Teaching platform management method, teaching platform, electronic equipment and storage medium

Abstract

The invention discloses a teaching platform management method, a teaching platform, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the extension processing of preset financial data information through a trained data extension model, so as to generate a first number of teaching use financial data; and selecting a second quantity of the teaching use financial data according to a teaching target to perform teaching case configuration so as to generate and store a target teaching case corresponding to the teaching target. According to the technical scheme, data expansion can be performed on the preset financial data through the trained data expansion model, so that sufficient teaching use financial data with relatively high reliability can be obtained, and then sufficient teaching use financial data can be selected to configure teaching cases according to teaching targets (or financial teaching requirements). Therefore, the technical scheme of the invention can ensure that the teaching platform has sufficient teaching data so as to support various financial teaching requirements.

Description

Technical Field

[0001] The present invention relates to the field of accounting teaching, and in particular to a teaching platform management method, a teaching platform, an electronic device and a computer-readable storage medium. Background Art

[0002] Most universities have added big data accounting majors on the basis of the original courses, but the teaching content only comes from the compiled textbooks, lacking actual cases and actual scenarios to support students' operations. The teaching platforms currently used by most universities follow the business logic of "management accounting" and cover four modules: investment management, strategic management, business management, and business analysis. However, these platforms mainly provide public financial report data from enterprises. Their data sources are relatively single and the data volume is insufficient, making it difficult to support various financial teaching needs (such as financial data analysis needs, etc.). Therefore, how to ensure that the teaching platform has sufficient teaching data to support various financial teaching needs is a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0003] The purpose of this application is to provide a teaching platform management method, teaching platform, electronic equipment and storage medium, which can ensure that the teaching platform has sufficient teaching data to support various financial teaching needs.

[0004] To achieve the above objectives:

[0005] In a first aspect, an embodiment of the present application provides a management method for a teaching platform, comprising: expanding and processing preset financial data information through a trained data expansion model to generate a first quantity of financial data for teaching use; selecting a second quantity of financial data for teaching use for teaching case configuration according to teaching objectives to generate and store target teaching cases corresponding to the teaching objectives.

[0006] In one embodiment, the step of selecting a second amount of financial data for teaching use to configure teaching cases according to the teaching objectives to generate target teaching cases corresponding to the teaching objectives includes: configuring a financial data analysis table according to the second amount of financial data for teaching use; the financial data analysis table includes one or more of a cost basis table, an expense detail bill, and an actual profit and loss summary table; configuring the teaching case corresponding to the teaching objective based on the second amount of financial data for teaching use and / or at least one data analysis table.

[0007] In one embodiment, the preset financial data information is expanded and processed through a trained data expansion model to generate a first amount of financial data for teaching use, including: determining a teaching scenario; determining the dimensional requirements and / or numerical requirements of the required financial data for teaching use according to the teaching scenario; selecting financial data information that matches the dimensional requirements and / or numerical requirements from a database as the preset financial data information; and expanding the preset financial data information through a trained data expansion model to generate financial data for teaching use that meets the teaching scenario.

[0008] In one embodiment, the data expansion model includes a clustering model preset data processing algorithm; the preset financial data information is expanded and processed through the trained data expansion model to generate a first number of financial data for teaching use, including: constructing a data set of preset financial data; clustering the dimensional data corresponding to each preset financial data in the data set through the clustering model to obtain at least one clustering result; based on at least one clustering result and the preset data processing algorithm, generalizing the numerical data to output the financial data for teaching use.

[0009] In one embodiment, the data expansion model includes a preset data processing algorithm; the step of expanding the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use includes: when the preset financial data information is customized financial data information, based on the customized financial data information and the preset data processing algorithm, generalizing the numerical data to output the financial data for teaching use.

[0010] In one embodiment, after selecting a second number of teaching use financial data for teaching case configuration according to the teaching objectives to generate a target teaching case corresponding to the teaching objectives, it also includes: in response to a display operation for the target teaching case, loading and displaying all or part of the data in the stored target teaching case through a preset database component, the preset database component includes a doris component; and / or, providing a question and answer service for the analysis results of the financial data of the displayed data through a financial big language model; and / or, performing online analysis on the target teaching case to obtain the analysis results corresponding to the target teaching case, and associating and storing the target teaching case with the corresponding analysis results so that when the target teaching case is called, the analysis results are called at the same time; the analysis results include the comprehensive financial status of the target teaching case.

