Enterprise service management cloud storage system

Abstract

The invention belongs to the technical field of cloud storage, and particularly relates to an enterprise service management cloud storage system which comprises a system initialization module, a distributed data storage module, a data encryption module, an authority control module, a multi-level cache module, a data backup and recovery module, a log monitoring module and a task scheduling and dynamic extension module. The distributed data storage module is responsible for dividing data in enterprise service management into a plurality of data blocks; the data encryption module is used for encrypting the enterprise service data stored in the cloud; the authority control module is responsible for managing the access authority of the user to the data; the multi-level cache module accelerates data access by configuring a local cache and a global cache; the data backup and recovery module is responsible for backing up the stored data regularly; the log monitoring module records various operation logs of the system; and the task scheduling and dynamic extension module is used for dynamically distributing calculation and storage resources according to the priority of the task and the utilization rate of the system resources.

Description

Technical Field

[0001] The present invention relates to the field of cloud storage technology, and in particular to an enterprise service management cloud storage 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 on dedicated servers. Cloud storage combines technologies such as parallel computing 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. It also supports multiple platforms, multiple languages, and global deployment. Cloud storage is widely used in enterprise service management systems. As an important part of enterprise informatization, enterprise service management systems are increasingly relying on cloud storage to manage massive amounts of service data.

[0003] Although traditional cloud storage systems provide basic storage functions, they have the following disadvantages when processing complex data:

[0004] 1. Traditional cloud storage systems have limitations when dealing with complex data distribution. These systems often rely on static storage structures and simple data sharding strategies, making it difficult to efficiently manage dynamically changing business data. When the amount of data increases dramatically or the data access pattern changes, traditional systems may not be able to adjust storage strategies and load balancing in a timely manner, resulting in system performance degradation and data access delays.

[0005] 2. Traditional cloud storage systems usually provide basic encryption and access control functions in terms of data security, but they are difficult to cope with complex security threats and compliance requirements. Data encryption may be limited to transmission encryption, and encryption support for static data is limited. Permission management often relies on a static role model, which is difficult to adapt to the dynamic changes in permissions within the enterprise.

[0006] 3. The lack of comprehensive auditing and monitoring capabilities makes it difficult for the system to effectively detect and respond to potential security incidents, increasing data leakage and compliance risks;

[0007] 4. Traditional cloud storage systems provide basic storage services, but they are insufficient in optimizing access performance. The system may face problems of high latency and low throughput, especially when processing high-concurrency requests. Traditional systems usually lack advanced caching mechanisms and data pre-fetching strategies, which leads to frequent disk read and write operations, thus affecting data access speed and system response time.

[0008] 5. The backup and recovery functions of traditional cloud storage systems are often inefficient. The system may rely on full backup, which consumes a lot of storage space and time each time, causing the backup process to affect the production system.

[0009] 6. Traditional cloud storage systems lack scalability and flexibility. The system usually requires manual configuration and adjustment to increase storage capacity or computing resources, which may make operations complex and prone to errors.

[0010] Therefore, the present invention proposes an enterprise service management cloud storage system, which realizes intelligent debugging and optimization of the enterprise service management process by integrating advanced optimization algorithms and complex mathematical models.

[0011] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0012] To solve the above problems, the present invention provides the following technical solutions:

[0013] A cloud storage system for enterprise service management includes a system initialization module, a distributed data storage module, a data encryption module, an authority control module, a multi-level cache module, a data backup and recovery module, a log monitoring module, and a task scheduling and dynamic expansion module. The system initialization module and the distributed data storage module are responsible for dividing the data in the enterprise service management into multiple data blocks and evenly distributing the data blocks to different storage nodes; the data encryption module is used to encrypt the enterprise service data stored in the cloud; the authority control module is responsible for managing the user's access rights to the data; the multi-level cache module accelerates data access by configuring local cache and global cache; the data backup and recovery module is responsible for regularly backing up the stored data and quickly restoring the data when it is lost or damaged; the log monitoring module records various operation logs of the system, including user behavior, system status and data access; the task scheduling and dynamic expansion module dynamically allocates computing and storage resources according to the priority of the task and the utilization rate of the system resources.

