A cloud-computing-based education resource sharing method and system

Abstract

The application belongs to the technical field of cloud computing and information security, and discloses an education resource sharing method and system based on cloud computing. The method carries out structural analysis on online course resources uploaded to a cloud platform, defines a user role attribute model based on an education scene, constructs an access control policy tree based on an attribute-based encryption mechanism, adopts a distributed hash table to store fragments of the resource ciphertext, and generates a resource positioning index. When a course resource access request is received, the education attribute set of the requesting user is verified, and a learning path perception mechanism is combined to determine whether the user meets the decryption condition and chapter unlocking condition of the access control policy tree. The course access record, learning duration, test score and credit acquisition record of the learner are recorded to an education alliance chain to generate a verifiable learning record certificate, thereby realizing access tracing and auditing. The technical problems of coarse education resource access control granularity, weak anti-stealing broadcast capability and low learning record credibility are solved.

Description

Technical Field

[0001] This invention belongs to the field of cloud computing and information security technology, and in particular relates to a cloud computing-based method and system for sharing educational resources. Background Technology

[0002] With the rapid development of cloud computing technology and the popularization of online education, higher education institutions and training platforms have accumulated a wealth of online course resources, including video lectures, e-textbooks, lab guides, and test question banks. Sharing these high-quality educational resources across institutions is crucial for promoting educational equity and improving teaching quality. However, sharing online course resources faces numerous technical challenges: on the one hand, core resources such as course videos have high intellectual property value, requiring protection against unauthorized access and illegal dissemination; on the other hand, different learners have varying learning needs and access levels, making traditional coarse-grained access control mechanisms insufficient for meeting the requirements of refined access management.

[0003] Existing educational resource sharing solutions have the following shortcomings: First, access control policies are too simplistic, typically relying solely on user identity for permission checks, failing to support dynamic authorization based on educational attributes such as learning progress, course selection status, and credit acquisition. Second, there is a lack of effective anti-piracy mechanisms, making course videos susceptible to screen recording, downloading, and illegal dissemination, thus harming the rights of content providers. Third, learning records are stored in centralized databases, which are prone to data tampering and lack of credibility, making it difficult to provide reliable proof for cross-institutional credit recognition. Fourth, resource storage and transmission efficiency is low, easily leading to performance bottlenecks during large-scale concurrent access.

[0004] Therefore, there is an urgent need for a method for sharing educational resources that can balance security, flexibility, and traceability to solve the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud computing-based method and system for sharing educational resources, in order to solve the technical problems in the prior art such as coarse granularity of educational resource access control, weak anti-piracy capabilities, and low credibility of learning records.

[0006] The specific technical solution is as follows: On the one hand, this invention provides a cloud computing-based method for sharing educational resources, comprising the following steps: Step S1 involves performing structured parsing on the online course resources uploaded to the cloud platform, extracting the course chapter tree, knowledge point tags, and prerequisite dependencies, constructing a course knowledge graph, and performing multi-level encryption on the course content to generate ciphertext of the resources and corresponding resource feature vectors.

[0007] Step S2: Define a user role attribute model based on the education scenario, construct a user education attribute certificate that includes identity role, affiliated institution, course selection status, learning progress and credit acquisition status, and construct an access control policy tree based on the attribute base encryption mechanism, and embed the access control policy tree into the resource metadata.

[0008] Step S3: The encrypted resource is fragmented and stored using a distributed hash table, and a resource location index is generated.

[0009] Step S4: When a course resource access request is received, the educational attribute set of the requesting user is verified, and the learning path awareness mechanism is used to determine whether the user meets the decryption conditions of the access control policy tree and the chapter unlocking conditions.

[0010] Step S5: If the decryption conditions are met, the resource fragments are obtained from the distributed nodes according to the resource location index, reassembled, and then the decryption operation is performed. Anti-piracy secure transmission is achieved through dynamic watermark embedding and playback environment detection, and the decrypted course resources are transmitted to the requesting user.

[0011] Step S6: Record learners’ course access records, learning duration, test scores and credit acquisition status to the education alliance chain to generate verifiable learning history credentials, enabling access traceability and auditing.

[0012] Furthermore, the structured parsing and course knowledge graph construction method specifically includes the following steps: Step S2.1: Parse the course resource package and extract course metadata, including course identifier, course name, instructor, credit value, total class hours, and course category; construct a course chapter tree. ,in Let E be the set of chapter nodes, E be the set of parent-child relationship edges, and L be the chapter hierarchy mapping function; each chapter node contains chapter identifier, title, duration, and difficulty level attributes.

[0013] Step S2.2: Extract the set of knowledge point tags for each chapter and establish the prerequisite dependencies between knowledge points. Directed edge Representing knowledge points It is learning Prerequisites.

[0014] Step S2.3: For the current course C, find its prerequisite course set. Learners must complete the prerequisite courses to unlock the current course.

