156 results about "Optimal maintenance" patented technology

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 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 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 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 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.

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.

The invention discloses a toughness-driven underwater production system on-condition maintenance decision optimization method and system, and belongs to the field of underwater production system maintenance decision, and the method comprises the steps: predicting the remaining service life of a system, modeling the natural degradation performance of a component based on Weibull distribution, and quantifying the random impact degradation amount by combining a Poisson-normal impact model; performing superposition calculation on the total degradation performance of the system and predicting the remaining service life; predicting oil and gas yield, and fitting historical yield data by adopting an I-type generalized mathematical model; performing real-time toughness evaluation, fusing technical indexes and economic indexes, and calculating a comprehensive toughness value through a performance retention function and a yield stability function; and maintenance decision optimization: taking the real-time toughness value as an input variable, establishing a dual-objective decision model for maximizing the system toughness and minimizing the maintenance cost, and solving an optimal maintenance trigger threshold by adopting a multi-objective optimization algorithm, the maintenance decision is driven through the toughness index, and the reliability and economical efficiency of the underwater production system are effectively improved.

The invention discloses a concrete bridge preventive maintenance decision-making method based on degradation prediction, and relates to the technical field of bridge degradation prediction maintenance, and the method comprises the steps: collecting the degradation characteristic data of a concrete bridge through a bridge detection device; according to the method, various types of degradation characteristic data are collected through the bridge detection equipment, the standardized bridge state characteristic vector set is generated, the data-mechanism fusion inversion model is adopted, the actually collected data and the inherent mechanism of bridge degradation are combined, the estimation result is made to better conform to the actual situation, and the estimation accuracy is improved. Dynamic data assimilation is carried out according to a state space model, a probabilistic estimation set of a degradation driving state is input into a predetermined bridge degradation mechanism model for long-term deterministic simulation prediction to generate a plurality of future performance degradation paths, and all the paths form a degradation track cloud picture reflecting prediction uncertainty; the optimal maintenance strategy set can reduce the expected life cycle cost to the maximum extent on the premise that the bridge maintenance requirement is met.

The invention discloses a sensor dynamic maintenance and diagnosis method, system and device and a storage medium, and relates to the technical field of automatic monitoring and control, and the method comprises the steps: obtaining real-time data, receiving system state characterization, outputting an optimal maintenance action, and continuously monitoring. When a fault diagnosis triggering condition is met, a fault diagnosis process is activated, and a fault root cause is positioned. The system comprises a data acquisition module, a decision and output module, a monitoring and triggering module and a fault diagnosis module. A decision model dynamic optimization strategy replaces traditional fixed period calibration, and the monitoring precision and the operation and maintenance cost are effectively balanced; and an execution-monitoring-diagnosis feedback closed loop is constructed, the fault root cause of the sensor or associated equipment is quickly positioned, and low-efficiency and experience-dependent manual troubleshooting is avoided. In conclusion, full-process automation from dynamic maintenance to intelligent diagnosis is realized, and the reliability, the stability and the operation efficiency of the sewage treatment monitoring system are remarkably improved.

The invention provides a train bearing full life cycle adaptive optimization predictive maintenance method and system, and the method comprises the steps: obtaining the operation data of a train bearing, and carrying out the preprocessing; extracting a numerical index of the preprocessed operation data; constructing a digital geometric model of the train bearing, constructing a high-fidelity physical twinborn body, and constructing a data twinborn body according to the preprocessed operation data; performing parallel prediction on the numerical index based on the physical twinborn and the data twinborn to obtain a probabilistic prediction result, performing iterative fusion on the probabilistic prediction result through a combination rule to obtain a comprehensive RUL prediction result, and coordinating the data twinborn by adopting a federated average algorithm; and taking the minimization of the total expected cost as a target function, and carrying out global optimization by adopting a genetic algorithm in combination with a maintenance plan and a comprehensive RUL prediction result, so as to output an optimal maintenance decision. According to the invention, timely early warning can be carried out, and the confidence of the prediction result is improved.

The invention provides an intelligent inspection and remote diagnosis expert method and system for a fire pump station, and relates to the technical field of intelligent inspection, and the method comprises the steps: mapping the real-time operation data and historical maintenance records of the fire pump station to a domain knowledge graph, constructing a fault diagnosis reasoning chain, analyzing an abnormal state propagation path, generating maintenance suggestions, and retrieving historical cases. And evaluating scheme feasibility, constructing an optimal maintenance execution sequence and tracking a maintenance effect. The method can achieve the precise recognition of the abnormal state of the fire pump station, improves the fault diagnosis efficiency, optimizes the maintenance resource configuration, reduces the equipment fault rate, and guarantees the safe and reliable operation of the pump station.

