Multi-data center disaster recovery backup and rapid recovery method and system based on private cloud

Abstract

The invention provides a private cloud-based multi-data center disaster recovery backup and rapid recovery method and system, and the method comprises the steps: enabling each data center to be connected with a disaster recovery control platform, and carrying out the deployment management; arranging a backup strategy according to the disaster recovery control platform; monitoring the health state of each data center on the basis of a disaster recovery control platform according to the arrangement result and the deployment result, analyzing the monitoring data on the basis of an AI model, and judging whether disaster recovery switching is triggered or not; and when it is judged that disaster recovery switching is triggered, service recovery and resource redistribution are carried out based on the disaster recovery control platform. Through automatic fault sensing and scheduling, the fault response time is effectively improved, multi-center backup and configuration recovery can be effectively realized through a unified disaster recovery control platform, and the management cost is further reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for disaster recovery and rapid recovery of multiple data centers based on private cloud. Background Technology

[0002] Currently, with the rapid development of information technology, the amount of data in various industries is growing explosively, and data has become one of the core assets of enterprises and organizations. The integrity, availability, and security of data are crucial for the continuous operation of business, decision support, and customer trust. However, various natural disasters, human errors, technical failures, and other factors constantly threaten data security, which may lead to data loss, corruption, or business interruption, causing huge economic losses and reputational damage to enterprises.

[0003] However, in data-intensive industries such as finance, e-commerce, government affairs, and energy, the requirements for high availability and data continuity in business systems are increasing. Traditional single-center data disaster recovery models can no longer meet the business continuity goals of modern enterprises, which require "multi-site, multi-active, and second-level recovery." While some commercial disaster recovery and backup solutions exist, such as Veeam, Commvault, and Huawei BCManager, these solutions primarily focus on backup functions and still suffer from low automation, limited cross-platform support, and insufficient recovery efficiency.

[0004] Therefore, in order to overcome the above-mentioned technical problems, the present invention provides a method and system for disaster recovery and rapid recovery of multiple data centers based on private cloud. Summary of the Invention

[0005] This invention provides a method and system for disaster recovery and rapid restoration of multiple data centers based on a private cloud. By connecting and managing each data center to a disaster recovery control platform, it effectively improves automatic reporting of resource topology, backup capabilities, and network connectivity. The disaster recovery control platform orchestrates backup strategies, defining them based on service levels and data tiers, enabling tasks such as on-demand generation of scheduled snapshots and continuous incremental synchronization. The platform effectively monitors the health status of each data center (e.g., system status, network connectivity) and analyzes the data using AI models to accurately determine if a disaster recovery switchover is triggered. When a switchover is triggered, the platform performs business recovery and resource reallocation, rapidly switching applications and data resources to the backup center, automatically deploying templates to rebuild the environment, and notifying the application access layer to change routes. Automated fault detection and scheduling effectively improves fault response time. The unified disaster recovery control platform effectively enables multi-center backup and recovery configuration, thereby reducing management costs.

[0006] This invention provides a method for disaster recovery and rapid recovery of multiple data centers based on a private cloud, characterized by comprising:

[0007] Step 1: Connect each data center to the disaster recovery control platform and deploy and manage it;

[0008] Step 2: Arrange the backup strategy according to the disaster recovery control platform;

[0009] Step 3: Based on the orchestration and deployment results, monitor the health status of each data center using the disaster recovery control platform, and analyze the monitoring data using an AI model to determine whether a disaster recovery switchover should be triggered.

[0010] Step 4: When a disaster recovery switchover is triggered, business recovery and resource reallocation are performed based on the disaster recovery control platform.

[0011] Preferably, a multi-datacenter disaster recovery and rapid recovery method based on a private cloud includes, in step 1, connecting each data center to the disaster recovery control platform and deploying and managing it, including:

[0012] Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform;

[0013] Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data;

[0014] In each data center, a corresponding data messenger factor is established based on the corresponding third communication data, and the second communication data is loaded and configured in the data messenger factor. Based on the loading and configuration results, the corresponding data center is connected to the disaster recovery control platform according to the data messenger factor.

[0015] Once the connection is established, the reported information from each data center is obtained, and the reported information from each data center is deployed and managed in the disaster recovery control platform.

[0016] Preferably, a multi-datacenter disaster recovery and rapid recovery method based on a private cloud involves acquiring the reported information from each data center and deploying and managing the reported information from each data center within a disaster recovery control platform, including:

[0017] Obtain the central identifier of each data center, and build the management module of each data center in the disaster recovery control platform based on the central identifier of each data center;

[0018] Obtain the content type of the reported information and add management items in the management module according to the content type. At the same time, divide the reported information according to the content type and obtain the sub-information content corresponding to each content type.

[0019] Based on the preset information management database, information management file templates of various content types are retrieved, and sub-information content is matched and mapped with the corresponding information management file templates to obtain the target information management file;

[0020] Analyze the target information management files corresponding to each sub-information content to determine the information status of each data center;

[0021] The target information management files and the information status of the data centers are stored in the corresponding management modules to complete the deployment management of the reported information content of each data center.