[0014] Furthermore, the system initialization module includes configuring a virtualized storage environment and defining a data distribution hash function H, virtualizing physical nodes into logical storage nodes, assuming that the total number of nodes is N, and the storage capacity of each node is C = {C1, C2..., C i}, where i is a positive integer.

[0015] Furthermore, the data encryption storage module includes a distributed data storage unit and a data encryption unit. The distributed data storage unit is specifically analyzed as follows:

[0016] The distributed data storage unit divides the enterprise service data D into n data blocks D v , where v is a positive integer and the size of each data block is S f , where f is a positive integer, based on the hash function H(D i) Determine data block D i The storage nodes are N j =(H(D i )+P j )mod N

[0017] Where P j is the load weight of the node, and N is the total number of nodes.

[0018] Furthermore, the data encryption unit uses a hybrid encryption technology to protect the data, including symmetric encryption and asymmetric encryption, wherein each data block is symmetric encrypted, and the encrypted ciphertext is F g :

[0019] F g =E k (D i )

[0020] Where E k is a symmetric encryption function, k is the encryption key;

[0021] The symmetric key k is encrypted using asymmetric encryption to generate the encryption key K enc :

[0022] K enc =RSA(PubKey,k)

[0023] Among them, PubKey is the recipient's public key.

[0024] Furthermore, the multi-level cache module includes a local cache C local and the global cache C global Strategy, based on cache hit rate and data access time, optimizes data reading speed and reduces the overall latency of the system. The cache acceleration formula is as follows:

[0025]

[0026] Where p1 and p2 are the hit rates of the local and global caches, respectively, and W cache , W storage are the workloads of cache and storage, respectively, cache , U storage are the utilization of cache and storage respectively, t local ,t global is the access time of local and global cache, T access Indicates the average time for data access.

[0027] Furthermore, the permission control module adopts an attribute-based access control model for permission management, and the access control model function expression is:

[0028] P(u,r)=if(A u ∩B r ≠φthen 1else 0)

[0029] Among them A u is the user attribute set, B r A collection of data resource attributes.

[0030] Furthermore, the data backup and recovery module uses snapshot and incremental backup technology to achieve disaster recovery of data, and restores the latest state based on the full snapshot and incremental data. The recovery formula is:

[0031]

[0032] Where S t-k is a snapshot, ΔD t-i It is incremental data.

[0033] Furthermore, the log monitoring and anomaly detection module records the system operation log in real time, and detects abnormal behavior in the system in combination with the isolation forest algorithm. The detection algorithm calculates the anomaly score A(x) of each data and issues an early warning for high-risk operations. The A(x) anomaly score calculation formula is:

[0034] A(x)=IsolationForest(x,parameters)

[0035] Where x is the operation data and parameters is the parameter setting of the isolation forest algorithm.

[0036] Furthermore, the task scheduling and dynamic expansion module is based on the priority P of the task. t and resource utilization U r Perform task scheduling, the scheduling goal is to minimize the response time, the scheduling formula is:

[0037]

[0038] Among them, λ is the scheduling weight coefficient, W taskj , U nodej are the workload of the j-th task and the utilization of the j-th node respectively;

[0039] The dynamic expansion module is based on the node load C load and the extended threshold C threshold Implement automatic node expansion.

[0040] Furthermore, an enterprise service management cloud storage system method adopts an enterprise service management cloud storage system as described in any one of claims 1 to 9, characterized in that it includes the following steps:

[0041] Step 1: First, in the system initialization phase, the virtual storage nodes are configured and the data distribution hash function is defined;

[0042] Step 2: Then, the enterprise data is divided into blocks and encrypted to ensure data security;

[0043] Step 3: Optimize data access speed through multi-level caching strategy, and the permission control module manages user permissions through attribute-based models;

[0044] Step 4: Based on data backup and recovery, ensure data recovery capability through snapshot and incremental backup technology;

[0045] Step 5: Data backup and recovery: Ensure data recovery capabilities through snapshot and incremental backup technology;

