253 results about "System recovery" patented technology

The invention provides an intelligent control method and system for fault positioning and rapid reliability recovery of a power distribution network, and relates to the technical field of power grids, and the method comprises the steps: collecting real-time data of each node of the power distribution network, inputting the real-time data into a pre-trained deep learning model, rapidly recognizing the fault type, and accurately positioning a fault point. Generating a preliminary fault assessment report; based on the generated preliminary fault assessment report, starting a multi-objective optimization algorithm, simulating various possible fault isolation and power grid reconstruction schemes by using a reinforcement learning technology, and selecting an optimal fault isolation and power grid reconstruction scheme; after fault isolation and power grid reconstruction are completed, the system recovery state is continuously monitored, detailed event reports and optimization suggestions are generated, and data support is provided for future fault prevention and system improvement.

The invention provides a DCS system autonomous controllable upgrading and system fault prediction method and system, and relates to the technical field of fault prediction, and the method comprises the steps: obtaining historical operation data and real-time operation data of a DCS system, carrying out the preprocessing, and generating an autonomous controllable upgrading scheme through a deep reinforcement learning algorithm; the method comprises the following steps of: setting specified time for upgrading time, carrying out consistent backup on the state of the DCS, executing software updating, hardware configuration adjustment and system parameter optimization according to upgrading steps, analyzing indexes of the DCS in real time in the process of executing the autonomous controllable upgrading scheme, and recovering the DCS to the state before upgrading when an abnormal condition is detected. And verifying and optimizing the real-time fault prediction result based on a knowledge graph, and generating an explanatory report including a fault prediction and upgrade decision process according to the optimized fault prediction result and the autonomous controllable upgrade scheme.

The invention discloses an industrial firewall isolation method of a DCS (Distributed Control System). The objective of the invention is to solve the problem that existing industrial network security protection has defects for a DCS system. According to the method, firstly, DCS architecture analysis and risk assessment are carried out, and protection key points and data flow directions are accurately determined; then, a firewall strategy fitting the characteristics of each area (such as a core control unit, an operation station and an external network interface) is customized, and mechanisms such as multiple data encryption are covered; and then firewall deployment and configuration optimization are carried out to ensure protection continuity and rule timeliness. And abnormal conditions are timely handled through a real-time monitoring and dynamic adjustment mechanism, and system recovery under a security event is guaranteed through an emergency response and backup recovery mechanism. And a regular safety drill and evaluation mechanism is arranged to continuously strengthen the protection capability. According to the method, the network security level of the DCS is comprehensively improved, stable and safe operation of industrial production is guaranteed, and the method has remarkable practical value.

The invention provides a power system recovery method and system of a black-start power supply, a medium and electronic equipment. The method comprises the following steps: establishing a frequency-power response characteristic of cooperative work of a first preset unit and a second preset unit; according to frequency-power response characteristics of cooperative work, obtaining a target frequency response model of sudden load increase under power shortage; determining constraint conditions of the power system according to the target frequency response model; in combination with the target frequency response model, obtaining a target frequency response result of each black-start power supply to the power system when the black-start power supply is started; determining a plurality of power supply aggregation modes of each black-start power supply according to each target frequency response result in combination with a constraint condition; and determining an optimal recovery path of each black-start power supply under the shortest time consumption based on a shortest path algorithm and each power supply aggregation mode, so as to recover the power system according to the optimal recovery path. The problem that a black start method in the prior art is low in adaptability is solved.

