1692 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 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 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 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 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.

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 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 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 discloses an intelligent power distribution network fault analysis and maintenance system based on the Internet of Things, which belongs to the technical field of power distribution network fault analysis and maintenance and comprises a multi-source data acquisition preprocessing module, a dynamic digital twin modeling module, an edge cloud fusion analysis module, a transient fault topology positioning module, a self-healing control module and an intelligent maintenance scheduling module. According to the method, fault features are extracted through edge-side AI reasoning, global anomaly detection is realized in combination with cloud big data analysis, anomaly modes under different working conditions are adapted through a dynamic balance coefficient, and fault type tags are quickly generated through weighted fusion of feature vectors in edge-side real-time reasoning, so that the fault detection accuracy is improved. The global anomaly scoring mechanism greatly improves the fault classification accuracy, updates the node state and the probability density function by injecting a dynamic digital twin model, quantifies the distribution characteristics of the faults in space coordinates in combination with the topological weight, the fault influence function and the space attenuation coefficient, and accurately recognizes the fault high-incidence area.

The invention discloses a rapid N-1 fault analysis method and system based on a graph neural network. The method comprises the following steps: performing power transmission line parameter identification based on real-time measurement data to ensure the accuracy of physical parameters of a power system diagram; constructing a power system diagram; known and unknown features are processed in combination with a mask encoder; constructing a graph neural network architecture based on topological adaptive graph convolution; embedding the physical constraint of the power system into a loss function of the graph neural network; for each credible N-1 fault, training a special graph neural network model by using power flow calculation data including a normal operation state and an accident state; and carrying out fault rapid analysis by using the model. According to the GNN-based rapid N-1 fault analysis method and system provided by the invention, the branch power flow after the fault is predicted by training the special model, so that the analysis efficiency is remarkably improved, the calculation time is shortened, relatively high prediction precision is kept, and the operation safety and reliability of a power grid are effectively enhanced.

The invention discloses a gas pipe network maintenance method and system based on a large language model, and electronic equipment, and aims to solve the ubiquitous problems that data fusion is difficult, fault analysis depends on manpower, the operation and maintenance automation degree is low and the like in a gas pipe network. The method comprises the following steps: firstly, acquiring and processing multi-source heterogeneous data of the gas pipe network, then vectorizing the multi-source heterogeneous data to construct a unified multi-modal database, when monitoring that the data is abnormal, adopting retrieval enhancement to generate a large language model, retrieving associated information from the database, performing fault root cause intelligent reasoning, and generating an analysis report, and finally, automatically generating a structured maintenance instruction according to the report, and docking with an operation and maintenance system to form a whole-process automatic closed loop from monitoring to disposal. The system and the electronic equipment are used for executing the method. The method has the advantages that a data island is broken through, accurate situation awareness is achieved, the efficiency and accuracy of fault diagnosis are remarkably improved, and the effect of reducing the operation cost and the safety risk is remarkable.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The embodiment of the invention provides a fault cause determination method and device, a storage medium, electronic equipment and a computer program product, and relates to the field of data analysis, and the method comprises the steps: receiving a fault analysis request sent by a target object, and determining a preliminary fault cause list corresponding to the fault analysis request; an analysis task corresponding to the preliminary fault reason in the preliminary fault reason list is determined, the analysis task is executed to obtain abnormal data corresponding to the analysis task, and the abnormal data at least comprises one of abnormal log data, abnormal index data and abnormal service data; and determining the fault contribution evaluation information corresponding to the plurality of candidate fault reasons according to the abnormal data, and determining the target fault reason of the target fault in the plurality of candidate fault reasons according to the fault contribution evaluation information, thereby solving the problem of relatively long time consumption caused by determining the fault reason depending on personal experience in related technologies.

