Data storage method and system for smart education platform

Abstract

The invention relates to the field of big data processing and cloud computing, and discloses a data storage method and system for an intelligent education platform, and the method comprises the steps: executing a quantum encryption and data protection strategy, and encrypting a data file; splitting the encrypted data file into a plurality of data fragments, distributing all the data fragments to a plurality of independent storage nodes, and encrypting data operation steps; executing an intelligent compression algorithm strategy, and automatically selecting a compression mode; executing a dynamic load balancing strategy, and distributing the data files to storage systems of a plurality of cloud service providers; the cache is divided into a plurality of levels, a multi-level cache mechanism strategy is executed, the cache is subjected to priority ranking, and data is managed at different storage levels; executing a multi-level authority management strategy, and setting different access authority levels according to user roles; all the data files are backed up in real time; the series of encryption and protection measures ensure high security of user data.

Description

Technical Field

[0001] The present invention relates to the field of big data processing and cloud computing, and specifically to a data storage method and system for a smart education platform. Background Art

[0002] In the context of the rapid development of modern information technology, the rise of smart education platforms has brought revolutionary changes to the education industry. In such platforms, the optimization of data storage methods is crucial. Effective data storage strategies can not only enhance the security and privacy of data, but also improve the system's response speed and user experience. By implementing strategies such as multi-level encryption, dynamic load balancing, and intelligent compression, smart education platforms can better manage and protect the data of students and teachers, thus providing strong support for the fair distribution of educational resources.

[0003] However, there are some deficiencies in the existing technology during data storage. Many traditional data storage methods rely solely on centralized storage systems, resulting in insufficient data security and being vulnerable to single-point failures. At the same time, these methods are inefficient in processing large-scale data and fail to fully utilize the advantages of cloud computing and edge computing. In addition, the existing technology manages data access permissions inflexibly and is difficult to dynamically adjust according to user roles, increasing the risk of data leakage and abuse.

[0004] This solution significantly improves the security and efficiency of data storage by introducing a number of innovative measures such as advanced quantum encryption technology, dynamic load balancing, and intelligent caching mechanisms. Summary of the Invention

[0005] The present invention provides a data storage method and system for a smart education platform to help solve the problems mentioned in the above background art.

[0006] In a first aspect, the present invention provides the following technical solution: A data storage method for a smart education platform, including: obtaining the data files submitted by users, generating a hash value for each data file, storing it in the blockchain, implementing a quantum encryption and data protection strategy to encrypt the data files; Splitting the encrypted data files into multiple data segments, distributing all data segments to multiple independent storage nodes, independently encrypting each data segment, and generating independent encryption keys; Recording all data operation steps, where the data operation steps are such as uploading, modifying, and deleting, implementing a multi-level strong encryption processing strategy to encrypt the data operation steps; Implementing a quantum key distribution technology strategy to securely manage and transmit the keys; Implementing an intelligent compression algorithm strategy to automatically select a compression method according to the type, access frequency, and importance of the data files; Execute the dynamic load balancing strategy to allocate data files to the storage systems of multiple cloud service providers; Divide the cache into multiple levels, execute the multi-level cache mechanism strategy, perform intelligent scheduling and priority sorting on the cache, and manage data at different storage levels; When a user accesses, execute the multi-level permission management strategy and set different access permission levels according to the user role; Execute the edge computing optimization strategy, store and distribute data files to edge nodes closer to the user, and perform real-time processing and analysis on the data files; Perform real-time backup on all data files, execute the distributed disaster recovery technology strategy, and be able to recover data files at other backup nodes in case of failure.

[0007] By introducing quantum encryption and data protection strategies, the intelligent education platform is significantly enhanced in terms of data security and privacy protection. Quantum encryption uses the principles of quantum mechanics to create a secure shared key for data transmission, ensuring that data is not intercepted or tampered with during transmission. A unique hash value is generated for each data file and stored on the blockchain, ensuring the immutability and authenticity of the data. In addition, the data files are encrypted, split, and distributed for storage on multiple independent nodes, reducing the risk of single-point failure. Even if a node has problems, the data can still be accessed and recovered securely, and the independent encryption key management further enhances security. This multi-level security protection measure not only enhances users' trust in data security but also improves the overall reliability of the platform, providing a strong guarantee for the secure management of educational resources.

[0008] Preferably, the execution of the quantum encryption and data protection strategy to encrypt data files includes: Denote the obtained data file as D, D = {d1, d2,..., d n}, where d i is the i-th byte in the data file, and there are n bytes in total, and the byte is the size of the data file; Calculate the hash value h of the data file D according to the hash function H, h = H(D) = H(d1, d2,..., d n ), where h is the unique hash value of the data file; Store the generated hash value h into the blockchain; During data transmission, execute the quantum encryption and data protection strategy to generate a shared key K q through the quantum communication protocol, and the quantum communication protocol is a technology that uses the principles of quantum mechanics for information transmission and secure encryption; Perform quantum encryption on the shared key K q to obtain the encrypted shared key Kq '; Use the encrypted shared key K q ' to perform quantum encryption on the data file D to obtain the encrypted data file D'; Split the encrypted data file D' into multiple data segments, denoted as D′ = {D′1, D′2, …, D′ m}, where m is the number of data segments after splitting; According to the obtained data segment D′ i , generate an independent encryption key K i , and perform quantum encryption on each data segment; Distribute the encrypted data segments D′ i to multiple independent storage nodes; Obtain all operation step records of the user for the data file, execute a multi-level strong encryption processing strategy, and generate an encryption key K e for each operation step record; Use the encryption key K e to perform the first layer of encryption on the operation step record; Use the public key K g of the recipient to perform the second layer of encryption on the encrypted operation step record; Create a connection channel for the encryption key K i , execute the quantum key distribution strategy, perform quantum key distribution protection on the encryption key K i for each data segment, and transmit the encryption key K i in the connection channel.

[0009] By implementing the dynamic load balancing strategy, the intelligent education platform can efficiently allocate data files among multiple cloud service providers, thereby improving the overall efficiency of data storage and access. The platform monitors the load conditions of each storage node in real time to ensure that data is always stored on the node with the lowest load, thus avoiding performance degradation caused by resource overload. This strategy improves the utilization rate of the storage system and also optimizes the data transmission rate, ensuring that users enjoy faster response times when accessing data. Dynamic load balancing can also be flexibly adjusted according to the storage capacity and bandwidth limitations of cloud service providers to ensure the efficiency and security of data storage. Such a resource management mechanism not only improves the stability of the system but also provides strong support for processing large-scale user data.

