Cloud storage protection system based on big data network

Abstract

The invention belongs to the technical field of cloud storage, and particularly relates to a cloud storage protection system based on a big data network. Comprising a data acquisition and storage module, a segmentation and multiple dynamic encryption module, a distributed storage and dynamic allocation module, a data verification and progressive recovery module, a dynamic redundancy and load balancing module, a multi-layer intrusion detection and protection module and an expansibility optimization and adaptive scheduling module, and the data acquisition and storage module acquires and stores big data information; the data segmentation and multiple dynamic encryption module segments the data into a plurality of sub-data blocks and performs double-layer dynamic encryption; the distributed storage and dynamic distribution module dynamically distributes the encrypted data blocks; the data verification and progressive recovery module carries out local recovery on data damage or loss; the dynamic redundancy and load balancing module dynamically adjusts the redundancy of the data; the multi-layer intrusion detection and protection module monitors system safety in real time and responds to potential threats; and the expansibility optimization and self-adaptive scheduling module dynamically expands the storage nodes.

Description

Technical Field

[0001] The present invention relates to the field of cloud storage technology, and in particular to a big data network cloud storage protection system. Background Art

[0002] Cloud storage is an online storage model that stores data on multiple virtual servers hosted by a third party rather than dedicated servers. Cloud storage combines parallel computing and other technologies and is accessed through Web service application programming interfaces or Web-based user interfaces. It provides advantages such as reliability, security, cost savings, stable operation, and elastic scalability, and supports multi-platform, multi-language, and global deployment. With the rapid development of cloud computing and big data technologies, cloud storage systems have been widely used in remote storage and management of corporate and personal data. However, existing cloud storage technologies have the following deficiencies in encryption, security, and dynamic scalability:

[0003] 1. Single and static encryption mechanism: Existing cloud storage systems usually use static symmetric or asymmetric encryption mechanisms, which are difficult to deal with potential dynamic threats during data storage. Once the key is leaked, all data may be destroyed.

[0004] 2. Inflexible storage allocation: Traditional cloud storage systems mostly use static node allocation, which makes it difficult to dynamically adjust storage strategies based on data importance, node load, and available resources, which can easily lead to uneven load and reduce system efficiency;

[0005] 3. Lack of effective protection during transmission: During the transmission process of data from the client to the cloud, the existing technology has insufficient security protection and is vulnerable to man-in-the-middle attacks and data theft;

[0006] 4. Low data recovery efficiency: When node data is lost or damaged, existing cloud storage systems often rely on global backups, which makes the recovery process time-consuming and labor-intensive, affecting system availability.

[0007] 5. Insufficient scalability: When the amount of data surges, traditional cloud storage systems have bottlenecks in resource expansion and dynamic adjustment, resulting in reduced system performance and delayed response time;

[0008] 6. Imperfect multi-layer protection mechanism: The security protection mechanism of existing cloud storage systems usually relies on a single-layer firewall or simple access control, which makes it difficult to cope with complex network attacks such as APT (advanced persistent threat) and DDoS (distributed denial of service attack).

[0009] Therefore, the present invention proposes a big data network cloud storage protection system, which realizes intelligent debugging and optimization of the enterprise service management process by integrating advanced optimization algorithms and complex mathematical models.

[0010] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0011] To solve the above problems, the present invention provides the following technical solutions:

[0012] A big data network cloud storage protection system includes a data acquisition and storage module, a segmentation and multiple dynamic encryption module, a distributed storage and dynamic allocation module, a data verification and progressive recovery module, a dynamic redundancy and load balancing module, a multi-layer intrusion detection and protection module, and an extensibility optimization and adaptive scheduling module. The data segmentation and multiple dynamic encryption module divides data into multiple sub-data blocks and performs double-layer dynamic encryption; the distributed storage and dynamic allocation module dynamically allocates encrypted data blocks based on the load and resource conditions of the storage nodes; the data verification and progressive recovery module regularly checks the integrity of the data and performs local recovery after the data is damaged or lost; the dynamic redundancy and load balancing module dynamically adjusts the redundancy of the data based on the importance and access frequency of the data to ensure the security and availability of high-frequency access data; the multi-layer intrusion detection and protection module monitors the system security in real time and responds to potential threats through a multi-layer firewall and an intrusion detection system based on machine learning; the extensibility optimization and adaptive scheduling module dynamically expands the storage nodes according to the system's resource usage and data volume to ensure that the system remains efficient under high load.

