666 results about "Maintenance strategy" patented technology

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

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

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable operation state real-time evaluation system based on multi-mode deep learning, and the system comprises the steps: synchronously collecting the load, partial discharge and temperature strain data of a cable through a current and voltage sensor, an ultrahigh frequency sensor and a distributed optical fiber sensor; feature extraction and cross-dimension fusion are carried out on the multi-source data, and a multi-dimensional feature matrix is constructed; a pre-trained deep learning model is utilized to analyze internal association between the features, and cable health degree scores, fault risk levels and defect type identification results are output; automatically generating and executing a load adjustment, loop switching or precise maintenance strategy according to an evaluation result; according to the method, the problems of inaccurate evaluation and early warning lag caused by data isolation analysis of a traditional monitoring method are solved, and real-time accurate evaluation and intelligent closed-loop operation and maintenance of the operation state of the cable are realized.

The invention provides a port facility management and maintenance large model report review agent construction method and system, and the method comprises the steps: collecting a cross-modal original data set, constructing a multi-modal feature fusion perception layer, and generating facility damage feature alignment data; constructing a cognitive neural network four-level architecture, and generating an inference decision tree; constructing a root cause-path-result causal chain, and generating a fault attribution analysis report; constructing a prediction-intervention-verification active defense closed loop, and generating a Pareto optimal maintenance strategy set; and executing an intervention strategy and feeding back a verification result by using the digital twin verification platform and the block chain evidence storage system. According to the method, cross-modal data deep semantic alignment is realized through the multi-modal feature fusion perception layer, a data island is broken, and the damage feature extraction accuracy is improved; an interpretable causal chain is constructed based on related architecture and modules, the decision black box problem is solved, and a maintenance strategy has causal logic support; and real-time verification and credible tracing of a strategy effect are realized through an active defense closed loop.

The invention discloses an industrial park intelligent operation and maintenance optimization method based on big data analysis, and relates to the technical field of maintenance management, and the method comprises the steps: collecting multi-source heterogeneous operation and maintenance data in an industrial park, carrying out the preprocessing, and generating a standardized multi-modal time series data set; calculating a causal contribution degree integral value in the multi-modal time sequence causal graph, and obtaining a fault root cause node; constructing an industrial park working condition simulation model according to the fault root cause nodes and the multi-modal time sequence cause and effect graph, and retrieving matched candidate strategies from a strategy library to generate a plurality of candidate operation and maintenance strategies; and performing strategy simulation verification on the plurality of candidate operation and maintenance strategies in the industrial park working condition simulation model, reconstructing a causal graph structure by blocking an intervention variable, and performing weighted comprehensive evaluation to obtain an optimal operation and maintenance strategy. According to the method, the causal graph structure is reconstructed, so that scientific decision-making jump of operation and maintenance of the industrial park from experience driving to data-causal-simulation cooperative driving is finally achieved.

The invention discloses an intelligent concrete automatic curing system and method based on real-time monitoring, and the system comprises a multi-parameter sensing module which collects the environment parameters and internal structure parameters of a concrete structure through a plurality of sensors and label identification codes, and an edge calculation module which is used for carrying out the preprocessing, feature extraction and emergency decision-making of collected data. The cloud intelligent module is used for storing data, establishing a prediction model and generating a dynamic maintenance strategy; the maintenance execution module is used for adjusting and executing spraying, temperature control or crack repair operation according to the maintenance strategy; by integrating structural strain, crack detection and environment air pressure monitoring, the limitation of traditional single temperature and humidity monitoring is broken through, multi-dimensional sensing and accurate diagnosis of concrete are achieved, meanwhile, the system combines a physical model and a data driving model, the self-adaptive optimization capability is achieved, and the system is suitable for large-scale popularization and application. And the edge gateway is adopted to process emergencies, and the cloud platform is responsible for global optimization, so that the system gives consideration to the real-time performance and the calculation depth, and the maintenance efficiency and the concrete quality are effectively improved.

