512 results about "Disaster recovery" patented technology

The invention relates to the technical field of power monitoring system network security and real-time micropatch hot deployment, and discloses a power disaster recovery system-oriented micropatch non-inductive deployment engine, a resource scheduling method, a system, equipment and a medium, and the method comprises the steps: capturing system events through a kernel eBPF probe, and carrying out feature extraction and model reasoning; generating and transmitting an encrypted scheduling token; loading and verifying a patch fragment by a patch agent, inserting a jump instruction through a kernel interface to redirect an execution stream, and maintaining multi-kernel cache consistency; fusing multi-source telemetry data to carry out fusing judgment, realizing network isolation and calling a key service to cancel a key; and collecting runtime indexes and performing trend prediction, triggering a recovery or rollback operation according to a result, and storing an operation result and data through a block chain. According to the method, through combination of deep fusion of multi-source heterogeneous data, dynamic reasoning of a knowledge graph and strategy optimization of reinforcement learning, efficient perception and defense of a complex attack scene of a digital power grid are realized.

The invention discloses a physical-data dual-drive retaining wall catastrophe robustness evaluation method, and relates to the technical field of structure catastrophe evaluation and computer science crossing. Aiming at the problems that an existing retaining wall catastrophe robustness evaluation method cannot correct physical model errors in real time and is insufficient in accuracy, the invention provides a retaining wall catastrophe process robustness evaluation method driven by fusion of a physical model and a neural network. The method comprises the following steps: 1, carrying out time discretization and parameter vectorization on retaining wall catastrophe monitoring parameters; 2, constructing a performance attenuation function based on the physical model; 3, updating an implicit damage state in combination with a neural network Cell module; 4, correcting an error through a feedforward network to obtain a total performance attenuation function; and 5, finally calculating a robustness index and outputting a corresponding robustness grade, thereby realizing quantitative evaluation of the retaining wall catastrophe robustness. Reliable quantitative support can be provided for retaining wall design optimization, operation monitoring and post-disaster recovery evaluation.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention discloses an archive data security backup method and system based on cloud computing, and the method comprises the steps: obtaining archive data and a cloud backup pool, obtaining an edge verification package based on to-be-backed-up data, carrying out the grouping encryption of the edge verification package through a national password, and enabling the cloud backup pool to carry out the splitting and storage of the backup data according to a regeneration code strategy, the method comprises the following steps: acquiring data fragments and fragment roots, writing Hash of the fragment roots into an alliance chain channel, hosting a decryption key of a confidential envelope according to a storage period, storing fragments of the decryption key into a cloud HSM, and if a cloud backup pool monitors a disaster trigger event, pushing decryption key sub-fragments to a disaster recovery undertaking pool according to a disaster level, the subset of the readable file data is recovered to the edge node, and a recovery log is written back to the alliance chain. According to the method, data backup, safety and reliability are improved through desensitization grading, differential safety protection, regeneration code strategy splitting and multi-node disaster recovery.

Techniques are described for maintaining data consistency when failure events occur during file storage replications between file systems in different cloud infrastructure regions. In certain embodiments, two generation numbers (or different identifications) are assigned to two groups of processed B-tree key-value pairs, one before and one after a failure event, within a key range. In some embodiments, the two generation numbers are assigned to a group of B-tree key-value pairs processed by a failed thread and another group of B-tree key-value pairs processed by a substitute thread taking over the failed thread to avoid potential data corruption.

The invention discloses a disaster recovery resource scheduling method and device based on a heterogeneous cloud environment, and the method comprises the steps: constructing a multi-dimensional resource portrait model and a cross-cloud network topological graph in the heterogeneous cloud environment, and generating a standardized resource vector set and a topological affinity scoring matrix; aggregating the local SLA default risk prediction model of each cloud node through federated learning, and performing fine tuning through transfer learning to obtain a global prediction model; calculating load fluctuation entropy based on the SLA constraint template and real-time load data, and inputting the load fluctuation entropy into a global model to predict and obtain an SLA default probability set; constructing and solving a dynamic weight multi-objective optimization function to obtain an optimal takeover cloud node list and a migration strategy identifier set; a target cloud node and a migration strategy are determined through SLA simulation verification, and an adaptive execution adapter is called to complete virtual machine incremental snapshot migration or cross-cloud arrangement deployment of containerized services. According to the invention, intelligent and automatic scheduling of disaster recovery resources is realized, and the fault takeover speed and the cross-cloud resource utilization rate are improved.

