CDM-based off-site disaster recovery cloud, disaster recovery method, storage medium and electronic device

Abstract

The application discloses a kind of off-site disaster recovery cloud based on CDM, disaster recovery method, storage medium and electronic equipment, and disaster recovery cloud includes: main server module, for dividing the system data set of enterprise operation system into multiple system data blocks, determine the storage level of each system data block, system data block is stored to local storage cloud server module according to storage level, generate the copy data of system data set, and send copy data to off-site disaster recovery server module;Local storage cloud server module is used to store each system data block received according to storage level;Off-site disaster recovery cloud server module is used to receive and manage copy data, and off-site disaster recovery cloud server module includes CDM unit, and CDM unit is used to realize the copy data management service of enterprise operation system based on CDM technology.Using the embodiment of the application, efficient management and value maximization of data assets can be realized, and sustainable operation and development of data assets are promoted.

Description

Technical Field

[0001] This invention belongs to the field of disaster recovery technology, specifically a CDM-based off-site disaster recovery cloud, disaster recovery method, storage medium, and electronic equipment. Background Technology

[0002] With the continuous development of information technology, enterprise information systems are becoming increasingly complex, and the amount of data is growing exponentially. Enterprise operating systems not only need to handle the massive amounts of data generated in daily operations, but also need to ensure the security, availability, and persistence of that data. Traditional storage solutions often cannot meet the requirements of modern enterprises for high availability, high reliability, and rapid recovery capabilities. Therefore, how to effectively manage and back up enterprise operating system data has become a significant challenge in enterprise information management. Summary of the Invention

[0003] The purpose of this invention is to provide a disaster recovery cloud based on CDM, a disaster recovery method, a storage medium, and electronic equipment to overcome the shortcomings of the prior art. By combining CDM technology with a disaster recovery cloud, real-time backup and rapid recovery of data can be achieved, reducing the risk of data loss and optimizing the utilization efficiency of storage resources.

[0004] One embodiment of this application provides an off-site disaster recovery cloud for enterprise operating systems based on CDM technology, the disaster recovery cloud comprising:

[0005] The main server module is used to divide the system dataset of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data blocks in the local storage cloud server module according to the storage level, generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module.

[0006] The local storage cloud server module is used to store the received system data blocks according to the storage hierarchy.

[0007] The off-site disaster recovery cloud server module is used to receive and manage the replica data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement replica data management services for enterprise operating systems based on CDM technology.

[0008] Optionally, determining the storage level of each system data block includes:

[0009] For each system data block, determine the data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size of the system data block to calculate the storage priority index of the system data;

[0010] The storage level of the system data block is determined based on the storage priority index.

[0011] Optionally, the local storage cloud server module includes multiple local storage units, and the storage of each system data block received according to the storage hierarchy includes:

[0012] Determine the local storage unit corresponding to the storage level of each system data block, obtain the available storage space of the local storage unit, and calculate the storage stability index of each local storage unit.

[0013] For each system data block, if the available storage space of the local storage unit corresponding to the system data block is less than the data block size of the system data block, or the storage stability index of the local storage unit corresponding to the system data block is less than the preset stability threshold, find a local storage unit whose available storage space is greater than or equal to the data block size of the system data block and whose storage stability index is greater than or equal to the preset stability threshold.

[0014] From the found local storage units, select the local storage unit with the highest storage level as the final local storage unit for the system data block, and store the system data block in the final local storage unit.

[0015] Optionally, calculating the storage stability index of each local storage unit includes:

[0016] For each local storage unit, the utilization rate of the local storage unit is calculated based on the used space and total capacity of the local storage unit;

[0017] The normalized failure rate of the local storage unit is calculated based on the historical failure count of the local storage unit and the maximum historical failure count among all local storage units.

[0018] The normalized mean time between failures (MTBF) of the local storage unit is calculated based on the longest historical continuous MTBF of the local storage unit and the longest historical continuous MTBF among all local storage units.

[0019] Calculate the latency impact factor of the local storage unit based on the average latency of the local storage unit's response to the upper-level request;

[0020] The storage stability index of the local storage unit is calculated based on the utilization rate, the normalized failure rate, the normalized time to failure, and the latency impact factor.