The invention relates to the technical field of power grids, in particular to a load shedding anti-overspeed control method during power grid decoupling of a compressed air energy storage system. Acquiring rotating speed data of the compressed air energy storage system when the power grid is decoupled, and monitoring the rotating speed data; a load shedding mode is preset, and block valves and exhaust valves at the front ends of all stages of expansion machines are controlled; and executing a load shedding mode according to the change of the rotating speed data, and controlling the rotating speed of the system to be reduced to a preset range. The load shedding mode is divided into two stages, wherein in the first load shedding stage, rotating speed control is conducted by closing block valves at the front ends of all stages of expansion machines; and in the second load shedding stage, the residual working medium is discharged by opening the exhaust valve. A system standby recovery strategy is established, and stable transition of the system from an emergency state to a normal operation state is realized. According to the method, the problems of inaccurate rotating speed control, insufficient residual working medium treatment and the like during power grid decoupling of the CAES system in the prior art are effectively solved, and the operation safety and reliability of the system are improved.

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 an intelligent early warning system for laboratory hazardous gas leakage, and the system comprises a system initialization and configuration module which is used for carrying out sensor deployment, system configuration and network connection; the real-time monitoring and data acquisition module is used for continuously monitoring sensor data and preprocessing the data; the data analysis and judgment module is used for threshold comparison and trend analysis to obtain alarm information and notification information; the alarm and notification module is used for sending out alarm information and notification information; the linkage control and emergency response module is used for automatically closing a dangerous source and starting a ventilation system; the recording and reporting module is used for recording events and generating reports; and the system recovery and evaluation module is used for alarm release, system inspection and safety evaluation. According to the invention, more comprehensive and intelligent laboratory hazardous gas leakage early warning can be carried out.

The present invention relates to the fields of computer science and information technology, and specifically to a fault isolation and rapid recovery system based on a microservice architecture, which integrates automated fault detection and identification technology, intelligent grayscale release mechanism, fault isolation technology, rapid recovery strategy and elastic microservice design to provide efficient and reliable fault management and system recovery solutions. By analyzing the operating indicators and log data of microservices, using machine learning algorithms to detect abnormal behaviors, automatic fault detection is achieved; using containerization technology and Kubernetes clusters for intelligent grayscale release, and real-time monitoring of the application status of new versions; achieving fault isolation through service grids, and using Kubernetes automatic expansion strategies to increase the number of healthy Pods and quickly isolate faults; when a fault occurs, through the rollback operation in the CI / CD process, the above technical measures are used in combination to effectively improve the stability, flexibility and maintainability of the system, and are suitable for complex and changeable production environments.

The invention discloses a cluster backup recovery system based on multiple data centers, and belongs to the technical field of cluster backup recovery, and the system comprises a plurality of data centers with dispersed geographic positions, which are used for deploying and operating the same business application and database service at the same time; the global synchronous optimization module is used for dynamically selecting an optimal data transmission channel to transmit the data fragments to a target data center in parallel according to a scheduling instruction of the system and network conditions among the data centers; the distribution consistency guarantee module is used for enabling each data center to backup and write correct data in parallel according to a Multi-Paxos consistency protocol, log synchronization and conflict detection; and the remote parallel recovery module is used for extracting the decompressed target backup data from each data center in parallel according to the task number, and transmitting the decompressed target backup data to the to-be-recovered data center for data recovery. The problems that the backup system recovery task recognition efficiency is low, and scheduling mechanism optimization is insufficient are solved.

The invention discloses an alarm flow control method based on consistent Hash of alarm priority and related equipment, the cache size is firstly configured according to the actual physical memory size to ensure that alarm software can process alarm information as efficiently as possible on the premise of not exhausting system resources, then priority arrangement is carried out, and the alarm flow control efficiency is improved. Priority processing of high-priority alarms can ensure that key safety problems are responded in time, potential risks are reduced, setting of an avalanche value provides an early warning mechanism for the system, the system can respond in advance before the number of alarms increases sharply, the system state is automatically adjusted according to real-time changes of the number of alarms, and the system safety is improved. The method ensures that the processing capability can be quickly recovered after the system returns to normal, finally ensures that all nodes can obtain complete alarm information and keep data consistency, improves the stability and reliability of the system under extreme conditions through a series of optimization measures, preferentially processes high-priority alarms, and improves the reliability of the system. And key safety problems can be timely responded and processed.