The invention relates to the technical field of power system automation, discloses a remote operation and maintenance method, system and equipment for wide-area substation automation equipment, and aims to solve the problems of data islanding, inaccurate diagnosis, strategy stiffness, low safety cooperation efficiency and the like in remote operation and maintenance of the wide-area substation automation equipment. According to the method and the system, a comprehensive platform is constructed, and multi-source heterogeneous data acquisition, intelligent fusion analysis, fault diagnosis and prediction, adaptive strategy generation, security remote control and block chain evidence storage and collaborative operation and maintenance are integrated. The method can solve the problem of data islands, improve the diagnosis and prediction precision and timeliness, optimize the operation and maintenance strategy, prolong the service life of equipment, and enhance the remote operation and maintenance safety and cooperation efficiency.

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 is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention provides a fly ash composite material goaf filling body interface quality intelligent evaluation method, and belongs to the technical field of mining engineering and artificial intelligence detection crossing. The method comprises the steps that firstly, filling body interface quality characteristic data are collected and comprise interface sound wave signals, stress strain, coal ash composite material physical parameters and environment working condition data; secondly, constructing a multi-physical field data completion model, performing unsupervised learning on the acquired sound wave, stress, temperature and moisture content data, and generating completion data of global spatial distribution; secondly, constructing a multi-field fusion interface quality index prediction model, and inputting multi-source data into the model to obtain an interface quality index; and finally, combining the quality index to realize interface defect mode classification and grade evaluation, and generating a targeted maintenance strategy. The invention provides an intelligent evaluation method which fuses multi-source data and gives consideration to real-time performance and comprehensiveness, so as to solve the industrial pain points of interface quality evaluation lag, low precision, large destructiveness and the like.

The invention provides an aero-engine remaining service life prediction method based on a space-time knowledge graph and SDCNN, and belongs to the field of aero-engine health management. According to the method, a space-time knowledge graph and SDCNN neural network architecture is constructed. According to the method, a spatio-temporal knowledge graph is innovatively constructed for an aero-engine, a BERT model is adopted to carry out data type conversion, and a multi-head graph attention network and a pooling graph attention network complete feature extraction and feature fusion to obtain fusion features; and finally, inputting the fusion features into a stacked expansion convolutional neural network to carry out regression learning on feature data, and then carrying out residual life prediction on the aero-engine. According to the method, modeling and prediction are carried out on complex spatial-temporal characteristic data, the remaining service life of the aero-engine can be effectively predicted under limited data, data support is provided for formulating an aero-engine maintenance strategy, and meanwhile a new thought is provided for predicting the remaining service life of other industrial equipment.

The invention relates to the technical field of power station operation and maintenance, and discloses a new energy power station intelligent operation and maintenance system based on big data analysis. The system comprises a data acquisition module used for acquiring and preprocessing operation data and maintenance logs of power station equipment; the diagnosis evidence generation module is used for generating diagnosis evidences of three dimensions of performance degradation, mechanical abnormity and repeated fault risk through parallel analysis; the data analysis module maps the diagnosis evidence to a state space to construct an equipment health track, calculates a health index based on a mahalanobis distance and predicts a change trend; the operation and maintenance decision module is used for performing significance verification on the health trend, generating a dynamic risk score, automatically matching a maintenance strategy and establishing a feedback optimization mechanism; according to the invention, accurate evaluation and predictive maintenance of the equipment health state are realized, and the operation and maintenance efficiency and the equipment reliability of the new energy power station are effectively improved.