[0013] Furthermore, the data acquisition and storage module mainly stores big data information, and marks the acquired and stored data as D.

[0014] Furthermore, the data segmentation and multiple dynamic encryption module includes a segmentation strategy unit and a dual dynamic encryption unit. The segmentation strategy unit is based on the original data D in the data acquisition storage module, and the original data D is segmented into n data blocks D according to the importance and access frequency of the data. i , the size of each data block is based on the weight matrix W i Determine, the weight matrix W i The specific analysis is dynamically generated based on the access frequency and importance of the data as follows:

[0015]

[0016] V i =α·f(d s )+β·g(a f )

[0017] Where V i is the weight, d s is the data block size, a f represents the access frequency, α and β represent the weight coefficients;

[0018] The dynamic re-encryption module performs double encryption on each segmented data block. The double encryption includes a symmetric encryption algorithm and an asymmetric encryption algorithm. The specific formula of the symmetric encryption algorithm is as follows:

[0019]

[0020] Where K i By time stamp T i and random seed S, K i The specific formula is as follows:

[0021]

[0022] Where P(ξ) represents a random process, represents the data of the i-th data block after AES encryption, E AES Indicates the AES encryption algorithm, using the key K i For data D i Encryption is performed;

[0023] The specific formula of the asymmetric encryption algorithm is as follows:

[0024]

[0025] in represents the final data of the i-th data block after ECC encryption, E ECC Indicates ECC encryption, the encrypted data Perform a second layer of encryption.

[0026] Furthermore, the distributed storage and dynamic allocation module includes a distributed storage unit and a dynamic allocation unit. The distributed storage and dynamic allocation module dynamically allocates encrypted data blocks based on the data segmentation and multiple dynamic encryption modules according to the load and resource conditions of the storage nodes. The distributed storage unit monitors the load L of each storage node in real time. j and Resources j Usage, select the best node to store encrypted data C i , the optimal storage location is:

[0027]

[0028] Where j = 1, 2, ..., m, L j The current load of the jth storage node represents the workload of the data stored on the node, R j It represents the available resources of the j-th storage node, represents the remaining capacity of the node that can be used to store or process data, is the optimal storage node for storing the ith data block, and m represents the total number of available storage nodes.

[0029] The dynamic allocation encryption unit encrypts the data block C i Store to the node with the lightest load N i Ensure load balancing.

[0030] Furthermore, the data verification and progressive recovery module includes a data verification unit and a progressive data recovery unit: the data verification unit stores the data blocks C stored on each node. i To verify, use a hash verification mechanism based on the Merkle tree:

[0031] V i =H(C i )

[0032] The progressive data recovery unit detects a data block C i When the node is damaged or lost, a gradual recovery mechanism is started, using redundant data R distributed on other nodes. j =(D i ) to recover the damaged data block:

[0033]

[0034] After the recovery is complete, the repaired data blocks are restored.

[0035] Where V i represents the checksum of the ith data block, H(C i ) represents the hash function, generating data block C i The check code, R j (D i ) represents the redundant data block D on the jth node i , k represents the number of redundant nodes.

[0036] Furthermore, the dynamic redundancy and load balancing module includes a dynamic redundancy adjustment unit and a load balancing unit. The dynamic redundancy adjustment unit adjusts the access frequency a of each data block according to the access frequency a of each data block. f And importance s dynamically adjusts its redundancy R(D), the formula is as follows:

[0037] where a f represents the access frequency of data, s represents the importance weight of data, R(D) represents the redundancy of data D, λ represents the adjustment factor, and S represents the upper limit of the importance of data.

[0038] The load balancing unit optimizes the storage location of redundant data in real time based on the load condition of each node in the system.

[0039] Furthermore, the multi-layer intrusion detection and protection module includes a real-time monitoring and detection unit and a multi-layer protection unit. The real-time monitoring and detection unit monitors and analyzes all access behaviors in real time through a machine learning model, identifies potential attacks, and calculates the attack probability P(A) of each access based on feature analysis:

[0040]

[0041] Where X i Characterizes the access pattern, λ i represents the weight of the feature, n represents the number of access pattern features, and P(A) represents the probability of the system being attacked;

[0042] The multi-layer protection unit provides further protection through multi-layer firewalls, combined with access control mechanisms to prevent potential security threats, and automatically adjusts the system's protection strategy based on the type and severity of the detected attack.