[0011] In one embodiment, after selecting a second amount of teaching use financial data for teaching case configuration according to the teaching objectives to generate and store target teaching cases corresponding to the teaching objectives, it includes: visually displaying the life cycle of the financial data used to constitute the target teaching case in the form of a chart or a timeline to provide users with a logical process of financial data evolution.

[0012] In the second aspect, the present application provides a teaching platform, including:

[0013] A data generation module is used to expand and process preset financial data information through a trained data expansion model to generate a first amount of financial data for teaching use, and to select a second amount of financial data for teaching use for teaching case configuration according to teaching objectives to generate and store target teaching cases corresponding to the teaching objectives; a data application module is used to call the stored target teaching cases for application.

[0014] In a third aspect, the present application provides an electronic device, including:

[0015] processor;

[0016] a memory for storing processor-executable instructions;

[0017] Among them, the processor is configured to execute instructions to implement the management method of the teaching platform of any one of the first aspects.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor, a management method of a teaching platform as described in any one of the second aspects is implemented.

[0019] The management method of the teaching platform, the teaching platform, the electronic device and the computer-readable storage medium provided by the present application, the management method of the teaching platform includes: expanding the preset financial data information through the trained data expansion model to generate a first number of teaching financial data; selecting the second number of teaching financial data for teaching case configuration according to the teaching objectives to generate and store the target teaching cases corresponding to the teaching objectives. Therefore, the technical solution of the present application can perform data expansion on the preset financial data through the trained data expansion model to obtain sufficient and reliable teaching financial data, and then can select enough teaching financial data to configure teaching cases according to the teaching objectives (or financial teaching needs). Therefore, the technical solution of the present application can ensure that the teaching platform has sufficient teaching data to support various financial teaching needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a flow chart of a method for managing a teaching platform provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the process of preparing a financial data analysis table based on teaching-use financial data in the present invention;

[0022] Figure 3It is a schematic diagram of the process of performing data expansion through the data expansion model in the present invention;

[0023] Figure 4 A schematic diagram of the structure of a teaching platform provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0026] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0027] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate the existence of features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0028] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0029] It should be noted that in this article, step codes such as S1 and S2 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the order. When implementing the step, those skilled in the art may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0030] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0032] See also Figure 1 , is a flow chart of a management method of a teaching platform provided in an embodiment of the present application. The management method of the teaching platform can be executed by an electronic device provided in an embodiment of the present application. The management method of the teaching platform provided in this embodiment includes:

[0033] Step S1, expanding the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use.

[0034] In this embodiment, the data expansion model can adopt a combination of one or more models such as a linear regression model, a logistic regression model, a decision tree model, a random forest model, a support vector machine model, a neural network model, a clustering model, etc., which are not specifically limited here. The preset financial data information can be real financial data information or customized financial data information. Real financial data information can be obtained from business transactions, such as sales records, purchase orders, bank statements, etc. In addition, the financial data used for teaching includes financial data provided by users, financial data extracted by ETL (Extract-Transform-Load, ETL) tools, built-in data, and data generated by the data expansion model.

[0035] Since the amount, quantity and other factors in financial data must be reasonable to be used for financial teaching analysis, it is necessary to train the data extension model. The data extension model is trained using real financial data information, including basic management accounting and business management analysis data provided by accounting-related professional teachers in colleges and universities. Real financial data information can reflect the actual financial status and business management of enterprises, making model training closer to reality and helping to improve the prediction accuracy and practicality of the model. In addition, by using a variety of real financial data information, the data extension model can learn a wider range of financial characteristics and patterns, so that it can make more accurate predictions when facing new and unseen data. In addition, the financial data information provided by teachers of relevant majors in colleges and universities is usually professionally screened and sorted, ensuring the quality and relevance of financial data information. Such financial data information is more helpful for the model to capture the key factors in financial analysis.

