A security management system and method applied to a smart education platform

Abstract

The application provides a security management system and method applied to a smart education platform, wherein the system comprises: a platform application security management module, which is used for performing security management on a data application layer of the smart education platform; a data security management module, which is used for performing security management on a data storage layer of the smart education platform; and a big data component security management module, which is used for performing security management on a big data basic component layer of the smart education platform. The security management system applied to the smart education platform of the application performs security management on the data application layer, the data storage layer and the big data basic component layer of the smart education platform through the platform application security management module, the data security management module and the big data component security management module, so that the security of the big data platform is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of security management, in particular to a security management method and system applied to a smart education platform. BACKGROUND

[0002] At present, smart education, namely education informatization, refers to a process of comprehensively and deeply applying modern information technology in the field of education (education management, education teaching and education research) to promote education reform and development. The technical features are digitalization, networking, intelligentization and multi-media, and the basic features are openness, sharing, interaction, cooperation and ubiquity. The education informatization promotes education modernization, and changes the traditional mode by using information technology. The smart education platform, as a big data platform, is the core of smart education, and its security is extremely important. SUMMARY

[0003] One of the purposes of the present application is to provide a security management system applied to a smart education platform. The security management system manages the data application layer, the data storage layer and the big data basic component layer of the smart education platform through a platform application security management module, a data security management module and a big data component security management module, so as to ensure the security of the big data platform.

[0004] The security management system applied to the smart education platform provided by the embodiment of the present application comprises:

[0005] The platform application security management module is used for managing the security of the data application layer on the smart education platform.

[0006] The data security management module is used for managing the security of the data storage layer of the smart education platform.

[0007] The big data component security management module is used for managing the security of the big data basic component layer of the smart education platform.

[0008] Preferably, the platform application security management module comprises:

[0009] The interface security management submodule is used for managing the security of the data interface of the smart education platform.

[0010] The operation audit submodule is used for realizing the operation audit of the data application layer of the smart education platform.

[0011] The permission management submodule is used for realizing the permission management of the smart education platform.

[0012] The code review submodule is used for reviewing the code of the data application layer of the smart education platform.

[0013] Preferably, the data security management module comprises:

[0014] The data encryption submodule is used to encrypt data in the data storage layer of the smart education platform;

[0015] The data anonymization submodule is used to anonymize data in the data storage layer of the smart education platform;

[0016] The data auditing submodule is used to audit the data in the data storage layer of the smart education platform.

[0017] Preferably, the big data component security management module includes:

[0018] The user management and authentication submodule is used for user management and authentication.

[0019] The component access control submodule is used for component access control;

[0020] The data access control submodule is used to control data access;

[0021] The Big Data Component Audit Submodule is used to implement the auditing of big data components;

[0022] The component resource management submodule is used for component resource management.

[0023] Preferably, the user management and authentication submodule performs the following operations:

[0024] When a user logs in, the device information of the user's login device is obtained;

[0025] Determine user device permissions based on device information;

[0026] When a user accesses or downloads data on the smart education platform, permission determination is made based on the user's device permissions and user permissions.

[0027] And / or,

[0028] When a user logs in, the user's location information is obtained;

[0029] Determine the user's location permissions based on location information;

[0030] When a user accesses or downloads data on the smart education platform, permission determination is made based on the user's location permissions and user permissions.

