1427 results about "Disaster recovery" patented technology

The invention provides an unmanned aerial vehicle multi-system navigation decoy system based on intelligent algorithm optimization, and relates to the technical field of unmanned aerial vehicle processing, a sensing module comprises a cognitive radar, a photoelectric / infrared sensor, a magnetic and acoustic sensor and an electromagnetic environment scanning device, and a digital twinning and countermeasure AI module comprises a digital twinning model module and a countermeasure AI engine; the cross-domain multi-source cheating module comprises a multi-system navigation signal synthesis module and a sensor-level cheating module; the reconfigurable intelligent antenna array comprises a metamaterial unit, a real-time beam controller and a biological heuristic intelligent algorithm module, and the system security hiding module comprises an encryption access control unit, a micro-power frequency expansion and hopping unit and a redundant backup and disaster recovery unit. The response time of the system from detection to interference can be remarkably shortened by performing high-fidelity simulation on the flight control logic of the unmanned aerial vehicle and the actual electromagnetic environment in the digital twin environment and combining the hybrid decoy strategy generated by the adversarial AI.

The invention relates to the technical field of database disaster recovery backup, and discloses a multi-node database disaster recovery backup system. The system comprises a data fragmentation module, a real-time synchronization module, a fault detection module, a data recovery module and a strategy optimization module. The data fragmentation module performs logic fragmentation based on a consistent Hash algorithm to realize load balancing and redundant backup; the real-time synchronization module captures data change of the main node by using an incremental log synchronization technology; the fault detection module evaluates the health condition of the node through a self-adaptive heartbeat detection mechanism; the data recovery module recovers data by means of a multi-version snapshot recovery algorithm; and the strategy optimization module generates an optimal disaster recovery backup strategy by applying a dynamic weight adjustment algorithm. The system solves the problems of non-uniform data distribution, low synchronization efficiency, untimely fault detection, incomplete data recovery, lack of strategy optimization and the like in multi-node database disaster recovery backup, improves system performance, stability and data security, and meets diversified disaster recovery backup requirements.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention discloses an information security management method and system based on sensitive data, relates to the technical field of data management, and comprises the following steps: realizing quantum encryption transmission of remote high-sensitive data by combining a quantum key distribution link and a dynamic chaotic key stream; the partitioning and synchronization priority is dynamically adjusted according to the node load, the data integrity is verified through CRC, the distributed storage disaster recovery system with extremely high safety and usability is achieved, it is ensured that data can still be recovered even under extreme disasters, a zero-trust data channel based on an IPSec tunnel is constructed in the data transmission process, and the reliability of data transmission is improved. The tamper-proof, anti-counterfeiting and anti-leakage capabilities of a data access link are greatly improved, the access frequency abnormity is detected through the isolated forest model, the behavior trend is detected through the LSTM time sequence prediction model, the baseline deviation analysis is performed through user behavior portrait comparison, and the real-time, multi-modal and chained security monitoring of the user data operation behavior is realized.

The invention relates to the technical field of computer network security, in particular to a security guarantee system for cross-domain communication and data sharing of network platform software, which comprises a data security transmission module, an identity authentication and access control module, an encryption module, a security audit and anomaly detection module and an anti-interference disaster recovery module. According to the invention, through linkage of equipment fingerprint end-to-end encryption, threat adaptive dynamic key updating and a timestamp-random number anti-replay mechanism and AI risk pre-judgment, reinforcement learning path planning, digital twinning pre-verification and fault automatic switching technologies, collaborative guarantee of key security and transmission continuity in cross-domain audio transmission is realized; encryption intensity is dynamically upgraded along with threats, a transmission path is optimized in advance, and it is ensured that the audio stream resists various network attacks and interferences in low-delay transmission.

Examples of systems described herein provide for share-level disaster recovery on, for example, virtualized file servers by enabling additional snapshots, including self-service recovery (SSR) snapshot migration in addition to replication snapshot migration to a disaster recovery location. In examples, a disaster recovery location may receive a replication of a share of a file system, the snapshot configured for recovery of the share at the disaster recovery location. The replication snapshot may include one or more additional snapshots, each taken since a time of a previous replication snapshot. At least one of the one or more additional snapshots may include an SSR snapshot, identifiable based at least on a tag associated with a process used to create the SSR snapshot at a primary location. The at least one additional snapshot may be extracted for use by another instance of the process at the disaster recovery location.

