CN106375086A - Big data-based internet teaching system running method - Google Patents
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Abstract
The present invention discloses a big data-based internet teaching system running method. According to the method, wireless real-time network teaching is implemented, and remote interaction between teachers and students is realized. The system uses a big data processing technology, so as to process and analyze massive learning client data. According to the method, the advantage of a collaborative filtering recommendation algorithm in electronic commerce is used for reference, and the collaborative filtering technology and a learning platform are combined, so that the use ratio of the online learning resources is improved greatly, and the teaching data is encrypted and then is transmitted remotely, thereby being ensured to transmit safely.
Description
Technical Field
The invention relates to an operation method of an internet teaching system based on big data.
Background
Mobile learning is increasingly receiving attention as a branch of networked learning, and becomes a new focus for higher education techniques and related fields. The mobile learning has immeasurable application potential in the field of school education and training as a brand-new learning form. The main purpose of mobile learning is to utilize the mobile terminal and the wireless communication network to perform teaching-related activities, including class learning, access to teaching resources, course evaluation and testing, and the like.
With the development of the Internet, the learning activities are expanded from classrooms to the Internet, and a plurality of Internet teaching system platforms appear, but the current online learning system has a plurality of defects that the utilization rate of teaching resources is not high; the learning resources are too numerous, and learners such as a great sea fishing needle cannot quickly find the required resources; the learner needs to manually input description vocabularies for searching, and the system cannot actively recommend the description vocabularies according to the information of the user. These defects make network learning lose the original advantages, and therefore, it is urgently needed to integrate personalized services into an internet teaching system platform. Therefore, the user can quickly and accurately obtain the required resources through the active pushing of the system without searching and seeking the resources by himself, so that the efficiency of searching the resources by the user is improved, and a large amount of time is saved for the user. In addition, through the personalized recommendation technology, the quality of recommended resources can be guaranteed, the resource utilization rate is improved, and the learning direction is indicated for learners who are in learning lost.
In a big data application system, different data can be obtained from a plurality of data sources in a plurality of industries at present, particularly in the field of information analysis, and the data types comprise structured data, semi-structured data and unstructured data; the data content format is disordered and the information is combined in virtuality and reality. Therefore, value information useful in mining from massive multi-source heterogeneous data needs to be mined through a big data analysis technology, and data support is provided for various analysis applications. With the rapid development of internet teaching technology, mass information is presented simultaneously in the era of information explosion at present, and a large amount of unstructured data such as UGC (User generated content, meaning of User generated content) content, audio, text information, video, pictures and the like appears, so that it is necessary to introduce a big data technology into an internet teaching system.
In addition, in order to solve the security problem in the data communication of the internet teaching system, the encryption communication gradually comes into the visual field of people, the end-to-end whole-course encryption technology is mainly used for the encryption communication, and a ciphertext transmission mode is adopted between an air interface and a network in the whole course, so that the call is difficult to eavesdrop, and the security of the whole data communication process is ensured.
Disclosure of Invention
The invention provides an operation method of an internet teaching system based on big data, which can realize wireless real-time network teaching and remote interaction of teachers and students, the system can process and analyze massive learning client data by utilizing a big data processing technology, the advantage of a collaborative filtering recommendation algorithm in electronic commerce is used for reference, the collaborative filtering technology is combined with a learning platform, the utilization rate of online learning resources is greatly improved, the teaching data is subjected to remote transmission after being encrypted, and the safety of teaching data transmission is ensured.
In order to achieve the purpose, the invention provides a flow chart of an operation method of an internet teaching system based on big data. The method specifically comprises the following steps:
s1, a mobile learning terminal and a big data processing device are respectively connected with a teaching server, and the big data processing device is connected with the mobile learning terminal;
s2, acquiring mass data from a plurality of mobile learning terminals in real time by a big data processing device, processing the mass data, and establishing a library;
s3, receiving and transmitting data among the mobile learning terminal, the big data processing device and the teaching server in real time in a safe encryption mode;
and S4, the teaching server receives the data sent by the mobile teaching server and the big data processing device in real time, positions the interest of the mobile learning terminal in real time, recommends proper learning content, and realizes personalized interaction between the mobile learning terminal and the teaching server.
Preferably, the step S2 specifically includes the following steps:
extracting big data from a plurality of mobile learning terminals, and carrying out rule conversion on the big data;
carrying out data processing on the big data subjected to rule conversion;
and establishing a database according to the big data after data processing.