[0015] Step S2.4: Combine the course chapter tree, knowledge point tags, and prerequisite dependencies to construct a course knowledge graph and store it in a graph database.

[0016] Furthermore, the multi-level encryption process specifically includes the following steps: Step S3.1, for the original course resources The first layer of encryption is performed using a symmetric encryption algorithm to generate the first ciphertext. The encryption process is represented as follows: ,in Represents a symmetric encryption function. It is a symmetric key.

[0017] Step S3.2: Use an attribute-based encryption algorithm to process the symmetric key. Perform a second layer of encryption to generate the key ciphertext. ,in This represents the attribute-based encryption function. This is an access control policy tree, where PK is the system public key.

[0018] Step S3.3: Extract resource content features and construct resource feature vectors. ,in For feature dimensions.

[0019] Furthermore, the method for constructing user education attribute certificates includes the following steps: Step S4.1, Define the set of educational role types , These correspond to students, teachers, teaching assistants, and administrators, respectively.

[0020] Step S4.2: Construct a user education attribute vector, including: user unique identifier, role type, affiliated institution identifier, course selection record set, and course selection status.

[0021] Step S4.3, Define the learning progress mapping ,user For the course The learning progress is calculated as follows: ,in For chapters The weighting coefficients, Duration Difficulty level This indicates the chapter is complete.

[0022] Step S4.4, construct the credit mapping if and only if and At that time, the user will receive credits for the course, of which The progress threshold, As the performance threshold, For users In the course The results.

[0023] Step S4.5: The education attribute certification authority digitally signs the user attribute vector to generate an education attribute certificate. The certificate validity period is updated in sync with the semester cycle; For the certification authority to sign the attribute vector, The private key for the educational certification authority.

[0024] Furthermore, methods for constructing access control policy trees include: constructing the policy tree. Where N is the set of nodes and E is the set of edges. For threshold functions, This is an attribute mapping function; for non-leaf nodes in the policy tree Set threshold value ,in , For nodes The number of child nodes; when When represents the OR gate, when The time indicates an AND gate.

[0025] Furthermore, the fragmented storage method includes the following steps: Step S6.1, encrypt the resource. According to fixed size Perform segmentation to generate a set of fragments. The number of shards .

[0026] Step S6.2: Use erasure coding technology to perform redundant coding on the fragments and set the redundancy factor. Generate redundant fragment sets .

[0027] Step S6.3, for each fragment Calculate distributed hash value ,in Indicates a connection operation. For resource identification.

[0028] Step S6.4, based on the hash value Determine the sharded storage nodes .

[0029] Step S6.5: Construct resource location index It is encrypted and stored on a cloud metadata server.

[0030] Furthermore, learning path-aware access control methods include: Get the attribute set of the requesting user and the corresponding attribute private key Satisfaction function For nodes in the strategy tree ,like If it is a leaf node, then ;like If a node is a non-leaf node, then it is true if and only if the number of its child nodes that satisfy the condition is not less than the threshold value. hour, ;when Chapters can only be unlocked at certain times.

[0031] Chapter unlock condition function Regarding the chapter and users The unlocking conditions are: ;in, As a chapter requirement, it requires users to complete all the preceding chapters of the current chapter. As a prerequisite for the course, Conditions for accumulating study time: ,in To remove the effective viewing time after fast forwarding and background playback, The required cumulative duration is calculated recursively from the root node of the strategy tree. ,like and If the verification passes, the verification is successful; otherwise, an access denied flag is returned.

[0032] Furthermore, the specific methods for preventing piracy and ensuring secure transmission include: starting from resource location indexes. Extract the fragment location information and initiate fragment retrieval requests in parallel to the corresponding storage nodes; verify the integrity of the retrieved fragments, and if the number of corrupted fragments does not exceed the redundancy factor. Then erasure coding is used to recover the data and reassemble it to obtain the complete ciphertext. Use user attribute private key For key ciphertext Perform attribute-based decryption to recover the symmetric key. Using a symmetric key For ciphertext Decryption was performed to obtain the original course resources. .

[0033] Generate dynamic user watermark information and perform client playback environment detection. The detection items include screen recording software detection, virtual machine environment detection, debugger attachment detection, and device fingerprint consistency detection. Video decryption and playback are allowed only if all detections pass.

[0034] Furthermore, methods for on-chain evidence storage of learning behavior include: calculating effective learning time. , ,in, Total duration To fast forward and skip the duration, This refers to the background playback duration. The idle time when the user does not interact with the system for more than a threshold.

[0035] Learning records are submitted to the education consortium blockchain, which consists of nodes from participating universities, educational institutions, and certification centers. It uses the PBFT consensus algorithm, where blockchain nodes verify transactions. Once verified, the transaction is written into a new block. Once a user completes the course and passes the assessment, a credit certificate is generated.