The invention provides a disaster recovery data recovery method and system of a disaster recovery system, and the method comprises the steps: adjusting a network bandwidth allocation proportion according to a priority, and if the target timeliness is higher than a preset recovery time threshold, increasing a real-time power grid alarm data bandwidth proportion, and generating a bandwidth allocation scheme; dynamically scheduling computing resource allocation according to a bandwidth allocation scheme, and if the key evaluation characteristic exceeds a set upper limit, increasing a computing resource share of a service key level, and generating a resource allocation decision; and after the priority updating scheme is obtained, monitoring a system load in concurrent request processing through a disaster recovery system, and if the system load exceeds a preset threshold value, triggering rebalance of network bandwidth allocation in combination with timeliness deviation to obtain a resource optimization result.

Systems and methods for private application access continuity include providing access to one or more private applications for users associated with a tenant of a cloud-based system; detecting one or more criteria suggesting an outage of the cloud-based system; and responsive to activation of a disaster recovery mode based on the one or more criteria, providing access to the one or more private applications via an on-site disaster recovery system including a site controller, wherein providing the access via the site controller does not require communication with the cloud-based system.

The invention is suitable for the technical field of database management, and provides a distributed database management method and system based on artificial intelligence. The system comprises a database content analysis module, a storage layout adjustment module, an anomaly detection module, an unmanned aerial vehicle storage communication module, an access request guide module and an unmanned aerial vehicle node recovery module. The scheme that an unmanned aerial vehicle is adopted as a storage and communication node brings remarkable innovation and multiple advantages to a distributed database management method. Firstly, the mobility of the unmanned aerial vehicle enables the unmanned aerial vehicle to quickly take over a data storage task when a network is interrupted or a node fails, so that the integrity and availability of data are ensured, and the response capability in a traditional architecture is relatively limited. In complex scenes such as disaster recovery or remote area deployment, the unmanned aerial vehicle can break through network limitation through off-line transmission, and data safety and storage continuity are effectively guaranteed.

The invention relates to the field of data storage and backup, and discloses a method and a system for transmitting disaster recovery backup capability of an emergency recovery unit. The method comprises the steps of establishing a quantum encryption transmission channel, monitoring a network index in real time and predicting transmission quality to start preventive optimization, calculating a link quality score to carry out link switching, verifying integrity and consistency of received data and executing retransmission, carrying out multi-mode data backup, and switching to an emergency recovery unit when a main data center fails. The system comprises a quantum encryption transmission module, a transmission monitoring and switching module, a data verification and retransmission module, a multi-mode backup module and an emergency switching and synchronization module. Through the technical scheme, the problems of data transmission security and high efficiency can be effectively solved, the disaster recovery backup capability of the emergency recovery unit is improved, the service continuity is ensured, and the method and the device are suitable for scenes with high requirements on data security and service continuity.

The invention relates to a power equipment state self-healing operation and maintenance method, and belongs to the field of power equipment operation and maintenance, and the method comprises the steps: obtaining the real-time operation state data of a power supply in a current multi-source power supply system, collecting the data through a preset sampling frequency, and constructing a power supply state matrix; a conversion time constraint condition is set in advance, a zero-delay switching decision tree is constructed and a power supply switching decision is output in combination with a main power supply fault risk assessment result, and if the main power supply fault risk assessment result is abnormal, the standby power supplies are switched according to the determined switching priorities of the standby power supplies and the power supply switching decision; after the switching is completed, monitoring the power supply continuity index of the power equipment in real time, and if the power supply fluctuation time or the phase jump amplitude exceeds a preset allowable value, smoothing the power supply fluctuation by adopting the rapid power compensation function of the energy storage device; the stability and reliability of the multi-source power supply system are improved, and intelligent power supply switching and self-healing disaster recovery are realized.

