1269 results about "Root cause" patented technology

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses a cloud monitoring service operation and maintenance dynamic optimization system and method based on an AI intelligent agent, and relates to the technical field of cloud computing intelligent operation and maintenance. The method is used for solving the problems of hybrid cloud cross-layer data splitting, hidden fault association failure and operation and maintenance action conflict. Heterogeneous data of a physical layer, a virtual layer and an application layer are collected through a containerized probe, and a standardized cross-layer index is constructed through layered tagging and dynamic time sequence alignment. And constructing a fault propagation map through incremental correlation analysis and a dynamic time window, and identifying space-time coupling nodes. And based on the directional disturbance verification causal relationship, combining with the resource scheduling and fault chain incidence matrix fitting coupling degree index, and generating a root cause positioning instruction. And optimizing an action priority based on a propagation cost gradient and an asymmetric game strategy, and updating the model and the rule through a feedback closed loop. According to the invention, cross-layer data fusion and accurate fault positioning are realized, and the cloud service stability and the resource utilization rate are improved.

The invention discloses a self-adaptive operation and maintenance root cause positioning method and system based on deep learning. The method comprises the steps that firstly, multi-source data such as system logs, performance indexes, alarm events and configuration changes are collected, and time sequence features, text features and associated features are extracted; performing anomaly detection in combination with a 3-sigma criterion and an auto-encoder; when an exception is found, reasoning diagnosis is carried out by utilizing the fault knowledge graph, and fault diagnosis is realized by combining an XGBoost classification algorithm; and then fault propagation analysis and root cause positioning are carried out, and a fault processing flow is continuously optimized. The problems of incomplete fault features, low detection accuracy, limited diagnosis capability and the like in an existing operation and maintenance system are solved, more accurate fault detection and root cause positioning are realized through a mode of combining multi-source data fusion, deep learning detection and knowledge graph reasoning, and meanwhile, the method has adaptive optimization capability and is suitable for large-scale popularization and application. And the operation and maintenance fault processing efficiency is obviously improved.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

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.

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 relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system 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 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 provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

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 provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

The invention relates to the field of computers, in particular to a micro-service fault positioning method and device based on causal inference and a knowledge graph. The method comprises the following steps: firstly, collecting an original calling link, a multi-dimensional time sequence index and log data of a micro-service system, analyzing the correlation of micro-service indexes by using a partial correlation algorithm, then inferring the causal relationship of the indexes by using a structural causal model, and mapping the causal relationship of the indexes to specific service calling by using a principal component analysis method. And constructing a service dependency relationship. And then defining a knowledge graph ontology, constructing a fault dependency graph according to an index causal relationship and a service dependency relationship, and fusing the fault dependency graph with a calling link graph to form a complete fault dependency knowledge graph with multi-level influence. And finally, storing the graph in a graph database, taking exception as a service node starting point, and outputting a fault root cause candidate set through exception detection, random walk and node score calculation. According to the invention, the efficiency and accuracy of fault positioning are improved, and the stable operation of the micro-service system is ensured.

A method may include accessing data associated with a failure of a 5G network component. The method may include providing the data associated with the failure of the 5G network component to a first machine learning model, configured to determine and output data indicating a root cause of the failure. The method may include providing data indicating the root cause of the failure to a second machine learning model, configured to determine and output data indicating one or more service providers and respective destinations associated with the root cause. The method may include generating a service ticket may include data indicating the root cause and the respective destinations associated with the root cause. The method may include transmitting the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure.