The invention relates to the technical field of equipment maintenance and overhaul decision, and discloses a knowledge graph driven two-stage classification equipment condition-based overhaul decision method, which comprises the following steps: acquiring multi-source text data; performing first-stage classification on the equipment based on the equipment importance evaluation model, and performing second-stage classification on the parts based on the maintenance strategy; a device entity, a fault mode entity and a maintenance measure entity are taken as knowledge ontologies, state variable nodes are embedded, and a knowledge graph fusing state variables is constructed by using a long-short-term memory network; in combination with a semantic context sensing mechanism and a graph structure stability constraint mechanism, outputting candidate fault nodes; candidate maintenance measures corresponding to the candidate fault nodes are retrieved in the knowledge graph, candidate maintenance measure verification is carried out in combination with soft constraints and hard constraints, and a maintenance measure list is converted into an equipment maintenance decision scheme by utilizing an execution arrangement generator, so that intelligence of the maintenance decision scheme is realized in combination with the knowledge graph; and decision support is provided for maintenance personnel.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention relates to the cross technical field of artificial intelligence and traffic infrastructure monitoring, discloses an intelligent health monitoring and prediction method and system for a road roadbed, and aims to solve the problems of insufficient monitoring coverage, shallow data mining, low prediction model precision and disjunction of operation and maintenance decisions in the prior art. The method comprises the following steps: collecting roadbed multi-dimensional physical field data through a multi-source sensor network; denoising, abnormity correction, time alignment and feature compression are carried out at the edge end; fusing multi-scale time sequence modeling and spatial correlation analysis to extract health features; predicting a future health state and a risk probability by using an LSTM-Attention model in combination with historical data and environment variables; and triggering graded early warning based on the dynamic threshold and generating a maintenance strategy. According to the technical scheme, high-precision and high-timeliness roadbed health perception and prediction can be realized, and the early warning response speed and the maintenance decision intelligent level are improved.

The invention discloses a laser equipment fault prediction and maintenance method based on digital twinning, and the method comprises the following steps: S1, collecting structure parameters, control logic and historical data, and constructing a unified neural network digital twinning model; s2, collecting multi-source operation data in real time in an operation process, and constructing a standardized state data set; s3, mapping the data to a unified neural network digital twin model to realize state synchronization; s4, predicting a future key parameter trend by using an LSTM model; s5, a health score is calculated through an entropy weight method, and the equipment state is quantitatively evaluated; s6, triggering early warning based on the health threshold value, identifying abnormity and generating a maintenance strategy; s7, pushing the strategy to an operation and maintenance system for closed-loop execution; and S8, maintaining feedback for retraining and self-optimizing the unified neural network digital twin model. According to the method, high-fidelity digital twin modeling is realized, so that a prediction-maintenance-feedback-updating intelligent closed-loop system is formed, and the fault prediction accuracy and the maintenance response efficiency of the laser equipment are remarkably improved.

The invention discloses a permanent magnet frequency conversion terminal health big data fusion and anomaly prediction operation and maintenance management platform, and relates to the technical field of operation and maintenance management. According to the permanent magnet frequency conversion terminal health big data fusion and anomaly prediction operation and maintenance management platform, a data acquisition layer constructs a multi-source operation data set for time sequence synchronization and anomaly uploading; the data fusion and feature processing layer cleans and standardizes a multi-source operation data set, and extracts multi-dimensional features to form an abnormal feature vector; abnormal detection and classification layer fusion dual-channel detection are carried out, and an abnormal event database is established; the anomaly-operation and maintenance mapping and scheme generation layer constructs a three-dimensional sample library, and calculates a mapping similarity value to generate an operation and maintenance strategy; and the operation and maintenance execution and feedback layer issues a work order according to the operation and maintenance strategy and monitors the effect, and performs self-learning optimization based on feedback. According to the invention, the permanent magnet frequency conversion terminal abnormity identification and operation decision-making capability is effectively improved, and the problems of non-association between detection and operation and maintenance, blind maintenance and low efficiency of the existing method are solved.

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 relates to the technical field of numerical control machine tool monitoring, and discloses a numerical control machine tool intelligent monitoring system based on digital twinning. A digital twin modeling module of the system obtains geometric parameters, physical attributes and historical operation data of a numerical control machine tool in real time, and constructs a dynamically updated virtual machine tool model; the working condition sensing module collects vibration, temperature and current data through a multi-source sensor, and generates a running state feature set through time sequence alignment processing. The exception response module is used for matching an exception mode library according to the feature set and outputting an exception type identifier and an influence range parameter; the decision optimization module generates an optimization instruction set containing maintenance priority ranking based on the parameters; and the strategy iteration module monitors an instruction execution result, calculates a deviation value between an actual effect and a virtual model prediction effect, and dynamically updates the abnormal mode library. According to the system, accurate sensing of the operation state of the numerical control machine tool, quick response to abnormity and dynamic optimization of a maintenance strategy are realized, and the intelligent level of monitoring is improved.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention relates to the field of electrical power grid operation and maintenance, in particular to a differentiated operation and maintenance method and system based on real-time weak links of equipment. According to the method, online monitoring data and power supply reliability operation data of power distribution network equipment are collected in real time to form an equipment state multi-source data set; based on the data set, calculating an equipment health degree evaluation result by adopting a state evaluation model, and obtaining an importance degree evaluation result through an analytic hierarchy process in combination with the position and load importance of the equipment in the network topology; and inputting the two results into a preset operation and maintenance strategy matrix to match a corresponding operation and maintenance strategy type, and finally generating a differentiated operation and maintenance strategy instruction containing specific operation and maintenance measures, an execution time window and a resource allocation scheme. According to the method and the device, the problems of lack of equipment-level pertinence and unreasonable resource allocation of an operation and maintenance strategy in the prior art are effectively solved, and the operation and maintenance accuracy and the resource utilization efficiency are improved.