[0021] Another embodiment of this application provides a disaster recovery method applied to the main server module of an off-site disaster recovery cloud for an enterprise operating system based on CDM technology. The disaster recovery cloud further includes: a local storage cloud server module and an off-site disaster recovery cloud server module. The method includes:

[0022] The system dataset of the enterprise operation system is divided into multiple system data blocks, and the storage level of each system data block is determined.

[0023] The system data blocks are stored in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level.

[0024] A copy of the system dataset for disaster recovery backup is generated, and the copy data is sent to the off-site disaster recovery server module so that the off-site disaster recovery cloud server module can receive and manage the copy data for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology.

[0025] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0026] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0027] Compared with existing technologies, this invention provides an off-site disaster recovery cloud for enterprise operating systems based on CDM technology, comprising: a main server module, used to divide the system dataset of the enterprise operating system into multiple system data blocks, determine the storage level of each system data block, store the system data blocks according to the storage level to a local storage cloud server module, generate a copy of the system dataset, and send the copy data to the off-site disaster recovery server module; a local storage cloud server module, used to store the received system data blocks according to the storage level; and an off-site disaster recovery cloud server module, used to receive and manage the copy data. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology. This enables real-time data backup and rapid recovery through the off-site disaster recovery cloud combined with CDM technology, reducing the risk of data loss and optimizing the utilization efficiency of storage resources. Attached Figure Description

[0028] Figure 1A schematic diagram of the structure of an off-site disaster recovery cloud for an enterprise operating system based on CDM technology is provided for an embodiment of the present invention;

[0029] Figure 2 Hardware structure block diagram of a computer device for a disaster recovery method provided in an embodiment of the present invention;

[0030] Figure 3 This is a flowchart illustrating a disaster recovery method provided in an embodiment of the present invention. Detailed Implementation

[0031] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0032] This invention first provides an off-site disaster recovery cloud for enterprise operating systems based on CDM technology, see [link to relevant documentation]. Figure 1 It can include:

[0033] The main server module 101 is used to divide the system dataset of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data blocks in the local storage cloud server module according to the storage level, generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module.

[0034] The main server module is the core component of this invention. Its main function is to divide the dataset within the enterprise's operating system into multiple system data blocks and allocate an appropriate storage tier to each system data block. This process not only structures the data but also considers the importance and frequency of the data to ensure optimized storage strategies. Based on the set storage tiers, the system data blocks are stored in the local storage cloud server module. Furthermore, the main server module generates replicas of these system data blocks to facilitate rapid recovery in the event of data loss or system failure. The generated replicas are then sent to the off-site disaster recovery server module, thereby achieving effective disaster recovery and business continuity assurance.

[0035] The establishment of the main server module enables enterprises to adopt a more scientific and systematic approach to addressing the challenges of data management, storage, and recovery. Through reasonable system data block partitioning and storage tier determination, not only is storage efficiency improved, but data security and availability are also guaranteed. The generation of replica data and off-site backups significantly reduce the risk of data loss, enhance the enterprise's response and recovery capabilities in the event of emergencies, and achieve efficient business continuity management.

[0036] In its implementation, the main server module first receives the complete dataset from the enterprise's operating system and divides it into multiple system data blocks using existing chunking algorithms. Alternatively, it can package the data for each functional unit of the enterprise's operating system into a separate system data block. Each data block is analyzed and evaluated for factors such as access frequency, importance, and change history to determine its corresponding storage tier. Subsequently, these data blocks are stored in the local storage cloud server module according to their storage tier. Simultaneously, the main server module generates a copy of each data block and can encrypt the copy data to enhance security. Finally, these encrypted copies are transmitted over the network to the off-site disaster recovery server module to ensure that a backup is available to support data recovery in the event of a local system failure or data loss.

[0037] Specifically, to determine the storage level of each system data block, the data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size of each system data block can be determined to calculate the storage priority index of the system data.