The invention provides a hydroelectric generating set maintenance resource optimal configuration method based on a knowledge graph, and the method comprises the steps: converting unstructured historical maintenance data into a structured maintenance text through data preprocessing and large language model fine tuning; constructing a unit defect information ontology, a maintenance task ontology and a maintenance resource ontology, combining the ontology into a unit maintenance information knowledge graph ontology, and constructing a unit maintenance information knowledge graph after instantiating a structured maintenance text; through a similarity analysis and sorting fusion method of a TF-IDF algorithm and a BGE-M3 vector model, combining N-hop sub-graph extraction of a graph, designing instruction cue words and few sample examples, and guiding a large model to automatically generate a structured maintenance scheme of a newly input maintenance text; and analyzing the maintenance parameters based on the large model agent tool, calling a genetic algorithm to carry out iterative optimization on the maintenance resource optimization configuration model, and outputting an optimal maintenance resource configuration result. According to the method, the maintenance task processing efficiency and the intelligent level of resource scheduling are remarkably improved.

The invention provides a road facility intelligent inspection and maintenance scheduling method based on deep learning, and relates to the technical field of road facility maintenance, and the method comprises the steps: obtaining multi-dimensional historical inspection data, and carrying out the damage recognition; historical maintenance records are collected and classified, abnormal time points are eliminated, and the time sequence importance and the optimal maintenance time of each dimension are determined in combination with the maintenance cost; a multi-objective optimization problem is constructed based on the damage information and the optimal maintenance time, and a maintenance scheduling scheme is obtained through genetic algorithm solving and iterative optimization, so that the road facility maintenance efficiency can be improved, the maintenance cost can be reduced, and the service life of the facility can be prolonged.

This invention discloses a method and system for cable line condition assessment and maintenance decision-making, relating to the field of power system technology. The method includes: acquiring cable condition data and determining missing data information; the missing data information includes the location and type of the missing data; based on the missing data information, employing a corresponding data completion strategy to complete the cable condition data, obtaining completed values ​​and corresponding uncertainty indicators; inputting the completed values ​​and corresponding uncertainty indicators into a pre-constructed fault prediction model to obtain the cable's fault probability and confidence interval within a specified future time window; and determining the optimal maintenance strategy based on the fault probability. This invention enables accurate assessment and scientific decision-making regarding cable line conditions even with incomplete information.

The invention relates to a main oil pump boundary element multi-unit intelligent cooperative operation and maintenance system, and belongs to the technical field of intelligent operation and maintenance of industrial equipment. By fusing a boundary element method and a multi-unit collaborative optimization algorithm, a dynamic operation and maintenance model of a main oil pump cluster is constructed, and intelligent scheduling and resource optimization configuration of multi-unit maintenance tasks are achieved. The system comprises three core components including a main oil pump state monitoring network, a boundary element characteristic analysis module and a multi-unit collaborative decision engine, wherein the boundary element characteristic analysis module establishes a mapping relation between a main oil pump operation state and mechanical loss based on a three-dimensional field domain discretization method; and the multi-unit collaborative decision engine generates an optimal maintenance strategy sequence by adopting a dynamic programming algorithm. According to the system, the non-planned downtime can be reduced, the operation and maintenance cost is saved, and meanwhile, the overall reliability index of a multi-unit system is effectively improved.

The invention discloses a storage equipment predictive maintenance method and device, a storage medium and a computer program product, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: collecting multi-source modal data of storage equipment, and generating a structured feature vector; using a lightweight decision tree model to carry out fault positioning on error code associated features in the structured feature vector to obtain a preliminary fault positioning result; generating a cloud fault diagnosis report according to the preliminary fault positioning result, cloud fault diagnosis and pre-integrated supply chain information; generating a plurality of candidate maintenance schemes in combination with a priority function based on the preliminary fault positioning result and / or the cloud fault diagnosis report; and selecting an optimal maintenance scheme from the plurality of candidate maintenance schemes through a cost optimization module. On the basis of fusing the supply chain operation data, the diagnosis accuracy and the operation and maintenance economy of the predictive maintenance of the storage equipment are improved.

The present application relates to the technical field of micro-grid, and provides a maintenance plan generation method and system suitable for high-load local isolated network, comprising: for each month in each maintenance plan date combination, calculating the power shortage when each thermal power unit is shut down; based on the power shortage, combining the new energy output in previous years, calculating the new energy supplement power when each thermal power unit is shut down in each month; based on the new energy supplement power, calculating the daily power purchase amount when each thermal power unit is shut down in each month; for each maintenance plan date combination, accumulating the daily power purchase amount of all days to obtain the grid power purchase amount corresponding to the maintenance plan date combination, and taking the maintenance plan date combination corresponding to the minimum grid power purchase amount as the annual optimal maintenance plan. By using the certain periodicity of new energy in time, the power gap in the power grid power shortage state is made up, the multi-energy complementary effect is realized in the maintenance plan aspect, and the external power purchase amount is reduced.