[0031] Preferably, the data access control submodule includes:

[0032] Monitor the frequency of data access on the smart education platform;

[0033] When the access frequency exceeds the preset standard frequency value, mirror data is constructed based on the preset first rule;

[0034] Mirror data and original data are used to deal with the access of the user together;

[0035] And / or,

[0036] When the user accesses the data on the intelligent education platform,

[0037] Obtain the historical data access record of the user;

[0038] Determine the relevance of the data accessed by the current user and the data accessed in the historical access record;

[0039] Obtain the popular data on the current intelligent education platform;

[0040] Calculate the similarity between the popular data and the data accessed by the current user;

[0041] When the similarity is less than the preset similarity threshold and the data accessed by the current user is not associated with the historical access record, the user is set as an observation user, and an observation value is configured; the observation value is initially one;

[0042] Track and monitor the data access of the observation user;

[0043] When the data access of the observation user is abnormal, the observation value is incremented by one;

[0044] When the observation value is greater than the preset alert value, the observation user is isolated;

[0045] When the observation value meets the preset cancel observation condition, the tracking and monitoring of the data access of the observation user is canceled;

[0046] The first preset rule includes: based on the value of the ratio of the access frequency to the standard frequency after rounding, the corresponding mirror data is constructed;

[0047] The cancel observation condition includes:

[0048] When the number of data accesses of the observation user reaches the preset observation cancellation judgment number, the observation value does not reach the first preset value.

[0049] Preferably, the user management and authentication submodule performs the following operations:

[0050] When the user logs in, obtain the information of the device logged in by the user, the login time, the login location information and the login information of the nearest preset number of times;

[0051] Based on the device information, login time, login location information and login information of the nearest preset number of times of the user login, the target data of the user is predicted;

[0052] Obtain the permission verification requirement of the target data, and perform permission verification on the user based on the permission verification requirement;

[0053] The target data of the user is predicted based on information of a device logged in by the user, login time, login location information, and login information of a preset number of times recently; and the method comprises the following steps:

[0054] A prediction vector is constructed based on information of a device logged in by the user, login time, login location information, and login information of a preset number of times recently;

[0055] A preset prediction database is obtained, and the similarity between the prediction vector and a judgment vector in the prediction database is calculated, and the calculation formula is as follows:

[0056]

[0057] Wherein, sim i is the similarity between the prediction vector and the i-th judgment vector in the prediction database; x j is the j-th parameter value in the prediction vector; x k is the k-th parameter value in the prediction vector; y i,j is the j-th parameter value in the i-th judgment vector; y i,k is the k-th parameter value in the i-th judgment vector;

[0058] The data corresponding to the judgment vector with the largest similarity and a value of the similarity greater than a preset similarity threshold is obtained as the target data;

[0059] The permission verification requirement comprises: the user inputs verification through a smart pen;

[0060] When the user inputs verification through the smart pen, the user management and authentication submodule performs the following operations:

[0061] Obtain the handwriting information input by the user through the smart pen;

[0062] The handwriting information is analyzed to obtain the input information of the user;

[0063] When the input information is text, the stroke order of the text and the force of each stroke are determined;

[0064] Based on the stroke order and the force of each stroke, the verification stroke order and the force of each stroke in the verification stroke order input by the user during registration are matched, and when the matching is successful, the verification is passed.

[0065] Preferably, the data security management module further comprises:

[0066] A data security verification submodule is configured to perform validity verification on new data received by the smart education platform before storing the new data;

[0067] The data security verification submodule performs the following operations:

[0068] analyzing the new data to determine a classification to which the new data belongs;

[0069] obtaining a preset verification user list corresponding to the classification to which the new data belongs;

[0070] sending the new data to the users on the verification user list and receiving verification information of the new data from the users;

[0071] stopping the receiving of the verification information when the number of the received verification information or the sum of the confidence values of the corresponding users of all the received verification information satisfies a preset verification judgment condition;

[0072] analyzing the received verification information and determining whether the new data passes the verification based on the analysis result.