A network management system (NMS) generates a hierarchical attribution graph representing different scopes at different hierarchical levels of a wide area network (WAN); obtains logical path down data indicative of operational behavior including failure events associated with logical paths of network devices over the WAN; obtains total path data indicative of a historical number of active logical paths between the network devices; and determines a scope of a logical path down issue by, for a time period of the logical path down issue, determining a score for each scope of the different scopes based on the logical path down data aggregated across the respective scope and the total path data, and determining the scope of the logical path down issue as a particular scope of the different scopes having a highest score. The NMS may identify the particular scope as a root cause of the logical path down issue.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention discloses a wire harness manufacturing management system and method based on artificial intelligence, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: inputting a structured feature vector and a process constraint condition into a multi-target reinforcement learning model, and generating a process parameter instruction; the process parameter instruction is executed, the product yield is monitored in real time, and cross-process quality tracing is carried out on the abnormal product yield; historical data of abnormal batches in crimping, assembling and testing procedures are traced, the defect root cause probability of each procedure is calculated based on a Bayesian causal network, and primary and secondary root causes of quality defects are obtained and analyzed; and dynamically updating process constraint conditions according to primary and secondary root cause analysis results, adjusting a reward function of the multi-target reinforcement learning model, and re-optimizing a process parameter instruction. According to the invention, the operation state of the production equipment is accurately captured, the production process is optimized and adjusted in real time, and a closed-loop management process is formed, so that the production flexibility, efficiency and product quality are remarkably improved.

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 discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

A system obtains a graph representing a set of resources of a distributed system. At least one node of the graph represents a resource-metric pair. The system further obtains time series data that indicates anomalies from the system. Then, the system determines a root cause anomaly that caused other anomalies based at least in part on the graph and the time series data.

The invention provides a monitoring method and system based on a digital twin remote intelligent protection device, and the method comprises the steps: carrying out the synchronous processing and feature extraction of operation data, generating cross-device space-time alignment data, and enabling the operation data to be multi-source heterogeneous data collected from a plurality of devices; based on the space-time alignment data, constructing a multi-dimensional association relationship among the plurality of devices, the multi-dimensional association relationship being used for representing a potential fault propagation path among the devices; performing simulation verification on the multi-dimensional association relationship through a digital twinborn model, and determining root cause equipment and a propagation path of the implicit association fault; and generating a cooperative control instruction according to the root cause device and the propagation path, wherein the cooperative control instruction is used for indicating the root cause device to execute protection operation. By adopting the method, the hidden fault missing detection problem caused by energy transfer or control dependence between the devices can be explored based on the multi-dimensional incidence relation between the devices.

The invention relates to a self-healing operation and maintenance method and device for a big data component, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing and feature fusion of multi-modal data of the big data component, obtaining and inputting a high-dimensional component health state vector into a fault diagnosis large model, and outputting a target fault type and a root cause confidence coefficient; when the root cause confidence coefficient meets a preset confidence coefficient threshold value, determining a historical restoration strategy based on the target fault type, calculating cluster environment similarity based on the cluster environment characteristic parameters of the current cluster environment and the historical restoration strategy, and if the cluster environment similarity meets a preset cluster environment similarity threshold value, determining that the current cluster environment is abnormal. If yes, taking the historical restoration strategy as a candidate restoration strategy; if not, a candidate repair strategy is generated based on a reinforcement learning strategy generation engine, and the candidate repair strategy is executed based on the strategy level matched with the candidate repair strategy; compared with the prior art, the technical scheme of the invention can effectively solve the problems of leak detection, low efficiency, solidification and the like of traditional operation and maintenance.

The invention provides a fault root cause analysis method and device, electronic equipment and a storage medium, and can be applied to the technical field of computers. The method comprises the following steps: converting a fault description text input by a user into vector representation to obtain a query vector; performing query search in a preset operation and maintenance knowledge vector library according to the query vector, and obtaining an operation and maintenance knowledge vector meeting a similarity condition with the query vector in the operation and maintenance knowledge vector library; wherein the operation and maintenance knowledge vector is a vector representation of the operation and maintenance knowledge text; based on the operation and maintenance knowledge text corresponding to the operation and maintenance knowledge vector and the fault description text, obtaining fault prompt information; and inputting the fault prompt information into the large language model to obtain a fault root cause analysis result. According to the method, by integrating the large language model and the retrieval enhancement technology, fault root cause analysis can be automatically carried out, and the analysis efficiency of fault root causes is effectively improved.