[0043] Furthermore, the scalability optimization and adaptive scheduling module includes a real-time monitoring node resource unit and an adaptive expansion unit. The real-time monitoring node resource real-time monitoring storage node resource usage is U i .

[0044] Furthermore, when the system load exceeds a set threshold, the system automatically adds a new storage node based on the adaptive expansion unit:

[0045] Among them U i Resource utilization of the i-th node, E S It represents the average resource utilization of the system, and n represents the number of currently active storage nodes.

[0046] Furthermore, a method for a big data network cloud storage protection system adopts a big data network cloud storage protection system according to any one of claims 1 to 9, characterized in that it includes the following steps:

[0047] Step 1: First, the system receives the data uploaded by the user and generates a weight matrix based on its importance and access frequency, thereby dividing the data into multiple sub-data blocks and performing double dynamic encryption to ensure data security;

[0048] Step 2: Then, the system monitors the load and available resources of the storage nodes in real time and dynamically selects the best node for distributed storage, thus avoiding single point of failure;

[0049] Step 3: Check data regularly, use redundant data to recover lost or damaged data blocks, dynamically adjust redundancy based on data access frequency and importance, and optimize resource utilization through load balancing algorithms;

[0050] Step 4: Dynamically adjust redundancy based on data access frequency and importance to balance node load;

[0051] Step 5: The system combines machine learning models to monitor access behavior in real time, identify potential attacks, and activate multi-layer protection mechanisms to prevent potential attacks;

[0052] Step 6: The system continuously monitors resource usage and automatically expands storage nodes to support large-scale data processing and ensure the efficiency and stability of the cloud storage system.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. In the present invention, through the dual dynamic encryption mechanism, the system effectively improves the security of data during transmission and storage. The combination of AES and ECC makes it difficult to crack all encrypted data even if the key is obtained by an attacker, which significantly reduces the risk of data leakage and protects user privacy;

[0055] 2. In the present invention, the strategy based on dynamic selection of storage nodes ensures uniform distribution of data and avoids single point overload. This distributed storage method improves the utilization efficiency of storage resources, enables the system to better cope with high-concurrency access requests, and improves the overall access efficiency;

[0056] 3. In the present invention, a regular hash check mechanism is used to ensure data integrity, damaged data is discovered and processed in a timely manner, and a strategy of progressive recovery using redundant data is used, which not only reduces the time and resource consumption required for data recovery, but also improves the reliability of the system and reduces the possibility of data loss;

[0057] 4. In the present invention, the redundancy is dynamically adjusted according to the access frequency and importance of the data, so that the key data can be more strongly protected when facing high-frequency access. The flexible redundancy management strategy ensures that users can quickly access the required data, thereby improving the user experience;

[0058] 5. In the present invention, combined with the multi-layer intrusion detection and protection mechanism of machine learning, the system can monitor access behavior in real time, quickly identify and respond to potential security threats. This intelligent protection improves the system's anti-attack capability and effectively maintains the security of the cloud storage environment;

[0059] 6. In the present invention, the system can automatically increase storage nodes during peak load periods to support growing data demands. This adaptive expansion capability ensures the efficiency and stability of the cloud storage system during large-scale data processing, allowing enterprises to flexibly respond to changing market demands. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0061] Figure 1 The present invention is a schematic diagram of the steps of a big data network cloud storage protection system. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, a big data network cloud storage protection system includes a data acquisition and storage module, a segmentation and multiple dynamic encryption module, a distributed storage and dynamic allocation module, a data verification and progressive recovery module, a dynamic redundancy and load balancing module, a multi-layer intrusion detection and protection module, and a scalability optimization and adaptive scheduling module. The data segmentation and multiple dynamic encryption module divides the data into multiple sub-data blocks and performs double-layer dynamic encryption; the distributed storage and dynamic allocation module dynamically allocates the encrypted data blocks based on the load and resource conditions of the storage nodes; the data verification and progressive recovery module regularly checks the integrity of the data and performs local recovery after the data is damaged or lost; the dynamic redundancy and load balancing module dynamically adjusts the redundancy of the data based on the importance and access frequency of the data to ensure the security and availability of high-frequency access data; the multi-layer intrusion detection and protection module monitors the system security in real time and responds to potential threats through multi-layer firewalls and an intrusion detection system based on machine learning; the scalability optimization and adaptive scheduling module dynamically expands the storage nodes according to the system's resource usage and data volume to ensure that the system remains efficient under high load.