[0010] Preferably, the execution of the dynamic load balancing strategy to allocate the data file to the storage systems of multiple cloud service providers includes: Monitor the storage systems of each cloud service provider a in real time, and obtain the load condition L of the storage nodes of each cloud service provider a a=(L1, L2,..., L N ), where each L j represents the load of the j-th storage node on the provider a, N is the total number of N storage nodes, and the load includes storage capacity, storage utilization rate, and bandwidth limit; Obtain the load L aj of any storage node inside each cloud service provider a, and calculate the load utilization rate L a of each cloud service provider, where N a is the number of storage nodes under the cloud service provider a; Obtain the load utilization rates L1, L2,..., L M of all cloud service providers. Among them, there are M cloud service providers in total. Execute the dynamic load balancing strategy and select the cloud service provider with the minimum load utilization rate for data storage; Store the data file on the storage node with the minimum load utilization rate inside the cloud service provider with the minimum load utilization rate; Obtain the available storage capacity C a of each cloud service provider a. The total size of the data file allocated does not exceed the storage capacity size of the cloud service provider. The storage size s w of the data file needs to satisfy where w represents the w-th data file; Obtain the bandwidth limit B a of each cloud service provider a. The total transmission rate when allocating the data file does not exceed the bandwidth limit of the cloud service provider. The transmission rate v w of the data file needs to satisfy When the load of any cloud service provider changes, recalculate and adjust the allocation of the data file.

[0011] By implementing the intelligent compression algorithm strategy, the intelligent education platform can automatically select the best compression method according to the type, access frequency, and importance of the data file, thereby optimizing the use of storage resources. This strategy ensures that important data files use lossless compression to maintain the integrity and availability of the data, while less frequently used files can use lossy compression to save storage space. By dynamically adjusting the compression score and threshold, the platform can flexibly respond to the needs of different data files, enhancing the flexibility and adjustability of data storage. This intelligent compression mechanism not only improves the data processing efficiency but also optimizes the storage cost, providing a more efficient resource management solution for the intelligent education platform, ensuring that users can quickly access the required information and improving the overall user experience.

[0012] Preferably, the implementation of the intelligent compression algorithm strategy automatically selects the compression method according to the type, access frequency, and importance of the data file, including: Obtain the data type T, access frequency G, and importance I of all data files; Set the compression scoring functions corresponding to the data type T, access frequency G, and importance I as f1(T), f2(G), and f3(I); Calculate the compression score Y, Y = ω1·f1(T) + ω2·f2(G) + ω3·f3(I), where ω1, ω2, and ω3 are weight coefficients; Set the compression score threshold θ; When Y ≥ θ, the data file is an important file, and a lossless compression method is selected for compression; When Y < θ, the data file is an infrequently used file, and a lossy compression method is selected for compression; Dynamically adjust the threshold θ according to the access frequency and storage requirements of the data file, and recalculate the compression score Y to select the compression method.

[0013] By implementing a multi-level permission management strategy, the intelligent education platform has achieved higher flexibility and security in data access control. The platform realizes role-based dynamic access control by precisely mapping user roles to permission levels, ensuring that only authorized users can access sensitive data. By classifying data files into three categories: public, restricted, and confidential, and managing them according to permission levels, the platform reduces the risk of data leakage and effectively improves data security. At the same time, when a user requests access to a data file, the system verifies the permissions through an access control matrix to ensure that the user's access is compliant. This detailed permission management mechanism significantly enhances users' trust in this platform, ensures the secure and compliant use of educational data, and thus provides strong support for the effective development of educational work.

[0014] Preferably, the cache is divided into multiple levels, and a multi-level cache mechanism strategy is executed to perform intelligent scheduling and priority sorting on the cache, including: The cache storage space is sequentially divided into a high-level cache layer U1, a middle-level cache layer U2, and a low-level cache layer U3; Set the cache priority scoring functions corresponding to the data type T, access frequency G, and importance I as f′1(T), f′2(G), and f′3(I); calculate the cache priority score Y' of each data file, Y′ = ω1·f′1(T) + ω2·f′2(G) + ω3·f′3(I); Set the thresholds θ1 and θ2; When Y′ ≥ θ1, the data file is cached to the high-level cache layer U1; When θ2 ≤ Y′ < θ1, the data file is cached to the middle-level cache layer U2; When Y′ < θ2, the data file is cached to the low-level cache layer U3; When the storage space is full, the data file with the lowest access frequency is eliminated for cache replacement; Regularly calculate the change in the cache priority of data files, and dynamically adjust the cache location of data files according to the current cache priority. Y′(t) = ω1·f′1(T(t)) + ω2·f′2(G(t)) + ω3·f′3(I(t)), where t is the time when the cache priority of the data file changes.

[0015] Through the edge computing optimization strategy, the intelligent education platform can store and distribute data files to edge nodes closer to users, thus improving the real-time performance and efficiency of data processing. This strategy not only reduces the latency of data transmission but also reduces the dependence on the central server, further improving the overall system response speed. By calculating the storage priority of edge nodes, the platform can select the node with the lowest load and the best bandwidth for data storage and processing, thus ensuring fast response and efficient service when users access educational resources. In addition, edge computing supports real-time analysis of data stored in edge nodes, can respond to user needs in a timely manner, and improve the user experience. This efficient distributed storage solution provides scalability and flexibility for the intelligent education platform on a large user base, promoting the development of the education industry.

[0016] Preferably, when a user accesses, a multi-level permission management strategy is executed, and different access permission levels are set according to the user role, including: Obtain the user role type R = {r1, r2,..., r b} and the permission level X = {x1, x2,..., x l} corresponding to the user type. The mapping relationship between the user role and the permission level is F: R → X, where a user role can only be assigned one permission level; Divide data files into public data files, restricted data files, and confidential data files; Set the first-level access permission as accessible to all user roles, and the accessible data files are public data files; Set the second-level access permission as accessible to specified user roles, and the accessible data files are restricted data files; Set the third-level access permission as accessible to specific authorized user roles, and the accessible data files are confidential data files; Set the relationship between the user role R, the data file D, and the permission level X as the access control matrix A(R, D): Among them, x uo represents that the user role r u has access permission to the data file D o ; When a user requests to access the data file Do When, obtain the user role r u , according to the access control matrix A(R, D), judge X(r u , D o ) whether it is 1; When X(r u , D o ) = 1, the user access privilege level meets the access requirements, and the user is allowed to access; When X(r u , D o ) ≠ 1, the user access privilege level does not meet the access requirements, and the user access is refused.

