353 results about "Fault propagation" patented technology

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention provides a power transmission network equipment fault diagnosis and life prediction method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the time sequence electrical characteristic data of a plurality of monitoring nodes, carrying out the window segmentation and statistical characteristic extraction, building a dynamic association graph structure based on a space-time association constraint model, and obtaining the time sequence electrical characteristic data; and calculating the abnormal contribution degree of each node, marking candidate abnormal nodes, determining a fault propagation path through reverse tracing and path analysis, and finally outputting a fault positioning result. According to the invention, abnormal nodes can be accurately identified, a fault propagation path can be accurately tracked, and the accuracy and timeliness of power transmission network fault diagnosis are improved.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention belongs to the technical field of data analysis, and provides a multi-source operation and maintenance data anomaly analysis method and device, equipment, a medium and a product, the method comprises the steps that multi-source operation and maintenance data is acquired, and the multi-source operation and maintenance data comprises at least two of index time sequence data, application logs, call link tracking data, configuration change records, alarm events and work orders; carrying out joint anomaly modeling on the preprocessed multi-source operation and maintenance data based on a multi-model fusion architecture to identify an abnormal event in the multi-source operation and maintenance data; based on the operation and maintenance knowledge graph and the structured causal model, fault influence path tracing and root cause positioning are carried out on the abnormal event, a root cause analysis result is obtained, and the root cause analysis result is used for indicating a fault root cause node and a fault propagation path in the abnormal event. Therefore, the accuracy of anomaly analysis of the multi-source operation and maintenance data is remarkably improved.

The invention provides a server cluster operation and maintenance method based on multi-source heterogeneous data fusion and a dynamic knowledge graph, and the method comprises the following steps: collecting a performance index, a log text and topological structure data of a server cluster, splicing the performance data and the log data based on a unified time window, and generating a multi-modal feature sequence; and analyzing the sequence by using an unsupervised deep learning model, constructing a dynamic health baseline, and generating a health degree portrait through the deviation with real-time data. When an exception is detected, mapping an exception event into a dynamic topological graph constructed based on a topological structure; analyzing a fault propagation probability between nodes by using a graph neural network algorithm, positioning a root cause node, and generating a disposal strategy to execute disposal operation; and collecting the processed recovery data as a feedback signal, and updating the deep learning model by using incremental learning. The method has the beneficial effects that the fault discovery accuracy is improved, the alarm storm is effectively inhibited, the root cause is directly positioned, and the model self-iteration adaptability is higher.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to the technical field of power plant operation monitoring, in particular to an intelligent power plant operation data monitoring method based on artificial intelligence assistance. The method comprises the following steps: extracting multi-source power plant operation data, detecting an abnormal value, obtaining optimized power plant operation characteristics through interpolation optimization processing, analyzing the multi-source data by using an artificial intelligence technology, predicting aging state characteristics of equipment, carrying out difference comparison, identifying potential hardware operation faults, and carrying out fault diagnosis. The method comprises the following steps: identifying a cooperative connection path of equipment through operation data, predicting a cooperative fault risk node, mapping a risk physical connection relationship of the equipment, performing fault transfer simulation based on the risk physical connection relationship, generating hardware fault transfer data, and determining hidden danger data when each hardware has an operation fault; and integrating the hardware operation fault information and the corresponding data into operation fault monitoring data, and carrying out fault alarm. According to the invention, intelligent and systematized power plant operation monitoring is realized, and construction and development of an intelligent power plant are promoted.

The invention discloses a cooling tower fault tracing method based on a knowledge graph, and the method comprises the steps: collecting all kinds of operation, fault symptoms and environment condition data of a cooling tower in a distributed manner, carrying out the abnormal value processing, missing completion and normalization, and constructing a knowledge graph which takes equipment components, fault modes and symptoms as nodes and causes and effects and association as edges; a fault propagation path is generated in a knowledge graph by using a structured symptom vector, a semantic feature and a historical statistical feature are combined to dynamically evaluate a path weight, and multi-round iterative optimization and sorting are realized, so that a key path with a significant weight is identified as a final fault traceability basis, and a maintenance decision is further supported. According to the invention, the traceability accuracy of complex faults of the cooling tower and the adaptive capability of the knowledge graph are effectively improved.

