929 results about "Maintenance management" patented technology

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention discloses a new energy equipment intelligent operation and maintenance system and method based on digital twinning, and relates to the technical field of new energy equipment operation and maintenance management. The system comprises a data acquisition module, a digital twin construction module, a model adaptive module, an intelligent analysis module, a decision optimization module and a knowledge closed-loop module. The data acquisition module realizes multi-source heterogeneous data fusion and standardization; the digital twin construction module generates geometric, physical and behavior models and is linked with real-time data; the model adaptive module dynamically calibrates the key parameters; the intelligent analysis module completes anomaly detection, fault diagnosis and life prediction; the decision optimization module formulates a maintenance scheduling and operation strategy and realizes a control closed loop; and the knowledge closed-loop module constructs a structured fault graph through text analysis to continuously optimize the model. The method can be widely applied to wind power, photovoltaic, energy storage and other scenes, and efficient and intelligent operation and maintenance of new energy equipment are achieved.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

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 relates to the technical field of power distribution network monitoring, in particular to an intelligent operation and maintenance management platform for a power distribution network. Obtaining suspected abnormal points according to data discrete features and local data fluctuation features of the feature data sequence; according to the number characteristics of the suspected abnormal points in all the operation and maintenance characteristics and the data distribution characteristics of the suspected abnormal points, risk statistical characteristic values of the power distribution network equipment are obtained; clustering suspected abnormal points in the operation and maintenance features, and obtaining risk distribution feature values of the power distribution network equipment according to time interval features between the data point clusters and interval features of the suspected abnormal points in the data point clusters; and obtaining the risk degree of the power distribution network equipment according to the risk statistical characteristic value and the risk distribution characteristic value. According to the invention, the inspection sequence of all power distribution network equipment is sorted according to the risk degree, and inspection is carried out in sequence, so that the inspection efficiency and the power utilization stability are improved.

The invention provides an elevator intelligent operation and maintenance management method and system based on a multi-modal neural network, and relates to the technical field of operation and maintenance management. The method comprises the steps that 1, multi-source time sequence monitoring data are collected, the multi-modal neural network is constructed, the multi-source time sequence monitoring data are input into the multi-modal neural network, and the multi-source time sequence monitoring data are input into the multi-modal neural network; obtaining a vibration feature vector, an acoustic feature vector and a visual feature vector; 2, inputting the vibration feature vector, the acoustic feature vector and the visual feature vector into a cross-modal feature fusion layer in a multi-modal neural network for fusion, and generating a multi-modal joint feature vector; through multi-modal feature fusion of multi-source time sequence monitoring data, multi-dimensional health state representation modeling and fusion weight dynamic adjustment driven by data reliability evaluation, the accuracy of elevator fault diagnosis and the reasonability of maintenance decision are effectively improved, and the improvement of elevator intelligent operation and maintenance precision and efficiency is achieved.

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 low-altitude operation and maintenance management, and discloses a digital twinning-based low-altitude takeoff and landing field infrastructure full-process operation and maintenance system, which comprises a real-time digital twinning construction module, a digital twinning construction module, a low-altitude takeoff and landing field infrastructure full-process operation and maintenance module and a digital twinning construction module, an airspace conflict detection module; an intelligent route planning module; a control instruction generation and execution module; and a data driving optimization module. By integrating multi-source data, updating the digital twin model in real time, carrying out airspace conflict detection and optimizing flight path planning, the path and scheduling time slot of the aircraft can be dynamically adjusted, the aircraft efficiency and the resource utilization rate are maximized, meanwhile, real-time feedback and continuous optimization are provided, and the flight path planning efficiency is improved. And the operation and maintenance intelligence and automation level of the low-altitude take-off and landing field is remarkably improved.