The invention belongs to the technical field of fire-fighting facility control and management, and discloses a fire-fighting facility full-life-cycle intelligent operation and maintenance management control system. Comprising the following steps: acquiring real-time field state data; in a non-fire state, executing an active excitation and response test to obtain dynamic response data; processing the field state data and the dynamic response data by adopting an end-to-end multi-task learning framework, and outputting a structured diagnosis result containing the health states of the pump set, the valve and the pipe network; establishing an initial health baseline of each key device, and dynamically generating a self-adaptive alarm threshold value; a quantitative risk level is generated in combination with the fire safety level, and an operation and maintenance strategy instruction containing pilot operation and maintenance parameters is generated; main and standby equipment switching and strong health detection are automatically executed, and the fire protection function is preferentially guaranteed when a fire alarm or a manual forcing instruction is received; and executing grading alarm according to the quantitative risk grade. And intelligent operation and maintenance management control of the whole life cycle of fire-fighting equipment is realized.

The invention provides a task processing method and device based on an industrial large model, a medium and a product, and relates to the technical field of industrial automation. The method comprises the following steps: constructing a multi-source data set based on an industrial scene, and training a plurality of adapters corresponding to the multi-source data set by adopting an adaptive low-rank adaptation algorithm; according to the learnable weight, fusing the plurality of adapters to obtain a fine-tuned industrial large model; and inputting an operation and maintenance task of the intelligent equipment in the industrial scene into the fine-tuned industrial large model, outputting an operation and maintenance strategy of the intelligent equipment, and controlling the intelligent equipment in the industrial scene to execute the operation and maintenance strategy. According to the method, efficient fine tuning and capability integration of an industrial large model in a multi-source heterogeneous data scene are achieved by introducing a self-adaptive low-rank adaptation algorithm and a multi-task adapter fusion framework, the fusion conflict problem of multi-source heterogeneous data is solved, efficient reasoning of full-process intelligent interaction of the air compressor is achieved, and the intelligent interaction of the air compressor is achieved. And the stability and the precision of the large industrial model in a complex industrial task are improved.

The invention relates to the technical field of electric power, in particular to a primary and secondary fusion environment-friendly ring main unit intelligent monitoring system and method, and the system comprises a data collection module which is used for collecting multi-mode monitoring data of a ring main unit to obtain an original monitoring data set; the data processing module is used for performing standardization processing on the original monitoring data set to obtain a standardized multi-modal monitoring data set; the intelligent monitoring module is used for performing state evaluation based on the standardized multi-mode monitoring data set to obtain an equipment state monitoring evaluation result; the digital twinning module is used for constructing a digital twinning model and performing digital twinning analysis on the equipment state monitoring and evaluation result through the digital twinning model to obtain a health index and a fault risk prediction result; the strategy generation module is used for generating an operation and maintenance strategy set based on the health index and the fault risk prediction result; and the strategy execution module is used for executing operation and maintenance operation according to the operation and maintenance strategy set and recording an operation and maintenance execution effect.

