Intelligent education courseware big data detection and storage system based on block chain

Abstract

The invention discloses an intelligent education courseware big data detection and storage system based on a block chain, and relates to the field of data storage and transmission, and the system comprises a data collection unit, a data preprocessing module, a block chain node cluster, an intelligent contract module, a data analysis unit, and a data access control unit. According to the intelligent education courseware big data detection and storage system based on the block chain, the confidentiality and integrity of intelligent education courseware data in the transmission process are ensured by utilizing the high efficiency and safety of an ECC encryption transmission protocol and the flexibility of key negotiation, and meanwhile, the performance problem possibly existing in a traditional encryption algorithm is avoided; according to the invention, only the node with the corresponding private key can decrypt and access the encrypted data, the data encryption technology can effectively protect the privacy of the data and prevent the data from being acquired by an unauthorized user, and meanwhile, in the data transmission process, an encryption transmission protocol is also adopted to ensure the transmission security of the data in the network.

Description

Technical Field

[0001] The present invention relates to data storage and transmission technology, and in particular to a blockchain-based smart education courseware big data detection and storage system. Background Art

[0002] With the increasing popularity of computer, communication and Internet technologies, modern educational technology and learning models are gradually developing towards informatization. According to the requirements of the syllabus and the needs of teaching, after strict teaching design, and produced in a variety of media and hypertext structures, courseware is currently the most widely used informatization teaching method in the field of education. The presentation form of courseware is also constantly enriched, from the early simple text documents, videos, and audio courseware to the current multimedia courseware with sound and color. The requirements for production level are getting higher and higher, and professional courseware production teams and production tools are born.

[0003] At present, PowerPoint, one of the courseware production tools, is a software for producing slides produced by Microsoft. The electronic documents produced by this software are widely used in academic reports, conferences and other places. Using this software to produce courseware is also the most commonly used method by middle school teachers. As for this software, its advantage is that it is convenient to make courseware, and it does not require much learning. It is easy to use. The produced courseware can be played through IE on the Internet for presentation. However, its functions are relatively poor. It is relatively convenient to display pictures, videos, and text materials, and it is easy to play the role of material display. However, if you want to achieve a better interactive effect, it is more cumbersome. You can completely achieve button and area interaction. Since office software has a certain universality, the use of PowerPoint courseware generally does not require packaging and other processing, but you need to pay attention to the path of audio and video files when using it.

[0004] The current courseware production software has the following defects: after the courseware is produced, the user needs to publish the courseware on the learning platform provided by the courseware production manufacturer for learners to use. This process involves large file transfers, and there are problems with security and efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a blockchain-based smart education courseware big data detection and storage system to solve the problems existing in the prior art.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a blockchain-based smart education courseware big data detection and storage system, comprising:

[0007] A data collection unit, used to collect various big data related to educational courseware in real time, including courseware content, user interaction data, and learning progress data;

[0008] The data preprocessing module performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected big data to ensure the quality and consistency of the data;

[0009] The blockchain node cluster consists of multiple interconnected blockchain nodes and is used to store preprocessed data. Each node saves a complete copy of the data to ensure data security and reliability;

[0010] Smart contract module, which defines the rules and operation logic for data storage, access, sharing, encryption, and automatically executes the data management process through smart contracts;

[0011] The data analysis unit uses machine learning and data mining techniques to analyze the big data stored on the blockchain to mine valuable information, including student learning preferences and courseware effect evaluation;

[0012] The data access control unit controls the access and use of data on the blockchain based on the user's identity and permission information to ensure the security and privacy of the data.