[0017] By implementing the distributed disaster recovery technology strategy, the intelligent education platform ensures the real-time backup of data files, enhances the fault tolerance of the system and the data recovery speed. The data files are stored in real time on multiple backup nodes, enabling the platform to quickly identify available backup nodes in case of a failure, ensuring the rapid recovery of data and the continuity of business. This strategy not only improves the reliability of the system, but also effectively reduces the risk of data loss, providing users with a more reassuring usage experience. Each data file is stored in at least multiple backups to ensure that data can be effectively protected and recovered even in extreme cases. In addition, the implementation of this technology enables the platform to better cope with emergencies such as natural disasters and system failures, improving the overall service stability and user trust. This highly available design lays a solid foundation for the future development of the intelligent education platform and effectively supports the digital transformation of the education industry.

[0018] Preferably, when implementing the edge computing optimization strategy, the data files are stored and distributed to edge nodes closer to the user, and the data files are processed and analyzed in real time, including: Calculate the storage priority of edge nodes where α1, α2, α3 are weight coefficients, and the edge node is a node with storage capacity C aj , computing power A aj , network bandwidth B aj , current load L aj , access frequency G aj , latency requirement J aj ; Set a threshold P th ; When P * (D) > P th , the edge node storing the data file is the edge node closest to the user; Set the edge node set as Z = {Z1, Z2,..., Z y}}, where Z j is any edge node in the set; Calculate and select an edge node with the lowest load, optimal bandwidth, and most matching computing power where β1, β2, and β3 are adjustment coefficients; Perform real-time calculation and analysis on the data files stored on the edge node.

[0019] By recording all data operation steps and performing strong encryption processing, the intelligent education platform enhances transparency and traceability in data management and auditing. Whenever a user uploads, modifies, or deletes a data file, the system generates corresponding operation records. These records not only facilitate data management but also facilitate later auditing and problem tracking. Through a multi-level strong encryption processing strategy, these operation records are also protected, ensuring privacy and data security. Users can query historical operation records at any time, thus having a clear understanding of the data usage and modification process. This transparent operation mechanism not only enhances users' trust but also provides necessary support for the platform to comply with data compliance and security, ensuring that the education platform can follow best practices when managing data.

[0020] Preferably, all data files are backed up in real time, and a distributed disaster recovery technology strategy is implemented. When a failure occurs, the data files can be restored on other backup nodes, including: Set the process of data file D to be stored in real time on multiple backup nodes M′ = {M1, M2,..., M z}; Allocate R * independent backup nodes S′(D i′ ) = {M i′1 , M i′2 , …, M i′X′} for each data file, where X′ is the number of backup copies, and each data file is stored at least X′ times. S′(D i′ ) represents the set of backup nodes for data file D; Set the normal health status of each backup node M j′ to be γ; When H′(M j′ ) = γ, the backup node M j′ runs normally; When H′(M j′ ) ≠ γ, the backup node M j′ fails; When the backup node M j′ fails, find an available backup node S′ al (D i′ ) = M j′ ∈S′(D i′) Restore the data file directly in the backup node.

[0021] Through a flexible multi-level caching mechanism, the intelligent education platform can manage data access more intelligently, improving the system's response speed and user experience. The platform divides the cache storage space into high-level, intermediate-level, and low-level cache layers, and sets cache priority scores according to data types, access frequencies, and importance. By regularly evaluating the access frequency of data files, the platform can preferentially cache popular data to improve its access speed, while less frequently used files may be replaced or moved to the low-level cache layer. When the storage space is insufficient, the system will automatically eliminate the data files with the lowest access frequency to achieve dynamic adjustment. This intelligent scheduling mechanism ensures that users can obtain a faster response when accessing educational resources, enhancing the overall usage experience, helping to optimize the configuration and utilization of educational resources, and thus promoting the efficient operation of the education platform.

[0022] In a second aspect, the present application provides a data storage system for an intelligent education platform, adopting the following technical solutions: A data storage system for an intelligent education platform includes: Data acquisition and hash calculation module: Responsible for receiving the data files submitted by users and performing preprocessing, calculating the hash values of the data files and storing them in the blockchain; Quantum encryption and data protection module: Encrypt the data files using encryption keys, split the encrypted data into multiple data segments, and generate independent encryption keys for each data segment and encrypt them; Distributed storage and management module: Manage cloud service providers and their storage nodes, allocate data segments to the optimal storage nodes according to the load balancing strategy, detect the health status of the storage nodes, and perform storage migration; Data operation record and multi-level encryption module: Record all data operation steps and perform multi-level encryption on them. Use the encryption key to perform the first layer of encryption on the operation steps, and use the recipient's public key to encrypt the encrypted data again; Dynamic load balancing module: Obtain the real-time load information of each storage node, calculate the storage load utilization rate of the cloud service provider, and select the cloud storage node with the lowest load to store data; Intelligent compression algorithm module: Analyze the types, access frequencies, and importance of data files, calculate the compression score and select the compression method, and dynamically adjust the compression strategy according to the data usage situation; Multi-level cache management module: Divide the cache into high-level cache layer, intermediate-level cache layer, and low-level cache layer, calculate the cache priority score, and dynamically adjust the cache strategy according to the data access pattern; Multi-level permission management module: Define different user roles, set access permission levels, and restrict the access permissions of different users to data; Edge Computing Optimization Module: Store data files in the edge node closest to the user and perform calculations at the edge node for real-time data analysis; Distributed Disaster Recovery Module: Perform real-time backups on all data files, detect whether storage nodes fail, and recover data files from available backup nodes.

[0023] The present invention has the following beneficial effects: 1. For the data storage method of this intelligent education platform, by introducing quantum encryption and data protection strategies, the intelligent education platform has been significantly improved in terms of data security and privacy protection. First, the platform generates the hash value of each data file and stores it in the blockchain to ensure the uniqueness and immutability of the data. This measure not only improves the integrity of the data but also enhances users' trust in the platform. In addition, when the data file is uploaded, it will be encrypted and split into multiple data segments, which are distributed and stored on multiple independent storage nodes. In this way, even if a storage node fails, the data can still remain secure and available. Each independently encrypted data segment is equipped with an independent encryption key, which further improves the data security and makes it difficult for attackers to obtain the complete data. Through quantum key distribution technology, the management and transmission of keys are also guaranteed, greatly improving the security of data during transmission. In summary, this series of encryption and protection measures ensure the high security of user data and greatly improve the reliability of the intelligent education platform to provide services to users.

