Data storage method and system for a smart education platform

Abstract

The present invention relates to the field of big data processing and cloud computing, and discloses a data storage method and system for a smart education platform, comprising: executing quantum encryption and data protection strategies to encrypt data files; splitting the encrypted data files into multiple data fragments, distributing all the data fragments to multiple independent storage nodes, and encrypting data operation steps; executing an intelligent compression algorithm strategy to automatically select a compression method; executing a dynamic load balancing strategy to distribute data files to storage systems of multiple cloud service providers; dividing the cache into multiple levels, executing a multi-level cache mechanism strategy to prioritize the cache, and managing data at different storage levels; executing a multi-level permission management strategy to set different access permission levels according to user roles; and backing up all data files in real time. This series of encryption and protection measures ensures the 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] Against the backdrop of the rapid development of modern information technology, the rise of smart education platforms has revolutionized the education industry. Optimizing data storage methods is crucial for these platforms. Effective data storage strategies not only enhance data security and privacy, but also improve system responsiveness and user experience. By implementing strategies such as multi-layered encryption, dynamic load balancing, and intelligent compression, smart education platforms can better manage and protect student and teacher data, thereby providing strong support for the equitable distribution of educational resources.

[0003] However, existing technologies have several flaws in data storage. Many traditional data storage methods rely solely on centralized storage systems, resulting in insufficient data security and vulnerability to single points of failure. Furthermore, these methods are inefficient when processing large amounts of data and fail to fully leverage the advantages of cloud and edge computing. Furthermore, existing technologies lack flexibility in managing data access rights, making it difficult to dynamically adjust access based on user roles, increasing the risk of data leakage and misuse.

[0004] This solution significantly improves the security and efficiency of data storage by introducing a number of innovative measures, including 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, which are used to promote the solution of the problems mentioned in the above background technology.

[0006] In a first aspect, the present invention provides the following technical solutions: a data storage method for a smart education platform, comprising: obtaining data files submitted by users, generating a hash value for each data file, storing the data file in a blockchain, executing quantum encryption and data protection strategies, and encrypting the data file;

[0007] Split the encrypted data file into multiple data fragments, distribute all data fragments to multiple independent storage nodes, encrypt each data fragment independently, and generate independent encryption keys;

[0008] Record all data operation steps, including upload, modification, and deletion, and implement multi-level strong encryption processing strategies to encrypt data operation steps;

[0009] Implement quantum key distribution technology strategies to securely manage and transmit keys;

[0010] Execute intelligent compression algorithm strategy and automatically select compression method based on data file type, access frequency and importance;

[0011] Execute dynamic load balancing strategies to distribute data files to storage systems of multiple cloud service providers;

[0012] Divide the cache into multiple levels, implement multi-level cache mechanism strategies, intelligently schedule and prioritize the cache, and manage data at different storage levels;

[0013] When users access, multi-level permission management strategies are implemented, setting different access permission levels based on user roles;

[0014] Execute edge computing optimization strategies, distribute data file storage to edge nodes closer to users, and perform real-time processing and analysis of data files;

[0015] All data files are backed up in real time, and distributed disaster recovery technology strategies are implemented to enable data files to be restored on other backup nodes in the event of a failure.

[0016] By introducing quantum encryption and data protection strategies, the smart education platform significantly enhances data security and privacy. Quantum encryption leverages the principles of quantum mechanics to create a secure shared key for data transmission, ensuring that data cannot be intercepted or tampered with during transmission. Each data file generates a unique hash value and is stored on the blockchain, ensuring the data's immutability and authenticity. Furthermore, data files are encrypted, split, and distributed across multiple independent nodes, reducing the risk of single points of failure. Even if a node experiences an issue, data can still be securely accessed and recovered, and independent encryption key management further enhances security. This multi-layered security measure not only increases user confidence in data security but also strengthens the platform's overall reliability, providing a strong guarantee for the secure management of educational resources.

[0017] Preferably, executing quantum encryption and data protection strategy to encrypt data files includes:

[0018] The acquired data file is recorded as D, D = {d1, d2, ..., d n}, where d i is the i-th byte in the data file, there are n bytes in total, and the bytes are the size of the data file;

[0019] 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;

[0020] Store the generated hash value h in the blockchain;

[0021] During the data transmission process, quantum encryption and data protection strategies are implemented, and a shared key K is generated through the quantum communication protocol. q , the quantum communication protocol is a technology that uses the principles of quantum mechanics to transmit information and securely encrypt;

[0022] Shared key K q Perform quantum encryption to obtain the encrypted shared key K q ';

[0023] Use the encrypted shared key K q 'Perform quantum encryption on data file D to obtain encrypted data file D';

[0024] 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;

[0025] According to the acquired data segment D′ i , generate an independent encryption key K i , and quantum encrypt each data fragment;

[0026] The encrypted data segment D′ i Distributed to multiple independent storage nodes;

[0027] Obtain all user operation step records for data files, implement multi-level strong encryption processing strategy, and generate an encryption key K for each operation step record e ;

[0028] With encryption key K e Perform the first layer of encryption on the operation step records;

[0029] Use the recipient's public key K g Perform a second layer of encryption on the encrypted operation step records;

[0030] Create encryption key K i The connection channel executes the quantum key distribution strategy and encrypts the key K for each data fragment. i Perform quantum key distribution protection and transmit the encryption key K in the connection channel i .

[0031] By implementing a dynamic load balancing strategy, the Smart Education Platform can efficiently distribute data files across multiple cloud service providers, improving the overall efficiency of data storage and access. The platform monitors the load of each storage node in real time, ensuring that data is always stored on the node with the lowest load, thereby avoiding performance degradation caused by resource overload. This strategy improves storage system utilization while optimizing data transfer rates, ensuring faster response times when users access data. Dynamic load balancing can also flexibly adjust based on the storage capacity and bandwidth limitations of the cloud service provider, ensuring efficient and secure data storage. This resource management mechanism not only improves system stability but also provides strong support for processing large-scale user data.

