3077results about "Fault response" patented technology

The invention relates to the field of log fault analysis, in particular to a log aggregation fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal heterogeneous log, carrying out sliding time sequence slicing processing, carrying out time sequence association sequence reconstruction, and constructing a time sequence reconstruction log data stream; log event deep semantic analysis is carried out on the time sequence reconstruction log data stream, event semantic topological evolution is carried out, and a multi-dimensional event topological representation matrix is constructed; performing routine event behavior analysis and abnormal fault mode inference based on the multi-dimensional event topology representation matrix, and marking abnormal fault points; and the occurrence timestamp and the abnormal propagation rate of the abnormal fault point are calculated, fault space-time diffusion evolution is carried out, and a dynamic fault propagation path map is constructed. Through efficient and accurate fault traceability analysis, the fault diagnosis efficiency is greatly improved, and the stability and reliability of log data are improved.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

A computer-implemented method executed using one or more processors of an incident management system, the computer-implemented method comprising accessing one or more data sets of information associated with an incident event corresponding to an incident associated with a computer system; generating a prompt based on the one or more data sets of information, wherein generating the prompt comprises generating a plurality of sub-prompts to be provided to a machine-learning model for generating a report of the incident event in accordance with a predetermined criteria; inputting the prompt into a machine-learning model that has been trained to generate a report of the incident event based on the prompt; outputting, by the machine-learning model, the report of the incident event, wherein the report comprises an analysis of the incident event; transmitting the report to one or more computing devices associated with the computer system.

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 discloses an AIOps anomaly detection and root cause positioning method, and relates to the technical field of anomaly detection. The method comprises the following steps: 1, processing a preset time window according to services and instances under a unified timeline, generating monitoring type abnormal fragments for monitoring indexes, and extracting a client and server span in distributed tracking for fragment pairing; step 2, executing stitching by taking distributed tracking as guidance to obtain a candidate evidence chain set, and taking a segment at the tail end of each evidence chain unit as a candidate root cause direction; and step 3, outputting a root cause list for the candidate evidence chain set according to a deterministic rule, and giving a time range of a related template text and an adjacent monitoring type abnormal fragment. According to the method, the abnormal propagation path can be accurately identified in the multi-source heterogeneous data, association verification is carried out on the upstream representation and the downstream resource failure, and a clear root cause target and evidence explanation are provided.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention provides a server cluster operation and maintenance method based on multi-source heterogeneous data fusion and a dynamic knowledge graph, and the method comprises the following steps: collecting a performance index, a log text and topological structure data of a server cluster, splicing the performance data and the log data based on a unified time window, and generating a multi-modal feature sequence; and analyzing the sequence by using an unsupervised deep learning model, constructing a dynamic health baseline, and generating a health degree portrait through the deviation with real-time data. When an exception is detected, mapping an exception event into a dynamic topological graph constructed based on a topological structure; analyzing a fault propagation probability between nodes by using a graph neural network algorithm, positioning a root cause node, and generating a disposal strategy to execute disposal operation; and collecting the processed recovery data as a feedback signal, and updating the deep learning model by using incremental learning. The method has the beneficial effects that the fault discovery accuracy is improved, the alarm storm is effectively inhibited, the root cause is directly positioned, and the model self-iteration adaptability is higher.

Systems and methods for uses and / or improvements to artificial intelligence applications, particularly in the area of generating conversational dynamic responses. As one example, systems and methods are for generating conversational dynamic responses using a multi-tiered guardrail architecture. As one example, systems and methods are for generating conversational dynamic responses using a multi-tiered guardrail architecture in data sparse environments.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention discloses a fault determination method and device for a micro-service system, a product and electronic equipment. Relates to the technical field of artificial intelligence. The method comprises the following steps: determining an abnormal event flow according to abnormal monitoring information; an abnormal event causal graph is determined based on an abnormal event flow, and then a fault point, a fault chain and a fault classification are determined by utilizing an intelligent agent driven by a pre-training model and combining the event causal graph and a fault mode standard. According to the method, anomaly detection, fault classification and root cause positioning of the micro-service system are realized, and closed-loop diagnosis from anomaly discovery to root cause positioning is realized; secondly, performing depth feature engineering and semantic abstraction on the refined data from the perspective of events and causal relationships, fusing system behaviors and dependency relationships dispersed in different modal data into a unified event causal graph through a graph modeling technology, and providing comprehensive and high-dimensional input for fault reasoning of an intelligent agent; and the accuracy of micro-service fault determination is improved.