The invention provides a disaster recovery system, method and device for a large model service based on an AI agency, and the system comprises a large model service module which comprises a plurality of GPU servers, and is used for responding to the configuration request information of a user, and correspondingly configuring the service end points of the GPU servers; the detection state service module is used for monitoring the state information of the service end points of the large model service module and regularly updating the state information of the service end points of the large model service module stored in the state cache pool; and the AI proxy service module is used for sending a state query request of the large model service module to the detection state service module, receiving the returned state information of the service endpoints and analyzing the state information so as to determine the service endpoint of the abnormal GPU server, and automatically switching to the service endpoint of the normal GPU server. According to the invention, real-time monitoring, state analysis and automatic switching of the service endpoints of the large model service module are realized, and the disaster recovery capability of the system is improved.

The invention discloses a Raft consensus algorithm-based dynamic management method and a related device, and relates to the technical field of communication, when a slave node server does not receive a heartbeat packet of a master node server overtime, the slave node server is automatically converted into a candidate node server and broadcasts an election request; and if the number of the received feedback information for confirming that the candidate node server is changed into the master node server is greater than half of the total number of all the slave node servers, determining that the candidate node server is changed into the master node server. The delay risk that a master-slave centralized architecture depends on manual monitoring and manual switching is thoroughly eliminated, fault discovery and response are solidified on an algorithm level, and automatic disaster recovery without human intervention is achieved.

The invention provides a vehicle system upgrading method and device, computer equipment and a storage medium, and the method comprises the steps: creating a block chain block containing domain control upgrading information in response to a domain control upgrading instruction, and determining a to-be-upgraded domain control node; carrying out upgrading operation on the domain control node based on the domain control upgrading instruction to obtain an upgraded state parameter of the domain control node, and writing the upgraded state parameter into a block chain block; and verifying the upgraded state parameter based on the block chain block, if the upgraded state parameter of any domain control node is abnormal, performing version rollback on the domain control node, and writing a version rollback result into the block chain block. Based on the scheme, the state consistency, safety and traceability of the upgrading process of the multi-domain controller of the automatic driving vehicle are ensured, an automatic disaster recovery and rollback mechanism is provided, and the reliability and safety of the system are remarkably improved.

The invention discloses a gravity retaining wall toughness evaluation method based on performance degradation. Aiming at the problems that the existing gravity retaining wall toughness evaluation focuses on the static performance, the dynamic influence of the performance degradation in the service period on the toughness is difficult to quantify, and the evaluation result is insufficient in accuracy, the evaluation method comprises the following steps: 1, constructing the performance characterization index of the gravity retaining wall; 2, establishing a correlation model of the toughness and performance attenuation of the gravity retaining wall, dividing performance grades, determining a performance threshold value corresponding to each grade, and fitting a performance attenuation function; 3, calculating a comprehensive toughness index in an evaluation period under each performance characterization index based on the performance attenuation function; 4, establishing a weight calculation model, and determining the toughness index proportion of each performance characterization index; and 5, calculating a weighted comprehensive toughness index and outputting a toughness grade to realize accurate quantitative evaluation of the toughness of the gravity retaining wall. The invention provides a practical quantification method for design optimization, operation safety monitoring and recovery effect evaluation after disaster damage of the retaining wall.

The invention discloses a private cloud platform-based micro-service application remote main and standby disaster recovery rapid switching method, relates to the technical field of computers, and is used for solving the problems of long remote main and standby disaster recovery switching time, cold start storm, insufficient capacity evaluation and discharge strategies and complex back switching. The switching process is decomposed into controllable steps by combining real-time state feedback through a clear sequential arrangement and batch reconnection strategy of a data surface and a control surface, so that the switching time is remarkably shortened, the target of minute-level continuous availability is met, and meanwhile, the switching efficiency is greatly improved through heat set construction and standby side quasi real-time preheating before switching. Core hotspot data and indexes are loaded to the standby side in advance, it is ensured that the standby side has the high cache hit rate and index availability during master raising, and therefore the performance bottleneck caused by access of a large amount of cold data is avoided.

Approaches presented herein relate to the analysis and / or management of a communication network including resources from multiple, independent communications networks. At least one edge device can be placed within individual communication networks, allowing for the capture and anonymization of data from the various provider networks. The data for various users can be aggregated by location and associated with an identifying key. A cloud-based service can generate a snapshot using this data from the various individual networks, and a decision engine can be used to generate a set of recommendations or actions to improve operation (e.g., performance or security, etc.) of the network or perform actions such as disaster recovery. The recommendations or actions can be provided for individual devices, locations, communication networks, or providers but based upon the data aggregated and analyzed across multiple individual networks.