The invention relates to the technical field of power distribution station supervision, in particular to an intelligent auxiliary and artificial intelligence visual gateway control method and system for a station building, which monitors network state parameters in real time, calculates a network quality score and dynamically selects an optimal transmission path based on a reinforcement learning algorithm. Comprising a high bandwidth mode, a low delay mode and a disaster recovery backup mode, stage processing and dynamic compression are performed on transmission data according to data types and network quality scores, link switching and a local caching mechanism are automatically triggered when a network is abnormal, and finally, multi-gateway load balancing and visual monitoring are realized through a software defined network architecture. The problem of transmission delay or interruption caused by the fact that a static data transmission framework cannot adapt to network state changes in the prior art is effectively solved, and the reliability and the real-time performance of data transmission are remarkably improved through dynamic path optimization, intelligent hierarchical transmission and a fault self-healing mechanism.

A governance, risk, and compliance (GRC) platform that is all-inclusive and that utilizes the attributes of assets to drive activities and manage risk. The platform includes risk mapping and cost analysis for applications, vendor risk analysis, business continuity, disaster recovery, incident response, policy management, and evidence collection. The platform utilizes questionnaires and vulnerability scans to evaluate risk and compliance.

The invention provides an intelligent tool warehouse management system, which belongs to the field of tool management and comprises a multi-mode sensing module, an intelligent access control module, an edge calculation module, an anomaly analysis module and a visualization module. The multi-mode sensing module realizes identity recognition, real-time positioning and integrity verification of the tool through RFID and UWB dual-mode positioning, visual recognition and sensor fusion technologies; the intelligent access control module is based on dynamic authority control and gravity sensing goods shelves, and tool storing and taking compliance is ensured; the edge computing module adopts localized data processing and low-power-consumption communication, and supports network disconnection disaster recovery; the abnormity analysis module identifies violation operation through an LSTM model and triggers grading alarm; the visualization module displays the tool state and the warehouse thermodynamic diagram in real time based on the digital twinning technology. The problems that traditional warehouse management depends on manpower, efficiency is low and errors are prone to occurring are solved, and high-precision tracking, intelligent early warning and optimal scheduling of the tool in the whole life cycle are achieved.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.

The invention discloses a data disaster recovery method supporting multi-backup path switching, which comprises the following steps: generating a dynamic granularity data fragment based on the sensitivity of input data and the characteristics of a storage medium, and generating a metadata label containing a data fragment identifier, storage preference and recovery priority; constructing a heterogeneous transmission path performance model through metadata labels, generating a path quality map, distributing an optimal transmission path for each data fragment, and monitoring path performance; detecting anomalies through the standardized deviation of accumulated transmission delay and migrating fragments; carrying out parallel verification by adopting incremental integrity verification and Merkle proof stored in the block chain, and preferentially retransmitting fragments which fail in verification; according to the invention, through the cooperation of dynamic data fragmentation, backup path optimization and recovery verification, the reliability and stability of data disaster recovery are ensured so as to meet the actual business requirements.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

The invention discloses an intelligent operation and maintenance control platform and system for a secondary power system, belonging to field of power system operation and maintenance technology. The platform includes a interval layer, a transmission layer, a station control layer, a system layer, a platform layer, an application layer, and a disaster recovery backup centre. By collecting data from a security system and external information system, and sharing data with other application platforms of a secondary system, problems of omission, repetition, and isolation in current data collection are solved. Balancing the safety of production control area and the needs of advanced application development in information management area, ultimately achieving comprehensive data collection and deep mining, making the production and operation business of the power secondary system more smooth, meeting the professional needs of various professional operators, and achieving the improvement and efficiency of the secondary system.

The invention relates to the technical field of archive management, in particular to a cloud-based archive information management system, which comprises a data association analysis module, an intelligent storage strategy module, a dynamic resource management module and a disaster recovery regulation and control module. According to the invention, by analyzing the access timestamp and the user tag of the archive data in the cloud environment, the accurate identification of the archive access mode and the access frequency is realized, the association degree between the archives is analyzed, the archive management efficiency is improved, and the archive access delay is reduced according to the intelligent identification of the data access hotspot and the optimization of the selection of the storage position. And by dynamically adjusting the storage capacity and bandwidth resources, the use efficiency of cloud storage resources is improved, quick response can be ensured in the peak demand, real-time monitoring of disaster recovery and a multi-site remote backup strategy are ensured to effectively deal with potential risks, the continuity and safety of data are ensured, and the reliability and disaster resistance of the whole system are improved.

