151 results about "Root cause analysis" patented technology

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

This disclosure relates to a system (106) and a method (300) for blockchain-based root cause analysis of faults in a vehicle (102). In response to fault detection, the system (106) receives one or more DTCs, time-series data, and fault-related metadata from an electronic control unit (104). The system (106) generates one or more blocks (110) based on the received DTCs, time-series data, and metadata. The blocks (110) are sequentially added to a blockchain ledger (116), where they are validated to form a chain. The system (106) receives an update signal configured to address a root cause of the faults. This update signal is determined based on a root cause analysis of the chain formed by the validated blocks (110) in the blockchain ledger (116).

The application relates to a relay protection misoperation root cause analysis method, device, equipment, medium and program product. The method comprises the following steps: in response to detecting a relay protection misoperation event, acquiring a corresponding observation data set; extracting and locking an exogenous noise vector of the relay protection misoperation event by inverse operation of a mixed driving structure equation; calculating average causal effects of each environmental state variable on relay protection action behavior, and generating a root cause quantitative analysis result according to the average causal effects; receiving an intervention operation instruction input by a user for a target environmental state variable, keeping the locked exogenous noise vector unchanged, inputting the modified target environmental state variable into a structural causal model, performing forward prediction and waveform synthesis through a mixed deduction engine, generating a virtual fault waveform and corresponding relay protection logic simulation results; and comparing the time domain waveform and the action logic to generate a comparison result. The method can quickly reproduce an accident scene and quantitatively locate a misoperation root cause.

The application provides a dynamic monitoring and optimization method and system of an artificial intelligence sandbox environment, relates to the technical field of artificial intelligence safety, and comprises the following steps: obtaining execution state data of a model, constructing a directed dependency graph for abnormality identification and propagation analysis, and generating an abnormal feature vector. Root cause information is extracted based on the vector, resource demand is predicted through historical record clustering, adjustment demand is generated, the demand is corrected through loop dependency analysis, and adjustment components are divided. Finally, the resource allocation ratio is solved by comprehensively analyzing multi-model information, and environment optimization instructions are generated. The application realizes accurate positioning, root cause analysis and dynamic optimization of abnormal behaviors in the sandbox, and improves monitoring efficiency and resource utilization.

The application provides a sound stimulation-brain electrical response closed-loop analysis system and method, relates to the technical field of brain electrical data analysis, and comprises the following steps: obtaining real-time brain electrical data under sound stimulation based on a standardized interface, and performing artifact removal and data standardization operations to obtain standard brain electrical data; performing response latency analysis on the standard brain electrical data to obtain response latency data; performing response significance analysis on the standard brain electrical data to obtain response significance data; extracting an abnormal response event set from the standard brain electrical data, and performing context feature modeling and abnormal root cause analysis on the abnormal response event set based on the response latency data and the response significance data to obtain abnormal root cause data; and generating a brain electrical response analysis graph interface corresponding to the response latency data, the response significance data and the abnormal root cause data based on neural signal analysis logic, and visually displaying the brain electrical response analysis graph interface.

This application discloses a test data processing method, apparatus, equipment, and storage medium for production problem feedback, relating to the field of software test data processing technology. The method includes: structuring historical production problem data to obtain structured production problem data; constructing a problem database based on the structured production problem data; determining root cause analysis data based on the problem database in response to newly added production problem data; updating test assets in the problem database based on the root cause analysis data when the root cause analysis data passes correctness verification, obtaining updated test assets; and generating test case data based on the updated test assets in response to system function change data. Through the structured governance and knowledge base construction of historical production problem data, the method achieves automatic determination of the root causes of newly added production problems and dynamic mapping and updating of test assets.

This invention relates to the field of artificial intelligence technology and proposes a deep AI-driven method for data anomaly detection and self-healing. The steps include: constructing a data governance and preprocessing platform to preprocess multi-source heterogeneous data and obtain high-quality data sources; employing a multi-algorithm fusion mechanism to detect anomalies in the data sources; constructing a dynamic modeling and self-learning engine to rapidly adapt model parameters based on meta-learning and to automatically search and optimize the model structure based on reinforcement learning to adapt to changes in data distribution and business environment; and providing an intelligent repair and self-healing mechanism for detected abnormal data, automatically correcting, repairing, and performing root cause analysis on data source anomalies, geographical location anomalies, data push failures, and system operation failures. This invention achieves high-precision detection of complex anomalies through a cascaded multi-algorithm fusion detection framework and realizes fully automatic model evolution through a collaborative adaptive engine of meta-learning and reinforcement learning.

