213 results about "Optimal maintenance" patented technology

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are 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 relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.

The invention discloses a ship equipment maintenance support system and method based on artificial intelligence, and the system collects the operation data of equipment in real time through a data collection and integration module, and digitalizes and integrates the historical maintenance data into a unified database; a fault diagnosis model is constructed through the model diagnosis module to carry out fault diagnosis and output a diagnosis result; an optimal maintenance scheme is provided through a maintenance decision support system according to a fault diagnosis result in combination with multi-dimensional data; real-time monitoring and trend analysis are carried out on operation data of the equipment through the fault prediction module, potential faults of the equipment are predicted, and a maintenance plan is made in advance. According to the invention, equipment operation data is collected in real time through a sensor, accurate fault diagnosis and prediction are realized in combination with a deep learning algorithm, an optimal maintenance scheme is formulated in combination with multi-dimensional data, and a maintenance plan is dynamically adjusted.

The invention discloses a rural historical building protection evaluation system based on big data, and relates to the technical field of building protection. Comprising the following steps: a data processing module normalizes environmental data to generate an environmental degradation factor, extracts a crack depth parameter and a facade weathering parameter from structure detection data through a feature extraction algorithm, and generates a building life attenuation curve through correlation analysis; and the decision module establishes a maintenance optimization model according to the building life attenuation curve and historical maintenance data, generates a risk level through threshold comparison, evaluates expected effects of different maintenance modes on degradation delay, and outputs an optimal maintenance suggestion. According to the method, accurate prediction of the rural historical building degradation process and optimal recommendation of the maintenance strategy are realized, and the scientificity and efficiency of protection work are improved.

The invention discloses a municipal facility maintenance strategy optimization method and system based on big data driving, and relates to the technical field of urban operation and maintenance management and big data decision support, and the method comprises the steps: carrying out the data cleaning and standardization processing of multi-source operation state data, and forming a facility state database; the method comprises the following steps: extracting facility degradation trend characteristics, establishing a facility state degradation prediction model, calculating a comprehensive health degree index, establishing a multi-objective optimization model based on prediction of a degradation trend, solving an optimal maintenance opportunity and a resource allocation scheme, and generating a maintenance strategy list. And comparing and analyzing the maintenance strategy list with historical maintenance effect data in the facility state database to obtain strategy optimization feedback parameters, and dynamically updating parameter setting of the facility state degradation prediction model. According to the invention, the management mode from passive fault repair to active preventive maintenance is changed.

The invention relates to the technical field of equipment fault diagnosis, and discloses an intelligent fault detection method for a speed reducer, and the method comprises the steps: extracting fault feature knowledge from a source domain based on a domain adaptive transfer learning algorithm, migrating the fault feature knowledge to a target domain, and generating a synthetic fault sample based on physical model constraints; constructing interaction influence among the multi-subject causal capture equipment; executing causal intervention and anti-factual reasoning, and determining a fault root cause; constructing a fault knowledge graph and continuously optimizing the fault knowledge graph; and constructing a preventive maintenance decision system to generate an optimal maintenance strategy. According to the method, high-accuracy fault diagnosis can be realized under the condition of sample scarcity, interaction influence among multiple devices is analyzed, fault root causes are traced, and reliable preventive maintenance decision support is provided.

