Education platform operation data intelligent analysis management system

Abstract

The invention, which relates to the technical field of data analysis, discloses an intelligent analysis and management system for operation data of an education platform, comprising a data sampling module, a data processing module, an after-class achievement detection module, a data analysis module, a user login module, and a user information reading module. According to the intelligent analysis and management system for the operation data of the education platform, through the arrangement of the after-class achievement detection module, an after-class detection result of a user can be used as an operation result detection standard of the education platform, so that the detection stability of the education platform is improved; meanwhile, by arranging the detection content analysis module, the mastering degree of the user on the course knowledge can be detected, and by detecting the mastering degree of the user on the course knowledge, the marking accuracy of the education platform on the knowledge in the operation process can be obtained; the operation accuracy of the education platform can be improved by ensuring the marking accuracy of the knowledge in the operation process of the education platform.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent analysis and management system for operation data of an education platform. Background Art

[0002] In modern education management systems, with the diversification of teaching resources and the continuous expansion of metadata, the dynamic changes and complex correlations of data in time and space dimensions have become increasingly significant. As a core element in education management systems, resource metadata records key information such as the creation time, frequency of use, and author information of educational resources. However, due to the dynamic nature and diversity of metadata, it is prone to corrosion effects during data processing and long-term storage, that is, metadata may lose information, be distorted, or have weakened correlation over time or after multiple processing. This corrosion effect is often hidden in multi-level time and space dimensions and complex geometric structures, posing a major challenge to the stability and data integrity of education management systems.

[0003] Nowadays, there are many types of education platforms. If we cannot accurately analyze the operation of education platforms, it will seriously hinder the development of education. The existing control of the operation of education platforms either does not analyze or analyzes the operation of education platforms at a single level. The way of analyzing the operation of education platforms is one-sided and inaccurate. It is difficult to accurately judge the stability of the operation of education platforms and point out the shortcomings of the operation of education platforms, which hinders the development prospects of education platforms. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent analysis and management system for operation data of an education platform to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: an education platform operation data intelligent analysis and management system, comprising:

[0006] A data sampling module, which is used to start the data collection process according to preset data collection rules and preset data collection conditions and through a data collection program, and collect different learning knowledge according to different collection programs;

[0007] A data processing module, which is used to mine the learning knowledge collected by the data sampling module and to mark the mined learning knowledge through a data marking model, wherein the marking includes: key explanation content, self-study content, and after-class review content;

[0008] After-class achievement detection module, the after-class achievement detection module is used to detect the after-class learning achievements and judge the operation effect of the current education platform, save the data processing methods of the teaching modules with qualified operation effects, and send the data processing methods of the data processing modules with unqualified operation effects to the data analysis module;

[0009] A data analysis module, the data analysis module is used to receive the data processing method of the data processing module with unqualified operation results sent by the after-class achievement detection module, and compare the learning knowledge with different tags in the data processing module with the after-class learning achievement detection results through the data comparison model, and analyze the education platform operation data based on the comparison results and through the data analysis model;

[0010] A user login module, which is used for users to log in to the education platform for learning and send user information to the user information reading module;

[0011] A user information reading module, which is used to read the user information sent by the user login module and read the data collection rules and data collection conditions corresponding to the user in the database module;

[0012] The data sampling module is also used to start the data collection process through the data collection program according to the design data collection rules and preset data collection conditions read by the user information reading module, and collect different learning knowledge according to different user learning abilities.

[0013] Furthermore, the data sampling module includes:

[0014] A data collection rule preset module, which is used to receive and store data collection rules preset by management personnel;

[0015] A data collection condition preset module, which is used to receive and store data collection conditions preset by management personnel;

[0016] The data acquisition module is used to start the data acquisition process through a data acquisition program based on the preset data acquisition rules stored in the data acquisition rule preset module and the preset data acquisition conditions stored in the data acquisition condition preset module.

[0017] Furthermore, the data sampling module also includes:

[0018] A learning ability analysis module, which is used to analyze the learning ability of the logged-in user through a learning ability analysis model, and based on the user's learning ability, to modify the preset data collection rules and preset data collection conditions through a data correction model, thereby obtaining receiving user data collection rules and user data collection conditions respectively;

[0019] A data collection rule receiving module, wherein the data collection rule receiving module is used to receive user data collection rules;

[0020] A data collection condition receiving module, wherein the data collection condition receiving module is used to receive user data collection conditions;

[0021] The data collection module is also used to start the data collection process through the data collection program based on the user data collection rules received by the data collection rule receiving module and the user data collection conditions received by the data collection condition receiving module.

