CDM-based remote disaster recovery cloud, disaster recovery method, storage medium and electronic equipment

Abstract

The invention discloses a CDM-based remote disaster recovery cloud, a disaster recovery method, a storage medium and electronic equipment, and the disaster recovery cloud comprises a main server module which is used for dividing a system data set of an enterprise operation system into a plurality of system data blocks, determining the storage hierarchy of each system data block, and storing the storage hierarchy of each system data block; the system data blocks are stored in a local storage cloud server module according to the storage hierarchy, copy data of a system data set is generated, and the copy data is sent to a remote disaster recovery server module; the local storage cloud server module is used for storing the received system data blocks according to the storage hierarchy; and the remote disaster recovery cloud server module is used for receiving and managing the replica data, the remote disaster recovery cloud server module comprises a CDM unit, and the CDM unit is used for realizing replica data management service of the enterprise operation system based on a CDM technology. By means of the data asset management method and device, efficient management and value maximization of the data assets can be achieved, and sustainable operation and development of the data assets are promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster recovery, and in particular to a CDM-based remote disaster recovery cloud, a disaster recovery method, a storage medium and an electronic device. Background Art

[0002] With the continuous development of information technology, enterprise information systems are becoming increasingly complex, and the amount of data is growing exponentially. Enterprise operation systems not only need to process the massive amounts of data generated in daily operations, but also need to ensure the security, availability, and persistence of 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 operation system data has become an important challenge in enterprise information management. Summary of the invention

[0003] The purpose of the present invention is to provide a CDM-based off-site disaster recovery cloud, a disaster recovery method, a storage medium and an electronic device to address the deficiencies in the prior art. By combining the off-site disaster recovery cloud with CDM technology, real-time backup and rapid recovery of data can be achieved, the risk of data loss can be reduced, and the utilization efficiency of storage resources can be optimized.

[0004] An embodiment of the present application provides a remote disaster recovery cloud for an enterprise operation system based on CDM technology, the disaster recovery cloud comprising: The main server module is used to divide the system data set of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data block in the local storage cloud server module according to the storage level, and generate copy data of the system data set for disaster recovery backup, and send the copy data to the remote disaster recovery server module; A local storage cloud server module, used for storing each received system data block according to the storage hierarchy; The off-site disaster recovery cloud server module is used to receive and manage the copy data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, and the CDM unit is used to implement the copy data management service of the enterprise operation system based on the CDM technology.

[0005] Optionally, 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; A storage level of the system data block is determined according to the storage priority index.

[0006] Optionally, the local storage cloud server module includes a plurality of local storage units, and the storing of each system data block received according to the storage hierarchy includes: Determine the local storage units corresponding to the storage levels of the system data blocks, obtain the available storage space of the local storage units, and calculate the storage stability index of each local storage unit; For each system data block, when 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 a preset stability threshold, search for 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; From the local storage units found, a local storage unit with the highest storage level is selected as the final local storage unit of the system data block, and the system data block is stored in the final local storage unit.

[0007] Optionally, calculating the storage stability index of each local storage unit includes: For each local storage unit, calculating the usage rate of the local storage unit according to the used space and the total capacity of the local storage unit; Calculating a normalized failure rate of the local storage unit according to the number of historical failures of the local storage unit and the maximum number of historical failures among all local storage units; Calculate the normalized failure-free time of the local storage unit according to the longest continuous failure-free time in history of the local storage unit and the longest continuous failure-free time in history of all local storage units; Calculate a delay impact factor of the local storage unit according to an average delay of the local storage unit's response to the superior request; A storage stability index of the local storage unit is calculated according to the usage rate, the normalized failure rate, the normalized time between failures, and the delay impact factor.

