and classification, and automatically identify and classify the financial data related to financial vouchers; Adopt an accounting rule engine and an event-driven architecture to automatically execute account adjustments and accounting entries for the classified financial data related to financial vouchers, and generate financial data of entries; For the financial data of entries, through workflow automation control and report generation tools, automatically execute the monthly settlement process, perform cost calculation and automatically generate the corresponding financial statements for output.

[0006] In the above-mentioned monthly settlement processing method for financial data based on intelligent analysis, after the step of automatically executing the monthly settlement process, performing cost calculation and automatically generating the corresponding financial statements for output for the financial data of entries through workflow automation control and report generation tools, it includes: Adopt an anomaly detection algorithm and an automated reporting tool to perform financial anomaly detection on the automatically generated financial statements, generate a financial data anomaly detection report and output it.

[0007] The described monthly settlement processing method for financial data based on intelligent analysis, wherein the steps of automatically collecting and obtaining various types of enterprise data, integrating and preprocessing the collected various types of data, and converting them into integrated data after preprocessing include: Connect to the financial module, sales module, and procurement module within the enterprise through the API interface to automatically collect and obtain various types of enterprise data, where the various types of data include financial data, sales data, and procurement data; Use data cleaning technology to preprocess the collected various types of enterprise data to obtain preprocessed data, where the preprocessing includes handling missing values, outliers, and duplicate values; For the preprocessed data, use data quality monitoring and evaluation, and perform data quality control processing according to the evaluation results to obtain integrated data after preprocessing; where the data quality control processing includes removing duplicate records and filtering out null values.

[0008] The described monthly settlement processing method for financial data based on intelligent analysis, wherein the steps of using machine learning and deep learning algorithms to perform intelligent identification and classification on the integrated data after preprocessing, and automatically identifying and classifying the financial data related to financial vouchers include: For the integrated data after preprocessing, use machine learning algorithms to perform preliminary intelligent identification and classification of financial vouchers to obtain preliminary classified financial data related to financial vouchers; Use deep learning algorithms to build a deep learning model, and perform in-depth identification and classification on the preliminary classified financial data through the built deep learning model to obtain financial data related to financial vouchers.

[0009] The described monthly settlement processing method for financial data based on intelligent analysis, wherein the steps of using an accounting rule engine and an event-driven architecture to automatically execute account adjustments and accounting entries on the classified financial data related to financial vouchers, and generate financial data of entries include: According to the preset accounting rules and logics, automatically perform account adjustments and generate accounting entries for the classified financial data related to financial vouchers; and through the pre-set event-driven architecture, respond to specified business events, automatically trigger the accounting processing flow, and process the financial data of generated entries. The described monthly settlement processing method for financial data based on intelligent analysis, wherein the steps of automatically executing the monthly settlement process, calculating costs, and automatically generating corresponding financial statements for output through workflow automation control and report generation tools for the financial data of entries include: For the financial data of the journal entries, automatically execute the monthly closing automation process in a predetermined order, and through the report generation tool, perform cost calculation, profit analysis, generation of balance sheets and income statements, and automatically generate and output the corresponding financial statements.

[0010] The method for processing monthly closing of financial data based on intelligent analysis, wherein the step of using the anomaly detection algorithm and the automated reporting tool to perform financial anomaly detection on the automatically generated financial statements, generate a financial data anomaly detection report and output further includes: For the automatically generated financial statements, use statistical analysis, clustering analysis, and / or model-based anomaly detection methods to perform anomaly detection and identification, identify abnormal financial data; and generate and output a financial data anomaly detection report through an automated reporting generation tool.

[0011] A device for processing monthly closing of financial data based on intelligent analysis, wherein the device includes: A data integration and preprocessing module, used to control the automatic collection and acquisition of various types of enterprise data, perform data integration and preprocessing on the collected and acquired various types of data, and convert them into preprocessed integrated data; An intelligent identification and classification module, used to perform intelligent identification and classification on the preprocessed integrated data by using machine learning and deep learning algorithms, and automatically identify and classify the financial data related to financial vouchers; An automated accounting processing module, used to adopt an accounting rule engine and an event-driven architecture to automatically execute account adjustments and accounting entries on the classified financial data related to financial vouchers, and generate the financial data of the journal entries; A monthly closing automation module, used to automatically execute the monthly closing process, perform cost calculation and automatically generate and output the corresponding financial statements on the financial data of the journal entries through workflow automation control and a report generation tool; An anomaly detection and reporting module, used to perform financial anomaly detection on the automatically generated financial statements by using an anomaly detection algorithm and an automated reporting tool, generate a financial data anomaly detection report and output.

