Intelligent disaster recovery adaptation optimization system based on electric power big data construction

Abstract

The invention relates to the technical field of data disaster recovery backup, and particularly discloses an intelligent disaster recovery adaptation optimization system based on electric power big data construction, and the method comprises the steps: constructing calculation, storage and network resource pools of applications in a disaster recovery data center for application disaster recovery deployment, and dividing the applications into A-level applications, B-level applications and C-level applications; constructing the same-city and different-place drilling ability of the resource pool, and designing the overall architecture of the disaster recovery management platform; all systems, networks and storage components between the disaster recovery center and the production center are comprehensively tested and verified by simulating a real environment, and the switching capability is evaluated by verifying system compatibility and checking data consistency; by making a drilling plan, simulating a disaster scene, executing switching operation and verifying service continuity, the drilling process of the disaster recovery management platform is overall evaluated, the existing problems and defects are analyzed, and improvement measures are put forward.

Description

Technical Field

[0001] The present invention relates to the technical field of data disaster recovery and backup, and in particular to an intelligent disaster recovery adaptation and optimization system based on power big data construction. Background Art

[0002] Disaster recovery backup system refers to the establishment of two or more sets of IT systems with the same functions in remote locations. The health status can be monitored and functions can be switched between each other. When a system stops working due to an accident (such as fire, earthquake, etc.), the entire application system can be switched to another one so that the system functions can continue to work normally. Disaster recovery technology is a component of the system's high availability technology. The disaster recovery system emphasizes the impact of the external environment on the system, especially the impact of catastrophic events on the entire IT node, and provides node-level system recovery functions.

[0003] The existing technologies all adopt disaster recovery at the database and business system level, and no remote disaster recovery for specific applications is designed. In the remote disaster recovery system, to achieve complete application disaster recovery, it is necessary to include the security mechanism of the local system and the remote data replication mechanism, and also to have the remote fault switching capability and fault diagnosis capability within the wide area network. In other words, once a fault occurs, the system must have a powerful fault diagnosis and switching strategy formulation mechanism to ensure rapid response and rapid business takeover. In fact, the high availability capability of the wide area network and the high availability capability of the local system should form a whole to realize a multi-level fault switching and recovery mechanism to ensure the reliability and security of the system in all ranges. However, there is no reliable intelligent disaster recovery adaptation and optimization system for system application failures in the existing technology.

[0004] Therefore, how to provide an intelligent disaster recovery adaptation and optimization system based on power big data construction is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] To achieve the purpose of the present invention, the present application provides an intelligent disaster recovery adaptation optimization method based on power big data construction, including:

[0006] To achieve the same invention purpose, the present application also provides an intelligent disaster recovery adaptation optimization system based on power big data construction, including:

[0007] Disaster recovery resource pool construction module: used to build computing, storage, and network resource pools for applications in the disaster recovery data center for application disaster recovery deployment, and to divide the applications into Class A applications, Class B applications, and Class C applications;

[0008] Disaster recovery platform construction module: used to build the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform;

[0009] Data simulation evaluation module: used to comprehensively test and verify various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and to evaluate switching capabilities by verifying system compatibility and checking data consistency;

[0010] Disaster recovery platform improvement module: By formulating exercise plans, simulating disaster scenarios, executing switching operations, and verifying business continuity, the disaster recovery management platform exercise process is comprehensively evaluated, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

[0011] In some specific embodiments, the A-level applications, B-level applications and C-level applications are determined by the importance of the applications, the priority of A-level applications is higher than that of B-level applications, and the priority of B-level applications is higher than that of C-level applications.

[0012] In some specific embodiments, the overall architecture includes: a resource layer, an operation and maintenance layer, an execution layer, a management layer and a presentation layer.

[0013] In some specific embodiments, it also includes: building a monitoring system to monitor the operating status, performance indicators, and security status of the monitoring system of the disaster recovery equipment in real time; and setting reasonable alarm thresholds and alarm strategies to ensure that alarms can be issued in time and relevant personnel can be notified when abnormal situations occur.

