6981results about "Fault response" patented technology

The invention provides an intelligent visual management method and system for enterprise big data. The method comprises the following steps: extracting a space-time association rule of an operation and maintenance report fault field and an equipment log error code, and generating a dynamic mapping data stream to drive a three-dimensional visual association topology; the method comprises the following steps: collecting cabinet vibration energy data, and synchronizing a highlight energy sudden increase area and error log entries according to a timestamp; dynamically distributing a vibration energy weight, and generating a risk probability matrix in combination with an error increment; fusing the time sequence characteristics and the physical topology path, constructing an abnormal event timestamp graph, and marking a fault propagation chain; and based on the map density gradient and the causal association strength, superimposing and rendering the penetrating thermodynamic diagram, associating topology, a fault chain and an equipment structure, and adaptively adjusting the color gradation highlighting abnormal region. According to the technical scheme provided by the invention, the multi-source fault correlation analysis efficiency is improved, and the abnormal risk is dynamically, visually and accurately positioned.

The invention provides an operation and maintenance alarm processing method and system based on a knowledge graph enhanced large model, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting multi-source operation and maintenance alarm historical data, carrying out the adaptive hierarchical structured processing of the data, and constructing a space-time semantic multi-dimensional feature matrix; constructing a multi-dimensional alarm association network based on a dynamic weight fusion algorithm and forming a hierarchical knowledge graph; constructing a root cause inference rule base in combination with time sequence evolution, topology propagation and business dependence characteristics of the alarm; and when a real-time alarm is received, a diagnostic reasoning system is started to analyze a propagation link, a root cause is identified, and a processing suggestion is generated. The method can accurately identify the alarm root cause, improves the operation and maintenance efficiency, and reduces the false alarm rate.

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 proposes a distributed storage node fault detection system, and relates to the technical field of distributed storage, and the system comprises a data collection module which is configured to collect node operation state data; the feature extraction module is configured to perform feature calculation on the node operation state data to obtain a feature vector; the distributed anomaly detection module adopts a three-layer detection architecture and comprises a local layer, a region layer and a global layer; the fault diagnosis module is configured to receive the anomaly detection result of the global layer, perform fault classification and generate a fault diagnosis report; and the early warning decision module is configured to perform response strategy matching through a preset strategy template and a dynamic generation rule according to the fault diagnosis report, perform strategy priority ranking, and select a preset strategy with the highest priority as a final recommendation strategy. According to the method, the abnormal state of the storage node can be found in time, the fault type can be accurately identified, the fault root cause can be quickly positioned, and intelligent early warning decision support can be provided.

The invention relates to the field of solid state disk fault prediction, in particular to a solid state disk fault prediction method and system based on artificial intelligence. The method comprises the following steps: carrying out storage unit fine-grained division on the solid state disk, carrying out storage unit serialization, and constructing a storage unit sequence of each partition; obtaining a solid state disk running log; carrying out multi-time-point state evolution on the storage unit sequence of each partition based on a solid state disk running log, and carrying out deep dynamic evolution learning to generate a state evolution rule of each storage unit; periodic access load peak calculation is carried out on the solid state disk running log, so that a simulation access data packet of each storage unit is generated; and performing dynamic pressure access simulation based on the state evolution rule of each storage unit and the simulation access data packet of each storage unit to obtain response state evaluation data of each storage unit. According to the invention, efficient and accurate solid state disk fault prediction is realized.

The invention provides an operation and maintenance log event association analysis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an original operation and maintenance log set of a target system, the original operation and maintenance log set comprising a plurality of operation and maintenance log fragments; secondly, performing feature extraction processing on the original operation and maintenance log set to obtain context semantic features and time sequence behavior features of each operation and maintenance log fragment, and then calling a pre-trained association analysis model to perform association rule matching processing on the features to generate an association rule set among the operation and maintenance log fragments; the association rule set comprises a causal relationship definition and an event propagation path description, generating an operation and maintenance event optimization strategy based on the association rule set, and finally feeding back the operation and maintenance event optimization strategy to an operation and maintenance management interface of a target system to trigger system optimization operation. And the system operation and maintenance efficiency and stability are improved.

The invention provides a multi-task monitoring and scheduling method and system, and relates to the technical field of task scheduling, and the method comprises the steps: receiving a task submission request, and constructing a task execution time prediction model based on a historical execution time sequence and a bidirectional gating recurrent neural network; monitoring a task execution process, and identifying an abnormal task by using a hierarchical Markov decision and a spectral clustering algorithm; for an abnormal task, constructing a task dependency graph based on the resource competition degree, calculating a critical path, determining an optimal calculation node, and performing re-execution; according to the method, the task execution time can be accurately predicted, abnormal tasks can be found in time, resource optimization scheduling is carried out, and the system operation efficiency is improved.

