484 results about "Maintenance plan" patented technology

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention relates to the technical field of power system operation management, and particularly discloses a main power distribution network planned maintenance optimization method and system based on deep learning. The method comprises the following steps: constructing an energy state graph based on a power grid topological structure, equipment parameters and a main-distribution hierarchical relationship; establishing a mapping relation between the maintenance task and the power grid topology, and defining a task attribute, a time window, a resource demand and an edge available state variable; calculating energy state propagation and cross-layer influence in a task execution state, detecting consistency of capacity, redundancy and power failure influence, and generating a conflict set; and adjusting a task feasible region through a constraint propagation mechanism, selecting a replacement starting point according to a minimum disturbance principle, and outputting a final maintenance plan by combining the combinatorial optimization objective function and the interactive arrangement layer. According to the method, the scientificity, the safety and the economical efficiency of the collaborative maintenance plan of the main power distribution network can be effectively improved, and the defects of a traditional method in the aspects of cross-layer collaboration, dynamic response and conflict processing are overcome.

The invention relates to the technical field of electrical equipment maintenance, in particular to an electrical equipment maintenance plan intelligent decision-making system based on spatial-temporal characteristics and an application method. Comprising a space-time knowledge graph construction unit; the spatial-temporal feature extraction unit is used for extracting equipment spatial distribution features, load time sequence features and maintenance resource time constraint features from the power grid operation data, constructing a multi-dimensional feature vector, and realizing feature extraction based on a graph traversal algorithm and an LSTM neural network; an intelligent decision-making unit; a main distribution network space-time cooperation unit; and a space-time risk assessment unit. According to the invention, the splicing of the main network and the distribution network CIM model is realized through the main and distribution network space-time cooperation unit, the full-voltage class fusion topological graph is generated, and the influence range of main network maintenance on the power supply path of the distribution network can be automatically identified based on a cross-voltage class topology tracking method; the mechanism effectively reduces the problem of repeated power failure or forwarding path conflict caused by insufficient coordination of the main network and the distribution network.

The invention belongs to the technical field of wind turbine generator maintenance, and particularly relates to an offshore wind turbine generator two-stage rolling maintenance method considering subsystem correlation, which comprises the following steps: S1, constructing a comprehensive reliability model; s2, taking the minimization of the total maintenance cost in the operation cycle of the wind turbine generator as a target, and forming a target optimization problem based on the comprehensive reliability model; solving the target optimization problem to form a first-stage long-term maintenance strategy; s3, when an operation window meeting the storm accessibility condition appears, determining a subsystem of which the current reliability is lower than a preventive maintenance threshold value; s4, the opportunity maintenance benefit ratio is calculated, and if the opportunity maintenance benefit ratio is larger than a processing threshold value, advanced maintenance of the subsystem is triggered; and S5, after the advanced maintenance is executed each time, the optimal preventive maintenance time point is solved again so as to dynamically update the subsequent maintenance plan. According to the method, the fault coupling and degradation evolution law between subsystems can be accurately described, and the storm accessibility window is actively utilized to dynamically adjust the maintenance plan.

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

The invention provides an electric power overhaul decision-making method and device based on knowledge domain mapping map retrieval generation. The method comprises the following steps: performing deep semantic analysis on an electric power overhaul file through a prompt project guide large language model LLM, and constructing an electric power overhaul knowledge domain mapping map; generating communities with hierarchical structures by adopting a community detection algorithm Leiden based on the electric power overhaul knowledge domain mapping map, and creating a community abstract in a report form for each community; and mapping an electric power overhaul query request initiated by a user to the embedding space of the electric power overhaul knowledge domain mapping map through a vectorization technology to realize semantic matching with the community abstract, and outputting an electric power overhaul decision answer result corresponding to the electric power overhaul query request. According to the method, an intelligent question-answering system in the field of electric power overhaul is constructed by fusing knowledge domain mapping map retrieval enhancement generation and LLM, automatic knowledge extraction and map construction of unstructured overhaul texts are realized, and high-reliability decision support is provided for fault diagnosis, overhaul plan optimization and risk early warning of electric power equipment.