[0013] Further, the data acquisition unit comprises the following steps:

[0014] Collect special data in specific educational scenarios and generate unique identification information for each special data courseware data. The calculation formula is: ID = Generate (data), where Generate is the identification generation function, data is the collected courseware data, and ID is the generated identification information;

[0015] Perform hash calculation on the identification information, such as HashID=DCH(ID) mentioned above, and the DCH construction unit constructs a distributed consistent hash ring according to the hash value;

[0016] The blockchain storage unit stores the nodes including identification information and hash value related information in the blockchain;

[0017] The data retrieval unit searches the DCH ring according to the identification information of the courseware data to be detected, and determines the storage location and status of the courseware data by comparing it with the node information stored in the blockchain. Through the collaborative work of various units, the system realizes the efficient storage and rapid retrieval of smart education courseware big data, ensuring the reliability and availability of data.

[0018] Furthermore, the nodes in the blockchain node cluster adopt a distributed architecture, each node has independent computing and storage capabilities, and the nodes communicate through an encrypted network connection. The communication between the nodes adopts an elliptic curve encryption algorithm to encrypt and decrypt the transmitted data. Therefore, when each node joins the blockchain node cluster, it needs to pass identity authentication and authorization to ensure the legitimacy and security of the node.

[0019] Furthermore, the rules defined in the smart contract module include data storage period rules, that is, different data storage periods are set according to the type and importance of the data. For some important courseware data and user interaction data, a long-term storage period is set to meet long-term teaching evaluation and research needs; for some temporary intermediate data, a short-term storage period can be set to clean up unnecessary data in time. At the same time, the rules also include a dynamic adjustment mechanism for data access rights, which automatically adjusts the user's access rights to data according to changes in the user's role and changes in teaching tasks.

[0020] Furthermore, the machine learning algorithm adopted by the data analysis unit includes a clustering algorithm, which is used to aggregate similar educational courseware data or user behavior data into one category for better analysis and understanding. The clustering algorithm can be used to aggregate courseware with similar teaching content or teaching style into one category to provide reference and selection for teachers; students with similar learning behavior characteristics can also be aggregated into one category so that teachers can provide personalized teaching. In addition, the data analysis unit also adopts an association rule mining algorithm to discover potential correlations between data. Through association rule mining, it can be found that there is a correlation between certain courseware content and students' high academic performance, providing a basis for courseware optimization and the formulation of teaching strategies.

[0021] Furthermore, in the process of data storage, data encryption technology is used to encrypt each data block stored on the blockchain, and the data is encrypted using an asymmetric encryption algorithm, where the public key is used to encrypt the data and the private key is used to decrypt the data. The calculation formula is:

[0022] Ciphertext=ECCEncrypt(plaintext,publickey)

[0023] Where plaintext is the original courseware data, publickey is the public key of the sender, and Ciphertext is the encrypted ciphertext. At the same time, the shared key is negotiated with the receiver through a specific key negotiation algorithm. The calculation formula is:

[0024] Sharedkey=Keyagreement(ECCprivatekey,ECCpublickey)

[0025] Among them, ECCprivatekey is the private key of the sender, ECCpublickey is the public key of the receiver, and Sharedkey is the shared key obtained through negotiation. During the transmission process, the ciphertext is transmitted through the blockchain network, and the receiver uses the shared key to decrypt the ciphertext. The calculation formula is:

[0026] Plaintext=ECCDecrypt(Ciphertext,sharedkey).

[0027] Further, the following steps are included:

[0028] First, the data collection unit collects big data related to educational courseware in real time;

[0029] Then, the collected data is transferred to the data preprocessing module for preprocessing, including cleaning, deduplication, and format conversion operations;

[0030] Next, the preprocessed data is stored in each node in the blockchain node cluster, and each node stores a complete copy of the data;

[0031] While storing data, the smart contract module automatically executes the settings of data storage period and access rights according to preset rules;

[0032] Subsequently, the data stored on the blockchain is analyzed using machine learning algorithms in the data analysis unit to mine valuable information;

[0033] Finally, based on the results of data analysis, the educational courseware is optimized and improved, and the data storage and management strategies are adjusted and optimized.

