Vocational education auditing system based on big data

Abstract

The present application relates to the technical field of big data and vocational education auditing, and particularly relates to a vocational education auditing system based on big data. The system comprises a teaching data collection unit, a student data collection unit and a management data collection unit, and is used for collecting various types of education data in real time. A data processing module comprises a data cleaning unit, a data standardization unit and a data integration unit; an auditing analysis module comprises a teaching auditing unit, a student auditing unit and a management auditing unit; a security management module comprises a data encryption unit, a data backup unit and a data monitoring unit; and a user training module comprises a training course generation unit and a training evaluation unit. The present invention significantly improves the accuracy and security of vocational education auditing by means of an intelligent auditing model and risk assessments, and meets the requirements of modern vocational education.

Description

A big data-based vocational education auditing system TECHNICAL FIELD

[0001] The present application belongs to the technical field of vocational education and big data auditing, and specifically relates to a big data-based vocational education auditing system. BACKGROUND

[0002] With the rapid development of information technology, the digital transformation of vocational education has become a trend. The application of big data auditing systems in vocational education not only improves teaching quality and efficiency, but also effectively discovers and prevents risks, ensuring the safety of educational data. However, existing big data auditing systems still have some deficiencies in terms of function and performance, affecting the intelligent management and risk prevention and control capabilities of vocational education.

[0003] After searching, a big data auditing scene analysis method and system applied to a smart education system (CN112948822B) was disclosed on October 18, 2024. This patent proposes a method of dividing the smart education platform into multiple auditing units according to different functions, and through the embedding of hook functions to monitor the event messages generated by each auditing unit, the messages sent to the target window are intercepted and analyzed to determine whether there are risks in the big data processing process. However, this technical solution mainly focuses on event message monitoring and risk assessment, lacks in-depth analysis and auditing of specific vocational education scenarios, and cannot fully cover various complex situations in vocational education. In addition, the division of auditing units and the selection of hook points in this system may have limitations, affecting the accuracy and comprehensiveness of the audit.

[0004] After searching, a financial data security management processing system (CN116628722B) was disclosed on July 30, 2024. This patent provides a financial data security management processing system, including data standardization, data protection, data monitoring, data analysis and other modules, which can standardize and convert financial data, and monitor financial data anomalies and risks in real time or periodically. However, this technical solution mainly targets the security management of financial data and does not optimize for the specific needs of vocational education. The vocational education field involves a large amount of teaching data, student data and management data, and the characteristics and management requirements of these data are significantly different from financial data, so this system has certain limitations in the auditing and security management of vocational education data. In addition, this system lacks advanced function training and user education modules for vocational education, and cannot meet the needs of user operation and system maintenance in the field of vocational education. TECHNICAL PROBLEM

[0005] The above problems show that the existing big data audit system has insufficient functions and performance in the application of the field of vocational education, cannot fully cover the complex situation of vocational education, the accuracy and comprehensiveness of the audit are affected, and there is a lack of optimization of specific needs of vocational education and user training module. Technical solution

[0006] The present application provides a big data-based vocational education audit system, aiming to provide a more comprehensive, accurate and intelligent vocational education audit system to meet the needs of modern vocational education for efficient audit and safety management.

[0007] The technical solution adopted by the present application to solve the above technical problems is: a big data-based vocational education audit system, comprising a data acquisition module, a data processing module, an audit analysis module, a safety management module, a user training module and a system maintenance module, wherein the data acquisition module is connected with the data processing module, the data processing module is connected with the audit analysis module, the audit analysis module is connected with the safety management module, the safety management module is connected with the user training module, and the user training module is connected with the system maintenance module.

[0008] The data acquisition module comprises a teaching data acquisition unit, a student data acquisition unit and a management data acquisition unit, the teaching data acquisition unit is used for real-time acquisition of various data in the teaching process, including course content, teaching progress, teaching effect, etc., the student data acquisition unit is used for real-time acquisition of student learning behavior data, including learning progress, learning achievement, learning habit, etc., and the management data acquisition unit is used for real-time acquisition of various data in the management process, including training plan, resource allocation, education evaluation, etc. The data acquisition module is connected with various education platforms and devices through a network interface to realize real-time data transmission.