This invention relates to the field of supply chain management technology, and in particular to a supply chain knowledge graph-driven method for diagnosing inventory anomalies. This method involves multi-dimensional data collection and preprocessing of a retail FMCG supply chain system to obtain an inventory-related dataset; constructing a multi-dimensional inventory knowledge graph based on this dataset; using the knowledge graph in conjunction with a pre-defined anomaly detection algorithm to detect inventory turnover anomalies and obtain anomaly event data; extracting anomaly event features and constructing a root cause analysis model based on the knowledge graph, performing root cause reasoning, and generating a candidate set of anomaly root causes; deeply verifying and locating key anomaly sources through the confidence propagation mechanism and path analysis technology of the knowledge graph; generating dynamic inventory adjustment strategies based on key anomaly sources and the knowledge graph, conducting simulations and effect evaluations, optimizing and implementing the adjustment plan, achieving accurate diagnosis and dynamic adjustment of inventory anomalies, and significantly improving supply chain operational efficiency and risk resistance.

A method of performing cross-domain root cause analysis (RCA) includes determining whether a node of a parent domain violates a predefined parent domain condition, based on determining that the node of the parent domain violates the predefined parent domain condition, determining whether a node of at least one child domain violates a predefined child domain condition, the at least one child domain being interconnected with the parent domain that violates the predefined parent domain condition, based on determining that the node of the at least one child domain violates the predefined child domain condition, performing at least one corrective action on the at least one child domain, and based on determining that the node of the at least one child domain does not violate the predefined child domain condition, performing at least one corrective action on the parent domain that violates the predefined parent domain condition.

The present application relates to a kind of energy efficiency dynamic monitoring and evaluation technical field, propose a kind of ship overall technical energy efficiency dynamic monitoring and evaluation method and system, can eliminate environmental influence, realize the transparent, fair, dynamic evaluation of ship " pure technology " energy efficiency state. Including the following steps: S100: carry out quasi-steady state trend test: for the parameter susceptible to ship maneuvering, system uses sliding time window to combine linear regression slope test method to distinguish;S200: carry out environmental self-adaptive threshold adjustment: for the fluctuation screening of speed, shaft speed and other parameters, system introduces the correlation factor of sea state, no longer uses fixed standard deviation threshold;S300: carry out physical validity final screening: the data reserved after above-mentioned step, finally carries out physical boundary check;S400: through core calculation engine module, input cleaned data set, calculate output index and its confidence interval;S500: carry out fault root cause analysis;S600: complete evaluation.

The present application belongs to the field of abnormal root cause analysis, and relates to a system performance bottleneck detection method based on reinforcement learning. The system performance bottleneck detection method is as follows: first, extract system performance index data; second, find the first abnormal time period and abnormal dimension using threshold method; third, perform causal inference on abnormal data and further root cause analysis. The present application can effectively solve the system performance bottleneck detection problem in a high-load environment, help system administrators identify and solve problems faster, reduce the risk of system crashes, and improve the stability and reliability of the system, so that the system can respond to user requests faster and improve user satisfaction. The present application can be applied to a wider range of root cause analysis problems, effectively helping operation and maintenance personnel solve system performance bottleneck detection problems using artificial intelligence methods, and has good applicability and robustness.

This invention discloses a method, device, and system for automatically generating power outage event analysis reports in distribution networks using an artificial intelligence large-scale model. The method includes: automatically acquiring multi-source data (unstructured data) in response to a power outage event in the distribution network; using artificial intelligence technology to extract and clean the acquired multi-source data into a structured form, creating a unified event data model; performing FA action inversion, terminal status analysis, network weak point diagnosis, and emergency repair process inversion based on the line topology and the event data model to generate analysis results; and inputting the analysis results into a pre-trained power large-scale language model for root cause analysis, problem exposure summary, and rectification measure recommendation to automatically generate a structured fault analysis report. This invention can completely solve the problems of difficult data collection, low analysis efficiency, strong report subjectivity, and poor implementation of rectification plans in existing technologies.