[0036] On the other hand, the present invention provides a cloud computing-based educational resource sharing system, which includes: a course parsing and knowledge graph engine, a resource encryption module, an educational attribute authentication center, an access control module, a distributed storage module, a learning path access controller, an anti-piracy secure transmission module, a resource acquisition module, and an educational consortium blockchain evidence storage platform.

[0037] The course parsing and knowledge graph engine is used to perform structured parsing of uploaded online course resources, extract course metadata, construct chapter trees, identify knowledge point tags and prerequisite dependencies, and generate and maintain a course knowledge graph.

[0038] The resource encryption module is used to perform multi-level encryption processing on course resources, including a symmetric encryption submodule and an attribute-based encryption submodule, to generate resource ciphertext and key ciphertext.

[0039] The Education Attribute Certification Center is used to define and manage the education role attribute model, including user identity role, institution affiliation, course selection status, learning progress and credit information, and is responsible for the issuance, updating and revocation of education attribute certificates.

[0040] The access control module is used to build and manage the access control policy tree, including a policy editor, property manager, and threshold configurator, to achieve fine-grained access control.

[0041] The distributed storage module is used to fragment, redundantly encode, and distribute the encrypted resources. It includes a fragmentation engine, erasure coding encoder, and distributed hash table, and generates and maintains a resource location index.

[0042] The learning path access controller is used to build and evaluate a multi-dimensional dynamic access control strategy that integrates the completion of prerequisite chapters, prerequisite course credits, and accumulated learning time, so as to achieve progressive content unlocking based on learning progress.

[0043] The anti-piracy secure transmission module is used to perform dynamic watermark embedding, segmented key authorization, playback environment detection, and session heartbeat monitoring. It includes a DRM copyright protection engine, a token issuer, and a client environment detection component.

[0044] The resource acquisition module is used to acquire fragments in parallel according to the resource location index, perform integrity verification, data reassembly and decryption operations, and transmit the decrypted resources to the user through a secure channel.

[0045] The educational consortium blockchain evidence storage platform is used to store learning behavior records and credit certificate records on the blockchain. It includes PBFT consensus nodes, smart contract executors, and automated audit engines to achieve tamper-proof access traceability.

[0046] Compared with the prior art, the beneficial effects of this invention are: This invention supports dynamic authorization based on multi-dimensional educational attributes such as user roles, course selection status, learning progress, and credit acquisition through an attribute-based encryption mechanism and access control policy tree, overcoming the shortcomings of coarse-grained access control in traditional solutions. Through a learning path awareness mechanism, chapter unlocking is linked to the completion of preceding chapters, prerequisite course credits, and accumulated learning time, effectively preventing learners from skipping key content and ensuring learning quality. Learning behavior and credit credentials are stored through an educational consortium blockchain, leveraging the immutability of blockchain to provide reliable proof for cross-institutional credit recognition. Distributed hash table sharding and erasure coding redundancy enable efficient resource storage and parallel access, effectively handling large-scale concurrent access scenarios. Attached Figure Description

[0047] Figure 1 This is a flowchart of a cloud computing-based educational resource sharing method according to the present invention. Figure 2 This is a schematic diagram of the components of a cloud computing-based educational resource sharing system according to the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of this invention, not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0049] like Figure 1 The diagram shown is a flowchart of a cloud computing-based educational resource sharing method according to the present invention, which includes the following steps: Step S1 involves performing structured parsing on the online course resources uploaded to the cloud platform, extracting the course chapter tree, knowledge point tags, and prerequisite dependencies, constructing a course knowledge graph, and performing multi-level encryption on the course content to generate ciphertext of the resources and corresponding resource feature vectors.

[0050] As a concrete example, let's take the "Data Structures and Algorithms" course for a computer science major at a university as an example. This course contains 12 chapters, with a total of 64 class hours and 4 credits. The system first parses the course resource package and extracts the course metadata: CourseID = "CS201", Course Name = "Data Structures and Algorithms", Instructor = "Professor Zhang", Credits = 4, Total Class Hours = 64, Course Category = "Required for Major". Then, a chapter tree is constructed, where the root node is the course itself, and the first-level child nodes include 12 chapter nodes such as "Chapter 1 Binding Theory", "Chapter 2 Linear Lists", and "Chapter 3 Stacks and Queues". Each chapter node records its identifier, title, duration (e.g., Chapter 1 has a duration of 4 class hours), and difficulty level (e.g., Chapter 1 has a difficulty level of 1, indicating basic difficulty). At the same time, the system identifies knowledge point tags, such as "linked list", "binary tree", "sorting algorithm", etc., and establishes prerequisite dependencies between knowledge points. For example, "binary search tree" depends on "binary tree basics", and "graph traversal" depends on "stack" and "queue".