[0022] Preferably, a method for disaster recovery and rapid restoration of multiple data centers based on private cloud reports information including: resource topology, data center backup capabilities, and network connectivity.

[0023] Preferably, in a multi-datacenter disaster recovery backup and rapid recovery method based on a private cloud, step 2 involves orchestrating a backup strategy according to the disaster recovery control platform, including:

[0024] Based on the disaster recovery control platform, the business services of each data center are read, and the first importance level of each business service is obtained. At the same time, the business services are sorted according to the first importance level to obtain the business service sequence.

[0025] Read the business data corresponding to each business service, evaluate the second importance level of the business data according to the preset evaluation indicators, and sort the business data according to the second importance level to obtain the business data sequence in each business service.

[0026] Based on preset management standards, the backup strategy for the data to be backed up is determined according to the business service sequence and business data sequence. At the same time, the available backup methods are obtained from the disaster recovery control platform, and the backup business attributes of the available backup methods are determined.

[0027] Based on the backup policy, the backup service attributes of the available backup methods are parsed to determine the combination of available backup methods under the backup policy and the configuration parameters of the available backup methods under each combination.

[0028] The backup strategy is arranged according to the combination of available backup methods under the backup strategy and the configuration parameters of the available backup methods under each combination.

[0029] Preferably, in a multi-datacenter disaster recovery backup and rapid recovery method based on a private cloud, step 3 involves monitoring the health status of each data center based on the orchestration and deployment results using a disaster recovery control platform, and analyzing the monitoring data using an AI model to determine whether a disaster recovery switchover has been triggered, including:

[0030] Obtain the monitoring dimensions for monitoring the health status of each data center;

[0031] The disaster recovery control platform collects operational data from each data center in real time and divides the operational data of each data center according to the monitoring dimensions to obtain the sub-operational dataset corresponding to each monitoring dimension.

[0032] Retrieve historical disaster recovery switchover events, and retrieve the sub-historical running dataset corresponding to each monitoring dimension from the historical disaster recovery switchover events;

[0033] Learn from each sub-historical operational dataset to determine the first operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is triggered and the second operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is not triggered.

[0034] Analyze and obtain the trend change characteristics when the second operating characteristic transforms into the first operating characteristic;

[0035] An AI model is constructed based on the first operational characteristic, the second operational characteristic, and the trend change characteristic.

[0036] The sub-operational dataset corresponding to each monitoring dimension is input into the AI ​​model for analysis, and the analysis results are used to determine whether to trigger a disaster recovery switch.

[0037] Preferably, a multi-datacenter disaster recovery backup and rapid recovery method based on private cloud, which determines whether to trigger disaster recovery switchover based on analysis results, includes:

[0038] If the sub-run dataset meets the first running characteristic, then a disaster recovery switch is triggered.

[0039] When the sub-run dataset meets the second run feature, the real-time status is matched with the trend change feature to determine the degree of matching between the real-time status and the trend change feature.

[0040] When the matching degree between the sub-run dataset and the trend change characteristics reaches the preset matching degree threshold, it is determined that a disaster recovery switch will be triggered.

[0041] When the matching degree between the sub-run dataset and the trend change characteristics is less than the preset matching degree threshold, it is determined that the disaster recovery switch will not be triggered.

[0042] When a disaster recovery switch is triggered, the target sub-run dataset corresponding to the triggering disaster recovery switch is obtained, and the corresponding target monitoring dimension is located based on the target sub-run dataset. At the same time, an early warning report for triggering the disaster recovery switch is generated based on the target sub-run dataset and the target monitoring dimension, and transmitted to the monitoring terminal.

[0043] Preferably, in a multi-datacenter disaster recovery backup and rapid recovery method based on a private cloud, step 4 involves performing business recovery and resource reallocation based on the disaster recovery control platform when a disaster recovery switch is triggered, including:

[0044] When a disaster recovery switch is triggered, the running services in the abnormal data center are locked, and the basic configuration parameters of the running services in the abnormal data center are retrieved from the disaster recovery control platform based on the locking result.

[0045] The basic configuration parameters are parsed to determine the overall architecture and business execution logic of the running business. At the same time, the business recovery process of the running business is initiated based on the disaster recovery control platform, and the conditions of the business recovery process are limited based on the overall architecture and business execution logic.

[0046] Based on the conditional constraints, the running services are switched to the backup data center according to the business recovery process, and based on the switching results, the business modules in the backup data center are restored and associated according to the business execution logic.

[0047] Based on business recovery and correlation results, the integrity of data in the backup data center is verified, and the business attributes of each business model are extracted after the verification is passed.

[0048] Based on business attributes, the business priority of each business module is determined, and based on the determined business priority, the resources required by each business module are reallocated in the backup data center.

[0049] This invention provides a multi-datacenter disaster recovery and rapid recovery system based on a private cloud, comprising:

[0050] The data access and management module is used to connect various data centers to the disaster recovery control platform and to manage their deployment.