The invention relates to an alarm event root cause analysis method, device and equipment based on a large language model and business topology, and the method comprises the steps: based on an analysis engine, according to an alarm event sent by an alarm system, obtaining an association node set, collecting multi-source data, generating a preliminary context packet, carrying out the extraction of abnormal information according to the preliminary context packet, and carrying out the analysis of the abnormal information. And generating a refined context package by removing irrelevant node information, and generating an interpretable root cause analysis report through large language model constraint and reasoning based on the constructed large language cue word template. According to the method, the initial context packet covering the fault propagation link is constructed based on the service topological graph, rapid modeling of the context is achieved, the high-frequency error mode template is generated through clustering, irrelevant nodes are dynamically pruned to generate the refined context packet, accurate focusing and data filtering are achieved, and the fault propagation efficiency is improved. And embedding the refined evidence chain through a structured cue word template, and driving the large language model to output an interpretable root cause report.

The invention discloses a complex scene multi-source heterogeneous data acquisition and monitoring diagnosis method and device, and the method comprises the steps: collecting multi-source heterogeneous data of an object to be monitored and diagnosed, and carrying out the data cleaning, data preprocessing, feature extraction and fusion of the multi-source heterogeneous data, so as to obtain a feature data set; and constructing and training a causal decoupling representation learning model based on input and output driving by using the feature data set and the data after data cleaning, so as to extract decoupling representation of a to-be-monitored diagnosis object, deduce a causal relationship between variables, and obtain a to-be-monitored diagnosis object. Wherein the causal decoupling characterization learning model comprises an external source control conversion network and a double-view evolution modeling and causal inference network, and the trained causal decoupling characterization learning model is used as a heterogeneous signal monitoring and diagnosis model and is used for monitoring the state of an object to be monitored and diagnosed and diagnosing the root cause of a fault of the object to be monitored and diagnosed. According to the invention, data acquisition, state monitoring and fault reason diagnosis can be integrally realized, the cost is saved, and the application prospect is wide.

The invention belongs to the technical field of multi-source heterogeneous data quality and analysis, and relates to a data management and intelligent analysis method oriented to power multi-source heterogeneous. According to the method, ontology model alignment, temporary ontology generation and multi-mode semantic embedding technologies are adopted, semantic unification of cross-data types is achieved, meanwhile, a spatio-temporal joint indexing mechanism is established, geographic coordinates and timestamps are bound and stored, efficient multi-dimensional query is supported, and data integration efficiency and semantic consistency are improved; the problem of poor adaptability of a fixed threshold value is solved by dynamically adjusting the operation data safety threshold value and evaluating the abnormal confidence coefficient, the detection precision and credibility of the power multi-source heterogeneous data anomaly are improved, the anomaly detection accuracy is improved, and the false alarm rate is reduced; by constructing an abnormity confirmation logic and combining equipment parameters, environment data and communication states, reasons such as equipment faults, environment interference and communication abnormity can be accurately positioned, root causes can be quickly positioned, and time can be shortened.

The invention provides a full-link dynamic monitoring and anomaly simulation test system and method. The system comprises a cross-level I / O load simulation engine module, a multi-level collaborative fault injection module, a full-link dynamic monitoring module and an intelligent root cause analysis module. The cross-level I / O load simulation engine module is used for simulating business pressure fluctuation, the multi-level collaborative fault injection module is used for synchronously injecting configurable anomalies and realizing cross-level fault conduction, and the full-link dynamic monitoring module realizes request-level end-to-end tracking and acquires monitoring data through a distributed probe. And the intelligent root cause analysis module positions a fault root and generates a repair suggestion in combination with LSTM time sequence prediction and Bayesian causal reasoning. According to the invention, the overall performance and fault response capability of the system can be comprehensively evaluated, and the accuracy and efficiency of system testing are improved.

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.