The invention discloses a factory equipment predictive maintenance scheme optimization method based on reinforcement learning, and relates to the technical field of equipment maintenance scheme prediction, and the method comprises the steps: carrying out the fusion through employing a space-time attention mechanism based on an equipment health index, equipment operation duration and a current load level, generating an equipment state vector, constructing a reinforcement learning framework, and carrying out the optimization of a factory equipment predictive maintenance scheme. A multi-target reward function is embedded in the reinforcement learning framework, a PPO algorithm is adopted for optimization, a reinforcement learning model is generated, the reinforcement learning model is trained by using the equipment state vector, the trained reinforcement learning model is obtained, the trained reinforcement learning model is deployed to an actual industrial control unit, the latest equipment state vector is received in real time, and the final equipment state vector is obtained. And obtaining an optimal maintenance strategy. And converting the optimal maintenance strategy into a specific maintenance instruction and executing the specific maintenance instruction. Through the factory equipment predictive maintenance method, rapid response to emergencies is facilitated, greater loss is avoided, resource allocation is optimized, and enterprise competitiveness is enhanced.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention provides a bridge group multi-target maintenance decision-making method fusing an evolutionary algorithm and artificial intelligence, and relates to the technical field of civil engineering and artificial intelligence crossing. The method comprises the steps of defining bridge group maintenance cost and structure failure risks, representing preferences of decision makers for different decision targets by weight combinations, and constructing a multi-target maintenance decision optimization model; encoding the weight combination into an individual of a multi-objective evolutionary algorithm, and randomly generating an initial population; aiming at each generation of weight combination, constructing a bridge group Markov decision-making environment; learning an optimal maintenance strategy by adopting an A2C training reinforcement learning agent; the optimal maintenance strategy is evaluated, and an evaluation result is used as individual fitness to be fed back to the multi-objective evolutionary algorithm; using a multi-objective evolutionary algorithm to perform evolutionary search on the multi-objective weight combination; and through a closed-loop feedback mechanism, outputting a Pareto optimal solution set containing an optimal maintenance strategy under various weight combinations, thereby realizing collaborative optimization of weight optimization and strategy learning.

The invention discloses a bridge service intelligent evaluation and early warning method and system based on dynamic and static load-multi-modal data fusion, and the method comprises the steps: S1, installing a vibration acceleration sensor and a strain gauge at a bridge control part, and collecting data; locally preprocessing data by adopting an edge computing framework, extracting feature values, encrypting and transmitting the feature values to a cloud; identifying and collecting diseases, and combining a deep learning network model to realize pixel-level crack segmentation; s2, establishing a space-time-measuring point-disease three-dimensional correlation model; s3, a three-level early warning threshold system is set, different early warning levels have different requirements for vibration amplitude and crack length indexes, and when a monitoring index exceeds a threshold value, early warning information is pushed to a terminal in real time through 5G; S4, a decision engine is constructed based on a Q-Learning algorithm or a deep reinforcement learning model, early warning levels, residual life and maintenance cost parameters are input, and the early warning level, the residual life and the maintenance cost parameters are calculated. And outputting the optimal maintenance scheme.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention provides a flange assembly predictive maintenance method based on residual life distribution dynamic identification, which comprises the following steps: firstly, establishing a linear Wiener model of a flange assembly degradation process, and designing a Bayesian parameter dynamic updating mechanism based on normal-inverse gamma conjugate prior; secondly, deducing residual life complete probability distribution considering parameter uncertainty through a Monte Carlo sampling method, overcoming the limitation of point prediction, and providing a maintenance decision rule based on a time-probability threshold; and finally, establishing a decision parameter optimization model with the goal of minimizing the long-term average cost rate, and solving an optimal maintenance strategy through system simulation. Compared with the prior art, the residual life prediction accuracy is remarkably improved, the maintenance cost and the equipment reliability are effectively balanced through a probabilistic decision-making mechanism, the full-life-cycle maintenance cost is remarkably reduced while the flange sealing safety is ensured, and the method has important popularization value in engineering equipment predictive maintenance.

The invention relates to the technical field of power system state monitoring and operation and maintenance, and discloses a power distribution network equipment state intelligent sensing and predictive maintenance decision-making method, system, equipment and medium, and the method comprises the steps: collecting equipment operation data, monitoring data and environment data through a power distribution automation system, an online monitoring device, an environment sensor and other channels; performing time synchronization and quality verification on the multi-source data; extracting multi-dimensional characteristic parameters reflecting the running state of the equipment, and performing normalization processing and fusion analysis on the characteristics; establishing an equipment state evaluation model by using a deep learning algorithm, and carrying out quantitative scoring on the current health condition of the equipment; constructing a time sequence prediction model, and predicting the future degradation trend and residual life of the equipment; and establishing a multi-objective optimization model, and generating an optimal maintenance decision scheme. The whole process is from data acquisition, state perception and fault prediction to decision optimization, and intelligent management of power distribution network equipment is realized.