[0064] Specifically, the data collection and storage module mainly stores big data information, and marks the collected and stored data as D.

[0065] Specifically, the data segmentation and multiple dynamic encryption module includes a segmentation strategy unit and a dual dynamic encryption unit. The segmentation strategy unit is based on the original data D in the data acquisition storage module, and the original data D is segmented into n data blocks according to the importance and access frequency of the data. i , the size of each data block is based on the weight matrix W i Determine, the weight matrix W i The specific analysis is dynamically generated based on the access frequency and importance of the data as follows:

[0066]

[0067] V i =α·f(d s )+β·g(a f )

[0068] Where V i is the weight, d s is the data block size, a f represents the access frequency, α and β represent the weight coefficients;

[0069] The dynamic re-encryption module performs double encryption on each segmented data block. The double encryption includes a symmetric encryption algorithm and an asymmetric encryption algorithm. The specific formula of the symmetric encryption algorithm is as follows:

[0070]

[0071] Where K i By time stamp T i and random seed S, K i The specific formula is as follows:

[0072]

[0073] Where P(ξ) represents a random process, represents the data of the i-th data block after AES encryption, E AES Indicates the AES encryption algorithm, using the key K i For data D i Encryption is performed;

[0074] The specific formula of the asymmetric encryption algorithm is as follows:

[0075]

[0076] in represents the final data of the i-th data block after ECC encryption, E ECC Indicates ECC encryption, the encrypted data Perform a second layer of encryption.

[0077] The user uploads data D, first generates the weight matrix W i Divide the data into multiple sub-data blocks D according to its importance and access frequency i , and then use the dynamic key K i Each data block is encrypted with AES and further protected with ECC encryption. i , ensuring the security of data during transmission and storage.

[0078] Specifically, the distributed storage and dynamic allocation module includes a distributed storage unit and a dynamic allocation unit. The distributed storage and dynamic allocation module dynamically allocates encrypted data blocks based on the load and resource conditions of the storage nodes after data segmentation and multiple dynamic encryption modules. The distributed storage unit monitors the load L of each storage node in real time. j and Resources j Usage, select the best node to store encrypted data C i , the optimal storage location is:

[0079]

[0080] Where j = 1, 2, ..., m, L j The current load of the jth storage node represents the workload of the data stored on the node, R j It represents the available resources of the j-th storage node, represents the remaining capacity of the node that can be used to store or process data, is the optimal storage node for storing the ith data block, and m represents the total number of available storage nodes.

[0081] Dynamically allocate encryption units to encrypt data block C i Store to the node with the lightest load N i Ensure load balancing.

[0082] Through dynamic allocation strategies, data blocks are evenly distributed on multiple nodes to prevent single-point overload and improve the overall access efficiency of the system. Encrypted data blocks are dynamically allocated to multiple storage nodes based on the node load and available resources to ensure load balance on each node and avoid single-point bottlenecks.

[0083] The system monitors the load L of each storage node in real time j and available resources R j , through the dynamic selection algorithm to determine the storage encryption data block C i By evenly distributing data on nodes with lighter loads, the system effectively prevents single point failures and improves the utilization efficiency of storage resources.

[0084] Specifically, the data verification and progressive recovery module includes a data verification unit and a progressive data recovery unit: the data verification unit stores the data block C on each node. i To verify, use a hash verification mechanism based on the Merkle tree:

[0085] V i =H(C i )

[0086] When the progressive data recovery unit detects a data block C iWhen the node is damaged or lost, a gradual recovery mechanism is started, using redundant data R distributed on other nodes. j =(D i ) to recover the damaged data block:

[0087]

[0088] After the recovery is complete, the repaired data blocks are restored.

[0089] Where V i represents the checksum of the ith data block, H(C i ) represents the hash function, generating data block C i The check code, R j (D i ) represents the redundant data block D on the jth node i , k represents the number of redundant nodes.

[0090] Periodically store data blocks C i Perform hash verification to ensure data integrity. If the verification finds data corruption, the system will use redundant data R from other nodes. j (D i ) to perform local recovery to ensure that damaged data blocks can be repaired quickly and reduce the risk of user data loss.