[0073] Preferably, the prediction database is established according to the historical login data of the users, the information of the device, the login time, the login location information and the preset number of login information in the historical login data of the users are constructed into a judgment vector, and the target data in the historical login data of the users is correspondingly associated with the judgment vector;

[0074] and / or,

[0075] obtaining a prediction database of all users in the smart education platform, which is constructed based on the historical login data of the users;

[0076] constructing common prediction data based on the prediction databases of all the users,

[0077] adding the prediction data into the prediction databases of all the users in the smart education platform;

[0078] wherein, constructing the common prediction data based on the prediction databases of all the users comprises:

[0079] obtaining a common value corresponding to the target data, and when the common value is greater than a preset common threshold value, extracting a corresponding judgment vector from the prediction database of each user based on the target data to construct a judgment vector set;

[0080] calculating the similarity between two vectors in the judgment vector set, taking the number of the similarities greater than a preset boundary value as an identification value of the judgment vector, and associating the judgment vector with the target data as the common prediction data when the identification value is the largest;

[0081] wherein, the common value is the ratio of the number of the prediction databases having the target data to the total number of the users.

[0082] The application further provides a security management method applied to the intelligent education platform.

[0083] Additional features and advantages of the application will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

[0084] The technical solutions of the application are described in detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0085] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, illustrate the application, and are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0086] Figure 1 It is a schematic diagram of a security management system applied to an intelligent education platform in an embodiment of the application;

[0087] Figure 2 It is a schematic diagram of a platform application security management module in an embodiment of the application;

[0088] Figure 3 It is a schematic diagram of a data security management module in an embodiment of the application;

[0089] Figure 4 It is a schematic diagram of a big data component security management module in an embodiment of the application. DETAILED DESCRIPTION

[0090] The preferred embodiments of the application are described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the application, and do not limit the application.

[0091] The embodiment of the application provides a security management system applied to an intelligent education platform, as shown in Figure 1 The security management system comprises:

[0092] A platform application security management module 1 is used to perform security management on a data application layer of the intelligent education platform;

[0093] A data security management module 2 is used to perform security management on a data storage layer of the intelligent education platform;

[0094] The big data component security management module 3 is used for security management of a big data basic component layer of the smart education platform.

[0095] Preferably, as shown in the figure, the platform application security management module 1 comprises: Figure 2

[0096] The interface security management sub-module 11 is used for security management of a data interface of the smart education platform.

[0097] The operation audit sub-module 12 is used for operation audit of the data application layer of the smart education platform.

[0098] The permission management sub-module 13 is used for permission management of the smart education platform.

[0099] The code review sub-module 14 is used for code review of the data application layer of the smart education platform.

[0100] Preferably, as shown in the figure, the data security management module 2 comprises: Figure 3

[0101] The data encryption sub-module 21 is used for data encryption in a data storage layer of the smart education platform.

[0102] The data desensitization sub-module 22 is used for data desensitization in the data storage layer of the smart education platform.

[0103] The data audit sub-module 23 is used for data audit in the data storage layer of the smart education platform.

[0104] Preferably, as shown in the figure, the big data component security management module 3 comprises: Figure 4

[0105] The user management and authentication sub-module 31 is used for user management and authentication.

[0106] The component permission control sub-module 32 is used for component permission control.

[0107] The data access control sub-module 33 is used for data access control.

[0108] The big data component audit sub-module 34 is used for big data component audit.

[0109] The component resource management sub-module 35 is used for component resource management.

[0110] The working principle and beneficial effects of the above technical solutions are as follows:

[0111] ​​​Under the big data platform, the core can be divided into three levels, one is the big data basic component layer, that is, each basic component used for constructing the big data platform based on the Hadoop ecological system, two is the core data storage layer used for storing data, three is each data processing application service based on the big data platform and data, and the three levels have included the construction of the big data platform, the collection, storage, processing and external service of data, and the protection target of the big data security and reliable platform is to ensure the security of the three levels, therefore, the big data security and reliable platform configures corresponding security measures in each level according to the principle of security partition design according to the core three levels under the big data environment, to ensure the security of each link in the whole life cycle of the big data. The big data security and reliable system aims to provide a series of protection measures and control such as audit, access control, encryption and desensitization for the data assets in the big data environment, and automatically monitor and real-time process each link in the whole life cycle of data collection, processing, storage and application. Provide security response scheme and processing mechanism before, during and after the event, form a safe closed loop that can predict before the event, have a method during the event and can be tracked after the event.