This invention discloses a business model orchestration and collaborative execution method for diagnosing complex business logic. The method comprises six parts: First, business model abstraction and layered construction, including component-based business modeling, domain-driven design-based business context partitioning, and aggregation of related services. Second, intelligent orchestration engine design, including dynamic process assembly, execution strategy configuration, and diagnostic probe implantation. Third, a distributed collaborative execution framework, including event-driven architecture, distributed transaction control, and resource scheduling optimization. Fourth, real-time diagnosis and root cause analysis, including multi-dimensional monitoring, dynamic topology analysis, intelligent diagnostic engine, and root cause localization. Fifth, collaborative governance and optimization closed loop, including knowledge accumulation, automatic repair, and process optimization suggestions. Sixth, collaborative task execution, allocating resources according to business type and collaboratively executing concurrent tasks. Finally, the results of business model orchestration and collaborative execution are output. This invention achieves efficient collaborative execution and automatically diagnoses problems during the execution process.

The application discloses a fault root cause positioning method, device, equipment and readable storage medium, the method comprises determining a plurality of source abnormal characteristics; acquire the field knowledge graph; retrieve analysis basis from the field knowledge graph; utilize the preliminary screening layer, combine the analysis basis to carry out root cause tracing, generate root cause reasoning process and reasoning complexity score; when the reasoning complexity score is lower than the complexity threshold, the conclusion generation layer is utilized to generate the root cause analysis report; when the reasoning complexity score is not lower than the complexity threshold, the deep reasoning layer is called to carry out deep causal reasoning, generate reasoning train of thought and root cause propagation path; the conclusion generation layer is utilized, and the root cause analysis report is generated based on the reasoning train of thought and the root cause propagation path. It can be seen that the application can improve the reasoning efficiency and the fault root cause positioning rate through the hierarchical reasoning architecture and the field knowledge graph, clearly present the reasoning train of thought of different difficulty faults, and improve the credibility and verifiability of various fault root cause positioning conclusions.

The innovative method allows to determine root cause of an anomaly in a turbomachine and comprises the steps of: a) receiving measurement data from a set of feature sensors installed on the turbo machine relating to a time frame, b) receiving an identifier of a target feature sensor, the anomaly appearing in its measurement data, c) receiving start time and end time of a non-anomalous time subframe, d) receiving start time and end time of an anomalous time subframe, e) receiving identifiers of a plurality of feature sensors associated to features of the turbo machine that could be root causes of the anomaly, and f) deriving at least one feature of the turbo machine to be considered a root cause of the anomaly based on “contrastive analysis” of “feature importance” values of the feature sensors during the non-anomalous and the anomalous time subframes.

The application relates to the technical field of intelligent manufacturing, and discloses a mold life cycle and product process quality information linkage management and control system, which is characterized in that: a mold multi-dimensional state sensing module is used for accurately identifying micro-damage of the mold; a process quality real-time acquisition module is used for millisecond-level synchronous capture of process-quality data; a data feature extraction module is used for high-dimensional feature extraction of heterogeneous data flow; a mold health state diagnosis module is used for state evaluation and trend prediction of the mold; a process parameter self-adaptive optimization module is used for ternary dynamic collaborative optimization of mold health-process parameter-quality targets; a quality defect linkage identification module is used for quantitative root cause analysis of defect contribution degree; a predictive maintenance decision module is responsible for intelligent decision-making of maintenance urgency classification and an optimal time window; and a production execution module is used for accurate issuing of decision-making instructions, so that all the modules and a production management module cooperatively construct the linkage management and control system of the mold full life cycle and product process quality full elements.

The application provides a fault root cause association method, device, equipment and readable storage medium, through changing the time limit of root cause association from a global fixed time window to a differentiated configuration according to a network element-service combination, that is, defining a special association time limit window for each root cause association rule in combination with the network element-service context of each of the two faults involved, avoiding alarm omission or misassociation caused by the fixed window, significantly improving the accuracy of root cause analysis, and taking the association time limit window as a time rationality criterion for establishing a causal relationship, ensuring that root cause association is only allowed to be constructed under the premise that the propagation delay is reasonable, a directional hierarchical causal chain is constructed, and the fault propagation path in a complex heterogeneous environment is restored.

