7740results about "Non-redundant fault processing" patented technology

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention discloses an intelligent operation and maintenance management and alarm system based on a large model agent, and the system comprises a data collection and fusion module which is used for processing original data collected from an IT system to obtain an operation and maintenance data graph, and the original data at least comprise logs, indexes and link tracking data; the anomaly detection module is used for performing analysis based on the preprocessed data and the time sequence characteristics to determine abnormal data; the alarm optimization module is used for analyzing the semantic information corresponding to the abnormal data so as to determine the priority of the corresponding alarm; the root cause analysis module is used for performing fault reasoning on the abnormal data based on a pre-constructed knowledge graph and generating fault interpretation according to data corresponding to the abnormal data; and the automatic operation and maintenance decision module is used for generating an automatic repair strategy according to the abnormal data and the fault interpretation and executing the automatic repair strategy to repair the abnormal data.

A system, device and method are provided for accelerating transfers via intermediaries. The illustrative method includes receiving a request to perform a transfer of funds and routing the first request through an account management service to a payment service. The method can include populating a second request to the interbank intermediary to initiate the transfer, and receiving, at the payment service, confirmation of transfer from the interbank intermediary, the received funds being routed by the payment service to a multi-tenant account. The method can include generating an event for the account management service that represents that the confirmation has been received and initiating, via the account management service, completion of the transfer to the first account by request to the multi-tenant account. The method can include generating, by the account management service, an event to update a distribution subsystem of the completed transfer.

Examples of the present disclosure describe systems and methods for automatically detecting and repairing reliability issues in operating systems and applications using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request to evaluate a detected issue in a software service or application. In response, the system analyzes error information associated with the detected issue to build an error context for the software code that caused the detected issue. The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and / or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.

Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

A network system to use machine learning systems to create chaos testing scenarios on cloud-based applications. The system uses inputs from applications that are implemented on user computing devices to allow users to interface with a network or other system. The system creates a model of the application based on input data received from a network of applications, the model representing a structure, method, and dependencies of the application. The system identifies points of failure of the application and generates one or more chaos testing simulation scenarios that target the identified points of failure. The system performs the chaos testing based on the received simulation scenarios and logs the results of the testing. The system generates recommendations to revise code of the application based on the outcome of the chaos testing. A large language model may be used to provide documentation and analysis of the chaos testing.

The invention relates to the technical field of network detection optimization, and discloses a network anomaly detection and automatic processing method and system, a storage medium and equipment, and the method comprises the steps: collecting multi-dimensional data related to network performance, carrying out the preprocessing of the collected data, and carrying out the feature engineering construction of the preprocessed data; constructing a multi-dimensional anomaly detection model, and performing time sequence anomaly detection, multi-dimensional feature clustering analysis and rule engine detection through the multi-dimensional anomaly detection model; performing classification and automatic processing on anomalies detected and output through the multi-dimensional anomaly detection model; point burying is carried out on network performance indexes and abnormal events, states of all layers of the network are monitored in real time, the abnormal events are tracked and recorded, meanwhile, prediction analysis is carried out on network performance data, data values are mined through trend and correlation analysis, and optimization suggestions are generated based on prediction analysis results. Through the method, a network anomaly detection and automatic processing system is realized, and the network quality and experience are improved.

A set of incident records are received for a computing system. The incident records are analyzed to identify similar incident records which are then linked. Incident clusters are generated based upon the links and incident records in each cluster are ranked. A prompt is generated to an artificial intelligence (AI) model based on the ranked, related incidents and the AI model returns a response that identifies a root cause and mitigation steps corresponding to the ranked incidents.

The invention relates to the technical field of computer communication, provides a distributed accounting asynchronous data processing method and system based on an MEMO state machine, and is used for improving the intelligent adaptability of a distributed accounting network to a dynamic service scene. The method comprises the following steps: acquiring a transaction request set in a distributed accounting network, extracting an asynchronous processing trigger event of the transaction request set, analyzing the asynchronous processing trigger event, generating a state transition instruction sequence containing M state transition paths associated with K transaction data blocks, a parallel prediction module based on an MEMO state machine performs asynchronous processing on the M state transition paths to generate N prediction state vectors and corresponding conflict detection marks; performing feature fusion on the N prediction state vectors according to the conflict detection mark to generate a global state updating matrix; and writing the global state updating matrix into a consensus node set of the distributed accounting network, and generating an asynchronous processing state log matched with the transaction request set.