[0038] A comprehensive quantitative evaluation of each system data block is conducted, including five key indicators: data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size. These indicators collectively form the basis for calculating the storage priority index. The data access frequency index reflects how frequently the data block is accessed, while the data importance index determines the data's importance to enterprise operations. The unit storage cost index shows the resource consumption required to store the data block, the data change frequency measures how often the data block is updated, and finally, the data block size directly affects storage efficiency. Therefore, by comprehensively considering these indicators, the system can scientifically derive the storage priority index for each data block.

[0039] For example, a formula for calculating a storage priority index P can be:

[0040]

[0041] This formula aims to comprehensively evaluate the storage priority index P of data blocks through multiple key factors, helping enterprises formulate scientific and reasonable data storage strategies. The formula's multi-dimensional structure makes storage decisions more comprehensive and accurate, ensuring that enterprises can effectively utilize storage resources and improve data access efficiency and security.

[0042] in: This is the data access frequency index, representing the number of times the data block has been accessed within a certain period (which can be preset). This parameter reflects the activity level of the data by measuring the access frequency of the data block. Data blocks with high access frequency are considered more important and should be stored first to ensure fast access. This is a data importance index, which scores data based on business logic and its importance in enterprise operations. The value range is typically [0,1]. The higher the value, the more critical the data, and the higher its priority. The unit storage cost index represents the cost required to store each unit of data. A higher value indicates a higher storage cost, which affects priority assessment. This parameter evaluates the storage cost per unit of data; data with higher storage costs has a lower priority, helping to control a company's data storage expenses. This parameter represents the data change frequency, indicating the number of times the data block has been updated over a period of time, reflecting the data activity level. It records the update frequency of data blocks to reflect data activity and maintain data freshness. Frequently updated data blocks should be prioritized for storage. The normalized value for data block size ensures that its size relative to its proportion in the system does not affect priority assessment. This normalization process guarantees that data block size does not negatively impact priority evaluation, achieving a fair and reasonable storage decision. w_1, w_2, w_3, w_4, and w_5 are weighting factors, corresponding to access frequency, importance, storage cost, change frequency, and data block size, respectively. These weights can be adjusted based on specific application scenarios and enterprise strategies to reflect the degree of influence of different factors on storage priority.

[0043] In practical implementation, data analytics tools can be used to automatically monitor and evaluate data blocks in an enterprise database. First, the system aggregates access logs for each data block to determine its access frequency. Then, it assesses data importance by analyzing business needs and historical data. Next, the system queries storage service provider information to obtain unit storage costs and monitors data update logs to assess change frequency. Finally, it calculates the size of the data blocks. All this information is integrated into the formula for calculating a storage priority index, providing a basis for subsequent storage decisions.

[0044] The core function of this step is to provide a quantitative evaluation standard for each system data block, making data storage decisions more transparent and evidence-based. By accurately evaluating various metrics, enterprises can prioritize and optimize storage resources, ensuring that critical and frequently used data is stored on higher-performance storage tiers. This scientific management approach will greatly improve data access efficiency, reduce storage costs, and ensure greater flexibility and responsiveness in data management for enterprises.

[0045] The storage level of the system data block is determined based on the storage priority index.

[0046] By allocating storage tiers appropriately, the performance and efficiency of the entire data management system can be improved. For example, prioritizing the storage of critical data blocks in high-performance tiers can significantly reduce data retrieval time, thereby improving the efficiency of business operations. At the same time, this intelligent storage allocation method helps enterprises control storage costs, ensures that all types of data receive appropriate storage management, and effectively prevents business risks caused by data loss or access delays.

[0047] In practice, after calculating the storage priority index, it is determined which storage tier's threshold range the index falls into. Each storage tier corresponds to a threshold range for a priority index, thus determining the specific storage tier for each system data block. Through this targeted allocation, enterprises can not only optimize storage performance but also achieve intelligent management of data access.

[0048] Define classification criteria for storage tiers, for example, defining three storage tiers: high, medium, and low. Based on the calculated storage priority index, assign storage tiers accordingly. For example:

[0049] 1. High-level storage data blocks: Critical data blocks such as financial reports and customer information have a higher storage priority and will be allocated to high-performance SSD storage. This type of data is typically frequently queried and updated during business operations.