[0051] The structured parsing and course knowledge graph construction method specifically includes the following steps: Step S2.1: Parse the course resource package and extract course metadata, including course identifier, course name, instructor, credit value, total class hours, and course category; construct a course chapter tree. ,in Let E be the set of chapter nodes, E be the set of parent-child relationship edges, and L be the chapter hierarchy mapping function; each chapter node contains chapter identifier, title, duration, and difficulty level attributes.

[0052] Step S2.2: Extract the set of knowledge point tags for each chapter and establish the prerequisite dependencies between knowledge points. Directed edge Representing knowledge points It is learning Prerequisites.

[0053] Step S2.3: For the current course C, find its prerequisite course set. Learners must complete the prerequisite courses to unlock the current course.

[0054] Step S2.4: Combine the course chapter tree, knowledge point tags, and prerequisite dependencies to construct a course knowledge graph and store it in a graph database.

[0055] For example, for the "Data Structures and Algorithms" course mentioned above, the course chapter tree constructed by the system can be represented as follows: ,in ="Chapter 1 Binding Theory". Directed Edge Representing knowledge points It is learning The prerequisites, corresponding to the examples: ("Binary Tree Basics", "Binary Search Tree") The condition "Binary Tree Basics" indicates that learning "Binary Search Trees" is a prerequisite. The set of prerequisite courses, Pre(CS201) = {CS101}, indicates that learning "Data Structures and Algorithms" requires completion of the "Fundamentals of Programming" course. This information is integrated into a course knowledge graph, stored in graph databases such as Neo4j, supporting complex knowledge association queries.

[0056] Multi-level encryption processing specifically includes the following steps: Step S3.1, for the original course resources The first layer of encryption is performed using a symmetric encryption algorithm to generate the first ciphertext. The encryption process is represented as follows: ,in Represents a symmetric encryption function. It is a symmetric key.

[0057] Step S3.2: Use an attribute-based encryption algorithm to process the symmetric key. Perform a second layer of encryption to generate the key ciphertext. ,in This represents the attribute-based encryption function. This is an access control policy tree, where PK is the system public key.

[0058] Step S3.3: Extract resource content features and construct resource feature vectors. ,in For feature dimensions.

[0059] Assuming the video resource size for Chapter 3, Stacks and Queues, is 500MB, the system first generates a 256-bit AES symmetric key and uses the AES-256-GCM algorithm for the first layer of encryption of the video file. Then, based on the access policy of this chapter, "(Role = Student AND Institution = This School AND Selected Course = CS201) OR Role = Teacher," an access control policy tree T is constructed. The symmetric key is then encrypted a second time using the CP-ABE attribute-based encryption algorithm to generate the ciphertext key. Only users whose attribute sets satisfy the policy tree can decrypt the video to obtain the symmetric key and thus decrypt the video content. Simultaneously, the system extracts the video's content feature vector for subsequent copyright protection and content retrieval.

[0060] Step S2: Define a user role attribute model based on the education scenario, construct a user education attribute certificate that includes identity role, affiliated institution, course selection status, learning progress and credit acquisition status, and construct an access control policy tree based on the attribute base encryption mechanism, and embed the access control policy tree into the resource metadata.

[0061] The method for constructing a user education attribute certificate includes the following steps: Step S4.1, Define the set of educational role types , These correspond to students, teachers, teaching assistants, and administrators, respectively.

[0062] Step S4.2: Construct a user education attribute vector, including: user unique identifier, role type, affiliated institution identifier, course selection record set, and course selection status.

[0063] Step S4.3, Define the learning progress mapping ,user For the course The learning progress is calculated as follows: ,in For chapters The weighting coefficients, Duration Difficulty level This indicates the chapter is complete.

[0064] Step S4.4, construct the credit mapping if and only if and At that time, the user will receive credits for the course, of which The progress threshold, As the performance threshold, For users In the course The results.

[0065] Step S4.5: The education attribute certification authority digitally signs the user attribute vector to generate an education attribute certificate. Certificate validity period Updated in sync with the semester cycle; This is used by the certification authority to sign the attribute vector, preventing attribute tampering and ensuring the authenticity of the certificate. This is the private key of the educational certification authority, used to issue certificates.

[0066] For example, for student Li Ming (student ID 2023001), his user education attribute certificate contains the following information: UserID="2023001", role type role=Student, affiliated institution OrgID="PKU" (Peking University), course selection record set EnrollSet={CS101, CS201, CS301}, and course selection status is Active. Li Ming's learning progress calculation for the "Data Structures and Algorithms" course: Assuming the course has 12 chapters, and Li Ming has completed the first 6 chapters (difficulty levels 1, 2, 2, 3, 3, 4, and durations of 4, 5, 5, 6, 6, 8 class hours respectively), then the total weighted coefficient is 4×1+5×2+5×2+6×3+6×3+8×4=4+10+10+18+18+32=92. The weighted sum of the completed chapters is 92. Therefore, the learning progress P(Li Ming, CS201)=92 / (4×1+5×2+5×2+6×3+6×3+8×4+10×4+8×3+6×3+6×2+4×2+2×1)=92 / 184≈0.50, meaning he has completed 50% of the course content. The Education Attribute Authentication Center digitally signs this attribute vector and generates a validity period. Educational qualification certificate by the end of this semester.