[0022] Furthermore, the data processing module includes:

[0023] A knowledge mining module, which is used to mine the learning content required by the user based on the learning knowledge collected by the data sampling module and through the knowledge mining model;

[0024] A data classification module, which is used to classify the learning content required by the user mined by the knowledge mining module through a data classification model, wherein the classification includes the previous chapter course knowledge, the current chapter course knowledge, the next chapter course knowledge, and extracurricular interest knowledge;

[0025] Data association analysis module, which is used to analyze the data association C of the course knowledge in this chapter and the previous chapter based on the data association analysis model, and preset the association threshold C 1 and C 2 , and C 1 <C 2 , where C <C 1 The knowledge in this chapter is marked as the key points to be explained. 1 <C<C 2 The knowledge in this chapter is marked as after-class review content, C>C 2 The knowledge in this chapter is marked as self-study content;

[0026] The data association analysis module is also used to analyze the data association D between the knowledge in this chapter and the next chapter based on the data association analysis model, and preset the association threshold D 1 and D 2 , and D 1 <D 2 , where D <D 1The knowledge in this chapter is marked as the key points to be explained. 1 <D<D 2 The knowledge in this chapter is marked as after-class review content, D>D 2 The knowledge in this chapter is marked as independent learning content.

[0027] Furthermore, the after-class achievement detection module includes:

[0028] A user score collection module, which is used to collect the user's after-class test scores;

[0029] A test content analysis module, which is used to analyze the mastery of different knowledge points in the user's after-class test scores;

[0030] A data comparison module, which is used to compare the user's mastery of different knowledge points obtained in the detection content analysis module with the marks of the corresponding knowledge points by the data processing module, wherein the user's mastery of different knowledge points according to different knowledge point marks is ranked from high to low in the following order: key explanation content > after-class review content > independent learning content;

[0031] A data re-marking module, wherein the data re-marking module is used to re-mark the marks of the knowledge points with different comparison results in the data comparison module;

[0032] An education platform operation analysis module, wherein the education platform operation analysis module is used to use the accuracy rate in the data comparison results as the platform operation analysis standard, preset the platform operation analysis threshold, mark the education platform operation results whose accuracy rate in the data comparison results is higher than the platform operation analysis threshold as normal operation, and mark the education platform operation results whose accuracy rate in the data comparison results is lower than the platform operation analysis threshold as abnormal operation.

[0033] Furthermore, the detection content analysis module includes the following working steps:

[0034] A1, taking the activation function Tanh, the user's mastery of different knowledge points in the previous t-1 tests can be expressed as:

[0035]

[0036] Among them A t-1 Indicates the user's mastery of different knowledge points in the previous t-1 tests, v t-1 Is the answer sequence X t-1 The embedding vector representation of A and b A They are the weight matrix and bias vector of the fully connected layer respectively;

[0037] A2, using the relevant weight matrix wt To calculate the relationship between the students' mastery of knowledge points and the knowledge concepts tested in this after-class test, the expression is:

[0038] A t-1 =w t (i)*A t-1 ;

[0039] A3, calculate the student's mastery of knowledge points and the input vector k t Relationship

[0040] A′ t-1 =A t-1 *k t ;

[0041] A4, read the relevant knowledge state r from the value memory matrix t Combined with the students’ mastery of relevant knowledge, they are input into a fully connected layer with a Than activation function to calculate the new joint vector f′ t , and the joint vector f′ t As the updated students’ mastery of knowledge points, the formula is as follows:

[0042]

[0043] Further, the following steps are included:

[0044] S1, the user logs in to the education platform through the user login module;

[0045] S2, the user information reading module reads the user information logged in in step S1, and reads the data collection rules and data collection conditions of the corresponding user in the database. If the data collection rules and data collection conditions of the corresponding user are not read, step S31 is executed; if the data collection rules and data collection conditions of the corresponding user are not read, step S32 is executed;

[0046] S31, the data sampling module starts the data collection process according to the preset data collection rules and preset data collection conditions and through the data collection program;