[0046] Step 6: Finally, dynamically schedule tasks and automatically scale based on node load to ensure efficient operation and scalability of the system.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. In the present invention, through the local cache and global cache strategy of the multi-level cache module, the system significantly reduces the need for direct access to storage nodes, thereby reducing data reading latency, improving the speed of data access and the response performance of the system. This cache mechanism ensures that users can quickly obtain the required data and optimizes the overall user experience;

[0049] 2. In the present invention, the data encryption module combines symmetric encryption and asymmetric encryption technology to provide strong protection for data in storage and transmission. This dual encryption scheme ensures the confidentiality and integrity of data, and can effectively prevent unauthorized access and data leakage even in a cloud environment, thereby enhancing the security protection capability of the system;

[0050] 3. In the present invention, the permission control module using the attribute-based access control model can dynamically adjust the access rights according to the attributes of users and data resources. This flexible permission management method allows precise control of data access, ensuring that only qualified users can access sensitive data, thereby improving data security and compliance;

[0051] 4. In the present invention, efficient backup and rapid recovery of data are achieved through the combination of full snapshot and incremental backup. The full snapshot provides a complete data state, while the incremental backup records the changed data, reducing the backup overhead and enabling rapid recovery when data is lost or damaged, thus ensuring business continuity and data persistence.

[0052] 5. In the present invention, computing and storage resources are automatically adjusted according to task priority and resource utilization, and new nodes are automatically expanded when the node load is higher than the preset threshold. This dynamic scheduling and expansion mechanism ensures that the system can maintain efficient operation when facing different load conditions, improves the elasticity and scalability of the system, and avoids resource bottlenecks and system crashes. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0054] Figure 1 A schematic diagram of the steps of an enterprise service management cloud storage system of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] like Figure 1 As shown, an enterprise service management cloud storage system includes a system initialization module, a distributed data storage module, a data encryption module, a permission control module, a multi-level cache module, a data backup and recovery module, a log monitoring module, a task scheduling and dynamic expansion module, the system initialization module, the distributed data storage module is responsible for dividing the data in the enterprise service management into multiple data blocks, and evenly distributing these data blocks to different storage nodes; the data encryption module is used to encrypt the enterprise service data stored in the cloud; the permission control module is responsible for managing the user's access rights to the data; the multi-level cache module accelerates data access by configuring local cache and global cache; the data backup and recovery module is responsible for regularly backing up the stored data and quickly restoring the data when it is lost or damaged; the log monitoring module records various operation logs of the system, including user behavior, system status and data access; the task scheduling and dynamic expansion module dynamically allocates computing and storage resources according to the priority of the task and the utilization of system resources.

[0057] Specifically, the system initialization module includes configuring the virtualized storage environment and defining the data distribution hash function H, virtualizing the physical nodes into logical storage nodes, assuming that the total number of nodes is N, and the storage capacity of each node is C = {C1, C2..., C i}, where i is a positive integer.

[0058] Specifically, the data encryption storage module includes a distributed data storage unit and a data encryption unit. The distributed data storage unit is specifically analyzed as follows:

[0059] The distributed data storage unit divides the enterprise service data D into n data blocks D v , where v is a positive integer and the size of each data block is S f , where f is a positive integer, based on the hash function H(D i ) Determine data block D i The storage nodes are N j =(H(D i )+P j )mod N

[0060] Where P j is the load weight of the node, and N is the total number of nodes.

[0061] The distributed data storage unit is responsible for dividing the data in enterprise service management into multiple data blocks and evenly distributing these data blocks to different storage nodes. It ensures the load balancing of data between physical nodes through data sharding and hash functions. Distributed storage means that data is no longer concentrated on a single server, but distributed in multiple storage nodes or servers. This can improve the fault tolerance and availability of the system. When a node fails, other nodes can continue to provide services to ensure the stability of the system. Distributed data storage also improves access performance because users can read data from multiple nodes in parallel.