[0050] 2. Mid-level storage blocks: such as sales data and production records, have a medium priority index and will be stored in storage devices with moderate performance, such as high-speed HDDs. This can reduce storage costs while maintaining a certain level of performance.

[0051] 3. Low-level storage blocks: such as expired historical data or archived information that is no longer frequently used. These blocks have a lower priority index and will be stored in the lowest-cost storage solutions, such as cloud storage services or low-speed HDDs.

[0052] This tiered storage strategy enables enterprises to effectively manage storage resources, achieving efficient data access and reasonable cost control. Furthermore, the system can periodically monitor data access frequency and importance, dynamically adjusting the storage tier of data blocks to adapt to ever-changing business needs.

[0053] The local storage cloud server module 102 is used to store the received system data blocks according to the storage hierarchy;

[0054] The local storage cloud server module is a core component of an enterprise's off-site disaster recovery cloud system. Its main function is to systematically store received system data blocks according to the storage tiers defined by the main server module. This module not only handles the storage of data blocks but also ensures that data is stored optimally according to different priorities. Based on preset storage tier standards, this module achieves a balance between performance, cost, and access speed, ensuring that critical data blocks are stored on high-performance storage media (such as solid-state drives), while relatively unimportant data blocks are stored on lower-cost or lower-performance media. This tiered storage strategy enables enterprises to achieve greater flexibility and efficiency in data management while also improving the utilization of storage resources.

[0055] The local storage cloud server module plays a crucial role in enterprise data management, not only in the physical storage of data but also in optimizing data access speed and storage security. By storing data in an orderly manner according to storage tiers, enterprises can ensure that critical business data can be accessed quickly, thereby improving the efficiency of daily operations. At the same time, it reduces storage costs, enhances system scalability, and allows enterprises to flexibly adjust storage strategies according to changes in business needs. This module provides a solid foundation for the long-term development of enterprises through reasonable storage management.

[0056] Specifically, the local storage cloud server module may include multiple local storage units. Based on the storage hierarchy of the received system data blocks, the local storage units corresponding to the storage hierarchy of each system data block can be determined, the available storage space of each local storage unit can be obtained, and the storage stability index of each local storage unit can be calculated.

[0057] The system first interacts with each local storage unit to determine the storage unit corresponding to the storage level of each system data block. This process requires obtaining the available storage space of each storage unit to ensure that the incoming data blocks can be stored. In addition, the system calculates a storage stability index for each storage unit, a key indicator for evaluating the reliability of storage devices, which includes factors such as utilization rate and failure rate. This provides fundamental data support for subsequent storage decisions.

[0058] By accurately determining the available space of storage units and calculating storage stability indices, enterprises can wisely select the most suitable storage units for data storage. This effectively avoids wasting storage resources and ensures that data blocks are stored securely and efficiently. Simultaneously, the calculation of stability indices helps enterprises identify poorly performing storage units, providing crucial protection for data security and reducing the risk of data loss due to storage failures.

[0059] Specifically, to calculate the storage stability index of each local storage unit, the utilization rate of each local storage unit can be calculated based on its used space and total capacity.

[0060] The utilization rate, U / C, represents the used space of a local storage unit, where U represents the total capacity of the local storage unit. It reflects the current load status of the storage unit and is an important indicator of storage stability. A lower utilization rate indicates less pressure on the storage unit and higher stability.

[0061] For each local storage unit, the ratio of its used storage space to its total storage capacity is evaluated to calculate the unit's utilization rate. This process provides a clear picture of the current load status of the storage unit, thus providing foundational data for subsequent storage stability analysis. Specifically, used space refers to the amount of data already stored in the storage unit, while total capacity is the unit's maximum storage capacity. The ratio of these two values ​​helps administrators understand whether the storage unit is nearing full capacity.

[0062] By calculating utilization rates, businesses can quickly identify the load on storage units and then take corresponding measures to optimize management. If the utilization rate of a storage unit is too high, it may affect its stability and data access speed. Therefore, monitoring utilization rates can effectively prevent storage resource shortages and ensure high system availability and data security.

