857 results about "Disaster recovery" patented technology

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.

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 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 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 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.

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 a smart power grid fault-tolerant communication system and method based on multi-operator channel switching, and the method comprises the steps: collecting heterogeneous network and equipment state data through an intelligent sensing layer, achieving the network quality prediction and multi-target optimization through an AI decision engine, integrating spectrum resources and network slices through a resource cooperative control layer, and achieving the fault-tolerant communication of a smart power grid. The edge execution layer executes hardware-level fast switching, the emergency disaster recovery layer provides multi-dimensional fault recovery, and the security trusted layer guarantees data security. The method breaks through the bottleneck of traditional network switching, realizes high-reliability and low-delay transmission of power business, is suitable for scenes of intelligent substations, distribution network automation and the like, and remarkably improves the fault-tolerant capability and resource utilization efficiency of intelligent power grid communication.

The invention provides a service system disaster recovery system based on cloud computing, and relates to the technical field of disaster recovery backup recovery and cloud computing, and the system comprises a software application registration and arrangement module which is used for registering an entity object of a service application to a software warehouse, arranging a topological structure of the service application according to a dependency relationship, and configuring a disaster recovery strategy; the software application deployment disaster recovery module is used for automatically deploying a pull-up service application and backing up the pull-up service application according to a disaster recovery strategy; the fault monitoring and damage decision-making module is used for performing fault monitoring and damage decision-making on the application instance, and sending out an alarm notification when a fault or a disaster is monitored; and the system recovery disaster recovery switching module is used for executing system recovery operation when receiving the alarm notification, executing disaster recovery switching operation such as upstream data flow and service access entry redirection, and realizing system recombination recovery and service replacement. The invention further provides a service system disaster recovery method based on cloud computing, electronic equipment and a storage medium.

The invention discloses a cultural heritage file multi-dimensional storage system, which relates to the field of cultural heritage digital protection and intelligent storage and comprises a data acquisition and processing module, a double-library management module, a multi-dimensional index retrieval engine, a security and convergence module and a backup and disaster recovery module. According to the method, structured texts and unstructured files are uniformly accessed through various interfaces, a special tool chain is adopted for format standardization, audios and videos are transcoded into an H.264 / AAC format through FFmpeg, images are subjected to resolution adjustment and compression through ImageMagick, plain texts are extracted from texts through ApacheTika and are converted into UTF-8 codes, standardized data are uniformly converted into an intermediate format, and the format of the intermediate format is converted into the intermediate format. According to the technical scheme, the method comprises the following steps of: standardizing input data by adopting a three-layer duplicate removal strategy, facilitating other modules to receive data, performing refined redundancy processing on the standardized input data by adopting a three-layer duplicate removal strategy, remarkably reducing storage redundancy, partitioning a cultural heritage data main library according to three layers of ''project type-medium type-age'', and partitioning an inheritor information library according to two levels of ''province-inheritor number prefix''.

The invention provides a bullying recognition notification method based on equipment cloud collaboration and AI keywords, and relates to the field of campus security and artificial intelligence, and the method comprises the steps: an equipment end collects environment audio in real time, detects bullying keywords in real time through a local AI model, and triggers data uploading; the cloud receives the audio and associates the audio with the scene video, performs secondary identification by using a multi-modal fusion model, and generates a behavior emergency index UI and a scene risk coefficient SRF; calculating a comprehensive risk value based on the UI and the SRF, and judging a bullying event in combination with a dynamic threshold value; and sending alarm information to a guardian, a teacher or a security platform according to the comprehensive risk value grade. According to the method, the recognition accuracy is remarkably improved through equipment cloud cooperative processing and multi-modal fusion analysis; a dynamically quantized UI / SRF evaluation system is introduced to realize scientific risk grading; an anti-disassembly mechanism, a local cache, breakpoint resume and an edge computing disaster recovery strategy are built in to guarantee the robustness of the system; the problems of inaccurate identification, risk quantification deficiency, response stiffness and easy equipment failure are solved.