[0051] The backup strategy orchestration module is used to orchestrate backup strategies according to the disaster recovery control platform.

[0052] The disaster recovery switchover judgment module is used to monitor the health status of each data center based on the orchestration and deployment results and the disaster recovery control platform, and to analyze the monitoring data based on the AI ​​model to determine whether to trigger a disaster recovery switchover.

[0053] The recovery management module is used to perform business recovery and resource reallocation based on the disaster recovery control platform when a disaster recovery switch is triggered.

[0054] Preferably, a multi-datacenter disaster recovery and rapid recovery system based on a private cloud includes a data access and management module, comprising:

[0055] The data acquisition unit is used for:

[0056] Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform;

[0057] Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data;

[0058] The data access unit is used to establish corresponding data messenger factors in each data center based on the corresponding third communication data, load and configure the second communication data in the data messenger factors, and connect the corresponding data center to the disaster recovery control platform based on the loading and configuration results and the data messenger factors.

[0059] The deployment management unit is used to obtain the reported information from each data center after the access is completed, and to deploy and manage the reported information from each data center in the disaster recovery control platform.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] By connecting and managing various data centers to the disaster recovery control platform, automatic reporting of resource topology, backup capabilities, and network connectivity can be effectively achieved and improved. The disaster recovery control platform orchestrates backup strategies, enabling the definition of backup policies based on service levels and data tiers, thus facilitating tasks such as on-demand generation of scheduled snapshots and continuous incremental synchronization. It also effectively monitors the health status of each data center (e.g., system status, network connectivity) and analyzes the data using AI models to accurately determine whether a disaster recovery switchover is triggered. When a switchover is triggered, the disaster recovery control platform performs business recovery and resource reallocation, rapidly switching applications and data resources to the backup center, automatically deploying templates to rebuild the environment, and notifying the application access layer to change routes. Automated fault detection and scheduling effectively improve fault response time. A unified disaster recovery control platform effectively enables multi-center backup and recovery configuration, thereby reducing management costs.

[0062] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0063] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0064] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0065] Figure 1 This is a flowchart illustrating a multi-datacenter disaster recovery and rapid recovery method based on a private cloud, as described in an embodiment of the present invention.

[0066] Figure 2 This is a flowchart of step 1 in a multi-datacenter disaster recovery and rapid recovery method based on a private cloud according to an embodiment of the present invention;

[0067] Figure 3 This is a structural diagram of a multi-datacenter disaster recovery and rapid recovery system based on a private cloud, as described in an embodiment of the present invention. Detailed Implementation

[0068] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0069] Example 1:

[0070] This embodiment provides a method for multi-datacenter disaster recovery and rapid recovery based on a private cloud, such as... Figure 1 As shown, it includes:

[0071] Step 1: Connect each data center to the disaster recovery control platform and deploy and manage it;

[0072] Step 2: Arrange the backup strategy according to the disaster recovery control platform;

[0073] Step 3: Based on the orchestration and deployment results, monitor the health status of each data center using the disaster recovery control platform, and analyze the monitoring data using an AI model to determine whether a disaster recovery switchover should be triggered.

[0074] Step 4: When a disaster recovery switchover is triggered, business recovery and resource reallocation are performed based on the disaster recovery control platform.

[0075] In this embodiment, the disaster recovery control platform is pre-built and used to manage data from different data centers.

[0076] In this embodiment, deployment management refers to the management of the data center's resource topology, data center backup capabilities, and network connectivity.

[0077] In this embodiment, the backup strategy refers to the specific methods or measures used when backing up data in the data center.

[0078] In this embodiment, orchestration refers to determining the backup strategy to be used under different circumstances in the disaster recovery control platform.

[0079] In this embodiment, the monitoring data refers to the real-time operating status of each data center obtained after monitoring the health status of each data center.

[0080] In this embodiment, disaster recovery switching refers to the ability to quickly switch services to a backup system in the event of a failure or unavailability of the primary system (such as a server, data center, etc.) to ensure business continuity and data availability.

[0081] In this embodiment, resource reallocation refers to the process of reallocating the operating resources required by each business module after the business has been restored through the disaster recovery control platform.

[0082] The working principle and beneficial effects of the above technical solution are as follows: By connecting and deploying each data center to the disaster recovery control platform, the automatic reporting of resource topology, backup capabilities, and network connectivity can be effectively realized and improved. The disaster recovery control platform orchestrates backup strategies, enabling the definition of backup strategies based on service levels and data classifications. This allows for on-demand generation of scheduled snapshots and continuous incremental synchronization. The platform effectively monitors the health status of each data center (e.g., system status, network connectivity), and uses AI models to analyze the detected data, accurately determining whether a disaster recovery switchover is triggered. When a switchover is triggered, the disaster recovery control platform performs business recovery and resource reallocation, quickly switching applications and data resources to the backup center. It automatically deploys templates to rebuild the environment and notifies the application access layer to change routes. Automated fault detection and scheduling effectively improve fault response time. A unified disaster recovery control platform effectively enables multi-center backup and recovery configuration, thereby reducing management costs.