[0047] S32, the data sampling module analyzes the learning ability of the logged-in user through the learning ability analysis model, and based on the user's learning ability, the preset data collection rules and preset data collection conditions are respectively corrected through the data correction model, and the data collection process is started through the data collection program according to the preset data collection rules and preset data collection conditions read by the user information reading module;

[0048] S4, the data processing module mines the learning knowledge collected by the data sampling module, and performs data labeling on the mined learning knowledge through the data labeling model;

[0049] S5, the after-class achievement detection module detects the after-class learning achievements and judges the operation effect of the current education platform, saves the data processing method of the teaching module with qualified operation effect, and stores the after-class test results of the corresponding user in the database as reference data for learning ability analysis when the user logs in next time;

[0050] S6, the education platform operation analysis module is used to use the accuracy rate in the data comparison results as the platform operation analysis standard, preset the platform operation analysis threshold, mark the education platform operation results whose accuracy rate in the data comparison results is higher than the platform operation analysis threshold as normal operation, and mark the education platform operation results whose accuracy rate in the data comparison results is lower than the platform operation analysis threshold as abnormal operation.

[0051] Compared with the prior art, the present invention provides an intelligent analysis and management system for operation data of an education platform. Through the setting of an after-class achievement detection module, the user's after-class detection results can be used as the operation result detection standard of the education platform, thereby increasing the stability of the education platform detection. At the same time, through the setting of the detection content analysis module, the user's mastery of course knowledge can be detected. By detecting the user's mastery of course knowledge, the accuracy of the education platform's marking of knowledge during operation can be obtained. By ensuring the accuracy of the education platform's marking of knowledge during operation, the accuracy of the education platform's operation can be increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 A system structure block diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0056] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0058] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0059] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0060] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0061] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of idealized schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings have schematic properties, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0062] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0063] See also Figure 1 , an education platform operation data intelligent analysis and management system, comprising:

[0064] The data sampling module is used to start the data collection process according to the preset data collection rules and preset data collection conditions and through the data collection program, and collect different learning knowledge according to different collection programs;

[0065] Data processing module: The data processing module is used to mine the learning knowledge collected by the data sampling module and mark the mined learning knowledge through the data marking model. The marking includes: key explanation content, self-study content and after-class review content;

[0066] After-class achievement detection module, which is used to detect after-class learning achievements and judge the operation effect of the current education platform, save the data processing methods of the teaching modules with qualified operation effects, and send the data processing methods of the data processing modules with unqualified operation effects to the data analysis module;

[0067] The data analysis module is used to receive the data processing method of the data processing module with unqualified operation results sent by the after-class achievement detection module, and compare the learning knowledge with different tags in the data processing module with the after-class learning achievement detection results through the data comparison model, and analyze the operation data of the education platform based on the comparison results and through the data analysis model;

[0068] User login module, which is used for users to log in to the education platform for learning and send user information to the user information reading module;

[0069] User information reading module, which is used to read the user information sent by the user login module and read the data collection rules and data collection conditions corresponding to the user in the database module;

[0070] The data sampling module is also used to start the data collection process through the data collection program according to the design data collection rules and preset data collection conditions read by the user information reading module, and collect different learning knowledge according to different user learning abilities.

[0071] The data sampling module includes:

[0072] A data collection rule preset module, which is used to receive and store data collection rules preset by management personnel;

[0073] A data collection condition preset module, which is used to receive and store data collection conditions preset by management personnel;

[0074] The data acquisition module is used to start the data acquisition process through the data acquisition program based on the preset data acquisition rules stored in the data acquisition rule preset module and the preset data acquisition conditions stored in the data acquisition condition preset module.

[0075] The data sampling module also includes:

[0076] The learning ability analysis module is used to analyze the learning ability of the logged-in user through the learning ability analysis model, and based on the user's learning ability, the preset data collection rules and preset data collection conditions are respectively modified through the data correction model, so as to obtain the receiving user data collection rules and user data collection conditions respectively;

[0077] A data collection rule receiving module, which is used to receive user data collection rules;

[0078] A data collection condition receiving module, which is used to receive user data collection conditions;

[0079] The data collection module is also used to start the data collection process through the data collection program based on the user data collection rules received by the data collection rule receiving module and the user data collection conditions received by the data collection condition receiving module.