[0034] Compared with the prior art, the blockchain-based smart education courseware big data detection and storage system provided by the present invention ensures the confidentiality and integrity of smart education courseware data during transmission by utilizing the efficiency and security of the ECC encryption transmission protocol and the flexibility of key negotiation, while avoiding the performance problems that may exist in traditional encryption algorithms. Only nodes with corresponding private keys can decrypt and access encrypted data. This data encryption technology can effectively protect the privacy of data and prevent data from being obtained by unauthorized users. At the same time, an encrypted transmission protocol is also used during data transmission to ensure the security of data transmission in the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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.

[0036] Figure 1 A schematic diagram of the overall structure provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0042] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0043] 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.

[0044] The embodiments herein may be described with reference to plan views and / or cross-sectional views with the aid 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 drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the drawings have schematic properties, and the shapes of the regions shown in the drawings illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0045] 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.

[0046] See also Figure 1 , a blockchain-based smart education courseware big data detection and storage system, including:

[0047] Data collection unit, used to collect various big data related to educational courseware in real time, including courseware content, user interaction data, and learning progress data;

[0048] The data preprocessing module performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected big data to ensure the quality and consistency of the data;

[0049] The blockchain node cluster consists of multiple interconnected blockchain nodes and is used to store preprocessed data. Each node saves a complete copy of the data to ensure data security and reliability;

[0050] Smart contract module, which defines the rules and operation logic for data storage, access, sharing, encryption, and automatically executes the data management process through smart contracts;

[0051] The data analysis unit uses machine learning and data mining techniques to analyze the big data stored on the blockchain to mine valuable information, including student learning preferences and courseware effect evaluation;

[0052] The data access control unit controls the access and use of data on the blockchain based on the user's identity and permission information to ensure the security and privacy of the data.

[0053] The data acquisition unit includes the following steps:

[0054] Collect special data in specific educational scenarios and generate unique identification information for each special data courseware data. The calculation formula is: ID = Generate (data), where Generate is the identification generation function, data is the collected courseware data, and ID is the generated identification information;

[0055] Perform hash calculation on the identification information, such as HashID=DCH(ID) mentioned above, and the DCH construction unit constructs a distributed consistent hash ring according to the hash value;

[0056] The blockchain storage unit stores the nodes including identification information and hash value related information in the blockchain;

[0057] The data retrieval unit searches the DCH ring according to the identification information of the courseware data to be detected, and determines the storage location and status of the courseware data by comparing it with the node information stored in the blockchain. Through the collaborative work of various units, the system realizes the efficient storage and rapid retrieval of smart education courseware big data, ensuring the reliability and availability of data.

[0058] The nodes in the blockchain node cluster adopt a distributed architecture. Each node has independent computing and storage capabilities, and the nodes communicate with each other through encrypted network connections. Under this distributed architecture, even if some nodes fail or are attacked, the entire system can still operate normally because other nodes can continue to provide services. At the same time, the encrypted network connection ensures the security of data transmission and prevents data from being stolen or tampered with during transmission. Specifically, the communication between nodes uses the elliptic curve encryption algorithm to encrypt and decrypt the transmitted data, so that each node needs to pass identity authentication and authorization when joining the blockchain node cluster to ensure the legitimacy and security of the node. This distributed architecture and encrypted communication mechanism provide a solid foundation for the storage and management of smart education courseware big data, ensuring the high availability of the system and the security of data.

[0059] The rules defined in the smart contract module include data storage period rules, that is, different data storage periods are set according to the type and importance of the data. For some important courseware data and user interaction data, a long-term storage period is set to meet long-term teaching evaluation and research needs; for some temporary intermediate data, a short-term storage period can be set to clean up unnecessary data in time and save storage space. At the same time, the rules also include a dynamic adjustment mechanism for data access rights, which automatically adjusts the user's access rights to data according to the user's role changes and teaching task changes. For example, when a teacher switches from teaching one course to teaching another, his access rights to the original course-related data will be automatically revoked, and he will gain access rights to the new course data. This dynamic adjustment mechanism can ensure the security of data and the rationality of access, and improve the flexibility and adaptability of the system.