[0012] An intelligent terminal, which includes a memory, and one or more programs, where one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include those for executing any one of the methods described above.

[0013] A computer-readable storage medium, wherein when the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute any one of the methods described above.

[0014] Advantages of the present invention: The present invention provides a method, apparatus, intelligent terminal and storage medium for monthly settlement processing of financial data based on intelligent analysis; the present invention can process complex and massive business and financial data in real time, accurately and completely according to the monthly financial settlement rules, mainly including data integration and preprocessing, intelligent recognition and classification, automated accounting processing, monthly settlement automation, anomaly detection and reporting. The present invention can realize the intelligent processing of the monthly settlement process of financial data, no longer requires a large number of manual operations, greatly saves labor costs, improves work efficiency, and reduces the possibility of errors, realizing intelligent monthly settlement of financial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of the method for monthly settlement processing of financial data based on intelligent analysis provided in Embodiment 1 of the present invention.

[0017] Figure 2 It is a schematic flowchart of the method for monthly settlement processing of financial data based on intelligent analysis provided in Embodiment 2 of the present invention.

[0018] Figure 3 It is a schematic flowchart of the method for monthly settlement processing of financial data based on intelligent analysis provided in Embodiment 3 of the present invention.

[0019] Figure 4 The principle block diagram of the embodiment of the device for monthly settlement processing of financial data based on intelligent analysis provided by the present invention.

[0020] Figure 5 It is the internal structure principle block diagram of the intelligent terminal provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the purpose, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] In the existing ERP system (Enterprise Resource Planning system), the financial monthly closing process usually requires a large amount of manual operations, including the verification of financial accounts, the preparation of financial statements, the handling of taxes, etc. These operations are not only time-consuming and laborious, but also very error-prone. Especially when dealing with a large amount of data, as the scale of the enterprise expands and the business becomes more complex, the difficulty and complexity of monthly closing are also increasing continuously.

[0023] With the continuous development of big data analysis, artificial intelligence algorithms, and automated process technologies, intelligent monthly closing has become possible. Embodiments of the present invention provide a method for processing financial data monthly closing based on intelligent analysis.

[0024] As Figure 1 shown, a method for processing financial data monthly closing based on intelligent analysis in Embodiment 1 of the present invention includes the following steps: Step S100: Automatically collect and obtain various types of enterprise data, perform data integration and preprocessing on the collected and obtained various types of data, and convert them into preprocessed integrated data; Embodiments of the present invention are applied to the financial monthly closing in an enterprise's ERP system (Enterprise Resource Planning system). Specifically, when implementing, various types of enterprise data need to be collected and obtained first, including financial data, sales data, procurement data, etc. within the enterprise.

[0025] Specifically, when implementing, web crawler technology, API interfaces, and data import tools can be used to automatically extract various data related to the enterprise from different sources (such as financial, sales, procurement, etc. modules within the enterprise). These data may include financial reports, sales data, customer feedback, market research data, etc.

[0026] In embodiments of the present invention, data integration and preprocessing are also performed on the collected and obtained various types of data. Specifically: Integrate the data collected from different sources, eliminate duplicates, and ensure data consistency. ETL (Extract, Transform, Load) technology can be used to integrate the data onto a unified platform. Then perform data preprocessing: including operations such as data cleaning, filling in missing values, data normalization, and standardization. Ensure the accuracy and availability of the data, and prepare a clean database for subsequent analysis.

[0027] After preprocessing, convert the data into a format suitable for analysis, for example, convert text data into numerical data, and divide the data into training sets and test sets, etc.

[0028] Through automated collection and preprocessing, the present invention can significantly reduce manual intervention and improve the data processing speed. And through standardized data preprocessing steps, errors caused by human factors can be reduced, and the overall quality of the data can be improved. And the need for manual operations can be reduced, and the labor cost can be lowered.

[0029] Step S200: For the preprocessed integrated data, use machine learning and deep learning algorithms for intelligent identification and classification to automatically identify and classify financial data related to financial vouchers. In the embodiment of this step, after completing data preprocessing, use machine learning and deep learning algorithms to conduct a more in-depth analysis of the data. Specifically, the embodiment of the present invention can select a suitable machine learning model or deep learning network for training according to the data type and analysis target. For example, decision trees, support vector machines, or neural networks can be used.