[0009] The data processing module comprises a data cleaning unit, a data standardization unit and a data integration unit, the data cleaning unit is used for cleaning the collected data to remove invalid and erroneous data, the data standardization unit is used for converting data from different sources into a unified format to ensure the compatibility between different data, and the data integration unit is used for integrating the cleaned and standardized data into structured data to provide a basis for subsequent audit analysis. The data processing module is connected with the data acquisition module through a data bus to ensure real-time processing and transmission of data.

[0010] The audit analysis module includes a teaching audit unit, a student audit unit, and a management audit unit. The teaching audit unit is used to analyze teaching data, evaluate teaching quality and effectiveness, and identify abnormalities and risks in the teaching process. The student audit unit is used to analyze student data, evaluate students' learning status and progress, and identify problems and risks in students' learning. The management audit unit is used to analyze management data, evaluate the rationality of education management and resource use, and identify problems and risks in the management process. The audit analysis module is connected to the data processing module through a data bus, and real-time processed data is obtained for comprehensive analysis and audit.

[0011] The security management module includes a data encryption unit, a data backup unit, and a data monitoring unit. The data encryption unit is used to encrypt sensitive data to protect data security. The data backup unit is used to regularly backup important data to prevent data loss. The data monitoring unit is used to monitor data usage in real time, identify and prevent data leakage and malicious access. The security management module is connected to the audit analysis module through a data bus to ensure the secure storage and transmission of audit results.

[0012] The user training module includes a training course generation unit and a training evaluation unit. The training course generation unit generates personalized training courses based on user needs and usage. The training evaluation unit evaluates user training effectiveness and provides feedback and improvement suggestions. The user training module is connected to user devices through a network interface to implement training course pushing and evaluation.

[0013] The system maintenance module includes a system monitoring unit and a system optimization unit. The system monitoring unit monitors the running state of the system in real time, identifies and solves system faults. The system optimization unit optimizes system performance based on system usage to improve system stability and efficiency. The system maintenance module is connected to the user training module through a data bus to ensure normal operation and continuous optimization of the system.

[0014] Preferably, the audit analysis module further includes an intelligent audit model generation unit, which includes a model training module, a model selection module, and a model deployment module. The model training module trains multiple audit models based on historical audit data and teaching data. The model selection module selects the most suitable audit model based on the nature and requirements of the audit task. The model deployment module deploys the selected audit model into the audit analysis module to realize intelligent audit analysis. The intelligent audit model generation unit is connected to the audit analysis module through a data bus to ensure real-time updating and optimization of the audit model.

[0015] Preferably, in the security management module, an intelligent risk assessment unit is further included, which comprises a risk identification module, a risk assessment module and a risk response module. The risk identification module is used to identify potential risk points in the audit process; the risk assessment module is used to assess the severity and impact range of the risk; and the risk response module is used to develop and implement risk response measures to reduce the impact of the risk. The intelligent risk assessment unit is connected with the security management module through a data bus, ensuring the security and reliability of the audit process.

[0016] Preferably, in the user training module, a virtual practical training platform is further included, which comprises a practical training environment generation module, a practical training task generation module and a practical training evaluation module. The practical training environment generation module is used to generate a virtual practical training environment according to the content of the vocational education course; the practical training task generation module is used to generate practical training tasks related to the course content; and the practical training evaluation module is used to evaluate the practical training performance of the user and provide feedback and improvement suggestions. The virtual practical training platform is connected with the user device through a network interface, providing an immersive learning experience.

[0017] Preferably, in the system maintenance module, an intelligent fault prediction unit is further included, which comprises a fault monitoring module, a fault prediction model training module and a fault response module. The fault monitoring module is used to monitor various indicators of the system in real time and identify potential fault points; the fault prediction model training module is used to train a fault prediction model based on historical data; and the fault response module is used to develop and implement fault response measures based on the prediction results of the fault prediction model. The intelligent fault prediction unit is connected with the system maintenance module through a data bus, ensuring the stable operation of the system and the timely handling of faults.

[0018] Preferably, the intelligent audit model generation unit is connected with the audit analysis module through a data bus to obtain the data of the audit task in real time, generate and optimize the audit model; the intelligent risk assessment unit is connected with the security management module through a data bus to obtain the data of the audit process in real time, assess and respond to risks; the virtual practical training platform is connected with the user device through a network interface to provide personalized practical training courses and evaluation; and the intelligent fault prediction unit is connected with the system maintenance module through a data bus to monitor the system status in real time, predict and respond to faults.