The present invention relates to the technical field of avionics equipment, and disclosed is a cross-domain maintenance decision-making method for coordinated optimization of maintenance service outlets, activities, and resources. The method comprises the steps of: on the basis of a fault phenomenon, acquiring a set of a variety of maintenance activities; determining whether resources required for a faulty product are missing at a current service outlet, and if the resources are missing, separately computing a resource scheduling time and cost required for each maintenance activity for which resources are missing; computing a fault repair time, repair costs, and repair probability of a certain maintenance activity; and determining a goal of a maintenance decision, and outputting an optimal maintenance scheme. The present invention can assist technicians in making maintenance decisions during maintenance of electronic products, reduce the waste of time and resources caused by the inconsistencies and uncertainties of maintenance activities, guide maintenance activities of remote operation and maintenance, reduce maintenance time, and reduce operation and maintenance costs.

The invention discloses an intelligent fire-fighting equipment fault prediction method, and belongs to the technical field of intelligent maintenance of fire-fighting equipment. According to the method, pressure, electrical waveform and environmental data are dynamically acquired through a multi-source sensor, spatial-temporal features are extracted in parallel by adopting a bidirectional LSTM and a multi-scale convolutional network, and feature fusion is realized by utilizing a cross-modal attention mechanism; outputting a fault probability based on a gradient boosting decision tree model, and predicting the remaining service life in combination with Bayesian regression; the firmware upgrading authority is controlled through a health degree dynamic scoring system, and the upgrading safety is guaranteed through AES-256-CBC encryption and SM3 signature verification; and generating an optimal maintenance path based on an improved genetic algorithm, and scheduling an engine to respond to a sudden fault in real time. Tests show that the fault early warning accuracy rate reaches 93.1%, the maintenance cost is reduced by 45%, the upgrade failure blocking rate is 100%, and the intelligent level and reliability of operation and maintenance of the fire-fighting equipment are remarkably improved.

The invention relates to the technical field of offshore wind turbine generator operation and maintenance, and discloses a failure evaluation system based on an offshore wind turbine generator. Comprising a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system obtains parameters such as vibration and temperature of the wind turbine generator in real time, after preprocessing, failure features are extracted through a deep convolutional neural network, and a dynamic Bayesian network model is constructed to calculate the component failure probability. An optimal maintenance path is generated by using an improved ant colony optimization algorithm, and meanwhile, a dynamic risk assessment module, an abnormal signal detection module and a maintenance strategy optimization module are arranged. The method can accurately evaluate the unit failure risk, timely discover abnormity, optimize the maintenance decision, improve the operation reliability of the offshore wind turbine generator, reduce the operation and maintenance cost, and guarantee the stability and high efficiency of offshore wind power generation.

The invention discloses a corrosion pipeline system maintenance strategy determination method, device and system, and the method comprises the steps: determining a pipeline limit state equation based on a defect growth model of a to-be-evaluated pipeline; according to the pipeline limit state equation, the failure probability of each defect is calculated through Monte Carlo simulation, and the failure probabilities of a single pipe section, a single pipeline section and a pipeline system are further calculated; the minimum total maintenance cost of the pipeline system and the maximum total conveying flow of the pipeline system serve as targets, an optimized target function is constructed based on the maintenance mode, the maintenance cost, the maintenance time, the failure probability of each pipe section and the initial capacity of the pipeline, and constraint conditions are set based on the failure probability threshold value of the pipe sections and the failure probability threshold value of the pipeline system. Generating a maintenance strategy multi-objective optimization model; the maintenance strategy multi-objective optimization model is solved, an optimal maintenance strategy is obtained, and the maintenance strategy comprises a maintenance mode and maintenance time. According to the invention, dual optimization of maintenance cost reduction and conveying efficiency improvement can be realized.