[0030] Then, feature extraction is performed, specifically extracting features related to financial vouchers from the preprocessed dataset, such as transaction amounts, transaction types, customer classifications, etc. And the model can be trained with the labeled historical data so that it can learn to recognize the features and patterns of financial vouchers.

[0031] Then, intelligent identification and classification are carried out. Specifically, the trained model can automatically identify new financial data and classify data related to financial vouchers. For example, invoice records can be automatically identified as "accounts payable" or "accounts receivable".

[0032] For example, if a company uses deep learning algorithms to analyze its financial records over the years, the model can automatically identify different financial vouchers, such as purchase orders, receipts, and invoices, and classify them into different financial categories. In this way, the enterprise's financial team can view the classification results and related reports in real time, thus making faster decisions.

[0033] In the embodiment of this step, machine learning and deep learning algorithms can quickly process large-scale data, which is more efficient than manual classification. And through the learning model, subjective biases can be reduced, and the accuracy of data classification can be improved. Moreover, it can be processed in real time, that is, it can monitor and classify continuously updated financial data in real time to help the enterprise keep track of its financial situation at any time.

[0034] In this way, through the combination of the above two steps, the present invention not only improves the efficiency and accuracy of data processing, but also provides an instant and reliable basis for financial data analysis for the enterprise, enabling the enterprise to make better decisions and plans.

[0035] Step S300: Adopt an accounting rule engine and event-driven architecture to automatically execute account adjustments and accounting entries for the classified financial data related to financial vouchers, and generate the financial data of the entries. The accounting rule engine in this step is a system that processes financial data based on preset accounting rules and logics; it can analyze the input financial data in real time to determine when and how to make account adjustments and generate entries. The accounting rule engine of the embodiments of the present invention supports configurability, allowing enterprises to customize rules according to their own business needs and financial regulations. For example, different accounting treatment methods can be applied to different types of transactions and account adjustments based on different amounts, time periods, or transaction natures.

[0036] The event-driven architecture (EDA) is a system design method based on events, which can trigger corresponding calculations or processes through monitored events (such as revenue recognition, invoice receipt, or payment processing), rather than in the traditional batch processing manner. Under this architecture in the present invention, when the data related to financial vouchers is classified and identified, events will be automatically generated to trigger the corresponding accounting logic processing.

[0037] Then the present invention will automatically perform account adjustments and accounting entries. That is, once the classified financial data and corresponding events are received in the embodiments of the present invention, account adjustments can be automatically performed based on the logic of the accounting rule engine. This includes making corresponding changes to existing accounts, correcting incorrect records, or adjusting account balances.

[0038] Subsequently, the present invention will generate accounting entries, that is, convert these adjustments into standard accounting items and update them into the enterprise's financial system. These entries will support financial reporting and subsequent audits.

[0039] For example, taking an e-commerce company that processes a large number of orders and payments daily as an example. When an order is completed and confirmed, this event will be captured using the method of the present invention; the accounting rule engine will automatically process the account adjustments related to the order based on the enterprise's rules (such as refund policies, discount strategies, etc.). If a refund occurs, the present invention will generate a negative accounting entry corresponding to the original order, automatically adjusting the "revenue" account and the "accounts receivable" account to ensure the accuracy of financial records. At the same time, all processing records will be updated in the financial information system in real time for management to view at any time.

[0040] It can be seen that the automatic account adjustment and accounting entry generation based on the accounting rule engine and event-driven architecture in the embodiments of this step can significantly improve the efficiency and accuracy of enterprise financial data processing. This can not only optimize the financial workflow but also enhance the timeliness and reliability of management decisions.

[0041] Moreover, automated account adjustments and entry generation can significantly improve processing speed, reduce the workload of finance staff, and shorten response time. The systematic processing method can reduce errors caused by manual input, improve the accuracy and consistency of entries, and minimize human errors. Additionally, by operating based on a rule engine, all interactions and adjustments are ensured to comply with established accounting standards and internal corporate policies, facilitating compliance management.

[0042] Step S400: For the financial data of the entries, automatically execute the monthly closing process, perform cost calculations, and automatically generate corresponding financial statement outputs through workflow automation control and report generation tools.