[0032] Preferably, executing a dynamic load balancing strategy to distribute data files to storage systems of multiple cloud service providers includes:

[0033] Monitor the storage system of each cloud service provider a in real time and obtain the load status L of each cloud service provider a's storage node a =(L1, L2, ..., L N ), where each L j represents the load of the jth storage node on provider a, where N is the total number of N storage nodes. The load includes storage capacity, storage utilization, and bandwidth limitations.

[0034] Get the load L of any storage node within each cloud service provider a aj , calculate the load utilization L of each cloud service provider a , Among them, N a is the number of storage nodes under cloud service provider a;

[0035] Get the load utilization L1, L2, ..., L of all cloud service providers M ,Among them, there are M cloud service providers in total, which implements a dynamic load balancing strategy and selects the cloud service provider with the minimum load utilization for data storage;

[0036] Store data files on the storage nodes with the lowest load utilization within the cloud service provider;

[0037] Get the available storage capacity C of each cloud service provider a a , the total size of the allocated data files does not exceed the storage capacity of the cloud service provider, and the storage size of the data files is s w Needs to be satisfied Among them, w represents the wth data file;

[0038] Get the bandwidth limit B of each cloud service provider aa , the total transfer rate when distributing data files does not exceed the bandwidth limit of the cloud service provider, and the transfer rate of data files v w Needs to be satisfied When the load of any cloud service provider changes, the distribution of data files is recalculated and adjusted.

[0039] By implementing intelligent compression algorithm strategies, the Smart Education Platform automatically selects the optimal compression method based on data file type, access frequency, and importance, thereby optimizing storage resource usage. This strategy ensures that important data files utilize lossless compression to maintain data integrity and availability, while less frequently used files utilize lossy compression to save storage space. By dynamically adjusting compression scores and thresholds, 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 data processing efficiency but also optimizes storage costs, providing the Smart Education Platform with a more efficient resource management solution, ensuring users can quickly access the information they need, and improving the overall user experience.

[0040] Preferably, the intelligent compression algorithm strategy is executed to automatically select a compression method based on the type, access frequency and importance of the data file, including:

[0041] Get the data type T, access frequency G and importance I of all data files;

[0042] Set the compression scoring functions of data type T, access frequency G and importance I to be f1(T), f2(G) and f3(I) respectively;

[0043] Calculate the compression score Y, Y = ω1·f1(T)+ω2·f2(G)+ω3·f3(I), where ω1, ω2, and ω3 are weight coefficients;

[0044] Set the compression score threshold θ;

[0045] When Y≥θ, the data file is an important file and lossless compression is selected for compression;

[0046] When Y<θ, the data file is an infrequently used file and lossy compression is selected for compression;

[0047] According to the access frequency and storage requirements of the data file, the threshold θ is dynamically adjusted, and the compression score Y is recalculated to select the compression method.

[0048] By implementing a multi-level permissions management strategy, the Smart Education Platform achieves greater flexibility and security in data access control. By precisely mapping user roles to permission levels, the platform implements dynamic role-based access control, 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. Furthermore, when users request access to data files, the system verifies permissions using the access control matrix to ensure compliance. This meticulous permissions management mechanism significantly enhances user trust in the platform, ensuring the secure and compliant use of educational data, and thus providing strong support for the effective implementation of educational work.

[0049] Preferably, the cache is divided into multiple levels, a multi-level cache mechanism strategy is implemented, and the cache is intelligently scheduled and prioritized, including:

[0050] The cache storage space is divided into the high-level cache layer U1, the middle-level cache layer U2, and the low-level cache layer U3;

[0051] Set the cache priority scoring functions for data type T, access frequency G, and importance I to 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);

[0052] Set thresholds θ1 and θ2;

[0053] When Y′≥θ1, the data file is cached in the advanced cache layer U1;

[0054] When θ2≤Y′<θ1, the data file is cached in the intermediate cache layer U2;

[0055] When Y′<θ2, the data file is cached in the low-level cache layer U3;

[0056] When the storage space is full, the least frequently accessed data files are eliminated for cache replacement;

[0057] Periodically calculate the cache priority changes of data files and dynamically adjust the cache position 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.

[0058] Through edge computing optimization strategies, the smart education platform can distribute data file storage to edge nodes closer to users, thereby improving the real-time and efficiency of data processing. This strategy not only reduces data transmission latency but also reduces dependence on central servers, further improving the response speed of the overall system. By calculating the storage priority of edge nodes, the platform can select nodes with the lowest load and optimal bandwidth for data storage and processing, ensuring that users receive fast responses and efficient services when accessing educational resources. In addition, edge computing supports real-time analysis of data stored on edge nodes, enabling timely response to user needs and improving the user experience. This efficient distributed storage solution provides scalability and flexibility for the smart education platform on a large user base, promoting the development of the education industry.

[0059] Preferably, when a user accesses the application, a multi-level authority management strategy is implemented, and different levels of access rights are set according to the user role, including:

[0060] Get user role type R = {r1, r2, ..., r b} and the corresponding permission level X={x1,x2,…,x l}, the mapping relationship between user roles and permission levels is F: R→X, where a user role can only be assigned one permission level;

[0061] Divide data files into public data files, restricted data files and confidential data files;

[0062] Set the first-level access permission to be accessible to all user roles, and the accessible data files are public data files;

[0063] Set the secondary access rights to designated user roles, and the accessible data files to restricted data files;

[0064] Set the third level of access rights to specific authorized user roles, and the accessible data files are confidential data files;

[0065] Assume that the relationship between user role R, data file D and permission level X is the access control matrix A(R,D):

[0066]

[0067] Among them, x uo Describing user roles u For data file D o have access rights;

[0068] When a user requests access to a data file D o When getting the user role r u , according to the access control matrix A(R,D), determine X(ru ,D o ) is 1;

[0069] When X(r u ,D o )=1, the user's access permission level meets the access requirements and the user is allowed to access;

[0070] When X(r u ,D o )≠1, the user's access permission level does not meet the access requirements and the user is denied access.