[0080] The data processing module includes:

[0081] The knowledge mining module is used to mine the learning content required by users based on the learning knowledge collected by the data sampling module and through the knowledge mining model;

[0082] Data classification module: The data classification module is used to classify the learning content required by users based on the learning content mined by the knowledge mining module, and the learning content is classified into the previous chapter course knowledge, the current chapter course knowledge, the next chapter course knowledge and extracurricular interest knowledge through the data classification model;

[0083] Data association analysis module: The data association analysis module is used to analyze the correlation C between the knowledge in this chapter and the data in the previous chapter based on the data association analysis model, and preset the correlation threshold C 1 and C 2 , and C 1 <C 2 , where C <C 1 The knowledge in this chapter is marked as the key points to be explained. 1 <C<C 2 The knowledge in this chapter is marked as after-class review content, C>C 2 The knowledge in this chapter is marked as self-study content;

[0084] The data association analysis module is also used to analyze the data association D between the knowledge in this chapter and the data in the next chapter based on the data association analysis model, and preset the association threshold D 1 and D 2 , and D 1 <D 2 , where D <D 1 The knowledge in this chapter is marked as the key points to be explained. 1 <D<D 2 The knowledge in this chapter is marked as after-class review content, D>D 2 The knowledge in this chapter is marked as independent learning content.

[0085] The after-class achievement test modules include:

[0086] User score collection module, which is used to collect user's after-class test scores;

[0087] The test content analysis module is used to analyze the mastery of different knowledge points in the user's after-class test results;

[0088] The data comparison module is used to compare the user's mastery of different knowledge points obtained from the detection content analysis module with the marks of the corresponding knowledge points by the data processing module, wherein the mastery of different knowledge points by users according to different knowledge point marks is ranked from high to low as follows: key explanation content > after-class review content > self-study content;

[0089] The data re-labeling module is used to re-label the labels of knowledge points with different comparison results in the data comparison module;

[0090] The education platform operation analysis module is used to use the accuracy rate in the data comparison results as the platform operation analysis standard, preset the platform operation analysis threshold, mark the education platform operation results whose accuracy rate in the data comparison results is higher than the platform operation analysis threshold as normal operation, and mark the education platform operation results whose accuracy rate in the data comparison results is lower than the platform operation analysis threshold as abnormal operation.

[0091] The detection content analysis module includes the following working steps:

[0092] A1, taking the activation function Tanh, the user's mastery of different knowledge points in the previous t-1 tests can be expressed as:

[0093]

[0094] Among them A t-1 Indicates the user's mastery of different knowledge points in the previous t-1 tests, v t-1 Is the answer sequence X t-1 The embedding vector representation of A and b A They are the weight matrix and bias vector of the fully connected layer respectively;

[0095] A2, using the relevant weight matrix w t To calculate the relationship between the students' mastery of knowledge points and the knowledge concepts tested in this after-class test, the expression is:

[0096] A t-1 =w t (i)*A t-1 ;

[0097] A3, calculate the student's mastery of knowledge points and the input vector k t Relationship

[0098] A′ t-1 =A t-1 *k t ;

[0099] A4, read the relevant knowledge state r from the value memory matrix t Combined with the students’ mastery of relevant knowledge, they are input into a fully connected layer with a Than activation function to calculate the new joint vector f t ′, and the joint vector f t ′ is the mastery degree of the students’ knowledge points after the update, and the formula is as follows:

[0100]

[0101] The following steps are involved:

[0102] S1, the user logs in to the education platform through the user login module;

[0103] S2, the user information reading module reads the user information logged in in step S1, and reads the data collection rules and data collection conditions of the corresponding user in the database. If the data collection rules and data collection conditions of the corresponding user are not read, step S31 is executed; if the data collection rules and data collection conditions of the corresponding user are not read, step S32 is executed;

[0104] S31, the data sampling module starts the data collection process according to the preset data collection rules and preset data collection conditions and through the data collection program;

[0105] S32, the data sampling module analyzes the learning ability of the logged-in user through the learning ability analysis model, and based on the user's learning ability, the preset data collection rules and preset data collection conditions are respectively corrected through the data correction model, and the data collection process is started through the data collection program according to the preset data collection rules and preset data collection conditions read by the user information reading module;