[0043] In the embodiments of the present invention, the workflow automation control refers to using software tools or systems to automatically manage and execute financial-related workflows. That is, in the financial monthly closing process of the embodiments of the present invention, once the data entry is completed, the system will automatically start the monthly closing process and process according to preset rules. The responsible persons and execution times of each link will be clarified at this stage, realizing visual management of the process, which helps to ensure that each step is completed on time and complies with the regulations.

[0044] The execution of the financial monthly closing process is an important link in enterprise financial management, mainly including the auditing, adjustment, and summarization of various accounts. With the support of workflow automation in the embodiments of the present invention, the status of each account can be automatically checked, and necessary account adjustments can be made, such as adjusting inventory, confirming revenue, and allocating expenses. In addition, the system of the present invention will automatically interface with relevant departments (such as sales and procurement) to obtain necessary support data, thereby ensuring the accuracy of the monthly closing.

[0045] Cost calculation is an important part of the monthly closing. The present invention will automatically summarize the cost data of each department, product, or project based on preset cost calculation rules and generate corresponding cost analyses. This can include calculations of different categories such as fixed costs, variable costs, and marginal costs. By analyzing different cost elements, enterprises can evaluate the profitability of products or services and make necessary adjustments.

[0046] Then, financial statements are generated. That is, in the embodiments of the present invention, after the monthly closing and cost calculation are completed, various financial statements will be automatically generated, such as income statements, balance sheets, and cash flow statements. These statements directly reflect the financial status and operating results of the company. The report generation tool in the present invention can provide multiple output formats according to different format requirements (such as spreadsheets, PDFs, etc.), facilitating timely access and viewing by management and relevant stakeholders.

[0047] For example, taking a manufacturing enterprise that needs to conduct monthly settlements on the last day of each month as an example, including revenue recognition and cost allocation. Using the method of the embodiment of the present invention for automated workflow management, a monthly settlement process is first set for this enterprise. When all sales and cost data are entered, the present invention automatically triggers the monthly settlement program. When implementing the method of the present invention, all sales records and expense records are first checked and necessary adjustments are made. Then, the production cost of the product is automatically calculated, including raw materials and manufacturing expenses. Finally, the present invention can automatically generate an income statement and a balance sheet based on these data, provide them to the finance department for review, and ensure that the financial report is released to management and other stakeholders within the specified time.

[0048] In this way, through workflow automation control and report generation tools, the monthly settlement process and financial statement generation are automatically executed, greatly improving the efficiency and accuracy of enterprise financial management, simplifying the decision-making process, and ensuring compliance, which helps the enterprise manage financial resources more efficiently.

[0049] And the present invention also has the following advantages: 1). It can improve efficiency: The automated workflow reduces the time of manual operations, improves the efficiency of monthly settlement and report generation, and ensures timely completion.

[0050] 2). It can enhance accuracy: Through systematic process control, the errors that may be brought by manual input are reduced, and the accuracy and consistency of data are improved.

[0051] 3). It can simplify the decision-making process: The automatically generated financial statements provide real-time and detailed data support for decision-makers, helping the enterprise make faster decisions.

[0052] 4). It can enhance compliance: The automated process can ensure that the corresponding operations follow accounting standards and enterprise internal control policies, enhancing compliance.

[0053] In another embodiment of the present invention, as Figure 2 shown, the method for monthly settlement processing of financial data based on intelligent analysis, in addition to including the steps of the above embodiment, after the steps of automatically executing the monthly settlement process, calculating costs, and automatically generating the corresponding financial statement output for the financial data of the entry through workflow automation control and report generation tools, further includes: S500. Using an anomaly detection algorithm and an automated reporting tool, conduct financial anomaly detection on the automatically generated financial statements, generate a financial data anomaly detection report and output it.

[0054] Anomaly detection algorithms are techniques used to identify and detect unusual or abnormal patterns and data points in financial data. They can be based on methods such as statistical analysis, machine learning, or deep learning to automatically identify data that deviates from normal behavior.

[0055] The anomaly detection methods adopted in the embodiments of the present invention include threshold-based methods (such as transactions exceeding a certain set range), clustering analysis (identifying data that does not conform to the main group), and model-based methods (such as Isolation Forest, Support Vector Machine, etc.).

[0056] The automated reporting tools in the embodiments of the present invention are used to organize the detected anomaly information into an easy-to-understand report. These tools can quickly convert data into charts, summaries, or detailed analysis reports to facilitate users in quickly obtaining key information. The report includes an overview of the detection results, specific details of the abnormal data (such as amount, time, possible reasons, etc.), and recommended follow-up handling measures.