[0063] The normalized failure rate of the local storage unit is calculated based on the historical failure count of the local storage unit and the maximum historical failure count among all local storage units.

[0064] The normalized failure rate is F / (F_max): F represents the number of historical failures of the local storage unit, and F_max is the maximum number of historical failures among all local storage units. By comparing the number of failures, a quantitative assessment of the storage unit's reliability is provided. A smaller normalized failure rate indicates fewer failure events, suggesting that the storage unit is more reliable.

[0065] The system evaluates historical failure data for each local storage unit and calculates its normalized failure rate (NFR). Specifically, the system first records the number of failures for each storage unit and determines the maximum number of historical failures across all storage units. Then, the NFR is obtained by dividing the number of failures for a single storage unit by this maximum value. This calculation standardizes the number of failures across different storage units, allowing them to be compared under the same metric.

[0066] The significance of calculating the normalized failure rate (NFR) lies in enabling enterprises to effectively identify the reliability of storage units. Fewer failures result in a lower NFR, indicating higher stability for the storage unit. By comparing failure rates, enterprises can quickly determine which more reliable storage systems to store their data in, thereby reducing the overall system failure risk and improving data management security.

[0067] The normalized mean time between failures (MTBF) of the local storage unit is calculated based on the longest historical continuous MTBF of the local storage unit and the longest historical continuous MTBF among all local storage units.

[0068] The normalized mean time between failures (MTBF) is calculated as MTBF / (MTBF_max): MTBF represents the longest consecutive MTBF in the history of a local storage unit, and MTBF_max is the longest consecutive MTBF among all local storage units. The normalized MTBF quantifies the performance of a storage unit during stable operation; a larger value indicates a longer MTBF and thus higher availability for the storage unit.

[0069] Analyze the historical records of each local storage unit to calculate the ratio of its longest continuous time between failures (MTBF) to the longest continuous time between failures across all storage units. This process helps assess the reliability of the storage units by collecting the longest period of time each storage unit has gone without failure during its operational lifespan and then comparing it to the maximum MTBF recorded across all storage units.

[0070] The calculation of normalized mean time between failures (MTBF) directly reflects the operational stability of each storage unit; a higher value indicates better performance in maintaining stable operation. This provides an important basis for storage strategies, allowing enterprises to prioritize storage units with longer MTBFs when storing data, thereby improving overall system stability and data security.

[0071] Calculate the latency impact factor of the local storage unit based on the average latency of the local storage unit's response to the upper-level request;

[0072] Among them, the time delay impact factor is T represents the average latency (in milliseconds) of the local storage unit's response to a request from the upper level. The latency impact factor evaluates the performance of the storage unit by the response delay; a smaller T value indicates a faster response speed, which helps ensure the system's efficiency.

[0073] The purpose of calculating the latency impact factor is to assess and optimize the access performance of storage units. High latency may mean slower response times for storage units, thus impacting the overall system performance. By monitoring this metric, enterprises can identify potential performance bottlenecks and optimize them, ensuring the efficient operation of business systems.

[0074] The storage stability index of the local storage unit is calculated based on the utilization rate, the normalized failure rate, the normalized time to failure, and the latency impact factor.

[0075] For example, the formula for calculating a storage stability index is:

[0076]

[0077] The Storage Stability Index (SSI) is a comprehensive metric designed to assess the stability and reliability of local storage units. It evaluates the overall performance of storage units across multiple key dimensions, providing a strong basis for enterprise decision-making.

[0078] in, These are the corresponding weighting coefficients used to adjust the relative importance of each indicator in the storage stability index. Using these weights, enterprises can flexibly adjust and optimize their overall storage management solutions based on different business needs and storage strategies.

[0079] For each system data block, if the available storage space of the local storage unit corresponding to the system data block is less than the data block size of the system data block, or the storage stability index of the local storage unit corresponding to the system data block is less than the preset stability threshold, find a local storage unit whose available storage space is greater than or equal to the data block size of the system data block and whose storage stability index is greater than or equal to the preset stability threshold.