The invention relates to the technical field of power distribution lines, in particular to a power distribution line abnormal line loss analysis and treatment method and system, and the method comprises the following steps: synchronously extracting real-time monitoring data and historical data based on a power distribution line network, carrying out the data alignment and data cleaning, screening features associated with current leakage and voltage drop, and carrying out the analysis and treatment of the abnormal line loss of the power distribution line. Comprising load change rate and temperature fluctuation. According to the invention, through application of real-time and historical data analysis, the detection and management efficiency of the abnormal line loss of the distribution line is improved, the accuracy of data processing is improved through synchronous extraction and data alignment, early recognition of abnormal modes is allowed, the possibility of energy loss and unplanned power failure is reduced, and through adoption of Fourier transform and wavelet transform, the detection efficiency of the abnormal line loss of the distribution line is improved. According to the method, abnormal signals are accurately recognized, the analysis capability of the signals is enhanced, the accuracy of abnormal detection is improved, fault positioning and maintenance plan making are effectively optimized through combination of the GIS system, and the pertinence and efficiency of maintenance work are remarkably improved.

The invention relates to a conveying belt tearing positioning and tracing system based on RFID and multi-sensor fusion, which effectively overcomes the influence of belt slipping and elastic deformation through multi-sensor fusion and motion compensation, and realizes accurate positioning of a tearing point. Meanwhile, through cross validation of vibration, vision and infrared data, the recognition accuracy is improved to 99.5% or above, false alarms caused by environmental factors (such as dust and moisture) are greatly reduced, stable operation in a severe industrial environment is ensured, and high reliability and low false alarm rate are achieved. And thirdly, the system supports long-term data tracing and intelligent operation and maintenance, and a user can analyze a tearing trend and identify a high-frequency fault area by storing historical data of at least 180 days and providing a visual tool, so that a maintenance plan is optimized, predictive maintenance is realized, and faults are reduced from the source.

The invention discloses a hydraulic motor abrasion monitoring method and system, and belongs to the technical field of abrasion fault prediction.The method specifically comprises the steps that multi-dimensional data signals of a hydraulic motor are collected in real time and preprocessed; the preprocessed multi-dimensional data of the hydraulic motor are processed, the early wear characteristics of the hydraulic motor are extracted, and the processing comprises decomposition of the preprocessed multi-dimensional data of the hydraulic motor, construction of a decomposition tree, reservation of nodes with preset frequency in the decomposition tree, and reconstruction, difference calculation and window moving average suppression of the reserved nodes; fusing the early wear characteristics of the hydraulic motor, and generating a comprehensive health index through a dimension reduction algorithm; judging whether the hydraulic motor has early-stage fine wear or not according to the comparison of the comprehensive health index and a dynamic threshold value, and giving an alarm according to a judgment result; according to the method, early-stage fine wear signs of the hydraulic motor can be found, a maintenance plan is made in advance, and production interruption caused by sudden equipment failures is avoided.

The invention relates to a power plant unit maintenance optimization method considering multiple uncertain factors of a power market, and belongs to the technical field of power system operation management. The method comprises the following steps: firstly, obtaining spot market electricity price and new energy output data in each time period of a historical year in a power plant; a random scene set considering spot market electricity price and new energy output is established based on historical data, time-varying features are described through data segmentation, state space division and a non-uniform Markov model, and the random scene set is generated by using a Monte Carlo method; and finally, taking maximization of the total expected income of the power plant as an objective function, constructing an optimization model in combination with preset constraint conditions of the unit, inputting the random scene set into the optimization model for solving, and outputting an optimized unit operation and maintenance strategy which comprises maintenance plans of wind power, photovoltaic and thermal power units and corresponding output plans. Multiple uncertain factors in the power market can be effectively handled, and the operation and maintenance benefits of a power plant are improved.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

The invention discloses a remote welding machine control system and method based on the Internet of Things, and the method comprises the following steps: S1, collecting and preprocessing multi-source time sequence data, and generating a time sequence data set; s2, constructing a variational auto-encoder model, extracting potential features and reconstructing original data; s3, using a bird flock optimization algorithm to optimize hyper-parameters of the VAE model; s4, performing feature extraction and data reconstruction through the optimized VAE model; s5, model output and actual data are compared, the welding quality is evaluated, and control parameters are adjusted; s6, real-time welding operation is carried out according to the adjusted control parameters, and equipment identity binding and permission verification are carried out; s7, performing real-name fire approval through the platform, and remotely starting the equipment after the approval is passed; s8, the welding state is monitored, early warning is conducted on abnormity, and a maintenance plan and reminding are pushed; and S9, uniformly managing equipment states, task approval, early warning and operation and maintenance, and generating an analysis report. Intelligent control and safety management of the remote welding machine are achieved, and automation and efficiency of welding operation are improved.