[0057] In the specific implementation of the embodiments of the present invention, an anomaly detection algorithm and an automated reporting tool are adopted to perform financial anomaly detection on automatically generated financial statements. After the anomaly detection is completed, a financial data anomaly detection report will be automatically generated. These reports will list all identified abnormal situations and provide possible hazard analysis, data background, and handling suggestions. This not only facilitates the financial team to take timely actions but also provides decision-making support for management.

[0058] For example, taking a company that is processing its quarterly financial data as an example, through the anomaly detection algorithm of the present invention, all transaction records and financial data are automatically analyzed, and it is found that a transaction amount is abnormally high, exceeding the normal transaction range. Subsequently, the automated reporting tool can generate an anomaly detection report, which details the time of this transaction, the accounts involved, the nature of the transaction, and its potential impact on the company's finances. The report also provides a possible analysis of this abnormal situation, such as system errors or fraud. In this way, the transaction can be quickly investigated, its legitimacy can be confirmed, and corrective measures can be taken in a timely manner to prevent further financial losses.

[0059] In this way, the present invention uses an anomaly detection algorithm and an automated reporting tool to monitor financial data, which can effectively identify and record financial anomalies, reduce risks for enterprises, improve work efficiency, and enhance financial transparency. At the same time, through automated anomaly detection, enterprises can timely discover potential financial anomalies before problems expand, reducing financial risks; and the automated report generation saves the time for financial personnel to manually check data and prepare reports, enabling them to concentrate their energy on more strategic work.

[0060] The following further describes the present invention in detail through another specific application embodiment, such as Figure 3As shown in the figure, a monthly settlement processing method for financial data based on intelligent analysis in this specific application embodiment includes the following steps: S10. Start and enter step S11; S11. Data integration and preprocessing; and enter step S12 and step S13; S12. Integrate enterprise internal modules through API and enter S14; In the embodiment of the present invention, the financial module, sales module, and procurement module within the enterprise can be connected through the API interface to automatically collect and obtain various types of enterprise data. Among them, the various types of data include financial data, sales data, and procurement data; S13. Perform data cleaning and quality control processing and enter S14; In the embodiment of the present invention, data cleaning technology can be used to preprocess various types of enterprise data collected and obtained to obtain preprocessed data, which can ensure data quality. Among them, the preprocessing includes processing missing values, outliers, and duplicate values.

[0061] For the preprocessed data, data quality monitoring and evaluation are adopted, and data quality control processing is performed according to the evaluation results to obtain preprocessed integrated data; among them, the data quality control processing includes removing duplicate record processing and filtering out null value processing.

[0062] In the embodiment of the present invention, through API integration and data cleaning technology, data synchronization with enterprise internal modules is achieved, and the quality and availability of data are ensured.

[0063] S14. Intelligent identification and classification, and enter S14 and S15; S15. Machine learning algorithm processing, and enter S17; S16. Intelligent identification and classification of financial vouchers, and enter S17; For the preprocessed integrated data, machine learning algorithms are used to perform preliminary intelligent identification and classification of financial vouchers to obtain preliminary classified financial data related to the financial vouchers; A deep learning model is constructed using deep learning algorithms, and the preliminary classified financial data is deeply identified and classified through the constructed deep learning model to obtain financial data related to the financial vouchers.

[0064] Among them, machine learning algorithms are used to perform intelligent identification and classification of financial vouchers. This includes supervised learning algorithms such as support vector machine (SVM), decision tree, random forest, etc., to reduce the workload of manual review.

[0065] Regarding deep learning frameworks, in order to handle the complex financial accounting processes, the present invention can use deep learning frameworks (such as TensorFlow or PyTorch) to build more complex models to improve the accuracy of recognition and classification.

[0066] In the embodiments of the present invention, by using machine learning and deep learning algorithms, financial vouchers can be automatically recognized and classified, reducing the workload of manual review.

[0067] S17. Perform automated accounting processing and proceed to S18 and S19; S18. Apply the accounting rule engine and proceed to S20; S19. Adjust accounts and generate accounting entries and proceed to S20; In the embodiments of the present invention, according to the preset accounting rules and logics, the financial data related to financial vouchers classified will be automatically adjusted for accounts and accounting entries will be generated; and through the pre-set event-driven architecture, in response to specified business events, the accounting processing flow will be automatically triggered to process the financial data for which entries are generated. In the embodiments of the present invention, regarding the accounting rule engine, according to the preset accounting rules and logics, accounts will be automatically adjusted and accounting entries will be generated. The present invention introduces a complex rule engine (Together) that can handle various accounting scenarios and regulatory requirements.