[0067] Methods for constructing an access control policy tree include: constructing the policy tree Where N is the set of nodes and E is the set of edges. For threshold functions, This is an attribute mapping function; for non-leaf nodes in the policy tree Set threshold value ,in , For nodes The number of child nodes; when When represents the OR gate, when The time indicates an AND gate.

[0068] Step S3: The encrypted resource is fragmented and stored using a distributed hash table, and a resource location index is generated.

[0069] The fragmented storage method includes the following steps: Step S6.1, encrypt the resource. According to fixed size Perform segmentation to generate a set of fragments. The number of shards .

[0070] Step S6.2: Use erasure coding technology to perform redundant coding on the fragments and set the redundancy factor. Generate redundant fragment sets .

[0071] Step S6.3, for each shard calculate the distributed hash value , where denotes the concatenation operation, is the resource identifier.

[0072] Step S6.4, based on the hash value determine the shard storage node .

[0073] Step S6.5, construct the resource location index , and encrypt it for storage in the cloud metadata server.

[0074] For example, for the 500MB encrypted video of Chapter 3 of the above "Data Structures and Algorithms" course, set the shard size b = 4MB, then l = ⌈500 / 4⌉ = 125 original shards are generated. Using Reed-Solomon erasure code, set the redundancy factor r = 25, and a total of 150 redundant shards are generated. Calculate the SHA-256 hash value for each shard as the distributed location key, and determine the storage node according to the hash value through the DHT protocol (such as Kademlia). The finally constructed resource location index contains 150 records, and the format of each record is (shard number, hash value, storage node address). This index is encrypted using AES and stored in the cloud metadata server. When any no more than 25 storage nodes fail, the complete data can still be recovered from the remaining 125 shards using erasure code.

[0075] When student Li Ming requests to access "Basic Concepts of Graphs" in Chapter 7, the system executes the following verification process: First, obtain Li Ming's attribute set {role = student, institution = PKU, enrolled courses = CS201} and its attribute private key. Verify the satisfaction of the attribute policy tree: Since Li Ming has the "student" role, belongs to the "PKU" institution, and has taken the CS201 course, which satisfies the AND gate condition in the policy tree, so Sat(root, SLi Ming) = 1. Then verify the chapter unlocking conditions: The system checks whether Li Ming has completed the study of Chapters 1 - 6 (prerequisite chapter condition), whether he has obtained the credits for the prerequisite course CS101 (prerequisite course condition), and whether the effective learning duration in the first 6 chapters meets the requirements (learning duration condition).

[0076] Suppose Li Ming has completed the first 6 chapters and obtained the CS101 credits, but the effective viewing duration of Chapter 6 is only 70% of the chapter duration (lower than the threshold of 80%), then Unlock = False, and the system returns a prompt of "Please complete the full study of Chapter 6 before accessing Chapter 7".

[0077] Step S4: When a course resource access request is received, the educational attribute set of the requesting user is verified, and the learning path awareness mechanism is used to determine whether the user meets the decryption conditions of the access control policy tree and the chapter unlocking conditions.

[0078] Learning path-aware access control methods include: Get the attribute set of the requesting user and the corresponding attribute private key Satisfaction function For nodes in the strategy tree ,like If it is a leaf node, then ;like If a node is a non-leaf node, then it is true if and only if the number of its child nodes that satisfy the condition is not less than the threshold value. hour, ;when Chapters can only be unlocked at certain times.

[0079] Chapter unlock condition function Regarding the chapter and users The unlocking conditions are: ;in, As a chapter requirement, it requires users to complete all the preceding chapters of the current chapter. As a prerequisite for the course, Conditions for accumulating study time: ,in To remove the effective viewing time after fast forwarding and background playback, The required cumulative duration is calculated recursively from the root node of the strategy tree. ,like and If the verification passes, the verification is successful; otherwise, an access denied flag is returned.

[0080] Step S5: If the decryption conditions are met, the resource fragments are obtained from the distributed nodes according to the resource location index, reassembled, and then the decryption operation is performed. Anti-piracy secure transmission is achieved through dynamic watermark embedding and playback environment detection, and the decrypted course resources are transmitted to the requesting user.

[0081] The specific methods for preventing piracy and ensuring secure transmission include: starting from resource location indexes. Extract the fragment location information and initiate fragment retrieval requests in parallel to the corresponding storage nodes; verify the integrity of the retrieved fragments, and if the number of corrupted fragments does not exceed the redundancy factor. Then erasure coding is used to recover the data and reassemble it to obtain the complete ciphertext. Use user attribute private key For key ciphertext Perform attribute-based decryption to recover the symmetric key. Using a symmetric key For ciphertext Decryption was performed to obtain the original course resources. .