[0091] Specifically, the dynamic redundancy and load balancing module includes a dynamic redundancy adjustment unit and a load balancing unit. The dynamic redundancy adjustment unit adjusts the access frequency of each data block according to a. f And importance s dynamically adjusts its redundancy R(D), the formula is as follows:

[0092]

[0093] Data blocks with high frequency of access and high importance will be allocated more redundant storage to ensure data availability and security.

[0094] where a f represents the access frequency of data, s represents the importance weight of data, R(D) represents the redundancy of data D, λ represents the adjustment factor, and S represents the upper limit of the importance of data.

[0095] The load balancing unit optimizes the storage location of redundant data in real time based on the system's load conditions on each node.

[0096] The system dynamically adjusts redundancy according to the access frequency and importance of data to improve the availability of key data. At the same time, through real-time monitoring and load balancing algorithms, the system ensures the uniform distribution of redundant data, optimizes the resource utilization of storage nodes, and avoids overload.

[0097] Specifically, the multi-layer intrusion detection and protection module includes a real-time monitoring and detection unit and a multi-layer protection unit. The real-time monitoring and detection unit monitors and analyzes all access behaviors in real time through a machine learning model, identifies potential attacks, and calculates the attack probability P(A) of each access based on feature analysis:

[0098]

[0099] Where X i Characterizes the access pattern, λ i represents the weight of the feature, n represents the number of access pattern features, and P(A) represents the probability of the system being attacked.

[0100] The multi-layer protection unit provides further protection through multi-layer firewalls, combined with access control mechanisms to prevent potential security threats. At the same time, it automatically adjusts the system's protection strategy according to the type and severity of the detected attack. Through multi-layer protection and machine learning models, and calculates the attack probability P(A), when high-risk behavior is detected, the system will immediately initiate protection measures. Through multi-layer firewalls and access control mechanisms, the security of the data storage environment is enhanced to ensure effective prevention of potential attacks.

[0101] Specifically, the scalability optimization and adaptive scheduling module includes a real-time monitoring node resource unit and an adaptive expansion unit. The real-time monitoring node resource and the real-time monitoring storage node resource usage are U i .

[0102] Specifically, the adaptive expansion unit automatically adds new storage nodes when the system load exceeds the set threshold:

[0103] Among them U i The resource utilization rate of the i-th node is the proportion of resources being used by the node, E S It represents the average resource utilization of the system, which is used to determine whether storage nodes need to be expanded. n represents the number of currently active storage nodes, which is used to calculate the overall resource utilization of the system.

[0104] After adding a new node, the system will reallocate some data blocks to the new node to alleviate the load pressure.

[0105] When the amount of data increases, the system can respond quickly and achieve smooth expansion of the system by adding new storage nodes, ensuring high performance under high load. When the system resource utilization exceeds the set threshold, the system will automatically add new storage nodes to ensure stable performance under high load.

[0106] The system continuously monitors the resource utilization of storage nodes and automatically adds new storage nodes when the load exceeds the set threshold. Through adaptive scheduling, the system can optimize storage resources based on real-time data growth, ensuring the efficiency and stability of the cloud storage system when processing large-scale data.

[0107] Specifically, a method for a big data network cloud storage protection system adopts a big data network cloud storage protection system according to any one of claims 1 to 9, comprising the following steps:

[0108] Step 1: First, the system receives the data uploaded by the user and generates a weight matrix based on its importance and access frequency, thereby dividing the data into multiple sub-data blocks and performing double dynamic encryption to ensure data security;

[0109] Step 2: Then, the system monitors the load and available resources of the storage nodes in real time and dynamically selects the best node for distributed storage, thus avoiding single point of failure;

[0110] Step 3: Check data regularly, use redundant data to recover lost or damaged data blocks, dynamically adjust redundancy based on data access frequency and importance, and optimize resource utilization through load balancing algorithms;

[0111] Step 4: Dynamically adjust redundancy based on data access frequency and importance to balance node load;

[0112] Step 5: The system combines machine learning models to monitor access behavior in real time, identify potential attacks, and activate multi-layer protection mechanisms to prevent potential attacks;

[0113] Step 6: The system continuously monitors resource usage and automatically expands storage nodes to support large-scale data processing and ensure the efficiency and stability of the cloud storage system.