[0036] In this embodiment, the trained data expansion model is supported to be customized to generate the financial data required for teaching. Specifically, by inputting financial parameter data, customizing variable factors and the type of model, the financial data required for teaching can be output.

[0037] Since real financial data information or customized financial data information has certain limitations and can only represent a small part of financial transactions and cannot cover the teaching materials required for university finance courses, this embodiment introduces a data extension model that can generate financial data on demand, increase the sources of financial data used in teaching, and improve the practicality of university finance courses.

[0038] In one embodiment, the preset financial data information is expanded and processed through a trained data expansion model to generate a first amount of financial data for teaching use, including: determining a teaching scenario; determining the dimensional requirements and / or numerical requirements of the required financial data for teaching use according to the teaching scenario; selecting financial data information that matches the dimensional requirements and / or numerical requirements from a database as the preset financial data information; and expanding the preset financial data information through a trained data expansion model to generate financial data for teaching use that meets the teaching scenario.

[0039] In this embodiment, the dimension requirement can represent the dimension data that must be included in the financial data information; the value requirement can represent the value range of the value data associated with the specific dimension data. In the financial data, the dimension data can include the unit price of goods, the quantity of goods, the cost of goods, etc.

[0040] Understandably, teaching scenarios are designed to simulate real financial events. For example, when learning how to evaluate the financial health of a company, the following scenario can be designed: an investment company is considering investing in a business called "XYZ Technology Company". Before making an investment decision, an in-depth analysis of the company's financial situation is required. In this process, it is necessary to understand XYZ Technology Company's balance sheet (total assets, current assets, fixed assets, total liabilities, current liabilities, long-term liabilities, etc.), income statement (total revenue, cost, gross profit, operating profit, net profit, etc.), cash flow statement (cash flow from operating activities, cash flow from investment activities, cash flow from financing activities, etc.), and financial ratios (current ratio, quick ratio, debt ratio, net profit margin, return on assets, etc.). However, since the existing financial data information about the company may not cover the above information, the missing financial data (which can include dimensional data and numerical data) can be generated based on the existing financial data to construct a complete financial event. In this way, reasonable and useful teaching cases can be provided for teaching to improve students' financial analysis skills.

[0041] In one embodiment, the data expansion model includes a preset data processing algorithm; the step of expanding the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use includes: when the preset financial data information is customized financial data information, based on the customized financial data information and the preset data processing algorithm, generalizing the numerical data to output the financial data for teaching use.

[0042] Here, the customized financial data information may represent the financial data information obtained after customizing the dimension data and / or the numerical data based on experience. When generalizing the data based on the customized financial data information, the numerical data may be directly generalized.

[0043] It is understandable that when dimensional data needs to be generated, the existing financial data can be clustered through the clustering model to obtain the corresponding dimensional data. In addition, when numerical data needs to be generated, the data expansion model can be configured through the linear regression model and the adversarial network algorithm. After the configuration is completed, the data generation interface is called to generate the financial data required for teaching. In this way, the model is configured and called according to the needs to improve the efficiency of data generation.

[0044] It is understandable that the system of the present invention is also configured with an open source DATAX plug-in, which can obtain data through the ETL tool, and the data can be directly used for teaching and can also be used for model training. Through the ETL tool and the system built-in open source DATAX plug-in, the data connection is configured to realize data extraction from the data table in the database to support the use of data on the teaching platform.

[0045] Step S2, selecting a second amount of teaching use financial data for teaching case configuration according to the teaching objectives, so as to generate and store target teaching cases corresponding to the teaching objectives.

[0046] In this embodiment, the teaching objectives can represent the teaching tasks determined based on the training direction of financial skills, and the teaching objectives include but are not limited to financial ratio analysis, time value of money, risk management, etc. Users can determine the dimensional data and numerical data to be selected according to the teaching objectives, and then configure teaching cases based on the selected data to improve the value and richness of the course. In addition, after the generated teaching cases are stored, users with call permissions can directly call them.