[0019] The structure, implementation and operation principle of the present application are as follows:

[0020] The data acquisition module: the teaching data acquisition unit, the student data acquisition unit and the management data acquisition unit are connected with various education platforms and devices through a network interface to acquire teaching, student and management data in real time. The data acquisition module transmits the collected data to the data processing module through a data bus for cleaning, standardization and integration.

[0021] Data processing module: data cleaning unit cleans the collected data to remove invalid and erroneous data; data standardization unit converts data from different sources into a unified format to ensure data compatibility; data integration unit integrates cleaned and standardized data into structured data. The data processing module transmits the processed data to the audit analysis module through the data bus to provide a basis for audit analysis.

[0022] Audit analysis module: teaching audit unit, student audit unit and management audit unit obtain processed data from data processing module through data bus, analyze teaching, student and management data respectively, evaluate teaching quality and effect, student learning status and progress, education management and resource use rationality, identify abnormalities and risks. The intelligent audit model generation unit generates and optimizes the audit model according to historical data and audit tasks to improve the accuracy and intelligence level of audit.

[0023] Security management module: data encryption unit encrypts sensitive data to ensure data security; data backup unit regularly backs up important data to prevent data loss; data monitoring unit monitors data usage in real time to identify and prevent data leakage and malicious access. The intelligent risk assessment unit obtains data of audit process from the audit analysis module through the data bus, identifies and assesses risks, formulates and executes risk response measures to ensure the safety and reliability of the audit process.

[0024] User training module: training course generation unit generates personalized training courses according to user needs and usage; training evaluation unit evaluates user training effectiveness and provides feedback and improvement suggestions. Virtual training platform generates virtual training environment and tasks to evaluate user training performance and provide immersive learning experience. The user training module connects with user devices through network interface to realize training course pushing and evaluation.

[0025] System maintenance module: system monitoring unit monitors system operation status in real time to identify and solve system faults; system optimization unit optimizes system performance according to system usage to improve system stability and efficiency. The intelligent fault prediction unit obtains system status data from the system monitoring unit through the data bus, trains the fault prediction model, predicts and responds to potential faults to ensure stable operation of the system. Beneficial effects

[0026] The beneficial effects of the present application are as follows: the intelligent audit model generation unit can generate and optimize the audit model according to the historical data and the audit task, significantly improve the accuracy and intelligent level of the audit, and effectively identify and prevent various risks in the vocational education. The intelligent risk assessment unit can identify and assess the risks in the audit process in real time, formulate and execute risk response measures, ensure the safety and reliability of the audit process, and effectively prevent data leakage and malicious access. The virtual training platform can generate personalized training environment and tasks, provide immersive learning experience, significantly improve the training effect and operation skills of users, and meet the needs of vocational education for advanced function training and user education. BRIEF DESCRIPTION OF DRAWINGS

[0027] Fig. 1 is a whole structure diagram of the vocational education audit system based on big data of the present application;

[0028] Fig. 2 is a detailed structure diagram of the data acquisition module;

[0029] Fig. 3 is a detailed structure diagram of the data processing module;

[0030] Fig. 4 is a detailed structure diagram of the audit analysis module;

[0031] Fig. 5 is a detailed structure diagram of the security management module;

[0032] Fig. 6 is a detailed structure diagram of the user training module and the system maintenance module;

[0033] In the drawings:

[0034] 1, data acquisition module; 11, teaching data acquisition unit; 12, student data acquisition unit; 13, management data acquisition unit;

[0035] 2, data processing module; 21, data cleaning unit; 22, data standardization unit; 23, data integration unit;

[0036] 3, audit analysis module; 31, teaching audit unit; 32, student audit unit; 33, management audit unit; 34, intelligent audit model generation unit; 341, model training module; 342, model selection module; 343, model deployment module;

[0037] 4, security management module; 41, data encryption unit; 42, data backup unit; 43, data monitoring unit; 44, intelligent risk assessment unit; 441, risk identification module; 442, risk assessment module; 443, risk response module;

[0038] 5, user training module; 51, training course generation unit; 52, training evaluation unit; 53, virtual training platform; 531, training environment generation module; 532, training task generation module; 533, training evaluation module;

[0039] 6、system maintenance module; 61、system monitoring unit; 62、system optimization unit; 63、intelligent fault prediction unit; 631、fault monitoring module; 632、fault prediction model training module; 633、fault response module. Embodiments of the present application

[0040] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application will be further described below in combination with specific embodiments.

