1185 results about "Maintenance strategy" patented technology

The invention discloses a power grid equipment intelligent operation and maintenance method based on a large language model and a knowledge graph, and relates to the technical field of intelligent power grid operation and maintenance, and the method comprises the steps: carrying out the operation and maintenance of power grid equipment through a power grid operation and maintenance knowledge graph constructed through a large language model and a knowledge federation technology, the power grid equipment operation and maintenance comprises one or more of health state evaluation, fault risk prediction and early warning, intelligent operation and maintenance strategy generation and health degree dialogue query of the power grid equipment. According to the invention, a power grid operation and maintenance mode can be effectively promoted to be transformed and upgraded from a traditional manual experience type and a passive maintenance type to a data-driven, intelligent and active preventive maintenance mode. Key intelligent operation and maintenance technical support is provided for building a novel electric power system with new energy as a main body, the novel electric power system is assisted to achieve the development goals of being safer, more efficient, cleaner and lower in carbon, and important industry strategic significance and social contribution are achieved.

The invention discloses an intelligent power distribution network equipment state perception and abnormity diagnosis system, and the system operation process specifically comprises the following steps: collecting the operation state data of power distribution network equipment in real time, carrying out the time-space alignment and feature fusion, and generating an equipment multi-dimensional state vector; inputting a pre-constructed equipment health dynamic baseline model, and outputting a real-time health deviation degree; when the real-time health deviation degree exceeds an early warning deviation threshold value, triggering an abnormal preliminary screening mechanism, and extracting abnormal feature fragments; inputting a multi-stage diagnosis knowledge graph model, and generating an abnormal cause hypothesis set; performing confidence ranking on the abnormal cause hypothesis set, and outputting first # imgabs0 diagnosis results and corresponding confidence weights; and generating an equipment maintenance strategy instruction set according to the diagnosis result. The method has the following advantages and effects: the dynamic baseline is adaptively generated from multi-source data, and a multi-stage diagnosis framework of a physical model, a power grid rule and a historical case is fused, so that the accuracy and timeliness of anomaly diagnosis are finally improved.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention discloses a petrochemical equipment fault prediction and safety management system and method, and belongs to petrochemical equipment management. The system comprises the following modules: a working condition sensing and classifying module for realizing real-time identification, classification and feature extraction of the equipment operation state; the health state evaluation module is used for calculating equipment health indexes and outputting an equipment performance degradation trend analysis result; the fault prediction and diagnosis module is used for predicting potential fault types and occurrence time, performing diagnosis analysis on fault mechanisms and providing credibility evaluation of diagnosis results; the risk assessment and early warning module comprehensively assesses multi-dimensional risk factors and generates graded early warning information through dynamic threshold optimization and a multi-stage early warning strategy; the maintenance decision support module is used for optimizing a maintenance strategy and providing resource scheduling and maintenance scheme suggestions based on the equipment health state, the fault prediction and the risk assessment result; and the management and optimization module ensures efficient cooperative operation of each module and continuously improves the system performance.

The invention relates to the technical field of power distribution operation and maintenance management, and discloses an intelligent power distribution operation and maintenance management system based on 5G transmission. The system comprises a 5G real-time acquisition module, a multi-dimensional feature fusion module, a dynamic topology generation module, an anomaly propagation analysis module and a strategy optimization feedback module. The 5G real-time acquisition module acquires operation state data streams such as current and voltage waveforms, an equipment temperature sequence and environment monitoring indexes of the power distribution equipment through a 5G network; the multi-dimensional feature fusion module is used for separating equipment state features, calculating mutual information amount and generating equipment state feature tensors; the dynamic topology generation module constructs an association intensity matrix according to the feature tensor, and generates a hierarchical connection path and a dynamic equipment topological graph; the abnormal propagation analysis module extracts a state fluctuation sequence, identifies an abnormal transmission path and marks a core propagation node; and a strategy optimization feedback module generates a maintenance strategy priority queue according to the dynamic topology map, and feeds back an execution result to update the dynamic topology map, so that the intelligence and accuracy of power distribution operation and maintenance management are improved.