[0024] 2. For the data storage method of this intelligent education platform, by implementing the dynamic load balancing strategy, the intelligent education platform can efficiently manage and allocate data files to the storage systems of multiple cloud service providers. This strategy relies on real-time monitoring of the load conditions of the storage nodes of cloud service providers to ensure that the storage capacity and utilization rate of each node are in the best state. Dynamic load balancing can not only effectively avoid resource overload but also improve the overall efficiency of data storage and access. When the platform selects a data storage node, it will automatically calculate the load utilization rate of each provider and then store the data on the node with the lowest load. This intelligent resource management method keeps the data transmission speed in the best state and reduces the delay caused by network congestion. In addition, the platform will continuously monitor the status of each cloud service provider. Once a load change is detected, the system will immediately make adjustments to ensure that users can always obtain a fast and stable data access experience. This flexibility not only improves user satisfaction but also provides a solid guarantee for the sustainable development of the platform.

[0025] 3. The data storage method of this intelligent education platform enables the platform to automatically select the best compression method according to the type, access frequency, and importance of data files by implementing an intelligent compression algorithm strategy, so as to achieve the optimal utilization of storage resources. After the data files are classified, the platform generates corresponding compression scores and selects lossless or lossy compression according to the set threshold. This strategy ensures the integrity of users' important data and avoids information loss caused by compression. For infrequently used files, lossy compression can be used to save storage space, thus making room for processing more important data. The intelligent compression algorithm also has the ability of dynamic adjustment, which can recalculate the scores and thresholds according to real-time access frequencies and storage requirements to ensure that the compression method is always in the optimal state. This flexible compression strategy not only improves storage efficiency but also reduces storage costs, providing a more efficient resource management solution for the intelligent education platform. Users can access the required data in a shorter time, further enhancing the user experience and system response speed.

[0026] 4. The data storage method of this intelligent education platform achieves higher flexibility and security in data access control by implementing a multi-level permission management strategy. The platform sets different access permission levels according to the user's role type to ensure that only authorized users can access sensitive data. Data files are divided into three categories: public, restricted, and confidential, and different categories of data files correspond to different access permissions to ensure data security. By setting up an access control matrix, the platform can quickly determine whether the user's access permission meets the requirements when the user requests to access a data file. This meticulous permission management mechanism not only improves the transparency of data access but also reduces the risk of data leakage. Users can clearly understand their own access permissions, while the platform can respond to users' requests in a timely manner to ensure the safe and legal use of data. In addition, flexible permission management facilitates future data auditing, providing necessary support for the platform to comply with relevant laws and regulations. This multi-level management strategy not only enhances users' trust in the platform but also provides strong guarantee for the security management of educational data.

[0027] 5. The data storage method of this intelligent education platform, through edge computing optimization strategies, enables the intelligent education platform to store and distribute data to edge nodes closer to users, thus significantly improving the real-time performance and efficiency of data processing. When selecting edge nodes, the platform considers multiple factors such as storage capacity, computing power, network bandwidth, current load, and access frequency to ensure data storage and processing at the node closest to the user. This method not only reduces the latency of data transmission but also decreases the over-reliance on the central server, contributing to improving the overall system response speed. In addition, the implementation of edge computing enables users to obtain a smoother experience when accessing educational resources and to process and analyze data files closer to them in real time. By optimizing the configuration of storage and computing resources, the platform can respond more efficiently to user needs and enhance user satisfaction. This flexible distributed storage solution not only provides scalability and flexibility for the intelligent education platform but also injects new vitality into the development of the education industry.

[0028] 6. The data storage method of this intelligent education platform, through the implementation of distributed disaster recovery technology strategies, ensures the real-time backup of data files and improves the fault tolerance and data recovery speed of the system. The platform stores data files in real time on multiple backup nodes, enabling the rapid identification of available backup nodes in case of a failure to ensure the quick recovery of data and the continuity of operations. Each data file is stored in at least multiple backups to ensure that data can be effectively protected and recovered even in extreme situations. This mechanism not only enhances the reliability of the system but also reduces the risk of data loss caused by disaster events, providing users with a more reassuring experience. In addition, the platform can monitor the health status of backup nodes to ensure timely adjustment in case of a backup node failure and ensure data security. This highly available design lays a solid foundation for the future development of the intelligent education platform, effectively supporting the digital transformation of the education industry and enabling it to better cope with various emergencies.

[0029] 7. The data storage method of this intelligent education platform significantly enhances transparency and traceability in data management and auditing by recording all data operation steps and performing strong encryption processing. Whenever a user uploads, modifies, or deletes a data file, the system automatically generates corresponding operation records, which not only helps with effective data management but also facilitates future auditing work. Through a multi-level strong encryption processing strategy, these operation records are protected, ensuring that user privacy is not leaked. When users query historical operation records, they can clearly understand the usage and modification process of the data. This transparent operation mechanism further enhances users' trust in the platform. Through strong audit tracking capabilities, the platform can ensure compliance in data usage, providing necessary guarantees for the management of educational data. This efficient data management method enables the intelligent education platform to better safeguard users' rights and interests and provides strong support for the development of educational work.

[0030] 8. The data storage method of this intelligent education platform realizes more intelligent cache management by dividing the cache into multiple levels, improving the system's response speed and user experience. The platform divides the cache storage space into high-level, middle-level, and low-level cache layers and sets cache priority scores according to data types, access frequencies, and importance. By dynamically adjusting the cache priority of data files, the platform can preferentially cache popular data, thereby improving its access speed without frequently reading from the main storage. This intelligent scheduling mechanism ensures that users can obtain a faster response when accessing educational resources and automatically eliminates the data files with the lowest access frequency when the storage space is full to achieve cache replacement. In addition, regularly calculating the change in the cache priority of data files and dynamically adjusting the cache location according to the current priority further optimizes the cache utilization efficiency. This efficient cache mechanism not only enhances the user experience but also optimizes the configuration and utilization of educational resources, providing strong guarantees for the efficient operation of the intelligent education platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic flowchart of the method of the present invention.

[0032] Figure 2 It is a schematic diagram of the system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] Example 1, refer to Figure 1 , a data storage method for an intelligent education platform, including: Obtain the data files submitted by users, generate the hash value of each data file, store it in the blockchain, execute the quantum encryption and data protection strategy, and encrypt the data files; Split the encrypted data files into multiple data segments, distribute all data segments to multiple independent storage nodes, encrypt each data segment independently, and generate independent encryption keys; Record all data operation steps, where the data operation steps are upload, modification, and deletion, execute the multi-level strong encryption processing strategy, and encrypt the data operation steps; Execute the quantum key distribution technology strategy to securely manage and transmit the keys; Execute the intelligent compression algorithm strategy, and automatically select the compression method according to the type, access frequency, and importance of the data files; Execute the dynamic load balancing strategy, and distribute the data files to the storage systems of multiple cloud service providers; Divide the cache into multiple levels, execute the multi-level cache mechanism strategy, perform intelligent scheduling and priority sorting on the cache, and manage data at different storage levels; When users access, execute the multi-level permission management strategy, and set different access permission levels according to the user roles; Execute the edge computing optimization strategy, store and distribute the data files to the edge nodes closer to the users, and perform real-time processing and analysis on the data files; Perform real-time backups on all data files, execute the distributed disaster recovery technology strategy, and be able to recover the data files at other backup nodes in case of failures.