[0041] The embodiment of the present application provides a vocational education auditing system based on big data, which solves the problems of insufficient function and performance of the existing big data auditing system in the application of vocational education field, inability to comprehensively cover the complex situation of vocational education, influence on auditing accuracy and comprehensiveness, and lack of optimization of specific needs of vocational education and user training module. The present application aims to provide a more comprehensive, accurate and intelligent vocational education auditing system to meet the needs of modern vocational education for efficient auditing and safety management.

[0042] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0043] Referring to FIGS. 1-6, a vocational education auditing system based on big data includes a data acquisition module 1, a data processing module 2, an auditing analysis module 3, a safety management module 4, a user training module 5 and a system maintenance module 6. The data acquisition module 1 is connected with the data processing module 2, the data processing module 2 is connected with the auditing analysis module 3, the auditing analysis module 3 is connected with the safety management module 4, the safety management module 4 is connected with the user training module 5, and the user training module 5 is connected with the system maintenance module 6.

[0044] Referring to FIG. 2, the data acquisition module 1 includes a teaching data acquisition unit 11, a student data acquisition unit 12 and a management data acquisition unit 13. The teaching data acquisition unit 11 is used for real-time acquisition of various data in the teaching process, including course content, teaching progress, teaching effect, etc.; the student data acquisition unit 12 is used for real-time acquisition of student learning behavior data, including learning progress, learning achievement, learning habit, etc.; the management data acquisition unit 13 is used for real-time acquisition of various data in the management process, including training plan, resource allocation, education evaluation, etc. The data acquisition module 1 is connected with various education platforms and devices through a network interface to realize real-time data transmission.

[0045] Referring to FIG. 3, the data processing module 2 includes a data cleaning unit 21, a data standardization unit 22, and a data integration unit 23. The data cleaning unit 21 is used to clean the collected data and remove invalid and erroneous data; the data standardization unit 22 is used to convert data from different sources into a unified format and ensure compatibility between different data; and the data integration unit 23 is used to integrate the cleaned and standardized data into structured data. The data processing module 2 is connected with the data acquisition module 1 through a data bus, ensuring real-time processing and transmission of data.

[0046] Referring to FIG. 4, the audit analysis module 3 includes a teaching audit unit 31, a student audit unit 32, and a management audit unit 33. The teaching audit unit 31 is used to analyze teaching data, evaluate teaching quality and effectiveness, and identify abnormalities and risks in the teaching process; the student audit unit 32 is used to analyze student data, evaluate students' learning status and progress, and identify problems and risks in students' learning; and the management audit unit 33 is used to analyze management data, evaluate the rationality of education management and resource use, and identify problems and risks in the management process. The audit analysis module 3 is connected with the data processing module 2 through a data bus, real-time access to processed data, and comprehensive analysis and audit.

[0047] Preferably, in the audit analysis module 3, an intelligent audit model generation unit 34 is also included, which includes a model training module 341, a model selection module 342, and a model deployment module 343. The model training module 341 is used to train multiple audit models according to historical audit data and teaching data; the model selection module 342 is used to select the most suitable audit model according to the nature and requirements of the audit task; and the model deployment module 343 is used to deploy the selected audit model to the audit analysis module 3, realizing intelligent audit analysis. The intelligent audit model generation unit 34 is connected with the audit analysis module 3 through a data bus, ensuring real-time updating and optimization of the audit model.

[0048] Referring to FIG. 5, the security management module 4 includes a data encryption unit 41, a data backup unit 42, and a data monitoring unit 43. The data encryption unit 41 is used to encrypt sensitive data to protect data security; the data backup unit 42 is used to regularly backup important data to prevent data loss; and the data monitoring unit 43 is used to monitor data usage in real time, identify and prevent data leakage and malicious access. The security management module 4 is connected with the audit analysis module 3 through a data bus, ensuring safe storage and transmission of audit results.