[0062] Specifically, the data encryption unit uses a hybrid encryption technology to protect the data, including symmetric encryption and asymmetric encryption, in which each data block is symmetric encrypted, and the encrypted ciphertext is F g :

[0063] F g =E k (D i )

[0064] Where E k is a symmetric encryption function, k is the encryption key;

[0065] The symmetric key k is encrypted using asymmetric encryption to generate the encryption key K enc :

[0066] K enc =RSA(PubKey,k)

[0067] Among them, PubKey is the recipient's public key.

[0068] The data encryption unit is used to encrypt enterprise service data stored in the cloud to ensure that the data will not be obtained by unauthorized third parties during transmission and storage. The data encryption unit uses symmetric encryption to quickly encrypt large quantities of data, and uses asymmetric encryption to encrypt the key to ensure that even if the storage node is invaded, the data cannot be decrypted or tampered with. This double encryption strategy ensures the confidentiality and integrity of the data.

[0069] Specifically, the multi-level cache module includes local cache C local and the global cache C global Strategy, based on cache hit rate and data access time, optimizes data reading speed and reduces the overall latency of the system. The cache acceleration formula is as follows:

[0070]

[0071] Where p1 and p2 are the hit rates of the local and global caches, respectively, and W cache , W storage are the workloads of cache and storage, respectively, cache , U storage are the utilization of cache and storage respectively, t local ,t global is the access time of local and global cache, T access Indicates the average time for data access.

[0072] The multi-level cache module configures the local cache C local and the global cache C global The strategy accelerates data access. The cache strategy gives priority to reading data from the local cache. When the local cache misses, it reads from the global cache or storage node. The multi-level cache module improves data reading speed, reduces latency, and optimizes user experience by reducing direct access to remote storage nodes. The local cache mainly stores frequently accessed data, and the global cache is responsible for cross-node data sharing. The multi-layer design of the cache enables the system to effectively reduce data access latency and improve overall performance.

[0073] Specifically, the permission control module uses an attribute-based access control model for permission management. The access control model function expression is:

[0074] P(u,r)=if(A u ∩B r ≠φthen 1else 0)

[0075] Among them A u is the user attribute set, B r A collection of data resource attributes.

[0076] The permission control module is responsible for managing user access rights to data. It adopts an attribute-based access control model and dynamically matches the user's attributes (such as identity, role, position) with the attributes of data resources to decide whether to grant the user access to specific data. The permission control module ensures that only users with legitimate permissions can access or operate specific data. By dynamically matching user and data attributes, this model is more flexible than traditional role-based access control (RBAC). It can dynamically adjust permissions to adapt to different security requirements and prevent data leakage or misoperation.

[0077] Specifically, the data backup and recovery module uses snapshot and incremental backup technology to achieve disaster recovery of data, and restores the latest state based on full snapshots and incremental data. The recovery formula is:

[0078]

[0079] Where S t-k is a snapshot, ΔD t-i It is incremental data.

[0080] The data backup and recovery module is responsible for regularly backing up stored data and quickly restoring data when it is lost or damaged. The module uses a combination of full snapshots and incremental backups to save multiple versions of data, and completes data recovery through a combination of snapshots and incremental data when necessary. The backup and recovery module ensures data persistence and security, especially rapid recovery when data is damaged or lost. The full snapshot provides a complete data state, while the incremental backup only records the changes since the last snapshot, thereby reducing the storage overhead and time of backup. The recovery mechanism combined with snapshots and incremental backups can quickly rebuild data and ensure business continuity.

[0081] Specifically, the log monitoring and anomaly detection module records the system operation log in real time, and combines the isolation forest algorithm to detect abnormal behavior in the system. The detection algorithm calculates the anomaly score A(x) for each data and issues an early warning for high-risk operations. The calculation formula for the A(x) anomaly score is:

[0082] A(x)=IsolationForest(x,parameters)

[0083] Where x is the operation data and parameters is the parameter setting of the isolation forest algorithm.