Disclosed in the present invention are an operation and maintenance method and apparatus for severe overload and three-phase imbalance of a distribution transformer, and a medium. The method comprises: by quantifying the degree of difference between devices as the distance between two points, performing clustering analysis on an operation and maintenance state of a distribution transformer, so as to obtain a classification result; and on the basis of a short-term single-phase severe overload category and a short-term three-phase severe overload category in the classification result, establishing a multi-objective optimization model and solving same, so as to obtain a multi-objective optimization result, and on this basis, formulating a day-ahead source-load distribution transformer operation and maintenance plan and performing operation and maintenance control on a distribution network. The present invention provides an operation and maintenance method and apparatus for severe overload and three-phase imbalance of a distribution transformer, and a medium. Solving a multi-objective optimization model to obtain a multi-objective optimization result can ensure that operational risks caused by three-phase imbalance are reduced while balancing three-phase loads, such that differentiated operation and maintenance can be achieved by means of a day-ahead source-load distribution transformer operation and maintenance plan, thereby solving the problem of difficulty in managing severe overload and three-phase imbalance of a distribution transformer to formulate a targeted operation and maintenance plan.

The invention discloses a fault early warning method and system for an electric pump, and relates to the technical field of fault early warning methods, and the method comprises the steps: determining an emergency floating parameter combination of the electric pump based on a plurality of working parameters and corresponding working stages of the electric pump; according to the emergency floating parameter combination of the electric pump, the multiple fault parts of the electric pump and the service life of the electric pump, the multiple abnormal fault parts and the conventional fault parts are determined, and the accuracy of the multiple abnormal fault parts and the conventional fault parts is improved. Therefore, a corresponding fault maintenance plan is determined based on each fault maintenance combination, the current working node of the electric pump and the working plan of the electric pump; according to the invention, the plurality of sub-fault maintenance items are determined based on the identification of the fault maintenance plan, and the dynamic maintenance events of the plurality of sub-fault maintenance items are determined according to the item content of each sub-fault maintenance item, the distribution positions of the plurality of fault parts and the fault correlation coefficient, so that the accuracy of the dynamic maintenance events of the plurality of sub-fault maintenance items is improved.

A system and method for automated post-mission logging of unmanned vehicle operations using generative AI. Flight telemetry, operator notes, and maintenance data are aggregated and normalized into a standardized dataset. A large language model interprets the dataset to generate user-readable debriefs, compliance records, and maintenance logs. The system ensures each post-mission report conforms to regulatory standards and automatically flags anomalies or compliance deviations. The generative AI module may be fine-tuned on domain-specific datasets to produce logs that are structurally consistent with predefined templates and compliance forms. The resulting reports can be stored, retrieved, and shared via secure cloud services, streamlining flight record-keeping and maintenance planning.

The invention relates to the technical field of wind power plant generating capacity prediction. The invention provides a wind power plant generating capacity prediction method and system considering a maintenance plan and an equipment health degree. The method comprises the following steps: collecting multi-source data of a wind turbine generator, and carrying out preprocessing and space-time alignment; based on the health monitoring data, state features are extracted, and a real-time health degree index is calculated by adopting a fuzzy comprehensive evaluation method; optimizing the maintenance plan according to the health degree index, and establishing a mapping relation among the health degree, the maintenance measures and the power generation loss; taking the meteorological data, the historical operation data, the health degree index and the maintenance influence coefficient as input and the historical power generation amount as output, and constructing and training a machine learning prediction model; and inputting data of a to-be-predicted time period to obtain a preliminary prediction result, and dynamically correcting model parameters through deviation analysis to obtain a final power generation prediction value. The problem that the prediction precision is insufficient due to the fact that an existing wind power plant generating capacity prediction method does not systematically consider the dynamic influence of the equipment health state and the maintenance plan is solved.