[0112] The security management system applied to the intelligent education platform of the application carries out security management on the data application layer, the data storage layer and the big data basic component layer of the intelligent education platform through the platform application security management module, the data security management module and the big data component security management module, and guarantees the security of the big data platform.

[0113] In one embodiment, the user management and authentication submodule performs the following operations:

[0114] When the user logs in, the device information of the login device of the user is acquired;

[0115] The device authority of the user is determined based on the device information;

[0116] When the user accesses or downloads data on the intelligent education platform, the authority is judged based on the device authority and the user authority of the user;

[0117] And / or,

[0118] When the user logs in, the positioning information of the user is acquired;

[0119] The positioning authority of the user is determined based on the positioning information;

[0120] When the user accesses or downloads data on the intelligent education platform, the authority is judged based on the positioning authority and the user authority of the user.

[0121] The working principle and beneficial effects of the above technical solution are as follows:

[0122] Based on the login device of the user, the permission of the login device is determined, so as to realize the permission judgment together with the user permission of the account of the user; and / or, the positioning permission is determined according to the positioning information of the user to participate in the judgment; that is, the access permission of the data stored in the intelligent education platform not only has the user permission of the user, but also needs the positioning permission and the device permission, further guaranteeing the security of the data; in addition, through the device permission, it can also prevent the user equipment and has the hardware condition of displaying the data of the intelligent education platform, and waste the download resource.

[0123] In one embodiment, the data access control submodule comprises:

[0124] The access frequency of the data on the intelligent education platform is monitored;

[0125] When the access frequency is greater than the preset standard frequency value, the mirror data of the data is constructed based on the preset first rule;

[0126] The mirror data and the original data are used together to respond to the access of the user;

[0127] And / or,

[0128] When the user accesses the data on the intelligent education platform,

[0129] The historical data access record of the user is obtained;

[0130] The relevance of the data accessed by the current user and the data accessed in the historical access record is determined;

[0131] The popular data on the current intelligent education platform is obtained;

[0132] The similarity between the popular data and the data accessed by the current user is calculated;

[0133] When the similarity is less than the preset similarity threshold value and the data accessed by the current user is not associated with the historical access record, the user is set as an observation user, and an observation value is configured; the observation value is initially one;

[0134] The data access of the observation user is tracked and monitored;

[0135] When the data access of the observation user is abnormal, the observation value is increased by one;

[0136] When the observation value is greater than the preset warning value, the observation user is isolated;

[0137] When the observation value meets the preset cancel observation condition, the tracking and monitoring of the data access of the observation user is cancelled;

[0138] The first preset rule comprises: based on the value obtained by rounding off the ratio of the access frequency to the standard frequency, the corresponding mirror data is constructed;

[0139] The canceling observation condition comprises:

[0140] When the number of data access of the observed user reaches the preset canceling observation judgment number, the observation value does not reach the first preset value.

[0141] The working principle and beneficial effects of the technical solution are:

[0142] The mechanism for generating mirror data ensures the access speed of data access. After the access frequency decreases, the mirror data can be deleted. The use of the observation mechanism regulates the data access of the user and monitors the account of the user, thereby ensuring that the data of the education data platform is not stolen by illegal persons.

