Disaster recovery method and device of system, electronic equipment and readable storage medium

Abstract

The invention provides a disaster recovery method and device of a system, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as cloud services, big data and big language models. The disaster recovery method of the system comprises the following steps: acquiring operation data of the system, and extracting target features from the operation data; inputting the target feature into a large language model to obtain a fault type output by the large language model; when it is determined that the fault type is a preset fault type, obtaining a plurality of target disaster recovery schemes corresponding to the fault type; and sequentially triggering the plurality of target disaster recovery schemes according to a sequence of disaster recovery levels from low to high until it is determined that the fault of the system is repaired. According to the method and the device, the timeliness of fault detection can be enhanced, the accuracy of the obtained fault type is improved, the disaster tolerance of the system can be enhanced, the fault repair efficiency is improved, and the service recovery time is shortened.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of artificial intelligence technology such as cloud services, big data, and large language models. A systematic disaster recovery method, device, electronic device, and readable storage medium are provided. Background Art

[0002] In the prior art, when solving the disaster recovery problem of a system (such as a system providing transaction services, order management services, etc.), a multi-instance deployment solution is usually adopted, that is, multiple instances are deployed in the same data center, so that when a certain instance fails, the fault can be repaired by switching the instance. However, when some core components of the system (such as a database) fail, it is still impossible to start the system to restore the service by only using a single disaster recovery solution of deploying a certain number of instances. Summary of the invention

[0003] According to a first aspect of the present disclosure, a disaster recovery method for a system is provided, comprising: obtaining operation data of the system, extracting target features from the operation data; inputting the target features into a large language model to obtain a fault type output by the large language model; in the case where it is determined that the fault type is a preset fault type, obtaining multiple target disaster recovery plans corresponding to the fault type; and triggering the multiple target disaster recovery plans in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired.

[0004] According to a second aspect of the present disclosure, a disaster recovery device for a system is provided, comprising: an extraction unit, configured to obtain operation data of the system and extract target features from the operation data; a processing unit, configured to input the target features into a large language model to obtain a fault type output by the large language model; an acquisition unit, configured to obtain, when determining that the fault type is a preset fault type, a plurality of target disaster recovery schemes corresponding to the fault type; and a repair unit, configured to trigger the plurality of target disaster recovery schemes in sequence according to the order of disaster recovery levels from low to high until determining that the fault of the system has been repaired.

[0005] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0006] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method as described above.

[0007] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described above when executed by a processor.

[0008] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0010] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0011] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0012] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0013] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0014] Figure 5 The block diagram is a electronic device used to implement the disaster recovery method of the system according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and mechanisms is omitted in the following description.

[0016] Figure 1 Schematic diagram of the first embodiment of the present disclosure. Figure 1 As shown, the disaster recovery method of the system in this embodiment specifically includes the following steps:

[0017] S101, obtaining operation data of the system, and extracting target features from the operation data;

[0018] S102, inputting the target feature into a large language model to obtain a fault type output by the large language model;

[0019] S103: when it is determined that the fault type is a preset fault type, obtaining multiple target disaster recovery solutions corresponding to the fault type;

[0020] S104: triggering the multiple target disaster recovery solutions in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired.

[0021] The disaster recovery method of the system of the present embodiment, on the one hand, uses a large language model to perform system fault detection based on target features extracted from operating data, which can enhance the timeliness of fault detection and improve the accuracy of the obtained fault type; on the other hand, multiple target disaster recovery schemes corresponding to the fault type are set to repair the fault, thereby avoiding the problem that the prior art relies on only a single disaster recovery scheme and may not be able to repair the fault, and can enhance the disaster recovery capability of the system. In addition, when the present embodiment detects that a system fault has occurred, it will automatically trigger the corresponding multi-level disaster recovery scheme to repair the fault, which can improve the efficiency of fault repair and reduce the service recovery time.

[0022] The system in this embodiment can be a transaction system, payment system and risk control system corresponding to the financial service platform, or an order management system, payment system and inventory management system corresponding to the e-commerce platform, or a user management system, messaging system and content recommendation system corresponding to the social media platform.