The application relates to the technical field of computer systems based on specific calculation models, and discloses a fault root cause analysis method fusing a graph neural network and a device topology relationship, which comprises the following steps: acquiring monitoring node topology and state data, constructing a static adjacency matrix, extracting a monitoring node time sequence feature tensor, determining time lag steps between nodes by calculating mutual correlation extreme values of node pairs with topological correlation, constructing a square order time delay offset index matrix, carrying out asynchronous sampling according to the index matrix, adjusting a feature dimension index offset, reconstructing an aligned space-time feature tensor, inputting the space-time feature tensor into a graph neural network model to generate a weight matrix, positioning a fault root cause node, and tracing a generated propagation path, the application quantitatively models a physical conduction time delay, eliminates time sequence waveform misplacement, ensures causality consistency, realizes accurate differentiation of root causes and follow-up nodes, and improves fault early warning sensitivity and diagnosis transparency.

The embodiment of the invention discloses a performance root cause positioning method and device for a micro-service system, a medium and a product, and relates to the technical field of micro-services. The method comprises the following steps: in response to an abnormal instruction of a service performance index of a front-end entry service, determining a candidate service set which is in the same service calling link as the front-end entry service, and obtaining a response time sequence of each candidate service in a preset time window; determining a baseline value, converting the original response time into a normalized relative change rate, and determining a service subset having fluctuation similarity with the front-end entry service; performing timing causal analysis on the service subset to determine a timing causal score; and determining a comprehensive root cause score based on the fluctuation similarity, the influence weight and the time sequence causal score, determining a root cause service with the highest confidence coefficient according to the comprehensive root cause score, and outputting a root cause report. According to the scheme of the invention, when the performance of the front-end entry service is abnormal, the root cause service is automatically identified, and the fault positioning efficiency is improved.

The present disclosure relates to a computer-implemented method for preventing an electrical submersible pump, ESP, failure, the method comprising obtaining real-time operation sensor data from one or more sensors (110), the real-time operation sensor data being indicative of the operation of the ESP, detecting, by an abnormality detection artificial intelligence, Al, engine (220), at least one abnormality in the real-time operation sensor data (120), the abnormality indicating an abnormal event of the operation of the ESP, generating, by a physics characteristics Al engine (230), at least one operating characteristic of the ESP based at least on the abnormality and on a physical model of the ESP (130), determining, by a root cause analysis Al engine (240), at least one root cause of the ESP failure based at least on the at least one abnormality and the at least one operating characteristic (140), and generating at least one failure prevention measure to reduce the risk that the ESP failure occurs (150). In addition, the present disclosure relates to a corresponding computer system and to a corresponding computer program.

The application provides a kind of application software development compatibility debugging method and device for heterogeneous equipment, and is related to application software debugging.The application constructs equipment dynamic characteristic map by collecting and integrating equipment hardware, system, resource, interface and running state characteristics through multi-modal feature fusion technology.Secondly, the real-time iterative optimization of compatibility calculation weight is realized by introducing reinforcement learning algorithm, and the feature adaptation accuracy is improved by combining the improved normalization model.Through abnormal fingerprint extraction and tracing algorithm, the precise positioning and root cause analysis of incompatibility problem are realized.Finally, based on dynamic confidence threshold, hierarchical adaptation, intelligent repair and cross-device debugging are executed.The application breaks through the limitations of traditional fixed strategy debugging, realizes the intelligentization, self-adaptation and precision of compatibility debugging, greatly improves the debugging efficiency and reduces the adaptation cost, and is suitable for various heterogeneous equipment platforms.

This application provides a method, apparatus, and device for analyzing the root causes of lag, relating to the field of data processing. The method includes: converting first log data in a first format into second log data in a second format; determining at least one lag event in the second log data based on a preset lag rule base, where a lag event is a set of log entries in the second log data that match a target lag rule in the lag rule base, and the target lag rule is any rule in the lag rule base; and determining the root cause of the lag event based on first device operation data corresponding to the target lag event and the second log data, where the target lag event is any one of the at least one lag event, and the first device operation data is device operation data within the time window corresponding to the target lag event. The method provided in this application improves the efficiency and accuracy of determining lag events and their root causes.