The invention relates to the technical field of airborne signal detection, and discloses an airborne signal anomaly detection and predictive maintenance system based on an AI model, which comprises a hardware part and a software part, the hardware part comprises a signal acquisition module, a data storage module, a data transmission module, an edge calculation module and a display alarm module; the software part comprises a data preprocessing module, an AI model construction module, an anomaly detection module, a fault prediction module and a maintenance decision module. According to the invention, through an integrated hardware architecture and an intelligent software algorithm, real-time acquisition of airborne signals, accurate anomaly detection, fault prediction in advance and intelligent making of a maintenance plan are realized, and the operation reliability and maintenance efficiency of airborne equipment are improved.

The invention relates to the technical field of distribution network maintenance, and discloses a distribution network maintenance plan auxiliary decision generation method and system based on large model fine tuning. The decision generation system comprises the following steps: acquiring distribution network maintenance related data; preprocessing the distribution network maintenance related data; selecting a basic large model; acquiring distribution network maintenance real-time demand data; calling an intelligent agent to carry out simulation evaluation and historical similar scheme matching on the preliminary maintenance plan auxiliary decision-making scheme; and aiding a decision scheme according to the final maintenance plan. The distribution network maintenance service logic is decoupled and a progressive three-class question and answer pair data set is constructed, so that accurate and efficient field fine tuning of a general large model is realized, and the fundamental transformation of a distribution network maintenance plan from traditional artificial experience driving to data and model intelligent driving is realized; the efficiency, standardization level and scientificity of decision making are greatly improved, and powerful intelligent support is provided for safe and economical operation of a power grid.

The invention discloses an RCM-based power plant maintenance strategy making method and a related device, and belongs to the technical field of power plant maintenance. The method comprises the following steps: constructing a maintenance database of power plant equipment according to historical maintenance records; acquiring operation parameters of the power plant equipment, inputting the operation parameters into a pre-established state analysis model, and outputting a fault type analysis result; matching the fault type analysis result with a maintenance database to generate a candidate maintenance strategy set; and based on the candidate maintenance strategy set, calculating a risk priority number RPN corresponding to each strategy, and generating a dynamic maintenance plan based on the RCM according to the risk priority number RPN. The maintenance plan can be dynamically generated according to the real-time operation state of the equipment and the fault type analysis result, and the flexibility and adaptability of the maintenance plan are improved.

The invention relates to a dynamic early warning and maintenance strategy adjusting method based on residual life, and belongs to the technical field of equipment management in the oil refining and chemical engineering field. The method comprises the following steps: acquiring multi-source operation data of equipment through an online acquisition module, and constructing a structured equipment state parameter set; based on the parameter set, establishing a residual life admission judgment model and generating a residual life accounting result of the current equipment; further adopting a corrosion rate-residual life two-factor coupling model to generate an equipment risk influence rate curve, and constructing a dynamic risk prevention and control boundary; and according to the risk level of boundary division, a dynamic maintenance strategy adjustment module is linked to perform early warning response, strategy execution, response recording and self-learning optimization are realized through a closed-loop management module, real-time evolution of a fortification value and a maintenance plan along with an equipment state is realized, and the accuracy of risk response and the intelligent level of equipment maintenance are improved.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

The invention relates to the technical field of safety measure ticket automatic optimization, and discloses a safety measure ticket automatic generation and checking method and system, and the method comprises the steps: obtaining a maintenance plan; generating a work ticket based on the maintenance plan; obtaining topological data and real-time operation data of the power grid; generating safety measure tickets based on the topological data and the real-time operation data of the work ticket and the power grid, wherein the safety measure tickets comprise a mounting safety measure ticket and a dismounting safety measure ticket; performing time sequence dependence analysis on the installation instructions of the safety measure items in the installation safety measure tickets, and generating an optimal operation sequence of each installation safety measure ticket; carrying out association analysis on the safety measure items in each safety measure ticket, and generating a mistaken dismounting verification strategy for dismounting the safety measure ticket; according to the method, the problems of safety measure redundancy, safety measure time sequence conflict and mistaken dismounting risk which often occur in a complex maintenance scene of multi-ticket parallel execution are effectively solved.