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention relates to the technical field of data recovery, and discloses a data recovery method and system based on a multi-cloud platform, and the method comprises the following steps: S1, constructing an intelligent data acquisition layer: carrying out the dynamic adaption of four data collection channels based on a multi-modal data recognition engine; s2, constructing an adaptive transmission protocol stack: dynamically selecting based on a network quality real-time evaluation model; s3, heterogeneous system architecture recovery: realizing architecture recovery through a virtual environment abstraction layer; s4, establishing a space-time dimension data management model, which is realized based on a cloud native storage architecture; and S5, automatically recovering an arrangement engine through a workflow engine. According to the method, the reliability and the stability of the system are improved while the labor cost is saved, high-frequency copy synchronization is supported, the resource utilization rate is improved, and the data reading efficiency can be improved through block-level data synchronization.

The invention discloses a system recovery method and system. The method comprises the following steps: configuring an environment variable partition; network rescue server parameters are pre-stored in the environment variable partition, a first-stage starting command, a second-stage starting command, a third-stage starting command, a network rescue command and a main starting command are defined, and the main starting command initially points to the first-stage starting command; starting for the first time, reading and executing a first-stage starting command from the environment variable partition by the universal boot loader; after execution of the first-stage starting command fails and a system is triggered to be restarted, the universal boot loader reads the updated main starting command from the environment variable partition, and the second-stage starting command and the third-stage starting command are executed in sequence; and when the third-stage starting command fails, the universal boot loader executes the network rescue command, downloads a recovery file from the network rescue server and repairs the system.

The invention discloses a BMC firmware starting method and device, equipment, a medium and a program product, and relates to the technical field of server management.The BMC firmware starting method comprises the steps that when a main mirror image file is damaged, BMC firmware is restarted on the basis of a standby mirror image file, if starting fails, the BMC firmware is restarted on the basis of a latest mirror image file of a remote server, and if starting fails, the BMC firmware is restarted on the basis of the latest mirror image file of the remote server; if the BMC firmware is started abnormally, the BMC firmware is restarted based on the local minimum mirror image file, so that the problem that a double-mirror image backup mechanism still faces system paralysis can be solved, a multi-stage mirror image file recovery mechanism is constructed through the multiple pre-deployed mirror image files, when the BMC firmware is started abnormally, the system is recovered step by step according to the multi-stage mirror image file recovery mechanism, and the system recovery efficiency is improved. The technical effects of ensuring the minimum availability of the system and improving the reliability and safety of the system are achieved.

The invention discloses a restart control method and device of a server processor, a storage medium and electronic equipment, and relates to the field of servers, and the method comprises the following steps: detecting the running state of the server processor, and storing a recovery file for the server processor according to the running state of the server processor, then the server processor is controlled to recognize the first controller as an external storage medium, and then the server processor is controlled to restart the external storage medium as a starting item which is preferentially executed in the starting process, so that the server processor can directly obtain a recovery file from the first controller when needing to be restarted; the executive program contained in the recovery file can guide the temporary operating system after the server processor is restarted, and the temporary operating system can provide an operating environment required by system recovery for the server processor, so that the technical problems of poor restarting reliability and the like of the server processor are solved; the technical effect of improving the restart reliability of the server processor is achieved.

The invention discloses a system quick recovery method and system based on system mirror image management, and the method comprises the steps: initializing a boot partition of a functional blade in a heterogeneous cluster system and a boot flag bit of the boot partition, the boot flag bit being used for BIOS reading to select a corresponding boot partition when the functional blade is started; the method comprises the following steps: triggering a job operating system or a boot operating system of a target function blade to restart through a soft shutdown interface, and modifying a start boot flag bit in a BMC (Baseboard Management Controller) of the target function blade, so that the target function blade is sequentially restarted and switched into the boot operating system from the job operating system; and executing system quick recovery or backup for the target system partition under the boot operating system, and then restarting and switching to run under the specified job operating system so as to complete system quick recovery or backup. According to the method, the characteristics of the heterogeneous cluster system are combined, and automatic and flexible system mirror image fast rollback recovery is achieved when the heterogeneous cluster system has a serious fault.