The invention belongs to the technical field of power equipment, and discloses a power equipment fault early warning method based on multi-source data fusion, and the method comprises the steps: constructing multi-dimensional feature association through multi-modal data time-space association collection and hierarchical fusion driven by a knowledge graph; a space-time weight matrix is used for correcting sampling deviation, fault mechanism knowledge is combined to strengthen key feature contribution degree, false alarm and missing alarm caused by data isolation are effectively avoided, early recognition of hidden defects of equipment is realized, and global perception capability of early warning is improved. A meta-learning enhanced cross-equipment early warning model and reinforcement learning dynamic threshold decision are adopted, cross-equipment rapid adaptation under a small number of samples is realized through a ''meta-micro'' double-circulation mechanism, and a nonlinear law of fault evolution can be accurately described by combining a three-dimensional dynamic threshold matrix to balance an equipment state, an environment and an operation and maintenance strategy. The model generalization problem of different types of equipment in a complex environment is solved, and the adaptability to scenes such as load fluctuation and environment sudden change is improved.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention provides a self-adaptive adjustment Internet operation and maintenance strategy generation method and system, and relates to the technical field of Internet, and the method comprises the steps: 1, dynamically collecting the operation state data of a target operation and maintenance environment through a distributed sensor and an edge node, and generating a multi-dimensional time series data set; 2, performing space-time correlation analysis on the multi-dimensional time sequence data set, determining a reference data node, constructing a two-dimensional correlation structure, and generating a dynamic judgment interval; and step 3, respectively selecting monitoring sample sets in the inner domain and the outer domain of the dynamic judgment interval, generating a trajectory feature sequence according to the time evolution relationship of the sample sets, and calculating a dynamic correction coefficient based on the trajectory feature sequence. According to the method, the self-adaptive circulation control is formed by dynamically adjusting the threshold baseline, the judgment interval and the strategy generation rule, the accuracy and effectiveness of the internet operation and maintenance strategy are improved, and the stability of internet operation is enhanced.

The invention relates to the technical field of the Internet of Things, and discloses an equipment fault prediction and diagnosis system for the Internet of Things. The system comprises a multi-source data acquisition module which is used for acquiring heterogeneous sensing data of Internet of Things equipment in real time; the data purification module is used for carrying out noise suppression and abnormal value repair on the data and generating a standardized time sequence data stream; the feature enhancement module is used for extracting equipment state features through a multi-scale decomposition algorithm; the fault prediction module is used for constructing an equipment degradation prediction model based on the cascade residual network and generating a dynamic evolution graph of an equipment health index; and the diagnosis decision module is used for generating a fault positioning result and a maintenance strategy optimization instruction through a hybrid inference engine based on the atlas. In addition, the system also comprises an equipment life calibration model, and a prediction model is dynamically adjusted by considering the individual difference of equipment. The system can effectively process heterogeneous data, accurately predict faults, accurately diagnose and optimize a maintenance strategy, and improve the operation reliability and maintenance efficiency of the Internet of Things equipment.

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention discloses a power distribution cabinet maintenance system based on artificial intelligence, and belongs to the technical field of intelligent power distribution cabinet diagnosis. Normalization, time sequence feature extraction and denoising are carried out on the collected data, an electrical topological graph is automatically constructed, node states are vectorized, text semantics are coded by utilizing a BERT class model, and a maintenance knowledge graph is generated; through fusion of multi-source data, a multi-branch neural network is constructed, state perception and risk determination are realized, and a fault trend and structure degradation are identified. Calculating a potential fault risk coefficient, and triggering a structure health monitoring mechanism; analyzing the structural health based on a topological graph and a graph neural network, and starting semantic strategy retrieval when the structure is abnormal; comparing the semantic conformity between the state and the historical strategy, and assisting in generating a precise maintenance strategy; according to the system, dynamic monitoring, intelligent early warning and strategy recommendation of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention discloses a water conservancy operation and maintenance platform based on a digital twinning technology, and relates to the technical field of digital twinning, and the platform comprises a digital twinning model building unit which is used for building a water conservancy facility digital twinning model comprising a physical mechanism model; the health state evaluation unit is used for calculating theoretical performance based on the mechanism model and comparing the theoretical performance with actual performance to obtain a performance residual error so as to accurately quantify the health state of the equipment; the intelligent scheduling decision-making unit adopts a reinforcement learning framework, performs decision-making deduction by using a composite reward function fusing business rewards and quantitative health loss cost in a simulation environment associated with digital twinning, and generates an optimal scheduling instruction giving consideration to business targets and equipment long-term health; and the closed-loop control and feedback unit is used for executing the instruction and forming a dynamic optimization closed loop. According to the method, abstract health loss is converted into computable decision cost, and global optimization of an operation and maintenance strategy is realized.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

The invention relates to the technical field of digital twinning, and particularly discloses a historical building structure evaluation system based on digital twinning, which comprises a multi-source data acquisition module, a dynamic digital twinning modeling module, a structure health evaluation module, an intelligent maintenance decision module and a collaborative optimization analysis module. According to the scheme, geometric, material and environment information of the historical building is captured, omnibearing monitoring of the historical building is realized through a digital twin modeling technology, an intelligent evaluation model is constructed in combination with finite element analysis and an SVM algorithm, a high-risk area is rapidly identified, and authenticity and accuracy of an evaluation result are ensured; a reinforcement learning algorithm is used to adapt to complex and changeable environmental requirements of historical buildings, a maintenance strategy is automatically generated, cooperation efficiency between modules is continuously optimized through collaborative optimization analysis, the overall operation performance is improved, evaluation precision is guaranteed, and meanwhile system resource consumption is effectively controlled.