[0106] S4, the data processing module mines the learning knowledge collected by the data sampling module, and performs data labeling on the mined learning knowledge through the data labeling model;

[0107] S5, the after-class achievement detection module detects the after-class learning achievements and judges the operation effect of the current education platform, saves the data processing method of the teaching module with qualified operation effect, and stores the after-class test results of the corresponding user in the database as reference data for learning ability analysis when the user logs in next time;

[0108] S6, the education platform operation analysis module is used to use the accuracy rate in the data comparison results as the platform operation analysis standard, preset the platform operation analysis threshold, mark the education platform operation results whose accuracy rate in the data comparison results is higher than the platform operation analysis threshold as normal operation, and mark the education platform operation results whose accuracy rate in the data comparison results is lower than the platform operation analysis threshold as abnormal operation.

[0109] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent analysis and management system for operation data of an education platform, characterized by: include: A data sampling module, which is used to start the data collection process according to preset data collection rules and preset data collection conditions and through a data collection program, and collect different learning knowledge according to different collection programs; A data processing module, which is used to mine the learning knowledge collected by the data sampling module and to mark the mined learning knowledge through a data marking model, wherein the marking includes: key explanation content, self-study content, and after-class review content; After-class achievement detection module, the after-class achievement detection module is used to detect the after-class learning achievements and judge the operation effect of the current education platform, save the data processing methods of the teaching modules with qualified operation effects, and send the data processing methods of the data processing modules with unqualified operation effects to the data analysis module; A data analysis module, the data analysis module is used to receive the data processing method of the data processing module with unqualified operation results sent by the after-class achievement detection module, and compare the learning knowledge with different tags in the data processing module with the after-class learning achievement detection results through the data comparison model, and analyze the education platform operation data based on the comparison results and through the data analysis model; A user login module, which is used for users to log in to the education platform for learning and send user information to the user information reading module; A user information reading module, which is used to read the user information sent by the user login module and read the data collection rules and data collection conditions corresponding to the user in the database module; The data sampling module is also used to start the data collection process through the data collection program according to the design data collection rules and preset data collection conditions read by the user information reading module, and collect different learning knowledge according to different user learning abilities.

2. The intelligent analysis and management system for operation data of an education platform according to claim 1, characterized in that: The data sampling module comprises: A data collection rule preset module, which is used to receive and store data collection rules preset by management personnel; A data collection condition preset module, which is used to receive and store data collection conditions preset by management personnel; The data acquisition module is used to start the data acquisition process through a data acquisition program based on the preset data acquisition rules stored in the data acquisition rule preset module and the preset data acquisition conditions stored in the data acquisition condition preset module.

3. The intelligent analysis and management system for operation data of an education platform according to claim 1, characterized in that: The data sampling module also includes: A learning ability analysis module, which is used to analyze the learning ability of the logged-in user through a learning ability analysis model, and based on the user's learning ability, to modify the preset data collection rules and preset data collection conditions through a data correction model, thereby obtaining receiving user data collection rules and user data collection conditions respectively; A data collection rule receiving module, wherein the data collection rule receiving module is used to receive user data collection rules; A data collection condition receiving module, wherein the data collection condition receiving module is used to receive user data collection conditions; The data acquisition module is also used to receive the user data acquisition rules received by the data acquisition rule receiving module and the user data acquisition conditions received by the data acquisition condition receiving module, and initiate the data acquisition process through the data acquisition program.

4. The intelligent analysis and management system for operation data of an education platform according to claim 1, characterized in that: The data processing module includes: A knowledge mining module, which is used to mine the learning content required by the user based on the learning knowledge collected by the data sampling module and through the knowledge mining model; A data classification module, which is used to classify the learning content based on the learning content required by the user mined by the knowledge mining module and through the data classification model. The classification includes knowledge of previous chapter courses, knowledge of this chapter courses, knowledge of next chapter courses, and extracurricular interest knowledge; A data correlation analysis module, which is used to analyze the data correlation C between the knowledge of this chapter courses and the data in the previous chapter courses based on the data correlation analysis model, preset the correlation thresholds C1 and C2, and C1 < C2. Among them, the knowledge of this chapter with C < C1 is marked as the key explanation content, the knowledge of this chapter with C1 < C < C2 is marked as the after-class review content, and the knowledge of this chapter with C > C2 is marked as the self-study content; The data correlation analysis module is also used to analyze the data correlation D between the knowledge of this chapter courses and the data in the next chapter courses based on the data correlation analysis model, preset the correlation thresholds D1 and D2, and D1 < D2. Among them, the knowledge of this chapter with D < D1 is marked as the key explanation content, the knowledge of this chapter with D1 < D < D2 is marked as the after-class review content, and the knowledge of this chapter with D > D2 is marked as the self-study content.