[0071] By implementing a distributed disaster recovery technology strategy, the Smart Education Platform ensures real-time backup of data files, enhancing the system's fault tolerance and data recovery speed. Real-time storage of data files on multiple backup nodes enables the platform to quickly identify available backup nodes in the event of a failure, ensuring rapid data recovery and business continuity. This strategy not only improves system reliability but also effectively reduces the risk of data loss, providing users with a more secure user experience. Each data file is stored in at least multiple copies, ensuring that data can be effectively protected and restored even in extreme circumstances. Furthermore, the implementation of this technology enables the platform to better respond to emergencies such as natural disasters and system failures, improving overall service stability and user trust. This high-availability design lays a solid foundation for the future development of the Smart Education Platform and effectively supports the digital transformation of the education industry.

[0072] Preferably, the execution of the edge computing optimization strategy, distributing data file storage to edge nodes closer to users, and performing real-time processing and analysis of data files, includes:

[0073] Calculate edge node storage priority Among them, α1, α2, and α3 are weight coefficients, and the edge node has a storage capacity of C aj 、Computing power A aj 、Network bandwidth B aj 、Current load L aj , access frequency G aj , delay requirementsJ aj Node;

[0074] Set the threshold P th ;

[0075] When P * (D)>P th When , the edge node storing the data file is the edge node closest to the user;

[0076] Set the edge node set to Z = {Z1, Z2, ..., Z y}, where Zj is any edge node in the set;

[0077] 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;

[0078] Perform real-time calculation and analysis on data files stored on edge nodes.

[0079] By recording all data manipulation steps and implementing strong encryption, the Smart Education Platform enhances transparency and traceability in data management and auditing. Whenever a user uploads, modifies, or deletes a data file, the system generates a corresponding operation record. This record not only aids data management but also facilitates subsequent audits and issue tracking. These operation records are protected through a multi-level strong encryption strategy, ensuring privacy and data security. Users can query historical operation records at any time, gaining a clear understanding of the data usage and modification process. This transparent operation mechanism not only enhances user trust but also provides essential support for the platform's adherence to data compliance and security, ensuring that the education platform adheres to best practices in data management.

[0080] Preferably, all data files are backed up in real time, and a distributed disaster recovery technology strategy is implemented to enable data files to be restored on other backup nodes in the event of a failure, including:

[0081] Assume that the data file D is stored in multiple backup nodes M′={M1, M2, ..., M z}Real-time storage of the process;

[0082] Assign R to each data file * An independent backup node S′(D i′ )={M i′1 , M i′2 ,…,M i′X′}, where X′ is the number of backup copies, and each data file stores at least X′ copies, S′(D i′ ) represents the backup node set of data file D;

[0083] Set each backup node M j′ The normal health condition is γ;

[0084] When H′(M j′ )=γ, the backup node M j′ Normal operation;

[0085] When H′(M j′ )≠γ, the backup node M j′ A malfunction occurs;

[0086] Backup node M j′ When a failure occurs, find an available backup node S' al (D i′ )=M j′ ∈S′(D i′ ), restore the data files directly in the backup node.

[0087] Through a flexible multi-level caching mechanism, the smart education platform can manage data access more intelligently, improving the system's response speed and user experience. The platform divides cache storage space into high-level, medium-level, and low-level cache layers, and sets cache priority scores based on data type, access frequency, and importance. By regularly evaluating the access frequency of data files, the platform can prioritize caching popular data to increase its access speed, while infrequently used files may be replaced or moved to low-level cache layers. When storage space is insufficient, the system automatically eliminates the least frequently accessed data files to achieve dynamic adjustment. This intelligent scheduling mechanism ensures that users can get a faster response when accessing educational resources, improves the overall user experience, helps optimize the configuration and utilization of educational resources, and thus promotes the efficient operation of the education platform.

[0088] In a second aspect, the present application provides a data storage system for a smart education platform, which adopts the following technical solution: A data storage system for a smart education platform, comprising:

[0089] Data acquisition and hash calculation module: responsible for receiving and preprocessing the data files submitted by users, calculating the hash value of the data files and storing them in the blockchain;

[0090] Quantum encryption and data protection module: encrypts data files using encryption keys, splits the encrypted data into multiple data fragments, generates independent encryption keys for each data fragment, and encrypts them;

[0091] Distributed storage and management module: manages cloud service providers and their storage nodes, allocates data fragments to the optimal storage nodes based on load balancing strategies, detects the health of storage nodes, and performs storage migration;

[0092] Data operation record and multi-level encryption module: records all data operation steps and performs multi-level encryption on them. The operation steps are encrypted with the encryption key as the first layer, and the encrypted data is encrypted again with the recipient's public key.

[0093] Dynamic load balancing module: obtains real-time load information of each storage node, calculates the storage load utilization of the cloud service provider, and selects the cloud storage node with the lowest load to store data;

[0094] Intelligent compression algorithm module: Analyzes the type, access frequency, and importance of data files, calculates compression scores and selects compression methods, and dynamically adjusts compression strategies based on data usage;

[0095] Multi-level cache management module: divides the cache into high-level cache layer, mid-level cache layer, and low-level cache layer, calculates cache priority scores, and dynamically adjusts cache strategies based on data access patterns;

[0096] Multi-level permission management module: define different user roles, set access permission levels, and limit different users' access rights to data;

[0097] Edge computing optimization module: stores data files on the edge node closest to the user, and performs calculations on the edge node for real-time data analysis;

[0098] Distributed disaster recovery module: performs real-time backup of all data files, detects whether storage nodes fail, and restores data files from available backup nodes.

[0099] The present invention has the following beneficial effects:

[0100] 1. This data storage method for a smart education platform significantly enhances data security and privacy by introducing quantum encryption and data protection strategies. The platform first generates a hash value for each data file and stores it in the blockchain, ensuring data uniqueness and immutability. This measure not only improves data integrity but also strengthens user trust in the platform. Furthermore, data files are encrypted upon upload and split into multiple data fragments, distributed and stored across multiple independent storage nodes. This ensures that even if a storage node fails, the data remains secure and available. Each independently encrypted data fragment is equipped with a separate encryption key, further enhancing data security and making it difficult for attackers to obtain the complete data. Quantum key distribution technology also ensures key management and transmission, significantly improving data security during transmission. In summary, this series of encryption and protection measures ensures the high security of user data and significantly improves the reliability of the services provided by the smart education platform.