The invention belongs to the technical field of glue filling control, and particularly relates to a glue filling amount control method and system. According to the method, the glue filling deviation can be controlled within an extremely small range through multi-time iteration control of a stage theoretical value and real-time feedback, the filling consistency and the bonding strength are remarkably improved, initial compensation is conducted in combination with various working condition parameters such as temperature, pressure and materials, the pressure and the diffusion dynamic state are monitored in real time, and the working efficiency is improved. The system can automatically adapt to environmental fluctuation and glue performance change, accurately control the filling amount, respond to overflow and viscosity abnormity in time, reduce glue waste, avoid product reworking caused by excess or insufficient glue, combine closed-loop feedback with maintenance strategies, automatically identify and process abnormal states, improve the automation degree and operation stability of a production line, and improve the production efficiency. Manual intervention is reduced, rapid configuration and expansion can be carried out according to different workpiece shapes, materials and production working conditions, and the device is suitable for various glue filling scenes.

The invention discloses a digital twin-driven deep and far sea fan platform prediction operation and maintenance method and system. The method comprises the following steps: acquiring real-time multi-source heterogeneous data of a deep and far sea target fan platform based on a sensor network; constructing a digital twinborn body of a multi-physics field integrated target fan platform, and inputting the real-time multi-source heterogeneous data into the digital twinborn body for simulation processing to obtain fault probability distribution of the target fan platform; and executing an operation and maintenance strategy corresponding to the fault probability distribution. According to the deep and far sea fan platform prediction operation and maintenance method provided by the embodiment of the invention, the deep and far sea fan platform prediction operation and maintenance efficiency is improved.

The invention discloses a railway vehicle test data management and analysis system, and the system comprises a multi-modal data collection module which collects the heterogeneous data of tests such as airtightness and weighing in real time; the block chain credible evidence storage module is used for performing time-space stamp marking and hash encryption on the data and verifying the integrity; the space-time atlas analysis module is used for constructing a space-time heterogeneous atlas and generating vehicle-level feature vectors through graph convolutional network fusion data; the predictive maintenance decision module is used for predicting subsystem performance degradation and fault risks and generating a hierarchical maintenance strategy; and the adaptive visual platform integrates a large screen and a mobile terminal to display data and decisions. According to the system, efficient integration and credible evidence storage of multi-source data are realized, the data utilization efficiency and analysis depth are improved, accurate maintenance decision is supported, and safe operation of railway vehicles is guaranteed.

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 discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention relates to the field of computing power center operation and maintenance, in particular to a computing power center multi-robot collaborative operation and maintenance system and method based on a multi-modal large model, and the method comprises the steps: collecting equipment operation data in real time; carrying out data preprocessing on the acquired multi-modal data; the preprocessed single-mode features are input into a multi-mode large model, a unified multi-mode feature vector is generated through an adaptive feature fusion algorithm, the equipment fault type, position and occurrence probability are predicted based on the fused features, and a multi-robot cooperative operation and maintenance strategy is generated; the cooperative control center distributes tasks to the optimal robot combination through a Hungary algorithm according to an operation and maintenance strategy; a reinforcement learning algorithm is adopted to plan a collision-free path for each robot, and real-time dynamic adjustment is carried out to cope with environmental changes; and the operation and maintenance robot executes inspection, fault positioning and basic maintenance tasks, and uploads execution data to the cooperative control center in real time. The complex operation and maintenance requirements of the computing power center are effectively met, and the operation and maintenance efficiency is improved.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention discloses an industrial internet of things real-time monitoring and predictive maintenance system based on digital twinning, and relates to the technical field of industrial digital twinning operation and maintenance, the system comprises a multi-modal data acquisition module, a sensor network is deployed, and edge calculation preprocessing is carried out; the digital twinning construction module is used for constructing a high-precision model by fusing a physical law and deep learning; the real-time monitoring module is used for detecting abnormity by using a space-time diagram neural network; the predictive maintenance module is used for optimizing a maintenance strategy in combination with a probabilistic algorithm; and the man-machine interaction module supports AR / VR and brain-computer interface operation. In addition, the system integrates functions of block chain security, energy management and the like, and realizes full-life-cycle intelligent management of equipment. The operation and maintenance efficiency of the industrial equipment is greatly improved. The data acquisition precision reaches the nanoscale, and the early warning time is advanced to 72 hours; the maintenance cost is reduced, and the equipment availability is improved; the AR interaction enables the operation efficiency to be improved and the training period to be shortened. And meanwhile, energy consumption reduction is realized.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention discloses a die life prediction and maintenance decision system based on digital twinning. The system comprises a data acquisition module, a data fusion module, a digital twinning body construction and updating module, a residual life prediction module, a maintenance decision and optimization module and a closed-loop execution and feedback module. The system collects working condition data and production parameters of a physical mold in real time, generates a comprehensive health state index after fusion processing, constructs a dynamic digital twin, simulates a future production plan based on the digital twin, predicts the remaining service life of the mold, and combines a production schedule, a resource inventory and a cost model. And generating, issuing and executing an optimal maintenance decision scheme. The system continuously updates and optimizes the digital twin by using the maintained result data through a closed-loop feedback mechanism, so that the health state evaluation, the life prediction and the dynamic optimization of the maintenance strategy of the mold are realized, the mold management level is effectively improved, and the maintenance cost and the production shutdown risk are reduced.