The invention provides a diesel-storage hybrid power anti-overload system and device based on a network-forming inverter, and a medium. A pre-synchronization module realizes seamless switching between a VSG mode and a PQ mode based on dynamic voltage amplitude matching, phase-locked loop frequency synchronization and phase real-time alignment technologies; the hardware current limiting module adopts high-speed current monitoring and hardware-level protection logic to cut off an overcurrent pulse in real time and prevent a power device from failing due to transient impact; the power suppression module optimizes the power distribution of the diesel generator and the energy storage by judging an overload threshold value and a steady state recovery condition in real time; the logic judgment module coordinates the diesel generator dynamic frequency modulation and energy storage mode switching, the diesel generator preferentially bears the transient power difference when the load suddenly changes, and the energy storage mode is seamlessly switched to the network construction mode after the system recovers the steady state; according to the system, through software and hardware full-link collaborative design, the problems of transient overload and mode switching instability of the diesel storage system are solved, and the stability and energy efficiency of the diesel storage hybrid power system are improved.

The invention discloses an intelligent early warning and decision making system and method based on large model driving. The intelligent early warning and decision making system comprises a server log data layer which is responsible for collecting various log data generated in the operation process of a server; the filtering layer is used for undertaking a task of quickly screening abnormal log information; the retrieval layer is used for matching similar or relevant information in a risk library for each abnormal log; the Agent analysis and decision-making layer is used for carrying out deep analysis on abnormal conditions by utilizing natural language processing and knowledge reasoning capability of an intelligent deep large model so as to identify attack types and formulate personalized defense schemes; the defense layer is responsible for calling a built-in defense tool library to execute a defense scheme; the execution layer is used for converting an instruction of the defense layer into a specific operation automatically executed through a script; the monitoring layer is used for monitoring results in and after scheme execution in real time; the inspection layer is used for automatically inspecting the production line after monitoring is normal; and the analysis layer is used for carrying out automatic chart analysis on attack or risk data. According to the invention, automatic processing from attack detection to system recovery is realized, and the response efficiency is improved.

The embodiment of the invention provides a state storage and recovery method and device, electronic equipment and a storage medium. The method comprises the steps that when a snapshot task of state data runs, detailed information of the state data is collected, and in response to completion of the snapshot task, the detailed information is stored in a storage system outside the distributed system, according to the method, the state data of the distributed system is stored in the storage system, so that a management platform of the distributed system reads detailed information of the state data from the storage system, the distributed system is controlled to recover the previous state according to the detailed information, the detailed information of the state data is collected in real time after a snapshot task when a program in the distributed system runs, the real-time performance is high, and the reliability is high. Therefore, frequent sending of requests to the distributed system is avoided, network broadband and system resources are saved, and performance and stability of the distributed system are improved.

The invention relates to the technical field of heat supply system recovery, and discloses a heat supply system heat transfer intelligent recovery method based on an LSTM neural network. The method comprises the following steps: firstly, collecting thermodynamic parameter time sequences of water supply temperature, water return temperature, pipe section flow, environment temperature and the like of each monitoring node of the heat supply pipe network; performing standardization processing on the time sequences, eliminating influence of different physical quantity lines, and generating a standardized thermodynamic time sequence; then, the heat supply pipe network is divided into a plurality of functional areas according to the topological structure and the thermal characteristics, and each area comprises a plurality of associated monitoring nodes; the LSTM neural network is used for deeply analyzing processed data and divided areas, intelligent heat transfer recovery of the heat supply system is achieved, the dynamic change rule of parameters can be accurately captured, the method adapts to complex working conditions, heat transfer recovery is more targeted and reasonable, heat energy waste is reduced, and the operation efficiency of the heat supply system is improved.