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 relates to a digital twin offshore wind turbine all-working-condition state monitoring and operation and maintenance optimization system, and belongs to the technical field of offshore wind power generation. The system comprises a physical sensing layer, an edge computing layer, a cloud digital twin platform and an operation and maintenance application terminal, the physical sensing layer collects multi-source heterogeneous data in the operation process of an offshore wind turbine in real time through a multi-type sensor, the multi-source heterogeneous data at least comprises environment data, structural vibration data and operation condition data, and the multi-source heterogeneous data is transmitted to the edge calculation layer; the edge calculation layer performs preprocessing, feature extraction and real-time anomaly analysis on the data to realize rapid fault early warning of the operation state; the cloud digital twinborn platform carries out fusion processing on the data, constructs and dynamically updates a digital twinborn model mapped with a physical entity in real time, and carries out fault diagnosis, health assessment, residual life prediction and operation and maintenance decision optimization; and the operation and maintenance application terminal visually displays the fan state, the early warning information and the operation and maintenance strategy.

The invention belongs to the technical field of data processing, and discloses an active intelligent operation and maintenance monitoring method for a data center, which comprises the following steps: step 1, component modeling and topology configuration; 2, carrying out distributed health detection and data acquisition; 3, performing real-time health assessment and anomaly detection; 4, performing intelligent alarm and root cause analysis; 5, unified operation and maintenance and closed-loop control are carried out; and step 6, dynamically optimizing the intelligent operation and maintenance strategy. According to the method, a monitoring object and a dependency relationship are clarified through component modeling and topological configuration, and specific scenes, such as multi-mode acquisition, active detection, index pulling, log analysis, coverage message queue theme accumulation and database connection pool exhaustion, of distributed health detection are combined. The quantitative health score is calculated based on the preset scoring model, and by combining with the dynamic baseline learned by the ARIMA or LSTM algorithm, the module abnormity can be actively detected in different periods and weekly updating, and the problems of fault discovery lagging and incomplete monitoring coverage are solved.

The invention relates to an energy storage power station maintenance method and device, computer equipment, a medium and a program product. The method comprises the following steps: acquiring operation data of each device in the energy storage power station; based on the operation data, constructing a plurality of objective functions by taking available capacity loss minimization, total maintenance cost minimization, maintenance time minimization and reliability maximization of each device as objectives; determining a maintenance objective function based on the plurality of objective functions; solving the maintenance objective function based on the constraint condition to obtain a maintenance strategy of the energy storage power station; and overhauling each device in the energy storage power station based on the overhauling strategy. By adopting the method, the power generation efficiency of the energy storage power station and the operation reliability of the energy storage power station can be improved.

The invention relates to the technical field of monitoring operation and maintenance, and discloses a method and system for monitoring operation and maintenance of equipment in a purification machine room, and the method comprises the steps: obtaining a surface deposition image of a related witness surface of a micropore array airflow component, analyzing the arrangement and construction information of a micropore array from the surface deposition image, and calculating the effective permeability of the micropore; positioning the center of the micropore according to the arrangement construction information, performing hole circumference radial statistics on the deposition intensity field, and generating a hole circumference jet flow deposition radial distribution map; within a limited microjet influence domain, calculating a microjet deposition diffusion attenuation factor based on the map; carrying out physical field coupling on the attenuation factor and the effective permeability of the micropore, analyzing a micropore flow field inertia enhancement coefficient and a macroscopic particle deposition mode weight, and further synthesizing an equipment operation comprehensive risk index; and finally, in response to the index and the permeability attenuation degree, generating an operation and maintenance strategy containing the surface clean recovery strength of the microporous panel and the airflow organization current-sharing correction amplitude.

The invention discloses a ring main unit state online monitoring and intelligent operation and maintenance system based on big data, and relates to the technical field of data monitoring, and the system comprises a ring main unit state data collection module which collects operation parameters in real time based on a sensor array disposed in a ring main unit, and generates a standardized feature vector; the equipment health monitoring module is used for performing state recognition and life prediction on the standardized feature vector based on a deep learning model, and generating an equipment health degree index; the intelligent operation and maintenance decision-making module is used for establishing an intelligent operation and maintenance decision-making model based on the health degree index and a preset operation and maintenance strategy library, and generating a maintenance work order based on the output of the intelligent operation and maintenance decision-making model; and the communication module transmits the monitoring data and receives the control instruction. According to the invention, fault early warning, state identification, life estimation and health degree index generation are carried out by combining the sensor array and edge calculation with deep learning, the operation and maintenance strategy is optimized, and the cost and downtime are reduced.