[0068] Regarding the event-driven architecture, the present invention can, through the event-driven architecture, respond to specific business events and automatically trigger the accounting processing flow.

[0069] In the embodiments of the present invention, through the accounting rule engine and the event-driven architecture, the adjustment of accounts and the generation of accounting entries are automatically executed, improving the efficiency of accounting processing.

[0070] S20. Automate monthly closing and proceed to S21 and S22; S21. Automate the workflow and proceed to S23; S22. Generate financial statements and proceed to S23; In the embodiments of the present invention, for the financial data of the entries, the monthly closing automation process will be automatically executed in a predetermined order, and through the report generation tool, cost calculation, profit analysis, generation of balance sheets and income statements will be carried out, and the corresponding financial statements will be automatically generated and output.

[0071] Regarding workflow automation, the present invention will automatically execute the monthly closing process, and specifically, workflow automation technology (Flowable) can be used to ensure that each step is executed in a predetermined order.

[0072] Regarding the report generation tool, including cost calculation, profit analysis, and the generation of balance sheets and income statements, it can be implemented using libraries such as Pandas in Python or professional report tools.

[0073] In the embodiments of the present invention, through workflow automation and report generation tools, the monthly closing process is automatically executed, including cost calculation and the generation of financial statements.

[0074] S23. Anomaly detection and reporting, and then enter S24; S24. Application of anomaly detection algorithm, and then enter S26; S25. Generation of anomaly report, and then enter S26; In the embodiments of the present invention, for the automatically generated financial statements, statistical analysis, clustering analysis, and / or model-based anomaly detection methods are used to perform anomaly detection and identification, and identify abnormal financial data; and a financial data anomaly detection report is generated and output through an automated report generation tool.

[0075] Regarding the anomaly detection algorithm, the present invention can identify possible financial anomalies through the anomaly detection algorithm; statistical analysis, clustering analysis, or model-based anomaly detection methods can be used.

[0076] Then regarding report automation, reports are generated for further analysis by financial personnel. Through automated report generation tools such as Tableau or Power BI, the abnormal situations can be intuitively displayed.

[0077] In the embodiments of the present invention, anomaly detection algorithms and automated report tools are used to identify financial anomalies and generate reports for further analysis by financial personnel.

[0078] S26. End.

[0079] Exemplary device Such as Figure 4 As shown, the embodiments of the present invention provide a financial data monthly closing processing device based on intelligent analysis. The device includes: A data integration and preprocessing module 310, which is used to control the automatic collection and acquisition of various types of enterprise data, perform data integration and preprocessing on the collected various types of data, and convert them into preprocessed integrated data; An intelligent recognition and classification module 320, which is used to perform intelligent recognition and classification on the preprocessed integrated data by using machine learning and deep learning algorithms, and automatically identify and classify financial data related to financial vouchers; An automated accounting processing module 330, which is used to adopt an accounting rule engine and event-driven architecture to automatically perform account adjustments and accounting entries on the classified financial data related to financial vouchers, and generate recorded financial data; The monthly closing automation module 340 is used to automatically execute the monthly closing process, perform cost calculations, and automatically generate corresponding financial statement outputs for the financial data of entries through workflow automation control and report generation tools. The anomaly detection and reporting module 350 is used to perform financial anomaly detection on the automatically generated financial statements by using anomaly detection algorithms and automated reporting tools, generate and output financial data anomaly detection reports, as described above.

[0080] Based on the above embodiments, the present invention also provides an intelligent terminal, and its principle block diagram can be as Figure 5 shown. The intelligent terminal includes a processor, a memory, a network interface, a display screen, and a database connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for processing monthly closing of financial data based on intelligent analysis. The database of the intelligent terminal is used to store a program for processing monthly closing of financial data based on intelligent analysis.

[0081] Those skilled in the art can understand that Figure 5 the principle block diagram shown in

[0082] merely shows the block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the intelligent terminal to which the solution of the present invention is applied. The specific intelligent terminal may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. Automatically collect and obtain various types of data of an enterprise, perform data integration and preprocessing on the collected various types of data, and convert them into preprocessed integrated data; For the preprocessed integrated data, use machine learning and deep learning algorithms for intelligent identification and classification, and automatically identify and classify the financial data related to financial vouchers; Adopt an accounting rule engine and event-driven architecture to automatically execute account adjustments and accounting entries for the classified financial data related to financial vouchers, and generate the financial data of entries; Automatically execute the monthly closing process, perform cost calculations, and automatically generate corresponding financial statement outputs for the financial data of the entries through workflow automation control and report generation tools, as described above.