[0060] The machine learning algorithms used by the data analysis unit include clustering algorithms, which are used to aggregate similar educational courseware data or user behavior data into one category for better analysis and understanding. The clustering algorithm can be used to aggregate courseware with similar teaching content or teaching style into one category to provide reference and selection for teachers; students with similar learning behavior characteristics can also be aggregated into one category so that teachers can provide personalized teaching. In addition, the data analysis unit also uses association rule mining algorithms to discover potential associations between data. Through association rule mining, it can be found that there is a correlation between certain courseware content and students' high academic performance, providing a basis for courseware optimization and teaching strategy formulation. The application of these machine learning algorithms can mine valuable information from a large amount of smart education courseware big data, providing strong support for the improvement and innovation of education and teaching.

[0061] In the process of data storage, data encryption technology is used to encrypt each data block stored on the blockchain. The data is encrypted using an asymmetric encryption algorithm, where the public key is used to encrypt the data and the private key is used to decrypt the data. The calculation formula is:

[0062] Ciphertext=ECCEncrypt(plaintext,publickey)

[0063] Where plaintext is the original courseware data, publickey is the public key of the sender, and Ciphertext is the encrypted ciphertext. At the same time, the shared key is negotiated with the receiver through a specific key negotiation algorithm. The calculation formula is:

[0064] Sharedkey=Keyagreement(ECCprivatekey,ECCpublickey)

[0065] Among them, ECCprivatekey is the private key of the sender, ECCpublickey is the public key of the receiver, and Sharedkey is the shared key obtained through negotiation. During the transmission process, the ciphertext is transmitted through the blockchain network, and the receiver uses the shared key to decrypt the ciphertext. The calculation formula is:

[0066] Plaintext=ECCDecrypt(Ciphertext,sharedkey).

[0067] This encrypted transmission protocol utilizes the efficiency and security of ECC and the flexibility of key negotiation to ensure the confidentiality and integrity of smart education courseware data during transmission, while avoiding the performance problems that may exist in traditional encryption algorithms. Only nodes with corresponding private keys can decrypt and access encrypted data. This data encryption technology can effectively protect the privacy of data and prevent data from being obtained by unauthorized users. At the same time, an encrypted transmission protocol is also used during data transmission to ensure the security of data transmission in the network.

[0068] The following steps are involved:

[0069] First, the data collection unit collects big data related to educational courseware in real time;

[0070] Then, the collected data is transferred to the data preprocessing module for preprocessing, including cleaning, deduplication, and format conversion operations;

[0071] Next, the preprocessed data is stored in each node in the blockchain node cluster, and each node stores a complete copy of the data;

[0072] While storing data, the smart contract module automatically executes the settings of data storage period and access rights according to preset rules;

[0073] Subsequently, the data stored on the blockchain is analyzed using machine learning algorithms in the data analysis unit to mine valuable information;

[0074] Finally, based on the results of data analysis, the educational courseware is optimized and improved, and the data storage and management strategies are adjusted and optimized.

[0075] 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. A blockchain-based smart education courseware big data detection and storage system, characterized in that: include: A data collection unit, used to collect various big data related to educational courseware in real time, including courseware content, user interaction data, and learning progress data; The data preprocessing module performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected big data; The blockchain node cluster consists of multiple interconnected blockchain nodes, which are used to store preprocessed data. Each node stores a complete copy of the data. Smart contract module, which defines the rules and operation logic for data storage, access, sharing, encryption, and automatically executes the data management process through smart contracts; The data analysis unit uses machine learning and data mining techniques to analyze the big data stored on the blockchain to mine valuable information, including student learning preferences and courseware effect evaluation; The data access control unit controls the access and use of data on the blockchain based on the user's identity and permission information.