[0084] The log monitoring module records various operation logs of the system, including user behavior, system status, data access, etc. It monitors system operations in real time, identifies abnormal behaviors, and uses anomaly detection algorithms (such as the isolation forest algorithm) to issue early warnings for potential security threats. The log monitoring module is a key component of system security. By monitoring user and system operations, it can detect and prevent abnormal operations and security threats in a timely manner. Abnormal behaviors may include abnormal data reading, abnormal writing, system performance degradation, etc. Combined with log auditing and anomaly detection algorithms, the system can take preventive measures before problems occur to improve the overall security of the system.

[0085] The Isolation Forest algorithm is an unsupervised learning method for anomaly detection. It is good at identifying anomalies that show unusual characteristics in large-scale data sets. The core idea is to isolate anomalies as early as possible by recursively partitioning the data space. The Isolation Forest algorithm assumes that abnormal data points are easier to isolate in the feature space. Normal data point groups are usually concentrated in certain areas and require more partitions to isolate them. In contrast, anomalies are usually far away from other data and require fewer partitioning steps. Therefore, the Isolation Forest partitions the data by constructing a series of random decision trees. Each node is recursively partitioned according to randomly selected features and random partitioning points until each data point is isolated. The steps of the Isolation Forest algorithm are as follows:

[0086] Step 1: Data preprocessing: The input of the isolation forest algorithm is high-dimensional data. First, features need to be extracted from system logs, user behaviors, and data operation records. Possible features include user access behaviors, file operation patterns, and system resource usage, as follows:

[0087] User access behavior: access frequency, access time period, access resource types, etc.;

[0088] File operation mode: upload, download, modify file size, type, time interval, etc.;

[0089] System resource usage: memory, CPU, bandwidth consumption, etc.; data transmission: amount of data transmitted, transmission time, destination address, etc.

[0090] Step 2: Algorithm training and modeling: Data segmentation and tree construction: Randomly select features (such as user behavior characteristics, operation modes, etc.) from the collected data set, and use the isolation forest algorithm to build a series of isolated trees. Through these trees, the data space is continuously segmented, and the anomalies are gradually isolated. Then, anomaly score calculation is established: According to the length of the isolation path of the data point in the tree, the anomaly score of each data point is calculated. The higher the score, the greater the difference between the data point and other normal data, and the more likely it is an anomaly point.

[0091] Step 3: Detection process: During the actual operation, the log monitoring module of the enterprise cloud storage system will collect user operation and system status data in real time. At regular intervals, these real-time data will be input into the isolation forest algorithm for detection. If a user's access pattern (such as frequent file access or abnormal large data volume transmission) is significantly different from the normal user's behavior, the system will mark the behavior as abnormal; if the system resource consumption (such as CPU or network bandwidth) suddenly surges and does not match the normal load, this may indicate that the system has suffered some kind of attack or abnormal use.

[0092] Step 4: Response mechanism: Once the system detects abnormal behavior through the isolation forest algorithm, it can trigger corresponding security response measures. The specific measures are as follows:

[0093] Send alerts: Send alerts of abnormal behavior to system administrators and provide relevant operation logs and anomaly scores;

[0094] Permission restriction: immediately restrict the permissions of suspicious users to prevent specific potential malicious behavior;

[0095] Data rollback: For abnormal file operations, the system can automatically trigger the data rollback mechanism to restore the data to a safe state.

[0096] Specifically, the task scheduling and dynamic expansion module is based on the priority P of the task. t and resource utilization U r Perform task scheduling, the scheduling goal is to minimize the response time, the scheduling formula is:

[0097]

[0098] Among them, λ is the scheduling weight coefficient, W taskj , U nodej are the workload of the j-th task and the utilization of the j-th node respectively;

[0099] Dynamic expansion module according to node load C load and the extended threshold C threshold Implement automatic node expansion.

[0100] The task scheduling and dynamic expansion module dynamically allocates computing and storage resources according to the priority of the task and the utilization of system resources. When the system load reaches the preset threshold, the module will automatically expand new storage nodes or computing nodes to ensure system performance and stability. The task scheduling module ensures the optimal use of system resources and dynamically adjusts resource allocation according to the urgency of the task and the availability of resources. The dynamic expansion function ensures that the system can automatically increase computing or storage capabilities when facing high concurrency or large amounts of data processing requirements, avoiding system crashes or slow responses due to overload. This flexible scalability makes the system more elastic and scalable, and can cope with fluctuations in business volume.

