2198 results about "Fault analysis" patented technology

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

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The invention discloses a fault analysis method and system for computer room equipment, and particularly relates to the technical field of computer room equipment. Accurate fault traceability and equipment positioning are realized by acquiring operation data of machine room equipment, analyzing an abnormal data mode in real time by utilizing a machine learning model, generating preliminary fault early warning and adopting a multi-source data fusion technology in combination with a historical fault database and a correlation analysis algorithm, and meanwhile, based on an equipment topological relation and service correlation analysis, the fault positioning accuracy is improved. Quantifying the influence range of the fault on the whole machine room system, calculating the influence level, and automatically generating an optimized fault processing scheme in combination with a fault case library; after the fault is processed, the equipment is continuously monitored, whether the fault is thoroughly eliminated or not is verified, and stable operation of the system is ensured; according to the method, the fault detection accuracy, the traceability and the repair efficiency of the computer room are effectively improved, the risk of false report and missing report is reduced, and the service continuity and the data integrity are improved, so that the intelligent level of operation and maintenance of the computer room is enhanced.

The invention relates to the technical field of data processing, and discloses a fuel gas monitoring fault analysis method and system and a storage medium. The method comprises the following steps: acquiring multi-dimensional data through a sensor group, and transmitting the multi-dimensional data to a processing unit for pre-screening through an Internet of Things protocol; carrying out filtering, feature calculation and dimension reduction processing on the data to obtain a feature vector; establishing a fault database and generating a mapping relation; time sequence and space data are fused to calculate a fault judgment value; an early warning result is obtained by combining sensor state data evaluation; historical data is retrieved, similarity is calculated, and a disposal instruction is generated. According to the method, the accuracy of gas pipe network fault diagnosis is improved through fusion analysis of multi-source data, and early recognition and early warning of faults are achieved.

The invention relates to the technical field of fault analysis, in particular to a power grid fault intelligent diagnosis and analysis system which comprises a fault signal detection module, a propagation path analysis module, a fault positioning calculation module, a fault type analysis module and a fault influence analysis module. According to the invention, by monitoring the voltage transient data of the plurality of nodes of the power grid in real time, the instantaneous offset and the time sequence difference of the voltage can be accurately calculated, so that the voltage mutation exceeding the preset threshold value can be quickly identified in the continuous time window, the early identification of the power grid fault is quicker, the risk of false alarm is reduced, and the accuracy of the early identification is improved. By analyzing the propagation path and speed of the sudden change quantity along the power grid, the fault positioning precision is improved, rapid and accurate fault source tracing is realized, and by careful analysis of the current waveform of a fault point and combination of real-time tripping current data, the fault type can be identified more accurately, the fault handling strategy can be optimized, and the fault handling efficiency is improved. And the power grid fault processing efficiency and safety are improved.

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.

A multi-fault diagnosis method for a power distribution network is provided. The method includes: performing short-circuit fault analysis on a line of a power distribution network by using a MATLAB platform, so as to obtain an electrical quantity-based fault information decision table; performing modeling and simulation on the fault information decision table by using a Simulink platform; denoising and collecting output training data of a neural network of the power distribution network by using a wavelet transformation method, and forming a related fault information decision table as a training sample of the neural network; and optimizing weights and thresholds of the neural network by using an improved Artificial Tree intelligent optimization algorithm, selecting some of the data as fault data, and using the neural network trained to perform fault detection.

The invention provides a power distribution network system fault monitoring method and system based on multi-source information, and the method comprises the steps: obtaining a multi-source monitoring data set of a power distribution network system, carrying out the data feature extraction of the multi-source monitoring data set, and obtaining the time sequence change feature of operation state data and the space correlation feature of environment influence data; calling a pre-constructed fault detection model to carry out cooperative fault analysis on the time sequence change characteristics and the space correlation characteristics, and generating a fault detection result of the power distribution network system; determining the fault type in the power distribution network system and the position characteristic of the fault in the network structure according to the fault detection result; and generating a fault early warning instruction containing the fault positioning coordinate based on the fault type and the position feature. The overall practicability and reliability of power distribution network system fault monitoring are effectively improved.

The invention discloses a high-voltage equipment monitoring system and method based on thermal imaging and dynamic sensing fusion of a power plant, and belongs to the field of high-voltage equipment monitoring. The edge intelligent processing module is used for outputting a real temperature field matrix, separating equipment vibration characteristics from environment noise and generating a fused characteristic vector; the multi-modal analysis module is used for inputting the feature vectors into a constructed equipment topological graph neural network and outputting a health index and an abnormal probability distribution diagram of each monitoring point; the environment coupling fault analysis module is used for generating a fault diagnosis report; and the dynamic early warning module is used for executing graded early warning according to the health index and the fault diagnosis report. The fault detection rate and the operation and maintenance efficiency can be improved.