[0008] Another embodiment of the present application provides a disaster recovery method, which is applied to a main server module of a remote disaster recovery cloud of an enterprise operation system based on CDM technology, wherein the disaster recovery cloud further includes: a local storage cloud server module and a remote disaster recovery cloud server module; the method includes: Divide the system data set of the enterprise operation system into multiple system data blocks and determine the storage level of each system data block; storing the system data blocks in the local storage cloud server module according to the storage hierarchy, so that the local storage cloud server module stores each received system data block according to the storage hierarchy; Generate copy data of the system data set for disaster recovery backup, and send the copy data to an 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, and the CDM unit is used to implement copy data management services for the enterprise operation system based on CDM technology.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present 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 execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides an off-site disaster recovery cloud for an enterprise operation system based on CDM technology, comprising: a main server module, used to divide a system data set 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 a local storage cloud server module according to the storage level, generate copy data of the system data set, and send the copy data to the off-site disaster recovery server module; a local storage cloud server module, used to store each received system data block according to the storage level; an off-site disaster recovery cloud server module, used to receive and manage the copy data, the off-site disaster recovery cloud server module comprises a CDM unit, and the CDM unit is used to implement a copy data management service for the enterprise operation system based on the CDM technology, thereby enabling real-time backup and rapid recovery of data to be achieved through an off-site disaster recovery cloud combined with the CDM technology, thereby reducing the risk of data loss and optimizing the utilization efficiency of storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the structure of a remote disaster recovery cloud for an enterprise operation system based on CDM technology provided by an embodiment of the present invention; Figure 2 A hardware structure block diagram of a computer device for a disaster recovery method provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process flow of a disaster recovery method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention first provides a remote disaster recovery cloud for an enterprise operation system based on CDM technology. Figure 1 , which may include: The main server module 101 is used to divide the system data set of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data block in the local storage cloud server module according to the storage level, and generate copy data of the system data set for disaster recovery backup, and send the copy data to the remote disaster recovery server module; The main server module is the core component of the present invention. Its main function is to divide the data set in the enterprise operation system into multiple system data blocks and assign an appropriate storage level to each system data block. This process not only structures the data, but also takes into account the importance and frequency of the data to ensure the optimization of the storage strategy. According to the set storage level, the system data blocks will be stored in the local storage cloud server module. In addition, the main server module will generate copy data of these system data blocks to facilitate rapid recovery in the event of data loss or system failure. The generated copy data will then be sent to the off-site disaster recovery server module to achieve effective disaster recovery and business continuity assurance.

[0015] The establishment of the main server module enables enterprises to adopt a more scientific and systematic approach to data management, storage and recovery challenges. The reasonable division of system data blocks and determination of storage levels not only improves storage efficiency, but also ensures data security and availability. The generation of replica data and off-site backup can significantly reduce the risk of data loss, improve the enterprise's ability to respond and recover in emergencies, and achieve efficient business continuity management.

[0016] In the specific implementation, the main server module will first receive the complete data set in the enterprise operation system, and divide the data set into multiple system data blocks through the existing block algorithm. It can also package the data of each functional unit into a separate system data block according to the functional unit of the enterprise operation system. Each data block will be analyzed and evaluated for factors such as its access frequency, importance, and change status to determine the corresponding storage level. Subsequently, these data blocks are stored in the local storage cloud server module according to the storage level. At the same time, the main server module generates a copy of each data block, and can also encrypt the copy data to improve security. Finally, these encrypted copy data will be transmitted to the off-site disaster recovery server module through the network to ensure that there is an available backup to support data recovery when the local system fails or data is lost.

[0017] Specifically, the storage level of each system data block is determined, and for each system data block, the data access frequency index, data importance index, unit storage cost index, data change frequency, and data block size of the system data block can be determined to calculate the storage priority index of the system data; Through a comprehensive quantitative evaluation of each system data block, including five key indicators: data access frequency index, data importance index, unit storage cost index, data change frequency and data block size. These indicators together form the basis for calculating the storage priority index. The data access frequency index reflects how frequently the data block is accessed, and the data importance index is used to determine the importance of data to enterprise operations. The unit storage cost index shows the resource consumption required to store the data block, while the data change frequency measures the update frequency of the data block. Finally, the data block size directly affects the efficiency of storage. Therefore, through the comprehensive consideration of these indicators, the system can scientifically derive the storage priority index of each data block.