Preferably, the big data extracted from the plurality of mobile learning terminals includes: structured data and unstructured data, the way of regular transformation of big data comprises data cleaning and data preprocessing, the data cleaning and data preprocessing comprises at least one of the following:
format standardization, abnormal data clearing, error correction and duplication removal.
Preferably, in step S4, the following steps are adopted to realize real-time locating of the interest of the mobile learning terminal, and recommend suitable learning content:
s41, acquiring a user-resource scoring matrix; the method comprises the steps that m mobile learning clients score n resources to form an m multiplied by n matrix;
s42, selecting a similarity calculation formula, and calculating to generate a nearest neighbor set; setting a user common score number threshold T, and if the common score number is less than T, using an improved similarity calculation method; otherwise, a cosine similarity or Pearson correlation coefficient similarity calculation method is used;
and S43, introducing a time function, scoring, predicting and calculating, and generating a recommendation result.
Preferably, in step S1, in the process of establishing a connection between the mobile learning terminal and the big data processing apparatus and the teaching server, the steps include:
the mobile learning terminal and the big data processing device initiate TCP connection to the teaching server;
checking and judging whether the sigs provided by the mobile learning terminal and the big data processing device are legal or not by using a public key of the teaching server; if the mobile learning terminal and the parent terminal are judged to be connected with the base station, the mobile learning terminal and the parent terminal are connected with the base station.
Preferably, the step of sending and receiving data in the secure encryption manner in S3 specifically includes the following steps:
s31, enabling the teaching server to accept the setting of at least one public and private key pair, and enabling the mobile learning terminal and the big data processing device to have public keys;
s32, the teaching server receives the session request of the mobile learning terminal and the big data processing device and distributes the session;
s33, the teaching server sets a session identifier and obtains a session key according to the session identifier;
s34, encrypting the session identifier and the session key, and transmitting the encrypted session identifier and the encrypted session key to the mobile learning terminal and the big data processing device by using a private key signature;
and S35, encrypting and decrypting data information by using the session key to perform data information security transmission.
Preferably, in step S31, at least one public-private key pair is set in the data security control unit of the tutorial control module of the tutorial server, and the mobile learning terminal and the big data processing device are provided with public keys; the public and private key pair corresponding encryption algorithm can use an ECC algorithm, and the specific process is as follows:
s311, calculating a first Part of key agreement Part1 by using T _ SKA/T _ PKA and NB _ SKB/NB _ PKB through an elliptic curve point multiplication algorithm;
the first Part of key agreement Part1 DPSM2(TSKA，NBPKB)；
S312: calculating a second Part of key agreement Part2 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and T _ SKB/T _ PKB;
the second Part of key agreement Part2 DPSM2(NBSKA，TPKB)；
S313: calculating a third Part of key agreement Part3 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and NB _ SKB/NB _ PKB;
third Part3 ═ DPSM2(NBSKA，NBPKB)；
S314: connecting a first key agreement Part1, a second key agreement Part2 and a third key agreement Part3 into a key component KM;
the key component KM Part1 Part2 Part 3);
s315: compressing the key component KM and the first character string into a parent rolling representation initial key N _ CC of 256 bits by using an SM3 algorithm;
initial key N _ CC ═ HSM3(KM | | | first string)
According to the characteristics of the elliptic curve point multiplication algorithm, through the calculation process, the two communication parties calculate a consistent parent rolling representative initial key N _ CC.
Preferably, in the step S32, when the mobile learning terminal and the big data processing device need to exchange data with the teaching server, a session request is sent to the data security control unit; and after receiving the session request, the data security control unit allocates a session to the mobile learning terminal and the big data processing device.
Preferably, in step S33, the data security control unit sets a session identifier for the allocated session, and obtains a session key according to the session identifier, where the session identifier can uniquely identify the session; the session key may encrypt the session.
The invention has the following advantages and beneficial effects: the system can realize wireless real-time network teaching, can realize remote interaction of teachers and students, can process and analyze massive learning client data by utilizing a processing technology of big data, references the advantages of collaborative filtering recommendation algorithm in electronic commerce, combines a collaborative filtering technology with a learning platform, greatly improves the utilization rate of online learning resources, and ensures the safety of teaching data transmission by carrying out remote transmission after the teaching data is encrypted.