The invention relates to the field of hard disk data protection, in particular to a hard disk data protection method and device based on artificial intelligence. The method comprises the following steps: acquiring a hard disk history monitoring log; performing multi-period hard disk load analysis on the historical hard disk monitoring log, and performing load peak fluctuation fitting so as to construct a hard disk load peak fluctuation curve; carrying out peak value trend evolution analysis on the hard disk load peak value fluctuation curve, and carrying out abnormal load peak value detection to obtain a time period where an abnormal peak value point is located; carrying out current time period redundant data calculation on the hard disk according to the time period of the abnormal peak point, and carrying out dynamic global data backup decision, so as to generate an abnormal peak global backup strategy; and performing abnormal write behavior detection and attack node tracing positioning on the hard disk historical monitoring log, and marking a hostile attack tracing positioning point. According to the invention, safe and reliable hard disk data protection is realized.

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention provides a fault prediction method and device and a baseboard management controller, and is applied to the technical field of fault prediction, and the method comprises the steps: obtaining the real-time operation data of a server; the real-time operation data comprises operation parameters of the hardware equipment, log data generated in the operation process of the hardware equipment, hardware data of the hardware equipment and software data of the software system. And determining fault prediction characteristics of the server according to the operation parameters, the log data, the hardware data and the software data. The fault prediction features comprise hard disk fault prediction features and memory fault prediction features. And inputting the hard disk fault prediction features and the memory fault prediction features into the trained fault prediction model, and determining a predicted fault type of the server. And on the basis of the predicted fault type, generating a fault diagnosis suggestion, thereby solving the problem that the fault diagnosis efficiency of the server is relatively low due to the fact that a technician needs to perform offline determination when the fault in the server is predicted at present.

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.

An anomaly detection system may receive uniformly spaced digital data from a plurality of sensors connected to the monitored system and physical model parameters specific to components of the monitored system. An orthogonal function processor generates an initial set of orthogonal kernel basis functions to process the digital data to detect signature anomalies. An adaptive analysis is performed based on the number of feature components and the width of each feature component. When the number of feature components is greater than the basis functions, additional basis functions are used for reprocessing. When the feature width is less than the basis function, the resolution of the basis function is increased and reprocessing of digital data is performed. Based on the adaptive analysis of each feature component, signature anomalies are identified and displayed.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

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.

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention relates to the technical field of ETC intelligent early warning, and discloses an ETC abnormal condition identification intelligent early warning system. According to the system, ETC related data including vehicle passing records, transaction amount and payment states, vehicle driving speed data and real-time road information are collected through a data acquisition unit, and the collected data are denoised through a data processing unit. The data processing unit is used for transmitting data, calculating real-time traffic flow, vehicle passing transaction success rate, vehicle passing time, average transaction amount and charging processing response time, carrying out license plate recognition and passing record data consistency check, and the abnormity recognition unit is used for carrying out ETC abnormity recognition based on data transmitted by the data processing unit and transmitting abnormal information to the early warning unit; the early warning unit generates corresponding early warning information and exception handling measures based on an exception detection result, summarizes and sends the early warning information and the exception handling measures to the information display unit, and the information display unit displays the early warning information and the exception handling measures on the visual terminal for information reminding and exception condition improvement.

A method for product fault root cause analysis based on knowledge graphs includes (i) acquiring product-associated design data, wherein the design data comprises semi-structured data and / or unstructured data, (ii) acquiring product-associated production data, wherein the production data comprises structured data, (iii) performing information extraction on the design data to acquire structured design data, (iv) performing a knowledge fusion of knowledge generated separately based on the production data and the structured design data to construct a target knowledge graph, and (v) and performing product fault root cause analysis tasks based on the target knowledge graph.

The invention provides an intelligent memory leak prediction and tracking method and system for a micro-service architecture, and relates to the technical field of memory leak prediction, and the method comprises the steps: collecting the runtime data of a service node, constructing a service calling topological graph, extracting the time sequence and topological features, and carrying out the feature fusion. And constructing a deep learning prediction model by combining a bidirectional long-short term memory network with a multi-head attention mechanism and a graph attention network, and predicting a memory use trend and a risk score of a service node. Potential memory leak nodes are identified according to the risk score and the abnormal degree predicted value, a key propagation path is identified through a service calling topological graph and a minimum cut algorithm, and finally a root service of memory leak is positioned by using a belief propagation algorithm, so that accurate prediction and rapid positioning of the memory leak in the micro-service system are realized. And the system stability and reliability are improved.

The invention relates to an alarm root cause positioning method of an operation and maintenance platform. The method comprises the following steps: acquiring and processing monitoring data; constructing a software and hardware topological knowledge graph; analyzing call chain data; merging the maps; processing the alarm data, and classifying the alarm data; finding an alarm causal relationship; predicting time sequence performance index data; constructing a monitoring operation and maintenance knowledge base; carrying out alarm convergence and root cause positioning; and performing alarm interpretation and processing opinions. According to the method, the topological graph is created and configured on the basis of the CMDB, related information such as call chains, alarm causal relationships and faults of performance index classes is added, and the physical link logic relationships between the components and the virtual machine or the host machine where the components are located are obtained through the CMDB so as to discover the mutual call relationships of application and service levels between the components. And the monitoring operation and maintenance knowledge base is used for carrying out professional interpretation on alarms and abnormal events and giving expert-level disposal suggestions. The expandability is considered in the system design, and more data sources and algorithms can be conveniently integrated in the future.