[0014] To achieve the same invention purpose, the present application also provides an intelligent disaster recovery adaptation optimization device based on power big data construction, including:

[0015] Memory for storing computer programs;

[0016] The processor is used to call and execute the computer program to implement the steps of the intelligent disaster recovery adaptation optimization method based on power big data construction as described above.

[0017] To achieve the same invention purpose, the present application also provides a storage medium, including a software program, which is suitable for a processor to execute the steps of the intelligent disaster recovery adaptation optimization method based on power big data construction as described above.

[0018] Beneficial effects of the above technical solution:

[0019] This application provides an intelligent disaster recovery adaptation optimization system based on power big data construction, which continuously optimizes and improves the cloud platform operation and maintenance system based on the problems found in the operation and maintenance process and the feedback from users. This includes improvements in resource optimization, performance improvement, security reinforcement, etc. It can ensure the stable, efficient and safe operation of the cloud platform through scientific planning and effective implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flowchart of an intelligent disaster recovery adaptation optimization method based on power big data construction provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the structure of an intelligent disaster recovery adaptation optimization system based on power big data construction provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of the structure of an intelligent disaster recovery adaptation and optimization device based on power big data construction is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0026] Embodiment 1

[0027] An embodiment of the present invention provides an intelligent disaster recovery adaptation optimization method based on power big data construction, referring to Figure 1 As shown, including:

[0028] Step S1: construct computing, storage, and network resource pools for applications in a disaster recovery data center for application disaster recovery deployment, and divide the applications into Class A applications, Class B applications, and Class C applications.

[0029] In a specific embodiment of the present invention, the A-level application, B-level application and C-level application are determined by the importance of the application, and the priority of the A-level application is higher than that of the B-level application, and the priority of the B-level application is higher than that of the C-level application. Examples of setting levels for specific levels of applications are shown in the following table:

[0030]

[0031] Specifically, in two data centers, a cloud platform active-standby disaster recovery scenario is built to facilitate cloud host failover when the entire site fails. When data is replicated synchronously (RPO=0), the recommended distance between data centers is <100 km, and the network latency RTT is <2ms (<1ms when database performance requirements are high); when asynchronous storage replication is used (RPO>=10 minutes), the recommended distance is <3000 km, and the network latency RTT is <100ms.

[0032] This application is applicable to old business systems that cannot be separated into WEB+APP+DB, all deployed with cloud servers (ECS). The business system itself has no disaster recovery capabilities, and the cloud platform needs to provide disaster recovery protection for cloud servers. It can cope with equipment failures, data center failures, regional disasters, and planned downtime, such as planned power outages, daily operations, etc. Synchronous replication RPO = 0, asynchronous replication RPO is above the minute level, and RTO is at the hour level.

[0033] Step S2: Build the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform.

[0034] In a specific embodiment of the present invention, the overall architecture includes: a resource layer, an operation and maintenance layer, an execution layer, a management layer and a presentation layer.

[0035] Step S3: Comprehensively test and verify the various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and evaluate the switching capability by verifying system compatibility and checking data consistency.

[0036] In a specific embodiment of the present invention, the disaster recovery center and the production center utilize the remote data connection function of the storage devices separated in physical locations to remotely maintain a set of data mirrors. Once a disaster occurs, the data backups distributed on the off-site storage can still provide services, which are divided into synchronous remote replication and asynchronous remote replication.

[0037] Specifically, the disaster recovery management platform is used for disaster recovery switching drills in the same city and across regions for applications at all levels. Through the steps of formulating a drill plan, simulating disaster scenarios, executing switching operations, and verifying business continuity, the disaster recovery management platform disaster recovery drill process is evaluated overall, and existing problems and deficiencies are analyzed and improvement measures are proposed. By verifying system compatibility and checking data consistency to evaluate switching capabilities, it can be ensured that the disaster recovery management platform can support the scheduling, execution, and monitoring of disaster recovery tasks. It ensures that the disaster recovery of the two locations and three centers has a lower switching delay, ensures better business continuity, and improves emergency response capabilities in disaster situations.