[0101] 2. This smart education platform's data storage method, through the implementation of a dynamic load balancing strategy, enables efficient management and distribution of data files across the storage systems of multiple cloud service providers. This strategy relies on real-time monitoring of the load on each cloud service provider's storage nodes to ensure optimal storage capacity and utilization for each node. Dynamic load balancing not only effectively prevents resource overload but also improves the overall efficiency of data storage and access. When selecting a data storage node, the platform automatically calculates the load utilization of each provider and stores data on the node with the lowest load. This intelligent resource management approach maintains optimal data transmission speeds and reduces latency caused by network congestion. Furthermore, the platform continuously monitors the status of each cloud service provider and, upon detecting load changes, immediately adjusts the system to ensure users consistently receive fast and stable data access. This flexibility not only improves user satisfaction but also provides a solid foundation for the platform's sustainable development.

[0102] 3. This data storage method for a smart education platform implements an intelligent compression algorithm strategy. The smart education platform automatically selects the optimal compression method based on the type, access frequency, and importance of data files to achieve optimal utilization of storage resources. Once the data files are classified, the platform generates a corresponding compression score and selects lossless or lossy compression based on a set threshold. This strategy ensures the integrity of the user's important data and avoids information loss due to compression. For infrequently used files, lossy compression can save storage space, freeing up space for more important data. The intelligent compression algorithm also has the ability to dynamically adjust, recalculating scores and thresholds based on real-time access frequency and storage requirements to ensure that the compression method is always optimal. This flexible compression strategy not only improves storage efficiency but also reduces storage costs, providing a more efficient resource management solution for the smart education platform. Users can access the data they need in a shorter time, further improving the user experience and system responsiveness.

[0103] 4. This data storage method for a smart education platform achieves greater flexibility and security in data access control by implementing a multi-level permissions management strategy. The platform sets different access rights levels based on user role type, ensuring that only authorized users can access sensitive data. Data files are categorized as public, restricted, and confidential, with different access rights assigned to each category to ensure data security. By establishing an access control matrix, the platform can quickly determine whether a user's access rights meet the requirements when requesting access to a data file. This meticulous permissions management mechanism not only improves data access transparency but also reduces the risk of data leakage. Users can clearly understand their access rights, and the platform can respond promptly to user requests, ensuring data security and legal use. Furthermore, flexible permissions management facilitates future data audits and provides essential support for the platform's compliance with relevant laws and regulations. This multi-level management strategy not only enhances user trust in the platform but also provides strong protection for the secure management of educational data.

[0104] 5. This data storage method for a smart education platform uses edge computing optimization strategies to distribute data storage to edge nodes closer to users, significantly improving the real-time 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 that data is stored and processed at the nodes closest to the user. This approach not only reduces data transmission latency but also reduces over-reliance on central servers, helping to improve the overall system's responsiveness. Furthermore, the implementation of edge computing enables users to enjoy a smoother experience when accessing educational resources, allowing for real-time processing and analysis of data files closer to the user. By optimizing the allocation of storage and computing resources, the platform can respond to user needs more efficiently and improve user satisfaction. This flexible distributed storage solution not only provides scalability and flexibility for the smart education platform but also injects new vitality into the development of the education industry.

[0105] 6. This data storage method of a smart education platform ensures real-time backup of data files by implementing a distributed disaster recovery technology strategy, improving the system's fault tolerance and data recovery speed. The platform stores data files in real time on multiple backup nodes, so that in the event of a failure, available backup nodes can be quickly found, ensuring rapid data recovery and business continuity. Each data file stores at least multiple backups to ensure that data can be effectively protected and restored even in extreme cases. This mechanism not only improves the reliability of the system, but also reduces the risk of data loss due to disaster events, providing users with a more secure user experience. In addition, the platform can monitor the health of the backup nodes to ensure that timely adjustments can be made when a backup node fails to ensure data security. This high-availability design has laid a solid foundation for the future development of the smart education platform, effectively supporting the digital transformation of the education industry and enabling it to better respond to various emergencies.

[0106] 7. This data storage method of a smart 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 will automatically generate a corresponding operation record, which not only helps to effectively manage the data, but also facilitates future audit work. Through a multi-level strong encryption processing strategy, these operation records are protected to ensure that user privacy is not leaked. When users query historical operation records, they can clearly understand the use 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 the compliance of data use and provide necessary guarantees for the management of educational data. This efficient data management method enables the smart education platform to better protect the rights and interests of users and provide strong support for the development of educational work.

[0107] 8. This data storage method for a smart education platform achieves more intelligent cache management by dividing the cache into multiple levels, thereby improving the system's response speed and user experience. The platform divides the cache storage space into high-level, medium-level, and low-level cache layers, and sets cache priority scores based on data type, access frequency, and importance. By dynamically adjusting the cache priority of data files, the platform can prioritize caching popular data, thereby increasing its access speed without the need to frequently read from the main storage. This intelligent scheduling mechanism ensures that users can obtain faster responses when accessing educational resources, and automatically eliminates the data files with the lowest access frequency when the storage space is full, realizing cache replacement. In addition, the cache priority changes of data files are regularly calculated, and the cache position is dynamically adjusted according to the current priority, further optimizing cache utilization efficiency. This efficient caching mechanism not only improves the user experience, but also optimizes the configuration and utilization of educational resources, providing a strong guarantee for the efficient operation of the smart education platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 Schematic diagram of the process of the present invention.