The invention discloses a server fault diagnosis method and electronic equipment, and relates to the technical field of server operation and maintaining.The server fault diagnosis method comprises the steps that firstly, by collecting operation state data of a server and extracting structured events containing index types, context tags and attribute information, associated contexts and dynamic characteristics between indexes are captured; the method comprises the following steps of: converting dispersed operation data into a node and edge relationship with logic association based on a fact graph constructed by a structured event, and intuitively presenting a multi-dimensional dependency relationship related to a fault; the pre-trained graph neural network extracts causal embedding features from a graph, identifies causal association between nodes, captures a conduction path with abnormal indexes in a complex fault mode, and reduces missed report caused by neglected index linkage change; the causal embedding features and the original operation data are fused to form joint state representation, collaborative decisions are generated through a fault diagnosis model, abnormal changes of complex scenes are recognized, the false alarm rate is reduced, and reliable diagnosis results and disposal strategies are provided for operation and maintenance.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The embodiment of the invention provides a code repairing method, device and equipment based on a large language model.The method comprises the following steps that a code to be repaired is analyzed through a code scanning tool on the basis of a coding specification, error information of the code to be repaired is obtained, the error information comprises an error number, a violation rule, a supplementary description, an error example, a correction example, an error type and an error code segment; generating a cue word template according to a historical repair sample and a coding specification of the large language model, and filling the cue word template with the error information to generate a filled cue word; and inputting the filled cue word into the large language model to generate a repair code segment, judging whether the repair code segment accords with a set conversion specification, and if the repair code segment accords with the conversion specification, replacing the code to be repaired with the repair code segment to generate a repaired code. According to the scheme, the code is repaired through an end-to-end automatic process, and the code repairing efficiency is improved.

The invention provides a root cause analysis method combining eBPF and traditional observability data, and belongs to the technical field of root cause analysis, the method comprises the following steps: S1, collecting events to obtain a structured eBPF index sequence; s2, time axis alignment and entity matching are carried out, a node calling graph is constructed, and a fused observation entity structure body is generated; s3, trend change detection and anomaly screening are carried out on the fused observation entity structure body; and S4, based on a node calling graph, the abnormal index list, the abnormal timestamp record and the abnormal score matrix, performing root cause path derivation to obtain a root cause service node and an abnormal propagation path. The eBPF and traditional observable data are fully combined, and the root cause positioning efficiency and precision are effectively improved.

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 root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

A computer-implemented method for simulating an aircraft system in a closed-loop functional simulation environment is disclosed. The computer-implemented method may include: receiving, at a computer system, one or more control inputs from an interface; processing, by a control system model, the one or more control inputs to generate one or more system command signals; applying the one or more system command signals to one or more virtual system models; simulating a physical response of the aircraft based on output generated by the one or more virtual system models in response to application of the one or more system command signals; generating, responsive to monitoring the output generated by the one or more virtual system models, sensor data; transmitting the sensor data to the control system model; and outputting simulation results, wherein the simulation results at least comprise an indication of the physical response of the aircraft.

The invention discloses a root cause analysis method based on an IT operation and maintenance system, and the method comprises the steps: obtaining the physical position data and real-time environment data of IT equipment, carrying out the matrix construction through a dual topological relation based on the physical position data and the real-time environment data, and obtaining an IT equipment spatial topological matrix containing environmental impact factors. And obtaining operation state data of the IT equipment, performing spatio-temporal conjoint analysis and dimension reduction based on the operation state data and the IT equipment spatial topology matrix to obtain IT equipment dynamic prediction data, and obtaining an IT equipment state dynamic prediction curve according to the IT equipment dynamic prediction data. If the IT equipment state dynamic prediction curve monitors abnormity, a fault propagation path is generated through a reinforcement learning algorithm, and IT equipment fault root causes are determined in combination with a multivariate fault knowledge graph and a causal graph model. According to the method, the problem of early warning lag of the IT operation and maintenance system can be solved, and the problem that root cause positioning has high dependence on artificial experience is solved.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention provides a fault root cause analysis method and device for a full link, and relates to the technical field of data processing.The method comprises the steps that after abnormal events are obtained from operation data of multiple department platforms, the abnormal events are matched with fault factors in a preset knowledge base; determining a fault factor corresponding to each abnormal event and instantiation information of the fault factor to form an associated event set; then, according to an association map in the knowledge base and a static factor corresponding to the fault factor, performing multi-dimensional contribution degree quantification on each fault factor in the association event set to obtain a fault contribution degree of the fault factor; and finally, updating the association map based on the abnormal event, the association event set and the fault contribution degree of each fault factor, analyzing the association map through a root cause analysis engine, and determining a fault evidence chain and a fault root cause. Therefore, the accuracy of fault root cause analysis is improved.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.