[0049] Preferably, in the security management module 4, an intelligent risk assessment unit 44 is also included, which comprises a risk identification module 441, a risk assessment module 442, and a risk response module 443. The risk identification module 441 is used to identify potential risk points in the audit process; the risk assessment module 442 is used to assess the severity and impact of the risk; the risk response module 443 is used to develop and implement risk response measures to reduce the impact of the risk. The intelligent risk assessment unit 44 is connected with the security management module 4 through a data bus, ensuring the security and reliability of the audit process.

[0050] Referring to FIG. 6, the user training module 5 comprises a training course generation unit 51 and a training evaluation unit 52. The training course generation unit 51 is used to generate personalized training courses according to the needs and usage of users; the training evaluation unit 52 is used to evaluate the training effect of users and provide feedback and improvement suggestions. The user training module 5 is connected with user devices through a network interface to realize the pushing and evaluation of training courses.

[0051] Preferably, in the user training module 5, a virtual practical training platform 53 is also included, which comprises a practical training environment generation module 531, a practical training task generation module 532, and a practical training evaluation module 533. The practical training environment generation module 531 is used to generate a virtual practical training environment according to the content of vocational education courses; the practical training task generation module 532 is used to generate practical training tasks related to the course content; the practical training evaluation module 533 is used to evaluate the practical training performance of users and provide feedback and improvement suggestions. The virtual practical training platform 53 is connected with user devices through a network interface to provide an immersive learning experience.

[0052] Referring to FIG. 6, the system maintenance module 6 comprises a system monitoring unit 61 and a system optimization unit 62. The system monitoring unit 61 is used to monitor the running state of the system in real time, identify and solve system failures; the system optimization unit 62 is used to optimize the performance of the system according to the usage of the system, and improve the stability and efficiency of the system. The system maintenance module 6 is connected with the user training module 5 through a data bus to ensure the normal operation and continuous optimization of the system.

[0053] Preferably, in the system maintenance module 6, an intelligent fault prediction unit 63 is also included, which comprises a fault monitoring module 631, a fault prediction model training module 632, and a fault response module 633. The fault monitoring module 631 is used to monitor various indicators of the system in real time to identify potential fault points; the fault prediction model training module 632 is used to train a fault prediction model according to historical data; the fault response module 633 is used to develop and implement fault response measures according to the prediction results of the fault prediction model. The intelligent fault prediction unit 63 is connected with the system maintenance module 6 through a data bus to ensure the stable operation of the system and the timely handling of faults.

[0054] The operating principle of the present application is as follows:

[0055] The teaching data acquisition unit 11, the student data acquisition unit 12, and the management data acquisition unit 13 are connected to various education platforms and devices through network interfaces and real-time collect teaching, student, and management data. These data are transmitted to the data processing module 2 through the data bus.

[0056] The data cleaning unit 21 cleans the collected data to remove invalid and erroneous data; the data standardization unit 22 converts data from different sources into a unified format; and the data integration unit 23 integrates the cleaned and standardized data into structured data. The processed data are transmitted to the audit analysis module 3 through the data bus.

[0057] The teaching audit unit 31, the student audit unit 32, and the management audit unit 33 analyze the teaching, student, and management data, respectively, to evaluate the teaching quality and effectiveness, the student's learning status and progress, the rationality of education management and resource use, and identify abnormalities and risks. The intelligent audit model generation unit 34 generates and optimizes audit models based on historical data and audit tasks to improve the accuracy and intelligence level of the audit.

[0058] The data encryption unit 41 encrypts sensitive data; the data backup unit 42 regularly backs up important data; and the data monitoring unit 43 monitors the use of data in real time. The intelligent risk assessment unit 44 identifies, assesses, and responds to risks to ensure the security and reliability of the audit process.

[0059] The training course generation unit 51 generates personalized training courses based on user needs; and the training evaluation unit 52 evaluates the training effectiveness of users. The virtual training platform 53 generates virtual training environments and tasks and evaluates the performance of users in the training.

[0060] The system monitoring unit 61 monitors the running state of the system in real time; and the system optimization unit 62 optimizes the performance of the system based on the use of the system. The intelligent fault prediction unit 63 monitors system indicators, trains fault prediction models, and develops countermeasures to ensure the stable operation of the system.

