393 results about "Business continuity" patented technology

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention relates to a traffic anomaly detection and network security reinforcement method for a full data center, and the method comprises the steps: achieving the precise recognition and active blocking of an attack chain through the real-time collection of full network traffic, the construction of a multi-dimensional time sequence model based on historical attack chain features, and the combination of a dynamic judgment threshold value and spatial and temporal distribution feature analysis. The method specifically comprises the steps of analyzing traffic in real time to generate an attack chain feature sequence; performing clustering and correlation analysis based on the multi-dimensional time sequence model; dynamically adjusting the monitoring density to focus the high-risk area; and generating a threat blocking instruction and executing network isolation. Aiming at the problems of high attack chain omission ratio, resource contention conflict and insufficient dynamic defense capability caused by dependence on a static rule and a fixed threshold value in the traditional scheme, the method solves the problem of weak perception capability of the traditional technology on a complex attack chain, remarkably reduces the omission rate and the error blocking rate, guarantees the service continuity through dynamic resource scheduling, and improves the service performance of the system. The method is suitable for real-time security protection of a large-scale data center.

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 discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the system.

The invention discloses a project side end data fragmentation recombination and service breakpoint resuming method in a weak network environment, and the method comprises the steps: receiving original service data collected on site through a project side end, carrying out the fragmentation according to the division of a construction region and a time interval, and generating a fragmentation data package with a time-space identifier and a check code; verifying the received fragmented data packet, and generating a fragmented cache set with a processing progress; when all fragments of a certain region-time fragment in the fragment cache set are received, the preprocessing progress of the corresponding service is read from the persistent file, and remaining service processing continues to be executed from the progress interruption position; and integrating the recombined original data of each region-time slice, the business processing result, the breakpoint resume record and the fragment transmission log, and generating a business data file containing a complete data chain and a processing track. By utilizing the embodiment of the invention, the data transmission efficiency and the service continuity in a weak network environment can be improved, and the reliability requirement of a project side end scene is met.

The invention relates to a lightweight AI outbound model hot switching framework construction method. Defining a runtime context state unit to form an independent data structure; the AI model is divided into modules responsible for process reasoning execution; the module is in communication with the module responsible for dialogue memory and intention tracking state scheduling through a lightweight intermediate interface; establishing a set of semantic alignment mapping table based on the semantic difference between the models; performing semantic label conversion on a current dialogue state through a mapping table, and injecting a pseudo state vector into the new model to reconstruct an initial reasoning condition; calculating a tension score based on the intention hopping frequency, the slot position switching frequency and the user reply uncertainty; if the controller judges that the switching window is a safe switching window, hot switching logic can be triggered; a prediction module is introduced before switching to perform rehearsal on the reachable intention coverage of the new model, and switching is delayed if the risk is too high. The whole model hot switching process is not perceived by users, dialogue continuity and service continuity are guaranteed, the system resource utilization rate is improved, and model scheduling intelligence is enhanced.

The invention relates to the technical field of data center high availability, in particular to a cross-data center fault isolation and switching method and system in a multi-tenant environment. The resource mapping table is constructed by taking the tenants as the minimum control units, the affected tenants are accurately identified, logic isolation is executed, the problem of waste of full-tenant service migration resources caused by node-level or cluster-level switching in the prior art is avoided, and the fault influence range is minimized. The optimal data center is dynamically selected by combining the multi-factor objective function with the tenant SLA level, the defects that an existing scheduling strategy is opaque and tenant priorities cannot be distinguished can be overcome, and it is ensured that key tenant services are preferentially recovered. And finally, through combination of a gray takeover mechanism and health feedback confirmation and multi-dimensional recovery verification after migration, the condition that an existing recovery mechanism is extensive can be changed, the fault processing precision and the resource scheduling efficiency of the data center in a multi-tenant environment are remarkably enhanced, and the service continuity is guaranteed.