The invention belongs to the technical field of steel production equipment fault diagnosis, and particularly relates to a steel production equipment fault diagnosis system based on a large language model. The system comprises a data processing module, a large language model training module and a retrieval enhancement generation module. Through domain data processing and model training, an intelligent diagnosis platform specially used for steel production equipment is constructed. The data processing module is responsible for performing feature extraction on the sampled data and constructing a vector database to ensure that the model can perform efficient semantic matching; the training module performs field adaptive adjustment on the large language model to improve the reasoning ability of the large language model in a specific field; the retrieval enhancement generation module realizes fault analysis and technical guidance with strong pertinence and high accuracy by integrating a vector database and a generative answer mode; the system runs locally or on a privatized server, all data processing, model calculation and generation processes are ensured to be completed in a controlled environment, and the privacy of user data is fully protected.

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 IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention relates to the technical field of power generation equipment control, and discloses an intelligent control method and system for a hydroelectric generating set speed regulator, and the method comprises the steps: employing an introduction variable structure and variable parameter control technology, and dynamically adjusting the control parameters of a power closed loop and an opening closed loop; real-time calculation and feedback control are carried out, and a speed regulator responds to a power grid frequency modulation Operating data of key parts of the speed regulator are collected in real time through a sensor, feature extraction and mode recognition are conducted on the data in combination with an artificial intelligence algorithm, and the operating state and potential faults of the speed regulator are recognized; constructing a fault tree model based on the speed regulator state monitoring parameters, carrying out hierarchical analysis on the speed regulator, and positioning the specific position and reason of the fault; and verifying and optimizing the fault diagnosis result, and generating a targeted fault processing suggestion. The system comprises a dynamic adjustment module, a state evaluation module and a fault analysis module. According to the invention, the operation efficiency and safety of the hydroelectric generating set speed regulator in a complex power grid environment are obviously improved.

The invention relates to the technical field of communication, and discloses an optical fiber performance automatic test method, system and device based on optical power detection. The method comprises the following steps: carrying out intelligent scanning detection on an optical fiber network, generating optical fiber resource mapping data, obtaining an OTDR measurement configuration table through dynamic analysis, executing optical pulse injection and signal acquisition to obtain a time domain response characteristic curve, obtaining an optical fiber performance parameter set through wavelet denoising and peak value identification processing, and obtaining the optical fiber performance parameter set. And performing fault analysis by using deep learning and expert rules to obtain diagnosis decision data, and finally generating an optical fiber state evaluation report through digital twin modeling. According to the invention, remote automatic testing, intelligent fault diagnosis and visual monitoring management of the optical fiber network can be realized, and the operation and maintenance efficiency and reliability of the optical fiber communication network are improved.

The invention relates to the technical field of cable fault detection, in particular to a cable fault analysis system based on big data, which comprises a disturbance feature extraction module, a time sequence hierarchy judgment module, a reflection characteristic analysis module, a topological section identification module and a fault path mapping module. According to the method, gradient inflection points are extracted by calculating voltage and current increment included angles at adjacent moments, spatial-temporal characteristics of disturbance signals are correlated, interference of noise to a starting point mark is reduced, time difference is fitted in combination with cable length, deviation over-threshold abnormal points are dynamically filtered, influences of synchronization errors on sorting are eliminated, and fault path mapping stability is improved; the method comprises the following steps: generating characteristic parameters by fusing amplitude values and wave crest intervals of main waves and reflection waves, quantifying amplitude attenuation and phase offset coupling, enhancing the multi-path superposed signal separation capability, judging the reflection characteristic similarity of a physical section based on a Manhattan distance and a support vector machine, and dynamically dividing a fault section in combination with line topology. The problem of fuzzy positioning in a branch parallel scene in a traditional method is solved.

The invention is suitable for the field of transformer fault analysis, and provides a transformer potential fault mode identification method and device based on reverse derivation, and the method comprises the steps: generating a multi-field coupled feature puzzle model through real-time collection of four-dimensional physical quantity spatio-temporal data of temperature, vibration, oil chromatography, partial discharge and the like, and carrying out the topological matching of the feature puzzle model and a historical health file, and reversely deducing a fault coupling path for the abnormal vacancy region through a constraint satisfaction algorithm, reconstructing a missing feature block, decoupling and extracting interface abnormal features, dynamically optimizing model topology in combination with an equipment aging factor, and finally quantifying the full-life-cycle risk accumulation intensity and positioning a composite fault source. The method breaks through the limitation of a traditional single-parameter threshold value, achieves the early recognition and precise traceability of the multi-field coupling fault through a reverse derivation mechanism of the feature jigsaw blocks, is suitable for the health management of the whole life cycle of the transformer, and can remarkably improve the timeliness and accuracy of potential fault early warning.

