Intelligent educational administration management system and method based on big data

Abstract

The invention discloses an intelligent educational administration management system and method based on big data, and relates to the technical field of educational administration management, and the method comprises the following steps: 1, setting a class educational administration analysis sequence of each class in the system; 2, periodically carrying out educational administration analysis on each class according to a class educational administration analysis sequence, and obtaining an educational administration analysis record of the class; 3, according to a comparison result between the educational administration evaluation value recorded by the educational administration analysis and the threshold value, whether the class is marked and the educational administration analysis sequence of the class is adjusted is judged; and 4, after the educational administration analysis is completed, intervening educational administration management on each class in sequence. According to the method, the educational administration analysis sequence can be reasonably set for each class, the educational administration management is adjusted according to different educational administration management analysis conditions, and the reasonability and adaptability of educational administration analysis and educational administration management are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of educational affairs management, and more specifically, to an intelligent educational affairs management system and method based on big data. Background Art

[0002] Academic management is a very important management area in schools. Its main responsibilities are to comprehensively plan, organize, coordinate, supervise and evaluate the teaching work of each class in the school. At present, schools will regularly review the teaching work of each class according to the teaching plan or teaching stage of each class. The review of teaching work needs to consider various academic management data, such as the class's teaching quality data and teaching resource data. The review and evaluation of teaching work is time-consuming and labor-intensive, and it is impossible to conduct accurate evaluation, which makes it impossible for schools to carry out effective academic management in a targeted manner. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent teaching management system and method based on big data.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] Intelligent teaching management system based on big data, including teaching sequence setting module, teaching sequence analysis module, teaching sequence adjustment module, and teaching management intervention module;

[0006] The teaching affairs sequence setting module is used to set the class teaching affairs analysis sequence of each class in the system;

[0007] The teaching affairs sequence analysis module is used to periodically perform teaching affairs analysis on each class according to the class teaching affairs analysis sequence, and obtain the teaching affairs analysis records of the class;

[0008] The teaching affairs order adjustment module is used to determine whether to mark a class and adjust the teaching affairs analysis order of the class according to the comparison result of the teaching affairs evaluation value recorded in the teaching affairs analysis with its threshold value;

[0009] The educational affairs management intervention module is used to intervene in the educational affairs management of each class in sequence after the educational affairs analysis is completed.

[0010] Furthermore, the class teaching affairs analysis order is obtained by the following method: obtaining the teaching affairs order value of each class in the system, sorting all classes from large to small according to the teaching affairs order value, and generating the class teaching affairs analysis order after sorting.

[0011] Furthermore, the class's teaching order value is obtained by the following method: obtain all the class's teaching analysis records before the current system time, sum up the teaching evaluation values ​​of all teaching analysis records and take the average value to obtain the average teaching evaluation value C mg, sort all the teaching affairs analysis records in the order of recording time, calculate the difference between the teaching affairs evaluation values ​​of two adjacent teaching affairs analysis records after sorting and take the absolute value to obtain the teaching affairs management fluctuation value, sum up all the teaching affairs management fluctuation values ​​and take the average value to obtain the teaching affairs management fluctuation mean value L kd , using the formula Sx = C mg *b1+L kd *b2 gets the class's academic affairs sequence value Sx, where b1 is the average academic affairs evaluation coefficient and b2 is the mean of the academic affairs management fluctuations.

[0012] Furthermore, the academic analysis records include class name, academic evaluation, and recording time.