[0109] Figure 2 Schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0110] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0111] Example 1, refer to Figure 1 , a data storage method for a smart education platform, comprising:

[0112] Obtain the data files submitted by the user, generate the hash value of each data file, store it in the blockchain, execute quantum encryption and data protection strategies, and encrypt the data files;

[0113] Split the encrypted data file into multiple data fragments, distribute all data fragments to multiple independent storage nodes, encrypt each data fragment independently, and generate independent encryption keys;

[0114] Record all data operation steps, including upload, modification, and deletion, and implement multi-level strong encryption processing strategies to encrypt data operation steps;

[0115] Implement quantum key distribution technology strategies to securely manage and transmit keys;

[0116] Execute intelligent compression algorithm strategy and automatically select compression method based on data file type, access frequency and importance;

[0117] Execute dynamic load balancing strategies to distribute data files to storage systems of multiple cloud service providers;

[0118] Divide the cache into multiple levels, implement multi-level cache mechanism strategies, intelligently schedule and prioritize the cache, and manage data at different storage levels;

[0119] When users access, multi-level permission management strategies are implemented, setting different access permission levels based on user roles;

[0120] Execute edge computing optimization strategies, distribute data file storage to edge nodes closer to users, and perform real-time processing and analysis of data files;

[0121] All data files are backed up in real time, and distributed disaster recovery technology strategies are implemented to enable data files to be restored on other backup nodes in the event of a failure.

[0122] By introducing quantum encryption and data protection strategies, the smart education platform has achieved significant results in ensuring data security and user privacy. The application of quantum encryption technology prevents unauthorized access or tampering during data transmission, ensuring the confidentiality of information. A unique hash value is generated for each data file and stored on the blockchain, ensuring data integrity and immutability. By encrypting and splitting data files and distributing them across multiple independent nodes, the risk of single points of failure is reduced. This architecture ensures that even if a single node fails, data integrity and accessibility are maintained on other nodes. Independent encryption key management further enhances data security, making the entire system more robust against potential security threats. This multi-layered security mechanism not only strengthens user trust in the platform but also provides important support for the secure storage and management of educational resources.

[0123] Execute quantum encryption and data protection strategies to encrypt data files, including:

[0124] The acquired data file is recorded as D, D = {d1, d2, ..., d n}, where d i is the i-th byte in the data file, there are n bytes in total, and the bytes are the size of the data file;

[0125] 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;

[0126] Store the generated hash value h in the blockchain;

[0127] During the data transmission process, quantum encryption and data protection strategies are implemented, and a shared key K is generated through the quantum communication protocol. q , the quantum communication protocol is a technology that uses the principles of quantum mechanics to transmit information and securely encrypt;

[0128] Shared key K q Perform quantum encryption to obtain the encrypted shared key K q ';

[0129] Use the encrypted shared key K q 'Perform quantum encryption on data file D to obtain encrypted data file D';

[0130] 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;

[0131] According to the acquired data segment D′ i , generate an independent encryption key K i , and quantum encrypt each data fragment;

[0132] The encrypted data segment D′ i Distributed to multiple independent storage nodes;

[0133] Obtain all user operation step records for data files, implement multi-level strong encryption processing strategy, and generate an encryption key K for each operation step record e ;

[0134] With encryption key K e Perform the first layer of encryption on the operation step records;

[0135] Use the recipient's public key K g Perform a second layer of encryption on the encrypted operation step records;

[0136] Create encryption key K i The connection channel executes the quantum key distribution strategy and encrypts the key K for each data fragment. i Perform quantum key distribution protection and transmit the encryption key K in the connection channel i .

[0137] By implementing a dynamic load balancing strategy, the Smart Education Platform optimizes data storage and access efficiency across multiple cloud service providers. This strategy monitors the load of each storage node in real time, ensuring that data is always stored on the node with the lowest load, thereby avoiding performance degradation caused by resource overload. Dynamic load balancing not only improves storage system utilization but also optimizes data transmission rates, increasing user response time when accessing educational resources. This strategy allows the platform to flexibly adjust data distribution across different cloud service providers, ensuring efficient and secure storage. By effectively managing storage resources, the platform can better cope with high concurrent access demands and enhance user experience, providing strong technical support for the education industry.

[0138] Execute dynamic load balancing strategy to distribute data files to storage systems of multiple cloud service providers, including: real-time monitoring of the storage system of each cloud service provider a, obtaining the load status of each cloud service provider a's storage node L a =(L1, L2, ..., L N ), where each L j represents the load of the jth storage node on provider a, where N is the total number of N storage nodes. The load includes storage capacity, storage utilization, and bandwidth limitations.

[0139] Get the load L of any storage node within each cloud service provider a aj , calculate the load utilization L of each cloud service provider a , Among them, N a is the number of storage nodes under cloud service provider a;

[0140] Get the load utilization L1, L2, ..., L of all cloud service providers M ,Among them, there are M cloud service providers in total, which implements a dynamic load balancing strategy and selects the cloud service provider with the minimum load utilization for data storage;

[0141] Store data files on the storage nodes with the lowest load utilization within the cloud service provider;

[0142] Get the available storage capacity C of each cloud service provider a a , the total size of the allocated data files does not exceed the storage capacity of the cloud service provider, and the storage size of the data files is s w Needs to be satisfied Among them, w represents the wth data file;

[0143] Get the bandwidth limit B of each cloud service provider a a, the total transfer rate when distributing data files does not exceed the bandwidth limit of the cloud service provider, and the transfer rate of data files v w Needs to be satisfied When the load of any cloud service provider changes, the distribution of data files is recalculated and adjusted.

[0144] By implementing an intelligent compression algorithm strategy, the Smart Education Platform achieves more efficient resource utilization for data storage. This strategy automatically selects the optimal compression method based on the type, access frequency, and importance of data files to optimize storage space. For example, lossless compression is used for important data files to maintain integrity, while lossy compression is used for less frequently used files to save storage space. By dynamically adjusting compression scores and thresholds, the platform can flexibly respond to the needs of different data files and more rationalize resource allocation. This intelligent compression process not only improves storage efficiency but also reduces storage costs, providing a foundation for the platform's sustainable development. Users can quickly access the data they need, improving the overall user experience and meeting the requirements for efficient data storage and access in education.