The invention discloses a power grid operation fault prediction and abnormal trend early warning method, equipment and a medium, and aims to construct a tetrad fault sequence based on power grid multi-source data and realize second-level fault evolution dynamic tracking. By updating the power grid topology base map in real time, the problem of model lag is solved, and the propagation path precision is improved. The electrical coupling strength is introduced as the edge weight of the graph convolutional network, the electrical topography of the power grid is duplicated in the vector space, the calculation is simplified, and the accuracy is improved. And similarity diffusion is carried out by using the topological embedded vector, so that a high-precision influence range sub-graph can be generated in milliseconds, and prediction distortion is avoided. And converting the subgraph into a local monitoring area, quickly constructing a low-dimensional fault state vector, integrating the low-dimensional fault state vector into a dynamic fault propagation map, and clearly displaying fault traceability, path and termination logic. And finally, the node risk probability and the multi-dimensional influence index are output through one key by means of the graph attention network, an early warning instruction is automatically generated, and the second-level control and protection capability of the power grid is remarkably enhanced.

The invention relates to the field of chip fault positioning, in particular to a chip fault rapid positioning method and device, equipment and a storage medium. The method comprises the following steps: acquiring temperature sensing data and time sequence signals in a chip operation process; performing multi-index deviation calculation on the temperature sensing data and the time sequence signal to generate a state deviation index; performing real-time comparison detection on the state deviation degree index based on a preset first deviation threshold value, and when the state deviation degree of the index deviation curve exceeds the preset first deviation threshold value, triggering a fault early warning mode; identifying abnormal function parameters based on the fault early warning mode; identifying the dependency relationship of each functional module of the chip; constructing a fault propagation model based on the dependency relationship; and inputting the abnormal function parameters into the fault propagation model for analysis, and outputting a fault source candidate set. According to the method, by focusing on a high-probability area, the defect searching time is remarkably shortened, and the chip fault position is efficiently and quickly positioned.

The invention discloses a smart home equipment state monitoring and abnormity early warning method and system, and relates to the technical field of smart home equipment state monitoring and abnormity early warning, and the method comprises the steps: constructing a dynamic space-time topological graph between home equipment; monitoring state data of each node device in the topological graph in real time; identifying a source abnormal node in the topological graph based on the state data and a preset abnormal rule; by taking the source abnormal node as input, executing fault propagation simulation deduction in combination with the direction and the weight of the edge to obtain a risk equipment node set and a fault propagation path; and based on the set and the propagation path, generating diagnosis early warning information used for positioning a fault root cause and representing a propagation link. According to the invention, through dynamic topology modeling, equipment physical connection and a logic dependency relationship are uniformly represented, root causes can be quickly locked and potential affected equipment can be predicted when multiple equipment are abnormal at the same time, prospective early warning of hidden cascading failures is realized, and operation and maintenance efficiency and system reliability are improved through a visual report.