[0023] The system in this embodiment is used to provide corresponding services, such as transaction services, order management services, payment services, etc.; the system is composed of a service cluster (including multiple service instances) component, a database component, a cache component, etc.

[0024] The operating data obtained by executing S101 in this embodiment includes service logs of various components of the system, machine performance indicators (such as CPU usage, memory usage, network traffic, etc.), service performance indicators (such as response time), error information, etc.

[0025] After executing S101 to obtain the system's operating data, this embodiment can extract target features from the obtained operating data; the target features extracted by executing S101 in this embodiment include abnormal response time, sudden increase in resource usage, frequent error logs, etc., which are used for large language models to perform system fault detection.

[0026] After executing S101 to extract the target features, this embodiment executes S102 to input the extracted target features into the large language model to obtain the fault type output by the large language model.

[0027] The Large Language Model (LLM) in this embodiment, after being pre-trained, has the ability to perform fault detection based on input features.

[0028] When pre-training the large language model, this embodiment can first obtain the characteristics of the system when it is operating normally and the characteristics of the system when a fault occurs, and then use the obtained characteristics and the system status (normal or faulty) to train the large language model, so that the trained large language model can be used for system fault detection.

[0029] In addition, when training the large language model, this embodiment can further obtain characteristics when the system is at risk of failure, and use the obtained characteristics and the system status (there is a risk of failure) to train the large language model, so that the trained large language model can be used for system failure warning.

[0030] The fault types obtained by executing S102 in this embodiment include database fault, cache fault, network fault, instance fault, etc. For database fault, this embodiment can further divide it into database operation fault, database data missing fault, etc.

[0031] It can be understood that when the present embodiment uses a large language model to perform system fault detection, the large language model can further output the fault level in addition to the fault type. For example, the output fault type is a database fault, and the fault level is level 1; wherein, the larger the fault level value, the more serious the database fault. For example, the fault level of a database operation fault is level 1, and the fault level of a data missing fault in the database is level 2.

[0032] After executing S102 to obtain the fault type output by the large language model, this embodiment executes S103 to obtain multiple target disaster recovery solutions corresponding to the fault type when it is determined that the fault type is a preset fault type.

[0033] In this embodiment, the preset fault type is a part of all fault types. For example, fault types such as database fault, cache fault, and network fault are used as preset fault types, while other fault types such as instance fault are not used as preset fault types.

[0034] That is to say, this embodiment can take the failures of core components (such as database components and cache components) in the system that provides services as preset failure types, and then set up multiple disaster recovery plans corresponding to the preset failure types, so that the core components in the system can be restored through multiple disaster recovery plans after failures occur, thereby avoiding the problem of the service being unable to start for a long time.

[0035] In this embodiment, when executing S103 to obtain multiple target disaster recovery solutions corresponding to the fault type, the disaster recovery solution corresponding to the fault type may be directly used as the target disaster recovery solution according to the preset correspondence between the fault type and the disaster recovery solution.

[0036] For example, the disaster recovery plans corresponding to database failures include memory disaster recovery plan (level one disaster recovery plan), data snapshot disaster recovery plan (level two disaster recovery plan), full data disaster recovery plan (level three disaster recovery plan), and multi-data center disaster recovery plan (level four disaster recovery plan).

[0037] In this embodiment, the memory disaster recovery solution is to obtain the key data of the database (the key data is the basic data required for the normal operation of the database) through local memory or a distributed cache system (such as Redis); wherein the local memory or distributed cache system stores the key data of the database.

[0038] In this embodiment, the data snapshot disaster recovery solution is to obtain the key data of the database through the locally stored data snapshot; wherein, the locally stored data snapshot stores the key data of the database; it can be understood that the locally stored data snapshot needs to be compressed, encrypted, signature verified and other operations before being stored locally to prevent the data snapshot from being tampered with.