[0145] Execute intelligent compression algorithm strategies and automatically select compression methods based on the type, access frequency, and importance of data files, including:

[0146] Get the data type T, access frequency G and importance I of all data files;

[0147] Set the compression scoring functions of data type T, access frequency G and importance I to be f1(T), f2(G) and f3(I) respectively;

[0148] Calculate the compression score Y, Y = ω1·f1(T)+ω2·f2(G)+ω3·f3(I), where ω1, ω2, and ω3 are weight coefficients;

[0149] Set the compression score threshold θ;

[0150] When Y≥θ, the data file is an important file and lossless compression is selected for compression;

[0151] When Y<θ, the data file is an infrequently used file and lossy compression is selected for compression;

[0152] According to the access frequency and storage requirements of the data file, the threshold θ is dynamically adjusted, and the compression score Y is recalculated to select the compression method.

[0153] By implementing a multi-level permissions management strategy, the Smart Education Platform has achieved significant improvements in data security and access control. This strategy precisely maps user roles to permission levels, ensuring that only authorized users can access sensitive data. The platform categorizes data files into three categories: public, restricted, and confidential, and manages them accordingly based on permission levels, reducing the risk of data leakage. Furthermore, when users request access to data files, the system verifies permissions using an access control matrix to ensure compliance. This permissions management mechanism not only enhances data security but also strengthens user trust in the platform. By ensuring legal access to data, the platform provides strong protection for the secure and compliant use of educational data and promotes the rational flow of educational resources.

[0154] Divide the cache into multiple levels, implement a multi-level cache mechanism strategy, and intelligently schedule and prioritize the cache, including:

[0155] The cache storage space is divided into the high-level cache layer U1, the middle-level cache layer U2, and the low-level cache layer U3;

[0156] Set the cache priority scoring functions for data type T, access frequency G, and importance I to 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);

[0157] Set thresholds θ1 and θ2;

[0158] When Y′≥θ1, the data file is cached in the advanced cache layer U1;

[0159] When θ2≤Y′<θ1, the data file is cached in the intermediate cache layer U2;

[0160] When Y′<θ2, the data file is cached in the low-level cache layer U3;

[0161] When the storage space is full, the least frequently accessed data files are eliminated for cache replacement;

[0162] Periodically calculate the cache priority changes of data files and dynamically adjust the cache position 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.

[0163] Through edge computing optimization strategies, the smart education platform is able to distribute data storage to edge nodes closer to users, thereby improving the real-time and efficiency of data processing. This strategy significantly reduces data transmission latency while reducing reliance on central servers, improving the overall responsiveness of the system. By calculating the storage priority of edge nodes, the platform selects nodes with the lowest load and optimal bandwidth for data storage and processing, ensuring that users receive a fast response when accessing educational resources. In addition, edge computing allows for real-time analysis of data stored on nodes, enabling rapid response to user needs. This distributed storage solution provides the education platform with scalability and flexibility to support growing user needs and advance the digital transformation of the education industry.

[0164] When users access, a multi-level permission management strategy is implemented, setting different access rights levels based on user roles, including:

[0165] Get user role type R = {r1, r2, ..., r b} and the corresponding permission level X={x1,x2,…,x l}, the mapping relationship between user roles and permission levels is F: R→X, where a user role can only be assigned one permission level;

[0166] Divide data files into public data files, restricted data files and confidential data files;

[0167] Set the first-level access permission to be accessible to all user roles, and the accessible data files are public data files;

[0168] Set the secondary access rights to designated user roles, and the accessible data files to restricted data files;

[0169] Set the third level of access rights to specific authorized user roles, and the accessible data files are confidential data files;

[0170] Assume that the relationship between user role R, data file D and permission level X is the access control matrix A(R,D):

[0171]

[0172] Among them, x uo Describing user roles u For data file D o have access rights;

[0173] When a user requests access to a data file D o When getting the user role r u , according to the access control matrix A(R,D), determine X(r u ,D o) is 1;

[0174] When X(r u ,D o )=1, the user's access permission level meets the access requirements and the user is allowed to access;

[0175] When X(r u ,D o )≠1, the user's access permission level does not meet the access requirements and the user is denied access.

[0176] By implementing distributed disaster recovery technology, the Smart Education Platform ensures real-time backup of data files, 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 the event of a failure, ensuring rapid data recovery and business continuity. This strategy not only improves system reliability but also effectively reduces the risk of data loss, providing users with a more secure user experience. Furthermore, the platform stores at least multiple backups of each data file, ensuring data integrity and availability even in extreme situations. This high-availability design lays a solid foundation for the future development of the education platform, ensuring the stability of the education industry in the face of various emergencies.

[0177] Execute edge computing optimization strategies, distribute data file storage to edge nodes closer to users, and perform real-time processing and analysis of data files, including:

[0178] Calculate edge node storage priority Among them, α1, α2, and α3 are weight coefficients, and the edge node has a storage capacity of C aj 、Computing power A aj 、Network bandwidth B aj 、Current load L aj , access frequency G aj , delay requirementsJ aj Node;

[0179] Set the threshold P th ;

[0180] When P * (D)>P th When , the edge node storing the data file is the edge node closest to the user;

[0181] Set the edge node set to Z = {Z1, Z2, ..., Z y}, where Z j is any edge node in the set;

[0182] 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;

[0183] Perform real-time calculation and analysis on data files stored on edge nodes.

[0184] By precisely recording all data manipulation steps and implementing strong encryption, the Smart Education Platform achieves greater transparency and traceability in data management and auditing. Whenever a user uploads, modifies, or deletes a data file, the system generates a corresponding operation record. This record not only facilitates data management but also facilitates subsequent audits and issue tracking. Multi-level strong encryption effectively protects these operation records, ensuring privacy and data security. Users can query historical operation records at any time, gaining a clear understanding of data usage and modification processes. This transparent operation mechanism effectively enhances user trust and provides essential support for the platform's adherence to data compliance and security, ensuring that the education platform adheres to best practices in data management and improving the quality of educational services.