The invention discloses an automatic UI self-healing test method and system based on multi-agent AI, and belongs to the technical field of automatic UI tests.The automatic UI self-healing test method comprises the steps that an original test script is semantized through a test reasoning interpretation class, and an expected intention is output; when the test fails, synchronously collecting multi-source data and extracting multi-modal features, constructing a fault propagation graph according to an expected intention and the multi-modal features, and performing fault propagation analysis by using a graph attention network to obtain a fault diagnosis result; according to a fault diagnosis result, a deep Q network, a greedy strategy and a Byzantine fault-tolerant algorithm are adopted to negotiate and decide a repair strategy through a distributed agent cooperation mechanism, and feedback is obtained; optimizing and dynamically adjusting the repair strategy and the test reasoning interpretation class through a meta-learning algorithm based on feedback; a test intention is deeply understood by constructing a test reasoning interpretation class, and fault diagnosis and automatic repair are cooperatively completed by adopting a distributed intelligent agent, so that the semantic understanding capability, the diagnosis accuracy and the repair success rate are improved.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention provides a multi-modal neural causal inference micro-service fault positioning method and system, and the method comprises the steps: accessing observability data in a service operation process, and representing the tracking information of each request as a directed acyclic graph of a multi-modal feature; performing multi-modal feature coding and graph self-coding anomaly detection on the calling graph, and identifying an abnormal node through a reconstruction error; based on service topology prior, learning a sparse causal relationship graph between services by adopting a multi-scale neural causal inference method; calculating a node root cause score according to the causal relationship graph and the abnormal score, and executing causal path search to generate a fault propagation path; and marking the potential root cause according to the path weight of the propagation graph and the node popularity, and outputting a visual diagnosis result. According to the method, the system operation state is comprehensively described by fusing three kinds of micro-service system multi-modal data of logs, indexes and Trace in the micro-service system, and the structure-perceived causal diagram is constructed, so that accurate and explainable root cause positioning is realized.

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

The invention relates to the technical field of automatic operation and maintenance of a power system, in particular to an automatic operation and maintenance method and system for plant-station equipment based on a knowledge graph, and the method comprises the steps: collecting static ledger data, real-time operation data and historical operation and maintenance data of the plant-station equipment, constructing a multi-dimensional association knowledge graph, creating a digital twinborn body for target equipment in a plant station, and carrying out the operation and maintenance of the plant-station equipment. Establishing a bidirectional data channel between the digital twin and a corresponding equipment entity node in the knowledge graph; and based on a state evaluation rule and a fault propagation path model preset in the knowledge graph, performing real-time health state evaluation on the target equipment by using the state evaluation rule and the fault propagation path model to generate a health index, and when an abnormality is monitored, tracing along the topological relation between the electrical connection and the equipment in the knowledge graph, the fault source is positioned, the confidence probability is calculated, risk level evaluation and processing priority ranking are carried out according to the confidence probability, an operation and maintenance diagnosis work order containing an execution time sequence is obtained, and the fault reason can be quickly traced and positioned.

The invention discloses a power distribution network fault processing method, system and device based on a digital twinborn technology, and a medium. The method comprises the following steps: collecting real-time operation data of a power distribution network, constructing a digital twinborn model of the power distribution network, identifying a potential fault source through the digital twinborn model, and generating a candidate fault propagation path according to the potential fault source; calculating a propagation damping score for each candidate fault propagation path; comparing each propagation damping score with a propagation score threshold value corresponding to the current operation stage; and according to the deviation condition of the propagation damping score and the suppression result of the candidate fault propagation path in the actual fault response, a scoring factor is adjusted, and the suppression strategy of the candidate fault propagation path is adjusted. According to the power distribution network fault processing method based on the digital twinning technology, the digital twinning model comprising the equipment layer, the connection layer and the state layer is constructed, real-time mapping of the structure and the operation state of the power distribution network is achieved, and the accuracy of fault source positioning is improved.

The invention provides a rail transit weak current system operation and maintenance management method. The method comprises the following steps: S1, data acquisition and preprocessing; s2, fault probability and residual service life (RUL) prediction based on an LSTM / Transform + attention mechanism; s3, fault propagation analysis and root positioning based on a graph neural network (GNN); and S4, operation and maintenance decision optimization based on reinforcement learning. According to the invention, through fusion of deep learning, a graph neural network and a reinforcement learning algorithm, a full-process and closed-loop rail transit weak current system operation and maintenance management system is constructed.