[0047] In one embodiment, the step of selecting a second amount of teaching financial data for teaching case configuration according to the teaching objectives to generate a target teaching case corresponding to the teaching objectives includes: configuring a financial data analysis table according to the second amount of teaching financial data; the financial data analysis table includes but is not limited to one or more of a cost basis table, an expense detail bill, and an actual profit and loss summary table; configuring a teaching case corresponding to the teaching objectives based on the second amount of teaching financial data and / or at least one data analysis table.

[0048] See also Figure 2 , which is a flow chart of data expansion through the data expansion model in this embodiment. By inputting the sales order information table and the voucher detail data into the data expansion model, the data expansion model processes the sales order information table and the voucher detail data and outputs a sales revenue detail table. Figure 2 The sales order information table and voucher detail data shown in the figure can be generated or extracted through ETL, or data from built-in templates. This process includes configuring data sources (sales order information table, voucher detail data), data information association, data processing, and generating data sets, where the data sets include financial data analysis tables.

[0049] Configuring teaching cases according to teaching objectives helps bridge the gap between theory and practice and provides students with a comprehensive and practical learning experience. In addition, through specific data and cases, students can understand financial concepts at a practical level and improve their financial awareness.

[0050] The following three cases are used to illustrate the configuration of the teaching cases in this embodiment.

[0051] Teaching Case 1: Financial Statement Analysis

[0052] Teaching objectives: Learn how to analyze financial statements and understand the relationship between financial statements.

[0053] Case content: Using a fictitious company's cost basis statement and actual profit and loss summary statement, students need to analyze the company's revenue, costs and profits.

[0054] Data analysis tables: including balance sheet, income statement and cash flow statement.

[0055] Teaching Case 2: Cost Control

[0056] Teaching objectives: Master cost analysis and management, and learn how to increase corporate profits through cost control.

[0057] Case content: Given a detailed expense account for a manufacturing company, students are required to identify areas where costs are too high and come up with strategies to reduce them.

[0058] Data analysis table: mainly includes cost basis table and expense detail bill.

[0059] Teaching activities: Students analyze cost structures, compare the cost effectiveness of different products, and develop cost-saving measures.

[0060] Teaching Case 3: Budgeting and Financial Planning

[0061] Teaching Objectives: Learn how to prepare a budget and perform financial planning.

[0062] Case content: A service company plans to expand its business, and students need to prepare a budget for the company for the next fiscal year based on the actual profit and loss summary and expense detailed bills.

[0063] Data analysis tables: including actual profit and loss summary table and budget table.

[0064] Activities: Students analyze historical data, forecast future revenue and expenses, and prepare budgets, taking into account the company's financial goals and strategies.

[0065] In one embodiment, after selecting a second number of teaching use financial data for teaching case configuration according to the teaching objectives to generate a target teaching case corresponding to the teaching objectives, it also includes: in response to a display operation for the target teaching case, loading and displaying all or part of the data in the stored target teaching case through a preset database component, the preset database component includes a doris component; and / or, providing a question and answer service for the analysis results of the financial data of the displayed data through a financial big language model; and / or, performing online analysis on the target teaching case to obtain the analysis results corresponding to the target teaching case, and associating and storing the target teaching case with the corresponding analysis results so that when the target teaching case is called, the analysis results are called at the same time; the analysis results include the comprehensive financial status of the target teaching case.

[0066] Understandably, financial data usually has the characteristic of too much data for report analysts. In this embodiment, by building an analytical database component Doris at the front end of the system, the speed of loading and displaying financial data can be improved. In addition, this application also builds Fanruan BI components and related BI teaching cases, and users can learn or develop related front-end BI development modules. At the same time, the interface of the Doris component remains open to the intranet. When connected to the intranet, users can choose the front-end display component by themselves.

[0067] The financial language model in this embodiment can be BloombergGPT deployed through SAAS, with 50 billion parameters, using a financial field data set containing 363 billion tokens and a general data set of 345 billion tokens. BloombergGPT performs well in financial field tasks. Students and teachers can obtain financial data through AI analysis plug-ins, and use AI analysis plug-ins to assist in the analysis of calculated and processed data.