The invention relates to a fault diagnosis method for a server. Efficient fault positioning and processing are achieved by constructing a multi-dimensional intelligent diagnosis system. Constructing a server virtual model at each node and establishing a connection relationship, and collecting various data and mapping the data to the virtual model; performing weighted fusion on the data through correlation analysis, and extracting fault features including time and space correlation by using a bidirectional long-short-term memory network; fault probability distribution is obtained by means of a dynamic fault knowledge graph and graph neural network reasoning, and a fault evaluation result is visualized in combination with a virtual model; and finally analyzing the behavior deviation through a neural network and generating a processing strategy. Real-time processing of multi-source data, intelligent extraction of fault features and dynamic deduction of fault propagation are achieved, the accuracy, the real-time performance and the automation level of server fault diagnosis are effectively improved, and predictive maintenance and intelligent decision making of a complex server system are achieved.

Systems, methods, and computer program products for using artificial intelligence (AI) models, machine learning, and large language models to identify root causes of issues in a computing environment where multiple applications and computing devices operate. One or more AI models may determine adverse trends from one or more issue metrics, where an issue metric corresponds to issues occurring in a computing system. The AI models may identify issues corresponding to the adverse trends. From the identified issues, the AI models may determine root causes from the issues and the impacted area information and process information from the issues. From the issues and the impacted area information and process information from the issues the AI models may determine recommendations for rectifying the issues. The relations between the root causes, the impacted area information, the process information, and the issues may be formatted and displayed as a traversable network graph.

The invention provides a server voltage monitoring system and method, electronic equipment and a storage medium, and relates to the technical field of computers, the system comprises a complex programmable logic device CPLD, and an initial timing module, a voltage acquisition module, an abnormal data storage module, a CPU processor and a baseboard management controller BMC module which are respectively in communication connection with the CPLD. The initial timing module activates the monitoring function of the CPLD through a startup signal; the voltage acquisition module acquires voltage data under the control of the CPLD so as to read the voltage data of the server mainboard; the CPLD obtains voltage data, compares the voltage data with a reference voltage range of the abnormal data storage module, triggers an interrupt signal after identifying abnormal fluctuation voltage, and feeds back the interrupt signal to the CPU and the BMC, so that the CPU and the BMC quickly output an alarm. The system covers full-cycle monitoring from startup to BMC startup, supports efficient acquisition, dynamic detection and low-delay response of multi-path voltage, relieves the voltage pressure of a BMC monitoring mainboard, and provides reliable guarantee for power supply safety of a server.

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 an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The invention relates to the technical field of fault prediction, in particular to a switch equipment fault early warning method based on multi-source data fusion, which comprises the following steps of: acquiring a channel transition time sequence to generate a time interval, marking a behavior label to generate a time sequence, constructing a sliding window to form a combined alarm, analyzing a cross matrix to extract an abnormal time period, and performing early warning. And identifying the leading channel and sending out early warning. According to the method, alignment processing is carried out through the time intervals of multi-channel state transition responses, dynamic linkage recognition among multiple signals is achieved, time sequence correlation among abnormal behaviors is enhanced by means of behavior label combination and a sliding statistical strategy, and the sensitivity and accuracy of abnormal recognition are enhanced through aggregation expression of composite alarm events. In combination with cross-channel behavior label matrix analysis, the continuity of abnormal labels is clear and key channels are identified, so that the accuracy and timeliness of early warning of the switch equipment are improved, the risk of false alarm and missing alarm is reduced, and the actual demand of multi-source sensing data fusion analysis of the switch equipment is met.

The invention provides an electronic equipment anomaly detection system and method, and belongs to the technical field of electronic equipment, the electronic equipment anomaly detection system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a threshold dynamic adjustment module, a model establishment module and an alarm module, the data acquisition module uses a sensor to acquire operation parameter data of electronic equipment in real time; the feature extraction module extracts key features from the preprocessed data by using a feature extraction algorithm; the threshold value dynamic adjustment module is used for adjusting the detection threshold value according to the running state of the equipment and the environment change, and the threshold value dynamic adjustment module comprises an association rule mining unit, a classification model construction unit and a D-AHP analysis unit; and the model building module is used for carrying out model training on normal behaviors and abnormal behaviors of the electronic equipment. The problems that the manual inspection efficiency of a traditional anomaly detection method is low, real-time monitoring of the equipment is difficult to realize, dynamic changes of the equipment are difficult to adapt, and false alarms and missing alarms are easy to generate are solved.

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.