[0101] Specifically, an enterprise service management cloud storage system method adopts an enterprise service management cloud storage system according to any one of claims 1 to 9, characterized in that it includes the following steps:

[0102] Step 1: First, in the system initialization phase, the virtual storage nodes are configured and the data distribution hash function is defined;

[0103] Step 2: Then, the enterprise data is divided into blocks and encrypted to ensure data security;

[0104] Step 3: Optimize data access speed through multi-level caching strategy, and the permission control module manages user permissions through attribute-based models;

[0105] Step 4: Based on data backup and recovery, ensure data recovery capability through snapshot and incremental backup technology;

[0106] Step 5: Data backup and recovery: Ensure data recovery capabilities through snapshot and incremental backup technology;

[0107] Step 6: Finally, dynamically schedule tasks and automatically scale based on node load to ensure efficient operation and scalability of the system.

[0108] The working principle of the present invention is as follows: First, in the system initialization phase, virtual storage nodes are configured and a data distribution hash function is defined to virtualize physical nodes into logical storage nodes. The total number of nodes is N, and the storage capacity of each node is C = {C1, C2..., C i}, where i is a positive integer. Then, the enterprise data is stored and encrypted in blocks, and the enterprise service data D is divided into n data blocks D using a distributed data storage unit. v , where v is a positive integer and the size of each data block is S f , where f is a positive integer, based on the hash function H(D i ) Determine data block D i The storage nodes are N j =(H(D i )+Pj )mod N, where P j is the load weight of the node, N is the total number of nodes to store data, and a hybrid encryption technology is used to protect data based on the data encryption unit, including symmetric encryption and asymmetric encryption, in which each data block is symmetric encrypted, and the encrypted ciphertext is F g :

[0109] F g =E k (D i )

[0110] Where E k is a symmetric encryption function, k is the encryption key;

[0111] The symmetric key k is encrypted using asymmetric encryption to generate the encryption key K enc :

[0112] K enc =RSA(PubKey,k)

[0113] Among them, PubKey is the recipient's public key, which ensures the security of data. At the same time, the multi-level cache module includes local cache C local and the global cache C global The strategy optimizes data access speed and reduces the overall latency of the system. The permission control module manages user permissions through an attribute-based model. Data backup and recovery ensure data recovery capabilities through snapshot and incremental backup technology to achieve data disaster recovery. The latest status is restored based on full snapshots and incremental data. The system also implements log monitoring and anomaly detection, and uses the isolated forest algorithm to detect abnormal behavior. Finally, tasks are dynamically scheduled and automatically expanded according to node load to ensure efficient operation and expansion capabilities of the system. The enterprise service management cloud storage system in the present invention not only focuses on efficient data management, but also improves data security, reliability and scalability through encryption, permission control, backup, anomaly monitoring and other technical measures. Each module cooperates with each other to jointly build a cloud storage system with stable performance, security, reliability, flexibility and scalability to meet the various needs of modern enterprises in large-scale data management.

[0114] 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. An enterprise service management cloud storage system, characterized in that: It includes a system initialization module, a distributed data storage module, a data encryption module, a permission control module, a multi-level cache module, a data backup and recovery module, a log monitoring module, a task scheduling and dynamic expansion module. The system initialization module and the distributed data storage module are responsible for dividing the data in the enterprise service management into multiple data blocks and evenly distributing these data blocks to different storage nodes; the data encryption module is used to encrypt the enterprise service data stored in the cloud; The authority control module is responsible for managing the user's access rights to data; The multi-level cache module accelerates data access by configuring local cache and global cache; the data backup and recovery module is responsible for regularly backing up stored data and quickly restoring data when it is lost or damaged; the log monitoring module records various operation logs of the system, including user behavior, system status and data access; the task scheduling and dynamic expansion module dynamically allocates computing and storage resources according to the priority of the task and the utilization of system resources.