[0083] Example 2:

[0084] Based on Example 1, this example provides a method for multi-datacenter disaster recovery and rapid recovery based on a private cloud, such as... Figure 2 As shown, in step 1, each data center is connected to the disaster recovery control platform and deployed and managed, including:

[0085] Step 101: Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform;

[0086] Step 102: Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data;

[0087] Step 103: In each data center, establish a corresponding data messenger factor based on the corresponding third communication data, load and configure the second communication data in the data messenger factor, and connect the corresponding data center to the disaster recovery control platform according to the data messenger factor based on the loading and configuration results.

[0088] Step 104: After the access is completed, obtain the reported information content of each data center, and deploy and manage the reported information content of each data center in the disaster recovery control platform.

[0089] In this embodiment, the first communication data refers to parameters such as data bandwidth, communication rate, and communication data format when each data center conducts data communication.

[0090] In this embodiment, the second communication data refers to the specific parameters that the disaster recovery control platform limits or requires for the communication process, including the format requirements for interfacing with the data center and the specific configuration requirements during interfacing.

[0091] In this embodiment, the data access standard refers to the format requirements of the disaster recovery control platform when accessing data.

[0092] In this embodiment, the third communication data refers to the result obtained after converting the format of the first communication data according to the data access standard.

[0093] In this embodiment, the data messenger factor is established based on the third communication data and is used to interface with the second communication data, thereby enabling each data center to access the disaster recovery control platform.

[0094] The beneficial effects of the above technical solution are as follows: by determining the first communication data of each data center and the second communication data of the disaster recovery control platform, and by combining the data access standard of the disaster recovery control platform to convert the format of the first communication data, the data center and the disaster recovery control platform can be connected according to the conversion result, ensuring the convenience and reliability of the disaster recovery control platform in managing each data center. Finally, the reported information of each data center is deployed and managed in the disaster recovery control platform, providing convenience and guarantee for multi-data center disaster recovery backup and rapid recovery.

[0095] Example 3:

[0096] Building upon Example 2, this example provides a method for multi-datacenter disaster recovery and rapid recovery based on a private cloud. This method acquires the reported information from each data center and manages the reported information within the disaster recovery control platform, including:

[0097] Obtain the central identifier of each data center, and build the management module of each data center in the disaster recovery control platform based on the central identifier of each data center;

[0098] Obtain the content type of the reported information and add management items in the management module according to the content type. At the same time, divide the reported information according to the content type and obtain the sub-information content corresponding to each content type.

[0099] Based on the preset information management database, information management file templates of various content types are retrieved, and sub-information content is matched and mapped with the corresponding information management file templates to obtain the target information management file;

[0100] Analyze the target information management files corresponding to each sub-information content to determine the information status of each data center;

[0101] The target information management files and the information status of the data centers are stored in the corresponding management modules to complete the deployment management of the reported information content of each data center.

[0102] In this embodiment, the central identifier is a marking symbol used to mark different data centers, which can be used to distinguish data centers.

[0103] In this embodiment, the management module refers to the tool in the disaster recovery control platform that manages the data working in different data centers.

[0104] In this embodiment, "management project" refers to the specific business category that requires management of the reported information content.

[0105] In this embodiment, sub-information content refers to the specific data set corresponding to different content types obtained after dividing the reported information content according to content type.

[0106] In this embodiment, the preset information management database is pre-set.

[0107] In this embodiment, matching mapping refers to determining the correspondence between sub-information content and different record areas in the information management archive template.

[0108] The beneficial effects of the above technical solution are as follows: by determining the central identifier of each data center, a management module is built for each data center in the disaster recovery control platform based on the central identifier. Secondly, the reported information content is analyzed and processed through the built management module, and finally the reported information content of each data center is accurately and effectively deployed on the disaster recovery control platform, which provides a guarantee for disaster recovery backup and rapid recovery.

[0109] Example 4:

[0110] Based on Example 2, this example provides a method for disaster recovery and rapid restoration of multiple data centers based on a private cloud. The method is characterized by reporting information including: resource topology, data center backup capabilities, and network connectivity.

[0111] Example 5:

[0112] Based on Example 1, this example provides a method for multi-datacenter disaster recovery and rapid recovery based on a private cloud. Step 2 involves orchestrating backup strategies according to the disaster recovery control platform, including:

[0113] Based on the disaster recovery control platform, the business services of each data center are read, and the first importance level of each business service is obtained. At the same time, the business services are sorted according to the first importance level to obtain the business service sequence.

[0114] Read the business data corresponding to each business service, evaluate the second importance level of the business data according to the preset evaluation indicators, and sort the business data according to the second importance level to obtain the business data sequence in each business service.

[0115] Based on preset management standards, the backup strategy for the data to be backed up is determined according to the business service sequence and business data sequence. At the same time, the available backup methods are obtained from the disaster recovery control platform, and the backup business attributes of the available backup methods are determined.