[0185] All data files are backed up in real time, and distributed disaster recovery technology strategies are implemented to enable data files to be restored on other backup nodes in the event of a failure, including:

[0186] Assume that the data file D is stored in multiple backup nodes M′={M1, M2, ..., M z}Real-time storage of the process;

[0187] Assign R to each data file * An independent backup node S′(D i′ )={M i′1 , M i′2 ,…,M i′X′}, where X′ is the number of backup copies, and each data file stores at least X′ copies, S′(D i′ ) represents the backup node set of data file D;

[0188] Set each backup node M j′ The normal health condition is γ;

[0189] When H′(M j′ )=γ, the backup node M j′ Normal operation;

[0190] When H′(M j′ )≠γ, the backup node M j′ A malfunction occurs;

[0191] Backup node M j′When a failure occurs, find an available backup node S' al (D i′ )=M j′ ∈S′(D i′ ), restore the data files directly in the backup node.

[0192] Through a flexible multi-level caching mechanism, the smart education platform achieves intelligent management of data access, further improving the system's response speed and user experience. The cache storage space is divided into high-level, medium-level, and low-level cache layers, and the platform sets cache priority scores based on data type, access frequency, and importance. When storage space is insufficient, the system automatically eliminates the data files with the lowest access frequency to achieve dynamic adjustment. This intelligent scheduling mechanism ensures that users can get a faster response when accessing educational resources, improving the overall user experience. In addition, the cache priority changes of data files are calculated regularly, allowing the platform to dynamically adjust the cache location of data files according to the current cache priority, effectively optimizing the utilization of storage resources, promoting the efficient operation of the education platform, and providing users with a smoother education experience.

[0193] Example 2, refer to Figure 2 , a data storage system for a smart education platform, comprising:

[0194] Data acquisition and hash calculation module: responsible for receiving and preprocessing the data files submitted by users, calculating the hash value of the data files and storing them in the blockchain;

[0195] Quantum encryption and data protection module: encrypts data files using encryption keys, splits the encrypted data into multiple data fragments, generates independent encryption keys for each data fragment, and encrypts them;

[0196] Distributed storage and management module: manages cloud service providers and their storage nodes, allocates data fragments to the optimal storage nodes based on load balancing strategies, detects the health of storage nodes, and performs storage migration;

[0197] Data operation record and multi-level encryption module: records all data operation steps and performs multi-level encryption on them. The operation steps are encrypted with the encryption key as the first layer, and the encrypted data is encrypted again with the recipient's public key.

[0198] Dynamic load balancing module: obtains real-time load information of each storage node, calculates the storage load utilization of the cloud service provider, and selects the cloud storage node with the lowest load to store data;

[0199] Intelligent compression algorithm module: Analyzes the type, access frequency, and importance of data files, calculates compression scores and selects compression methods, and dynamically adjusts compression strategies based on data usage;

[0200] Multi-level cache management module: divides the cache into high-level cache layer, mid-level cache layer, and low-level cache layer, calculates cache priority scores, and dynamically adjusts cache strategies based on data access patterns;

[0201] Multi-level permission management module: define different user roles, set access permission levels, and limit different users' access rights to data;

[0202] Edge computing optimization module: stores data files on the edge node closest to the user, and performs calculations on the edge node for real-time data analysis;

[0203] Distributed disaster recovery module: performs real-time backup of all data files, detects whether storage nodes fail, and restores data files from available backup nodes.

[0204] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0205] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A data storage method for a smart education platform, characterized in that: include: Obtain the data files submitted by the user, generate the hash value of each data file, store it in the blockchain, execute quantum encryption and data protection strategies, and encrypt the data files; Split the encrypted data file into multiple data fragments, distribute all data fragments to multiple independent storage nodes, encrypt each data fragment independently, and generate independent encryption keys; Record all data operation steps, such as upload, modification, and deletion, and implement multi-level strong encryption processing strategies to encrypt data operation steps; Implement quantum key distribution technology strategies to securely manage and transmit keys; Execute intelligent compression algorithm strategy and automatically select compression method based on data file type, access frequency and importance; Execute dynamic load balancing strategies to distribute data files to storage systems of multiple cloud service providers; Divide the cache into multiple levels, implement multi-level cache mechanism strategies, intelligently schedule and prioritize the cache, and manage data at different storage levels; When users access, multi-level permission management strategies are implemented, setting different access permission levels based on user roles; Execute edge computing optimization strategies, distribute data file storage to edge nodes closer to users, and perform real-time processing and analysis of data files; Back up all data files in real time and implement distributed disaster recovery technology strategies to enable data files to be restored on other backup nodes in the event of a failure; The execution of edge computing optimization strategies, distributing data file storage to edge nodes closer to users, and performing real-time processing and analysis of data files, includes: Calculate edge node storage priority ,in, 、 、 is the weight coefficient, the edge node has a storage capacity , computing power , network bandwidth , current load , access frequency , latency requirements Node; Setting thresholds ; when > When , the edge node storing the data file is the edge node closest to the user; Set the edge node set to Z= ,in, is any edge node in the set; Calculate and select an edge node with the lowest load, optimal bandwidth, and most matching computing power ,in, 、 、 is the adjustment coefficient; Perform real-time calculation and analysis on data files stored on edge nodes; The method backs up all data files in real time and implements a distributed disaster recovery technology strategy to restore data files on other backup nodes in the event of a failure, including: Set data file D on multiple backup nodes The upper process is stored in real time; Allocate for each data file Independent backup nodes ,in, is the number of backup copies, each data file stores at least share, Represents the set of backup nodes for data file D; Set up each backup node The normal health condition is ; when = When the backup node Normal operation; when When the backup node A malfunction occurs; Backup Node When a failure occurs, find an available backup node , restore the data file directly in the backup node, where It is an identifier used to find available backup nodes when executing a distributed disaster recovery technology strategy.