[0018] For example, a calculation formula for the storage priority index P may be:

[0019] This formula is designed to comprehensively evaluate the storage priority index P of data blocks through multiple key factors, helping enterprises to formulate scientific and reasonable data storage strategies. The multi-dimensional structure of the formula makes storage decisions more comprehensive and accurate, ensuring that enterprises can effectively use storage resources and improve data access efficiency and security.

[0020] in: The data access frequency index indicates the number of times the data block has been accessed in the past period of time (a preset time period). This parameter reflects the activity 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. The data importance index is a score based on the business logic and the importance of data in enterprise operations. The value range is usually [0,1]. The higher the value, the more critical the data is, and the higher the priority is. The unit storage cost index is expressed as the cost per unit of data storage. A higher value means a higher storage cost, which affects the priority assessment. This parameter assesses the storage cost per unit of data. The higher the storage cost, the lower the priority of the data storage, which helps control the data storage expenses of the enterprise. The frequency of data changes indicates the number of times the data block has been updated in the past period of time, reflecting the activity of the data. This parameter records the update frequency of the data block, which is used to reflect the activity of the data and maintain the freshness of the data. Data blocks that are updated frequently should be given priority for storage. is the normalized value of the data block size. The data block size is normalized to ensure that the size relative to its proportion in the system does not affect the priority evaluation. Through normalization, it is ensured that the data block size does not have a negative impact on the priority evaluation, and a fair and reasonable storage decision is achieved. w_1, w_2, w_3, w_4, w_5 are weight factors, corresponding to access frequency, importance, storage cost, change frequency and data block size respectively. These weights can be adjusted according to specific application scenarios and enterprise policies to reflect the impact of different factors on storage priority.

[0021] In the actual implementation process, data analysis tools can be used to automatically monitor and evaluate data blocks in the enterprise database. First, the system will summarize the access logs of each data block to determine its access frequency, and then evaluate the importance of the data by analyzing business needs and historical data. Next, the system will query the storage service provider's information to obtain the unit storage cost, monitor the data update log to evaluate the change frequency, and finally calculate the size of the data block. All of this information will be integrated into the calculation formula of the storage priority index to provide a basis for subsequent storage decisions.

[0022] The core role of this step is to provide a quantitative evaluation standard for each system data block, making data storage decisions more transparent and based on evidence. By accurately evaluating each indicator, enterprises can prioritize and optimize storage resources to ensure that critical and frequently used data can be stored in higher-performance storage tiers. This scientific management method will greatly improve the efficiency of data access, reduce storage costs, and ensure that enterprises have greater flexibility and adaptability in data management.

[0023] A storage level of the system data block is determined according to the storage priority index.

[0024] Through reasonable storage tier allocation, the performance and benefits of the entire data management system can be improved. For example, by giving priority to storing key data blocks in the high-performance tier, the data reading time can be significantly shortened, thereby improving the efficiency of business operations. At the same time, this intelligent storage allocation method helps enterprises control storage costs, ensure that all types of data can be properly stored and managed, and effectively prevent business risks caused by data loss or access delays.

[0025] In specific implementation, after calculating the storage priority index, determine which storage level the storage priority index falls into. Each storage level corresponds to a threshold range of the priority index, thereby determining the specific storage level of each system data block. Through this directional allocation, enterprises can not only optimize storage performance, but also achieve intelligent management of data access.