Drawings
FIG. 1 shows a block diagram of an Internet instructional system utilizing big data technology in accordance with the present invention.
Fig. 2 is a flowchart illustrating an operation method of the big data based internet teaching system according to the present invention.
Detailed Description
Fig. 1 is a view showing an internet teaching system using big data technology of the present invention, which includes a plurality of mobile learning terminals 1, a teaching server 2 and a big data processing apparatus 3;
wherein, the mobile learning terminal 1 includes:
the learning terminal interaction module 11 is used for collecting videos and audios of students, playing teaching audios and videos and realizing interaction between the students and the teaching server;
the learning data storage and processing module 12 is used for storing and processing mobile learning terminal data;
the learning end wireless data transmission interface 14 is used for wirelessly receiving and transmitting data at the learning end and can be used for wirelessly communicating with the teaching server and the big data processing device;
a learning control module 13, which is used for controlling and coordinating each module of the mobile learning terminal;
the teaching server 2 includes:
the teaching terminal interaction module 23 is used for collecting teaching videos and audios, playing the audio and videos of students and realizing interaction with the mobile learning terminal;
the teaching data storage and processing module 22 is used for storing and processing the data of the mobile teaching terminal;
the teaching end wireless data transmission interface 21 is used for the teaching end to wirelessly receive and transmit data and can be used for wireless communication with the mobile learning terminal and the big data processing device;
the learning resource positioning recommendation module 25 can accurately position the learning interest of the learning terminal by analyzing the big data of the learning terminal, so as to perform personalized recommendation on the learning resources of the learning terminal;
a teaching control module 24 for controlling and coordinating the modules of the teaching server;
the big data processing device 3 is used for processing massive client data and constructing a database so as to provide data support for various data analysis, client interest analysis learning and relationship discovery, and comprises:
a processing device wireless data transmission interface 31, which is used for the processing device to receive and transmit data and can be used for wireless communication with the teaching server and the mobile learning terminal;
an extraction conversion module 32, configured to extract big data from multiple learning terminals, and perform rule conversion on the big data;
a big data processing module 33, configured to perform data processing on the big data subjected to rule conversion;
and the database building module 34 is used for building a database according to the big data after data processing.
Preferably, the learning resource positioning recommendation module 25 includes a management unit, a recommendation policy unit, and a database unit, where:
a management unit: the method is mainly used for managing user information and learning resources;
the recommendation strategy unit is used for recommending learning resources to a user logging in a learning platform;
the database unit is used for storing various basic data including information tables required by the system, the database unit and the management unit are in data storage relation, the data tables generated in the management unit can be stored in the database, and the management of the user information and the learning resource information in the management unit can generate corresponding data tables.
Preferably, the management unit includes a user management subunit and a learning resource management subunit, and the user management subunit mainly manages login and registration information of the user; the learning resource management subunit mainly comprises the type management of learning resources and the operation of a user on the resources, the type of the learning resources mainly comprises video resources and text resources, and the operation of the user on the resources mainly comprises scoring, praise, downloading and sharing.
Preferably, the recommendation policy unit mainly includes a hot learning resource recommendation subunit and a collaborative filtering recommendation subunit: wherein
The hot learning resource recommendation subunit is mainly used for recommending new users through hot learning resource ranking after the new users enter the system for the first time, and requesting the new users to score the hot learning resources so as to predict the learning interest of the users for the first time;
and the collaborative filtering recommendation subunit is mainly used for aiming at non-new users, calculating the similarity between the users by analyzing the scores of the users on the learning resources, finding out a nearest neighbor set, and recommending the target user according to the learning experiences of the similar users.
Preferably, the database unit stores a database including: the resource learning system comprises a user information table, data table information of resource learning by users, a learning resource type table and a resource rating table.
Preferably, the big data extracted from the plurality of learning terminals includes: structured data and unstructured data.
Preferably, the manner of rule transformation for big data includes data cleansing and data preprocessing, and the data cleansing and data preprocessing include at least one of the following:
format standardization, abnormal data clearing, error correction and duplication removal.
Preferably, the teaching control module 24 includes a data security control unit, and when the mobile learning terminal 1 and the big data processing device 2 need to exchange data with a teaching server, a session request is sent to the data security control unit; and after receiving the session request, the data security control unit needs to allocate sessions for the mobile learning terminal 1 and the big data processing device 2.