The invention discloses an electronic contract signing method based on block chain evidence storage, and particularly relates to the technical field of electronic evidence preservation and intelligent contracts. Obtaining original data of the electronic contract and generating a hash abstract; setting a plurality of candidate block chain networks as evidence storage target chains; the transaction density index and the network propagation lag degree of each chain are collected in real time; inputting the parameters into a risk scoring model, and calculating a comprehensive risk score; executing a strategic broadcast operation according to a scoring result; monitoring a transaction confirmation state, and selecting a first block chain reaching a set confirmation number as a main storage certificate chain; when the evidence storage fails, under-chain supplementary evidence storage or re-broadcasting is triggered; according to the method, the chain state can be dynamically sensed, the optimal evidence storage path can be adaptively selected, the uplink success rate and the legal efficacy of the electronic contract are effectively improved, and the method has high disaster tolerance and cross-chain applicability and is suitable for scenes such as judicial, finance and government affairs with high evidence reliability requirements.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

The invention discloses a digital archive multi-protection-based data security transmission method, and relates to the field of data security transmission, and the method comprises the steps: generating a random seed in real time based on a quantum state, carrying out the iterative blocking of the quantum seed through combining with Logistic chaotic mapping, outputting variable-length secret key fragmentation, carrying out the symmetric encryption of archive data through employing a lightweight SM4 algorithm, and carrying out the encryption of the archive data. Obtaining encrypted data of the fragments; a behavior anomaly probability is calculated through a hidden Markov model, a risk score is calculated through a safety interval, and when a score value exceeds a safety threshold value, a grading response is triggered; key fragments are generated by adopting n nodes, at least k fragments can reconstruct signatures to monitor the node state of the block chain, and when the node anomaly rate exceeds a node threshold value, a three-layer architecture is automatically switched. The method has the advantages that an active multi-protection system is constructed through a quantum dynamic key, risk-driven hierarchical response and block chain disaster recovery switching, and quantum attack resistance and dynamic adaptation to transmission of network risks are realized.

The invention discloses a method and a system for cooperatively controlling post-disaster recovery and multi-source resource coordination of three networks, and belongs to the technical field of post-disaster recovery and toughness improvement of a multi-infrastructure system. The method comprises the following steps: obtaining a node importance evaluation function through multi-index fusion, and calculating a recovery priority score of a grey starting power supply; estimating the node state probability to obtain the node state probability; setting boundary conditions of recovery feasibility of the subarea power grid; optimizing a recovery sequence through a grey starting power supply-load-net rack path matching model, and generating a recovery path set; a reinforcement learning algorithm is adopted, node states, resource availability and a path connection matrix are used as state spaces, and an optimal recovery sequence and a resource scheduling scheme are output; and generating a three-network recovery path recommendation, a partition power grid recovery strategy and a multi-element flexible resource scheduling sequence. According to the invention, efficient scheduling and cross-network cooperative control of multi-source flexible resources are realized, and the recovery efficiency and toughness of three networks in a post-disaster scene are effectively improved.

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.

A power distribution network elasticity improvement method based on distributed new energy aggregation relates to the technical field of power systems, and comprises the following steps: S1, establishing a device-cluster-system three-level collaborative architecture, and collecting operation data of distributed energy nodes in real time through digital twin dynamic modeling; s2, dynamically adjusting a virtual power plant aggregation boundary based on a deep reinforcement learning algorithm, and optimizing a resource response strategy; s3, a multi-time-scale optimization framework is adopted, and space-time cooperative elastic enhancement is achieved in the pre-disaster stage, the in-disaster stage and the post-disaster stage; s4, realizing credible evidence storage and compensation liquidation of the resource scheduling instruction through the block chain; aiming at the problems of dynamic response delay and insufficient space-time dimension collaboration in the existing distributed energy aggregation technology, the invention realizes full-chain elastic improvement from minute-level dynamic aggregation to hour-level recovery optimization by constructing a three-level collaboration architecture, and solves the problems of low resource utilization rate and slow after-disaster recovery speed in the traditional method.

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

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 provides a power distribution network post-disaster intelligent recovery method and device based on a RainbowDQN algorithm. The method comprises the steps of obtaining a power distribution network model and a preset N-3 fault combination, and constructing a Markov decision environment model of a power distribution network load recovery task when a line has an N-3 fault; setting a target function and a constraint condition of the Markov decision environment model; training an intelligent agent in the Markov decision environment model by adopting a RainbowDQN algorithm added with a self-adaptive action mask to obtain a trained intelligent agent; and parameter information of the power distribution network with the N-3 line fault is transmitted to the trained intelligent agent, and the intelligent agent outputs a load recovery strategy of the power distribution network with the N-3 line fault. According to the method, the flexible soft switching technology is utilized to quickly isolate an affected area when a fault is caused by a natural disaster, so that the risk of fault diffusion is reduced, the power supply grid is quickly subjected to necessary topological adjustment after the disaster, and efficient distribution of power support after the disaster is realized.