The invention discloses an operation and maintenance knowledge base analysis system and method based on deep learning, and is applied to unified operation and maintenance management of a hospital information system and medical equipment. The system collects operation and maintenance data such as operation logs, monitoring indexes, alarm events, work orders, maintenance records and the like, constructs a minute-level time window through equipment identification and technology triggering time, and merges multi-source heterogeneous data into operation and maintenance sub-events and operation and maintenance events. Log, index and text feature extraction is performed on each operation and maintenance sub-event, multi-modal feature fusion is performed to generate an operation and maintenance event feature vector, the operation and maintenance event feature vector is stored in a vector index database and is associated with an operation and maintenance knowledge graph, and similar historical event retrieval and root cause and processing scheme recommendation of a new operation and maintenance event are realized. In combination with the adoption and processing results of recommended schemes by operation and maintenance personnel, the system performs hierarchical management on the operation and maintenance knowledge and performs incremental updating on the feature modeling network, so that the operation and maintenance knowledge retrieval accuracy and the fault positioning efficiency are improved.

The invention relates to a multi-dimensional intelligent cooperative detection method, system and device based on a tunnel inspection robot and a medium. The method comprises the steps that initialization is carried out based on tunnel environment parameters and task requirements, and a standardized initialization reference data set is generated; based on the data set, starting an inspection robot to carry out data acquisition, preprocessing original data, and generating a synchronized multi-source data acquisition stream; then abnormal event monitoring, structure health detection and environment perception detection are executed in parallel, and a standardized detection result set is generated; and performing multi-source data fusion processing on the standardized detection result set to obtain a core detection result and a comprehensive confidence coefficient, and performing matching in combination with a preset decision rule base to generate a standardized decision instruction set. According to the method, through multi-module collaborative data acquisition and multi-source data fusion processing, the comprehensiveness, accuracy and efficiency of tunnel inspection can be improved, and then the safety and efficiency of tunnel operation and maintenance management are effectively improved.

The invention discloses an industrial equipment remote operation and maintenance management method based on digital twinning, and aims to solve the problems of deduplication, association and suppression of events and alarms and root cause fusion positioning under complex working conditions. According to the method, a multi-relation layered signed causal graph is constructed, a dynamic causal mask is generated according to a twinning state, causal structure learning is carried out under constraint, and graph anti-fact intervention and simulation of physical budget constraint and historical dual-channel consistency verification are combined; and the root cause priority and the evidence chain are generated by adopting topology perception contribution degree sorting, and the suppression strategy parameters are formed, so that the technical effects of reducing false alarms and repeated alarms, improving the root cause identification accuracy and processing efficiency and stably outputting the linkage work order are realized.

The invention provides a high and low voltage power distribution cabinet remote operation and maintenance management and control system based on cloud computing, relates to the field of remote operation and maintenance management, and improves the accuracy and operation and maintenance efficiency of power distribution cabinet fault early warning. The method comprises the following steps: firstly, acquiring sensing data through an edge intelligent sensing module, performing multi-dimensional correlation analysis based on a static safety threshold, identifying an abnormal trend with spatial-temporal correlation, and generating a preliminary early warning; then, a cloud platform prediction decision module drives the digital twin model to carry out dynamic simulation, and a predictive maintenance strategy and a dynamic safety threshold are generated in combination with a prediction algorithm of a historical performance decline curve; and finally, the adaptive control execution module optimizes subsequent analysis by using a dynamic threshold value, and triggers a differential control action from monitoring adjustment to emergency isolation according to a strategy type. According to the invention, by constructing the closed-loop intelligent operation and maintenance architecture of cloud-side cooperation, the operation and maintenance mode transformation from passive response to active prediction is realized, and the reliability and safety of the operation of the power distribution system are significantly 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 provides a rail transit weak current system operation and maintenance management method. The method comprises the following steps: S1, data acquisition and preprocessing; s2, fault probability and residual service life (RUL) prediction based on an LSTM / Transform + attention mechanism; s3, fault propagation analysis and root positioning based on a graph neural network (GNN); and S4, operation and maintenance decision optimization based on reinforcement learning. According to the invention, through fusion of deep learning, a graph neural network and a reinforcement learning algorithm, a full-process and closed-loop rail transit weak current system operation and maintenance management system is constructed.