[0114] Working principle of the present invention: First, the system receives the data D uploaded by the user and generates a weight matrix W according to its importance and access frequency. i , thereby dividing the data into multiple sub-data blocks D i , and double dynamic encryption is performed. Double encryption includes symmetric encryption algorithm and asymmetric encryption algorithm. The specific formula of the symmetric encryption algorithm is as follows:

[0115]

[0116] It is generated by timestamp and random seed, and the specific formula is as follows:

[0117]

[0118] Wherein represents the random process, represents the data of the i-th data block after being encrypted by AES, represents the AES encryption algorithm, and uses the key to encrypt the data;

[0119] The specific formula of the asymmetric encryption algorithm is as follows:

[0120]

[0121] Wherein, represents the final data of the i-th data block after ECC encryption, represents ECC encryption, and the encrypted data is encrypted in the second layer to ensure data security. Then the system monitors the load and available resources of the storage nodes in real time, and dynamically selects the optimal node for distributed storage to avoid single point failure. Subsequently, the stored data is hashed regularly to ensure data integrity; if damage is found, the system uses redundant data for local recovery. In addition, the system dynamically adjusts redundancy according to the access frequency and importance of the data, and optimizes resource utilization through load balancing algorithms. To enhance security, the system combines machine learning models to monitor access behavior in real time, identify potential attacks and start multi-layer protection mechanisms. Finally, the system continuously monitors resource usage and automatically expands storage nodes to support large-scale data processing, ensuring the efficiency and stability of the cloud storage system. This technical solution integrates multiple aspects such as data segmentation, dynamic encryption, distributed storage, data verification, redundancy management and intrusion detection, ensuring the security and efficiency of the cloud storage system in a big data environment. This flexible design can quickly adapt to changing storage needs while protecting user data from various network threats.

[0122] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A big data network cloud storage protection system, characterized in that: It includes a data acquisition and storage module, a segmentation and multiple dynamic encryption module, a distributed storage and dynamic allocation module, a data verification and progressive recovery module, a dynamic redundancy and load balancing module, a multi-layer intrusion detection and protection module, and an extensibility optimization and adaptive scheduling module. The data acquisition and storage module collects and stores information on big data; the data segmentation and multiple dynamic encryption module divides the data into multiple sub-data blocks and performs double-layer dynamic encryption; the distributed storage and dynamic allocation module dynamically allocates the encrypted data blocks based on the load and resource conditions of the storage nodes; the data verification and progressive recovery module regularly checks the integrity of the data and performs local recovery after the data is damaged or lost; the dynamic redundancy and load balancing module dynamically adjusts the redundancy of the data based on the importance and access frequency of the data; the multi-layer intrusion detection and protection module monitors the system security in real time and responds to potential threats through a multi-layer firewall and an intrusion detection system based on machine learning; the extensibility optimization and adaptive scheduling module dynamically expands the storage nodes according to the system's resource usage and data volume.

2. According to claim 1, a big data network cloud storage protection system is characterized in that: The data collection and storage module mainly stores big data information, and marks the collected and stored data as D.

3. A big data network cloud storage protection system according to claim 2, characterized in that: The data segmentation and multiple dynamic encryption module includes a segmentation strategy unit and a dual dynamic encryption unit. The segmentation strategy unit is based on the original data D in the data acquisition storage module, and the original data D is segmented into n data blocks according to the importance and access frequency of the data. i , the size of each data block is based on the weight matrix W i Determine, the weight matrix W i The specific analysis is dynamically generated based on the access frequency and importance of the data as follows: V i =α·f(d s )+β·g(a f ) Where V i is the weight, d s is the data block size, a f represents the access frequency, α and β represent the weight coefficients; The dynamic re-encryption module performs double encryption on each segmented data block. The double encryption includes a symmetric encryption algorithm and an asymmetric encryption algorithm. The specific formula of the symmetric encryption algorithm is as follows: Where K i By time stamp T i and random seed S, K i The specific formula is as follows: Where P(ξ) represents a random process, represents the data of the i-th data block after AES encryption, E AES Indicates the AES encryption algorithm, using the key K i For data D i Encryption is performed; The specific formula of the asymmetric encryption algorithm is as follows: in represents the final data of the i-th data block after ECC encryption, E ECC Indicates ECC encryption, the encrypted data Perform a second layer of encryption.