[0039] In this embodiment, the full data disaster recovery solution is to obtain all the data of the database through a full database backup; wherein the full database backup stores all the data of the database.

[0040] In this embodiment, the multi-data center disaster recovery solution is to obtain all the data in the database by switching data centers; wherein, this embodiment can deploy different data centers in different geographical locations, and different data centers include the same service instances and database data (such as database, memory, data snapshots, database backup, etc.), thereby preventing service interruptions caused by failure of a single data center.

[0041] In addition, in this embodiment, data synchronization can be performed between different data centers through high-speed dedicated lines, thereby ensuring data consistency and real-time performance of each data center.

[0042] For example, the disaster recovery plans corresponding to cache failures include a backup cache disaster recovery plan (first-level disaster recovery plan) and a database read disaster recovery plan (second-level disaster recovery plan); among them, the backup cache disaster recovery plan is to switch to the backup cache instance to obtain data; the database read disaster recovery plan is to obtain data directly from the database.

[0043] For example, the disaster recovery plans corresponding to network failures include a backup line disaster recovery plan (first-level disaster recovery plan) and a multi-data center disaster recovery plan (second-level disaster recovery plan); among them, the backup line disaster recovery plan is to switch to a backup network line, and the multi-data center disaster recovery plan is to switch to other data centers.

[0044] When executing S103 to obtain multiple target disaster recovery plans corresponding to the fault type, this embodiment can also adopt the following methods: obtain the fault level. This embodiment can obtain the fault level according to the output result of the large language model; obtain multiple initial disaster recovery plans according to the fault type, for example, use the disaster recovery plan corresponding to the fault type in the preset corresponding relationship as the initial disaster recovery plan; select the disaster recovery plan corresponding to the obtained fault level from the multiple initial disaster recovery plans as multiple target disaster recovery plans.

[0045] For example, if the fault type is a database fault, if the initial disaster recovery plan obtained is a first-level disaster recovery plan, a second-level disaster recovery plan, a third-level disaster recovery plan, and a fourth-level disaster recovery plan, if the fault level obtained is level 1, if the disaster recovery plan corresponding to level 1 is a first-level disaster recovery plan and a second-level disaster recovery plan, when executing S103, this embodiment obtains the first-level disaster recovery plan and the second-level disaster recovery plan as the target disaster recovery plan; if the fault level obtained is level 2, if the disaster recovery plan corresponding to level 2 is a third-level disaster recovery plan and a fourth-level disaster recovery plan, when executing S103, this embodiment obtains the third-level disaster recovery plan and the fourth-level disaster recovery plan as the target disaster recovery plan.

[0046] That is to say, this embodiment also acquires the target disaster recovery solution in combination with the fault level, so that the acquired target disaster recovery solution corresponds to both the fault type and the fault level, thereby improving the accuracy of the acquired target disaster recovery solution.

[0047] After executing S103 to obtain multiple target disaster recovery plans, this embodiment executes S104 to trigger the multiple target disaster recovery plans in order from low to high disaster recovery levels until it is determined that the system failure has been repaired.

[0048] In this embodiment, different target disaster recovery schemes correspond to different disaster recovery levels. For example, the first-level disaster recovery scheme corresponds to the first-level disaster recovery level, the second-level disaster recovery scheme corresponds to the second-level disaster recovery level, and so on.

[0049] In this embodiment, when executing S104, multiple target disaster recovery plans are triggered in sequence from low to high disaster recovery levels until it is determined that the system fault has been repaired. The implementation method that can be adopted is: according to the disaster recovery level, determine the target disaster recovery plan with the lowest current disaster recovery level; use the determined target disaster recovery plan to repair the system fault; in response to determining that the system fault has not been repaired, switch to executing the step of determining the target disaster recovery plan with the lowest current disaster recovery level according to the disaster recovery level, and repeat this cycle until it is determined that the system fault has been repaired.

[0050] That is to say, this embodiment triggers different target disaster recovery solutions in sequence according to the order of disaster recovery levels to repair system failures, thereby improving the orderliness of fault repair and further ensuring that system failures can be repaired, thereby restoring services as soon as possible.