[0068] In one embodiment, after selecting a second amount of teaching use financial data for teaching case configuration according to the teaching objectives to generate and store target teaching cases corresponding to the teaching objectives, it includes: visually displaying the life cycle of the financial data used to constitute the target teaching case in the form of a chart or a timeline to provide users with a logical process of financial data evolution.

[0069] Understandably, through real-time online analysis of teaching cases, students can give timely feedback on the content of teaching cases, which helps to quickly understand and absorb the concepts in financial teaching. By associating and storing teaching cases with their corresponding analysis results, a structured learning resource library can be built to facilitate data management and information retrieval. At the same time, displaying financial data in the form of charts or timelines can help students understand the changes and trends of data more intuitively. Visual display helps students understand the logical process of financial data evolution and improve logical thinking and data analysis capabilities.

[0070] In one embodiment, the management method of the teaching platform provided in the present application also includes: isolating data of users from different schools, and also being able to perform teacher-student interaction behaviors such as homework content customization, homework reception, and homework submission based on the teaching platform.

[0071] The teaching platform management method provided in this application improves the data processing capability of the financial teaching platform by introducing big data and machine learning related technology systems, so that students can be exposed to multi-source, automated, high-performance standard data processing solutions during the teaching process, and have stronger accounting data processing capabilities. This solution also solves the current problem of missing teaching data and insufficient data volume to support financial data and management financial data analysis needs, and can combine actual corporate financial and business analysis from the data source to the application layer.

[0072] Based on the same inventive concept as the above-mentioned embodiment, the process of preparing a financial data analysis table according to teaching using financial data in the present invention is described below by a specific example. Figure 3 .

[0073] Select the sales order information table in the historical financial data, classify the data in the sales order information table according to dimensional data and numerical data, and configure the data extension model according to the category. For dimensional data, data extension can be performed through the built-in model in the data extension model 1 (which can be a clustering model). For numerical data, you can select the built-in model 2 in the data extension model (which can be a data regression model). After selecting the model form, adjust the parameters (for example, the loss factor α can be determined to be 0.25), and then call the built-in data generation interface to generate financial data information such as company information (including company name, code, business unit code, business unit name, effective time, factory), order information (order type, order code, creation time), and product information (product type, product model, material code, material description). In addition, when the financial data information used is numerical data, linear regression analysis, label selection, and parameter adjustment are performed on the numerical data based on the custom model, and a generator model is learned by training a generative adversarial network. Then, this generator model and random noise vector are used to generate new data samples, thereby achieving data extension.

[0074] Based on the same inventive concept as the above embodiments, the present invention provides a teaching platform. Figure 4 The teaching platform provided by the present invention includes: a data generation module, which is used to expand and process the preset financial data information through a trained data expansion model to generate a first amount of financial data for teaching use, and select a second amount of financial data for teaching use according to the teaching objectives to configure teaching cases, so as to generate and store target teaching cases corresponding to the teaching objectives; a data application module, which is used to call the stored target teaching cases for application.

[0075] In one embodiment, the teaching platform also includes a rights management module and a data map module. The rights management module is used to assign roles to users, and different roles have different operating permissions. The platform has a built-in administrator role. Users who use the administrator identity in the platform can assign directory-level data permissions to other roles. Data permissions are assigned through the established college class organizational structure. The administrator role can create a teacher role, and the teacher role can create other roles and assign permissions to the roles it creates. Different factory permissions can be assigned to students to avoid copying and pasting assignments without actual operations. The teacher role can also create class roles to manage students. The data map module is used to visualize the life cycle of financial data used to constitute the target teaching case in the form of a chart or a timeline, so as to provide users with a logical process of financial data evolution. Specifically, the lineage ingestion function of the DataHub framework can be used, which allows users to automatically obtain the lineage relationship between data tables by configuring database links, and the lineage relationship can be detailed to the column level. Therefore, users can use DataHub to track the flow of data, not only knowing which tables are related, but also knowing how the data in which specific columns flows and is processed. In other words, the data map module can visually show students the flow of data from the business system to the final data report. In addition, since financial data is usually large and complex, data maps can help users quickly find the required data, understand the relationship between data, and the source and purpose of data. Through the data map module, users can conduct financial data analysis and decision-making more effectively.