Preferably, the data security control unit sets a session identifier for the allocated session, and obtains a session key according to the session identifier, where the session identifier can uniquely identify the session; the session key may encrypt the session.
Preferably, the data security control unit transmits the session identifier and the session key to the mobile learning terminal 1 and the big data processing device 2 in an encrypted manner, and signs with a private key in the data security control unit, so that the mobile learning terminal and the big data processing device can be verified with the received public key of the data security control unit, thereby enhancing the session security; the mobile learning terminal and the big data processing device can encrypt data information by using the session key and transmit the data information to the data security control unit, or decrypt the encrypted data information acquired from the data security control unit and perform data screening and orthogonal processing on the key.
Preferably, the data security control unit may also encrypt and decrypt data information using the session key to exchange data with the mobile learning terminal and the big data processing device; when the mobile learning terminal and the big data processing device need to send data information to the teaching server, the data security control unit receives the encrypted data information sent by the mobile learning terminal and the big data processing device, and decrypts the ciphertext information by using the session key to obtain original plaintext information; when the mobile learning terminal and the big data processing device need to acquire data information from the teaching server, the data security control unit encrypts the data information needed by the mobile learning terminal and the big data processing device by using a session key, transmits the encrypted data information to the mobile learning terminal and the big data processing device, and decrypts ciphertext information by using the session key to obtain original plaintext information; making the data exchange secure.
Fig. 2 is a flowchart illustrating an operation method of the big data based internet teaching system according to the present invention. The method specifically comprises the following steps:
s1, a mobile learning terminal and a big data processing device are respectively connected with a teaching server, and the big data processing device is connected with the mobile learning terminal;
s2, acquiring mass data from a plurality of mobile learning terminals in real time by a big data processing device, processing the mass data, and establishing a library;
s3, receiving and transmitting data among the mobile learning terminal, the big data processing device and the teaching server in real time in a safe encryption mode;
and S4, the teaching server receives the data sent by the mobile teaching server and the big data processing device in real time, positions the interest of the mobile learning terminal in real time, recommends proper learning content, and realizes personalized interaction between the mobile learning terminal and the teaching server.
Preferably, the step S2 specifically includes the following steps:
extracting big data from a plurality of mobile learning terminals, and carrying out rule conversion on the big data;
carrying out data processing on the big data subjected to rule conversion;
and establishing a database according to the big data after data processing.
Preferably, the big data extracted from the plurality of mobile learning terminals includes: structured data and unstructured data, the way of regular transformation of big data comprises data cleaning and data preprocessing, the data cleaning and data preprocessing comprises at least one of the following:
format standardization, abnormal data clearing, error correction and duplication removal.
Preferably, in step S4, the following steps are adopted to realize real-time locating of the interest of the mobile learning terminal, and recommend suitable learning content:
s41, acquiring a user-resource scoring matrix; the method comprises the steps that m mobile learning clients score n resources to form an m multiplied by n matrix;
s42, selecting a similarity calculation formula, and calculating to generate a nearest neighbor set; setting a user common score number threshold T, and if the common score number is less than T, using an improved similarity calculation method; otherwise, a cosine similarity or Pearson correlation coefficient similarity calculation method is used;
and S43, introducing a time function, scoring, predicting and calculating, and generating a recommendation result.
Preferably, in step S42, the improved similarity calculation method in the step is:
wherein SU,VRepresenting the similarity between the user U and the user V; rU,VA common scoring resource set representing a user U and a user V; r isU,iRepresenting the scoring of resource i by user U; r isV,iRepresents the rating of resource i by user V;representing the average rating of the user U on the resource;represents the average rating of user V for the resource;
F(rU,i,rV,i) A score constraint function representing a score containing the score of the user u on the resource i and the score of the user V on the resource i; diRepresenting the absolute distance of the user U and the user V for scoring the resource i; r ismA median score value representing a score range set by the system; RatingStart represents its essence of the system setting the score range; RatingEnd represents the end value of the score range set by the system.
Preferably, in step S43, the introducing the time function specifically includes:
wherein,representing the average rating of the user U on the resource;
SU,Vrepresenting the similarity between the user U and the user V;
rX,irepresents the user X's score for resource i;
NUrepresents the set of neighbors that is most similar to user U;
x represents one of the users in the neighbor set with the most similar user U;
ftrepresenting a time function, the larger the time function, the more recent the user's interest, tuiRepresenting the scoring time of the learning resources by the user;
n and μ are time decay parameters.