[0013] Furthermore, the academic evaluation of the academic analysis record is obtained in the following way:

[0014] Obtain various teaching management data of the class, obtain teaching management models of various teaching management data, input various teaching management data into corresponding teaching management models, obtain teaching management values ​​of various teaching management data, sum up the teaching management values ​​of all teaching management data, obtain teaching management and value, and mark it as H jw , obtain the teaching management extreme values ​​of various teaching management data, compare the teaching management values ​​of the same teaching management data with the teaching management extreme value, when the teaching management value of the teaching management data is greater than the teaching management extreme value, mark the teaching management data as intervention teaching management data, when the teaching management value of the teaching management data is less than or equal to the teaching management extreme value, do not process it, obtain the knowledge graph of the teaching management data, compare all the intervention teaching management data with each other, when there is a correlation between two intervention teaching management data, calculate the difference between the teaching management values ​​of the two intervention teaching management data and take the absolute value, obtain the teaching management prominent value, and mark it as Dj, j = 1, 2, ..., J, when there is no correlation between the two intervention teaching management data, increase the number of independent interventions by one, set the teaching management prominent value coefficient to Ks, sum up all the independent intervention numbers, and obtain the total number of independent interventions A gs , using the formula Get academic evaluation G cw , where a1 is the educational management and value coefficient, a2 is the total number of independent intervention coefficients, and J is the total number of educational management highlights.

[0015] Further, according to the comparison result between the teaching affairs evaluation value recorded in the teaching affairs analysis and the threshold value thereof, it is determined whether to mark the class and adjust the teaching affairs analysis order of the class, specifically: setting the teaching affairs threshold evaluation value, when the teaching affairs evaluation value recorded in the teaching affairs analysis is greater than the teaching affairs threshold evaluation value, marking the class as a teaching affairs management intervention class, marking the remaining classes after the teaching affairs management intervention class in the class teaching affairs analysis order as order-adjusted classes, obtaining the order adjustment value of each order-adjusted class, sorting the order-adjusted classes in order from small to large according to the order adjustment value, and performing teaching affairs analysis on the order-adjusted classes in the sorted order;

[0016] When the teaching affairs evaluation value of the teaching affairs analysis record is less than or equal to the teaching affairs threshold evaluation value, the teaching affairs analysis is performed on the next class in the order of class teaching affairs analysis.

[0017] Furthermore, the order adjustment value of the order-adjusted class is obtained by the following method: obtain m consecutive academic analysis records of the class with academic management intervention before the current system time, obtain m consecutive academic analysis records of the order-adjusted class before the current system time, calculate the difference between the academic evaluation value of the academic analysis record of the class with academic management intervention in the same period and the academic evaluation value of the academic analysis record of the order-adjusted class, and obtain the academic gap value Bi, i = 1, 2, ..., m, set the academic gap value coefficient to Fr, and use the formula Get the order adjustment value E of the order adjustment class zc .

[0019] Furthermore, after the academic affairs analysis is completed, the academic affairs management is intervened for each class in sequence. Specifically, when the academic affairs analysis of all classes in the current cycle is completed, the academic affairs evaluation of each class is obtained, and the classes are sorted in descending order according to the academic affairs evaluation value, and the academic affairs management is intervened for each class in the sorted order.

[0020] Furthermore, the intelligent teaching management method based on big data includes the following steps:

[0021] Step 1: Set the order of class academic analysis for each class in the system;

[0022] Step 2: Periodically conduct academic affairs analysis on each class according to the class academic affairs analysis sequence, and obtain the class academic affairs analysis records;

[0023] Step 3: Based on the comparison result of the teaching evaluation value recorded in the teaching analysis with the threshold value, determine whether to mark the class and adjust the teaching analysis order of the class;

[0024] Step 4: After completing the academic analysis, intervene in the academic management of each class in order.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The method of the present invention can rationally set the order of teaching affairs analysis for each class, adjust the teaching affairs management according to different teaching affairs management analysis situations, and ensure the rationality and adaptability of teaching affairs analysis and teaching affairs management;

[0027] 2. Set up the teaching affairs sequence setting module, which can set a reasonable teaching affairs analysis sequence according to the teaching affairs management situation of each class. Set up the teaching affairs sequence analysis module, teaching affairs sequence adjustment module and teaching affairs management intervention module, which can conduct in-depth and comprehensive analysis of the class's teaching affairs management data. Through the analysis of the teaching affairs evaluation, it can be determined whether it is necessary to intervene in the teaching affairs management of the class, and the subsequent class analysis sequence can be flexibly adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of the intelligent teaching management method based on big data;