The invention relates to the technical field of computers, in particular to a dynamic configuration initialization and version synchronization method and system for multi-agent service, and the method comprises the following steps: obtaining a database update lock, constructing a database migration script dependency relationship, determining a current version of a database mode, upgrading and deploying the database mode, and verifying agent versions. The method has the beneficial effects that a rigorous database updating lock control mechanism, perfect migration script dependency chain verification, agent version management, port availability detection, idempotence and transaction safeguard measures are introduced, so that the processes of database structure upgrading and agent version initialization in a distributed deployment environment can be effectively supported; and the data consistency, the operation stability and the service continuity of the system are ensured.

The invention discloses a multi-role collaborative management architecture based on RBAC + HIS synchronization, and the architecture comprises a role authority management module which builds a static role authority baseline based on an RBAC model, and associates the real-time organization architecture data of an HIS system through a dynamic rule engine, and generates a multi-level role authority tree; the data synchronization middleware adopts an API gateway to realize one-way data push between the HIS system and the psychological assessment platform, comprises a field-level encryption unit and an incremental data verification unit, and supports isolated transmission of patient medical records, medical advices and assessment tasks according to inpatient areas; the multi-terminal cooperation engine realizes authority mapping of the doctor workstation, the mobile nursing terminal and the patient self-service terminal through distributed session management, a conflict detection mechanism is built in, and a secondary authentication process is triggered when cross-inpatient-area operation is detected. According to the invention, dynamic authority management is realized, data secure transmission and multi-terminal cooperation are guaranteed, auditing tracing is enhanced, and business continuity when the system is abnormal is ensured.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

The invention relates to the technical field of computers, in particular to a storage cluster system upgrading method and device, electronic equipment, a medium and a product. The method comprises the steps that a current upgrading package is received, and the state of a current rapid upgrading function is obtained; under the condition that the current upgrade package meets the pre-checking condition, decompressing the current upgrade package, and performing pre-upgrading checking on the decompressed upgrade package to obtain a pre-upgrading checking result; and determining a target upgrading mode of the current storage cluster system according to the pre-upgrading check result and the state of the current rapid upgrading function, and performing staged upgrading on the grouped nodes of the storage cluster based on the target upgrading mode so as to complete upgrading of the current storage cluster system. Therefore, the problems of service interruption, long consumed time and reduced reliability caused by forcing the controller to restart in the existing storage cluster upgrade are solved, the non-interruption upgrade of the storage system is realized, the controller restart is avoided while the system consistency is ensured, and the service continuity and the upgrade efficiency are remarkably improved.

The invention relates to the technical field of quantum cryptography, in particular to a dynamic key generation system and method based on multi-source QRNG and QKD, and the system comprises an interface registration method, a calling method, related equipment and a storage medium, is used for generating a high-security quantum key, and is a quantum key service system with a layered architecture. By abstracting, integrating and managing bottom-layer multi-source QRNG and QKD equipment and fully utilizing the flexible scheduling of quantum equipment, the security of key generation is ensured, so that the key service has higher performance, usability and security, and unified, dynamic and high-security quantum key generation and supply services are provided for various applications of the upper layer. The method is widely applied to the industries and fields of government, finance, energy, traffic, electric power and the like, meets the requirements of quantum key acquisition of various business applications in cloud computing and non-cloud environments, and ensures information security and business continuity.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