A unit maintenance plan double-layer optimization method considering new energy grid connection comprises the steps that an upper-layer optimization model is established, and the minimum unit operation cost and the minimum unit conventional maintenance cost in the planned maintenance period serve as target functions; solving a unit start-stop plan during a maintenance plan period by using a genetic algorithm, and arranging a historical maintenance plan as an initial population; establishing a lower-layer optimization model, and taking the minimum unit fault risk cost and the minimum wind and light abandoning and load shedding expected cost in the maintenance plan period as a target function; according to the obtained unit start-stop plan, solving the optimal output and scheduling arrangement of the unit during the maintenance plan by using an improved particle swarm algorithm; and solving the total economic cost of the system according to the obtained optimal output and scheduling arrangement of the unit, and obtaining an optimal maintenance plan according to optimization iteration. According to the method, a basis can be provided for a maintenance plan of a power grid worker, the load loss amount is minimum, the economical efficiency is optimal, and specific guidance is provided for maintenance of equipment with different voltage grades in a power grid.

The invention discloses an intelligent production line operation and maintenance strategy optimization method based on a condition generation model, and the method comprises the specific steps: (1) collecting time series data of an equipment operation state, and constructing a sample data set; (2) preprocessing the sample data set to obtain a preprocessed data set, and dividing the preprocessed data set into a training set and a verification set; (3) a conditional VAE world model is constructed through a conditional variation auto-encoder, a Transform decoder and an RUL prediction module based on a historical sequence; (4) training a reinforcement learning agent based on the world model, and learning an optimal maintenance strategy; and (5) carrying out equipment state prediction and maintenance decision making by utilizing the trained world model and the intelligent agent. According to the method, state transition dynamics is modeled through conditional VAE, state transition is predicted by using a Transform decoder, an independent structure is designed to predict RUL and rewards, the prediction precision is improved in combination with historical sequence information, and decision support is provided for intelligent maintenance.

Provided are an intelligent operation and maintenance optimization method and intelligent production optimization method for an offshore oil subsea production system (SPS), and a system, relating to the field of petroleum engineering. The operation and maintenance optimization method includes: determining whether there is a leak in an SPS; and if there is a leak, performing emergency repair; or if there is no leak, performing intelligent operation and maintenance optimization. The performing intelligent operation and maintenance optimization includes: calculating a static health index of the SPS based on ashore sensor status data and Christmas tree sensor data; obtaining dynamic health indexes at different moments through a Kalman filtering algorithm; and formulating an optimal maintenance strategy for the SPS with optimization objectives of maximizing toughness and minimizing a maintenance cost.

The invention provides intelligent assistant software of a motor train unit and a fault diagnosis and disposal method thereof, and belongs to the technical field of railway transportation. Through cooperative work of the central control unit and multiple modules, intelligent closed-loop management of fault processing of the motor train unit is realized: the system can analyze HMI fault information in real time and automatically match an optimal maintenance scheme, and by combining three-dimensional space positioning, a dynamic circuit schematic diagram and a fault processing scheme, the fault positioning and processing efficiency is remarkably improved; through torque standard automatic pushing and visual guidance, the risk of manual operation errors is effectively reduced; the emergency response time is greatly shortened based on an automatic notification mechanism of a fault level and generation of a personalized disposal scheme; through intelligent linkage and data sharing among the modules, whole-process auxiliary decision support from fault discovery, diagnosis analysis to disposal implementation is formed, normalization and accuracy of maintenance operation are improved, and system performance is continuously optimized through historical data accumulation and adaptive learning.

The invention belongs to the technical field of hydraulic engineering supervision, and particularly relates to a hydraulic engineering supervision system based on big data. Comprising a data acquisition and preprocessing module used for collecting and processing a multi-source heterogeneous data set; the multi-modal feature extraction and alignment module is used for generating a node feature matrix; the space-time degradation modeling and fusion module is used for predicting and generating an evolution trajectory of the key performance indexes of the water conservancy project; the health trajectory prediction and risk decision module is used for calculating the future failure probability of the structure and determining whether to start intervention or not by adopting a reinforcement learning decision agent; the active intervention strategy optimization module is used for generating a group of optimal maintenance strategies; the parameterization maintenance instruction generation module is used for converting the operation instruction into a standardized operation instruction; and the digital twin visualization and execution feedback module is used for feeding back and updating the time-space diagram attention network model. According to the method, the accuracy and the reliability of predicting the long-term evolution trend of the key performance index are remarkably improved.