[0026] Set the classification criteria for storage tiers, such as defining three storage tiers: high, medium, and low. Based on the calculated storage priority index, the storage tiers are assigned to the corresponding storage tiers. For example: 1. High-storage-level data blocks: such as financial reports, customer information and other key data blocks, which have a higher storage priority index, will be allocated to high-performance SSD storage. This type of data is usually frequently queried and updated in enterprise operations. 2. Middle storage level data blocks: Data 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 performance. 3. Low-storage-level data blocks: such as expired historical data or archived information that is no longer frequently used, have a lower priority index. These data blocks will be stored in the lowest-cost storage solution, such as cloud storage services or low-speed HDDs. Through this tiered storage strategy, enterprises can effectively manage storage resources, achieve efficient data access and reasonable cost control. In addition, the system can also regularly monitor the access frequency and importance of data and dynamically adjust the storage level of data blocks to adapt to changing business needs.

[0027] The local storage cloud server module 102 is used to store each received system data block according to the storage hierarchy; The local storage cloud server module is one of the core components of the off-site disaster recovery cloud of the enterprise operation system. Its main function is to store the received system data blocks in order according to the storage level divided and determined by the main server module. This module not only undertakes the storage task of the data blocks, but also ensures that the data is stored in an optimized manner according to different priorities. According to the preset storage level standards, the module can achieve a balance between performance, cost and access speed, ensuring that key data blocks are in high-performance storage media (such as solid-state drives), while relatively unimportant data blocks are stored in lower-cost or lower-performance media. This tiered storage strategy enables enterprises to gain greater flexibility and efficiency in data management, while also improving the utilization of storage resources.

[0028] The local storage cloud server module occupies an important position in enterprise data management. Its role is not only reflected in the physical storage of data, but also in optimizing the access speed and storage security of data. By storing data in an orderly manner according to the storage hierarchy, enterprises can ensure that key business data can be accessed quickly, thereby improving the efficiency of daily operations. At the same time, it also reduces storage costs and enhances the scalability of the system, allowing 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.

[0029] Specifically, the local storage cloud server module may include multiple local storage units. According to the storage hierarchy, each system data block received can be stored, and the local storage units corresponding to the storage hierarchy of each system data block can be determined, the available storage space of the local storage unit can be obtained, and the storage stability index of each local storage unit can be calculated; The system will first interact 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 it can store the data blocks to be received. In addition, the system will also calculate the storage stability index of each storage unit, which is a key indicator for evaluating the reliability of storage devices, including utilization rate, failure rate and other factors. This provides basic data support for subsequent storage decisions.

[0030] By accurately obtaining the available space of storage units and calculating the storage stability index, enterprises can wisely select the most appropriate storage unit for data storage. This measure effectively avoids the waste of storage resources and ensures that data blocks can be stored safely and efficiently. At the same time, the calculation of the stability index helps enterprises identify storage units with poor performance, providing important protection for data security, thereby reducing the risk of data loss caused by storage failures.

[0031] Specifically, the storage stability index of each local storage unit is calculated, and for each local storage unit, the utilization rate of the local storage unit can be calculated according to the used space and total capacity of the local storage unit; The usage rate is U / C: U represents the used space of the local storage unit, and C 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. The smaller the usage rate, the less pressure on the storage unit and the higher the stability.

[0032] The ratio of used storage space to total storage capacity is evaluated for each local storage unit to calculate the storage unit utilization rate. This process can intuitively reflect the current load status of the storage unit, thus providing basic data for subsequent storage stability analysis. Specifically, the used space refers to the size of the data stored in the storage unit, while the total capacity is the maximum storage capacity of the storage unit. The ratio of the two can help administrators understand whether the storage unit is close to full load.

[0033] By calculating the utilization rate, enterprises can quickly identify the load of storage units and 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 the utilization rate can effectively prevent the problem of insufficient storage resources and ensure the high availability of the system and the security of data.

[0034] Calculating a normalized failure rate of the local storage unit according to the number of historical failures of the local storage unit and the maximum number of historical failures among all local storage units; Among them, 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 evaluation of the reliability of the storage unit is provided. The smaller the value of the normalized failure rate, the fewer failure events there are, indicating that the storage unit is more reliable.