The invention relates to the technical field of computers, in particular to a containerized scene fault prediction and self-healing method and device, equipment and a storage medium. The containerized scene fault prediction and self-healing method comprises the following steps: obtaining resource use data related to a container in real time by using a sliding window, carrying out noise filtering on the resource use data, generating time sequence characteristics, and obtaining preprocessed data; analyzing the preprocessed data by adopting an isolated forest model to obtain an abnormal score, and judging whether a node of the container has a fault risk or not according to the abnormal score; and carrying out corresponding self-healing operation on the fault node according to the fault type by adopting a pre-constructed self-healing strategy. By analyzing the container resource data, the fault nodes and the types of the fault nodes can be effectively identified, the self-healing operation is pertinently executed, the resource utilization rate is effectively increased, and the core service continuity is guaranteed.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a fault insight and self-healing method and system based on chain control. According to the chain control-based fault insight and self-healing method, indexes, logs and tracking data of a micro-service environment are dynamically collected and fused; the method comprises the following steps: dynamically constructing and visualizing a service dependent topology, realizing dynamic topology construction and intelligent visual rendering, performing intelligent root cause analysis and predictive diagnosis based on multi-dimensional data, realizing self-adaptive early warning, generating an intelligent decision, performing closed-loop learning optimization based on an execution result, and realizing automatic fault repair and continuous optimization. According to the fault insight and self-healing method and system based on chain control, a predictive early warning mechanism is established, automatic fault self-healing is realized, and through intelligent and automatic means, manual intervention is reduced, the operation and maintenance cost is reduced, the average repair time is shortened, the service continuity is guaranteed, and the system availability is improved.

The invention discloses a fault positioning method, device and equipment and a computer program product, and relates to the technical field of computer software, and the method comprises the steps: carrying out the anomaly detection of a to-be-detected business system, and obtaining a plurality of pieces of anomaly information; analyzing the multiple pieces of abnormal information to obtain an abnormal association relationship; and identifying the abnormal association relationship, and performing layer-by-layer recursive positioning of a fault cause according to the obtained abnormal topological relationship until analysis of each layer in the abnormal topological relationship is completed. The abnormal information in the service system is detected in real time, and data support is provided for fault positioning. By analyzing the abnormal association relationship between the abnormal information and further identifying the abnormal topological relationship, the service influence range can be identified. And according to the topological relation, recursively drilling down to a downstream service and resource level layer by layer until the root cause of the fault is positioned, so that the fault type and the fault point position are accurately positioned, and guarantee is provided for service continuity.

The invention belongs to the technical field of network management, and particularly relates to a road toll collection system network security task dynamic allocation and load balancing method, which comprises a road toll collection system, and the road toll collection system comprises a network security task dynamic allocation system and a load balancing system. The network security task dynamic allocation system serves as an intelligent security protection scheduling center, is used for coping with continuous evolution threats, and comprises a real-time threat perception and response scheduling integration, a resource elastic scaling system and a strategy self-adaptive adjustment system. The load balancing system serves as a foundation stone of service continuity and performance, ensures stable operation of high-concurrency transactions, and comprises an intelligent flow distribution system, an automatic fault isolation and recovery system and a load balancing system. The method can deal with sudden security events, optimize the resource utilization rate, improve the protection precision, guarantee high availability, improve the user experience and support the elastic expansion of the system.

The invention discloses a network security protection strategy generation method based on multi-source data fusion. The method comprises the following steps of collecting security data of a network side, a terminal side, an identity side, an asset and service topology side and an external threat intelligence side, and performing time alignment and missing supplementation; standardly generating an evidence unit sequence and an evidence credibility vector, and aggregating and constructing an evidence unit long sequence according to an entity and a sliding time window; inputting the evidence unit long sequence, the evidence credibility vector and the service topology condition vector into an improved Mamba-2 model, and outputting an attack intention distribution, risk situation embedding and key evidence reference set; generating a strategy candidate set; optimizing an output strategy packet under the constraint of a service link, a change window and compliance auditing; executing protection and monitoring indexes, and if a rollback condition is met, performing rollback; and collecting feedback update credibility mapping parameters and trigger thresholds. According to the invention, closed-loop self-adaptive protection is formed, and the strategy effectiveness and the service continuity guarantee capability are improved.

