1076 results about "Root cause" patented technology

The invention relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

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

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

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

The invention discloses an AIOps anomaly detection and root cause positioning method, and relates to the technical field of anomaly detection. The method comprises the following steps: 1, processing a preset time window according to services and instances under a unified timeline, generating monitoring type abnormal fragments for monitoring indexes, and extracting a client and server span in distributed tracking for fragment pairing; step 2, executing stitching by taking distributed tracking as guidance to obtain a candidate evidence chain set, and taking a segment at the tail end of each evidence chain unit as a candidate root cause direction; and step 3, outputting a root cause list for the candidate evidence chain set according to a deterministic rule, and giving a time range of a related template text and an adjacent monitoring type abnormal fragment. According to the method, the abnormal propagation path can be accurately identified in the multi-source heterogeneous data, association verification is carried out on the upstream representation and the downstream resource failure, and a clear root cause target and evidence explanation are provided.

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

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

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

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

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

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

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention relates to the technical field of substation operation and maintenance management, in particular to a three-dimensional visual substation operation and maintenance management method and system based on digital twinning. The method comprises the following steps: acquiring real-time working condition data of equipment; acquiring equipment distribution data and space electric field intensity distribution; inputting the equipment distribution data and the historical operation and maintenance data into a physical information neural network model to obtain a health index and a residual service life prediction value; when the real-time working condition data is abnormal or the health index is lower than a preset threshold value, message transmission and node updating are carried out in the substation graph neural network model, a fault source is positioned, and the cascading fault probability is evaluated; and generating an operation and maintenance work order based on the fault source, the health index and the cascading fault probability, and planning a safe operation and maintenance path by using an A-star algorithm. According to the scheme, the limitation that traditional operation and maintenance depend on surface monitoring and experience judgment can be overcome, the depth and the breadth of fault diagnosis are improved, and the safety of field operation is enhanced.

The invention discloses a switch fault diagnosis and intelligent analysis management method and device, equipment and a storage medium, and relates to the technical field of network equipment fault diagnosis and intelligent analysis, and the method comprises the steps: obtaining monitoring container index information, carrying out the data preprocessing based on the monitoring container index information, synchronizing a preset time sequence database, and determining synchronous data; extracting time sequence common characteristics based on the synchronous data, matching a corresponding fault mode, and determining a root cause probability sequence; and positioning a fault propagation path based on the root cause probability sequence, triggering topology to generate a marked target fault path, determining a display topological graph, and completing switch fault diagnosis and intelligent analysis management based on the display topological graph. According to the method, the time sequence features are extracted, the fault modes are matched to determine the root cause probability sorting, and the fault paths are reasoned to dynamically mark the topological graph, so that data islands are broken, multi-source data association analysis is realized, the diagnosis time is shortened, the real-time performance and accuracy are improved, and the visualization effect is optimized.

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.

A disclosed method for incident dependency prediction includes identifying, for a newly-occurring network incident of interest, one or more similar historical incidents; obtaining causal dependency data for the one or more similar historical incidents, the causal dependency data identifying one or more historical responsible incidents responsible for causing the similar historical incidents; identifying candidate root cause incidents each occurring within a recent time interval and satisfying similarity criteria with a corresponding one of the one or more historical responsible incidents; and transmitting a prompt to an incident dependency prediction model, the prompt requesting prediction of a root cause incident responsible for causing the newly-occurring network incident of interest based on feature information for each of the candidate root cause incidents and for the incident of interest.

The invention discloses a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and relates to the technical field of fault diagnos.The method comprises the steps that a graph neural network with a liquid time constant network unit as a node is constructed through a dynamic topology dependency relationship and a multi-dimensional key performance index flow; each unit describes state evolution through a coupled ordinary differential equation system, and a liquid state time constant can be adaptively adjusted. A time back propagation algorithm is adopted to train a model to learn a normal behavior track contour reference, and anomaly is detected through a dynamic time warping distance. And determining a fault propagation path and a root cause through anti-fact intervention and forward integral solution. And generating an optimal diagnosis action sequence in a liquid graph neural network simulation environment, and calculating a reward value based on execution efficiency, accuracy and a repair effect to carry out strategy optimization. The abnormal detection accuracy and the root cause positioning precision are improved, the fault repair time is shortened, the operation and maintenance cost is reduced, and an intelligent fault diagnosis solution is provided for a complex information technology system.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention discloses an equipment state monitoring and anomaly analysis strategy method and system based on a PMS, and belongs to the technical field of equipment management.The method comprises the steps that equipment basic information, real-time operation state data, historical maintenance records and risk level parameters in an equipment management system are obtained, a comprehensive equipment state data set is generated, and a dynamic health attention index is calculated; and generating a strategy chain instruction set to schedule the sensor group to execute data acquisition operation, generating a target monitoring data set, inputting the target monitoring data set into the multi-stage adaptive analysis tree in combination with an analysis strategy instruction, generating a structured analysis report, and matching maintenance decision suggestions from the maintenance knowledge base and outputting the maintenance decision suggestions when a preset condition of a decision triggering strategy instruction is met. According to the method, the dynamic health attention index is adopted to drive strategy chain instruction generation, and the multi-stage adaptive analysis tree hierarchical diagnosis and closed-loop parameter optimization mechanism is combined, so that accurate distribution of monitoring resources, accurate positioning of fault root causes, economic optimization of maintenance decision and adaptive continuous upgrading of the system can be realized.