[0080] Each system data block is evaluated to check whether its corresponding local storage unit has sufficient storage space and a stable storage index. If the available storage space of the storage unit is insufficient to store the data block, or its stability index is below a preset threshold, the system will automatically query other local storage units to locate a suitable storage medium that meets the storage space and stability requirements. This process ensures that data blocks are not stored in an insecure or unstable storage environment, thereby guaranteeing data integrity and availability.

[0081] By conducting dual checks on storage space and stability, enterprises can effectively avoid potential risks during the storage process, ensuring that data is stored on the most suitable devices as needed. This measure not only improves the security of data storage but also enhances the flexibility of enterprise data management, guaranteeing that storage solutions are always available and preventing data loss and business interruptions due to insufficient storage space.

[0082] During implementation, the system monitors the status of each storage unit in real time and maintains a database containing information about all storage units. Once it is found that the storage conditions of a data block in the corresponding storage unit are not met, the system will automatically search for storage units, filter out those storage units with available space that meet the data block size and whose stability index is higher than the threshold, and generate a list of available storage units for subsequent steps.

[0083] From the found local storage units, select the local storage unit with the highest storage level as the final local storage unit for the system data block, and store the system data block in the final local storage unit.

[0084] The system will further filter all eligible storage units found in the previous step, selecting the unit with the highest storage level as the final storage target. This selection considers not only the available space and stability of the storage unit but also the priority of the storage level, ensuring that critical data blocks are stored in the best-performing storage medium. Finally, the system will store the system data block in the selected final storage unit to maximize data access efficiency.

[0085] This selection process ensures that data is stored in the highest-performing storage units, improving access speed and system responsiveness. This priority storage strategy effectively enhances overall data management performance, giving enterprises a stronger competitive edge in data processing and business operations. Simultaneously, it reduces potential risks caused by insufficient storage device performance, significantly improving the security and reliability of data storage.

[0086] In its implementation, the system sorts the available storage units by their storage hierarchy and selects the highest-level unit as the final destination. Subsequently, the system securely and efficiently stores the data blocks into this storage unit using data transmission protocols, ensuring a fast and seamless storage process. This process also logs the storage operations for subsequent data management and auditing.

[0087] The off-site disaster recovery cloud server module 103 is used to receive and manage the replica data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit 1031, which is used to implement replica data management services for enterprise operating systems based on CDM technology.

[0088] The primary function of the off-site disaster recovery cloud server module is to receive and manage copy data used for disaster recovery backup. The CDM (Copy Data Management) unit, as a core component of this module, is responsible for providing copy data management services for the enterprise's operating systems. Utilizing advanced copy data management technology, the CDM unit efficiently identifies, classifies, and stores received copy data, ensuring data integrity and consistency across different storage environments. Simultaneously, the CDM unit also provides functions such as data deduplication, intelligent backup, and recovery strategy optimization to improve storage efficiency and data recovery flexibility.

[0089] In the implementation process, the off-site disaster recovery cloud server module first needs to establish a secure communication channel with the main server module to ensure the security and reliability of data transmission. This process typically employs encryption technologies, such as SSL / TLS protocols, to prevent data interception or tampering during transmission. Regular backup tasks are triggered by the main server module, and the generated backup data is divided into two types: incremental backups and full backups. Incremental backups only include data that has changed since the last backup, significantly reducing the amount of data transmitted and improving backup efficiency.

[0090] Once the replica data is generated, the master server module will transmit the data in batches to the off-site disaster recovery cloud server via an encrypted channel. After receiving this data, the CDM unit will first perform data deduplication. This process identifies identical data blocks or files, eliminating duplicate copies and thus saving storage space. After deduplication, the CDM unit will classify and store the replica data, organizing it into different storage partitions based on timestamps, data types, or business criticality for easy access and management later.

[0091] To ensure the availability and integrity of backup data, the CDM unit periodically performs data consistency checks. This process includes comparing the hash values ​​of the replica data with those of the master data to ensure that no corruption or tampering has occurred during the entire transmission and storage process. In addition, the CDM unit records detailed log information for each data backup, including backup time, data size, and transmission status, to facilitate auditing and troubleshooting when needed.