[0061] Specific application scenarios

[0062] Suppose a vocational school needs to conduct a comprehensive audit of teaching quality and student learning. First, the teaching data collection unit 11 collects various data in the teaching process in real time, such as course content and teaching progress; the student data collection unit 12 collects learning behavior data in real time, such as student learning progress and learning performance; the management data collection unit 13 collects various data in the management process in real time, such as training plans and resource allocation. These data are transmitted to the school's data center through the network interface and transmitted to the data processing module 2 through the data bus.

[0063] In the data processing module 2, the data cleaning unit 21 cleans up invalid and erroneous data; the data standardization unit 22 converts data from different sources into a unified format; the data integration unit 23 integrates the cleaned and standardized data into structured data. These processed data are transmitted to the audit analysis module 3 through the data bus.

[0064] In the audit analysis module 3, the teaching audit unit 31 analyzes various data in the teaching process, evaluates teaching quality and identifies abnormalities in the teaching process; the student audit unit 32 analyzes student learning behavior, evaluates student learning status and identifies learning problems; the management audit unit 33 analyzes various data in the management process, evaluates the rationality of education management and resource use and identifies problems in the management process. The intelligent audit model generation unit 34 trains multiple audit models according to historical audit data and selects the most suitable model for deployment.

[0065] In the security management module 4, the data encryption unit 41 encrypts sensitive teaching and student information; the data backup unit 42 regularly backs up important teaching and student information; the data monitoring unit 43 monitors the use of these information in real time. The intelligent risk assessment unit 44 identifies, assesses and responds to risk points to ensure the security and reliability of the audit process.

[0066] In the user training module 5, the training course generation unit 51 generates personalized training courses according to the needs of teachers; the training evaluation unit 52 evaluates the training effect of teachers and provides feedback. The virtual training platform 53 generates virtual teaching environments and tasks and evaluates the teaching performance of teachers.

[0067] In the system maintenance module 6, the system monitoring unit 61 monitors the running state of the system in real time; the system optimization unit 62 optimizes the performance according to the use of the system. The intelligent fault prediction unit 63 ensures the stable operation of the system by monitoring system indicators, training fault prediction models and developing countermeasures.

[0068] The above describes and shows the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and the description in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of the present application is defined by the appended claims and their equivalents.

[0069] As used in the specification and claims, certain terminology is used to describe features. Those of ordinary skill in the art will appreciate that one hardware manufacturer can use different names for the same feature. The specification and claims are not limited to the names specifically used to describe features, but include everything within the meaning of those names. As used throughout the specification and claims, "comprising" is to be read as "comprising, without limitation." "Approximately" means within an acceptable error range for the corresponding technical field. "Substantially" means within an acceptable error range for the corresponding technical field.

[0070] It should be noted that the terms "comprising," "including," and "having" or any other variation thereof, are intended to cover a non-exclusive inclusion. As such, a product or system that comprises a list of elements is not necessarily limited to only those elements, but can include other elements not expressly listed or inherent to such product or system. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the product or system that includes the element.

[0071] The above description shows and describes several preferred embodiments of the present application, but as mentioned above, it should be understood that the present application is not limited to the forms disclosed herein, and should not be considered as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the inventive concept described herein, by the above teachings or related art or knowledge. Any modification and change made by those skilled in the art without departing from the spirit and scope of the present application should be within the scope of protection of the appended claims of the present application.

Claims

1. A big data based vocational education auditing system, characterized in that, It comprises a data acquisition module (1), a data processing module (2), an audit analysis module (3), a security management module (4), a user training module (5) and a system maintenance module (6), the data acquisition module (1) is connected with the data processing module (2), the data processing module (2) is connected with the audit analysis module (3), the audit analysis module (3) is connected with the security management module (4), the security management module (4) is connected with the user training module (5), and the user training module (5) is connected with the system maintenance module (6).

2. The big data based vocational education auditing system as claimed in claim 1, wherein, The data acquisition module (1) comprises a teaching data acquisition unit (11), a student data acquisition unit (12) and a management data acquisition unit (13), the teaching data acquisition unit (11) is used for collecting various data in the teaching process in real time, the student data acquisition unit (12) is used for collecting student learning behavior data in real time, and the management data acquisition unit (13) is used for collecting various data in the management process in real time, and the data acquisition module (1) is connected with various education platforms and devices through a network interface, and realizes real-time data transmission.