[0076] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, the electronic device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 5 The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 5 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the management method of the teaching platform is implemented.

[0077] The electronic device may also include: at least one network interface 312. The various components in the electronic device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 313 is not used in the following examples. Figure 5 Various buses are labeled as bus system 313.

[0078] The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM). The memory 311 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memory.

[0079] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program used to operate on the electronic device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0080] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored, and the computer-readable storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM), etc.; or it may be various devices including one or any combination of the above-mentioned memories, such as a server, etc. When the instructions stored therein are executed by a processor, the management method of the teaching platform described in the above-mentioned embodiment is implemented.

[0081] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0083] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A management method for a teaching platform, characterized in that: include: Performing expansion processing on the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use; A second quantity of the teaching use financial data is selected according to the teaching objectives to configure teaching cases, so as to generate and store target teaching cases corresponding to the teaching objectives.

2. The method according to claim 1, characterized in that: The step of selecting a second amount of the teaching use financial data for teaching case configuration according to the teaching objective to generate a target teaching case corresponding to the teaching objective includes: According to the second amount of the teaching use financial data, a financial data analysis table is configured; the financial data analysis table includes one or more of a cost basis table, a detailed expense bill, and an actual profit and loss summary table; Based on the second amount of the teaching usage financial data and / or at least one of the data analysis tables, the teaching cases corresponding to the teaching objectives are configured.

3. The method according to claim 1, characterized in that The step of performing expansion processing on the preset financial data information by using the trained data expansion model to generate a first amount of financial data for teaching use includes: Determine the teaching scenario; Determine the dimension requirements and / or value requirements of the financial data required for teaching use according to the teaching scenario; Selecting financial data information matching the dimension requirement and / or value requirement from a database as the preset financial data information; The preset financial data information is expanded and processed through the trained data expansion model to generate the teaching financial data that meets the teaching scenario.

4. The method according to claim 1, characterized in that The data expansion model includes a clustering model and a preset data processing algorithm; The step of performing expansion processing on the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use includes: Construct a dataset of preset financial data; Performing clustering processing on the dimensional data corresponding to each preset financial data in the data set by using the clustering model to obtain at least one clustering result; Based on the at least one clustering result and the preset data processing algorithm, numerical data generalization is performed to output the teaching use financial data.

5. The method according to claim 1, characterized in that: The data expansion model includes a preset data processing algorithm; The step of performing expansion processing on the preset financial data information through the trained data expansion model to generate a first amount of financial data for teaching use includes: When the preset financial data information is customized financial data information, numerical data generalization is performed based on the customized financial data information and the preset data processing algorithm to output the financial data for teaching use.

6. The method according to claim 1, characterized in that After selecting the second quantity of the teaching use financial data according to the teaching objective to configure teaching cases to generate target teaching cases corresponding to the teaching objective, the method further includes: In response to the display operation for the target teaching case, all or part of the data stored in the target teaching case is loaded and displayed through a preset database component, wherein the preset database component includes a doris component; and / or, Providing Q&A services based on the analysis results of the financial data displayed through the financial big language model; and / or, Performing online analysis on the target teaching case to obtain an analysis result corresponding to the target teaching case, and associating and storing the target teaching case with the corresponding analysis result, so that when the target teaching case is called, the analysis result is called at the same time; The analysis results include the comprehensive financial status of the target teaching case.

7. The method according to claim 1, characterized in that After selecting the second quantity of the teaching use financial data according to the teaching objective to configure teaching cases to generate and store target teaching cases corresponding to the teaching objective, the method further comprises: The life cycle of the financial data used to constitute the target teaching case is visualized in the form of a chart or a timeline to provide users with a logical process of the evolution of the financial data.

8. A teaching platform, characterized in that: include: A data generation module, configured to perform expansion processing on preset financial data information through a trained data expansion model to generate a first quantity of teaching financial data, and to select a second quantity of the teaching financial data for teaching case configuration according to a teaching objective to generate and store a target teaching case corresponding to the teaching objective; The data application module is used to call the stored target teaching case for application.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the steps of the teaching platform management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor, the steps of the teaching platform management method as described in any one of claims 1 to 7 are implemented.

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