[0038] Step S4: By formulating a drill plan, simulating disaster scenarios, executing switching operations, and verifying business continuity, the overall evaluation of the disaster recovery management platform drill process is conducted, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

[0039] In a specific embodiment of the present invention, it also includes: building a monitoring system to monitor the operating status, performance indicators, and security status of the monitoring system of the disaster recovery equipment in real time; and setting reasonable alarm thresholds and alarm strategies to ensure that alarms can be issued in time and relevant personnel can be notified when abnormal situations occur.

[0040] This application provides an intelligent disaster recovery adaptation optimization system based on power big data construction, which continuously optimizes and improves the cloud platform operation and maintenance system based on the problems found in the operation and maintenance process and the feedback from users. This includes improvements in resource optimization, performance improvement, security reinforcement, etc. It can ensure the stable, efficient and safe operation of the cloud platform through scientific planning and effective implementation.

[0041] Embodiment 2

[0042] An embodiment of the present invention provides an intelligent disaster recovery adaptation optimization system based on power big data construction, referring to Figure 2 As shown, including:

[0043] Disaster recovery resource pool construction module 10: used to build computing, storage, and network resource pools for applications in the disaster recovery data center for application disaster recovery deployment, and to divide the applications into Class A applications, Class B applications, and Class C applications;

[0044] Disaster recovery platform construction module 20: used to build the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform;

[0045] Data simulation evaluation module 30: used to comprehensively test and verify various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and to evaluate the switching capability by verifying system compatibility and checking data consistency;

[0046] Disaster Recovery Platform Improvement Module 40: By formulating drill plans, simulating disaster scenarios, executing switching operations, and verifying business continuity, the overall evaluation of the disaster recovery management platform drill process is conducted, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

[0047] In a specific embodiment of the present invention, the A-level application, B-level application and C-level application are determined by the importance of the application, the priority of the A-level application is higher than that of the B-level application, and the priority of the B-level application is higher than that of the C-level application.

[0048] In a specific embodiment of the present invention, the overall architecture includes: a resource layer, an operation and maintenance layer, an execution layer, a management layer and a presentation layer.

[0049] In a specific embodiment of the present invention, it also includes: building a monitoring system to monitor the operating status, performance indicators, and security status of the monitoring system of the disaster recovery equipment in real time; and setting reasonable alarm thresholds and alarm strategies to ensure that alarms can be issued in time and relevant personnel can be notified when abnormal situations occur.

[0050] In a specific embodiment of the present invention, the disaster recovery center and the production center utilize the remote data connection function of the storage devices separated in physical locations to remotely maintain a set of data mirrors. Once a disaster occurs, the data backups distributed on the off-site storage can still provide services, which are divided into synchronous remote replication and asynchronous remote replication.

[0051] Specifically, the disaster recovery management platform is used for disaster recovery switching drills in the same city and across regions for applications at all levels. Through the steps of formulating a drill plan, simulating disaster scenarios, executing switching operations, and verifying business continuity, the disaster recovery management platform disaster recovery drill process is evaluated overall, and existing problems and deficiencies are analyzed and improvement measures are proposed. By verifying system compatibility and checking data consistency to evaluate switching capabilities, it can be ensured that the disaster recovery management platform can support the scheduling, execution, and monitoring of disaster recovery tasks. It ensures that the disaster recovery of the two locations and three centers has a lower switching delay, ensures better business continuity, and improves emergency response capabilities in disaster situations.

[0052] This application provides an intelligent disaster recovery adaptation optimization system based on power big data construction, which continuously optimizes and improves the cloud platform operation and maintenance system based on the problems found in the operation and maintenance process and the feedback from users. This includes improvements in resource optimization, performance improvement, security reinforcement, etc. It can ensure the stable, efficient and safe operation of the cloud platform through scientific planning and effective implementation.

[0053] Embodiment 3

[0054] Corresponding to the method embodiment, in the embodiment of the present invention, there is also provided an intelligent disaster recovery adaptation optimization device based on power big data construction, such as a terminal, a server, etc. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this.