The invention provides an on-orbit maintenance ground test method under the cabin condition of a manned spacecraft, and the method comprises the steps: S1, employing maintenance simulation software to carry out maintainability simulation under a cabin environment, and determining a maintenance pose; s2, simulating a surrounding layout environment of the on-orbit maintenance unit, and setting a proper position according to a simulation result, so that the relative poses of the simulated astronaut and the on-orbit maintenance unit are kept consistent during ground maintenance; s3, performing maintenance operation verification by the maintenance personnel by adopting the relative poses and the maintenance tools according to the maintenance operation requirements, and verifying accessibility, visibility and operability in the maintenance operation process; s4, carrying out trafficability verification by the on-orbit maintenance unit, and verifying whether the maintenance product can pass through the transfer channel or not; and S5, the maintenance pose is adjusted, and the optimal maintenance state is optimized. According to the invention, maintainability verification in an on-orbit zero-gravity whole cabin environment can be effectively verified, a zero-gravity verification problem under a ground cabin body is solved, and design optimization of on-orbit maintainability can be effectively supported.

The invention discloses a side cloud collaborative port equipment maintenance decision optimization method and system, and relates to the technical field of port equipment maintenance. The method comprises the following steps: collecting multi-source heterogeneous data through an edge terminal, extracting a feature vector, and constructing a semantic dynamic digital twinborn model; a lightweight analysis model is built by embedding physical constraints based on a GCN architecture, and is deployed in an edge gateway to output equipment health indexes, fault modes and data quality scores in real time; and when the data quality does not reach the standard, the edge side uploads the related data and the local sub-model to the cloud, the cloud constructs a target optimization model containing structural constraints by adopting a PINN algorithm in combination with a physical model library, a multi-target optimization problem is solved by improving an NSGA-II algorithm, and an optimal maintenance scheme considering the maintenance cost, the equipment availability rate and the fault risk is generated. Physical constraint and digital twinning technologies are integrated to guarantee decision reliability, real-time performance and accuracy of maintenance decision are effectively improved, and the method is suitable for efficient operation and maintenance of special equipment in ports.

A system is provided.SOLUTION: A system comprising: means for collecting basic building information, maintenance history, and building material characteristics; means for analyzing the collected information by extracting features using a generative AI model; means for creating an optimum maintenance schedule based on the analysis result and notifying a user of the maintenance schedule; and means for collecting feedback information from the user who has received the notification and updating the database.SELECTED DRAWING: Figure 1

The invention discloses an aero-engine gear residual life prediction model based on stress correction and polynomial chaos. The method comprises the following steps: realizing fault robust classification under a strong interference working condition by fusing uncertainty quantization through a double-branch deep network; based on an improved GD-YOLOv12 architecture, a lightweight self-attention mechanism is integrated, and sub-pixel positioning of micron-sized damage is achieved; constructing a surface topography-wear depth two-dimensional fusion model, and generating a damage index in combination with image correction and an Archard theory; a Walker index and a Manson-Halford factor are adopted to correct the average stress effect to establish a fatigue damage model, and material and load uncertainty is quantified through a polynomial chaos theory; and designing a dynamic decision engine driven by a risk increasing factor, and fusing multi-objective optimization to generate an optimal maintenance strategy. According to the technology, the defects of insufficient recognition precision and unquantified uncertainty of a traditional method are overcome, closed-loop management of damage recognition, accurate quantification, probability life prediction and optimization decision is achieved, the reliability of the engine is remarkably improved, and the operation and maintenance cost is reduced.

The application relates to an intelligent maintenance decision method and system based on a knowledge graph, relates to the fields of intelligent operation and maintenance and decision support and information technology, and comprises the following steps: acquiring industrial equipment operation state data and historical maintenance data; then, constructing an industrial knowledge graph and generating a correlation relationship model; based on the analysis of the relationship between fault types and maintenance paths, an optimal maintenance path is obtained; through the optimal maintenance path, the correlation relationship model and maintenance resource constraint conditions, a maintenance task allocation scheme is optimized; then, optimal maintenance strategies are obtained through dynamic adjustment analysis of maintenance time windows; finally, multi-objective optimization iteration analysis is performed to obtain a final maintenance decision; the scheme can comprehensively consider various factors, and the scientificity and accuracy of the maintenance decision of the industrial equipment are improved.