2267 results about "Maintenance management" patented technology

The invention discloses an intelligent operation and maintenance management and alarm system based on a large model agent, and the system comprises a data collection and fusion module which is used for processing original data collected from an IT system to obtain an operation and maintenance data graph, and the original data at least comprise logs, indexes and link tracking data; the anomaly detection module is used for performing analysis based on the preprocessed data and the time sequence characteristics to determine abnormal data; the alarm optimization module is used for analyzing the semantic information corresponding to the abnormal data so as to determine the priority of the corresponding alarm; the root cause analysis module is used for performing fault reasoning on the abnormal data based on a pre-constructed knowledge graph and generating fault interpretation according to data corresponding to the abnormal data; and the automatic operation and maintenance decision module is used for generating an automatic repair strategy according to the abnormal data and the fault interpretation and executing the automatic repair strategy to repair the abnormal data.

The invention relates to the technical field of charging pile collaborative management, in particular to a new energy automobile charging pile intelligent operation and maintenance management system, which comprises a data acquisition and transmission unit, an edge computing unit and a cloud server unit, and is characterized in that the data acquisition and transmission unit collects multi-aspect information of a charging pile and transmits the multi-aspect information to the edge computing unit; the edge computing unit preprocesses the data, starts an emergency protection mechanism when the power grid fluctuates, and uploads the data and decision results, the cloud server unit constructs a cloud computing platform, a data storage service stores the data, a data analysis service excavates a charging rule, and an algorithm model service deploys an intelligent adaptive power distribution algorithm. The system comprises a dynamic priority determination module, a real-time power adjustment module and a cross-station cooperation power scheduling module which are respectively used for determining the vehicle priority, adjusting the power according to the power grid load and guiding the vehicle to perform cross-station charging, thereby solving the problems of unreasonable power distribution and insufficient cross-station cooperation in the operation and maintenance management of the charging pile, and improving the use efficiency of the charging pile.

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention provides an operation and maintenance log event association analysis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an original operation and maintenance log set of a target system, the original operation and maintenance log set comprising a plurality of operation and maintenance log fragments; secondly, performing feature extraction processing on the original operation and maintenance log set to obtain context semantic features and time sequence behavior features of each operation and maintenance log fragment, and then calling a pre-trained association analysis model to perform association rule matching processing on the features to generate an association rule set among the operation and maintenance log fragments; the association rule set comprises a causal relationship definition and an event propagation path description, generating an operation and maintenance event optimization strategy based on the association rule set, and finally feeding back the operation and maintenance event optimization strategy to an operation and maintenance management interface of a target system to trigger system optimization operation. And the system operation and maintenance efficiency and stability are improved.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

The invention relates to the technical field of hydropower station operation and maintenance management, in particular to an operation and maintenance management method and system for a hydropower station, and aims to improve operation and maintenance efficiency and equipment reliability. Then dynamic feature extraction operation is executed, a state feature set containing equipment operation stability, environment coupling and abnormal fluctuation correlation features is generated, then abnormal state matching processing is conducted on the state feature set through a preset abnormal detection strategy network, the abnormal level and influence range parameters are determined, and the abnormal state is obtained. And finally, generating an equipment operation and maintenance optimization strategy set according to the parameters, including an equipment control instruction sequence and a maintenance resource scheduling scheme, and feeding back to a hydropower station control center to trigger operation and maintenance operation, thereby realizing intelligentization and precision of hydropower station operation and maintenance management, and ensuring safe and stable operation of the hydropower station.

The invention discloses a dispatching automation fault intelligent processing system based on knowledge reasoning navigation. The dispatching automation fault intelligent processing system comprises a dispatching automation system environment sensing module which obtains machine room environment multi-source data in real time to serve as a data source; the intelligent analysis module performs preprocessing and labeling on the image type data and the text type data, then performs knowledge extraction to obtain entities and relationships among the entities, performs data fusion on the obtained entities, constructs an automatic duty knowledge graph, forms a scheduling automation system fault research and judgment mechanism reasoning model, and performs fault diagnosis on the scheduling automation system. A fault alarm event is generated, a knowledge reasoning environment is constructed, and a fault research and judgment conclusion is obtained through reasoning of a knowledge reasoning algorithm; an auxiliary decision-making module performs reasoning matching on the fault alarm event and the automatic duty knowledge graph through an RE2 model to obtain a fault handling auxiliary decision-making opinion; and constructing a fault exception handling flow navigation chart, and generating a fault handling coping strategy. The operation and maintenance management level of the dispatching automation machine room is improved.