[0051] For example, if the target disaster recovery plan includes a memory disaster recovery plan and a data snapshot disaster recovery plan, when executing S104, this embodiment will first trigger the memory disaster recovery plan to obtain key data from the local memory or distributed cache system, and then use the obtained key data to restart the database to restore the service; if the key data cannot be obtained, the data snapshot disaster recovery plan will be triggered again to obtain the key data from the locally stored data snapshot, and then use the obtained key data to restart the database to restore the service.

[0052] It is understandable that after completing the system fault repair, this embodiment can further record information such as the cause of the fault, the recovery process, and the recovery time, so as to analyze the recorded information to continuously optimize the selection of disaster recovery plans, the determination of fault types, etc.

[0053] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure. Figure 2 As shown, after executing S102 “inputting the target feature into the large language model”, this embodiment may further include the following contents:

[0054] S201, obtaining a fault prediction result output by the large language model;

[0055] S202: Generate warning information according to the fault prediction result, and send the warning information to an operation and maintenance device.

[0056] That is to say, this embodiment can also issue fault warnings based on the output results of the large language model. Before the fault actually occurs, the operation and maintenance personnel can check and handle it based on the warning information in the operation and maintenance equipment, thereby avoiding system failures that affect services.

[0057] The fault prediction result obtained by executing S201 in this embodiment is used to reflect the existence of fault risk in the system. The fault prediction result may also include information of the component with fault risk, that is, the fault prediction result can be used to determine which component in the system has the fault risk.

[0058] After executing S201 to obtain the fault prediction result, this embodiment may also include the following contents: in response to determining that the component with failure risk is a database according to the fault prediction result, performing a data backup operation, such as obtaining key data from the database and storing it in local memory or generating a data snapshot, or generating a full database backup based on the full data in the database.

[0059] It can be understood that the fault prediction results output by the large language model in this embodiment may also include predicted fault levels, and then when executing S201 in response to determining that the component with a fault risk is a database according to the fault prediction results, a data backup operation is performed according to the predicted fault level.

[0060] For example, if the predicted failure level is level 1, an operation of obtaining key data from the database and storing it in local memory or generating a data snapshot is performed. If the predicted failure level is level 2, an operation of generating a full database backup based on the full data in the database is performed.

[0061] That is to say, this embodiment can periodically store key data in the local memory or distributed cache system, periodically generate data snapshots based on the key data, or periodically generate a full database backup based on all the data. It can also store key data, generate data snapshots, or generate a full database backup after obtaining the fault prediction results, thereby improving the accuracy of data backup.

[0062] In addition, after executing S201 to obtain the fault prediction result, this embodiment can also perform predictive maintenance such as updating the software version and optimizing the system configuration, so as to further prevent the occurrence of faults.

[0063] The warning information generated by executing S202 in this embodiment includes information such as the name of the component that may fail, the type of the failure that may fail, etc.

[0064] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure. Figure 3The disaster recovery flow chart of the system of this embodiment is shown in FIG. 301 ; S301, obtaining the operation data of the system, the operation data obtained includes the service logs, machine performance indicators, service performance indicators, etc. of each component in the system; S302, extracting target features from the obtained operation data, and before extracting the target features, this embodiment can also perform pre-processing operations such as data grouping and data desensitization on the obtained operation data; S303, calling the large language model to process the extracted target features, and obtaining the fault detection results output by the large language model, and the fault detection results are the fault types of the faults that have occurred and the fault prediction results of the fault risks that exist. , where the fault types that have occurred may include database faults, cache faults, network faults, service abnormality faults, service performance degradation faults, etc.; S304, according to the obtained fault detection results, adopt corresponding processing means to handle; S305, if the obtained fault detection result is a fault type, obtain the target disaster recovery plan corresponding to the fault type to perform fault repair; S306, if the obtained fault detection result is a fault prediction result, generate early warning information according to the fault prediction result to notify the corresponding operation and maintenance personnel, and further perform predictive maintenance and other operations on the system according to the fault prediction result.