[0092] In terms of data recovery, when a company's main server module fails or data is lost, the CDM unit can respond quickly and provide flexible recovery options based on the company's specific needs. The company can choose to rapidly restore the entire system to a specific point in time, or only restore specific datasets. During the recovery process, the CDM unit quickly retrieves the required copy data based on previously saved index information and securely transmits the data back to the main server module through a dedicated recovery channel. After recovery is complete, the CDM unit also performs a post-recovery data consistency check to ensure that the recovered data matches expectations, thus confirming the success of the recovery process. Throughout the implementation process, the CDM unit ensures maximum business continuity and data security for the company in the event of a disaster through efficient resource scheduling and management.

[0093] This off-site disaster recovery cloud server module, through the introduction of the CDM unit, effectively enhances enterprises' ability to manage backup data, ensuring rapid business recovery in the event of an emergency. By centrally managing replica data, enterprises can better conduct data audits and compliance checks, reducing the risk of data loss or corruption. Simultaneously, the application of CDM technology significantly reduces the storage requirements for redundant data, thereby lowering storage costs and improving resource utilization. This efficient data management approach allows enterprises to implement disaster recovery strategies more flexibly and efficiently, enabling them to quickly respond to various emergencies and ensure business continuity.

[0094] This invention also provides a disaster recovery method that can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0095] The following detailed explanation uses a computer terminal as an example. Figure 2 This is a hardware structure block diagram of a computer terminal for a disaster recovery method provided in an embodiment of the present invention. Figure 2 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0096] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any disaster recovery method.

[0097] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0098] Internal memory provides an environment for the execution of computer programs stored on non-volatile storage media. When these computer programs are executed by a processor, the processor can perform any disaster recovery method.

[0099] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0101] See Figure 3 The present invention provides a disaster recovery method, the method comprising:

[0102] S301, divide the system dataset of the enterprise operation system into multiple system data blocks and determine the storage level of each system data block;

[0103] S302, the system data blocks are stored in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level;

[0104] S303, Generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module so that the off-site disaster recovery cloud server module can receive and manage the copy data for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology.

[0105] As can be seen, by dividing the system dataset of the enterprise operating system into multiple system data blocks and determining the storage level of each system data block; storing the system data blocks in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level; generating a copy of the system dataset for disaster recovery backup, and sending the copy data to the off-site disaster recovery server module, so that the off-site disaster recovery cloud server module receives and manages the copy data for disaster recovery backup, wherein the off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology, thereby enabling real-time backup and rapid recovery of data through off-site disaster recovery cloud combined with CDM technology, reducing the risk of data loss, and optimizing the utilization efficiency of storage resources.

[0106] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0107] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0108] S301, divide the system dataset of the enterprise operation system into multiple system data blocks and determine the storage level of each system data block;

[0109] S302, the system data blocks are stored in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level;

[0110] S303, Generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module so that the off-site disaster recovery cloud server module can receive and manage the copy data for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology.

[0111] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0112] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0113] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0114] S301, divide the system dataset of the enterprise operation system into multiple system data blocks and determine the storage level of each system data block;

[0115] S302, the system data blocks are stored in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level;

[0116] S303, Generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module so that the off-site disaster recovery cloud server module can receive and manage the copy data for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology.

[0117] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A disaster recovery cloud for enterprise operating systems based on CDM technology, characterized in that, The disaster recovery cloud includes: The main server module is used to divide the system dataset of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data blocks in the local storage cloud server module according to the storage level, generate a copy of the system dataset for disaster recovery backup, and send the copy data to the off-site disaster recovery server module. A local storage cloud server module is used to store received system data blocks according to the storage hierarchy. The local storage cloud server module includes multiple local storage units. Storing the received system data blocks according to the storage hierarchy includes: determining the local storage unit corresponding to the storage hierarchy of each system data block; obtaining the available storage space of the local storage unit; and calculating the storage stability index of each local storage unit. For each system data block, if the available storage space of the local storage unit corresponding to the system data block is less than the data block size, or the storage stability index of the local storage unit corresponding to the system data block is less than a preset stability threshold, a local storage unit with available storage space greater than or equal to the data block size and a storage stability index greater than or equal to the preset stability threshold is searched. From the searched local storage units, the local storage unit with the highest storage hierarchy is selected as the final local storage unit for the system data block, and the system data block is stored in the final local storage unit. The off-site disaster recovery cloud server module is used to receive and manage the replica data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement replica data management services for enterprise operating systems based on CDM technology.