The invention provides a system abnormity intelligent diagnosis and recovery method and system fusing time sequence logs, and relates to the field of distributed system operation and maintaining.The method comprises the steps that operation logs are collected from a plurality of service nodes of a distributed system, time sequence alignment is conducted, and calling relation and performance measurement data are extracted; time sequence change features are calculated in the sliding time window, dynamic feature vectors are generated through fusion coding, and a cross-service time sequence association graph is constructed; identifying an abnormal service node and matching the abnormal service node with historical fault data to obtain a historical propagation path and a feature vector; calculating a propagation convergence coefficient and a characteristic deviation degree based on a historical propagation path to obtain a causal intensity score; and identifying a fault influence degree according to the score, and adaptively adjusting a resource isolation and flow scheduling strategy. According to the invention, accurate diagnosis and efficient recovery of distributed system faults are realized.

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 discloses an operation and maintenance data management system based on cloud computing, particularly relates to the field of data management, and comprises a distributed data acquisition module, a cloud native data processing module, an intelligent analysis module, a visualization and decision module and a cloud resource management center module. According to the method, heterogeneous data are collected in real time through the lightweight agent, and high efficiency is guaranteed by adopting multi-thread and self-adaptive sampling; data cleaning, standardization and mixed storage are realized by using a stream batch integrated architecture; the intelligent analysis module fuses an improved Robust Z-Score, an LSTM-Attention model and a complex event processing engine, and supports dynamic anomaly detection and fault tracing; the visual assembly builds an instrument panel in a dragging mode, alarm strategies are aggregated in a grading mode, and storm is restrained; the cloud resource management center deeply integrates Kubernetes, predicts demands based on reinforcement learning, automatically expands and shrinks capacity and optimizes cost, and deals with burst traffic in combination with a burst capacity pool.

The invention discloses a suspension control fault detection and recovery method and device for a maglev train and a medium, and relates to the field of maglev trains, and the method comprises the following steps: S1, constructing a linearized suspension system model; s2, reconstructing a state vector, and generating a suspension gap estimation value; s3, constructing a health probe set, and executing a signal selection strategy; s4, detecting a slow change fault based on the statistical accumulation characteristic of the residual error, and outputting a slow change fault detection result; s5, monitoring the variation trend of the residual error, and triggering a reset mechanism to clear historical accumulated data when a reset condition is met; and S6, generating a final fault mark, and forcibly updating the fault mark to a normal state if a reset mechanism is triggered. According to the hierarchical collaborative decision-making architecture based on the maglev train lap joint structure dynamic model, the condition of performance degradation or failure does not occur in a real system by the algorithm, so that the engineering applicability and reliability of fault diagnosis are improved.

The invention relates to an alarm rule automatic generation and closed loop optimization method and system, and relates to the field of computer system monitoring and testing. The method comprises the following steps: acquiring and cleaning a cluster to generate a standardized enhanced alarm event table based on multi-source heterogeneous data fusion; performing feature extraction on the enhanced alarm event table, constructing a feature vector, performing feature importance analysis on the feature vector by using a tree model, and outputting the feature importance analysis; according to statistical distribution of features in the structured feature vector, automatically generating a rule for identifying an alarm scene in combination with business knowledge; converting the alarm scene rule into a structured format, performing logic conversion through a test case generation engine, and outputting an automatic test case; and executing the automated test case and verifying the accuracy of the automated test case so as to dynamically optimize and adjust the alarm scene rule. According to the method, automatic rule generation and closed-loop optimization of a complex alarm scene can be realized, the alarm test efficiency and the scene recognition accuracy are remarkably improved, and the manual intervention requirement is reduced.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

A method of hybrid technical support includes receiving, by a network-connected device, a first user prompt including at least one technical support query and generating, by a language model executed by the network-connected device, a first natural-language response to the first user prompt, the first natural-language response configured to elicit first additional information describing the at least one technical support query. The method further includes receiving, by the network-connected device, a second user prompt including the first additional information describing the at least one technical support query, generating a pre-summarization prompt based on the first user prompt and the second user prompt, generating a summarization of the pre-summarization prompt using a language summarization model executed by the network-connected device, and providing the summarization to a support technician device configured to be operated by a support technician. The language summarization model is configured to generate summaries of text prompts.