Techniques discussed herein relate to improved data recovery techniques within cloud computing environments. The disclosed techniques utilize consistency groups that are identified for volumes corresponding to a compute instance. A plurality of resource identifiers uniquely identifying a respective cloud computing resource of the cloud computing environment can be maintained. A cloud computing instance corresponding to a resource identifier may be identified and volume metadata associated with that resource identifier is obtained. The volume metadata identifying at least one of a block volume or boot volume. A consistency group is generated for the identified volume devices for the computing instance and one or more schedules can be generated with which various snapshots of the volume devices of the group are to be subsequently generated.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling intelligent optimization method and system.The distributed computing power scheduling intelligent optimization method comprises the steps that a multi-level system dependency graph and a causal relationship model are constructed; a multi-dimensional anomaly detection and fault prediction technology is utilized to monitor the system state in real time and predict potential faults; when an anomaly is detected or a potential fault is predicted, performing key path analysis by combining a multi-level system dependency graph and a causal relationship model, identifying a fault propagation path and a key influence node, executing a fault isolation strategy, blocking fault propagation and implementing preventive resource scheduling; implementing intelligent repair and resource recombination; according to the method, early prediction, accurate positioning and efficient processing of faults in the distributed system are realized, allocation and scheduling of computing power resources are optimized, and the reliability, availability and performance of the system are improved.

A device fault diagnosis method, comprising: acquiring data for diagnosis of a target device; determining whether one or more abnormal sequences exist in the data for diagnosis; when it is determined that one or more abnormal sequences exist, on the basis of a statistical feature of each abnormal sequence, forming a feature vector of the abnormal sequence; and on the basis of the feature vector and a preset fault classification model, determining a fault type corresponding to the abnormal sequence.

The invention provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system are improved.

A system for automating information technology software actions using advanced AI techniques including a processing system, storage medium, a communications interface, a user interface, a natural language processing model or neural network, operable to interface with at least one of an embedded prompt or chatbot prompt and hosted on a control node which communicates with one or more nodes comprised by a node network via the communications interface, and program instructions on storage medium that direct the processing system to receive an instruction from the user interface, process the instruction using the one or more natural language processing model or neural network, along with one or more AI agents, and execute the instruction one of locally or on a node of the node network via the communications interface.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

The invention relates to the technical field of cloud computing, in particular to an intelligent capacity planning method and system in a hybrid cloud environment. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, heterogeneous resources in a hybrid cloud are modeled by using a declarative description language, a dependency relationship among the resources is identified, and a directed acyclic graph (DAG) is constructed; the resource operation tasks are divided in batches, and the tasks without the dependency relationship are executed in parallel; synchronizing the running state of each resource in real time, and performing state consistency verification and abnormity marking; triggering a retry mechanism when an exception occurs; and after the resource arrangement process is completed, summarizing data in the whole process, and performing performance evaluation and strategy optimization on resource operation. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, the use condition of resources can be monitored and analyzed in real time, the resource demand can be accurately predicted, a reasonable resource scheduling strategy can be generated, manual intervention is reduced, and elastic expansion and optimal configuration of cloud resources are realized.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

A computerized method is disclosed for automated handling of data ingestion anomalies. The method features operations of detecting a data ingestion anomaly and determining a cause for the data ingestion anomaly. The causal determination may be conducted by at least (i) determining features of an anomalous data ingestion volume, (ii) training a second data model, after a first data model being used to detect the data ingestion anomaly, with data sets consistent with the determined features, (iii) applying the second data model to predict whether a data ingestion sub-volume is anomalous, (iv) obtaining system state information during ingestion of the anomalous data ingestion sub-volume, and (v) determining the cause of the anomalous data ingestion volume based on the system state information.

The invention discloses a memory fault repairing method and device, equipment, a medium and a computer program product, and relates to the technical field of computers.The memory fault repairing method includes the steps that memory error information sent by a memory controller is obtained, a fault target memory page can be positioned, and a standby memory page is obtained from a standby memory pool; the memory address mapping table is updated, the physical address of the target memory page with the fault is mapped to the physical address of the standby memory page, the repairing process does not depend on triggering of a system management interrupt mechanism, memory repairing in the system running stage is achieved, the system downtime caused by memory fault repairing is shortened, and therefore the memory repairing efficiency is improved. The problem of high delay in memory fault processing can be solved, and the technical effect of improving the memory fault repairing efficiency is achieved.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

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

A determination is made that anomaly analysis is to be performed with respect to an application. An anomaly score of the application is generated with respect to observed values of a plurality of metrics of the application. Generation of the anomaly score comprises computing an anomaly score contribution associated with an analysis of a correlation between values of a pair of metrics of the application. In response to a detection that the anomaly score exceeds a threshold, an anomaly response operation is initiated.

The invention provides an intelligent operation and maintenance and fault prediction system and method for a data center, and the system comprises a self-healing decision module, a closed-loop verification module, an automatic execution module, a monitoring feedback module, and a strategy optimization module, and is characterized in that the self-healing decision module is used for determining the type and severity of a fault. The self-healing decision tree is generated according to the fault type and severity in combination with expert knowledge and historical data, the self-healing accuracy and safety are improved, the self-healing decision is executed through the automatic execution module, manual intervention is reduced, the operation and maintenance efficiency is improved, the self-healing process is monitored through the monitoring feedback module, feedback information is collected, and the operation and maintenance efficiency is improved. The self-healing effect is evaluated, the correctness and effectiveness of self-healing operation are ensured, a self-healing strategy is continuously optimized through a strategy optimization module according to feedback information and new data, the self-adaptive capacity and long-term performance of the system are improved, the system can continuously learn and adapt to a new fault mode, and therefore the self-healing capacity is continuously improved.