[0065] In addition, after completing the system fault repair or fault warning, this embodiment can also record the repair process or warning process, such as the type of fault detected, the target disaster recovery plan used during repair, the repair time, the generated warning information, etc., so as to optimize the large language model in combination with the recorded relevant information and improve the fault detection capability of the large language model.

[0066] Figure 4 is a schematic diagram according to the fourth embodiment of the present disclosure. Figure 4 As shown, the disaster recovery device 400 of the system in this embodiment includes:

[0067] The extraction unit 401 is used to obtain the operation data of the system and extract the target features from the operation data;

[0068] A processing unit 402 is used to input the target feature into a large language model to obtain a fault type output by the large language model;

[0069] The acquisition unit 403 is used to acquire multiple target disaster recovery solutions corresponding to the fault type when it is determined that the fault type is a preset fault type;

[0070] The repair unit 404 is used to trigger the multiple target disaster recovery solutions in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired.

[0071] The operation data obtained by the extraction unit 401 includes service logs of various components of the system, machine performance indicators (such as CPU usage, memory usage, network traffic, etc.), service performance indicators (such as response time), error information, etc.

[0072] After the extraction unit 401 obtains the system's operating data, it can extract target features from the obtained operating data; the target features extracted by the extraction unit 401 include abnormal response time, sudden increase in resource usage, frequent error logs, etc., which are used for large language models to detect system faults.

[0073] In this embodiment, after the extraction unit 401 extracts the target feature, the processing unit 402 inputs the extracted target feature into the large language model to obtain the fault type output by the large language model.

[0074] The Large Language Model (LLM) in this embodiment, after being pre-trained, has the ability to perform fault detection based on input features.

[0075] When pre-training the large language model, this embodiment can first obtain the characteristics of the system when it is operating normally and the characteristics of the system when a fault occurs, and then use the obtained characteristics and the system status (normal or faulty) to train the large language model, so that the trained large language model can be used for system fault detection.

[0076] In addition, when training the large language model, this embodiment can further obtain characteristics when the system is at risk of failure, and use the obtained characteristics and the system status (there is a risk of failure) to train the large language model, so that the trained large language model can be used for system failure warning.

[0077] The fault types obtained by the processing unit 402 include database fault, cache fault, network fault, instance fault, etc. For database fault, this embodiment can further divide it into database operation fault, database data missing fault, etc.

[0078] It can be understood that when the present embodiment uses a large language model to perform system fault detection, the large language model can further output the fault level in addition to the fault type. For example, the output fault type is a database fault, and the fault level is level 1; wherein, the larger the fault level value, the more serious the database fault. For example, the fault level of a database operation fault is level 1, and the fault level of a data missing fault in the database is level 2.

[0079] In this embodiment, after the processing unit 402 obtains the fault type output by the large language model, the obtaining unit 403 obtains multiple target disaster recovery solutions corresponding to the fault type when it is determined that the fault type is a preset fault type.

[0080] In this embodiment, the preset fault type is a part of all fault types. For example, fault types such as database fault, cache fault, and network fault are used as preset fault types, while other fault types such as instance fault are not used as preset fault types.

[0081] That is to say, this embodiment can take the failures of core components (such as database components and cache components) in the system that provides services as preset failure types, and then set up multiple disaster recovery plans corresponding to the preset failure types, so that the core components in the system can be restored through multiple disaster recovery plans after failures occur, thereby avoiding the problem of the service being unable to start for a long time.

[0082] When acquiring multiple target disaster recovery solutions corresponding to the fault type, the acquisition unit 403 may directly use the disaster recovery solution corresponding to the fault type as the target disaster recovery solution according to the preset correspondence between the fault type and the disaster recovery solution.

[0083] When acquiring multiple target disaster recovery plans corresponding to the fault type, the acquisition unit 403 can also adopt the following methods: acquiring the fault level; acquiring multiple initial disaster recovery plans based on the fault type; and selecting a disaster recovery plan corresponding to the acquired fault level from the multiple initial disaster recovery plans as multiple target disaster recovery plans.