The invention provides an AI-based multi-node rapid disaster recovery switching method and system, and the method comprises the steps: obtaining multi-dimensional monitoring indexes of the CPU usage rate, the memory occupancy rate, the network IO, the disk IO, the application response time and the error rate of each node, and constructing a time sequence feature matrix; extracting space correlation features among nodes by using a graph convolutional network, extracting time sequence features through a long-short-term memory network, constructing a deep space-time graph neural network to identify a fault precursor, and predicting a node fault 30-120 seconds in advance; generating a node health score based on a node fault prediction result and capacity estimation, executing a target node optimization algorithm, and selecting an optimal disaster recovery backup node; the method comprises the following steps: constructing a hot backup by adopting a CRI U-based lightweight process migration technology, and generating a check point and an incremental snapshot of a source container; and automatic disaster recovery switching is executed before the node fails, the service flow is redirected, and the service is ensured not to be interrupted. According to the method, the disaster recovery switching time can be shortened to a millisecond level, and the system availability is remarkably improved.

The invention discloses a disaster recovery method and device for a service system, a storage medium and a product, and relates to the technical field of network systems, and the method comprises the steps: carrying out the anti-fact simulation operation of each candidate disaster recovery action in a preset sandbox when a system behavior of a target service system is monitored to meet a disaster recovery operation triggering condition in a disaster recovery strategy file, determining an execution result index vector corresponding to each candidate disaster recovery action; based on a preset service weight in the disaster recovery strategy file and the execution result index vector corresponding to each candidate disaster recovery action, determining a target disaster recovery action from each candidate disaster recovery action; performing first verification on the target disaster recovery action based on a first verification mode; performing second verification on the target disaster recovery action passing the first verification based on a second verification mode at the disaster recovery end of the target business system; and executing the target disaster recovery action passing the second verification to complete the disaster recovery operation of the target service system. Therefore, the reliability of disaster recovery operation of the system can be improved.

The invention relates to a big data different-domain disaster recovery method and device, equipment, a storage medium and a program product, and the method comprises the steps: executing a daily batch task through a main cluster under the condition that the main cluster is in an available state, and synchronizing the metadata, business data and scheduling configuration data of the main cluster to a slave cluster according to a target hierarchical collaborative synchronization mechanism; executing a service analysis task through the slave cluster; under the condition that the master cluster is in an unavailable state, switching the working mode of the slave cluster into a disaster recovery mode, and taking over daily batch tasks through the slave cluster; after the master cluster recovers the available state, incremental data generated by the slave cluster in the disaster recovery mode is written back to the master cluster. The dual functions of daily analysis and disaster recovery takeover are achieved through the slave cluster, a special disaster recovery cluster does not need to be deployed, the data synchronization efficiency can be greatly improved under the condition that idle hardware resources are reduced, bidirectional synchronization can be automatically achieved between the master cluster and the slave cluster, and the data collaboration degree is effectively improved.

The invention discloses a post-disaster recovery large-scale city power grid toughness evaluation method, device and medium, and the method comprises the steps: constructing a large-scale city power grid fault scene and a power transmission line time-varying fault probability model under an earthquake disaster, generating an earthquake-fault combined scene based on Monte Carlo simulation, and selecting k high-risk typical scenes, and then, by taking the maximum weighted load recovery total amount in the system full recovery period as a core optimization target, constructing a power grid post-disaster collaborative recovery model, constructing a multi-dimensional dynamic toughness index system based on the grid robustness, the system self-healing capability and the post-disaster first-aid repair capability, and evaluating the large-scale urban power grid toughness level of power grid post-disaster collaborative recovery. And outputting a good and bad sequence of the optimization strategy. By evaluating the toughness level of the large-scale urban power grid subjected to post-disaster collaborative recovery of the power grid, the performance of the whole recovery process can be comprehensively quantified, the advantages and disadvantages of strategies can be effectively distinguished, and the recovery speed and the recovery degree can be accurately quantified, so that an effective recovery strategy cannot be adopted, and a reliable basis is provided for disaster prevention and emergency of the power grid.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention discloses a disaster recovery switching method and device for a vehicle management system and a storage medium, and relates to the technical field of digital information transmission, and the disaster recovery switching method for the vehicle management system comprises the steps: responding to a region fault alarm triggered for a fault region, executing a main and standby database switching action based on the databases of the fault area and the to-be-transferred area; in response to operation and maintenance input for the fault area, updating a target protocol address and a target area address, corresponding to a to-be-transferred area, of each vehicle type on the basis of the to-be-transferred area indicated by the operation and maintenance input; and controlling the cluster management platform of the fault area to disconnect the service connection with each vehicle type based on the fault protocol address. The technical effect of improving the fault service recovery efficiency of the server area can be achieved.