5. The intelligent analysis and management system for operation data of an education platform according to claim 4, characterized in that: The after-class achievement detection module includes: A user score acquisition module, which is used to acquire the user's after-class detection scores; A detection content analysis module, which is used to analyze the mastery level of different knowledge points in the user's after-class detection scores; A data comparison module, which is used to compare the mastery level of different knowledge points obtained by the detection content analysis module with the marks of the corresponding knowledge points by the data processing module. Among them, according to the marks of different knowledge points, the mastery level of the user for different knowledge points from high to low is: key explanation content > after-class review content > self-study content; A data re-marking module, which is used to re-mark the marks of the knowledge points with different comparison results in the data comparison module; An education platform operation analysis module, which is used to use the correct rate in the data comparison result as the platform operation analysis standard, preset the platform operation analysis threshold, mark the operation result of the education platform with a correct rate higher than the platform operation analysis threshold as normal operation, and mark the operation result of the education platform with a correct rate lower than the platform operation analysis threshold as abnormal operation.

6. The intelligent analysis and management system for operation data of an education platform according to claim 5, characterized in that: The detection content analysis module includes the following working steps: A1. Take the activation function Tanh, then the mastery level of the user for different knowledge points in the previous t - 1 detections can be expressed as: Among them A t-1 Indicates the user's mastery of different knowledge points in the previous t-1 tests, v t-1 Is the answer sequence X t-1 The embedding vector representation of A and b A They are the weight matrix and bias vector of the fully connected layer respectively; A2, using the relevant weight matrix w t To calculate the relationship between the students' mastery of knowledge points and the knowledge concepts tested in this after-class test, the expression is: A t-1 =w t (i)*A t-1 ; A3, calculate the student's mastery of knowledge points and the input vector k t Relationship A′ t-1 =A t-1 *k t ; A4, read the relevant knowledge state r from the value memory matrix t Combined with the students’ mastery of relevant knowledge, they are input into a fully connected layer with a Than activation function to calculate the new joint vector f t ′, and the joint vector f t ′ is the mastery degree of the students’ knowledge points after the update, and the formula is as follows:

7. The intelligent analysis and management system for operation data of an education platform according to claim 1, characterized in that: It includes the following steps: S1. The user logs in to the education platform through the user login module; S2, the user information reading module reads the user information logged in in step S1, and reads the data collection rules and data collection conditions of the corresponding user in the database. If the data collection rules and data collection conditions of the corresponding user are not read, step S31 is executed; if the data collection rules and data collection conditions of the corresponding user are not read, step S32 is executed; S31, the data sampling module starts the data collection process according to the preset data collection rules and preset data collection conditions and through the data collection program; S32, the data sampling module analyzes the learning ability of the logged-in user through the learning ability analysis model, and based on the user's learning ability, the preset data collection rules and preset data collection conditions are respectively corrected through the data correction model, and the data collection process is started through the data collection program according to the preset data collection rules and preset data collection conditions read by the user information reading module; S4, the data processing module mines the learning knowledge collected by the data sampling module, and performs data labeling on the mined learning knowledge through the data labeling model; S5, the after-class achievement detection module detects the after-class learning achievements and judges the operation effect of the current education platform, saves the data processing method of the teaching module with qualified operation effect, and stores the after-class test results of the corresponding user in the database as reference data for learning ability analysis when the user logs in next time; S6, the education platform operation analysis module is used to use the accuracy rate in the data comparison results as the platform operation analysis standard, preset the platform operation analysis threshold, mark the education platform operation results whose accuracy rate in the data comparison results is higher than the platform operation analysis threshold as normal operation, and mark the education platform operation results whose accuracy rate in the data comparison results is lower than the platform operation analysis threshold as abnormal operation.

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