2. The disaster recovery cloud according to claim 1, characterized in that, Determining the storage level of each system data block includes: For each system data block, determine the data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size of the system data block to calculate the storage priority index of the system data; The storage level of the system data block is determined based on the storage priority index.

3. The disaster recovery cloud according to claim 2, characterized in that, The calculation of the storage stability index for each local storage unit includes: For each local storage unit, the utilization rate of the local storage unit is calculated based on the used space and total capacity of the local storage unit; The normalized failure rate of the local storage unit is calculated based on the historical failure count of the local storage unit and the maximum historical failure count among all local storage units. The normalized mean time between failures (MTBF) of the local storage unit is calculated based on the longest historical continuous MTBF of the local storage unit and the longest historical continuous MTBF among all local storage units. Calculate the latency impact factor of the local storage unit based on the average latency of the local storage unit's response to the upper-level request; The storage stability index of the local storage unit is calculated based on the utilization rate, the normalized failure rate, the normalized time to failure, and the latency impact factor.

4. A disaster recovery method, characterized in that, The method includes: a main server module for an off-site disaster recovery cloud applied to an enterprise operating system based on CDM technology, wherein the disaster recovery cloud further includes: a local storage cloud server module and an off-site disaster recovery cloud server module; the method includes: The system dataset of the enterprise operation system is divided into multiple system data blocks, and the storage level of each system data block is determined. The system data blocks are stored in the local storage cloud server module according to the storage level, so that the local storage cloud server module stores the received system data blocks according to the storage level. The local storage cloud server module includes multiple local storage units. Storing the received system data blocks according to the storage level includes: determining the local storage unit corresponding to the storage level of each system data block; obtaining the available storage space of the local storage unit; and calculating the storage stability index of each local storage unit. For each system data block, if the available storage space of the local storage unit corresponding to the system data block is less than the data block size, or the storage stability index of the local storage unit corresponding to the system data block is less than a preset stability threshold, a local storage unit with available storage space greater than or equal to the data block size and a storage stability index greater than or equal to the preset stability threshold is searched. From the searched local storage units, the local storage unit with the highest storage level is selected as the final local storage unit for the system data block, and the system data block is stored in the final local storage unit. A copy of the system dataset for disaster recovery backup is generated, and the copy data is sent to the off-site disaster recovery server module so that the off-site disaster recovery cloud server module can receive and manage the copy data for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, which is used to implement copy data management services for the enterprise operating system based on CDM technology.

5. The method according to claim 4, characterized in that, Determining the storage level of each system data block includes: For each system data block, determine the data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size of the system data block to calculate the storage priority index of the system data; The storage level of the system data block is determined based on the storage priority index.

6. The method according to claim 5, characterized in that, The calculation of the storage stability index for each local storage unit includes: For each local storage unit, the utilization rate of the local storage unit is calculated based on the used space and total capacity of the local storage unit; The normalized failure rate of the local storage unit is calculated based on the historical failure count of the local storage unit and the maximum historical failure count among all local storage units. The normalized mean time between failures (MTBF) of the local storage unit is calculated based on the longest historical continuous MTBF of the local storage unit and the longest historical continuous MTBF among all local storage units. Calculate the latency impact factor of the local storage unit based on the average latency of the local storage unit's response to the upper-level request; The storage stability index of the local storage unit is calculated based on the utilization rate, the normalized failure rate, the normalized time to failure, and the latency impact factor.

7. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 4-6 when it is run.

8. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 4-6.

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