The invention discloses a Solar-Think photovoltaic power station intelligent construction platform, and the platform comprises a photovoltaic power station intelligent design module which is used for automatically generating design data; the photovoltaic power station construction collaboration module is used for converting a design model in the design data into a digital twin model, collecting construction data in real time through Internet of Things equipment, and dynamically updating a construction state in a virtual space; the predictive operation and maintenance module is used for predicting equipment faults through an LSTM neural network based on design parameters, construction records and real-time operation and maintenance data of the design model and generating an optimal maintenance path in combination with the digital twinborn model; and the photovoltaic power station full-process data closed-loop architecture module is used for automatically synchronizing the design model to the construction collaboration module, reversely correcting the design model based on construction data, creating association between the construction data and the operation and maintenance data, forming an equipment full-life-cycle file, and transmitting the equipment full-life-cycle file to the construction collaboration module. And feeding back the operation and maintenance data to the design module to support next-generation power station scheme optimization.

The invention relates to the field of tool management, and provides a coal-fired power plant tool intelligent management method and system. The method comprises the following steps: carrying out digital identity construction on a purchasing tool to obtain a tool digital identity file comprising a unique identification code of the tool; constructing a tool circulation record based on the tool digital identity file, and obtaining a block chain traceability record; monitoring an inventory tool state through the block chain traceability record to obtain a tool behavior baseline model; based on the tool behavior baseline model, attenuation prediction and maintenance scheduling are carried out on an inventory tool, and a tool health attenuation trend prediction result and an optimal maintenance scheduling scheme are obtained; and performing decision analysis based on the optimal maintenance scheduling scheme to obtain a tool management decision instruction set. According to the method and the system, non-tampering recording of tool full-life-cycle data, accurate health state prediction and scientific decision support are realized, and the management efficiency and the data credibility are improved.

The invention relates to a self-adaptive optimization decision-making method for preventive maintenance opportunity of a road surface, which comprises the following steps of: establishing a road surface performance prediction model based on multi-source data fusion based on historical road surface performance data, traffic load, climate environment and material structure characteristics; based on the performance data of the pavement before and after maintenance construction, extracting the instantaneous performance resilience value after maintenance completion and the performance attenuation rate after maintenance, and establishing a maintenance effect prediction model; constructing a double-layer optimization decision model taking the total cost minimization of the whole life cycle as an optimization target; the upper layer takes the maintenance opportunity threshold value as a decision variable to generate a corresponding maintenance demand; the lower layer solves the optimal maintenance schedule under the corresponding threshold value; in the lower-layer solving process, a maintenance effect prediction model is called to predict the maintenance effect; and an optimal maintenance opportunity threshold value is obtained through simulation optimization search. The method aims to realize closed-loop coupling of performance prediction, maintenance effect quantification and maintenance decision, and minimize the total cost of the whole life cycle on the premise of ensuring the pavement service level.

The invention discloses a predictive maintenance method for an implicit state of power equipment, and belongs to the technical field of intelligent operation and maintenance of the power equipment. According to the method, multi-mode information such as SCADA data, acoustic vibration data, infrared thermal image data and partial discharge data is collected, space-time alignment and attention mechanism fusion are carried out, and a hidden state code representing the internal health state of equipment is extracted; a dynamic state deduction model combining physical constraint and data driving is constructed, and prediction of the future state evolution trajectory of the equipment is achieved; and performing a virtual maintenance experiment in the digital twin based on a prediction result, and generating an optimal maintenance strategy through multi-objective optimization. According to the method, the problems that the hidden state of the equipment cannot be sensed and the prediction capability is lacked in the prior art are solved, the conversion from passive maintenance to predictive maintenance is realized, and the accuracy and foresight of operation and maintenance of the power equipment are improved.