[0143] In one embodiment, the user management and authentication submodule performs the following operations:

[0144] When the user logs in, the information of the device logged in by the user, the login time, the login location information and the login information of the nearest preset number of times are obtained;

[0145] The target data of the user is predicted based on the information of the device logged in by the user, the login time, the login location information and the login information of the nearest preset number of times;

[0146] The permission verification requirement of the target data is obtained, and the user is subjected to permission verification based on the permission verification requirement;

[0147] The target data of the user is predicted based on the information of the device logged in by the user, the login time, the login location information and the login information of the nearest preset number of times; comprising:

[0148] A prediction vector is constructed based on the information of the device logged in by the user, the login time, the login location information and the login information of the nearest preset number of times;

[0149] A preset prediction database is obtained, the similarity of the prediction vector and the judgment vector in the prediction database is calculated, and the calculation formula is as follows:

[0150]

[0151] Wherein, sim i is the similarity of the prediction vector and the i-th judgment vector in the prediction database; x j is the j-th parameter value in the prediction vector; x k is the k-th parameter value in the prediction vector; y i,j is the j-th parameter value in the i-th judgment vector; y i,k is the k-th parameter value in the i-th judgment vector;

[0152] acquire data corresponding to a judgment vector with the largest similarity and a value of the similarity greater than a preset similarity threshold as target data;

[0153] The permission verification requirement includes that the user inputs verification through the smart pen.

[0154] When the user inputs verification through the smart pen, the user management and authentication submodule executes the following operations:

[0155] Obtain handwriting information input by the user through the smart pen.

[0156] Analyze the handwriting information to obtain input information of the user.

[0157] When the input information is text, determine the stroke order of the text and the force of each stroke.

[0158] Based on the stroke order and the force of each stroke, match the verification stroke order and the force of each stroke in the verification stroke order input by the user during registration, and when the matching is successful, the verification is passed.

[0159] The working principle and beneficial effects of the above technical solution are:

[0160] In the smart education platform, the access of each data corresponds to different permission verification methods or permission requirements. When the user logs in, the target data of the user is predicted to provide a suitable verification method for the user, thereby realizing the intelligentization of the smart education platform.

[0161] In an embodiment, the data security management module further includes:

[0162] A data security verification submodule is configured to perform validity verification on new data received by the smart education platform before storing the new data.

[0163] The data security verification submodule executes the following operations:

[0164] Analyze the new data to determine the classification to which the new data belongs.

[0165] Obtain a preset verification user list corresponding to the classification.

[0166] Send the new data to the users on the verification user list and receive verification information of the new data from the users.

[0167] When the number of received verification information or the sum of confidence values of corresponding users of all received verification information satisfies a preset verification judgment condition, stop receiving the verification information.

[0168] Analyze the received verification information, and determine whether the new data passes the verification based on the analysis result.

[0169] The working principle and beneficial effects of the above technical solutions are:

[0170] The new data is verified in a manner of verifying user-assisted verification. For example, in a smart education platform, the new data is a mathematics subject classification, which needs to be sent to a verification user of the mathematics classification of the smart education platform, which can be a mathematics teacher. The confidence of the new data is determined by the feedback of the teacher. The mathematics teacher assigns different confidence values according to his experience and rating. When the proportion of trusted information reaches a preset first proportion value or the proportion of trusted confidence values reaches a preset second proportion, it can be determined that the new data passes the verification.

[0171] In one embodiment, the prediction database is established according to the user's historical login data, the information of the device, the login time, the login location information and the preset number of login information in the user's historical login data are constructed into a judgment vector, and the target data in the user's historical login data is correspondingly associated with the judgment vector;

[0172] And / or,

[0173] Obtain the prediction database of all users in the smart education platform based on the user's historical login data;

[0174] Based on the prediction database of all users, common prediction data is constructed,

[0175] The prediction data is added to the prediction database of all users in the smart education platform;

[0176] Among them, based on the prediction database of all users, common prediction data is constructed, including:

[0177] Obtain the common value corresponding to the target data. When the common value is greater than a preset common threshold, the corresponding judgment vector is extracted from each user's prediction database based on the target data, and a judgment vector set is constructed;

[0178] Calculate the similarity between the two vectors of the judgment vector set. The number of similarity values greater than a preset threshold is used as the identification value of the judgment vector. The judgment vector with the largest identification value is associated with the target data as the common prediction data;

[0179] Among them, the common value is the ratio of the number of prediction databases with target data to the total number of users.