Preferably, in step S1, in the process of establishing a connection between the mobile learning terminal and the big data processing apparatus and the teaching server, the steps include:
the mobile learning terminal and the big data processing device initiate TCP connection to the teaching server;
checking and judging whether the sigs provided by the mobile learning terminal and the big data processing device are legal or not by using a public key of the teaching server; if the mobile learning terminal and the parent terminal are judged to be connected with the base station, the mobile learning terminal and the parent terminal are connected with the base station.
Preferably, the step of sending and receiving data in the secure encryption manner in S3 specifically includes the following steps:
s31, enabling the teaching server to accept the setting of at least one public and private key pair, and enabling the mobile learning terminal and the big data processing device to have public keys;
s32, the teaching server receives the session request of the mobile learning terminal and the big data processing device and distributes the session;
s33, the teaching server sets a session identifier and obtains a session key according to the session identifier;
s34, encrypting the session identifier and the session key, and transmitting the encrypted session identifier and the encrypted session key to the mobile learning terminal and the big data processing device by using a private key signature;
and S35, encrypting and decrypting data information by using the session key to perform data information security transmission.
Preferably, in step S31, at least one public-private key pair is set in the data security control unit of the tutorial control module of the tutorial server, and the mobile learning terminal and the big data processing device are provided with public keys; the public and private key pair corresponding encryption algorithm can use an ECC algorithm, and the specific process is as follows:
s311, calculating a first Part of key agreement Part1 by using T _ SKA/T _ PKA and NB _ SKB/NB _ PKB through an elliptic curve point multiplication algorithm;
the first Part of key agreement Part1 DPSM2(TSKA，NBPKB)；
S312: calculating a second Part of key agreement Part2 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and T _ SKB/T _ PKB;
the second Part of key agreement Part2 DPSM2(NBSKA，TPKB)；
S313: calculating a third Part of key agreement Part3 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and NB _ SKB/NB _ PKB;
third Part3 ═ DPSM2(NBSKA，NBPKB)；
S314: connecting a first key agreement Part1, a second key agreement Part2 and a third key agreement Part3 into a key component KM;
the key component KM Part1 Part2 Part 3);
s315: compressing the key component KM and the first character string into a parent rolling representation initial key N _ CC of 256 bits by using an SM3 algorithm;
initial key N _ CC ═ HSM3(KM | | | first string)
According to the characteristics of the elliptic curve point multiplication algorithm, through the calculation process, the two communication parties calculate a consistent parent rolling representative initial key N _ CC.
Preferably, in the step S32, when the mobile learning terminal and the big data processing device need to exchange data with the teaching server, a session request is sent to the data security control unit; and after receiving the session request, the data security control unit allocates a session to the mobile learning terminal and the big data processing device.
Preferably, in step S33, the data security control unit sets a session identifier for the allocated session, and obtains a session key according to the session identifier, where the session identifier can uniquely identify the session; the session key may encrypt the session.
Preferably, the data security control unit transmits the session identifier and the session key to the mobile learning terminal and the big data processing device in an encrypted manner, and signs by using a private key in the data security control unit, so that the mobile learning terminal and the big data processing device can be verified by using the received public key of the data security control unit, thereby enhancing the session security; the mobile learning terminal and the big data processing device can encrypt data information by using the session key and transmit the data information to the data security control unit, or decrypt the encrypted data information acquired from the data security control unit and perform data screening and orthogonal processing on the key.
Preferably, the data security control unit may also encrypt and decrypt data information using the session key to exchange data with the mobile learning terminal and the big data processing device; when the mobile learning terminal and the big data processing device need to send data information to the information security equipment, the teaching server receives the encrypted data information sent by the mobile learning terminal and the big data processing device, and decrypts the ciphertext information by using the session key to obtain original plaintext information; when the mobile learning terminal and the big data processing device need to acquire data information from the teaching server, the data security control unit encrypts the data information needed by the mobile learning terminal and the big data processing device by using a session key, transmits the encrypted data information to the mobile learning terminal and the big data processing device, and decrypts ciphertext information by using the session key to obtain original plaintext information; making the data exchange secure.