The invention is suitable for the technical field of computers, and provides a containerized multi-copy deployment management method and device, terminal equipment and a storage medium, and the method comprises the steps that after a container copy is started, state information of the container copy is periodically updated to a real-time state list, and the state information comprises an IP address and a node health level; reading state information of other container copies in the real-time state list; dynamically electing a master container copy according to a preset master-slave election rule based on the IP address and the node health level in the real-time state list; when it is detected that the current main container copy is abnormal, an automatic switching process is triggered, and the main container copy is elected again. According to the method and the device, the efficiency and the reliability of copy management in a containerized multi-copy deployment scene can be improved, so that the service continuity and the data consistency are guaranteed.

The present invention discloses an e-commerce management method and system based on big data analysis, which relates to the field of data management technology. The present invention realizes the stable operation and efficient data transmission of the e-commerce platform in a complex network environment by introducing big data analysis and dynamic slicing scheduling mechanism. With the help of real-time evaluation and classification processing of the network status of user terminals, the present invention can intelligently adapt to different types of e-commerce business needs, thereby realizing personalized transmission strategies, and effectively alleviates the network congestion problem during the promotion period through time-series differential encoding and interpolation compensation of high-priority data such as flash sales, thereby improving the response speed and user experience of key businesses. Combined with dynamic delay floating analysis and packet loss rate evolution judgment, the present invention can adaptively adjust the data sending rhythm, ensure the smooth scheduling of slice data when bandwidth fluctuates, and significantly improve business continuity and system fault tolerance.

The invention discloses a disk scheduling method, and relates to the technical field of storage systems, and the method comprises the steps: collecting a historical input / output access mode of a disk array card, the mode covering access information of a disk requested to be input / output in a historical time period, and generating a disk scheduling strategy adaptive to a business rule by using a pre-trained disk scheduling model; and meanwhile, monitoring the interface traffic of the disk array card in real time, calculating the bandwidth utilization rate of the disk array card according to the interface traffic, and dynamically adjusting the number of active and dormant disks in combination with a strategy. Therefore, the disk number and bandwidth allocation are flexibly self-adapted, the problems of bandwidth imbalance, burst load response delay and the like caused by rigid configuration in the traditional technology are effectively solved, and the service continuity and the system reliability are guaranteed.

The invention is suitable for the field of business data scheduling, and provides a data-in-channel scheduling method and system adaptive to multiple business scenarios, and the method comprises the steps: collecting business feature data in real time, abstracting business departments participating in scheduling as game roles, and distributing resource demand attributes and game chip attributes for the game roles. And based on the task urgency degree coefficient, the resource utilization efficiency and the scene adaptation weight, constructing a bid calculation model to generate automatic bids, allocating exclusive resources to high-priority roles in combination with a resource allocation thermodynamic diagram, and scheduling low-priority roles as required through an elastic resource pool. The system dynamically monitors resource utilization rate and task indexes, evaluates bidding rationality and adjusts chip attributes. According to the method, roles are stimulated to compete efficiently through a game mechanism, resource allocation is optimized in combination with dynamic feedback, the problems that a traditional method is poor in adaptability and not public in allocation are solved, and the resource utilization efficiency and service continuity in a multi-service scene are remarkably improved.

The invention discloses a fault processing method of a multi-control storage system and electronic equipment, and relates to the technical field of data storage, and the fault processing method comprises the following steps: controlling a cache state machine to execute a first fault processing flow under the condition of identifying that a specified mirror image pair exists in at least one mirror image pair, the specified mirror image pair is a mirror image pair containing two fault controllers; and under the condition that the first fault processing flow is not executed completely and a specified controller is identified to exist, executing a second fault processing flow of the cache state machine based on an execution stage of the first fault processing flow so as to control the multi-control storage system to continue to perform service processing, the problem of long-time service interruption of the multi-control storage system in a fault scene in the prior art is solved, and the service continuity and the system stability are remarkably enhanced.

