1731 results about "Maintenance strategy" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

The invention discloses a predictive maintenance strategy optimization method for transformer area equipment maintenance, relates to the technical field of transformer area equipment maintenance, and is used for solving the problem of poor maintenance effect of transformer equipment. Sensors are installed in a transformer area to collect current, voltage, temperature and other key data, preprocessing, frequency alignment and fusion are carried out to generate the same equipment state data, and when data fluctuation exceeds a threshold value, re-collection and calibration are triggered, a health state model is constructed, and a health index is calculated to evaluate the equipment state. The method comprises the following steps: predicting a future health state through an LSTM model, triggering early warning when the future health state is lower than a baseline, dividing risk levels according to health indexes and risk factors, setting maintenance priorities based on health assessment and fault prediction, applying a multi-objective optimization algorithm in combination with an emergency degree and equipment importance to assess resource requirements, and dynamically monitoring resource use conditions. The prediction precision is improved, and the equipment resource allocation efficiency and the maintenance response speed are improved.

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention discloses a photovoltaic module fault diagnosis system and method based on deep learning, which significantly improves the accuracy and comprehensiveness of fault diagnosis through multi-modal data fusion and advanced deep learning technology, and improves the system operation efficiency through the application of an adaptive sampling strategy and edge calculation. The system can simultaneously execute fault classification, severity evaluation and life prediction through a multi-task learning framework, the diagnosis depth and breadth are enhanced, a predictive maintenance decision-making mechanism based on reinforcement learning optimizes a maintenance strategy, the service life of equipment is prolonged, the operation and maintenance cost is reduced, and the maintenance efficiency is improved. The distributed learning framework ensures data privacy and continuous optimization of the model, improves the overall power generation efficiency and economic benefits of the photovoltaic power station, and provides powerful support for large-scale application of renewable energy sources.

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

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 electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

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 an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

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 discloses a building operation and maintenance management method and system based on big data, and relates to the technical field of building intellectualization, and the method comprises the steps: collecting multi-source heterogeneous data, executing data standardization processing, and generating a standard data stream; constructing a virtual building model based on a digital twinning technology, mapping the standard data stream to a corresponding device node of the virtual building model, and generating a device feature matrix; inputting the equipment characteristic matrix into a deep learning network, training to generate an energy consumption prediction model, and outputting an energy consumption prediction sequence; a multilayer perceptron health scoring model is constructed based on the energy consumption prediction sequence, and a fault early warning priority list is generated in combination with an equipment weight coefficient; and automatically generating a maintenance work order according to the fault early warning priority list. Through the maintenance effect evaluation and secondary maintenance mechanism, a complete closed-loop management process is formed, the maintenance quality is ensured, the maintenance strategy is continuously optimized, and the overall operation and maintenance management level is improved.

The invention relates to a railway traction substation state monitoring method and system, equipment and a medium. The monitoring method comprises the following steps: acquiring real-time monitoring data of the equipment in a railway traction substation; performing data preprocessing on the real-time monitoring data to obtain a preprocessed monitoring data set, performing protocol identification, and converting heterogeneous data in the monitoring data set into structured data according to a preset protocol template library; based on the structured data, time-frequency domain characteristic parameters of the equipment are extracted, and a multi-dimensional characteristic matrix is constructed; inputting the multi-dimensional feature matrix into a pre-trained hybrid diagnosis model, and generating an equipment health degree score and a fault probability value; according to the health degree score and the fault probability value, generating an early warning instruction in combination with a dynamic threshold algorithm; and generating a priority maintenance strategy through a maintenance strategy optimization model based on the early warning instruction and the equipment maintenance resource constraint condition. According to the invention, accurate perception and intelligent decision making of the equipment state are realized in a multi-source heterogeneous data environment.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

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 provides a central air-conditioning system real-time fault prediction method and system based on digital twinning, and the method comprises the steps: deploying various types of sensors for multi-dimensional detection, and collecting the data of the sensors; edge computing nodes are deployed around the central air conditioning unit; a digital twinborn model based on the central air conditioning unit is constructed according to the preprocessed data, a dynamic view of key performance indexes is provided, and the state of the central air conditioning unit is mapped and simulated in real time; designing a fault prediction model, and predicting and identifying faults; deploying the fault prediction model at an edge computing node and operating the fault prediction model, and performing real-time fault monitoring and diagnosis; when a fault is found, the AI-drive maintains decision support, and automatically adjusts a fault prediction model parameter or arranges a maintenance strategy; and constructing a learning loop based on performance feedback, and optimizing a fault prediction model and a maintenance strategy. Quantum calculation, the digital twinning technology and edge calculation are fused, and the operation efficiency and reliability of the central air conditioning system are remarkably improved.

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 field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

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 discloses a use monitoring method and system for a knife gate valve, and relates to the technical field of knife gate valve monitoring, and the method comprises the steps: integrating a multi-mode sensor array at the root of a valve rod of the knife gate valve, an adjacent region of a sealing surface and in a valve cavity flow channel, and collecting multi-source signals in real time; performing dynamic fusion processing on the multi-source signal, and determining a feature extraction algorithm combination based on the current valve rod temperature and historical working condition data to extract a sealing surface damage feature, a transmission chain wear feature and a particle impact feature; constructing a dynamic anomaly judgment model, updating a clustering boundary of a vibration and torque characteristic space in real time, and performing decoupling diagnosis on a sealing surface dry friction risk, thread clearance expansion and gear wear in combination with time-frequency domain fluctuation analysis of a gear meshing frequency harmonic attenuation rate and an angular acceleration; and generating a three-level maintenance strategy according to the diagnosis result, dynamically adjusting the output torque of the execution mechanism and optimizing the switching frequency of the valve.