The invention belongs to the technical field of communication, and particularly relates to a router system service exception self-healing method based on cloud AI, which comprises the following steps: deploying an exception detection module at a router end, collecting system logs and state data in real time and generating a standardized exception report; uploading the report to a cloud AI server through an encrypted MQTT protocol; the cloud calls a hybrid analysis model composed of a rule matching engine, a machine learning classifier and a reinforcement learning decision network to generate a self-healing strategy instruction packet; the router end receives and executes the strategy, completes service restart, configuration rollback or hotfix loading and other operations, and verifies the self-healing effect; when communication interruption exceeds a threshold value, an embedded loopback self-healing subsystem is automatically activated, and abnormity is independently handled based on a local strategy library. According to the technical scheme, millisecond-level abnormal response and high-success-rate autonomous recovery can be achieved, the network availability and the service continuity are remarkably improved, and the disaster recovery self-healing capacity is still achieved when the cloud end is disconnected.

Cascading data replication for disaster recovery, including: establishing a cascading replication relationship between a plurality of storage nodes comprising a source endpoint node, a target endpoint node, and one or more intermediary nodes, wherein the cascading replication relationship comprises a chain of replication relationships between pairs of storage nodes from the source endpoint node to the target endpoint node via the one or more intermediary nodes; and replicating, based on the cascading replication relationship, a dataset from the source endpoint node to the target endpoint node via the one or more intermediary nodes.

The invention provides a disaster recovery switching processing method and device for an active-active architecture, equipment and a computer readable storage medium. The method comprises the following steps: acquiring multi-source monitoring data of a main node and a standby node in an active-active architecture; processing the multi-source monitoring data through a pre-trained fault prediction model, and predicting the fault risk probability of the main node and the standby node at the current moment; determining link quality scores of the main node and the standby node at the current moment based on the network layer index data; determining system load indexes of the main node and the standby node at the current moment based on the operation state data; according to the fault risk probability, the link quality score and the system load index, determining health state evaluation results of the main node and the standby node at the current moment; and under the condition that the health state evaluation result of the main node is abnormal and the health state evaluation result of the standby node is normal, triggering a disaster recovery switching process and completing the switching of the main and standby roles. The method ensures the reliability and continuity of the power grid service.

The invention discloses a distributed database load balancing and disaster recovery backup method, relates to the technical field of databases, and solves the problems that when a distributed database is used, a disaster recovery backup node is usually in a cold standby state and does not work, the resource utilization rate is low, waste is caused, and the disaster recovery backup node cannot be combined with load balancing for cooperative use. A distributed database load balancing and disaster recovery backup method comprises the following steps: collecting multi-dimensional performance indexes of all nodes in a distributed database cluster in real time, and constructing a global dynamic metadata view; according to the method, the health state of the cluster is continuously monitored, when the fault of the node or the data center is detected, the disaster recovery switching process is automatically triggered, and the role of the specified disaster recovery backup node is promoted to be a new main node, so that disaster recovery resources and cost are reduced, the disaster recovery backup node is kept in a hot standby state, and the disaster recovery efficiency is improved. Direct operation is facilitated, and the situation that the starting time needs to be responded when the device needs to be used is prevented.

A data management platform may group a first instance of a set of virtual machines into a backup group for performing a backup of data from a source data storage environment to a target data storage environment in accordance with a backup configuration. The data management platform may receive, from a user, an input to create a second instance of the set of virtual machines at the target data storage environment. In some examples, data backed up to the target data storage environment from the first instance may be configured as a source for the second instance. The data management platform may instantiate, in response to receiving the input, the second instance of the set of virtual machines using the backup configuration for the first instance of the set of virtual machines and the data backed up to the target data storage environment.

The invention relates to the technical field of cloud computing and data disaster recovery, in particular to a government affair cloud cross-data-center disaster recovery data replication method and a government affair cloud disaster recovery system. Generating an optimal strategy comprising an execution time sequence, a target backup virtual machine, a target encryption service and a target front node; issuing a backup instruction with a unique task ID to a backup agent in a specified user VPC according to the optimal strategy, and triggering data backup; after receiving a backup success feedback, scheduling the target encryption service to execute data encryption; and after the completion of encryption is confirmed, reading the encrypted data from the front node, writing the encrypted data into a disaster recovery center through a parallel transport stream, and triggering data consistency verification. According to the method, accurate resource allocation and automatic process connection are realized through intelligent scheduling and task IDs, the backup efficiency and the resource utilization rate are effectively improved, and the data reliability is enhanced through consistency verification.