The invention provides a business path tracking and fault tracing method and device for a video monitoring system, and relates to the technical field of video monitoring fault diagnos.The video monitoring system is divided into a plurality of layers, and multiple types of data acquisition probes are deployed at key nodes of each layer; when a user initiates a video request, generating a Trace ID to be spread along with a video streaming signaling, and reconstructing an end-to-end service path in combination with node information captured by a probe; associating and fusing the multi-dimensional performance data by taking the TraceID as a key, and establishing an end-to-end total delay decomposition and dynamic health baseline; and positioning a root cause node based on the weighted directed graph, the node anomaly confidence score and the fault propagation consistency, displaying a path, data and a traceability result through a visual interface, and generating a precise alarm. According to the method, service link visualization is realized, traditional equipment alarm is upgraded to service fault root cause positioning, the troubleshooting efficiency is improved, and the method is suitable for an isomerized large-scale video monitoring system.

The invention relates to the technical field of fault detection, in particular to a power distribution network system fault monitoring method and system based on multi-source information, and the method comprises the following steps: based on the operation state data of a power distribution network, extracting voltage, current and equipment states, recognizing and standardizing abnormal events, extracting timestamp recognition wave bands and propagation paths, and carrying out the statistics of type evaluation weights. According to the method, logical and sequential analysis is carried out on the multi-source operation data of the power distribution network, association recognition and hierarchical collection of abnormal events are achieved, abnormal association judgment and propagation rule recognition are enhanced, and the fault propagation and collection efficiency is improved. The fault data analysis and presentation mode is optimized, the fault traceability and path tracking accuracy is improved, abnormal wave bands are dynamically divided, priorities are sequenced, misjudgment and omission are reduced, accurate recognition of key nodes and influence ranges is ensured, and the power grid abnormity monitoring and risk early warning capacity is enhanced.

The invention relates to the technical field of power system automation and artificial intelligence, and particularly discloses a power network risk alarm event analysis method, which comprises the following steps of: firstly, carrying out standardization processing and space-time slicing on a multi-source heterogeneous original alarm flow, and constructing a dynamic fault propagation graph in combination with power grid physical topology; then, multi-dimensional indexes such as topology centrality, time sequence attenuation and semantic rare degree are comprehensively calculated, and the causal significance of each alarm node is quantified; on this basis, adaptive sparse sampling based on maximum interpretation gain is implemented, and high-score core events are precisely preserved and public precursor nodes with global collective power are forcibly recalled by evaluating common-mode interpretation utility and space-time coupling strength of nodes to downstream alarms on the premise of strictly complying with the limitation of context tokens of a large model. And finally, inputting the simplified high-value evidence chain into a large language model to carry out thinking chain reasoning, thereby realizing accurate positioning of complex fault root causes and automatic generation of a disposal strategy.

The invention discloses a fault detection method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data processing. The method comprises the steps that under the condition that a target system breaks down, fault warning information and a target knowledge graph of the target system are acquired, and the target knowledge graph comprises a plurality of nodes, potential fault probabilities of the nodes, fault propagation probabilities and edges between the nodes; determining a fault sub-graph of the target system according to the fault alarm information and the target knowledge graph, wherein the fault sub-graph is a sub-graph composed of abnormal nodes in a plurality of nodes in the target knowledge graph; and determining a root cause node in the plurality of abnormal nodes according to the association relationship of the plurality of abnormal nodes in the fault sub-graph, the potential fault probability and the fault propagation probability, and generating and displaying a fault root cause analysis result according to the root cause node and the target fault root cause probability thereof so as to efficiently and accurately determine the fault root cause.

The invention provides a circuit breaker monitoring method and system based on multi-state sensing, a medium and equipment, and relates to the technical field of electrical equipment monitoring. According to the technical scheme provided by the invention, the initial influence weight of the directed edge is dynamically updated into the target influence weight according to the weight adjustment rule corresponding to the event type of the operation event, so that the fault propagation map accurately reflects the actual change of the influence relationship of the current operation event on the components. And calculating the first node health degree of each node based on the updated target influence weight and the multi-dimensional state data, accurately evaluating the current health state of the circuit breaker, predicting the second node health degree within the duration of the operation event, and realizing the pre-judgment of the development trend of the health state. According to the first node health degree and the second node health degree, the monitoring mode is determined, and the early warning response of the corresponding level is triggered, so that the timeliness and pertinence of early warning are ensured, and the accuracy of monitoring and early warning of the circuit breaker can be improved.