The invention discloses a low-voltage-crossing adaptive control method and device of a wind turbine generator, electronic equipment and a medium, and aims to solve the problems of slow system recovery, voltage overshoot and current out-of-limit in the current low-voltage-crossing control technology. The method comprises the following steps: acquiring running state data of a wind turbine generator at a grid-connected point in a low-voltage period in real time, and calculating a system short-circuit ratio according to the running state data; considering a power adjustment coefficient, and constructing a current feedback controller based on the operation state data; introducing an adaptive weighting strategy of a system short-circuit ratio, and combining a power adjustment coefficient to construct a multi-objective optimization function; the multi-objective optimization function is subjected to parameter optimization solution combined with system short-circuit ratio self-adaptive correction, and an optimized power adjustment coefficient is obtained; and substituting the optimized power adjustment coefficient into a current feedback controller, solving a control current value, and performing current control on the wind turbine generator based on the control current value.

The invention relates to the technical field of system backup, in particular to a factory backup method and device for a domestic operating system, and the method comprises the following steps: after automatically deploying a customized operating system, running a UnisPareted.sh script and a UnisInstall.sh script in sequence in an OEM mode so as to perform partition operation and target lightweight operating system installation operation, and generating a FactoryBackup.ini file; the method comprises the following steps: starting a FactoryBackup.ini file, then restarting target factory equipment, reading backup indication information of the FactoryBackup.ini file through a BIOS (Basic Input / Output System), executing factory backup operation, and finally reporting a factory backup result to an MES (Manufacturing Execution System) server. According to the scheme, the automatic station crossing requirement is met, large-scale deployment is achieved, the production efficiency is improved, customized software needs can be recovered when the system is recovered, and the trouble of reinstallation and reconfiguration is avoided.

The invention belongs to the technical field of power system recovery control, and discloses a power coordination control method of a hybrid energy storage auxiliary black-start system and a related device.According to the method, the total active power requirement of the black-start system is obtained and decomposed into three time scale components of fundamental waves, intermediate frequencies and high frequencies; the power is dynamically distributed to the lithium battery and the super capacitor, the distributed power is corrected based on the temperature, the operation time and the rated parameters, and finally the frequency change rate and the direct-current voltage change rate are calculated according to the actual power instruction and the system parameters so that the controller can output the control quantity. By adopting the method, the contradiction between the energy and the power density of a single power supply is overcome, the response speed and the continuous power supply capability are synergistically improved, the voltage frequency instability risk caused by load input is effectively inhibited, and the reliability and the high efficiency of black start are remarkably improved by perfecting hybrid energy storage coordination control.

The invention relates to the technical field of power system operation and control, in particular to a power system recovery capability evaluation method, device and equipment considering unconventional risks and a medium, and the method comprises the steps: determining parameters for calculating the occurrence probability of earthquake disasters of different earthquake magnitudes; calculating intensity distribution by using an earthquake intensity diffusion model; calculating transmission tower and transmission line fault probabilities to obtain a fault line set; according to the operation power flow and the maximum allowable power flow, the cascading failure situation is divided into high risk and low risk; aiming at the two conditions, respectively enumerating a first-order fault state set and a second-order fault state set to obtain a system recovery capability initial value; the value serves as input, a convergence threshold value is set, a ULBI-IISE algorithm is adopted for iterative calculation, when the difference value of the upper cut set and the lower complementary set is smaller than the threshold value, the recovery capacity and the interval are output, and the method reduces high-order fault complete enumeration and the calculation amount while guaranteeing the precision, and is suitable for rapid evaluation of emergency scenes such as earthquakes.