[0083] After the steps of automatically executing the monthly closing process, performing cost calculations, and automatically generating corresponding financial statement outputs for the financial data of the entries through workflow automation control and report generation tools, the following steps are included: Adopt an anomaly detection algorithm and an automated reporting tool to perform financial anomaly detection on the automatically generated financial statements, generate a financial data anomaly detection report, and output it.

[0084] Among them, the steps of automatically collecting and obtaining various types of enterprise data, integrating and preprocessing the collected various types of data, and converting them into preprocessed integrated data include: Connect to the financial module, sales module, and procurement module within the enterprise through the API interface to automatically collect and obtain various types of enterprise data, where the various types of data include financial data, sales data, and procurement data; Adopt data cleaning technology to preprocess the various types of enterprise data collected and obtained to obtain preprocessed data, where the preprocessing includes handling missing values, outliers, and duplicate values; For the preprocessed data, adopt data quality monitoring and evaluation, and perform data quality control processing according to the evaluation results to obtain preprocessed integrated data; among them, the data quality control processing includes removing duplicate record processing and filtering out null value processing.

[0085] Among them, the steps of using machine learning and deep learning algorithms to perform intelligent identification and classification on the preprocessed integrated data, and automatically identifying and classifying the financial data related to financial vouchers include: For the preprocessed integrated data, adopt a machine learning algorithm to perform preliminary intelligent identification and classification on financial vouchers to obtain preliminary classified financial data related to financial vouchers; Adopt a deep learning algorithm to build a deep learning model, and perform deep identification and classification on the preliminary classified financial data through the built deep learning model to obtain financial data related to financial vouchers.

[0086] Among them, the steps of adopting an accounting rule engine and an event-driven architecture to automatically perform account adjustments and accounting entries on the classified financial data related to financial vouchers, and generate the financial data of the entries include: According to preset accounting rules and logic, automatically adjust the accounts and generate accounting entries for the classified financial data related to financial vouchers; and through a pre-set event-driven architecture, respond to specified business events, automatically trigger the accounting processing flow, and process the financial data for generating entries. Among them, the steps of automatically executing the end-of-month process, calculating costs, and automatically generating corresponding financial statement outputs for the financial data of the entries through workflow automation control and report generation tools include: For the financial data of the entries, automatically execute the end-of-month automation process in a predetermined order, and through the report generation tool, calculate costs, analyze profits, generate balance sheets and income statements, and automatically generate corresponding financial statements and output them.

[0087] Among them, the steps of using anomaly detection algorithms and automated reporting tools to detect financial anomalies in the automatically generated financial statements, generate and output financial data anomaly detection reports further include: For the automatically generated financial statements, use statistical analysis, clustering analysis, and / or model-based anomaly detection methods to detect anomalies, identify abnormal financial data; and generate and output financial data anomaly detection reports through an automated reporting generation tool, as described above.

[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to memory, storage, database, or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0089] In summary, the present invention provides a monthly financial data settlement processing method, device, intelligent terminal and storage medium based on intelligent analysis; the present invention can process complex and massive business and financial data in real time, accurately and completely according to the monthly financial settlement rules, mainly including data integration and preprocessing, intelligent identification and classification, automated accounting processing, monthly settlement automation, and anomaly detection and reporting. The present invention can realize the intelligent processing of the monthly financial data settlement process, no longer requires a large amount of manual operations, greatly saves labor costs, improves work efficiency, and reduces the possibility of errors, realizing the intelligent monthly financial data settlement.

Claims

1. A method for processing monthly financial data based on intelligent analysis, characterized in that: include: Automatically collect and obtain various types of enterprise data, integrate and preprocess the collected data, and convert them into preprocessed integrated data; For the pre-processed integrated data, use machine learning and deep learning algorithms to perform intelligent identification and classification, and automatically identify and classify the financial data related to financial vouchers; Based on accounting rule engine and event-driven architecture, it automatically performs account adjustments and accounting entries for classified financial data related to financial vouchers, and generates financial data for entries; For the financial data of the entries, the monthly closing process is automatically executed, cost calculation is performed, and the corresponding financial report output is automatically generated through workflow automation control and report generation tools.