[0055] An example of a hardware structure block diagram of an intelligent disaster recovery adaptation optimization device based on power big data construction provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, it may include:

[0056] Processor 1, communication interface 2, memory 3 and communication bus 4;

[0057] The processor 1, the communication interface 2, and the memory 3 communicate with each other via the communication bus 4;

[0058] Optionally, the communication interface 2 may be an interface of a communication module, such as an interface of a GSM module;

[0059] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0060] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0061] The processor 1 is specifically used to execute the computer program stored in the memory 3 to perform the following steps:

[0062] Step S1: constructing computing, storage, and network resource pools for applications in a disaster recovery data center for application disaster recovery deployment, and dividing the applications into Class A applications, Class B applications, and Class C applications;

[0063] Step S2: construct the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform;

[0064] Step S3: Comprehensively test and verify the various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and evaluate the switching capability by verifying system compatibility and checking data consistency;

[0065] Step S4: By formulating a drill plan, simulating disaster scenarios, executing switching operations, and verifying business continuity, the overall evaluation of the disaster recovery management platform drill process is conducted, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

[0066] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the intelligent disaster recovery adaptation optimization method based on power big data construction provided by the embodiment of the present invention.

[0067] Embodiment 4

[0068] In an embodiment of the present invention, a storage medium is further provided, which may store a program suitable for execution by a processor, wherein the program is used to:

[0069] Step S1: constructing computing, storage, and network resource pools for applications in a disaster recovery data center for application disaster recovery deployment, and dividing the applications into Class A applications, Class B applications, and Class C applications;

[0070] Step S2: construct the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform;

[0071] Step S3: Comprehensively test and verify the various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and evaluate the switching capability by verifying system compatibility and checking data consistency;

[0072] Step S4: By formulating a drill plan, simulating disaster scenarios, executing switching operations, and verifying business continuity, the overall evaluation of the disaster recovery management platform drill process is conducted, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

[0073] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0074] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the methods provided by other embodiments of the present invention.

[0075] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0076] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referenced to each other. The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention. Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0077] The method and device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

[0078] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "one specific embodiment" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent disaster recovery adaptation optimization system based on power big data construction, characterized in that: include: Disaster recovery resource pool construction module: used to build computing, storage, and network resource pools for applications in the disaster recovery data center for application disaster recovery deployment, and to divide the applications into Class A applications, Class B applications, and Class C applications; Disaster recovery platform construction module: used to build the same-city and remote-location drill capabilities of the resource pool and design the overall architecture of the disaster recovery management platform; Data simulation evaluation module: used to comprehensively test and verify various systems, networks, and storage components between the disaster recovery center and the production center by simulating a real environment, and to evaluate switching capabilities by verifying system compatibility and checking data consistency; Disaster recovery platform improvement module: By formulating exercise plans, simulating disaster scenarios, executing switching operations, and verifying business continuity, the overall evaluation of the disaster recovery management platform exercise process is carried out, and its existing problems and deficiencies are analyzed and improvement measures are proposed.

2. According to claim 1, the intelligent disaster recovery adaptation optimization system based on power big data construction is characterized in that: The A-level application, B-level application and C-level application are determined by the importance of the application, the priority of the A-level application is higher than that of the B-level application, and the priority of the B-level application is higher than that of the C-level application.

3. According to the intelligent disaster recovery adaptation optimization system based on power big data construction according to claim 1, it is characterized in that: The overall architecture includes: resource layer, operation and maintenance layer, execution layer, management layer and presentation layer.

4. According to claim 1, the intelligent disaster recovery adaptation optimization system based on power big data construction is characterized in that: Also includes: Build a monitoring system to monitor the operating status, performance indicators, and security status of the monitoring system of the disaster recovery equipment in real time; And set reasonable alarm thresholds and alarm strategies to ensure that alarms can be issued in time and relevant personnel can be notified when abnormal situations occur.

5. An intelligent disaster recovery adaptation and optimization device based on power big data construction, characterized in that: include: Memory for storing computer programs; A processor is used to call and execute the computer program to implement the program of the intelligent disaster recovery adaptation optimization system based on power big data construction as described in any one of claims 1 to 4.

6. A storage medium, characterized in that: It includes a software program, which is suitable for being executed by a processor as a program of an intelligent disaster recovery adaptation optimization system based on power big data construction as described in any one of claims 1-4.

Images