The invention discloses a data sharing and deployment system and method of a cross-hierarchical regional independent system, and a medium, and aims to solve the problem that global strong-consistency basic data is difficult to manage due to high autonomy of a regional system in the prior art. According to the method, three technologies of service double-instance disaster recovery, CDC real-time data synchronization and Nacos dynamic routing are fused, a mixed mode of central centralized reading and writing and local disaster recovery backup is constructed, two service instances connected with a central database and a local backup database are deployed in each region, data are synchronized to the local backup database in real time by utilizing the CDC technology, and the data are stored in the local backup database in real time. And the online state of the service instance is dynamically adjusted through Nacos, so that the central data is accessed when the network is normal to ensure strong consistency, and the local backup service is automatically switched to ensure high availability when the network fails.

The application discloses a data storage and data reading method and device, and relates to the technical field of computers. A specific implementation of the method comprises the following steps: saving a received order change message into a message queue of a plurality of clusters, and constructing order data by acquiring the order change message in the queue, and storing the order data in a storage medium of each cluster. The implementation realizes the reliability of data storage through the architecture of the plurality of clusters, so that subsequent data query can be realized through different clusters, and the disaster recovery of the system is improved.

The application discloses a virtual machine configuration data synchronization method and device, equipment and a storage medium, relates to the technical field of cloud computing, and is applied to a cloud platform with a dual-site disaster recovery system, and comprises the following steps: when it is monitored that a production site needs to perform a configuration data addition and deletion operation on a first virtual machine, full-amount configuration information data after the configuration data addition and deletion operation is calculated, then target addition and deletion operation tasks are generated based on the changed full-amount configuration information data and are delivered to a second virtual machine in a disaster recovery site, so that the target addition and deletion operation tasks are executed through the second virtual machine; if the execution result shows that the task execution is successful, the target addition and deletion operation tasks are executed through the first virtual machine. The application performs the configuration data addition and deletion operation in the disaster recovery site in advance, and performs the operation in the production site after the operation in the disaster recovery site is successful, so that the reliability and consistency during the addition and deletion operation synchronization are ensured, and the user business has flexibility while being protected by the disaster recovery security.

The invention discloses an industrial park micro-grid toughness planning method and system based on opportunity constraint, and relates to the technical field of power grid planning, and the method comprises the steps: building a combined scene pool containing scene weight according to the extreme strength probability of risk factors in a historical scene based on the coupling and association failure of multiple risk factors; constructing a pre-disaster protection planning layer according to opportunity constraints and protection dimensions; constructing a post-disaster recovery planning layer by using a staged recovery mechanism and an emergency operation mode; constructing an optimization objective function by minimizing the protection cost and the recovery cost; constructing a dual-stage planning model by using the pre-disaster protection planning layer, the post-disaster recovery planning layer and the optimization objective function; and matching the current triggering scene and the occurrence probability according to the real-time industrial park micro-grid operation data and the combined scene pool, inputting the current triggering scene and the occurrence probability into the double-stage planning model, and generating a tough protection planning strategy. The method has the beneficial effects that the protection planning strategy gives consideration to the toughness requirement and disaster recovery requirement of the micro-grid under the extreme event.

Examples described herein are generally directed towards generating, allocating, and assigning consistent file server user identifiers (IDs) (also described herein as a global identifier (GID)) domains subscribed to by file server(s) within a distributed file server environment. In operation, a virtualized file server of a distributed file server system may scan for and identify a trusted domain subscribed to by the virtualized file server. The virtualized file server may allocate to the trusted domain a range of file server user IDs. The virtualized file server may store a mapping between local user security identifiers (SIDs) in the trusted domain and a respective file server user IDs within the allocated range. The virtualized file server may provide the mapping to a replication target, such as during a replication, migration, and / or a disaster recovery event.

The invention discloses a scheduling method, system and device based on remote disaster recovery and electronic equipment. Relates to the field of container resource management, and the method comprises the following steps: generating a migration scheduling instruction which is used for requesting to migrate container resources of a first container cluster with faults to containers of a second container cluster; in response to the migration scheduling instruction, a first migration strategy is generated, and the first migration strategy comprises a resource migration strategy for migrating container resources; based on the first migration strategy, a target optimization strategy is adopted to adjust the first migration strategy to obtain a second migration strategy, and the target optimization strategy comprises a reinforcement learning strategy based on strategy gradient; and migrating the container resources based on the second migration strategy. According to the invention, the technical problem of poor scheduling effect of a cross-regional disaster recovery scheduling method based on a static rule in the related art is solved.