[0035] By introducing the quantum encryption and data protection strategy, the intelligent education platform has achieved remarkable results in ensuring data security and user privacy. The application of quantum encryption technology enables data to prevent unauthorized access or tampering during transmission, ensuring the confidentiality of information. Each data file generates a unique hash value and is stored in the blockchain to ensure the integrity and immutability of the data. By encrypting, splitting, and distributing the storage of data files on multiple independent nodes, the risk of single-point failure is reduced. This architecture ensures that even if a certain node fails, other nodes can still maintain the integrity and accessibility of the data. The independent encryption key management further enhances the data security, making the entire system more stable in the face of potential security threats. Such a multi-level security mechanism not only enhances users' trust in the platform but also provides important support for the secure storage and management of educational resources.

[0036] Implement quantum encryption and data protection strategies to encrypt data files, including: Record the obtained data file as D, D = {d1, d2, …, d n}, where d i is the i-th byte in the data file, and there are n bytes in total. The byte is the size of the data file; Calculate the hash value h of the data file D according to the hash function H, h = H(D) = H(d1, d2, …, d n ), where h is the unique hash value of the data file; Store the generated hash value h in the blockchain; During data transmission, implement quantum encryption and data protection strategies to generate a shared key K q through a quantum communication protocol. The quantum communication protocol is a technology that uses the principles of quantum mechanics for information transmission and secure encryption; Perform quantum encryption on the shared key K q to obtain the encrypted shared key K q '; Use the encrypted shared key K q ' to perform quantum encryption on the data file D to obtain the encrypted data file D'; Split the encrypted data file D' into multiple data segments, denoted as D′ = {D′1, D′2, …, D′ m}, where m is the number of split data segments; According to the obtained data segment D′ i , generate an independent encryption key K i , and perform quantum encryption on each data segment; Distribute the encrypted data segment D′ i to multiple independent storage nodes; Obtain all operation step records of the user for the data file, and implement a multi-level strong encryption processing strategy to generate an encryption key K e for each operation step record; Use the encryption key K e to perform the first layer of encryption on the operation step record; Use the public key K g of the recipient to perform the second layer of encryption on the encrypted operation step record; Create a connection channel for the encryption key K i , implement the quantum key distribution strategy, perform quantum key distribution protection on the encryption key K i of each data segment, and transmit the encryption key K i in the connection channel.

[0037] By implementing a dynamic load balancing strategy, the intelligent education platform can optimize data storage and access efficiency among multiple cloud service providers. This strategy monitors the load conditions of each storage node in real time to ensure that data is always stored on the node with the lowest load, thus avoiding performance degradation caused by resource overload. Dynamic load balancing not only improves the utilization rate of the storage system but also optimizes the data transmission rate, enhancing the response speed of users when accessing educational resources. This strategy allows the platform to flexibly adjust data allocation among different cloud service providers, ensuring efficiency and security during the storage process. By effectively managing storage resources, the platform can better handle high-concurrency access demands, enhancing the user experience and providing strong technical support for the education industry.

[0038] Execute the dynamic load balancing strategy to allocate data files to the storage systems of multiple cloud service providers, including: monitoring the storage system of each cloud service provider a in real time to obtain the load conditions L of the storage nodes of each cloud service provider a a =(L1, L2,..., L N ), where each L j represents the load of the jth storage node on this provider a, N is the total number of N storage nodes, and the load includes storage capacity, storage utilization rate, and bandwidth limit; Obtain the load L of any storage node within each cloud service provider a aj , calculate the load utilization rate L of each cloud service provider a , where N a is the number of storage nodes under cloud service provider a; Obtain the load utilization rates L1, L2,..., L of all cloud service providers M , where there are M cloud service providers in total, execute the dynamic load balancing strategy, and select the cloud service provider with the lowest load utilization rate for data storage; Store the data file on the storage node with the lowest load utilization rate within the cloud service provider with the lowest load utilization rate; Obtain the available storage capacity C of each cloud service provider a a , the total size of the data file allocated does not exceed the storage capacity size of the cloud service provider, and the storage size s of the data file w needs to satisfy where w represents the wth data file; Obtain the bandwidth limit B of each cloud service provider a a , the total transmission rate when allocating the data file does not exceed the bandwidth limit of the cloud service provider, and the transmission rate v of the data file w needs to satisfy When the load of any cloud service provider changes, recalculate and adjust the allocation of data files.

[0039] By implementing the intelligent compression algorithm strategy, the intelligent education platform has achieved more efficient resource utilization in data storage. This strategy automatically selects the best compression method according to the type, access frequency, and importance of data files to optimize the storage space. For example, lossless compression is used for important data files to maintain integrity, while lossy compression is used for infrequently used files to save storage space. By dynamically adjusting the compression score and threshold, the platform can flexibly respond to the needs of different data files, making the resource allocation more reasonable. This intelligent compression process not only improves the storage efficiency but also reduces the storage cost, providing a foundation for the sustainable development of the platform. Users can quickly access the required data, enhancing the overall user experience and meeting the high-efficiency requirements for data storage and access in educational work.

[0040] Implement the intelligent compression algorithm strategy, and automatically select the compression method according to the type, access frequency, and importance of data files, including: Obtain the data type T, access frequency G, and importance I of all data files; Set the compression score functions corresponding to the data type T, access frequency G, and importance I as f1(T), f2(G), and f3(I); Calculate the compression score Y, Y = ω1·f1(T) + ω2·f2(G) + ω3·f3(I), where ω1, ω2, and ω3 are weight coefficients; Set the compression score threshold θ; When Y ≥ θ, the data file is an important file, and select the lossless compression method for compression; When Y < θ, the data file is an infrequently used file, and select the lossy compression method for compression; Dynamically adjust the threshold θ according to the access frequency and storage requirements of the data file, and recalculate the compression score Y to select the compression method.

[0041] By implementing the multi-level permission management strategy, the intelligent education platform has achieved significant improvement in data security and access control. This strategy accurately maps user roles to permission levels to ensure that only authorized users can access sensitive data. The platform classifies data files into three categories: public, restricted, and confidential, and manages them accordingly based on the permission level to reduce the risk of data leakage. At the same time, when a user requests access to a data file, the system uses an access control matrix for permission verification to ensure that the user's access is compliant. Such a permission management mechanism not only enhances the security of data but also increases users' trust in the platform. By ensuring the legitimate access to data, the platform provides strong protection for the secure and compliant use of educational data and promotes the reasonable flow of educational resources.