The invention belongs to the technical field of fault analysis and monitoring, and particularly relates to an intelligent monitoring and fault isolation method and system for a branch box, and the method comprises the steps: carrying out the multi-physical field data fusion collection of the branch box, and obtaining a spatial-temporal feature matrix; performing dynamic feature extraction on the multi-physical field data of the branch box, and outputting a fused dynamic feature vector of the branch box; obtaining a branch box topological graph structure, and obtaining a branch box node fault probability matrix; building a physically constrained fault propagation model based on the branch box node fault probability matrix, obtaining a pre-isolation set, and outputting a dynamic blocking instruction; taking the pre-isolation set as an initial solution space constraint, outputting a fault type and obtaining an isolation instruction; and setting a branch box topology reconstruction constraint, and performing closed-loop control on each branch box node in combination with the isolation instruction to obtain a switching action sequence of each branch box node and a reconstructed power supply path. Precise monitoring, rapid isolation and intelligent reconstruction of the fault of the branch box can be realized, and the operation and maintenance efficiency of the power distribution network is remarkably improved.

A method and system for analyzing fault data features of an active distribution network. The method comprises: analyzing a voltage variation after a fault in an active distribution network and analyzing a single-phase ground fault (S1); acquiring distribution network fault data by means of analog simulation (S2); and establishing a fault location prediction model to analyze fault data features and realize fault localization (S3). In the method, a voltage variation after a fault in an active distribution network is analyzed, the voltage variation is equivalently represented using an injected current, a single-phase ground fault is analyzed, and the localization of a fault point is realized. By simulating a current variation during an actual fault injection process by means of an injected current function, a model can more closely reflect an actual operating state, thereby enhancing the authenticity of fault simulation and the accuracy of prediction. A complex relationship between a current variation and node impedance is deeply analyzed by means of a complex information filtering function, and more refined fault features are extracted, thereby optimizing the precision of fault localization, and enhancing the adaptability of the model to different fault scenarios.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an execution method of a large model graph retrieval enhancement system oriented to software and hardware monitoring operation and maintenance, which comprises the following steps: S1, inputting fault information to the graph retrieval enhancement system; s2, a graph construction and updating module receives and processes the fault information, generates a triple, and writes the triple into an operation and maintenance / fault knowledge graph; s3, the fault information is input into a graph retrieval enhancement module for two-stage filtering retrieval, and sub-graph information is screened out; s4, the prompt word construction module converts the fault information and the screened sub-graph information into structured natural language prompt segments; and S5, a reasoning generation module performs natural language question and answer to generate a fault analysis result. Based on the above scheme, the execution method enhances the adaptive capacity of knowledge reasoning and fault positioning, gives play to the generalization reasoning capacity of a large language model while ensuring the accuracy, and enhances the practical value.

The invention provides a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence, and the system comprises an intelligent monitoring and predictive maintenance module which is used for achieving the real-time monitoring, abnormality recognition and performance prediction of an IT system through collection of multi-dimensional time series data, and when an abnormal condition is monitored or a prediction result does not meet a preset requirement, the intelligent monitoring and predictive maintenance module carries out the operation and maintenance of the IT system; if so, triggering intelligent early warning; the AI-driven fault diagnosis and self-healing module is used for carrying out intelligent fault analysis and repair in combination with IT system logs and historical fault data based on the detected abnormal condition; the self-adaptive resource management module is used for predicting a future load trend and a future performance trend, and performing self-adaptive resource management based on the predicted future load trend and the future performance trend in combination with cost and energy consumption factors; and the security operation and maintenance automation module is used for monitoring security threats of the IT system in real time and automatically executing security strategies.

The invention relates to the technical field of industrial equipment diagnosis, in particular to an AI-driven industrial equipment fault analysis method and system, and solves the problems of insufficient multi-source data fusion, weak working condition adaptability and inaccurate fault positioning in the prior art. According to the method, high-frequency time sequence signals are synchronously collected through a vibration sensor, a temperature sensor and the like, working condition parameters are marked, a dynamic confidence coefficient weight is generated based on a working condition matching historical fault library, and cross-modal features are extracted through a space-time convolutional network after multi-source data are fused. A fault mode separation module is used for decoupling the fault contribution degree of mechanical transmission, electric power and heat dissipation systems, precise diagnosis is achieved in combination with frequency domain fingerprints, time domain waveform analysis and cross decision verification, finally, the remaining life is predicted through a long-short-term memory network and a Weibull model, and the system integrates multi-source sensing, dynamic fusion and intelligent diagnosis units. The fault early warning accuracy and the equipment maintenance scientificity under complex working conditions are improved, and a solution is provided for intelligent diagnosis of industrial equipment.