[0084] That is to say, the acquisition unit 403 also acquires the target disaster recovery solution in combination with the fault level, so that the acquired target disaster recovery solution corresponds to both the fault type and the fault level, thereby improving the accuracy of the acquired target disaster recovery solution.

[0085] In this embodiment, after the acquisition unit 403 acquires multiple target disaster recovery solutions, the repair unit 404 triggers the multiple target disaster recovery solutions in order from low to high disaster recovery levels until it is determined that the system failure has been repaired.

[0086] In this embodiment, different target disaster recovery schemes correspond to different disaster recovery levels. For example, the first-level disaster recovery scheme corresponds to the first-level disaster recovery level, the second-level disaster recovery scheme corresponds to the second-level disaster recovery level, and so on.

[0087] The repair unit 404 triggers multiple target disaster recovery plans in order from low to high disaster recovery levels until it is determined that the system failure has been repaired. The implementation method that can be adopted is: according to the disaster recovery level, determine the target disaster recovery plan with the lowest current disaster recovery level; use the determined target disaster recovery plan to repair the system failure; in response to determining that the system failure has not been repaired, go to the step of determining the target disaster recovery plan with the lowest current disaster recovery level according to the disaster recovery level, and repeat this cycle until it is determined that the system failure has been repaired.

[0088] That is, the repair unit 404 triggers different target disaster recovery solutions in sequence according to the order of disaster recovery levels to repair system failures, thereby improving the orderliness of fault repair and further ensuring that system failures can be repaired, thereby restoring services as soon as possible.

[0089] It is understandable that after completing the system fault repair, this embodiment can further record information such as the cause of the fault, the recovery process, and the recovery time, so as to analyze the recorded information to continuously optimize the selection of disaster recovery plans, the determination of fault types, etc.

[0090] The disaster recovery device 400 of the system of this embodiment may also include an early warning unit 405, which is used to execute the following contents: after the processing unit 402 inputs the target feature into the large language model, a fault prediction result output by the large language model is obtained; early warning information is generated according to the fault prediction result, and the early warning information is sent to the operation and maintenance equipment.

[0091] That is to say, in this embodiment, the warning unit 405 can also issue a fault warning based on the output results of the large language model. Before the fault actually occurs, the operation and maintenance personnel can check and process it based on the warning information in the operation and maintenance equipment, so as to avoid system failures that affect services.

[0092] The fault prediction result obtained by the early warning unit 405 is used to reflect the existence of a fault risk in the system. The fault prediction result may also include information about the component with the fault risk, that is, the fault prediction result can be used to determine which component in the system has the fault risk.

[0093] After obtaining the fault prediction result, the early warning unit 405 may also include the following contents: in response to determining that the component with failure risk is the database according to the fault prediction result, perform a data backup operation, such as obtaining key data from the database and storing it in the local memory or generating a data snapshot, or generating a full database backup based on the full data in the database.

[0094] It can be understood that the fault prediction results output by the large language model in this embodiment may also include predicted fault levels. In response to determining that the component with failure risk is a database based on the fault prediction results, the early warning unit 405 performs a data backup operation based on the predicted fault level.

[0095] That is to say, this embodiment can periodically store key data in the local memory or distributed cache system, periodically generate data snapshots based on the key data, or periodically generate a full database backup based on all the data. It can also store key data, generate data snapshots, or generate a full database backup after obtaining the fault prediction results, thereby improving the accuracy of data backup.

[0096] In addition, after obtaining the fault prediction result, the early warning unit 405 can also perform predictive maintenance such as updating the software version and optimizing the system configuration, so as to further prevent the occurrence of faults.

[0097] The warning information generated by the warning unit 405 includes information such as the name of the component that may fail, the type of failure that may fail, etc.

[0098] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0099] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0100] like Figure 5 , is a block diagram of an electronic device according to a disaster recovery method of a system according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0101] like Figure 5As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0102] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the disaster recovery method of the system. For example, in some embodiments, the disaster recovery method of the system may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508.