[0042] The cache is divided into multiple levels, and a multi-level cache mechanism strategy is implemented to perform intelligent scheduling and priority sorting on the cache, including: The cache storage space is sequentially divided into a high-level cache layer U1, a middle-level cache layer U2, and a low-level cache layer U3; Set the cache priority scoring functions for data type T, access frequency G, and importance I to be f′1(T), f′2(G), and f′3(I) respectively; calculate the cache priority score Y’ of each data file, Y′ = ω1·f′1(T) + ω2·f′2(G) + ω3·f′3(I); Set thresholds θ1 and θ2; When Y′ ≥ θ1, the data file is cached in the high-level cache layer U1; When θ2 ≤ Y′ < θ1, the data file is cached in the middle-level cache layer U2; When Y′ < θ2, the data file is cached in the low-level cache layer U3; When the storage space is full, the data file with the lowest access frequency is eliminated for cache replacement; Regularly calculate the change in the cache priority of the data file, and dynamically adjust the cache location of the data file according to the current cache priority, Y′(t) = ω1·f′1(T(t)) + ω2·f′2(G(t)) + ω3·f′3(I(t)), where t is the time when the cache priority of the data file changes.

[0043] Through the edge computing optimization strategy, the intelligent education platform can store and distribute data to edge nodes closer to users, thereby improving the real-time performance and efficiency of data processing. This strategy significantly reduces the latency of data transmission, while reducing the dependence on the central server and improving the overall response speed of the system. By calculating the storage priority of edge nodes, the platform selects the node with the lowest load and the optimal bandwidth for data storage and processing to ensure a fast response when users access educational resources. In addition, edge computing allows real-time analysis of the data stored on the nodes to quickly respond to user needs. This distributed storage solution provides scalability and flexibility for the education platform, can support the growing user needs, and promotes the process of digital transformation in the education industry.

[0044] When a user accesses, a multi-level permission management strategy is implemented, and different access permission levels are set according to the user role, including: Obtain the user role type R = {r1, r2,..., r b} and the permission levels X = {x1, x2,..., x l} corresponding to the user type. The mapping relationship between the user role and the permission level is F: R → X, where a user role can only be assigned one permission level; Divide the data files into public data files, restricted data files, and confidential data files; Set the first-level access permission to be accessible to all user roles, and the accessible data files are public data files; Set the second-level access permission to be accessible to specified user roles, and the accessible data files are restricted data files; Set the third-level access permission to be accessible to specific authorized user roles, and the accessible data files are confidential data files; Set the relationship between user role R, data file D, and permission level X as access control matrix A(R, D): where x uo represents user role r u has access to data file D o has access rights; When a user requests access to data file D o , obtain user role r u , and according to the access control matrix A(R, D), judge whether X(r u , D o ) is 1; When X(r u , D o ) = 1, the user's access permission level meets the access requirements, and the user is allowed to access; When X(r u , D o ) ≠ 1, the user's access permission level does not meet the access requirements, and the user's access is denied.

[0045] By implementing distributed disaster recovery technology, the intelligent education platform ensures real-time backup of data files, thereby enhancing the system's fault tolerance and data recovery speed. By storing data in real time on multiple backup nodes, the platform can quickly locate available backup nodes in case of a failure, ensuring rapid data recovery and business continuity. This strategy not only improves the reliability of the system but also effectively reduces the risk of data loss, providing users with a more reassuring usage experience. In addition, the platform stores at least multiple copies of each data file, ensuring data integrity and availability even in extreme cases. This highly available design lays a solid foundation for the future development of the education platform, ensuring that the education industry can maintain business stability in the face of various emergencies.

[0046] Execute the edge computing optimization strategy, store and distribute the data files to edge nodes closer to the users, and perform real-time processing and analysis on the data files, including: Calculate the storage priority of edge nodes Among them, α1, α2, and α3 are weight coefficients, and the edge node has a storage capacity C aj , computing power A aj , network bandwidth B aj , current load L aj , access frequency G aj , latency requirement J aj ; Set a threshold P th ; When P * (D) > P th , the edge node storing the data file is the edge node closest to the user; Set the edge node set as Z = {Z1, Z2,..., Z y}, where Z j is any edge node in the set; Calculate and select an edge node with the lowest load, optimal bandwidth, and most matching computing power Among them, β1, β2, and β3 are adjustment coefficients; Perform real-time calculation and analysis on the data files stored on the edge nodes.

[0047] By accurately recording all data operation steps and performing strong encryption processing, the intelligent education platform has achieved higher transparency and traceability in data management and auditing. Whenever a user uploads, modifies, or deletes a data file, the system generates corresponding operation records. These records not only help with data management but also facilitate later auditing and problem tracking. Through multi-level strong encryption processing, these operation records are effectively protected, ensuring privacy and data security. Users can query historical operation records at any time, thus having a clear understanding of the data usage and modification process. This transparent operation mechanism effectively enhances users' trust and provides necessary support for the platform to comply with data compliance and security, ensuring that the education platform can follow best practices in data management and improve the quality of educational services.

[0048] Perform real-time backup on all data files and execute a distributed disaster recovery technology strategy to be able to recover data files on other backup nodes in case of a failure, including: Set the data file D to be stored in real-time on multiple backup nodes M' = {M1, M2,..., M z}; Allocate R * independent backup nodes S'(D i′ ) = {M i′1 , M i′2 ,..., M i′X′}, where X′ is the number of backup copies, and each data file is stored in at least X′ copies. S′(D i′ ) represents the set of backup nodes of data file D; Set the normal health status of each backup node M j′ to be γ; When H′(M j′ ) = γ, the backup node M j′ operates normally; When H′(M j′ ) ≠ γ, the backup node M j′ fails; When the backup node M j′ fails, search for an available backup node S′ al (D i′ ) = M j′ ∈S′(D i′ ), and directly recover the data file in the backup node.

[0049] Through a flexible multi-level caching mechanism, the intelligent education platform realizes the intelligent management of data access, further improving the system's response speed and user experience. The cache storage space is divided into high-level, intermediate-level, and low-level cache layers. The platform sets cache priority scores according to data types, access frequencies, and importance. When the storage space is insufficient, the system will automatically eliminate the data files with the lowest access frequency to achieve dynamic adjustment. This intelligent scheduling mechanism ensures that users can obtain a faster response when accessing educational resources, enhancing the overall usage experience. In addition, regularly calculating the changes in the cache priorities of data files allows the platform to dynamically adjust the cache locations of data files according to the current cache priorities, effectively optimizing the utilization of storage resources, promoting the efficient operation of the education platform, and providing users with a smoother education experience.