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention relates to the field of battery car charger detection, in particular to a charger health state assessment method and system, and the method comprises a data collection step, a predicted electric quantity calculation step, a health state assessment step and a fault feature analysis step. A sliding time window algorithm is used for dynamically calculating electric quantity loss, a self-adaptive dynamic threshold system based on an operation state and an environment working condition is constructed, fault characteristics are analyzed in combination with a nonlinear fusion model and an equipment aging factor, and accurate evaluation of the health state of the charger and rapid fault positioning are achieved. And the evaluation comprehensiveness, accuracy, threshold dynamic nature and fault analysis precision are improved.

The invention discloses an electronic information system multi-fault diagnosis method and system based on artificial intelligence, and belongs to the technical field of electronic information system fault diagnosis, and the method comprises the steps: data collection and preprocessing, feature extraction and selection, multi-fault diagnosis model construction, real-time fault diagnosis and analysis, and fault recovery and feedback. The system comprises a data acquisition module, a preprocessing module, a feature extraction and selection module, a multi-fault diagnosis model module and a fault analysis and repair module, and all the modules cooperate to achieve full-process automation from data acquisition to fault repair. The method has a self-learning optimization capability, can significantly improve the fault diagnosis efficiency and reliability of a complex electronic information system, and is suitable for real-time fault diagnosis scenes of various types of electronic equipment.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The invention discloses a fault analysis method and device for a switch chip, and relates to the field of computers, and the method comprises the steps: monitoring an error event, and instantly generating an error log containing an error code and a port number when a target link is abnormal; then, through accurate fault mapping, based on an error code mapping table pre-stored in a nonvolatile medium, dynamically converting an original error code into a readable fault description, and in combination with a data packet associated physical position captured by a trigger, realizing fault tracing; through structured storage management, the fault description and the original log are stored in a partitioned mode according to port numbers, and a traceable historical database is formed; and finally, real-time alarm and deep diagnosis are triggered through system collaborative optimization. Therefore, the technical effects of operation and maintenance efficiency rising, fault tracing enhancement and system reliability enhancement are realized, and the technical problem of low fault analysis efficiency of the switch chip in the related technology can be solved.

The invention relates to the technical field of software testing, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a fault simulation method, device and equipment based on a code probe and a medium. Constructing a corresponding fault logic code according to the payment data link; injecting the fault logic code into the to-be-tested fault object to obtain a target fault object; performing payment fault simulation on the target fault object according to a preset payment fault simulation condition and the fault logic code to obtain fault response data corresponding to the target fault object; and performing fault analysis on the target fault object according to the fault response data to obtain a target fault analysis report of the target fault object. According to the invention, full-link intelligent fault simulation can be realized and the verification reliability of each transaction of the target system can be improved.

The invention relates to an arc fault detection method and system based on a dynamic fuzzy threshold and a storage medium, and the method comprises the steps: collecting the current circuit data of to-be-detected electrical equipment, and carrying out the feature extraction of the current circuit data, and obtaining a current feature parameter; the method comprises the following steps: training historical circuit data by taking a decision tree algorithm as a model architecture, and in the training process, performing parameter updating through a double-sliding window mechanism and optimizing a fuzzy threshold band range through an information gain maximization principle to obtain a dynamic fuzzy threshold decision model; and performing arc fault analysis on the current characteristic parameters through the dynamic fuzzy threshold decision model to obtain a fault detection result, and outputting early warning information when the fault detection result is that an arc fault occurs. According to the method, the dynamic fuzzy threshold value band is constructed through the Gaussian mixture model, and the self-adaptive adjustment of the threshold value is realized in combination with a double-sliding-window online updating mechanism. According to the technical scheme, the accuracy of arc faults can be remarkably improved, and particularly the false alarm rate is reduced.

The embodiment of the invention provides an urban rail signal system fault diagnosis method based on a knowledge graph and a large model, and the method comprises the steps: collecting system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and standardization; based on historical fault data, a fault classification and prediction model is constructed through feature extraction and mode recognition, and automatic fault recognition and risk early warning are achieved; constructing a fault diagnosis knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large language model and a LoRA technology are adopted for efficient fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the knowledge graph and large model output are fused, real-time and historical data are combined, multi-path fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and technical support is provided for intelligent operation and maintenance management.