4. According to claim 1, a big data network cloud storage protection system is characterized in that: The distributed storage and dynamic allocation module includes a distributed storage unit and a dynamic allocation unit. The distributed storage and dynamic allocation module dynamically allocates encrypted data blocks based on the data segmentation and multiple dynamic encryption modules according to the load and resource conditions of the storage nodes. The distributed storage unit monitors the load L of each storage node in real time. j and Resources j Usage, select the best node to store encrypted data C i , the optimal storage location is: Where j = 1, 2, ..., m, L j The current load of the jth storage node represents the workload of the data stored on the node, R j represents the available resources of the jth storage node, represents the remaining capacity of the node that can be used to store or process data, and is the optimal storage node for storing the i-th data block. m represents the total number of available storage nodes; The dynamic allocation encryption unit encrypts the data block C i Store to the node with the lightest load N i Ensure load balancing.

5. The big data network cloud storage protection system according to claim 1 is characterized in that: The data verification and progressive recovery module includes a data verification unit and a progressive data recovery unit: the data verification unit stores the data blocks C on each node. i To verify, use a hash verification mechanism based on the Merkle tree: V i =H(C i ) The progressive data recovery unit detects a data block C i When the node is damaged or lost, a gradual recovery mechanism is started, using redundant data R distributed on other nodes. j =(D i ) to recover the damaged data block: After the recovery is completed, the repaired data blocks are stored again; Where V i represents the checksum of the ith data block, H(C i ) represents the hash function, generating data block C i The check code, R j (D i ) represents the redundant data block D on the jth node i , k represents the number of redundant nodes.

6. The big data network cloud storage protection system according to claim 1 is characterized in that: The dynamic redundancy and load balancing module includes a dynamic redundancy adjustment unit and a load balancing unit. The dynamic redundancy adjustment unit adjusts the access frequency a of each data block according to the access frequency a of each data block. f And importance s dynamically adjusts its redundancy R(D), the formula is as follows: where a f represents the access frequency of data, s represents the importance weight of data, R(D) represents the redundancy of data D, λ represents the adjustment factor, and S represents the upper limit of the importance of data; The load balancing unit optimizes the storage location of redundant data in real time based on the load condition of each node in the system.

7. The big data network cloud storage protection system according to claim 1 is characterized in that: The multi-layer intrusion detection and protection module includes a real-time monitoring and detection unit and a multi-layer protection unit. The real-time monitoring and detection unit monitors and analyzes all access behaviors in real time through a machine learning model, identifies potential attacks, and calculates the attack probability P(A) of each access based on feature analysis: Where X i Characterizes the access pattern, λ i represents the weight of the feature, n represents the number of access pattern features, and P(A) represents the probability of the system being attacked; The multi-layer protection unit provides further protection through multi-layer firewalls, combined with access control mechanisms to prevent potential security threats, and automatically adjusts the system's protection strategy based on the type and severity of the detected attack.

8. The big data network cloud storage protection system according to claim 1 is characterized in that: The scalability optimization and adaptive scheduling module includes a real-time monitoring node resource unit and an adaptive expansion unit. The real-time monitoring node resource real-time monitoring storage node resource usage is U i .

9. The big data network cloud storage protection system according to claim 8 is characterized in that: The adaptive expansion unit automatically adds new storage nodes when the system load exceeds the set threshold: Among them U i Resource utilization of the i-th node, E S It represents the average resource utilization of the system, and n represents the number of currently active storage nodes.

10. A method for protecting a big data network cloud storage system, using a big data network cloud storage protection system as described in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: First, the system receives the data uploaded by the user and generates a weight matrix based on its importance and access frequency, thereby dividing the data into multiple sub-data blocks and performing double dynamic encryption to ensure data security; Step 2: Then, the system monitors the load and available resources of the storage nodes in real time and dynamically selects the best node for distributed storage, thus avoiding single point of failure; Step 3: Check data regularly, use redundant data to recover lost or damaged data blocks, dynamically adjust redundancy based on data access frequency and importance, and optimize resource utilization through load balancing algorithms; Step 4: Dynamically adjust redundancy based on data access frequency and importance to balance node load; Step 5: The system combines machine learning models to monitor access behavior in real time, identify potential attacks, and activate multi-layer protection mechanisms to prevent potential attacks; Step 6: The system continuously monitors resource usage and automatically expands storage nodes to support large-scale data processing and ensure the efficiency and stability of the cloud storage system.

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