[0180] The working principle and beneficial effects of the above technical solutions are:

[0181] There are two ways to establish prediction data in the prediction database: one is the user's own historical login data; the other is the common data of all users on the platform. The prediction database established based on the two ways is more comprehensive and representative, which improves the accuracy and applicability of the prediction.

[0182] The application further provides a security management method applied to the intelligent education platform, constructs any of the security management systems applied to the intelligent education platform, and performs security management on a data application layer of the intelligent education platform through a platform application security management module; performs security management on a data storage layer of the intelligent education platform through a data security management module; and performs security management on a big data basic component layer of the intelligent education platform through a big data component security management module. The security management method applied to the intelligent education platform performs security management on the data application layer, the data storage layer and the big data basic component layer of the intelligent education platform through the platform application security management module, the data security management module and the big data component security management module, and guarantees the security of the big data platform

[0183] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the application. Accordingly, such modifications and changes are intended to fall within the scope of the application as defined in the following claims and their equivalents.

Claims

1. A security management system applied to a smart education platform, characterized in that, The application relates to a smart education platform security management system, which comprises the following modules: a platform application security management module for managing the data application layer of a smart education platform; a data security management module for managing the data storage layer of the smart education platform; a big data component security management module for managing the big data basic component layer of the smart education platform; the big data component security management module comprises the following sub-modules: a user management and authentication sub-module for managing and authenticating users; a component permission control sub-module for controlling the permissions of components; a data access control sub-module for controlling data access; a big data component audit sub-module for auditing big data components; a component resource management sub-module for managing the resources of components; the data access control sub-module comprises the following steps: monitoring the access frequency of data on the smart education platform; when the access frequency is greater than a preset standard frequency value, constructing mirror data of the data based on a preset first rule; using the mirror data and the original data to jointly respond to user access; and / or, when a user accesses data on the smart education platform, obtaining the historical data access records of the user; determining the relevance of the data accessed by the current user and the historical access records; obtaining popular data on the smart education platform; calculating the similarity between the popular data and the data accessed by the current user; when the similarity is less than a preset similarity threshold value and the data accessed by the current user is irrelevant to the historical access records, the user is regarded as an observation user, and an observation value is configured for the observation user; the observation value is initially one; tracking and monitoring the data access of the observation user; when the data access of the observation user is abnormal, the observation value is increased by one; when the observation value is greater than a preset warning value, the observation user is isolated; when the observation value satisfies a preset cancel observation condition, the tracking and monitoring of the data access of the observation user is cancelled; wherein the first rule comprises: constructing corresponding mirror data based on the integral value of the ratio of the access frequency to the standard frequency; the cancel observation condition comprises: when the data access frequency of the observation user reaches a preset cancel observation judgment frequency, the observation value does not reach a first preset value. 2.The security management system applied to the smart education platform of claim 1, wherein, the platform application security management module comprises the following sub-modules: an interface security management sub-module for managing the data interface of the smart education platform; an operation audit sub-module for realizing the operation audit of the data application layer of the smart education platform; a permission management sub-module for realizing the permission management of the smart education platform; a code review sub-module for reviewing the code of the data application layer of the smart education platform. 3.The security management system applied to the smart education platform of claim 1, wherein, the data security management module comprises the following sub-modules: a data encryption sub-module for encrypting data in the data storage layer of the smart education platform; a data desensitization sub-module for desensitizing data in the data storage layer of the smart education platform; a data audit sub-module for auditing data in the data storage layer of the smart education platform.

4. The security management system applied to a smart education platform as described in claim 1, characterized in that, The user management and authentication submodule performs the following operations: When a user logs in, obtain the device information of the user's login device; Determine the device permissions of the user based on the device information; When the user accesses or downloads data on the smart education platform, determine the permissions based on the user's device permissions and user permissions; And / or, When a user logs in, obtain the user's location information; Determine the location permissions of the user based on the location information; When the user accesses or downloads data on the smart education platform, determine the permissions based on the user's location permissions and user permissions.