[0050] Embodiment 2. Refer to Figure 2 , a data storage system for an intelligent education platform, including: Data acquisition and hash calculation module: responsible for receiving the data files submitted by users and performing preprocessing, calculating the hash values of the data files, and storing them in the blockchain; Quantum encryption and data protection module: encrypting the data files using encryption keys, splitting the encrypted data into multiple data segments, and generating and encrypting independent encryption keys for each data segment; Distributed storage and management module: managing cloud service providers and their storage nodes, allocating data segments to the optimal storage nodes according to the load balancing strategy, detecting the health status of the storage nodes, and performing storage migration; Data Operation Record and Multi - level Encryption Module: Record all data operation steps and perform multi - level encryption on them. Use the encryption key to perform the first - layer encryption on the operation steps, and use the recipient's public key to encrypt the encrypted data again; Dynamic Load Balancing Module: Obtain the real - time load information of each storage node, calculate the storage load utilization rate of the cloud service provider, and select the cloud storage node with the lowest load to store data; Intelligent Compression Algorithm Module: Analyze the type, access frequency, and importance of data files, calculate the compression score and select the compression method, and dynamically adjust the compression policy according to the data usage; Multi - level Cache Management Module: Divide the cache into a high - level cache layer, a middle - level cache layer, and a low - level cache layer, calculate the cache priority score, and dynamically adjust the cache policy according to the data access pattern; Multi - level Permission Management Module: Define different user roles, set access permission levels, and restrict different users' access permissions to data; Edge Computing Optimization Module: Store the data file in the edge node closest to the user and perform calculations at the edge node for real - time data analysis; Distributed Disaster Recovery Module: Perform real - time backups on all data files, detect whether the storage node fails, and recover the data file from the available backup node.

[0051] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0052] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A data storage method for an intelligent education platform, characterized in that, Including: Obtain the data files submitted by users, generate the hash value of each data file, store it in the blockchain, execute the quantum encryption and data protection strategy, and encrypt the data files; Split the encrypted data files into multiple data segments, distribute all data segments to multiple independent storage nodes, independently encrypt each data segment, and generate independent encryption keys; Record all data operation steps, where the data operation steps are such as upload, modification, deletion, execute the multi-level strong encryption processing strategy, and encrypt the data operation steps; Execute the quantum key distribution technology strategy to securely manage and transmit the keys; Execute the intelligent compression algorithm strategy, and automatically select the compression method according to the type, access frequency, and importance of the data files; Execute the dynamic load balancing strategy to distribute the data files to the storage systems of multiple cloud service providers; Divide the cache into multiple levels, execute the multi-level cache mechanism strategy, perform intelligent scheduling and priority sorting on the cache, and manage data at different storage levels; When a user accesses, execute the multi-level permission management strategy, and set different access permission levels according to the user role; Execute the edge computing optimization strategy, store and distribute the data files to edge nodes closer to the user, and perform real-time processing and analysis on the data files; Perform real-time backup on all data files, execute the distributed disaster recovery technology strategy, and be able to recover the data files at other backup nodes in case of failure.

2. The data storage method of an intelligent education platform according to claim 1, characterized in that, The execution of the quantum encryption and data protection strategy to encrypt the data files includes: Record the obtained data file as D, D = {d1, d2,..., d n}, where d i is the i-th byte in the data file, and there are n bytes in total. The byte is the size of the data file; Calculate the hash value h of the data file D according to the hash function H, h = H(D) = H(d1, d2, …, d n ), where h is the unique hash value of the data file; Store the generated hash value h in the blockchain; During the data transmission process, quantum encryption and data protection strategies are executed to generate a shared key K through a quantum communication protocol q , and the quantum communication protocol is a technology that uses the principles of quantum mechanics for information transmission and secure encryption; Quantum encrypt the shared key K q to obtain the encrypted shared key K q '; Use the encrypted shared key K q 'Perform quantum encryption on the data file D to obtain the encrypted data file D'; The encrypted data file D’ is split into multiple data segments, denoted as D′={D′1, D′2, …, D′ m}, where m is the number of data segments after splitting; Based on the obtained data segment D′ i , generate an independent encryption key K i , and perform quantum encryption on each data segment; Distribute the encrypted data segment D′ i to multiple independent storage nodes; Obtain all the operation step records of the user for the data file, and execute a multi-level strong encryption processing strategy to generate an encryption key K for each operation step record e ; Using encryption key K e Perform the first layer of encryption on the operation step record; Use the recipient's public key K g Perform a second layer of encryption on the encrypted operation step record; Create an encryption key K i 's connection channel, execute the quantum key distribution strategy, and encrypt the encryption key K for each data segment i Perform quantum key distribution protection and transmit the encryption key K within the connection channel i .

3. The data storage method of an intelligent education platform according to claim 1, characterized in that, The execution of the dynamic load balancing strategy to distribute the data files to the storage systems of multiple cloud service providers includes: Monitor the storage system of each cloud service provider a in real time, and obtain the load situation L of the storage nodes of each cloud service provider a a =(L1, L2,..., L N ), where each L j represents the load of the j-th storage node on the provider a, N is the total number of N storage nodes, and the load includes storage capacity, storage utilization rate, and bandwidth limit; Obtain the load L of any storage node within each cloud service provider a aj , calculate the load utilization rate L of each cloud service provider a , where N a is the number of storage nodes under cloud service provider a; Obtain the load utilization rates L1, L2, …, L of all cloud service providers M , where there are a total of M cloud service providers, execute the dynamic load balancing policy, and select the cloud service provider with the lowest load utilization rate for data storage; Store the data files on the storage node with the lowest load utilization within the cloud service provider with the lowest load utilization; Obtain the available storage capacity C of each cloud service provider a a , and allocate the total size of the data files not exceeding the storage capacity size of the cloud service provider. The storage size s of the data files w needs to satisfy where w represents the w-th data file; Obtain the bandwidth limit B of each cloud service provider a a , when allocating data files, the total transmission rate does not exceed the bandwidth limit of the cloud service provider, and the transmission rate v of the data file w needs to satisfy When the load of any cloud service provider changes, recalculate and adjust the distribution of the data files.