[0035] Evaluate the historical failure data of each local storage unit and calculate its normalized failure rate. Specifically, the system first records the number of failures of each storage unit and determines the maximum number of historical failures among all storage units. Then, the normalized failure rate is obtained by dividing the number of failures of a single storage unit by the maximum value. This calculation can standardize the number of failures of different storage units so that they can be compared under the same metric.

[0036] The significance of calculating the normalized failure rate is that enterprises can effectively identify the reliability of storage units. The fewer the number of failures, the lower the normalized failure rate, which means that the stability of the storage unit is higher. By comparing the failure rates, enterprises can quickly decide which more reliable storage systems to store data in, thereby reducing the failure risk of the entire system and improving the security of data management.

[0037] Calculate the normalized failure-free time of the local storage unit according to the longest continuous failure-free time in history of the local storage unit and the longest continuous failure-free time in history of all local storage units; Among them, the normalized time between failures is MTBF / (MTBF_max): MTBF represents the longest continuous time between failures in the history of the local storage unit, and MTBF_max represents the longest continuous time between failures in the history of all local storage units. The normalized time between failures quantifies the performance of the storage unit in stable operation. The larger the value of the normalized time between failures, the longer the time between failures, indicating that the availability of the storage unit is higher.

[0038] Analyze the history of each local storage unit and calculate the ratio of its maximum mean time between failures (MTBF) to the maximum mean time between failures among all storage units. This process helps assess the reliability of the storage units by collecting the maximum time each storage unit has been without failure during its operational lifecycle and then comparing it with the maximum mean time between failures recorded among all storage units.

[0039] The calculation of normalized time between failures can directly reflect the operational stability of each storage unit. The larger the value, the better the storage unit performs in maintaining stable operation. This also provides an important basis for storage strategies. Enterprises can give priority to storage units with longer time between failures when storing data, thereby improving the overall stability of the system and data security.

[0040] Calculate a delay impact factor of the local storage unit according to an average delay of the local storage unit's response to the superior request; Among them, the delay impact factor is : T represents the average latency (in milliseconds) of the local storage unit responding to requests from the upper level. The latency impact factor evaluates the performance of the storage unit by response delay. The smaller the T value, the faster the response speed, which helps to ensure the efficiency of the system.

[0041] The purpose of calculating the latency impact factor is to evaluate and optimize the access performance of the storage unit. High latency may mean that the storage unit responds slowly, thus affecting the overall performance of the system. By monitoring this indicator, enterprises can identify potential performance bottlenecks and optimize them to ensure the efficient operation of business systems.

[0042] A storage stability index of the local storage unit is calculated according to the usage rate, the normalized failure rate, the normalized time between failures, and the delay impact factor.

[0043] For example, a storage stability index is calculated as:

[0044] The Storage Stability Index (SSI) is a comprehensive indicator designed to evaluate the stability and reliability of local storage units. It evaluates the comprehensive performance of storage units through indicators of multiple key dimensions, thus providing a strong basis for enterprise decision-making.

[0045] in, is the corresponding weight coefficient, which is used to adjust the relative importance of each indicator in the storage stability index. Through these weights, enterprises can flexibly adjust according to different business needs and storage strategies to optimize the overall storage management solution.

[0046] For each system data block, when 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 a preset stability threshold, search for 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; Each system data block will be evaluated to check whether the corresponding current local storage unit has sufficient storage space and storage stability index. If it is found that the available storage space of the storage unit is insufficient to store the data block, or its stability index is lower than the preset threshold, the system will automatically query other local storage units to locate the appropriate storage media that can currently meet the storage space and stability requirements. This process ensures that the data block will not be stored in the current unsafe or unstable storage environment, thereby ensuring the integrity and availability of the data.