The invention provides a BIM-fused port infrastructure digital twinborn operation and maintenance management system and method. The method comprises the steps of collecting a port infrastructure BIM full-life-cycle multi-source heterogeneous data set, constructing a port infrastructure real-time three-dimensional digital twinborn body, constructing an equipment health prediction model and generating a fault diagnosis result; simulating tide influence to form a stress distribution prediction result; a three-level linkage optimization model is constructed, a scheduling scheme is generated, a multi-energy collaborative optimization engine optimization scheme is used, a three-dimensional safety matrix is constructed through unmanned aerial vehicle inspection and visual recognition, risks are monitored in real time, and safety early warning is generated. The prediction model is constructed, faults are accurately diagnosed, the tide influence is quantified, and the prediction precision is improved; multi-target collaborative optimization is realized through a linkage optimization model and an engine, and the problem of single-target optimization is solved; and on the basis of a monitoring and early warning system, rapid identification and hierarchical response are realized, a closed-loop link is formed, and the port operation and maintenance adaptability is improved.

The invention discloses an urban sewage pipe network system operation and maintenance effect evaluation system and method, and belongs to the technical field of sewage pipe network operation and maintenance management. According to the method, through four core models of data quality scoring, improved AHP-entropy weight method combination weighting, pipe network toughness approximate quantification and public security risk Bayesian updating, robust evaluation of data incomplete and error scenes is realized; and in combination with a core-maneuvering-wide area three-layer three-dimensional perception network and a four-step progressive data governance process, outputting a system health index with a confidence interval, designing a double-layer performance payment function associated with data quality, and generating a data short board diagnosis report. According to the method, the problem that efficiency quantification is difficult in the landing of an effect-based payment mechanism is solved, an enhanced closed loop of evaluation, data improvement and accurate operation and maintenance is constructed, a scientific quantification basis is provided for performance management of the sewage pipe network, and the method is suitable for operation and maintenance evaluation of the urban sewage pipe network under various data conditions.

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 relates to the technical field of heat meter performance test and operation and maintenance management, in particular to a heat supply working condition multi-source parameter real-time acquisition heat meter performance intelligent analysis system, which comprises a working condition simulation subsystem, a multi-source parameter real-time acquisition module, an intelligent analysis engine and data management and visual interaction interface, and a digital twinning virtual calibration module. Multi-dimensional variable working conditions are constructed through the working condition simulation subsystem, the acquisition module realizes high-precision synchronous acquisition, the intelligent analysis engine integrates multiple algorithms to complete data fusion, performance prediction, health degree evaluation and fault early warning, and the data management module realizes whole-process closed-loop management. The heat meter test accuracy and the working condition coverage are improved, intelligent analysis and accurate calibration are achieved, the operation and maintenance cost is reduced, and stable and efficient operation of a heat supply system is guaranteed.

The invention provides an operation and maintenance performance assessment data management system based on dynamic weight optimization, and relates to the technical field of operation and maintenance management, and the system comprises a quantification module which is used for obtaining an original operation and maintenance data sequence through a data collection interface, and mapping the operation and maintenance data sequence to a multi-dimensional feature space; in the multi-dimensional feature space, quantitative analysis is carried out on spatial distribution of data points, and data density distribution characteristics of all areas are determined; the calculation module is used for executing unsupervised clustering operation based on the density distribution characteristics and the proximity relation of the data points to obtain a group of data sets with internal consistency; core statistical features are calculated for each generated data set, and a specific calibration coefficient is derived for the data set based on the core statistical features. According to the invention, precision, scenario and high efficiency of operation and maintenance performance assessment are realized.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention provides an operation and maintenance alarm intelligent filtering and grading processing method based on a self-adaptive algorithm, and relates to the technical field of computer operation and maintenance management, and the method comprises the steps: obtaining an alarm event, extracting semantic and time sequence characteristics, carrying out the dynamic clustering through employing a self-adaptive similarity measurement mechanism, calculating a priority score based on an influence range and an emergency degree, and carrying out the calculation of an alarm result. And a self-adaptive filtering strategy is implemented in combination with the operation and maintenance resource state, and finally, filtered alarms are distributed to corresponding operation and maintenance units for processing, collection and feedback. According to the method, redundant alarms can be effectively reduced, resource allocation is optimized, and the operation and maintenance efficiency and the alarm processing accuracy are improved.