5. The security management system applied to a smart education platform as described in claim 1, characterized in that, The user management and authentication submodule performs the following operations: When a user logs in, obtain the information of the device the user logs in, the login time, the login location information, and the last preset number of login information; Based on the information of the device the user logs in, the login time, the login location information, and the last preset number of login information, predict the target data of the user; Obtain the permission verification requirements of the target data, and perform permission verification on the user based on the permission verification requirements; Wherein, based on the information of the device the user logs in, the login time, the login location information, and the last preset number of login information, predict the target data of the user; including: Based on the information of the device the user logs in, the login time, the login location information, and the last preset number of login information, construct a prediction vector; Obtain a preset prediction database, calculate the similarity of the prediction vector and the judgment vector in the prediction database, and the calculation formula is as follows: ; in, The prediction vector and the first prediction vector in the prediction database The similarity of the judgment vectors; For the prediction vector, the first... Each parameter value; For the prediction vector, the first... Each parameter value; For the first The first of the judgment vectors Each parameter value; For the first The first of the judgment vectors Each parameter value; Obtain the data corresponding to the judgment vector with the largest similarity and a value greater than a preset similarity threshold in the similarity as the target data; Wherein, the permission verification requirements include: the user inputs verification through a smart pen; When the user inputs verification through a smart pen, the user management and authentication submodule performs the following operations: Obtain the handwriting information input by the user through the smart pen; Parse the handwriting information to obtain the input information of the user; When the input information is text, determine the stroke order of the text and the force of each stroke; Based on the stroke order and the force of each stroke, match the verification stroke order input by the user when registering and the force of each stroke in the verification stroke order, and when the matching is successful, the verification is passed. 6.The security management system applied to the smart education platform of claim 1, wherein, The data security management module further comprises: A data security verification submodule for performing validity verification on new data received by the smart education platform before storing the new data; The data security verification submodule performs the following operations: Parse the new data to determine the classification to which the new data belongs; Obtain a preset verification user list corresponding to the classification; Send the new data to the users on the verification user list and receive the verification information of the new data from the users; When the number of received verification information or the sum of the confidence values of the corresponding users of all received verification information meets a preset verification judgment condition, stop receiving the verification information; Analyzing the received verification information, and determining whether the new data passes verification based on the analysis result. 7.The security management system applied to the smart education platform of claim 5, wherein, The prediction database is established according to the historical login data of the user, and the information of the device, the login time, the login location information and the preset number of login information in the historical login data of the user are constructed as the judgment vector, and the target data in the historical login data of the user is correspondingly associated with the judgment vector; And / or, Obtaining the prediction database of all users in the intelligent education platform based on the historical login data of the user; Based on the prediction database of all users, common prediction data is constructed, Adding the prediction data into the prediction database of all users in the intelligent education platform; Based on the prediction database of all users, common prediction data is constructed, Obtaining the common value corresponding to the target data, when the common value is greater than the preset common threshold value, the corresponding judgment vector is extracted from the prediction database of each user based on the target data, and a judgment vector set is constructed; Calculate the similarity between the two vectors of the judgment vector set, and take the number of the similarity greater than the preset boundary value as the identification value of the judgment vector, and take the judgment vector with the maximum identification value as the common prediction data associated with the target data. Wherein, the common value is the ratio of the number of the prediction database with the target data to the total number of users. 8.A security management method applied to a smart education platform, characterized in that, Construct the security management system applied to the intelligent education platform as claimed in any one of claims 1 to 7, and apply the security management module to the data application layer of the intelligent education platform; through the data security management module, the data storage layer of the intelligent education platform is managed; through the big data component security management module, the big data basic component layer of the intelligent education platform is managed.

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