The invention discloses a power grid fault positioning system and method based on traveling wave distance measurement, relates to the technical field of fault analysis, and solves the technical problems that traveling wave feature analysis, temperature compensation and high-precision synchronization technologies are not effectively integrated, and rapid and accurate fault positioning of a smart power grid is difficult to meet. Through the technical combination of dynamic temperature compensation, high-precision time synchronization and intelligent fault classification, the bottlenecks of traditional traveling wave distance measurement in the aspects of precision, reliability and intelligence are systematically solved, temperature sensors are deployed at key nodes of a cable, the temperature is monitored in real time, the wave speed is dynamically corrected through a formula, the overall error is reduced, and the fault location accuracy is improved. Meanwhile, an optical fiber two-way time transmission method is combined, transmission delay errors are reduced, synchronization reliability is improved, finally, a mapping library is constructed through laboratory simulation and field data, a Euclidean distance matching algorithm is combined, fault reasons are automatically recognized, and rapid positioning of the faults and accurate recognition of the fault reasons are achieved.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

The invention belongs to the technical field of network communication, and particularly relates to an operation and maintenance network fault solving system and method.The operation and maintenance network fault solving method comprises the steps that a data collection module collects multi-source original network data of target network equipment and preprocesses the multi-source original network data to obtain multi-source standard network data; a semantic reconstruction module identifies non-business purpose fields in the multi-source standard network data and performs semantic reconstruction processing on the non-business purpose fields to obtain a target state set; the fault diagnosis module carries out fault analysis on the target state set and determines a fault type and a root position; and the execution module generates and executes a predictive isolation strategy according to the fault type and the root position, records a fault processing process and updates a dynamic coding rule and a diagnosis weight distribution strategy. According to the invention, the problems of low efficiency and insufficient accuracy caused by dependence on artificial experience and rule analysis in a network fault diagnosis method can be solved.

The invention relates to the field of intelligent operation and maintenance of rail train traction transmission systems, and discloses an intelligent operation and maintenance method and system for a rail train traction transmission system. The method comprises the following steps: firstly, collecting multi-source heterogeneous knowledge, and defining a fault knowledge graph body and a relationship thereof to construct a knowledge graph; and analyzing and segmenting the text data in different types, and storing the text data into a vector database so as to support subsequent accurate retrieval and reasoning. Establishing a multi-source knowledge retrieval mechanism, and extracting unstructured texts and structured knowledge highly related to fault query by retrieving a vector database and a knowledge graph; aiming at unstructured text knowledge, carrying out sequence optimization on the knowledge by adopting a reordering model; and a cue word template is constructed, texts and structured knowledge are integrated, a large language model is guided to carry out fault analysis, and maintainability decision support is provided. The accuracy and reliability of a large language model in intelligent operation and maintenance in the field can be improved.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

The invention relates to the technical field of power distribution network fault processing, in particular to a power distribution network fault automatic reconstruction control method of a power system. The method comprises the following steps: collecting operation state data of the power distribution network; dividing the power distribution network operation state data into power distribution network operation fault data and power distribution network operation state candidate data to be detected; performing disturbance interference potential state simulation analysis on the to-be-detected candidate data of the operation state of the power distribution network to generate disturbance interference potential state simulation data of the power distribution network; performing disturbance fault analysis through the power distribution network disturbance potential state simulation data to generate power distribution network disturbance fault data; based on the power distribution network operation fault data and the power distribution network disturbance fault data, intelligent reconstruction control decision design is carried out, and power distribution network intelligent reconstruction control decision data is generated; and power distribution network fault automatic reconstruction control operation is executed through the power distribution network intelligent reconstruction control decision data. According to the invention, efficient automatic reconstruction control is realized when the power distribution network fails.