The invention relates to a power transmission line operation state intelligent identification and analysis method, and belongs to the technical field of power transmission line state analysis. The method comprises the following steps: acquiring soil water content and foundation settlement data in real time, recognizing tower node displacement accumulation and a stress transfer path in combination with environmental factors such as temperature, humidity and wind speed, accurately judging a structural deformation abnormal region, and generating a stress concentration and transfer prediction chart through multi-source data fusion and historical data contrastive analysis. According to the method, the coordinates of potential safety hazard parts are dynamically calibrated, infinitesimal displacement monitoring errors are eliminated, finally, a tower overall structure safety evaluation report and maintenance management measures are output, through full-link data analysis and dynamic calibration, the monitoring precision and early warning capacity of tower stability under geological condition changes are remarkably improved, and safe operation of power transmission facilities is guaranteed.

The invention discloses a knowledge graph-AI fusion fault diagnosis method for operation and maintenance management of a power distribution network, and relates to the technical field of artificial intelligence. The problems of low initial diagnosis accuracy, insufficient diagnosis adaptation capability in different grounding forms, long fault positioning time and the like caused by external interference of zero-sequence current signals are solved, and the conditions that the operation and maintenance efficiency is reduced and the power supply loss is increased are finally avoided. The method comprises the following steps: acquiring multi-source data and constructing a dynamically updated power distribution network knowledge graph; multi-mode fault feature extraction is carried out; using an adaptive AI model to identify a fault type and performing primary judgment; and accurately positioning a fault section and analyzing a spreading path in combination with a knowledge graph. According to the method, the single-phase earth fault preliminary diagnosis accuracy is improved to reduce invalid troubleshooting of operation and maintenance, cross-scene diagnosis errors are reduced to adapt to diversified operation and maintenance scenes, average positioning time consumption is shortened, and the supporting capacity of fault diagnosis on operation and maintenance management of the power distribution network is remarkably improved.

The invention relates to the technical field of operation and maintenance management, and discloses a historical building operation and maintenance management method and system based on digital twinning, and the method comprises the steps: building a digital substrate based on historical data and point cloud, dividing a monitoring region according to a bearing structure, value elements and a function space, and associating an identifier. And mapping the operation data to a corresponding component storage space according to the area to which the operation data belongs, and forming a dynamic operation and maintenance file and a monitoring data stream. And performing visual coding on the data stream by taking the acquisition time as a horizontal axis and the spatial topology as a longitudinal axis to generate a spatial-temporal distribution map. And determining an abnormal area according to fluctuation form difference and trend deviation degree of real-time and historical data in the map, and binding identifiers and time periods to generate an abnormal list. Updating the file by taking the list as an index, and matching the disease record with a historical maintenance record to generate an operation and maintenance management scheme; the efficiency of historical building operation and maintenance management based on digital twinning can be improved.

The invention belongs to the technical field of nuclear power station maintenance management, and particularly relates to a nuclear power maintenance decision-making system and method based on multi-Agent cooperation. The system comprises an input layer, a multi-Agent cooperation layer, a knowledge support layer, a decision processing layer, an output and interaction layer and a feedback learning layer. The input layer receives initial work order information including equipment basic information and fault description; the multi-Agent collaboration layer allocates sub-tasks to predefined six types of professional Agents according to work order information and completes information interaction; the knowledge support layer comprises a nuclear power professional knowledge base, a historical maintenance database and a rule and regulation library; the decision processing layer performs conflict detection, negotiation and decision fusion on Agent output; the output and interaction layer provides a decision result display and man-machine interaction interface; and the feedback learning layer realizes comprehensive improvement of nuclear power maintenance decision-making efficiency and safety. The method has the beneficial effects that a multi-professional Agent collaborative network technical means is adopted, and the technical effects of maintenance decision cross-professional information instant sharing and efficient collaboration are realized.