The application discloses a cloud-native application operation and maintenance troubleshooting method and device and a storage medium, and the method comprises the following steps: sending alarm information; acquiring topology information of a Pod instance corresponding to the alarm information; acquiring a near-time period alarm list and a near-time period index monitoring graph of an associated resource object from the topology information; determining an abnormal resource object based on a large language model and a multi-modal image model inference; extracting a feature vector of a current fault from the alarm information, near-time period log information and the near-time period index monitoring graph of the abnormal resource object, and screening a similar historical fault according to the similarity between the feature vector of the current fault and the feature vector of the historical fault; and performing root cause analysis based on the similar historical fault and technical document information of the abnormal resource object to determine a fault root cause. The above technical solution can at least accurately determine the fault root cause, improve the fault troubleshooting efficiency, and reduce the risk of business interruption caused by inaccurate or delayed manual judgment.

This invention provides an interactive software debugging and automatic repair method based on a large language model, addressing the problems of traditional software debugging relying on manual operation and being time-consuming, as well as the low repair accuracy caused by the lack of a dynamic operating environment in existing static automatic program repair technologies. The method first triggers a program exception by executing a reproduction command and captures the original crash stack information; then, it uses a large model to extract a simplified core function call sequence; subsequently, it constructs a closed-loop debugging mechanism, controlling the large model to generate hypotheses about the root cause of the defect and executing debugger probe commands, iteratively revising the hypotheses based on the dynamic information obtained by the debugger until the defective code is accurately located; finally, it combines the debugging context with the large model to complete root cause analysis and automatically generate and verify executable repair patches. This invention alleviates the "illusion" of large model inference by continuously capturing the underlying dynamic operating state, significantly improving the automation and accuracy of defect repair.

Systems and procedures are provided for comprehensive observability across a cluster of nodes in a container management system to enable proactive, compliance-driven root cause detection within the cluster. Examples train a root cause analyzer for a container management system cluster by applying historical cluster observability data to a set of machine learning (ML) models. Examples detect technological anomalies in the container management system and identify compliance events at compute nodes in the cluster based on cluster observability data received from the cluster. Examples predict a root cause for one or more technological anomalies based on the compliance events and prioritize remediation actions to address the root cause based on both the compliance events and the technological anomalies.

This invention discloses a self-optimizing control method and system for a fabric spreading machine based on multi-source scoring feedback, belonging to the field of textile machinery automation technology. The method includes: real-time acquisition of multi-source data from the fabric spreading machine; calculation of parameter jitter score Q1, operational continuity score Q2, and fabric smoothness score Q3 respectively, and weighted fusion to obtain a comprehensive quality score; when the comprehensive quality score is below a threshold, root cause analysis is performed to generate parameter tuning suggestions; parameter regression prediction is performed using historical high-scoring samples, optimized parameter combinations are output and executed, and the score is updated based on newly acquired data, forming a self-optimizing closed loop. This invention, through multi-dimensional quantitative evaluation and data-driven closed-loop optimization, solves problems such as reliance on experience in manual parameter tuning, subjective quality assessment, and difficulty in fault attribution, significantly improving the consistency of fabric spreading quality and the level of equipment intelligence.

Techniques are disclosed for using prompt engineering and statistical root cause analysis (RCA) to increase the accuracy of machine learning RCA. In an example, a computing system generates a prompt for root cause analysis of an application-layer anomaly within system of elements across a plurality of layers. The prompt comprises a cross-layer topology graph of the elements across the plurality of layers, diagnostics information for the elements, and a list of the elements and corresponding diagnostics information ordered according to a likelihood of being a source of a root cause of the application-layer anomaly. The computing system provides the generated prompt to a machine learning model to obtain, based at least in part on the generated prompt, a response comprising the root cause of the application-layer anomaly. The computing system outputs the response.

The application discloses a micro-service root cause positioning method and system based on fault snapshots and large language models, and the method comprises the following steps: an intelligent monitoring and alarming agent captures system abnormal alarms in real time; an intelligent preprocessing agent performs an aggressive preprocessing strategy on the aggregated telemetry data; the generated "fault snapshot" is taken as input and submitted to a domain-specific large language model trained in a specific composite manner; and a structured root cause analysis report is generated based on the inference result of the model. The application overcomes the context limitation of large models, realizes rapid and accurate automatic root cause diagnosis, and significantly reduces the average fault repair time.