[0082] Generate dynamic user watermark information and perform client playback environment detection. The detection items include screen recording software detection, virtual machine environment detection, debugger attachment detection, and device fingerprint consistency detection. Video decryption and playback are allowed only if all detections pass.

[0083] For example, when student Li Ming watches the Chapter 3 video, the system first generates dynamic watermark information, encrypting and encoding information such as user ID, session ID, and timestamp, embedding it into the video frames in a way invisible to the naked eye. If the video is illegally recorded and disseminated, the watermark can be extracted from the leaked video to trace the responsible party. Simultaneously, the client player performs environment checks: checking if screen recording software such as OBS or Bandicam is running (playback is blocked if detected), checking if it is in a virtual machine environment such as VMware or VirtualBox (playback is blocked if detected), checking if it is attached to a debugger (such as Frida or Xposed, playback is blocked if detected), and verifying if the device fingerprint matches the one used during registration (if inconsistent, identity re-verification is required). Only when all checks pass does the player obtain a short-term segmented decryption token from the server, decrypt and play the video segment as needed. During playback, the client sends a heartbeat packet to the server every 10 seconds. If three consecutive heartbeats are lost or the environment hash is abnormal, the server immediately revokes the playback permission for the current session.

[0084] Step S6: Record learners’ course access records, learning duration, test scores and credit acquisition status to the education alliance chain to generate verifiable learning history credentials, enabling access traceability and auditing.

[0085] Methods for on-chain evidence storage of learning behavior include: calculating effective learning time. , ,in, Total duration To fast forward and skip the duration, This refers to the background playback duration. The idle time when the user does not interact with the system for more than a threshold; Learning records are submitted to the education consortium blockchain, which consists of nodes from participating universities, educational institutions, and certification centers. It uses the PBFT consensus algorithm, where blockchain nodes verify transactions. Once verified, the transaction is written into a new block. Once a user completes the course and passes the assessment, a credit certificate is generated.

[0086] When student Li Ming was studying Chapter 3 of the "Data Structures and Algorithms" course, the system recorded the following learning behaviors: total playback time was 60 minutes, including 5 minutes of fast-forwarding, 3 minutes of background playback (window minimized or tab switched), and 7 minutes of no interaction (more than 2 minutes of no mouse or keyboard operation). Therefore, the effective learning time = 60 - 5 - 3 - 7 = 45 minutes. This learning record was packaged into a transaction and submitted to the educational consortium blockchain. The transaction content included: User ID="2023001", Course ID="CS201", Chapter ID="Chap03", Effective Time = 45 minutes, Viewing Completion Rate = 90%, Chapter Quiz Score = 85 points, and Timestamp = "2024-03-15T14:30:00Z". The consortium blockchain consists of consensus nodes composed of participating universities (such as Peking University and Tsinghua University), educational institutions (such as China University MOOC), and certification centers. The transaction is written into the block after consensus verification using the PBFT algorithm.

[0087] When Li Ming completes all 12 chapters of study and achieves a final exam score of 60 or above, the system will automatically generate a credit certificate, recording the final grade, credit value, and issuing institution. This certificate can be used for cross-institutional credit recognition.

[0088] Example 2 like Figure 2 The diagram shows the composition of a cloud-based educational resource sharing system according to the present invention. The system includes: a course parsing and knowledge graph engine, a resource encryption module, an educational attribute authentication center, an access control module, a distributed storage module, a learning path access controller, an anti-piracy secure transmission module, a resource acquisition module, and an educational consortium blockchain evidence storage platform.

[0089] The course parsing and knowledge graph engine is used to perform structured parsing of uploaded online course resources, extract course metadata, construct chapter trees, identify knowledge point tags and prerequisite dependencies, and generate and maintain a course knowledge graph.

[0090] The resource encryption module is used to perform multi-level encryption processing on course resources, including a symmetric encryption submodule and an attribute-based encryption submodule, to generate resource ciphertext and key ciphertext.

[0091] The Education Attribute Certification Center is used to define and manage the education role attribute model, including user identity role, institution affiliation, course selection status, learning progress and credit information, and is responsible for the issuance, updating and revocation of education attribute certificates.

[0092] The access control module is used to build and manage the access control policy tree, including a policy editor, property manager, and threshold configurator, to achieve fine-grained access control.

[0093] The distributed storage module is used to fragment, redundantly encode, and distribute the encrypted resources. It includes a fragmentation engine, erasure coding encoder, and distributed hash table, and generates and maintains a resource location index.

[0094] The learning path access controller is used to build and evaluate a multi-dimensional dynamic access control strategy that integrates the completion of prerequisite chapters, prerequisite course credits, and accumulated learning time, so as to achieve progressive content unlocking based on learning progress.