[0104] In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 51, one or more steps of the system disaster recovery method described above may be executed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the system disaster recovery method in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a disaster recovery device of a general-purpose computer, a special-purpose computer, or other programmable system, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0112] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A systematic disaster recovery method, comprising: Acquire operation data of the system, and extract target features from the operation data; Inputting the target feature into a large language model to obtain a fault type output by the large language model; In the case where it is determined that the fault type is a preset fault type, obtaining multiple target disaster recovery solutions corresponding to the fault type; The multiple target disaster recovery plans are triggered in sequence in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired.

2. The method according to claim 1, wherein: The acquiring of multiple target disaster recovery solutions corresponding to the fault type comprises: Get the fault level; According to the fault type, multiple initial disaster recovery plans are obtained; A disaster recovery scheme corresponding to the fault level is selected from the multiple initial disaster recovery schemes as the multiple target disaster recovery schemes.

3. The method according to claim 1, wherein: The triggering of the plurality of target disaster recovery schemes in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired comprises: According to the disaster recovery level, determine the target disaster recovery plan with the lowest current disaster recovery level; Using the determined target disaster recovery solution, repair the failure of the system; In response to determining that the system failure has not been repaired, the step of determining the target disaster recovery plan with the lowest current disaster recovery level according to the disaster recovery level is executed, and this is repeated until it is determined that the system failure has been repaired.

4. The method according to claim 1, further comprising: After inputting the target feature into the large language model, obtaining a fault prediction result output by the large language model; Generate warning information according to the fault prediction result, and send the warning information to operation and maintenance equipment.

5. The method according to claim 4, further comprising: After obtaining the fault prediction result output by the large language model, in response to determining that the component with a fault risk is a database according to the fault prediction result, a data backup operation is performed.

6. The method according to claim 5, wherein: The performing of the data backup operation comprises: Get predicted failure level; A data backup operation is performed according to the predicted failure level.

7. A disaster recovery device for a system, comprising: An extraction unit, used to obtain operation data of the system and extract target features from the operation data; A processing unit, used for inputting the target feature into a large language model to obtain a fault type output by the large language model; An acquisition unit, configured to acquire, when determining that the fault type is a preset fault type, a plurality of target disaster recovery solutions corresponding to the fault type; The repair unit is used to trigger the multiple target disaster recovery schemes in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired.

8. The device according to claim 7, wherein: When the acquisition unit acquires multiple target disaster recovery solutions corresponding to the fault type, the acquisition unit specifically performs: Get the fault level; According to the fault type, multiple initial disaster recovery plans are obtained; A disaster recovery scheme corresponding to the fault level is selected from the multiple initial disaster recovery schemes as the multiple target disaster recovery schemes.

9. The device according to claim 7, wherein: The repair unit triggers the multiple target disaster recovery schemes in order from low to high disaster recovery levels until it is determined that the fault of the system has been repaired, specifically performing: According to the disaster recovery level, determine the target disaster recovery plan with the lowest current disaster recovery level; Using the determined target disaster recovery solution, repair the failure of the system; In response to determining that the system failure has not been repaired, the step of determining the target disaster recovery plan with the lowest current disaster recovery level according to the disaster recovery level is executed, and this is repeated until it is determined that the system failure has been repaired.

10. The device according to claim 7, further comprising an early warning unit, configured to execute: After the processing unit inputs the target feature into the large language model, obtaining a fault prediction result output by the large language model; Generate warning information according to the fault prediction result, and send the warning information to operation and maintenance equipment.

11. The device according to claim 10, wherein the early warning unit is further configured to execute: After obtaining the fault prediction result output by the large language model, in response to determining that the component with a fault risk is a database according to the fault prediction result, a data backup operation is performed.

12. The device according to claim 11, wherein When performing the data backup operation, the early warning unit specifically performs: Get predicted failure level; A data backup operation is performed according to the predicted failure level.

13. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images