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

The invention discloses a backup disaster recovery optimization method, and relates to the technical field of IT operation and maintenance and disaster recovery, and the method comprises the steps: analyzing a statistical dependency relationship between basic performance indexes through employing a causal discovery algorithm based on a standardized monitoring data flow, and constructing a directed weighted graph; when a basic performance index in the directed weighted graph deviates from a normal performance baseline, triggering a reverse traceability positioning root cause component, identifying a service influence path through forward path analysis, and generating a backup enhancement instruction; in a digital twin environment, according to historical drill records and a strategy library, multiple potential disaster recovery switching schemes are generated, parallel deduction is carried out, the performance influence of each potential disaster recovery switching scheme on a virtual service link is quantitatively evaluated in the deduction process, and the optimal disaster recovery switching scheme is selected by comparing evaluation results. According to the invention, the prospective optimization of the disaster recovery decision and the minimization of the business influence are realized, and the automation, intelligence and business adaptation capabilities of the backup disaster recovery system are enhanced.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention discloses an encryption protection method for network data backup and recovery, which relates to the technical field of data protection, and comprises the following steps: respectively implementing differential backup strategies by adopting a local, cloud and remote three-terminal collaborative backup architecture; and performing local data backup by a combined scheme of complete backup and incremental backup to generate a chained hash value sequence. Three-terminal key isolation and dynamic derivation are adopted to guarantee data security, an independent key system is adopted locally, cloud and remotely, a chain type key is generated locally based on a hash chain, version independent keys are generated at the cloud and remotely, and data security of other terminals is not affected by key leakage of any storage terminal. The recovery priority is automatically switched from local to cloud to remote, the local decrypts incremental data layer by layer through a hash chain, the cloud directly calls an independent key of a target version and uses a synthetic backup complete copy in remote, and the disaster recovery efficiency is improved through layered recovery and synthetic backup.

The invention provides a service fault recovery method and system based on disaster recovery strategy optimization, and the method comprises the steps: obtaining a network topological graph and an enterprise digital twin model based on an enterprise IT architecture; obtaining an initial disaster recovery strategy based on the network topological graph; inputting the initial disaster recovery strategy to the enterprise digital twin model, optimizing the initial disaster recovery strategy until a preset convergence requirement is met, obtaining a target disaster recovery strategy, and storing the target disaster recovery strategy to a preset cloud strategy library; and when a service fault occurs, automatically switching a target disaster recovery strategy based on the preset cloud strategy library so as to recover the service fault. According to the invention, the problem of poor service fault recovery reliability caused by the fact that the disaster recovery scheme cannot be adaptively adjusted according to different service fault scenes in the prior art is solved. According to the invention, self-adaptive adjustment of the disaster recovery strategy is realized, and service fault recovery reliability is improved.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

Systems and methods for providing continued access via a tenant-specific private cloud include monitoring an operation state of a cloud-based system to detect disruptions based on predefined conditions; enabling a disaster recovery mode in response to detecting a disruption in the cloud-based system; and responsive to activation of the disaster recovery mode, redirecting traffic associated with a tenant from the cloud-based system to a tenant-specific private cloud, the tenant-specific private cloud being configured to enforce tenant-specific policies and maintain access to internet, Software-as-a-Service (SaaS) applications, and private applications.

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 provides an intelligent archive data sub-library storage system and method based on dynamic fragmentation and self-adaptive indexing, and relates to the technical field of intelligent archive data management. And efficient management of the archive data in storage and access levels is realized. Constructing an archiving attribute tensor matrix, performing popularity grouping according to the access frequency, the reference relationship and the modification frequency, and quickly identifying high-access-value data; then, a life cycle attribute is introduced to construct a stage weight, a fragment structure is dynamically optimized, copy information is updated in real time, and data distribution reasonability and disaster recovery robustness are effectively improved. Meanwhile, the system fuses access path optimization, index hop count compression and caching strategies, self-adaptive adjustment and version solidification of an index structure are achieved by evaluating an index response efficiency index, the average access time is effectively shortened, and the hit rate is improved.

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.