[0029] Figure 2 A flowchart for determining whether to mark a class and adjusting the order of class academic analysis;

[0030] Figure 3 This is a flowchart for intervening in academic management for each class in sequence. DETAILED DESCRIPTION

[0031] Example 1

[0032] Reference Figure 1 , the intelligent teaching management method based on big data includes the following steps:

[0033] Step 1: Set the order of class academic analysis for each class in the system;

[0034] Step 2: Periodically conduct academic affairs analysis on each class according to the class academic affairs analysis sequence, and obtain the class academic affairs analysis records;

[0035] Step 3: Based on the comparison result of the teaching evaluation value recorded in the teaching analysis with the threshold value, determine whether to mark the class and adjust the teaching analysis order of the class;

[0036] Step 4: After completing the academic analysis, intervene in the academic management of each class in order.

[0037] The method of the present invention can perform reasonable teaching management analysis on each class and adjust the teaching management according to different teaching management analysis situations.

[0038] Example 2

[0039] Reference Figure 2-Figure 3, an intelligent teaching affairs management system based on big data, including teaching affairs sequence setting module, teaching affairs sequence analysis module, teaching affairs sequence adjustment module, and teaching affairs management intervention module.

[0040] Academic affairs sequence setting module: set the order of class academic affairs analysis for each class in the system.

[0041] The class teaching affairs analysis order is obtained in the following way: obtain the teaching affairs order value of each class in the system, sort all classes from large to small according to the teaching affairs order value, and generate the class teaching affairs analysis order after sorting.

[0042] The class's teaching order value is obtained by the following method: obtain all the class's teaching analysis records before the current system time, sum up the teaching evaluation values ​​of all teaching analysis records and take the average value to obtain the average teaching evaluation value C mg , sort all the teaching affairs analysis records in the order of recording time, calculate the difference between the teaching affairs evaluation values ​​of two adjacent teaching affairs analysis records after sorting and take the absolute value to obtain the teaching affairs management fluctuation value, sum up all the teaching affairs management fluctuation values ​​and take the average value to obtain the teaching affairs management fluctuation mean value L kd , using the formula Sx = C mg *b1+L kd *b2 gets the class's academic affairs sequence value Sx, where b1 is the average academic affairs evaluation coefficient, b2 is the mean of the academic affairs management fluctuations, the value of b1 is 0.44, and the value of b2 is 0.51.

[0043] By setting up the teaching order setting module, you can set a reasonable teaching analysis order according to the teaching management situation of each class to ensure reasonable teaching analysis for each class.

[0044] Academic affairs sequence analysis module: periodically conducts academic affairs analysis on each class in the order of class academic affairs analysis (for example, if the order of class academic affairs analysis is class A-class C-class D-class B, then when the cycle node is reached, the class academic affairs analysis is conducted in the above order, and class A is analyzed first. The cycle can be adjusted and modified according to the teaching plan or teaching stage in the academic affairs management). When conducting academic affairs analysis on a class, an academic affairs analysis record of the class is generated, which includes the class name, academic affairs evaluation, and recording time.

[0045] The academic evaluation of the academic analysis record is obtained in the following ways:

[0046] Obtain various teaching management data of the class (teaching management involves various teaching management data, including but not limited to teaching quality data, teaching resource data, student affairs management data, and teaching plan data. The method of obtaining various teaching management data is not the focus of this application), obtain teaching management models of various teaching management data (each teaching management data corresponds to a teaching management model, such as teaching quality data corresponds to a teaching management model, and teaching resource data corresponds to a teaching management model), input various teaching management data into the corresponding teaching management model, obtain teaching management values ​​of various teaching management data, sum up the teaching management values ​​of all teaching management data, obtain teaching management and value, and mark it as H jw , obtain the teaching management extreme values ​​of various teaching management data (each teaching management data corresponds to a teaching management value, each teaching management value corresponds to a teaching management extreme value, the teaching management extreme value is the system preset value, and each teaching management extreme value must be greater than 0), compare the teaching management value of the same teaching management data with the teaching management extreme value, when the teaching management value of the teaching management data is greater than the teaching management extreme value, mark the teaching management data as intervening teaching management data, when the teaching management value of the teaching management data is less than or equal to the teaching management extreme value, do not process it, obtain the knowledge graph of the teaching management data, compare all the intervening teaching management data pairwise, when there is a correlation between two intervening teaching management data When there is a correlation, the difference between the teaching and management values ​​of the two intervention teaching and management data is calculated and the absolute value is taken to obtain the teaching and management prominent value, which is marked as Dj, j = 1, 2, ..., J. When there is no correlation between the two intervention teaching and management data, the number of independent interventions is increased by one, and the teaching and management prominent value coefficient is set to Ks, s = 1, 2, 3, ... s; K1 < K2 < K3 < ... < Ks, each teaching and management prominent value coefficient corresponds to a teaching and management prominent value within a range, such as [0, D1], (D1, D2], ..., (Dj-1, DJ], such as Dj∈[0, D1], the teaching and management prominent value coefficient is K1, all independent intervention numbers are summed up to obtain the total number of independent interventions A gs , using the formula Get academic evaluation G cw , where a1 is the coefficient of educational management and value, a2 is the coefficient of the total number of independent interventions, J is the total number of educational management prominent values, the value of a1 is 0.78, and the value of a1 is 0.92.

[0047] Construct a knowledge graph of the educational management data. Various educational management data are represented in the form of a knowledge graph to show their relationships with each other. When two educational management data are associated in the knowledge graph, it means that the two educational management data have a mutual influence relationship. For example, a decrease or increase in teaching resources will affect the teaching quality, and the teaching quality can indirectly provide feedback on the teaching resources. Therefore, the teaching resource data is associated with the teaching quality data.

[0048] Example: The teaching management data includes but is not limited to teaching quality data, teaching resource data, student affairs management data, and teaching plan data. In this embodiment, taking the teaching quality data as an example, the teaching management model training method of the public teaching quality data is as follows: a plurality of teaching quality data are collected, and the collected teaching quality data are all used for model training. The collected teaching quality data include real teaching quality data and virtual teaching quality data. The set ratio of the real teaching quality data to the virtual teaching quality data is 4:6, and the set ratio can be modified according to actual needs. Feature extraction is performed on each teaching quality data to obtain a plurality of teaching quality feature data, and a deep learning model is constructed. The teaching quality feature data is used as training data for the deep learning model, and a teaching management value is assigned to the training data. The training data is divided into a training set and a validation set according to a preset ratio. The preset ratio can be adjusted according to actual needs. The training set and the validation set are iteratively trained to obtain a teaching management model for the teaching quality data, wherein the value range of the teaching management value is (-1 to 1), and the larger the value of the teaching management value is, the less the teaching quality data does not meet the teaching management standard, and the smaller the value of the teaching management value is, the more the teaching quality data meets the teaching management standard.

[0049] Academic affairs order adjustment module: Set the academic affairs threshold evaluation value. The academic affairs threshold evaluation value is the threshold set by the system and can be modified according to actual needs. When the academic affairs evaluation value recorded in the academic affairs analysis is greater than the academic affairs threshold evaluation value, the class will be marked as an academic affairs management intervention class, and the remaining classes after the academic affairs management intervention class in the class academic affairs analysis order will be marked as order-adjusted classes. The order adjustment value of each order-adjusted class is obtained, and the order-adjusted classes are sorted in order from small to large according to the order adjustment value, and the order-adjusted classes are subjected to academic affairs analysis in the sorted order.