The invention discloses a cloud computing platform intelligent operation and maintenance method and system based on a knowledge graph, and relates to the technical field of cloud computing platform intelligent operation and maintenance, and the method comprises the following steps: a data collection and preprocessing step: collecting platform hardware operation, software logs, service calling and network transmission data; a knowledge graph construction and dynamic updating step: defining the entity type of the operation and maintenance field; a multi-dimensional real-time monitoring step: building a four-dimensional monitoring model based on the knowledge graph; an abnormity intelligent detection and positioning step of fusing association reasoning and a machine learning algorithm to detect abnormity; an operation and maintenance decision intelligent generation step: matching historical cases and combining resource loads; an operation and maintenance execution and effect feedback step: executing the operation and maintenance plan and monitoring the effect; and an operation and maintenance knowledge precipitation and optimization step: extracting operation and maintenance knowledge rules and updating the operation and maintenance knowledge rules to the knowledge graph. According to the method, full-process intelligent operation and maintenance of the cloud computing platform are realized based on the knowledge graph, resource utilization is balanced and optimized, and the platform stability and the operation and maintenance efficiency are greatly improved.

The invention discloses an electric furnace production line production plan automatic optimization scheduling method and system, and belongs to the field of automatic control. Actual production data and manufacturing standard data are collected from a manufacturing management and production process control system, and different steel grade characteristics and time standards are determined; extracting and importing a to-be-discharged heat plan from a manufacturing management system; according to a process rule condition and a shutdown maintenance plan, setting a production plan automatic scheduling constraint condition; determining an optimal production plan sorting result on the basis of a genetic algorithm model according to the plan of the heat needing to be arranged and the constraint condition; and according to the steel grade plan sequence and the corresponding time standard, with the process with the longest processing period as the center, the production time table of each process is determined in sequence, and the predicted yield, time consumption information and risk warning information in the plan period are displayed. The scheduling optimization model is used for calculation, the optimized scheduling of the production plan is completed, the planned yield, the planned time consumption and the planned risk point warning are calculated, and the scheduling efficiency, precision and intelligent level of the production plan of the electric furnace production line are improved.

The invention is suitable for the technical field of robots, and provides a robot management method and device, terminal equipment and a computer program product, and the method comprises the steps: receiving multi-source data associated with a target robot; analyzing the multi-source data to obtain a health state analysis report of the target robot; performing fault prediction on the target robot based on the health state analysis report and the multi-source data to obtain a fault prediction result; generating a prediction operation and maintenance plan based on the fault prediction result; and executing a target operation and maintenance operation corresponding to the predicted operation and maintenance plan on the target robot. Compared with the prior art, the method has the advantages that the abnormity and health state of the robot can be analyzed in real time through multi-source data, so that fine-grained health changes of machine parts are sensed in real time, and a health state analysis report is generated. Meanwhile, potential faults can be predicted, and an operation and maintenance plan is automatically generated and scheduled for execution, so that shutdown loss and operation cost can be reduced.

The invention relates to a spot market maintenance plan making method and system based on probability evaluation, and the method comprises the steps: determining a power flow expected value of a to-be-maintained line and a power flow change value caused by a maintenance plan according to the output probability distribution of each node unit in each time period and a power distribution transfer factor of a system; and according to the branch on-off distribution factor and the power flow change value of the to-be-overhauled line caused by the overhaul plan, obtaining the branch power flow change value of the branch caused by the power change of the to-be-overhauled line in each time period. The method aims at avoiding power flow out-of-limit and reserving the maximum margin. According to the maximum transmission capacity allowed by the line, the power flow value flowing through the branch when no maintenance plan exists, the power distribution transfer factor of the new energy unit to the line to be maintained, the branch on-off distribution factor, the output characteristic curve of the new energy unit and the branch power flow change value of the branch caused by the power change of the line to be maintained in each time period; and generating a maintenance plan of the to-be-maintained line.

The invention provides a motor maintenance decision-making method based on graph attention DQN reinforcement learning. Respectively extracting time domain features, frequency domain features and periodic fault features of the motor by adopting GRU, 1D-CNN and a bispectrum estimation method; the maintenance scheme nodes, the fault type nodes and the motor feature nodes are connected to construct a motor feature-fault type-maintenance scheme ternary graph; fusing the three motor features based on a cross-modal attention fusion method to obtain fault type node attributes in the ternary graph; adopting a GAT to aggregate fault type node and maintenance scheme node attributes in the ternary graph, and selecting a GRU as a DQN reinforcement learning agent to calculate an action and an evaluation value; and constructing a reward function including a fault mitigation score, a maintenance cost penalty, a maintenance step penalty and a stability score. According to the motor maintenance decision-making method based on graph attention DQN reinforcement learning, efficient learning of a motor maintenance scheme accurate recommendation method is realized.