[0047] By double-checking storage space and stability, enterprises can effectively avoid possible risks during the storage process and ensure that data is stored on the most suitable device as needed. This measure not only improves the security of data storage, but also enhances the flexibility of enterprise data management, ensuring that storage solutions are available at any time and avoiding data loss and business interruption due to insufficient storage space.

[0048] During the implementation process, the system will monitor the status of each storage unit in real time and maintain an information database containing all storage units. Once it is found that a data block does not meet the storage conditions of the corresponding storage unit, the system will automatically search for storage units, filter out those storage units with available space that meets the data block size and whose stability index is higher than the threshold, and generate a list of available storage units for subsequent steps.

[0049] From the local storage units found, a local storage unit with the highest storage level is selected as the final local storage unit of the system data block, and the system data block is stored in the final local storage unit.

[0050] The system will further screen all qualified storage units found in the previous step and select the unit with the highest storage level as the final storage target. This selection not only takes into account the available space and stability of the storage unit, but also focuses on the priority of the storage level, thereby ensuring that key data blocks are stored in the storage medium with the best performance. Finally, the system will store the system data blocks in the selected final storage unit to maximize data access efficiency.

[0051] Through this selection process, enterprises can ensure that data is stored in the storage unit with the best performance, improving access speed and system responsiveness. This priority storage strategy effectively improves the performance of overall data management, making enterprises more competitive in data processing and business operations. At the same time, it also reduces potential risks caused by insufficient storage device performance, greatly improving the security and reliability of data storage.

[0052] In the specific implementation, the system will sort the storage levels of the available storage units and select the storage unit with the highest level as the final destination. After that, the system will safely and efficiently store the data blocks in the storage unit through the data transmission protocol to ensure a fast and seamless storage process. The process also records the log of the storage operation for subsequent data management and auditing.

[0053] The off-site disaster recovery cloud server module 103 is used to receive and manage the copy data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit 1031, and the CDM unit 1031 is used to implement the copy data management service of the enterprise operation system based on the CDM technology.

[0054] The main function of the off-site disaster recovery cloud server module is to receive and manage the copy data used for disaster recovery backup, and the CDM (Copy Data Management) unit, as the core component of the module, is responsible for implementing the copy data management service of the enterprise operation system. The CDM unit uses advanced copy data management technology to efficiently identify, classify and store the received copy data to ensure the integrity and consistency of the data in different storage environments. At the same time, the CDM unit also provides functions such as data deduplication, intelligent backup and recovery strategy optimization to improve storage efficiency and data recovery flexibility.

[0055] In the specific implementation process, the remote disaster recovery cloud server module must first establish a secure communication channel with the main server module to ensure the security and reliability of the data transmission process. This process usually uses encryption technology, such as SSL / TLS protocol, to prevent data from being intercepted or tampered with during transmission. Regular backup tasks will be triggered by the main server module, and the replica data generated by these tasks are divided into two types: incremental backup and full backup. Incremental backup only contains data that has changed since the last backup, which greatly reduces the amount of data transmitted and improves backup efficiency. Once the replica data is generated, the master server module will transfer the data in batches to the off-site disaster recovery cloud server through an encrypted channel. After the CDM unit receives the data, it will first perform data deduplication, which eliminates duplicate copies by identifying identical data blocks or files, thereby saving storage space. After deduplication is completed, 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 subsequent quick access and management. To ensure the availability and integrity of backup data, the CDM unit regularly performs data consistency checks. This process includes comparing the hash values ​​of the replica data with the primary data to ensure that it has not been damaged or tampered with during the entire transmission and storage process. In addition, the CDM unit also records detailed log information for each data backup, covering backup time, data size, transmission status, etc., to facilitate auditing and troubleshooting when necessary. In terms of data recovery, when an enterprise's main server module fails or data is lost, the CDM unit can respond quickly and provide flexible recovery options based on the specific needs of the enterprise. The enterprise can choose to quickly restore the entire system state to a certain point in time, or only restore a specific data set. During the recovery process, the CDM unit will quickly retrieve the required copy data based on the previously saved index information, and securely transfer the data back to the main server module through a dedicated recovery channel. After the recovery is completed, the CDM unit will also perform a consistency check on the restored data to ensure that the restored data is consistent with expectations to confirm the success of the recovery process. Throughout the implementation process, the CDM unit ensures that the enterprise can achieve maximum business continuity and data security when a disaster occurs through efficient resource scheduling and management.