The invention provides a multi-agent cooperation-oriented process planning and execution system and method, and the system comprises an autonomous process planning engine which dynamically generates and optimizes a process path based on a reinforcement learning model according to a real-time business rule; the multi-Agent collaborative execution layer receives a flow path, performs task decomposition and adaptive load balancing to intelligently distribute sub-tasks to a plurality of Agents for parallel execution, and adopts a consensus protocol to guarantee the consistency of key data; and the fault self-healing guarantee system continuously monitors the running state of the system, identifies a source through an intelligent positioning module when detecting an abnormality, and automatically generates and executes a recovery strategy in combination with a preset template and a dynamic learning mechanism. By constructing an intelligent closed loop of planning, execution and self-healing, dynamic self-adaption, efficient cooperation and high-reliability operation of the business process are achieved, complex business changes and system anomalies can be coped with without manual intervention, and the intelligent level and the business continuity guarantee capability of an automatic system are remarkably improved.

The invention discloses a business topology automation construction method and system based on an industrial environment, belongs to the field of industrial business topology automation, and aims to analyze a logical relationship between devices in real time based on a neural network, dynamically adjust node priorities and combine a topology optimization mechanism of a genetic algorithm / particle swarm optimization algorithm. The method reduces the false alarm rate of the system under the scenes of equipment firmware upgrade, configuration change and the like, adopts an event-triggered topology automatic update mechanism, eliminates the requirements of manual maintenance of a fingerprint library and a rule library, and realizes automatic discovery and construction of industrial service topology by monitoring the network state in real time and triggering adaptive reconstruction. A complete and accurate topological graph can be quickly generated without manual intervention, and a configuration backup and progressive switching mechanism is implemented in a topology reconstruction process to ensure service continuity.

The invention provides an online migration management method and device for database hotspot data and a medium, and belongs to the technical field of database migration. The method comprises the following steps: generating a hotspot object list based on hotspot statistical information collected in a preset time period; and according to the hot object list, determining a corresponding online migration task, and allocating the online migration task to a migration thread pool, so that the migration thread pool executes the online migration task based on preset migration strategies corresponding to different granularity levels. In response to an execution instruction of the online migration task, generating a corresponding hotspot object access mapping table, and configuring a preset access agent module to call the hotspot object access mapping table after performing access interception on a client request; and after a data access request from a client is received, determining a target access object corresponding to the data access request based on a preset access agent module, and forwarding the target access object to a corresponding data physical address. Through the method, the hotspot data management efficiency and the service continuity are improved.

The large-model-based data security risk automatic research and judgment processing system comprises a fusion research and judgment module which is used for carrying out comprehensive research and judgment on the risk level, attack intention and potential influence of a security event based on security data in combination with deep learning and a large-scale language model, and transmitting a research and judgment conclusion to an automatic processing module; the business process disassembling and analyzing module is used for automatically discovering and modeling based on the security data through process mining and flow analysis, perceiving a business process and a dependency graph associated with a security event in real time, evaluating potential influences of different disposal measures on business continuity, and transmitting business influence evaluation to the automatic disposal module; and the automatic disposal module is used for selecting and executing an optimal risk disposal strategy from the security strategy library by adopting a distributed architecture of an AI intelligent agent according to the research and judgment conclusion and the business influence evaluation. The accuracy of alarm study and judgment is improved, 'alarm fatigue 'is relieved, and the method has high elasticity, high fault tolerance and strong cooperative capability.

The invention belongs to the technical field of computer network monitoring and database management, and discloses a database fault prediction and autonomous recovery system and method and related equipment, and the method comprises the steps: collecting multi-dimensional performance indexes on a database server in real time, extracting time features of different scales through discrete wavelet transform, and constructing feature vectors; and dynamically calculating a fault prediction threshold by using a sliding window technology, and predicting a fault risk probability by using a multi-scale attention-enhanced LSTM model. And according to a prediction result, the system automatically executes an autonomous recovery strategy, high-precision prediction and autonomous recovery of database faults are realized, and the stability and service continuity of the system are improved.