4. The data storage method of an intelligent education platform according to claim 1, wherein The execution of the intelligent compression algorithm strategy to automatically select the compression method according to the type, access frequency, and importance of the data files includes: Obtain the data type T, access frequency G, and importance I of all data files; Set the compression scoring functions corresponding to the data type T, access frequency G, and importance I as f1(T), f2(G), and f3(I); Calculate the compression score Y, Y = ω1·f1(T)+ω2·f2(G)+ω3·f3(I), where ω1, ω2, ω3 are weight coefficients; set the compression score threshold θ; When Y≥θ, this data file is an important file, and select the lossless compression method for compression; When Y<θ, this data file is an infrequently used file, and select the lossy compression method for compression; Dynamically adjust the threshold θ according to the access frequency and storage requirements of the data files, and recalculate the compression score Y to select the compression method.

5. The data storage method of an intelligent education platform according to claim 4, characterized in that The division of the cache into multiple levels, the execution of the multi-level cache mechanism strategy, the intelligent scheduling and priority sorting of the cache includes: Sequentially divide the cache storage space into a high-level cache layer U1, a middle-level cache layer U2, and a low-level cache layer U3; Set the cache priority scoring functions for data type T, access frequency G, and importance I as f′1(T), f′2(G), and f′3(I) respectively; calculate the cache priority score Y’ for each data file, Y′ = ω1·f′1(T) + ω2·f′2(G) + ω3·f′3(I); Set thresholds θ1 and θ2; When Y′ ≥ θ1, the data file is cached to the high - level cache layer U1; When θ2 ≤ Y′ < θ1, the data file is cached to the medium - level cache layer U2; When Y′ < θ2, the data file is cached to the low - level cache layer U3; When the storage space is full, the data file with the lowest access frequency is eliminated for cache replacement; Regularly calculate the change in the cache priority of the data file, and dynamically adjust the cache location of the data file according to the current cache priority, Y′(t) = ω1·f′1(T(t)) + ω2·f′2(G(t)) + ω3·f′3(I(t)), where t is the time when the cache priority of the data file changes.

6. The data storage method of an intelligent education platform according to claim 2, wherein When the user accesses, execute a multi - level permission management strategy, and set different access permission levels according to the user role, including: Obtain the user role type \(R = \{r_1, r_2, \ldots, r\ b \}\) and the permission levels \(X = \{x_1, x_2, \ldots, x\ l \}\) corresponding to the user type. The mapping relationship between the user role and the permission level is \(F: R ightarrow X\). Among them, only one permission level can be assigned to a user role; Divide the data files into public data files, restricted data files, and confidential data files; Set the first - level access permission as accessible to all user roles, and the accessible data files are public data files; Set the second - level access permission as accessible to specified user roles, and the accessible data files are restricted data files; Set the third - level access permission as accessible to specific authorized user roles, and the accessible data files are confidential data files; Set the relationship between user role R, data file D, and permission level X as the access control matrix A(R,D): where x uo represents the user role r u has access to the data file D o ; When the user requests access to data file D o obtain the user role r u , and according to the access control matrix A(R, D), determine whether X(r u , D o ) is 1; When X(r u , D o ) = 1, the user access privilege level meets the access requirements, and the user is allowed to access; When X(r u , D o ) ≠ 1, the user access privilege level does not meet the access requirements, and the user access is denied.

7. A data storage method for an intelligent education platform according to claim 2, characterized in that, Execute the edge computing optimization strategy, store and distribute the data files to edge nodes closer to the user, and perform real - time processing and analysis on the data files, including: Calculating the storage priority of edge nodes Among them, α1, α2, and α3 are weight coefficients, and the edge node has a storage capacity C aj , computing power A aj , network bandwidth B aj , current load L aj , access frequency G aj , latency requirement J aj ; Set the threshold P th ; When P * (D)>P th the edge node storing the data file is the edge node closest to the user; Set the edge node set as Z = {Z1, Z2,..., Z y}, where Z j is any edge node in the set; Calculate and select an edge node with the lowest load, optimal bandwidth, and most matching computing power Among them, β1, β2, and β3 are adjustment coefficients; Perform real - time calculation and analysis on the data files stored on the edge nodes.

8. The data storage method of an intelligent education platform according to claim 2, characterized in that ,All data files are backed up in real - time, and a distributed disaster recovery technology strategy is executed. When a failure occurs, the data files can be restored on other backup nodes, including: Set the data file D to be stored in real time by processes on multiple backup nodes M′ = {M1, M2, …, M z}. Allocate R for each data file * independent backup nodes S′(D i′ ) = {M i′1 , M i′2 , …, M i′X′}, where X′ is the number of backup copies, and each data file is stored at least X′ times. S′(D i′ ) represents the set of backup nodes for data file D; Set the normal health status of each backup node M j′ to be γ; When H′(M j′ ) = γ, the backup node M j′ operates normally; When H′(M j′ ) ≠ γ, the backup node M j′ fails; Backup node M j′ When a failure occurs, find an available backup node S′ al (D i′ ) = M j′ ∈ S′(D i′ ), directly recover the data file in the backup node.

9. A system for a data storage method of an intelligent education platform, applied to the data storage method of an intelligent education platform according to any one of claims 1-8, characterized in that, Including: Data acquisition and hash calculation module: Responsible for receiving the data files submitted by the user and performing pre - processing, calculating the hash value of the data file and storing it in the blockchain; Quantum encryption and data protection module: Encrypt the data file using the encryption key, split the encrypted data into multiple data segments, and generate independent encryption keys for each data segment and encrypt them; Distributed storage and management module: Manage cloud service providers and their storage nodes, allocate data segments to the optimal storage nodes according to the load - balancing strategy, detect the health status of the storage nodes, and perform storage migration; Data operation record and multi - level encryption module: Record all data operation steps and perform multi - level encryption on them. Use the encryption key to perform the first - layer encryption on the operation steps, and use the recipient's public key to encrypt the encrypted data again; Dynamic load - balancing module: Obtain the real - time load information of each storage node, calculate the storage load utilization rate of the cloud service provider, and select the cloud storage node with the lowest load to store the data; Intelligent Compression Algorithm Module: Analyze the type, access frequency, and importance of data files, calculate the compression score, select the compression method, and dynamically adjust the compression strategy according to the data usage situation; Multi-level Cache Management Module: Divide the cache into a high-level cache layer, a middle-level cache layer, and a low-level cache layer, calculate the cache priority score, and dynamically adjust the cache strategy according to the data access pattern; Multi-level Permission Management Module: Define different user roles, set access permission levels, and restrict different users' access rights to data; Edge Computing Optimization Module: Store data files in the edge node closest to the user, and perform calculations at the edge node for real-time data analysis; Distributed Disaster Recovery Module: Perform real-time backups on all data files, detect whether the storage nodes fail, and recover data files from available backup nodes.

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