2. According to the blockchain-based smart education courseware big data detection and storage system of claim 1, it is characterized in that: The data acquisition unit comprises the following steps: Collect special data in specific educational scenarios and generate unique identification information for each special data courseware data. The calculation formula is: ID = Generate (data), where Generate is the identification generation function, data is the collected courseware data, and ID is the generated identification information; Perform hash calculation on the identification information, such as HashID=DCH(ID) mentioned above, and the DCH construction unit constructs a distributed consistent hash ring according to the hash value; The blockchain storage unit stores the nodes including identification information and hash value related information in the blockchain; The data retrieval unit searches the DCH ring according to the identification information of the courseware data to be detected, and determines the storage location and status of the courseware data by comparing it with the node information stored in the blockchain.

3. According to the blockchain-based smart education courseware big data detection and storage system of claim 2, it is characterized in that: The nodes in the blockchain node cluster adopt a distributed architecture, each node has independent computing and storage capabilities, and the nodes communicate through encrypted network connections. The communication between nodes adopts an elliptic curve encryption algorithm to encrypt and decrypt the transmitted data, so that each node needs to pass identity authentication and authorization when joining the blockchain node cluster.

4. According to the blockchain-based smart education courseware big data detection and storage system of claim 3, it is characterized in that: The rules defined in the smart contract module include data storage period rules, that is, different data storage periods are set according to the type and importance of the data. For some important courseware data and user interaction data, a long-term storage period is set to meet long-term teaching evaluation and research needs; for some temporary intermediate data, a short-term storage period is set to clean up unnecessary data in a timely manner. At the same time, the rules also include a dynamic adjustment mechanism for data access rights, which automatically adjusts the user's access rights to data according to changes in the user's role and changes in teaching tasks.

5. According to the blockchain-based smart education courseware big data detection and storage system of claim 4, it is characterized in that: The machine learning algorithms used by the data analysis unit include clustering algorithms, which are used to aggregate similar educational courseware data or user behavior data into one category for better analysis and understanding. The clustering algorithm is used to aggregate courseware with similar teaching content or teaching style into one category to provide reference and selection for teachers; students with similar learning behavior characteristics are also aggregated into one category so that teachers can provide personalized teaching. In addition, the data analysis unit also uses an association rule mining algorithm to discover potential correlations between data. Through association rule mining, it is discovered that there is a correlation between certain courseware content and students' high academic performance, providing a basis for courseware optimization and the formulation of teaching strategies.

6. According to the blockchain-based smart education courseware big data detection and storage system of claim 5, it is characterized in that: In the process of data storage, data encryption technology is used to encrypt each data block stored on the blockchain. The data is encrypted using an asymmetric encryption algorithm, where the public key is used to encrypt the data and the private key is used to decrypt the data. The calculation formula is: Ciphertext=ECCEncrypt(plaintext,publickey) Where plaintext is the original courseware data, publickey is the public key of the sender, and Ciphertext is the encrypted ciphertext. At the same time, the shared key is negotiated with the receiver through a specific key negotiation algorithm. The calculation formula is: Sharedkey=Keyagreement(ECCprivatekey,ECCpublickey) Among them, ECCprivatekey is the private key of the sender, ECCpublickey is the public key of the receiver, and Sharedkey is the shared key obtained through negotiation. During the transmission process, the ciphertext is transmitted through the blockchain network, and the receiver uses the shared key to decrypt the ciphertext. The calculation formula is: Plaintext=ECCDecrypt(Ciphertext,sharedkey).

7. According to the blockchain-based smart education courseware big data detection and storage system of claim 6, it is characterized in that: The following steps are involved: First, the data collection unit collects big data related to educational courseware in real time; Then, the collected data is transferred to the data preprocessing module for preprocessing, including cleaning, deduplication, and format conversion operations; Next, the preprocessed data is stored in each node in the blockchain node cluster, and each node stores a complete copy of the data; While storing data, the smart contract module automatically executes the settings of data storage period and access rights according to preset rules; Subsequently, the data stored on the blockchain is analyzed using machine learning algorithms in the data analysis unit to mine valuable information; Finally, based on the results of data analysis, the educational courseware is optimized and improved, and the data storage and management strategies are adjusted and optimized.

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