As described above, although the embodiments and the drawings defined by the embodiments have been described, it is apparent to those skilled in the art that various modifications and variations can be made from the above description. For example, the present invention may be carried out in a different order from the method described in the technology described, or may be combined or combined in a different manner from the method described for the constituent elements such as the system, the structure, the device, the circuit, and the like described, or may be replaced or substituted with other constituent elements or equivalents. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications, which are equivalent in performance or use, should be considered to fall within the scope of the present invention without departing from the spirit of the invention.
Claims (9)
1. An operation method of an internet teaching system based on big data specifically comprises the following steps:
s1, a mobile learning terminal and a big data processing device are respectively connected with a teaching server, and the big data processing device is connected with the mobile learning terminal;
s2, acquiring mass data from a plurality of mobile learning terminals in real time by a big data processing device, processing the mass data, and establishing a library;
s3, receiving and transmitting data among the mobile learning terminal, the big data processing device and the teaching server in real time in a safe encryption mode;
and S4, the teaching server receives the data sent by the mobile teaching server and the big data processing device in real time, positions the interest of the mobile learning terminal in real time, recommends proper learning content, and realizes personalized interaction between the mobile learning terminal and the teaching server.
2. The method according to claim 1, wherein step S2 specifically comprises the steps of:
extracting big data from a plurality of mobile learning terminals, and carrying out rule conversion on the big data;
carrying out data processing on the big data subjected to rule conversion;
and establishing a database according to the big data after data processing.
3. The method of claim 2, wherein the big data extracted from the plurality of mobile learning terminals comprises: structured data and unstructured data, the way of regular transformation of big data comprises data cleaning and data preprocessing, the data cleaning and data preprocessing comprises at least one of the following:
format standardization, abnormal data clearing, error correction and duplication removal.
4. The method according to claim 3, wherein in step S4, the following steps are adopted to realize real-time positioning of the interests of the mobile learning terminal, and to recommend suitable learning content:
s41, acquiring a user-resource scoring matrix; the method comprises the steps that m mobile learning clients score n resources to form an m multiplied by n matrix;
s42, selecting a similarity calculation formula, and calculating to generate a nearest neighbor set; setting a user common score number threshold T, and if the common score number is less than T, using an improved similarity calculation method; otherwise, a cosine similarity or Pearson correlation coefficient similarity calculation method is used;
and S43, introducing a time function, scoring, predicting and calculating, and generating a recommendation result.
5. The method as claimed in claim 4, wherein in the step S1, in the process of establishing connection between the mobile learning terminal and the big data processing device and the teaching server, the steps include:
the mobile learning terminal and the big data processing device initiate TCP connection to the teaching server;
checking and judging whether the sigs provided by the mobile learning terminal and the big data processing device are legal or not by using a public key of the teaching server; if the mobile learning terminal and the parent terminal are judged to be connected with the base station, the mobile learning terminal and the parent terminal are connected with the base station.
6. The method as claimed in claim 5, wherein the step of transceiving data in a secure encrypted manner in S3 specifically comprises the steps of:
s31, enabling the teaching server to accept the setting of at least one public and private key pair, and enabling the mobile learning terminal and the big data processing device to have public keys;
s32, the teaching server receives the session request of the mobile learning terminal and the big data processing device and distributes the session;
s33, the teaching server sets a session identifier and obtains a session key according to the session identifier;
s34, encrypting the session identifier and the session key, and transmitting the encrypted session identifier and the encrypted session key to the mobile learning terminal and the big data processing device by using a private key signature;
and S35, encrypting and decrypting data information by using the session key to perform data information security transmission.