The embodiment of the invention provides a fault processing method and device for a control system. The method comprises the following steps: determining a current running state of the control system; under the condition that the current running state accords with the target abnormal state, target fault configuration information corresponding to the target abnormal state is obtained, and the target fault configuration information is configured with at least one fault reason in the target abnormal state and fault recovery programs corresponding to the fault reasons respectively; according to the target fault configuration information, a target fault reason of the control system is determined, a target fault recovery program corresponding to the target fault reason is executed, the target fault recovery program is used for enabling the control system to recover the normal state, and the target fault reason of the control system is determined according to the target fault configuration information. And the target fault recovery program corresponding to the target fault reason is executed, so that the system can be helped to perform autonomous recovery, the system is recovered to a normal state, the system reliability is improved, and the train driving safety and the passenger travel safety are guaranteed.

The invention discloses a server hardware link diagnosis method and a server hardware link diagnosis system, relates to the technical field of computer system fault diagnosis, and discloses the server hardware link diagnosis method and the server hardware link diagnosis system. Through the steps of obtaining a diagnosis rule configuration file, collecting software and hardware state data, carrying out matching analysis to generate a diagnosis path, executing diagnosis processing, carrying out correlation analysis, generating a fault report and the like, the problems of software and hardware diagnosis splitting and path stiffness in the prior art are solved, and collaborative diagnosis and dynamic path generation of software and hardware faults are realized. And the fault positioning accuracy and the system recovery timeliness are improved.

The invention discloses a satellite-borne router exception recovery system and method based on multistage redundancy, and supports the construction of intra-satellite, inter-satellite and satellite-ground data link communication schemes in a satellite constellation communication scene. The system-level recovery method based on multi-level redundancy is designed for various abnormal scenes faced by a satellite-borne router in a satellite operation environment, and in combination with telemetering state judgment and remote control instruction issuing, in-orbit recovery of data link communication service availability is achieved. Abnormalities related to the guiding system can be recovered through modes of redundant system switching, double-flash configuration synchronization, on-orbit system updating and the like. Abnormalities related to a network operating system can be recovered through self-adaptive network configuration reloading, system switching based on GPIO (General Purpose Input / Output) monitoring, redundant system switching, on-orbit system updating and the like. According to the satellite-borne router abnormity recovery method constructed based on the invention, a single device can be combined to generate 16 system recovery routes, and the availability of a satellite constellation data link communication service is improved.

The invention relates to a power grid recovery model rapid solving method based on a shortest circuit principle. The method comprises the following steps: constructing a system recovery two-stage robust optimization model considering the uncertainty of new energy output; the whole system recovery process is divided into two recovery periods, the first recovery period is the recovery period of units, corresponding net racks and important loads, and the second recovery period is the recovery period of residual networks and loads; for the first recovery period, converting the system recovery two-stage robust optimization model into a three-layer optimization problem, and constructing and solving a first recovery period model based on a shortest circuit principle; based on a solving result of the first recovery period model, constructing a robust optimization model of a load and a line in a second recovery period based on the gradeability of the unit, and solving the robust optimization model; and continuously solving the robust optimization model of the load and the line in the second recovery period based on a rapid solving strategy. According to the method, the time required for generating the system recovery scheme is shortened while the model solving precision is ensured.

The invention relates to the technical field of power distribution network recovery, and discloses a power distribution network recovery success rate prediction method and system, and the method comprises the steps: collecting the operation data of a power distribution network; constructing a Bayesian network, defining nodes according to characteristics of the power distribution network, determining a connection relation of edges, and constructing a conditional probability table; the method comprises the following steps: constructing a recovery strategy through a Markov decision process for dynamic optimization, training the recovery strategy through a reinforcement learning algorithm, introducing a Monte Carlo tree search algorithm to search an optimal solution for the recovery strategy, calculating the probability that the power distribution network is recovered to a target state, and generating success rate prediction results of different recovery strategies. According to the invention, the interpretability and adaptability of the model are improved; dynamic optimization of a recovery strategy is realized, and different fault scenes and system states can be adapted; the calculation efficiency is improved, factors such as system topology, load characteristics and new energy output are comprehensively considered, the prediction accuracy and practicability are improved, the system recovery efficiency is improved, and the power failure loss is reduced.