3. The big data based vocational education auditing system as claimed in claim 1, wherein, The data processing module (2) comprises a data cleaning unit (21), a data standardization unit (22) and a data integration unit (23), the data cleaning unit (21) is used for cleaning the collected data, the data standardization unit (22) is used for converting data of different sources into a unified format, and the data integration unit (23) is used for integrating the cleaned and standardized data into structured data, and the data processing module (2) is connected with the data acquisition module (1) through a data bus, so as to ensure real-time processing and transmission of data.

4. The big data based vocational education auditing system as claimed in claim 1, wherein, The audit analysis module (3) comprises a teaching audit unit (31), a student audit unit (32) and a management audit unit (33), the teaching audit unit (31) is used for analyzing teaching data, the student audit unit (32) is used for analyzing student data, and the management audit unit (33) is used for analyzing management data, and the audit analysis module (3) is connected with the data processing module (2) through a data bus, so as to obtain the processed data in real time, and perform comprehensive analysis and audit.

5. The big data based vocational education auditing system as claimed in claim 4, wherein, The audit analysis module (3) further comprises an intelligent audit model generation unit (34), the intelligent audit model generation unit (34) comprises a model training module (341), a model selection module (342) and a model deployment module (343), the model training module (341) is used for training various audit models according to historical audit data and teaching data, the model selection module (342) is used for selecting the most suitable audit model according to the nature and requirements of the audit task, the model deployment module (343) is used for deploying the selected audit model into the audit analysis module (3), so as to realize intelligent audit analysis, and the intelligent audit model generation unit (34) is connected with the audit analysis module (3) through a data bus, so as to ensure real-time updating and optimization of the audit model.

6. The big data based vocational education auditing system as claimed in claim 1, wherein, The security management module (4) includes a data encryption unit (41), a data backup unit (42), and a data monitoring unit (43). The data encryption unit (41) is used for encrypting sensitive data. The data backup unit (42) is used for regularly backing up important data. The data monitoring unit (43) is used for real-time monitoring of data usage. The security management module (4) is connected with the audit analysis module (3) through a data bus, ensuring the safe storage and transmission of audit results.

7. The big data based vocational education auditing system as claimed in claim 6, wherein, The security management module (4) further includes an intelligent risk assessment unit (44), which includes a risk identification module (441), a risk assessment module (442), and a risk response module (443). The risk identification module (441) is used for identifying potential risk points in the audit process. The risk assessment module (442) is used for assessing the severity and impact range of risks. The risk response module (443) is used for developing and implementing risk response measures. The intelligent risk assessment unit (44) is connected with the security management module (4) through a data bus, ensuring the security and reliability of the audit process.

8. The big data based vocational education auditing system as claimed in claim 1, wherein, The user training module (5) includes a training course generation unit (51) and a training evaluation unit (52). The training course generation unit (51) is used to generate personalized training courses based on user needs and usage. The training evaluation unit (52) is used to evaluate user training effectiveness, provide feedback and improvement suggestions. The user training module (5) is connected with user devices through a network interface, realizing the pushing and evaluation of training courses.

9. The big data based vocational education auditing system as claimed in claim 8, wherein, The user training module (5) further includes a virtual practical training platform (53), which includes a practical training environment generation module (531), a practical training task generation module (532), and a practical training evaluation module (533). The practical training environment generation module (531) is used to generate a virtual practical training environment based on the content of vocational education courses. The practical training task generation module (532) is used to generate practical training tasks related to course content. The practical training evaluation module (533) is used to evaluate user practical training performance, provide feedback and improvement suggestions. The virtual practical training platform (53) is connected with user devices through a network interface, providing an immersive learning experience.

10. The big data based vocational education auditing system as claimed in claim 1, wherein, The system maintenance module (6) includes a system monitoring unit (61) and a system optimization unit (62). The system monitoring unit (61) is used to monitor the running state of the system in real time, identify and solve system faults. The system optimization unit (62) is used to optimize system performance based on system usage, improving system stability and efficiency. The system maintenance module (6) is connected with the user training module (5) through a data bus, ensuring the normal operation and continuous optimization of the system.

Images