[0095] The anti-piracy secure transmission module is used to perform dynamic watermark embedding, segmented key authorization, playback environment detection, and session heartbeat monitoring. It includes a DRM copyright protection engine, a token issuer, and a client environment detection component.

[0096] The resource acquisition module is used to acquire fragments in parallel according to the resource location index, perform integrity verification, data reassembly and decryption operations, and transmit the decrypted resources to the user through a secure channel.

[0097] The educational consortium blockchain evidence storage platform is used to store learning behavior records and credit certificate records on the blockchain. It includes PBFT consensus nodes, smart contract executors, and automated audit engines to achieve tamper-proof access traceability.

[0098] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cloud computing-based method for sharing educational resources, characterized in that, The method includes the following steps: Step S1: Perform structured parsing on the online course resources uploaded to the cloud platform, extract the course chapter tree, knowledge point tags and prerequisite dependencies, construct the course knowledge graph, and perform multi-level encryption processing on the course content to generate resource ciphertext and corresponding resource feature vectors. Step S2: Define a user role attribute model based on the education scenario, construct a user education attribute certificate that includes identity role, affiliated institution, course selection status, learning progress and credit acquisition status, and construct an access control policy tree based on the attribute base encryption mechanism, and embed the access control policy tree into the resource metadata; Step S3: The encrypted resource is fragmented and stored using a distributed hash table, and a resource location index is generated. Step S4: When a course resource access request is received, the educational attribute set of the requesting user is verified, and the learning path awareness mechanism is used to determine whether the user meets the decryption conditions of the access control policy tree and the chapter unlocking conditions. Step S5: If the decryption conditions are met, the resource fragments are obtained from the distributed nodes according to the resource location index, reassembled, and then the decryption operation is performed. Anti-piracy secure transmission is achieved through dynamic watermark embedding and playback environment detection, and the decrypted course resources are transmitted to the requesting user. Step S6: Record learners’ course access records, learning duration, test scores and credit acquisition status to the education alliance chain to generate verifiable learning history credentials, enabling access traceability and auditing.

2. The method according to claim 1, characterized in that, The structured parsing and course knowledge graph construction method specifically includes the following steps: Step S2.1: Parse the course resource package and extract course metadata, including course identifier, course name, instructor, credit value, total class hours, and course category; construct a course chapter tree. ,in Let E be the set of chapter nodes, E be the set of parent-child relationship edges, and L be the chapter hierarchy mapping function; each chapter node contains chapter identifier, title, duration, and difficulty level attributes. Step S2.2: Extract the set of knowledge point tags for each chapter and establish the prerequisite dependencies between knowledge points. Directed edge Representing knowledge points It is learning Preconditions; Step S2.3: For the current course C, find its prerequisite course set. Learners must complete the prerequisite courses to unlock the current course. Step S2.4: Combine the course chapter tree, knowledge point tags, and prerequisite dependencies to construct a course knowledge graph and store it in a graph database.

3. The method according to claim 2, characterized in that, Multi-level encryption processing specifically includes the following steps: Step S3.1, for the original course resources The first layer of encryption is performed using a symmetric encryption algorithm to generate the first ciphertext. The encryption process is represented as follows: ,in Represents a symmetric encryption function. It is a symmetric key; Step S3.2: Use an attribute-based encryption algorithm to process the symmetric key. Perform a second layer of encryption to generate the key ciphertext. ,in This represents the attribute-based encryption function. This is an access control policy tree, where PK is the system public key; Step S3.3: Extract resource content features and construct resource feature vectors. ,in For feature dimensions.

4. The method according to claim 3, characterized in that, The method for constructing a user education attribute certificate includes the following steps: Step S4.1, Define the set of educational role types , These correspond to students, teachers, teaching assistants, and administrators, respectively. Step S4.2: Construct a user education attribute vector, including: user unique identifier, role type, affiliated institution identifier, course selection record set, and course selection status; Step S4.3, Define the learning progress mapping ,user For the course The learning progress is calculated as follows: ,in For chapters The weighting coefficients, Duration Difficulty level Indicates the chapter completion status; Step S4.4, construct the credit mapping if and only if and At that time, the user will receive credits for the course, of which The progress threshold, As the performance threshold, For users In the course Results; Step S4.5: The education attribute certification authority digitally signs the user attribute vector to generate an education attribute certificate. Certificate validity period Updated in sync with the semester cycle; For the certification authority to sign the attribute vector, The private key for the educational certification authority.

5. The method according to claim 4, characterized in that, Methods for constructing an access control policy tree include: constructing the policy tree Where N is the set of nodes and E is the set of edges. For threshold functions, This is an attribute mapping function; for non-leaf nodes in the policy tree Set threshold value ,in , For nodes The number of child nodes; when When represents the OR gate, when The time indicates an AND gate.