The disclosure relates to utilizing an anomaly mitigation proposal system to determine root causes, summarize anomalous metrics, and report mitigation actions for service incidents in cloud computing systems. Based on receiving an incident report request, the anomaly mitigation proposal system utilizes a two-layer approach that implements large generative language models to generate incident reports that include clear and concise text narratives summarizing metric anomalies, root causes, and corresponding mitigation actions. For example, the anomaly mitigation proposal system initially utilizes an online generative language model to provide these incident reports and, when unavailable within a time threshold, a fallback model that references root cause datastores.

The invention discloses a fault root cause analysis method and device for a distributed software system and a storage medium, and the method comprises the steps: obtaining fault data of the distributed software system, and extracting key information from the fault data; processing the key information according to a triple model to obtain a knowledge graph corresponding to the key information; when a fault analysis task is received, performing abnormal chain reasoning based on the knowledge graph, and generating a fault reason description text of the distributed software system based on an abnormal chain reasoning result; and inputting the fault reason description text into the large model to obtain a fault analysis result which corresponds to the fault analysis task and is deduced by the large model. According to the scheme, the technical problems that a traditional fault detection and root cause analysis method is low in analysis efficiency and inaccurate in root cause positioning are solved, and rapid and accurate fault attribution is achieved through the knowledge graph and the large language model.

The invention discloses a fault analysis method for vehicle-mounted automatic test equipment, and relates to the technical field of vehicle-mounted electronic equipment testing, and the method comprises the steps: collecting vehicle-mounted test data of the vehicle-mounted automatic test equipment, and storing the vehicle-mounted test data in a test database according to a time sequence; performing feature extraction on the test database through a deep learning model to generate a fault feature vector; inputting the fault feature vector into a fault diagnosis model, and generating a fault type judgment result and a fault probability value; and according to the fault type determination result and the fault probability values, screening corresponding fault recovery schemes from a preset fault recovery strategy library, and sorting the fault recovery schemes according to the fault probability values from high to low to form a fault processing report. According to the method, the fault processing report is generated based on the layered fault recovery strategy library and the priority grading mechanism, the technical problems that a traditional fault diagnosis method is insufficient in feature extraction and low in diagnosis precision are solved, and the accuracy and recovery efficiency of fault diagnosis are improved.

The present application relates to the technical field of the Internet, and provides a root cause localization method and apparatus, a device and a storage medium. The method comprises: when performing root cause localization, acquiring a timing heterogeneous graph; using attributes of nodes in the timing heterogeneous graph to delete normal nodes and edges connected to the normal nodes in the timing heterogeneous graph; reconstructing the timing heterogeneous graph having undergone the deletion processing, so as to obtain an abnormal sub-graph, wherein reachable edges exist between a failure node and other nodes in the abnormal sub-graph; and determining the root cause of the failure node in the abnormal sub-graph. By using the solution of the present application, the reconstructed abnormal sub-graph not only retains the reachability between the failure node and other nodes, but also reduces the scale of an original node and noise caused by the normal nodes, thereby improving the efficiency and accuracy of root cause localization.

A diagnostic system and method for identifying and resolving vehicle Controller Area Network (CAN) bus faults is disclosed. The system connects to a vehicle's data link connector (DLC) and automatically retrieves diagnostic trouble codes (DTCs) from multiple electronic control units (ECUs). It filters the DTCs to identify those related to CAN communication errors, groups repeated DTCs across ECUs to highlight systemic faults, and assigns repair priority based on status, scope, and criticality. A user interface displays prioritized DTCs with definitions, affected systems, possible causes, and repair guidance. Upon technician input, the system re-scans the network to confirm resolution and updates the display accordingly. The invention supports semantic and conceptual recognition of communication faults, enabling robust, language-agnostic analysis. This system reduces diagnostic time, improves repair accuracy, and supports multilingual or manufacturer-specific DTC definitions without requiring external infrastructure or advanced user expertise.

The invention relates to the field of intelligent inspection and resource allocation, in particular to an electric power information system inspection and resource allocation method based on a knowledge base and an intelligent agent, and the method comprises the steps: collecting the data of an electric power information system, carrying out the heterogeneous data integration, and constructing the knowledge base of the electric power information system; constructing an intelligent agent oriented to the electric power information system based on a large language model, and performing RAG on the intelligent agent based on a knowledge base and open source knowledge to enhance the performance of the intelligent agent; acquiring a routing inspection demand in a natural language form, automatically generating a routing inspection script by the intelligent agent based on the routing inspection demand, and compiling the routing inspection script to generate a routing inspection task; the intelligent agent automatically executes the inspection task and compares the inspection result with the knowledge base to generate an inspection report; and the intelligent agent performs intelligent analysis on the inspection report, and performs resource allocation on abnormal items in the inspection report in combination with the knowledge base. According to the method, the operation and maintenance efficiency of the electric power information system is effectively improved, and the operation and maintenance cost is reduced.