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 provides a cloud native system fault root cause positioning method and device, and the method comprises the steps: determining the abnormal performance index data of each instance and a server based on the micro-service instance of each target micro-service and the performance index abnormal score of the server, so as to construct the nodes corresponding to each micro-service instance and the server, according to a fault propagation direction between the abnormal performance index data, constructing an edge between the nodes to obtain an index-level cause and effect graph; normalizing the performance index anomaly score to obtain a target anomaly score of each node, and constructing a transition probability matrix; and obtaining the access frequency of each node in the causal graph by using a random walk algorithm so as to obtain fault root cause positioning result data of the cloud native system. According to the invention, the automation degree, efficiency and accuracy of fault root cause positioning of the cloud native system can be effectively improved, the efficiency and reliability of fault early warning and recovery of the cloud native system can be effectively improved, and the operation stability of the cloud native system can be improved.

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

The invention provides an anomaly detection method and system based on a knowledge graph and rule reasoning, and the method comprises the steps: obtaining the anomaly information of a power system: inputting the anomaly information into a preset operation inspection model, so as to enable the operation inspection model to generate standardized data in a preset format, extracting corresponding knowledge paths and node information from a preset knowledge graph according to the standardized data, and further generating a semantic reasoning result; inputting the standardized data into a preset inference engine, so that the inference engine performs rule inference according to a preset rule base, and further generates a rule inference result; each of the semantic reasoning result and the rule reasoning result comprises a judgment result of an abnormal state, cause analysis of the abnormal state and a fault root cause positioning path; and performing comparison verification according to the semantic reasoning result and the rule reasoning result, and generating a corresponding anomaly detection result according to a comparison verification result, thereby improving the accuracy and efficiency of anomaly detection.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention discloses an intelligent diagnosis method for electric power secondary equipment based on digital twinning, which relates to the technical field of operation and maintenance of electric power equipment, and comprises the following steps: uniformly accessing real-time data of the electric power secondary equipment and carrying out timestamp standardization to complete cross-channel alignment; constructing an equipment-level digital twinborn body, and calculating a key business volume, a contrast deviation of an output and a field volume and a credible interval; cross-channel transition parameters, key electrical parameters and link state parameters are extracted under the unified time axis and secondary network topology, and a constraint quantity set is generated; and constructing an evidence chain on the topology based on the contrast deviation and the constraint quantity, executing root cause convergence and conflict stripping, and forming a diagnosis conclusion with a confidence level and processing steps. Through unified time reference and multi-domain twinborn contrast, triple constraint quantity weighting and evidence chain reasoning, cooperative constraint of time sequence consistency, physical consistency and safety boundary is realized, positioning precision and closed loop efficiency are improved, false alarm and missing alarm are reduced, strong isolation of simulation and production links is guaranteed, and visual tracing is realized.

The invention discloses an intelligent supply chain management system and method based on artificial intelligence and big data, and belongs to the technical field of supply chain management and artificial intelligence, and the method comprises the steps: obtaining a state data sequence of a supply chain object, extracting abnormal features, and forming an abnormal feature data sequence, obtaining a supply chain environment and operation parameter time sequence aligned in time and space; and jointly inputting the abnormal feature data sequence and the supply chain environment and operation parameter time sequence into a pre-trained multi-modal deep learning model for fusion analysis, and outputting one or more key supply chain parameters causing the abnormal state and quantized abnormal fluctuation information thereof, accurately associating the key parameters with the specific physical position or visual form of the abnormal state on the supply chain object, and finally generating an association map; according to the invention, full-link closed loop from data perception, intelligent analysis to root cause visualization is realized, and the intelligent level and fault processing efficiency of supply chain management are improved.

The invention discloses an automatic operation and maintenance method based on an agent technology and a collaborative network, an operation and maintenance agent responds to an alarm event and initializes an operation and maintenance task, a data agent is triggered to collect and preprocess multi-source heterogeneous operation and maintenance data, the operation and maintenance agent initiates a deep root cause analysis request according to the preprocessed data, and the deep root cause analysis request is sent to the collaborative network. Driving the code agent to generate and execute an analysis code, positioning a root cause and generating a repair scheme by the operation and maintenance agent based on an execution result of the code agent, verifying a repair effect after execution, and presenting an agent cooperation path and an evidence chain in an operation and maintenance process in real time by the visual agent through a preset visual protocol. A structured report is generated by the reporting agent. Dynamic code generation and safe execution are driven through a multi-agent collaborative architecture, end-to-end automatic root cause positioning and closed loop repairing are achieved, meanwhile, an evidence chain and a collaborative process are presented in real time by means of protocol visualization, and the operation and maintenance intelligent level and fault diagnosis transparency are improved.