The invention provides an equipment telemetry data fault analysis method and system based on machine learning, and relates to the technical field of fault analysis, and the method comprises the steps: carrying out the standardization processing of multi-dimensional telemetry data, extracting time sequence features and statistical features, screening a core feature set, and constructing a lightweight feature extraction rule; according to the method, feature component contribution degree scores are calculated, adaptive weight coefficients are distributed, fault feature representation is generated, and a fault diagnosis rule base is matched, so that the fault type and probability are determined, edge end data processing lightweight and timely generation of fault early warning are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to an equipment fault intelligent question-answering method based on large model enhancement, and the method comprises the following steps: obtaining steel production line data, and constructing a fault knowledge graph; user questions are obtained and classified through semantic analysis, and classification results comprise single-hop questions, short-hop questions and multi-hop questions; calling a corresponding reasoning mechanism according to the category of the user question, generating an answer according to the fault knowledge graph, and outputting equipment fault analysis in a natural language form through a retrieval enhancement generation technology; for a single-hop problem, core entities and relationships are matched based on an Aho-Corasick automaton, and answers are directly retrieved from the fault knowledge graph; for a short jump problem, deriving an answer from the fault knowledge graph by adopting a single-agent inference model; for a multi-hop problem, a multi-agent collaborative reasoning model is adopted to generate a multi-step reasoning path based on the fault knowledge graph. Compared with the prior art, intelligent question answering with high reliability and high readability can be efficiently realized in a complex industrial scene.

The invention relates to the technical field of monitoring systems, in particular to an electric power internet of things transformer substation auxiliary equipment monitoring system and method, and the method comprises the steps: collecting the operation data of transformer substation equipment in real time; preprocessing the collected operation data by utilizing edge calculation; analyzing the preprocessed operation data based on a deep learning algorithm, and generating a preliminary fault diagnosis and prediction result; constructing a digital twinborn model of the substation equipment, and performing equipment health assessment and fault prediction by utilizing a preliminary fault diagnosis and prediction result and combining a physical model and historical data of the equipment; and according to the equipment health assessment and fault prediction result, data visualization display is carried out through the monitoring platform, and hierarchical alarm is carried out to inform operation and maintenance personnel. Through the deep learning algorithm and the digital twinborn model, deeper fault analysis and prediction can be provided, and the problem that the equipment is not maintained in time or is excessively maintained is avoided.

The invention relates to the technical field of fault diagnosis of power electronic equipment, and particularly discloses a fault analysis and diagnosis method and system for a mining high-voltage frequency converter, and the method comprises the steps: collecting the skin effect depth and parasitic parameter drift distance in real time under a high-frequency working condition through a multi-physics field sensor array; constructing three-dimensional current density distribution of the conductor based on a non-Euclidean space mesh generation technology, and dynamically correcting the equivalent resistivity of the conductor in combination with a metamaterial database to calculate a dynamic eddy current loss characteristic value; optimizing and solving the parasitic parameter time-varying evolution equation by using a quantum annealing algorithm to obtain a voltage peak sensitivity characteristic value; inputting the characteristic value into a pre-trained deep residual shrinkage network diagnosis model for multi-modal fusion analysis, and outputting a fault risk assessment result; according to the method, the problem of parasitic parameter time-varying characteristic modeling in the vibration environment is innovatively solved, and early warning of hidden faults is achieved.

The invention discloses an intelligent photovoltaic grid-connected adaptive system based on multi-modal sensing and predictive analysis, and the system comprises a multi-modal sensing unit which is used for collecting the multi-source information of a photovoltaic system; the predictive fault analysis module is used for predicting potential system faults based on the multi-source data; the self-adaptive phase-frequency synchronous control unit is used for dynamically adjusting the output parameters of the photovoltaic inverter, so that the output of the photovoltaic inverter is matched with the parameters of the power grid; the comprehensive decision engine is used for generating a grid-connected or off-grid instruction; the self-repairing execution unit is used for generating a specific response scheme and controlling related equipment to execute based on the fault early warning and the strategy instruction of the comprehensive decision engine; and the remote monitoring and manual intervention interface is used for providing a monitoring interface. Through cooperative work of multi-modal data perception, predictive fault analysis and adaptive control, safe, efficient and intelligent grid connection of the photovoltaic system and the power grid is realized, and the reliability, safety and economical efficiency of the photovoltaic power generation system are improved.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.