The invention relates to the technical field of operation and maintenance management, in particular to a digital twin hydraulic engineering operation and maintenance monitoring system and method. The method comprises the following steps: acquiring water conservancy project related data; performing three-dimensional geometric feature extraction on the related data of the water conservancy project to obtain three-dimensional geometric data of the water conservancy project; hydrological structure data association is carried out according to the three-dimensional geometric data of the water conservancy project, and a digital twinborn basic model is generated; global optimization is carried out on the digital twinborn basic model, and a dynamic digital twinborn hydraulic engineering model is generated; performing hydraulic engineering remote monitoring based on the dynamic digital twin hydraulic engineering model to generate hydraulic engineering monitoring data; and performing hydraulic engineering composite state sensing on the hydraulic engineering monitoring data to generate a hydraulic engineering dynamic monitoring map. According to the method, the dynamic model is constructed through the digital twinning technology, precision, intelligence and collaboration of operation and maintenance monitoring of the water conservancy project are achieved, and the precision and intelligent level of operation and maintenance monitoring of the water conservancy project are improved.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention belongs to the technical field of electromechanical equipment intelligent monitoring and operation and maintenance management, and discloses an electromechanical system health monitoring and optimization method based on artificial intelligence, and the method comprises the steps: building a digital twin architecture based on a simulation model, reasonably arranging a multi-source sensor, and carrying out the optimization of the health of an electromechanical system; key equipment is fully covered; the system acquires operation data at high frequency and synchronously transmits the operation data to the cloud through multiple channels to realize real-time monitoring; the collected data is cleaned and preprocessed to remove abnormities so as to ensure the data quality; inputting the cleaned data into a neural network, extracting features, and identifying an operation trend and a fault mode; establishing a prediction model in combination with historical data, and constructing a maintenance knowledge base; the equipment state is predicted in real time, and early warning is dynamically generated; through virtual-real synchronization, health and carbon emission states are mapped in real time, equipment operation is intelligently optimized, and cloud closed-loop monitoring is continuously iterated.

The invention discloses an intelligent campus operation and maintenance management system based on digital twinning, which relates to the technical field of intelligent campus operation and maintenance management and comprises a digital twinning monitoring module, an intelligent task scheduling and resource optimization module and a space optimization and path planning module. According to the system, the visual and intelligent level of equipment management is improved by monitoring the equipment state in real time and constructing an accurate equipment virtual model, the task execution efficiency is improved by optimizing task scheduling and resource allocation, meanwhile, the energy consumption of the equipment is reduced, the service life of the equipment is prolonged, the space conflict between the equipment is solved, and the equipment management efficiency is improved. The cooperative work capability of the equipment is improved, the path planning and task execution sequence is optimized, and the safety and stability of the intelligent campus operation and maintenance system are improved.

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

The invention relates to the technical field of power distribution operation and maintenance management, and discloses an intelligent power distribution operation and maintenance management system based on 5G transmission. The system comprises a 5G real-time acquisition module, a multi-dimensional feature fusion module, a dynamic topology generation module, an anomaly propagation analysis module and a strategy optimization feedback module. The 5G real-time acquisition module acquires operation state data streams such as current and voltage waveforms, an equipment temperature sequence and environment monitoring indexes of the power distribution equipment through a 5G network; the multi-dimensional feature fusion module is used for separating equipment state features, calculating mutual information amount and generating equipment state feature tensors; the dynamic topology generation module constructs an association intensity matrix according to the feature tensor, and generates a hierarchical connection path and a dynamic equipment topological graph; the abnormal propagation analysis module extracts a state fluctuation sequence, identifies an abnormal transmission path and marks a core propagation node; and a strategy optimization feedback module generates a maintenance strategy priority queue according to the dynamic topology map, and feeds back an execution result to update the dynamic topology map, so that the intelligence and accuracy of power distribution operation and maintenance management are improved.

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention discloses an intelligent multi-dimensional automatic inspection operation and maintenance management method, device and equipment, relates to the technical field of network inspection, and aims to solve the problems that the inspection efficiency of network equipment is low and fault early warning is lagged. The method comprises the following steps: scanning equipment in a target machine room to establish an equipment registration file, wherein the registration file comprises an equipment type identifier and an associated inspection strategy template; collecting multi-dimensional data of each registered device according to the inspection strategy template, wherein the multi-dimensional data comprises a hardware operation state, a business middleware state and an environment monitoring parameter; determining abnormal data from the multi-dimensional data according to a preset dynamic threshold value, and generating and issuing graded alarm information according to the influence degree of the abnormal data; and creating a work order according to the abnormal data and automatically distributing the work order so as to process the abnormal data.

The invention discloses an electromechanical engineering construction information operation and maintenance management method and system based on the Internet, and particularly relates to the technical field of data management. Constructing an initial BIM model based on the design drawing, and generating three-dimensional model data containing equipment geometric information and space coordinates; a three-dimensional laser scanning device or a high-precision positioning sensor is used for obtaining the geometrical morphology and actual coordinates of a real object on site; the collected point cloud data is compared with the initial BIM model through a spatial data matching algorithm, deviation is recognized, the BIM model is automatically updated, and model precision dynamic correction is achieved; binding the updated equipment information and an equipment entity with a two-dimensional code, and synchronously binding an actual position coordinate through an indoor positioning label; uploading the corrected model and equipment information to a cloud operation and maintenance platform to realize information centralized management and multi-terminal sharing; the method improves electromechanical engineering operation and maintenance efficiency and information precision, and has good application value.