[0116] Based on the backup policy, the backup service attributes of the available backup methods are parsed to determine the combination of available backup methods under the backup policy and the configuration parameters of the available backup methods under each combination.

[0117] The backup strategy is arranged according to the combination of available backup methods under the backup strategy and the configuration parameters of the available backup methods under each combination.

[0118] In this embodiment, the backup service attribute is used to indicate the backup scenario to which the available backup methods are applicable, such as global backup or local backup.

[0119] In this embodiment, business services refer to the specific business categories corresponding to each data center and the business management objectives that need to be performed.

[0120] In this embodiment, the first importance level refers to the degree of importance of each business service in the data center.

[0121] In this embodiment, the business service sequence refers to the result obtained by sorting business services in ascending or descending order of importance according to the obtained first importance level.

[0122] In this embodiment, business data refers to the specific data information related to service items corresponding to different business services.

[0123] In this embodiment, the preset evaluation indicators are pre-set.

[0124] In this embodiment, the second importance level refers to the degree of importance of different business data in each business service as determined by preset evaluation indicators.

[0125] In this embodiment, the business data sequence refers to the result obtained by sorting business data in ascending or descending order of importance according to the second importance level.

[0126] In this embodiment, the preset management standards are set in advance.

[0127] In this embodiment, the available backup methods include timed snapshots and continuous incremental synchronization.

[0128] In this embodiment, the configuration parameters of the available backup method refer to the specific requirements corresponding to the available backup method when performing backup operations, including the amount of data in a single backup and the data backup rate, etc.

[0129] The beneficial effects of the above technical solution are as follows: By ranking the importance of business services in each data center and the business data corresponding to each business service through the disaster recovery control platform, the backup strategy for the data to be backed up can be determined based on the importance ranking results. At the same time, the available backup methods are parsed to determine the combination of available backup methods and the configuration parameters of available backup methods. Finally, the backup strategy is orchestrated based on the combination of available backup methods and the configuration parameters of available backup methods under each combination, thereby ensuring the reliability of disaster recovery backup and rapid recovery.

[0130] Example 6:

[0131] Based on Example 1, this example provides a method for multi-datacenter disaster recovery backup and rapid recovery based on a private cloud. In step 3, the health status of each data center is monitored based on the orchestration and deployment results using a disaster recovery control platform, and the monitoring data is analyzed based on an AI model to determine whether a disaster recovery switchover is triggered, including:

[0132] Obtain the monitoring dimensions for monitoring the health status of each data center;

[0133] The disaster recovery control platform collects operational data from each data center in real time and divides the operational data of each data center according to the monitoring dimensions to obtain the sub-operational dataset corresponding to each monitoring dimension.

[0134] Retrieve historical disaster recovery switchover events, and retrieve the sub-historical running dataset corresponding to each monitoring dimension from the historical disaster recovery switchover events;

[0135] Learn from each sub-historical operational dataset to determine the first operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is triggered and the second operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is not triggered.

[0136] Analyze and obtain the trend change characteristics when the second operating characteristic transforms into the first operating characteristic;

[0137] An AI model is constructed based on the first operational characteristic, the second operational characteristic, and the trend change characteristic.

[0138] The sub-operational dataset corresponding to each monitoring dimension is input into the AI ​​model for analysis, and the analysis results are used to determine whether to trigger a disaster recovery switch.

[0139] In this embodiment, the monitoring dimension refers to the category of items used to monitor the health status of each data center.

[0140] In this embodiment, the sub-running dataset refers to the data set corresponding to each monitoring dimension obtained by splitting the running data of each data center according to the monitoring dimension.

[0141] In this embodiment, the sub-historical operational dataset refers to the historical data in historical disaster recovery switching events that correspond one-to-one with the monitoring dimensions.

[0142] In this embodiment, the first operational feature refers to determining the range of data values ​​and the composition of the data in the sub-historical operational dataset when a disaster recovery switch occurs, based on the sub-historical operational dataset.

[0143] In this embodiment, the first operational feature refers to the range of data values ​​and the composition of the data in the sub-historical operational dataset when no disaster recovery switch has occurred, as determined based on the sub-historical operational dataset.

[0144] In this embodiment, the trend change feature refers to the data change characteristics when the second operating feature transforms into the first operating feature, including the fluctuation of data values, etc.

[0145] The beneficial effects of the above technical solution are as follows: By acquiring monitoring dimensions for monitoring the health status of each data center (the monitoring dimensions are set according to actual needs, including but not limited to: data center system status, network connectivity, CPU / Disk, and other indicators), the collected operational data of each data center can be effectively divided through the monitoring dimensions, thereby determining the sub-operational dataset corresponding to each monitoring dimension. By retrieving historical disaster recovery switching events, the sub-historical operational dataset corresponding to each monitoring dimension can be effectively learned, thereby accurately constructing an AI model for disaster recovery switching analysis of the operational data of each data center. This effectively ensures the accuracy, intelligence, and effectiveness of the analysis, thus providing a timely response for subsequent disaster recovery switching operations when a disaster recovery switching is triggered.