The invention provides an electrical equipment remote maintenance guidance method and system. The method comprises the following steps: capturing an aperiodic torque waveform and a magnetic field gradient abnormal signal by deploying a torque fluctuation sensor and a magnetoresistive sensor; performing multi-scale frequency band division on the non-periodic torque waveform, screening out a transient high-frequency component in a start-stop stage of equipment, and separating out a magnetic field polarity reversal characteristic from a magnetic field gradient abnormal signal; performing cross-domain matching on the occurrence time of the transient high-frequency component and the spatial orientation of the magnetic field polarity reversal feature to generate a mixed feature mark; calculating a frequency band overlapping degree based on the time-frequency energy distribution marked by the mixed features and generating a coherence map; and matching a fault historical case library according to the coherence map, and outputting a maintenance strategy set aiming at the rotating shaft dynamic unbalance and electromagnetic interference superposition fault. According to the invention, through fusion of torque fluctuation and magnetic field distortion collaborative analysis, the precision of mechanical and electromagnetic composite fault diagnosis of the rotating shaft is improved.

The invention discloses an intelligent agent-based automatic operation and maintenance method and system, and relates to the technical field of computer operation and maintenance, and the method comprises the steps: constructing an intelligent agent comprising a data collection layer, a state sensing layer, a decision-making layer and an execution layer, the data collection layer collects IT system multi-source data, the state sensing layer analyzes the state of the data sensing system, and the decision-making layer determines the state of the data sensing system; the decision-making layer generates an operation and maintenance instruction based on a sensing result decision, and the execution layer executes the instruction; relevant knowledge of the IT system is collected and sorted, and a knowledge graph is constructed; the intelligent agent is trained by using historical operation and maintenance data, the intelligent agent is enabled to try and explore in different environments through a reinforcement learning algorithm, the behavior of the intelligent agent is continuously adjusted according to a feedback result, and a decision model and an operation and maintenance strategy of the intelligent agent are optimized; and the intelligent agent monitors the IT system in real time, starts the data acquisition layer, the state sensing layer, the decision-making layer and the execution layer when detecting an abnormal event, and automatically executes corresponding operation and maintenance operation according to a decision-making result. According to the invention, stable and efficient operation of the IT system can be guaranteed.

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention belongs to the technical field of wind power equipment fault diagnosis, and relates to a wind turbine generator maintenance method based on multi-modal data fusion and a knowledge graph. Comprising the steps of obtaining text description data, component image data and equipment operation time sequence data of a wind turbine generator; predicting the residual life of the component based on the equipment operation time sequence data, and generating a residual life prediction value; performing feature extraction on the data to obtain corresponding features; performing fusion processing on the features to obtain joint feature representation; analyzing a fault evolution time sequence mode of the wind turbine generator based on the joint feature representation; performing matching retrieval in a historical case library according to a fault evolution time sequence mode; and inputting a matching retrieval result into the dynamic knowledge graph for reasoning, generating a fault traceability result, and generating a maintenance decision scheme including a maintenance priority list in combination with the residual life prediction value. According to the method, accurate fault diagnosis, automatic source tracing of root causes and dynamic optimization of maintenance strategies are realized, the operation and maintenance efficiency is remarkably improved, and the cost is reduced.