2. The data storage method of a smart education platform according to claim 1, characterized in that: The execution of quantum encryption and data protection strategy to encrypt data files includes: The acquired data file is recorded as D. ,in, is the i-th byte in the data file, there are n bytes in total, and the bytes are the size of the data file; Calculate the hash value h of data file D according to the hash function H, h= , where h is the unique hash value of the data file; Store the generated hash value h in the blockchain; During data transmission, quantum encryption and data protection strategies are implemented, and shared keys are generated through quantum communication protocols. , the quantum communication protocol is a technology that uses the principles of quantum mechanics to transmit information and securely encrypt; Shared Key Perform quantum encryption to obtain the encrypted shared key '; Use the encrypted shared key 'Perform quantum encryption on data file D to obtain encrypted data file D'; Split the encrypted data file D' into multiple data fragments, denoted as , where m is the number of data fragments after splitting; According to the acquired data fragment , generate an independent encryption key , and quantum encrypt each data fragment; The encrypted data fragment Distributed to multiple independent storage nodes; Obtain all user operation step records for data files, implement multi-level strong encryption processing strategy, and generate an encryption key for each operation step record ; Use encryption key Perform the first layer of encryption on the operation step records; Use the recipient's public key Perform a second layer of encryption on the encrypted operation step records; Creating an encryption key connection channel, executes quantum key distribution strategy, and encrypts the key for each data fragment Perform quantum key distribution protection to transmit encryption keys within the connection channel .

3. The data storage method of a smart education platform according to claim 1, characterized in that: The implementation of a dynamic load balancing strategy to distribute data files to 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 of each cloud service provider a's storage node , where each represents the load of the jth storage node on provider a, where N is the total number of N storage nodes. The load includes storage capacity, storage utilization, and bandwidth limitations. Get the load of any storage node within each cloud service provider a , calculate the load utilization of each cloud service provider , ,in, is the number of storage nodes under cloud service provider a; Get the load utilization of all cloud service providers ,Among them, there are M cloud service providers in total, which implements a dynamic load balancing strategy and selects the cloud service provider with the minimum load utilization for data storage; Store data files on the storage nodes with the lowest load utilization within the cloud service provider; Get the available storage capacity of each cloud service provider a The total size of the allocated data files does not exceed the storage capacity of the cloud service provider, and the storage size of the data files Needs to be satisfied , where w represents the wth data file; Get the bandwidth limit of each cloud service provider a , the total transfer rate when allocating data files does not exceed the bandwidth limit of the cloud service provider, and the transfer rate of data files Needs to be satisfied ; When the load of any cloud service provider changes, the distribution of data files is recalculated and adjusted.

4. The data storage method of a smart education platform according to claim 1, characterized in that: The intelligent compression algorithm strategy automatically selects a compression method based on the type, access frequency, and importance of the data file, including: Get the data type T, access frequency G and importance I of all data files; The compression scoring function for the data type T, access frequency G, and importance I is: 、 and ; Calculate the compression score Y, ,in, 、 、 is the weight coefficient; Setting compression score thresholds ; when When the data file is important, select lossless compression to compress it; when When the data file is not frequently used, lossy compression is selected for compression; Dynamically adjust the threshold based on the access frequency and storage requirements of data files , and recalculate the compression score Y to select the compression method.

5. The data storage method of a smart education platform according to claim 4, characterized in that: The cache is divided into multiple levels, a multi-level cache mechanism strategy is implemented, and the cache is intelligently scheduled and prioritized, including: Divide cache storage space into advanced cache tiers , intermediate cache layer , low-level cache layer ; The cache priority scoring function with data type T, access frequency G and importance I is: 、 and ; Calculate the cache priority score Y' for each data file, ; Setting thresholds 、 ; when When the data file is cached in the advanced cache layer ; when When the data file is cached in the intermediate cache layer ; when When the data file is cached in the low-level cache layer ; When the storage space is full, the least frequently accessed data files are eliminated for cache replacement; Regularly calculate the cache priority changes of data files and dynamically adjust the cache location of data files according to the current cache priority. , where t is the time when the data file cache priority changes.

6. The data storage method of a smart education platform according to claim 2, characterized in that: When a user accesses an application, a multi-level permission management strategy is implemented, setting different access permission levels based on the user role, including: Get user role type R= Permission level X corresponding to the user type , the mapping relationship between user roles and permission levels 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 to be accessible to all user roles, and the accessible data files are public data files; Set the secondary access rights to designated user roles, and the accessible data files to restricted data files; Set the third level of access rights 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 : ； in, Describing user roles For data files have access rights; When a user requests access to a data file Get the user role , according to the access control matrix ,judge Is it 1? when =1, the user's access permission level meets the access requirements and the user is allowed to access; when 1, the user's access permission level does not meet the access requirements and the user is denied access.

7. A system for a data storage method of a smart education platform, applied to a data storage method of a smart education platform according to any one of claims 1 to 6, characterized in that: include: Data acquisition and hash calculation module: responsible for receiving and preprocessing the data files submitted by users, calculating the hash value of the data files and storing them in the blockchain; Quantum encryption and data protection module: encrypts data files using encryption keys, splits the encrypted data into multiple data fragments, generates independent encryption keys for each data fragment, and encrypts them; Distributed storage and management module: manages cloud service providers and their storage nodes, allocates data fragments to the optimal storage nodes based on load balancing strategies, detects the health of storage nodes, and performs storage migration; Data operation record and multi-level encryption module: records all data operation steps and performs multi-level encryption on them. The operation steps are encrypted with the encryption key as the first layer, and the encrypted data is encrypted again with the recipient's public key. Dynamic load balancing module: obtains real-time load information of each storage node, calculates the storage load utilization of the cloud service provider, and selects the cloud storage node with the lowest load to store data; Intelligent compression algorithm module: Analyzes the type, access frequency, and importance of data files, calculates compression scores and selects compression methods, and dynamically adjusts compression strategies based on data usage; Multi-level cache management module: divides the cache into high-level cache layer, mid-level cache layer, and low-level cache layer, calculates cache priority scores, and dynamically adjusts cache strategies based on data access patterns; Multi-level permission management module: define different user roles, set access permission levels, and limit different users' access rights to data; Edge computing optimization module: stores data files on the edge node closest to the user, and performs calculations on the edge node for real-time data analysis; Distributed disaster recovery module: performs real-time backup of all data files, detects whether storage nodes fail, and restores data files from available backup nodes.

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