[0146] Example 7:

[0147] Building upon Example 6, this example provides a method for multi-datacenter disaster recovery backup and rapid recovery based on a private cloud, which determines whether a disaster recovery switchover should be triggered based on analysis results, including:

[0148] If the sub-run dataset meets the first running characteristic, then a disaster recovery switch is triggered.

[0149] When the sub-run dataset meets the second run feature, the real-time status is matched with the trend change feature to determine the degree of matching between the real-time status and the trend change feature.

[0150] When the matching degree between the sub-run dataset and the trend change characteristics reaches the preset matching degree threshold, it is determined that a disaster recovery switch will be triggered.

[0151] When the matching degree between the sub-run dataset and the trend change characteristics is less than the preset matching degree threshold, it is determined that the disaster recovery switch will not be triggered.

[0152] When a disaster recovery switch is triggered, the target sub-run dataset corresponding to the triggering disaster recovery switch is obtained, and the corresponding target monitoring dimension is located based on the target sub-run dataset. At the same time, an early warning report for triggering the disaster recovery switch is generated based on the target sub-run dataset and the target monitoring dimension, and transmitted to the monitoring terminal.

[0153] In this embodiment, the preset matching threshold is set in advance.

[0154] In this embodiment, the monitoring terminal includes, but is not limited to, mobile phones, computers, etc.

[0155] In this embodiment, the preset matching threshold is set in advance and is used as a reference for determining whether to trigger disaster recovery switching.

[0156] In this embodiment, the target sub-run dataset refers to the specific sub-run dataset corresponding to the disaster recovery switchover trigger.

[0157] The beneficial effects of the above technical solution are: by effectively analyzing the sub-running dataset and its corresponding running characteristics, it is possible to accurately determine whether a disaster recovery switch has been triggered. Furthermore, when a disaster recovery switch is triggered, an early warning report can be generated to promptly understand the reason for the triggering of the disaster recovery switch, thereby improving the interpretability of the triggering of the disaster recovery switch.

[0158] Example 8:

[0159] Based on Example 1, this example provides a method for multi-datacenter disaster recovery backup and rapid recovery based on a private cloud. In step 4, when a disaster recovery switch is triggered, business recovery and resource reallocation are performed based on the disaster recovery control platform, including:

[0160] When a disaster recovery switch is triggered, the running services in the abnormal data center are locked, and the basic configuration parameters of the running services in the abnormal data center are retrieved from the disaster recovery control platform based on the locking result.

[0161] The basic configuration parameters are parsed to determine the overall architecture and business execution logic of the running business. At the same time, the business recovery process of the running business is initiated based on the disaster recovery control platform, and the conditions of the business recovery process are limited based on the overall architecture and business execution logic.

[0162] Based on the conditional constraints, the running services are switched to the backup data center according to the business recovery process, and based on the switching results, the business modules in the backup data center are restored and associated according to the business execution logic.

[0163] Based on business recovery and correlation results, the integrity of data in the backup data center is verified, and the business attributes of each business model are extracted after the verification is passed.

[0164] Based on business attributes, the business priority of each business module is determined, and based on the determined business priority, the resources required by each business module are reallocated in the backup data center.

[0165] In this embodiment, the abnormal data center refers to at least one data center among multiple data centers that triggers the disaster recovery switch.

[0166] In this embodiment, the basic configuration parameters refer to the specific operating standards corresponding to the services running in the abnormal data center when performing services.

[0167] In this embodiment, the condition constraint refers to limiting the execution steps and execution standards in the business recovery process based on the overall architecture and business execution logic of the running business.

[0168] In this embodiment, business attributes refer to information such as the business category executed by the business model and the conditions that need to be met during execution.

[0169] The beneficial effects of the above technical solution are as follows: By locking the running services in the abnormal data center when disaster recovery switching is triggered, and determining the basic configuration parameters of the running services in the abnormal data center, the overall architecture and business execution logic of the running services can be determined based on the basic configuration parameters. Secondly, by initiating the business recovery process, and as a result of the overall architecture and business execution logic, the business modules are restored and associated in the backup data center, which facilitates the restoration of normal operation of the running services. Finally, the business priority of each business module is determined, and the resources required by each business module are reallocated according to the priority, ensuring the speed and effectiveness of multi-data center recovery.

[0170] Example 9:

[0171] This embodiment provides a multi-datacenter disaster recovery and rapid recovery system based on a private cloud, such as... Figure 3 As shown, it includes:

[0172] The data access and management module is used to connect various data centers to the disaster recovery control platform and to manage their deployment.

[0173] The backup strategy orchestration module is used to orchestrate backup strategies according to the disaster recovery control platform.

[0174] The disaster recovery switchover judgment module is used to monitor the health status of each data center based on the orchestration and deployment results and the disaster recovery control platform, and to analyze the monitoring data based on the AI ​​model to determine whether to trigger a disaster recovery switchover.