2. The enterprise service management cloud storage system according to claim 1, characterized in that: The system initialization module includes configuring a virtualized storage environment and defining a data distribution hash function H, virtualizing physical nodes into logical storage nodes, assuming that the total number of nodes is N, and the storage capacity of each node is C = C1, C2, ..., C i , where i is a positive integer.

3. The enterprise service management cloud storage system according to claim 2, characterized in that: The data encryption storage module includes a distributed data storage unit and a data encryption unit. The distributed data storage unit is specifically analyzed as follows: The distributed data storage unit divides the enterprise service data D into n data blocks D v , where v is a positive integer and the size of each data block is S f , where f is a positive integer, based on the hash function H(D i ) Determine data block D i The storage nodes are N j =(H(D i )+P j )modN Where P j is the load weight of the node, and N is the total number of nodes.

4. The enterprise service management cloud storage system according to claim 3, characterized in that: The data encryption unit uses a hybrid encryption technology to protect the data, including symmetric encryption and asymmetric encryption, wherein each data block is symmetric encrypted, and the encrypted ciphertext is F g : F g =E k (D i ) Where E k is a symmetric encryption function, k is the encryption key; The symmetric key k is encrypted using asymmetric encryption to generate the encryption key K enc : K enc =RSA(PubKey,k) Among them, PubKey is the recipient's public key.

5. The enterprise service management cloud storage system according to claim 1, characterized in that: The multi-level cache module includes a local cache C local and the global cache C global Strategy, based on cache hit rate and data access time, optimizes data reading speed and reduces the overall latency of the system. The cache acceleration formula is as follows: Where p1 and p2 are the hit rates of the local and global caches, respectively, and W cache , W storage are the workloads of cache and storage, respectively, cache , U storage are the utilization of cache and storage respectively, t local ,t global is the access time of local and global cache, T access Indicates the average time for data access.

6. The enterprise service management cloud storage system according to claim 1, characterized in that: The permission control module adopts an attribute-based access control model for permission management. The function expression of the access control model is: P(u,r)=if(A u ∩B r ≠φthen1else0) Among them A u is the user attribute set, B r A collection of data resource attributes.

7. The enterprise service management cloud storage system according to claim 1, characterized in that: The data backup and recovery module uses snapshot and incremental backup technology to achieve disaster recovery of data, and restores the latest state based on the full snapshot and incremental data. The recovery formula is: Where S t-k is a snapshot, ΔD t-i It is incremental data.

8. The enterprise service management cloud storage system according to claim 1, characterized in that: The log monitoring and anomaly detection module records the system operation log in real time, and detects abnormal behavior in the system in combination with the isolation forest algorithm. The detection algorithm calculates the anomaly score A(x) for each piece of data and issues an early warning for high-risk operations. The A(x) anomaly score calculation formula is: A(x)=IsolationForest(x,parameters) Where x is the operation data and parameters is the parameter setting of the isolation forest algorithm.

9. The enterprise service management cloud storage system according to claim 1, characterized in that: The task scheduling and dynamic expansion module is based on the priority P of the task. t and resource utilization U r Perform task scheduling, the scheduling goal is to minimize the response time, the scheduling formula is: Among them, λ is the scheduling weight coefficient, W taskj , U nodej are the workload of the j-th task and the utilization of the j-th node respectively; The dynamic expansion module is based on the node load C load and the extended threshold C threshold Implement automatic node expansion.

10. An enterprise service management cloud storage system method, using an enterprise service management cloud storage system as described in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: First, in the system initialization phase, the virtual storage nodes are configured and the data distribution hash function is defined; Step 2: Then, the enterprise data is divided into blocks and encrypted to ensure data security; Step 3: Optimize data access speed through multi-level caching strategy, and the permission control module manages user permissions through attribute-based models; Step 4: Based on data backup and recovery, ensure data recovery capability through snapshot and incremental backup technology; Step 5: Data backup and recovery: Ensure data recovery capabilities through snapshot and incremental backup technology; Step 6: Finally, dynamically schedule tasks and automatically scale based on node load to ensure efficient operation and scalability of the system.

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