[0056] The remote disaster recovery cloud server module effectively improves the enterprise's backup data management capabilities through the introduction of CDM units, ensuring that business operations can be quickly restored in the event of an emergency. By centrally managing copy data, enterprises can better conduct data audits and compliance checks, reducing the risks caused by data loss or damage. At the same time, the application of CDM technology has greatly reduced the storage requirements for redundant data, thereby reducing storage costs and improving resource utilization. This efficient data management method makes enterprises more flexible and efficient in implementing disaster recovery strategies, and can quickly respond to various emergencies to ensure business continuity.

[0057] The embodiment of the present invention further provides a disaster recovery method, which can be applied to electronic equipment, such as a computer terminal, specifically a common computer, etc.

[0058] The following describes it in detail by taking running on a computer terminal as an example. Figure 2 The hardware structure block diagram of a computer terminal of a disaster recovery method provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0059] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any disaster recovery method.

[0060] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0061] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any disaster recovery method.

[0062] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0063] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0064] See also Figure 3 , an embodiment of the present invention provides a disaster recovery method, the method comprising: S301, dividing a system data set of an enterprise operation system into a plurality of system data blocks, and determining a storage level of each system data block; S302, storing the system data blocks in the local storage cloud server module according to the storage hierarchy, so that the local storage cloud server module stores each received system data block according to the storage hierarchy; S303, generate copy data of the system data set for disaster recovery backup, and send the copy data to an 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, and the CDM unit is used to implement copy data management services for the enterprise operation system based on CDM technology.

[0065] It can be seen that by dividing the system data set of the enterprise operation system into multiple system data blocks, 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 each received system data block according to the storage level; copy data of the system data set 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 receives and manages the copy data for disaster recovery backup, wherein the off-site disaster recovery cloud server module includes a CDM unit, and the CDM unit is used to implement the copy data management service of the enterprise operation system based on CDM technology, so that through the off-site disaster recovery cloud combined with CDM technology, real-time backup and rapid recovery of data can be realized, the risk of data loss can be reduced, and the utilization efficiency of storage resources can be optimized.

[0066] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0067] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S301, dividing a system data set of an enterprise operation system into a plurality of system data blocks, and determining a storage level of each system data block; S302, storing the system data blocks in the local storage cloud server module according to the storage hierarchy, so that the local storage cloud server module stores each received system data block according to the storage hierarchy; S303, generate copy data of the system data set for disaster recovery backup, and send the copy data to an 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, and the CDM unit is used to implement copy data management services for the enterprise operation system based on CDM technology.

[0068] An embodiment of the present invention further 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 execute the steps in any one of the above method embodiments.

[0069] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0070] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S301, dividing a system data set of an enterprise operation system into a plurality of system data blocks, and determining a storage level of each system data block; S302, storing the system data blocks in the local storage cloud server module according to the storage hierarchy, so that the local storage cloud server module stores each received system data block according to the storage hierarchy; S303, generate copy data of the system data set for disaster recovery backup, and send the copy data to an 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, and the CDM unit is used to implement copy data management services for the enterprise operation system based on CDM technology.