The invention relates to the technical field of smart park data processing and operation and maintenance management, in particular to a smart park full-life-cycle digital twin management system based on the Internet of Things, which comprises the following modules: a multi-dimensional state sensing aggregation module for collecting heterogeneous node data and generating a full-life-cycle state vector sequence through timestamp alignment and normalization processing; and the dynamic phase space reconstruction module is used for calculating mutual information and a false nearest neighbor number of the sequence so as to determine optimal delay time and an embedding dimension, and constructing a discrete attractor point cloud by utilizing a Tarkens embedding theorem. According to the method, phase change singular points are identified through reformed group flow, harmonic components are eliminated through Hough decomposition, and then cross-scale early warning and topological grooming of systematic paralysis are achieved, so that the problems that a traditional system mostly adopts numerical threshold monitoring, and due to the fact that topological structure deadlock is ignored, the system reliability is poor, and the system reliability is high are solved. And therefore, the problem that hidden paralysis is formed when the numerical value which cannot be recognized does not exceed the standard is solved.

This invention provides a comprehensive digital and intelligent operation and maintenance management method and system for power distribution networks, relating to the field of computer data processing technology. The method includes: S1, obtaining pole and tower information collected by drones during line inspections, performing defect mining and analysis on the pole and tower information, and integrating the analyzed defect information with the pole and tower information to form a pole and tower information collection table; S2, selecting and extracting corresponding data from the pole and tower information collection table, and visualizing the data on a GIS map containing the lines obtained from the pole and tower information collection table using preset markers to generate an inspection status map; S3, statistically classifying the analyzed defect information and matching it through a big data platform to generate a defect repair and management summary table; S4, analyzing the faults using an AI model and a big data platform, combining the inspection status map and the defect repair and management summary table, obtaining priority-ranked repair tasks, and proposing corresponding solutions.

The invention relates to the technical field of computers, and discloses a visual operation and maintenance supervision platform based on a digital twinning technology, and the platform comprises a data preprocessing module which is used for preprocessing a multi-source heterogeneous monitoring data flow; the data fusion module is used for constructing an equipment topological relation graph, extracting an initial feature vector set of equipment nodes and carrying out space-time fusion; the digital twin modeling module is used for performing parametric modeling and semantic annotation; the real-time synchronization module is used for extracting a model updating instruction set and carrying out asynchronous message transmission and parallel updating; the anomaly detection module is used for training an anomaly detection model and analyzing anomaly types and anomaly reasons; the root cause tracing module is used for constructing an associated sub-graph of abnormal equipment, predicting a root cause candidate equipment list and carrying out hybrid reasoning and evidence fusion; the visual presentation module is used for visualizing a fault influence range, dynamically displaying network traffic and performing multi-view roaming and information drilling; according to the invention, the fault positioning efficiency and the operation and maintenance management level are improved.

The invention provides a multi-scene equipment intelligent operation and maintenance management method and system based on artificial intelligence and Internet of Things. The method comprises the following steps: acquiring equipment data of each piece of medical production equipment; performing data comparison on the basis of the current operation electromagnetic data of each medical production device and preset reference operation electromagnetic data, determining an abnormal device, and performing case matching on the basis of the current operation electromagnetic data of the abnormal device and historical abnormal case data to obtain target abnormal case data; inputting the target abnormal case data into the AI decision reasoning model to obtain initial equipment operation and maintenance strategies, and performing equipment operation and maintenance simulation based on each initial equipment operation and maintenance strategy in combination with the equipment position of the abnormal equipment to obtain an optimal equipment operation and maintenance strategy; and generating an operation and maintenance task instruction based on the optimal equipment operation and maintenance strategy, and sending the operation and maintenance task instruction to the equipment terminal, so that an operation and maintenance user performs operation and maintenance management on the abnormal equipment based on the operation and maintenance task instruction in the equipment terminal. The production efficiency and the equipment resource utilization rate are improved.