7. The method as claimed in claim 6, wherein at step S31, at least one public-private key pair is set in a data security control unit of a tutorial control module of the tutorial server, and the mobile learning terminal and the big data processing apparatus are provided with public keys; the public and private key pair corresponding encryption algorithm can use an ECC algorithm, and the specific process is as follows:
s311, calculating a first Part of key agreement Part1 by using T _ SKA/T _ PKA and NB _ SKB/NB _ PKB through an elliptic curve point multiplication algorithm;
the first Part of key agreement Part1 DPSM2(TSKA，NBPKB)；
S312: calculating a second Part of key agreement Part2 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and T _ SKB/T _ PKB;
the second Part of key agreement Part2 DPSM2(NBSKA，TPKB)；
S313: calculating a third Part of key agreement Part3 by using an elliptic curve point multiplication algorithm for NB _ SKA/NB _ PKA and NB _ SKB/NB _ PKB;
third Part3 ═ DPSM2(NBSKA，NBPKB)；
S314: connecting a first key agreement Part1, a second key agreement Part2 and a third key agreement Part3 into a key component KM;
the key component KM Part1 Part2 Part 3);
s315: compressing the key component KM and the first character string into a parent rolling representation initial key N _ CC of 256 bits by using an SM3 algorithm;
initial key N _ CC ═ HSM3(KM | | | first string)
According to the characteristics of the elliptic curve point multiplication algorithm, through the calculation process, the two communication parties calculate a consistent parent rolling representative initial key N _ CC.
8. The method according to claim 7, wherein in step S32, when the mobile learning terminal and the big data processing device need to exchange data with the tutoring server, a session request is sent to the data security control unit; and after receiving the session request, the data security control unit allocates a session to the mobile learning terminal and the big data processing device.
9. The method according to claim 8, wherein in the step S33, the data security control unit sets a session identifier for the allocated session, and obtains a session key according to the session identifier, wherein the session identifier uniquely identifies the session; the session key may encrypt the session.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341408A (en) * | 2017-08-02 | 2017-11-10 | 武汉卓凡高科信息技术有限公司 | A kind of Informalized teaching method and device |
| CN107992999A (en) * | 2017-11-27 | 2018-05-04 | 西北工业大学 | A kind of multiagent dispatching method towards personalized production environment |
| CN111274278A (en) * | 2020-01-19 | 2020-06-12 | 托普朗宁(北京)教育科技有限公司 | Method and device for assisting learning and readable storage medium |
| WO2022116422A1 (en) * | 2020-12-01 | 2022-06-09 | 平安科技（深圳）有限公司 | Product recommendation method and apparatus, and electronic device and computer-readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104575140A (en) * | 2015-01-19 | 2015-04-29 | 深圳市时尚德源文化传播有限公司 | Intelligent terminal network teaching method |
| CN105491162A (en) * | 2016-01-19 | 2016-04-13 | 成都银事达信息技术有限公司 | Remote internet teaching system operation method |
| CN105590281A (en) * | 2015-12-29 | 2016-05-18 | 南京邮电大学 | System and method for education big data processing |
| CN105677710A (en) * | 2015-12-28 | 2016-06-15 | 曙光信息产业(北京)有限公司 | Processing method and system of big data |
| CN105718582A (en) * | 2016-01-25 | 2016-06-29 | 重庆邮电大学 | Personalized learning resource recommendation system and method under E-learning platform |
-
2016
- 2016-08-27 CN CN201610756914.XA patent/CN106375086A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104575140A (en) * | 2015-01-19 | 2015-04-29 | 深圳市时尚德源文化传播有限公司 | Intelligent terminal network teaching method |
| CN105677710A (en) * | 2015-12-28 | 2016-06-15 | 曙光信息产业(北京)有限公司 | Processing method and system of big data |
| CN105590281A (en) * | 2015-12-29 | 2016-05-18 | 南京邮电大学 | System and method for education big data processing |
| CN105491162A (en) * | 2016-01-19 | 2016-04-13 | 成都银事达信息技术有限公司 | Remote internet teaching system operation method |
| CN105718582A (en) * | 2016-01-25 | 2016-06-29 | 重庆邮电大学 | Personalized learning resource recommendation system and method under E-learning platform |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341408A (en) * | 2017-08-02 | 2017-11-10 | 武汉卓凡高科信息技术有限公司 | A kind of Informalized teaching method and device |
| CN107341408B (en) * | 2017-08-02 | 2020-04-17 | 武汉卓凡高科信息技术有限公司 | Informationized teaching method and device |
| CN107992999A (en) * | 2017-11-27 | 2018-05-04 | 西北工业大学 | A kind of multiagent dispatching method towards personalized production environment |
| CN111274278A (en) * | 2020-01-19 | 2020-06-12 | 托普朗宁(北京)教育科技有限公司 | Method and device for assisting learning and readable storage medium |
| WO2022116422A1 (en) * | 2020-12-01 | 2022-06-09 | 平安科技（深圳）有限公司 | Product recommendation method and apparatus, and electronic device and computer-readable storage medium |
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