[0071] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A remote disaster recovery cloud for enterprise operation systems based on CDM technology, characterized in that: The disaster recovery cloud includes: The main server module is used to divide the system data set of the enterprise operation system into multiple system data blocks, determine the storage level of each system data block, store the system data block in the local storage cloud server module according to the storage level, and generate copy data of the system data set for disaster recovery backup, and send the copy data to the remote disaster recovery server module; A local storage cloud server module, used for storing each received system data block according to the storage hierarchy; The off-site disaster recovery cloud server module is used to receive and manage the copy data used for disaster recovery backup. The off-site disaster recovery cloud server module includes a CDM unit, and the CDM unit is used to implement the copy data management service of the enterprise operation system based on the CDM technology.

2. The disaster recovery cloud according to claim 1, characterized in that: The determining of 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; A storage level of the system data block is determined according to the storage priority index.

3. The disaster recovery cloud according to claim 2, characterized in that: The local storage cloud server module includes a plurality of local storage units, and each system data block received by storing the storage level includes: Determine the local storage units corresponding to the storage levels of the system data blocks, obtain the available storage space of the local storage units, and calculate the storage stability index of each local storage unit; For each system data block, when 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 a preset stability threshold, search for 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; From the local storage units found, a local storage unit with the highest storage level is selected as the final local storage unit of the system data block, and the system data block is stored in the final local storage unit.

4. The disaster recovery cloud according to claim 3, characterized in that: The calculating of the storage stability index of each local storage unit includes: For each local storage unit, calculating the usage rate of the local storage unit according to the used space and the total capacity of the local storage unit; Calculating a normalized failure rate of the local storage unit according to the number of historical failures of the local storage unit and the maximum number of historical failures among all local storage units; Calculate the normalized failure-free time of the local storage unit according to the longest continuous failure-free time in history of the local storage unit and the longest continuous failure-free time in history of all local storage units; Calculate a delay impact factor of the local storage unit according to an average delay of the local storage unit's response to the superior request; A storage stability index of the local storage unit is calculated according to the usage rate, the normalized failure rate, the normalized time between failures, and the delay impact factor.

5. A disaster recovery method, characterized in that: A main server module applied to a remote disaster recovery cloud of an enterprise operation system based on CDM technology, wherein the disaster recovery cloud further includes: a local storage cloud server module and a remote disaster recovery cloud server module; the method includes: Divide the system data set of the enterprise operation system into multiple system data blocks and determine the storage level of each system data block; storing the system data blocks in the local storage cloud server module according to the storage hierarchy, so that the local storage cloud server module stores each received system data block according to the storage hierarchy; Generate copy data of the system data set for disaster recovery backup, and send the copy data to an 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, and the CDM unit is used to implement copy data management services for the enterprise operation system based on CDM technology.

6. The method according to claim 5, characterized in that The determining of 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; A storage level of the system data block is determined according to the storage priority index.

7. The method according to claim 6, characterized in that The local storage cloud server module includes a plurality of local storage units, and each system data block received by storing the storage level includes: Determine the local storage units corresponding to the storage levels of the system data blocks, obtain the available storage space of the local storage units, and calculate the storage stability index of each local storage unit; For each system data block, when 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 a preset stability threshold, search for 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; From the local storage units found, a local storage unit with the highest storage level is selected as the final local storage unit of the system data block, and the system data block is stored in the final local storage unit.

8. The method according to claim 7, characterized in that The calculating of the storage stability index of each local storage unit includes: For each local storage unit, calculating the usage rate of the local storage unit according to the used space and the total capacity of the local storage unit; Calculating a normalized failure rate of the local storage unit according to the number of historical failures of the local storage unit and the maximum number of historical failures among all local storage units; Calculate the normalized failure-free time of the local storage unit according to the longest continuous failure-free time in history of the local storage unit and the longest continuous failure-free time in history of all local storage units; Calculate a delay impact factor of the local storage unit according to an average delay of the local storage unit's response to the superior request; A storage stability index of the local storage unit is calculated according to the usage rate, the normalized failure rate, the normalized time between failures, and the delay impact factor.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 5 to 8 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 5 to 8.

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