[0175] The recovery management module is used to perform business recovery and resource reallocation based on the disaster recovery control platform when a disaster recovery switch is triggered.

[0176] The working principle and beneficial effects of the above technical solution are as follows: By connecting and deploying each data center to the disaster recovery control platform, the automatic reporting of resource topology, backup capabilities, and network connectivity can be effectively achieved and improved; the disaster recovery control platform orchestrates backup strategies, enabling the definition of backup strategies based on service levels, data classifications, etc., thereby achieving tasks such as generating scheduled snapshots and continuous incremental synchronization on demand; the disaster recovery control platform can effectively monitor the health status of each data center (such as system status, network connectivity, etc.), and effectively analyze the detection data based on AI models to accurately determine whether a disaster recovery switchover has been triggered. When a disaster recovery switchover is triggered, business recovery and resource reallocation are performed based on the disaster recovery control platform. That is, the disaster recovery control platform quickly switches applications and data resources to the backup center, rebuilds the environment through automatic deployment templates, and notifies the application access layer to change routes; through automated fault detection and scheduling, fault response time is effectively improved; and through a unified disaster recovery control platform, multi-center backup and recovery configuration can be effectively realized, thereby reducing management costs.

[0177] Example 10:

[0178] Based on Example 9, this example provides a multi-datacenter disaster recovery and rapid recovery system based on a private cloud, including a data access and management module, comprising:

[0179] The data acquisition unit is used for:

[0180] Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform;

[0181] Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data;

[0182] The data access unit is used to establish corresponding data messenger factors in each data center based on the corresponding third communication data, load and configure the second communication data in the data messenger factors, and connect the corresponding data center to the disaster recovery control platform based on the loading and configuration results and the data messenger factors.

[0183] The deployment management unit is used to obtain the reported information from each data center after the access is completed, and to deploy and manage the reported information from each data center in the disaster recovery control platform.

[0184] The beneficial effects of the above technical solution are as follows: by determining the first communication data of each data center and the second communication data of the disaster recovery control platform, and by combining the data access standard of the disaster recovery control platform to convert the format of the first communication data, the data center and the disaster recovery control platform can be connected according to the conversion result, ensuring the convenience and reliability of the disaster recovery control platform in managing each data center. Finally, the reported information of each data center is deployed and managed in the disaster recovery control platform, providing convenience and guarantee for multi-data center disaster recovery backup and rapid recovery.

[0185] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for disaster recovery and rapid recovery of multiple data centers based on private cloud, characterized in that, include: Step 1: Connect each data center to the disaster recovery control platform and deploy and manage it; Step 2: Arrange the backup strategy according to the disaster recovery control platform; Step 3: Based on the orchestration and deployment results, monitor the health status of each data center using the disaster recovery control platform, and analyze the monitoring data using an AI model to determine whether a disaster recovery switchover should be triggered. Step 4: When a disaster recovery switchover is triggered, business recovery and resource reallocation are performed based on the disaster recovery control platform.

2. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 1, characterized in that, Step 1 involves connecting each data center to the disaster recovery control platform and deploying and managing it, including: Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform; Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data; In each data center, a corresponding data messenger factor is established based on the corresponding third communication data, and the second communication data is loaded and configured in the data messenger factor. Based on the loading and configuration results, the corresponding data center is connected to the disaster recovery control platform according to the data messenger factor. Once the connection is established, the reported information from each data center is obtained, and the reported information from each data center is deployed and managed in the disaster recovery control platform.

3. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 2, characterized in that, Obtain the reported information from each data center and deploy and manage the reported information from each data center in the disaster recovery control platform, including: Obtain the central identifier of each data center, and build the management module of each data center in the disaster recovery control platform based on the central identifier of each data center; Obtain the content type of the reported information and add management items in the management module according to the content type. At the same time, divide the reported information according to the content type and obtain the sub-information content corresponding to each content type. Based on the preset information management database, information management file templates of various content types are retrieved, and sub-information content is matched and mapped with the corresponding information management file templates to obtain the target information management file; Analyze the target information management files corresponding to each sub-information content to determine the information status of each data center; The target information management files and the information status of the data centers are stored in the corresponding management modules to complete the deployment management of the reported information content of each data center.

4. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 2, characterized in that, The information to be reported includes: resource topology, data center backup capabilities, and network connectivity.

5. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 1, characterized in that, In step 2, the backup strategy is orchestrated according to the disaster recovery control platform, including: Based on the disaster recovery control platform, the business services of each data center are read, and the first importance level of each business service is obtained. At the same time, the business services are sorted according to the first importance level to obtain the business service sequence. Read the business data corresponding to each business service, evaluate the second importance level of the business data according to the preset evaluation indicators, and sort the business data according to the second importance level to obtain the business data sequence in each business service. Based on preset management standards, the backup strategy for the data to be backed up is determined according to the business service sequence and business data sequence. At the same time, the available backup methods are obtained from the disaster recovery control platform, and the backup business attributes of the available backup methods are determined. Based on the backup policy, the backup service attributes of the available backup methods are parsed to determine the combination of available backup methods under the backup policy and the configuration parameters of the available backup methods under each combination. The backup strategy is arranged according to the combination of available backup methods under the backup strategy and the configuration parameters of the available backup methods under each combination.

6. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 1, characterized in that, In step 3, based on the orchestration and deployment results, the health status of each data center is monitored using the disaster recovery control platform. The monitoring data is then analyzed using an AI model to determine whether a disaster recovery switchover has been triggered, including: Obtain the monitoring dimensions for monitoring the health status of each data center; The disaster recovery control platform collects operational data from each data center in real time and divides the operational data of each data center according to the monitoring dimensions to obtain the sub-operational dataset corresponding to each monitoring dimension. Retrieve historical disaster recovery switchover events, and retrieve the sub-historical running dataset corresponding to each monitoring dimension from the historical disaster recovery switchover events; Learn from each sub-historical operational dataset to determine the first operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is triggered and the second operational feature of the corresponding sub-historical operational dataset when the historical disaster recovery switch is not triggered. Analyze and obtain the trend change characteristics when the second operating characteristic transforms into the first operating characteristic; An AI model is constructed based on the first operational characteristic, the second operational characteristic, and the trend change characteristic. The sub-operational dataset corresponding to each monitoring dimension is input into the AI ​​model for analysis, and the analysis results are used to determine whether to trigger a disaster recovery switch.

7. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 6, characterized in that, Based on the analysis results, determine whether to trigger a disaster recovery switch, including: If the sub-run dataset meets the first running characteristic, then a disaster recovery switch is triggered. When the sub-run dataset meets the second run feature, the real-time status is matched with the trend change feature to determine the degree of matching between the real-time status and the trend change feature. When the matching degree between the sub-run dataset and the trend change characteristics reaches the preset matching degree threshold, it is determined that a disaster recovery switch will be triggered. When the matching degree between the sub-run dataset and the trend change characteristics is less than the preset matching degree threshold, it is determined that the disaster recovery switch will not be triggered. When a disaster recovery switch is triggered, the target sub-run dataset corresponding to the triggering disaster recovery switch is obtained, and the corresponding target monitoring dimension is located based on the target sub-run dataset. At the same time, an early warning report for triggering the disaster recovery switch is generated based on the target sub-run dataset and the target monitoring dimension, and transmitted to the monitoring terminal.

8. The method for disaster recovery and rapid recovery of multiple data centers based on private cloud according to claim 1, characterized in that, In step 4, when a disaster recovery switchover is triggered, service recovery and resource reallocation are performed based on the disaster recovery control platform, including: When a disaster recovery switch is triggered, the running services in the abnormal data center are locked, and the basic configuration parameters of the running services in the abnormal data center are retrieved from the disaster recovery control platform based on the locking result. The basic configuration parameters are parsed to determine the overall architecture and business execution logic of the running business. At the same time, the business recovery process of the running business is initiated based on the disaster recovery control platform, and the conditions of the business recovery process are limited based on the overall architecture and business execution logic. Based on the conditional constraints, the running services are switched to the backup data center according to the business recovery process, and based on the switching results, the business modules in the backup data center are restored and associated according to the business execution logic. Based on business recovery and correlation results, the integrity of data in the backup data center is verified, and the business attributes of each business model are extracted after the verification is passed. Based on business attributes, the business priority of each business module is determined, and based on the determined business priority, the resources required by each business module are reallocated in the backup data center.

9. A multi-datacenter disaster recovery and rapid recovery system based on a private cloud, characterized in that, include: The data access and management module is used to connect various data centers to the disaster recovery control platform and to manage their deployment. The backup strategy orchestration module is used to orchestrate backup strategies according to the disaster recovery control platform. The disaster recovery switchover judgment module is used to monitor the health status of each data center based on the orchestration and deployment results and the disaster recovery control platform, and to analyze the monitoring data based on the AI ​​model to determine whether to trigger a disaster recovery switchover. The recovery management module is used to perform business recovery and resource reallocation based on the disaster recovery control platform when a disaster recovery switch is triggered.

10. A multi-datacenter disaster recovery and rapid recovery system based on a private cloud as described in claim 9, characterized in that, The data access and management module includes: The data acquisition unit is used for: Read the first communication data from each data center and obtain the second communication data from the disaster recovery control platform; Obtain the data access standard of the disaster recovery control platform, and convert the format of the first communication data according to the data access standard to obtain the third communication data; The data access unit is used to establish corresponding data messenger factors in each data center based on the corresponding third communication data, load and configure the second communication data in the data messenger factors, and connect the corresponding data center to the disaster recovery control platform based on the loading and configuration results and the data messenger factors. The deployment management unit is used to obtain the reported information from each data center after the access is completed, and to deploy and manage the reported information from each data center in the disaster recovery control platform.

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