6. The method according to claim 5, characterized in that, The fragmented storage method includes the following steps: Step S6.1, encrypt the resource. According to fixed size Perform segmentation to generate a set of fragments. The number of shards ; Step S6.2: Use erasure coding technology to perform redundant coding on the fragments and set the redundancy factor. Generate redundant fragment sets ; Step S6.3, for each fragment Calculate distributed hash value ,in Indicates a connection operation. For resource identification; Step S6.4, based on the hash value Determine the sharded storage nodes ; Step S6.5: Construct resource location index It is encrypted and stored on a cloud metadata server.

7. The method according to claim 6, characterized in that, Learning path-aware access control methods include: Get the attribute set of the requesting user and the corresponding attribute private key Satisfaction function For nodes in the strategy tree ,like If it is a leaf node, then ;like If a node is a non-leaf node, then it is true if and only if the number of its child nodes that satisfy the condition is not less than the threshold value. hour, ;when Chapters can only be unlocked at certain times; Chapter unlock condition function Regarding the chapter and users The unlocking conditions are: ;in, As a chapter requirement, it requires users to complete all the preceding chapters of the current chapter. As a prerequisite for the course, Conditions for accumulating study time: ,in To remove the effective viewing time after fast forwarding and background playback, The required cumulative duration is calculated recursively from the root node of the strategy tree. ,like and If the verification passes, the verification is successful; otherwise, an access denied flag is returned.

8. The method according to claim 7, characterized in that, The specific methods for preventing piracy and ensuring secure transmission include: starting from resource location indexes. Extract the fragment location information and initiate fragment retrieval requests in parallel to the corresponding storage nodes; verify the integrity of the retrieved fragments, and if the number of corrupted fragments does not exceed the redundancy factor. Then erasure coding is used to recover the data and reassemble it to obtain the complete ciphertext. Use user attribute private key For key ciphertext Perform attribute-based decryption to recover the symmetric key. Using a symmetric key For ciphertext Decryption was performed to obtain the original course resources. ; Generate dynamic user watermark information and perform client playback environment detection. The detection items include screen recording software detection, virtual machine environment detection, debugger attachment detection, and device fingerprint consistency detection. Video decryption and playback are allowed only if all detections pass.

9. The method according to claim 8, characterized in that, Methods for on-chain evidence storage of learning behavior include: calculating effective learning time. , ,in, Total duration To fast forward and skip the duration, This refers to the background playback duration. The idle time when the user does not interact with the system for more than a threshold; Learning records are submitted to the education consortium blockchain, which consists of nodes from participating universities, educational institutions, and certification centers. It uses the PBFT consensus algorithm, where blockchain nodes verify transactions. Once verified, the transaction is written into a new block. Once a user completes the course and passes the assessment, a credit certificate is generated.

10. A cloud computing-based educational resource sharing system, used to perform the method according to any one of claims 1-9, characterized in that, The system includes: a course parsing and knowledge graph engine, a resource encryption module, an education attribute authentication center, an access control module, a distributed storage module, a learning path access controller, an anti-piracy secure transmission module, a resource acquisition module, and an education alliance chain evidence storage platform. The course parsing and knowledge graph engine is used to perform structured parsing of uploaded online course resources, extract course metadata, build chapter trees, identify knowledge point tags and prerequisite dependencies, and generate and maintain course knowledge graphs. The resource encryption module is used to perform multi-level encryption processing on course resources, including a symmetric encryption submodule and an attribute-based encryption submodule, to generate resource ciphertext and key ciphertext; The Education Attribute Certification Center is used to define and manage the education role attribute model, including user identity role, institution affiliation, course selection status, learning progress and credit information, and is responsible for the issuance, updating and revocation of education attribute certificates; The access control module is used to build and manage the access control policy tree, including a policy editor, property manager and threshold configurator, to achieve fine-grained access control. The distributed storage module is used to fragment, redundantly encode, and distribute the encrypted resources, including a fragmentation engine, erasure coding encoder, and distributed hash table, and generates and maintains a resource location index. The learning path access controller is used to build and evaluate a multi-dimensional dynamic access control strategy that integrates the completion of prerequisite chapters, prerequisite course credits, and accumulated learning time, so as to achieve progressive content unlocking based on learning progress. The anti-piracy secure transmission module is used to perform dynamic watermark embedding, segmented key authorization, playback environment detection and session heartbeat monitoring, and includes a DRM copyright protection engine, a token issuer and a client environment detection component. The resource acquisition module is used to acquire fragments in parallel according to the resource location index, perform integrity verification, data reassembly and decryption operations, and transmit the decrypted resources to the user through a secure channel; The educational consortium blockchain evidence storage platform is used to store learning behavior records and credit certificate records on the blockchain. It includes PBFT consensus nodes, smart contract executors, and automated audit engines to achieve tamper-proof access traceability.

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