2. The method for processing monthly financial data based on intelligent analysis according to claim 1, characterized in that: The steps of automatically executing the monthly closing process, performing cost calculation and automatically generating the corresponding financial report output for the financial data of the journal entries through workflow automation control and report generation tools include: Use anomaly detection algorithms and automated reporting tools to perform financial anomaly detection on automatically generated financial statements, generate and output financial data anomaly detection reports.

3. The method for processing monthly financial data based on intelligent analysis according to claim 1, characterized in that: The steps of automatically collecting and acquiring various types of enterprise data, integrating and preprocessing the collected and acquired various types of data, and converting them into preprocessed integrated data include: Connecting to the financial module, sales module, and procurement module within the enterprise through the API interface, automatically collecting and acquiring various types of data of the enterprise, wherein the various types of data include financial data, sales data, and procurement data; Using data cleaning technology to pre-process various types of enterprise data collected and obtained to obtain pre-processed data, wherein the pre-processing includes processing missing values, abnormal values, and duplicate values; The preprocessed data is subjected to data quality monitoring and evaluation, and data quality control processing is performed according to the evaluation results to obtain preprocessed integrated data; wherein the data quality control processing includes removing duplicate records and filtering out null values.

4. The method for processing monthly financial data based on intelligent analysis according to claim 1, characterized in that: The steps of intelligently identifying and classifying the preprocessed integrated data using machine learning and deep learning algorithms to automatically identify and classify the financial data related to the financial vouchers include: For the pre-processed integrated data, a machine learning algorithm is used to perform preliminary intelligent recognition and classification of financial vouchers to obtain preliminary classified financial data related to the financial vouchers; A deep learning algorithm is used to construct a deep learning model, and the constructed deep learning model is used to deeply identify and classify the preliminary classified financial data to obtain financial data related to the financial vouchers.

5. The method for processing monthly financial data based on intelligent analysis according to claim 1 is characterized in that: The steps of automatically performing account adjustments and accounting entries on the classified financial data related to the financial vouchers based on the accounting rule engine and event-driven architecture to generate the financial data of the entries include: According to the preset accounting rules and logic, the classified financial data related to the financial vouchers are automatically adjusted and accounting entries are generated; and through the pre-set event-driven architecture, the specified business events are responded to, the accounting processing flow is automatically triggered, and the financial data for the generated entries is processed.

6. The method for processing monthly financial data based on intelligent analysis according to claim 1, characterized in that: The steps of automatically executing the monthly closing process, performing cost calculation and automatically generating corresponding financial report outputs for the financial data of the journal entries through workflow automation control and report generation tools include: For the financial data of the entries, the monthly closing automation process is automatically executed in a predetermined order, and cost calculation, profit analysis, balance sheet and profit and loss statement are generated through report generation tools, and the corresponding financial statements are automatically generated and output.

7. The method for processing monthly financial data based on intelligent analysis according to claim 2 is characterized in that: The step of using anomaly detection algorithms and automated reporting tools to perform financial anomaly detection on automatically generated financial statements, generating and outputting a financial data anomaly detection report also includes: For automatically generated financial statements, statistical analysis, cluster analysis and / or model-based anomaly detection methods are used to perform anomaly detection and identification to identify abnormal financial data; and a financial data anomaly detection report is generated and output through an automated report generation tool.

8. A financial data monthly settlement processing device based on intelligent analysis, characterized in that: The device comprises: The data integration and preprocessing module is used to control the automatic collection and acquisition of various types of enterprise data, integrate and preprocess the collected data, and convert them into preprocessed integrated data; Intelligent recognition and classification module, which is used to intelligently recognize and classify the pre-processed integrated data using machine learning and deep learning algorithms, and automatically recognize and classify the financial data related to financial vouchers; The automated accounting processing module is used to automatically perform account adjustments and accounting entries for the classified financial data related to financial vouchers based on the accounting rule engine and event-driven architecture, and generate financial data for the entries; Monthly closing automation module is used to automatically execute the monthly closing process, perform cost calculation and automatically generate corresponding financial report output for the financial data of the entries through workflow automation control and report generation tools; The anomaly detection and reporting module is used to use anomaly detection algorithms and automated reporting tools to perform financial anomaly detection on automatically generated financial statements, generate financial data anomaly detection reports and output them.

9. An intelligent terminal, characterized in that: The device comprises a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include being used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of claims 1 to 7.

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