The invention relates to a medium wave transmitter fault prediction and adaptive operation and maintenance method based on artificial intelligence, and the method comprises the steps: constructing an electromagnetic coupling topological graph through analyzing a circuit board design file, and quantifying a fault propagation path between power amplifier modules; fault current signals are collected in real time, and risk quantitative indexes are generated in combination with topological characteristics; modeling and predicting a cascading risk probability based on the graph structure; dynamically adjusting a radio frequency power path and a carrier frequency according to a topological position evaluation result; and utilizing an execution feedback closed loop to optimize the prediction model. The method solves the problems that a traditional scheme cannot quantify an electromagnetic coupling propagation path, prediction and hardware control are separated, and a dynamic optimization mechanism is lacked, achieves accurate blocking of a fault propagation path and adaptive evolution of a prediction model, and remarkably improves the operation stability and fault response efficiency of a medium-wave transmitter.

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.

The invention discloses a risk assessment method for power grid cascading failures, and the method comprises the steps: firstly determining an assessment range and a target, and carrying out the data collection after the assessment range and the target are determined; establishing a system model, and constructing a digital model capable of accurately reflecting physical characteristics, operation states and control behaviors of a real power grid in a computer; constructing an initial fault scene; analyzing a cascading failure propagation path; quantifying risk indexes; and evaluating the cross-domain association influence, supplementing and evaluating the linkage influence on the association system in combination with the cross-domain association characteristics, and proposing a risk prevention and control and optimization strategy. According to the method, potential cascading failure inducements in a power grid can be actively mined through risk assessment of power grid cascading failures, a traditional passive mode of failure reprocessing is broken through, weak links are positioned, key equipment which easily triggers the cascading failures is found out through initial failure scene screening and propagation path simulation, and long-term latent hidden dangers are avoided.

The invention relates to the technical field of electrical equipment fault detection, and discloses an electrical equipment fault detection method and system based on reinforcement learning, and the method comprises the steps: collecting the multi-modal operation data of an electrical equipment group, and constructing a dynamic heterogeneous data set; according to the dynamic heterogeneous data set, a change mode of equipment operation is extracted, and distribution feature representation is determined; according to the distribution feature representation, constructing an inter-equipment association graph model, and generating an association-enhanced feature representation; carrying out anomaly detection on the association enhanced feature expression, judging node deviation and backtracking to obtain a potential fault starting point; according to the potential fault starting point, simulating a propagation dynamic state between nodes and tracking a chain reaction path to obtain a fault propagation sequence; and carrying out reaction intensity calculation on the fault propagation sequence, generating an early warning signal, outputting the early warning signal, backtracking and updating the graph model according to the early warning signal, and determining a final fault source position. According to the method, early warning, propagation tracking and accurate traceability of faults of the electrical equipment group can be realized.

The invention discloses a fault diagnosis method and device, a storage medium and electronic equipment, and relates to the technical field of fault diagnos.The fault diagnosis method comprises the steps that a fault information set is determined based on at least one fault of target equipment, and fault information in the fault information set is used for indicating a node where the corresponding fault is located; a node topological graph is generated according to the operation architecture of the target device, and nodes are used for indicating the relation between at least one service node of the target device; generating a fault propagation path corresponding to a target fault in the node topological graph by using the fault information set, the target fault being any one of the at least one fault; and under the condition that the fault propagation path comprises at least one reference fault, a target node is determined according to the fault propagation path, and the target node is used for indicating a root cause of the fault propagation path. The technical problem that a fault diagnosis method in the prior art is low in accuracy is solved.