[0050] When the teaching affairs evaluation value of the teaching affairs analysis record is less than or equal to the teaching affairs threshold evaluation value, the teaching affairs analysis is performed on the next class in the order of class teaching affairs analysis.

[0051] Educational affairs management intervention module: When the educational affairs analysis of all classes in the current cycle is completed, the educational affairs evaluation of each class is obtained, and the classes are sorted in descending order according to the educational affairs evaluation value, and educational affairs management is intervened for each class in the sorting order (for example, if the sorting order is class C, class B, and class A, then class C will be intervened in educational affairs management first).

[0052] The order adjustment value of the order-adjusted class is obtained by the following method: obtain m consecutive academic analysis records of the class with academic management intervention before the current time of the system, obtain m consecutive academic analysis records of the order-adjusted class before the current time of the system, calculate the difference between the academic evaluation value of the academic analysis record of the class with academic management intervention in the same period and the academic evaluation value of the academic analysis record of the order-adjusted class, and obtain the academic gap value Bi, i=1, 2, ..., m, set the academic gap value coefficient to Fr, r=1, 2, 3, ...r; F1<F2<F3<...<Fr, each academic gap value coefficient corresponds to an academic gap value within a range, such as [0, B1], (B1, B2], ..., (B i-1, Bm], such as Bi∈[0, B1], the academic gap value coefficient is F1, and the formula is used Get the order adjustment value E of the order adjustment class zc .

[0054] By setting up the teaching affairs sequence analysis module, teaching affairs sequence adjustment module and teaching affairs management intervention module, you can conduct an in-depth and comprehensive analysis of the class's teaching affairs management data. By analyzing the teaching affairs evaluation, you can determine whether it is necessary to intervene in the teaching affairs management of the class, and flexibly adjust the subsequent class analysis sequence. You can adjust the teaching affairs management according to different teaching affairs management analysis situations.

[0055] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0056] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0057] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0058] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0061] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0062] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent education management system based on big data, characterized by: It includes the teaching affairs sequence setting module, teaching affairs sequence analysis module, teaching affairs sequence adjustment module and teaching affairs management intervention module; The teaching affairs sequence setting module is used to set the class teaching affairs analysis sequence of each class in the system; The teaching affairs sequence analysis module is used to periodically perform teaching affairs analysis on each class according to the class teaching affairs analysis sequence, and obtain the teaching affairs analysis records of the class; The teaching affairs order adjustment module is used to determine whether to mark a class and adjust the teaching affairs analysis order of the class according to the comparison result of the teaching affairs evaluation value recorded in the teaching affairs analysis with its threshold value; The educational affairs management intervention module is used to intervene in the educational affairs management of each class in sequence after the educational affairs analysis is completed.

2. The intelligent education management system based on big data according to claim 1 is characterized in that: The class teaching affairs analysis order is obtained in the following way: obtain the teaching affairs order value of each class in the system, sort all classes from large to small according to the teaching affairs order value, and generate the class teaching affairs analysis order after sorting.

3. The intelligent education management system based on big data according to claim 2 is characterized in that: The class's teaching order value is obtained by the following method: obtain all the class's teaching analysis records before the current system time, sum up the teaching evaluation values ​​of all teaching analysis records and take the average value to obtain the average teaching evaluation value C mg , sort all the teaching affairs analysis records in the order of recording time, calculate the difference between the teaching affairs evaluation values ​​of two adjacent teaching affairs analysis records after sorting and take the absolute value to obtain the teaching affairs management fluctuation value, sum up all the teaching affairs management fluctuation values ​​and take the average value to obtain the teaching affairs management fluctuation mean value L kd , using the formula Sx = C mg *b1+L kd *b2 gets the class's academic affairs sequence value Sx, where b1 is the average academic affairs evaluation coefficient and b2 is the mean of the academic affairs management fluctuations.

4. The intelligent education management system based on big data according to claim 1 is characterized in that: The academic analysis records include class name, academic evaluation value, and recording time.

5. The intelligent education management system based on big data according to claim 4 is characterized in that: The academic evaluation of the academic analysis record is obtained in the following ways: Obtain various teaching management data of the class, obtain teaching management models of various teaching management data, input various teaching management data into corresponding teaching management models, obtain teaching management values ​​of various teaching management data, sum up the teaching management values ​​of all teaching management data, obtain teaching management and value, and mark it as H jw , obtain the teaching management extreme values ​​of various teaching management data, compare the teaching management values ​​of the same teaching management data with the teaching management extreme value, when the teaching management value of the teaching management data is greater than the teaching management extreme value, mark the teaching management data as intervention teaching management data, when the teaching management value of the teaching management data is less than or equal to the teaching management extreme value, do not process it, obtain the knowledge graph of the teaching management data, compare all the intervention teaching management data with each other, when there is a correlation between two intervention teaching management data, calculate the difference between the teaching management values ​​of the two intervention teaching management data and take the absolute value, obtain the teaching management prominent value, and mark it as Dj, j = 1, 2, ..., J, when there is no correlation between the two intervention teaching management data, increase the number of independent interventions by one, set the teaching management prominent value coefficient to Ks, sum up all the independent intervention numbers, and obtain the total number of independent interventions A gs , using the formula Get academic evaluation G cw , where a1 is the educational management and value coefficient, a2 is the total number of independent intervention coefficients, and J is the total number of educational management highlights.

6. The intelligent education management system based on big data according to claim 1 is characterized in that: According to the comparison result between the teaching affairs evaluation value recorded in the teaching affairs analysis and its threshold, determine whether to mark the class and adjust the teaching affairs analysis order of the class, specifically: set the teaching affairs threshold evaluation value, when the teaching affairs evaluation value recorded in the teaching affairs analysis is greater than the teaching affairs threshold evaluation value, mark the class as a teaching affairs management intervention class, mark the remaining classes after the teaching affairs management intervention class in the class teaching affairs analysis order as order-adjusted classes, obtain the order adjustment value of each order-adjusted class, sort the order-adjusted classes in order from small to large according to the order adjustment value, and perform teaching affairs analysis on the order-adjusted classes in the sorted order; When the teaching affairs evaluation value of the teaching affairs analysis record is less than or equal to the teaching affairs threshold evaluation value, the teaching affairs analysis is performed on the next class in the order of class teaching affairs analysis.

7. The intelligent education management system based on big data according to claim 6 is characterized in that: The order adjustment value of the order adjustment class is obtained by the following method: obtain m consecutive academic analysis records of the class with academic management intervention before the current system time, obtain m consecutive academic analysis records of the order adjustment class before the current system time, calculate the difference between the academic evaluation value of the academic analysis record of the class with academic management intervention in the same period and the academic evaluation value of the academic analysis record of the order adjustment class, and obtain the academic gap value Bi, i = 1, 2, ..., m, set the academic gap value coefficient to Fr, and use the formula Get the order adjustment value E of the order adjustment class zc .

8. The intelligent education management system based on big data according to claim 1 is characterized in that: After the academic affairs analysis is completed, the academic affairs management is intervened for each class in order. Specifically, when the academic affairs analysis of all classes in the current cycle is completed, the academic affairs evaluation of each class is obtained, and the classes are sorted in descending order according to the academic affairs evaluation value, and the academic affairs management is intervened for each class in the sorted order.

9. An intelligent teaching affairs management method based on big data, applied to the intelligent teaching affairs management system based on big data as claimed in claim 1, characterized in that: The steps include: Step 1: Set the order of class academic analysis for each class in the system; Step 2: Periodically conduct academic affairs analysis on each class according to the class academic affairs analysis sequence, and obtain the class academic affairs analysis records; Step 3: Based on the comparison result of the teaching evaluation value recorded in the teaching analysis with the threshold value, determine whether to mark the class and adjust the teaching analysis order of the class; Step 4: After completing the academic analysis, intervene in the academic management of each class in order.

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