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

The invention relates to the technical field of underground drainage pipeline monitoring and hidden danger analysis, and provides an underground drainage pipeline hidden danger data analysis method based on big data, which comprises the following steps: acquiring data such as flow, water level, pressure and pipeline structure by using a multi-source sensor to form an original monitoring data set; preprocessing the data, extracting time domain, frequency domain and spatial topological features, and constructing a multi-dimensional feature vector; and inputting the feature vectors into a pre-trained hidden danger prediction model, and outputting hidden danger types and position coordinates. And constructing a spatio-temporal data analysis model based on the hidden danger position, generating a spatio-temporal correlation feature matrix, and performing dynamic risk assessment to obtain hidden danger development trend parameters. And the hidden danger information is mapped to a geographic information system, a three-dimensional visual analysis result is generated, and a graded early warning instruction is transmitted to a drainage pipe network operation and maintenance management system through a communication interface. The operation and maintenance management efficiency of the drainage pipeline can be improved, the hidden danger checking cost is reduced, and the safety and reliability of the drainage system are enhanced.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention relates to the technical field of operation and maintenance management, in particular to an asset operation and maintenance decision management platform and management method based on data fusion, and the method comprises the steps: collecting asset full life cycle data, and constructing a standardized asset data set; utilizing ontology modeling and knowledge extraction technologies to construct a dynamic asset knowledge graph containing asset attributes, topological relations and operation states; training a deep learning model to realize asset fault diagnosis, constructing an asset operation and maintenance risk detection algorithm, performing real-time analysis on asset multi-dimensional data, identifying an asset security risk level, and triggering a corresponding operation and maintenance strategy; and converting the diagnosed asset fault and the generated operation and maintenance strategy into a natural language by using a natural language generation technology, and pushing the natural language to an operation and maintenance manager for execution. According to the method, multi-source heterogeneous data is fused, and intelligent operation and maintenance decision-making of the full life cycle of assets is realized.

The invention discloses a power equipment intelligent early warning and maintenance management system based on an AI Agent, and relates to the technical field of power equipment maintenance management, and the system comprises a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time sequence analysis and intelligent early warning module, and a fault simulation and maintenance optimization module. And the data preprocessing and weight correction module is used for preprocessing the operation data of the power equipment and carrying out weighted analysis on the data of the multi-source sensor by adopting a self-adaptive weight distribution algorithm. According to the method, the hidden deviation problem of the sensor is solved through the multi-source data correction and dynamic weight distribution technology, and key features are accurately extracted and noise is eliminated in combination with a deep learning feature enhancement and denoising method. And a dynamic prediction model and an intelligent early warning generation method are utilized to realize accurate fault prediction and graded early warning, a maintenance strategy is optimized, and the reliability and safety of a power system are improved.

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 relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention relates to the technical field of operation and maintenance management, in particular to an operation and maintenance management method based on digital twinning, and the method comprises the following steps: S1, building a corresponding digital twinning model through collecting the structure data and operation data of an actual operation and maintenance system; s2, collecting operation data of the operation and maintenance system in real time, transmitting the operation data to the cloud platform, and preprocessing the operation data transmitted in real time in the cloud platform; s3, performing real-time analysis on the preprocessed data through a digital twinborn model, and detecting a system operation state and an abnormal condition; s4, based on the historical data and the real-time data, using a machine learning algorithm to construct a fault prediction model, and predicting possible faults in the future; and S5, generating an optimization management scheme according to the fault prediction result and the system operation state. Through accurate data acquisition and real-time monitoring, potential faults and hidden dangers of equipment can be found in time, accidental shutdown of the equipment is avoided, and production loss caused by faults is greatly reduced.

The invention discloses an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

The embodiment of the invention discloses an optical module health state detection method and device, a storage medium and a program product. The method comprises the following steps: extracting time sequence features from time sequence data of an optical module, and extracting key features from system log data of the optical module; fusing the time sequence features and the key features based on the constructed hybrid model, and generating fused features to obtain a current health degree score of the optical module; collecting a historical health degree score of the optical module in a preset time range; detecting at least one environmental parameter of the environment where the optical module is located; obtaining a dynamic threshold value of the optical module based on the historical health degree score and the environmental parameters; and judging the health state of the optical module based on the relationship between the current health degree score and the dynamic threshold. According to the method, the health state of the optical module can be comprehensively, accurately and efficiently evaluated based on multi-dimensional data features, powerful support is provided for operation and maintenance management of the optical module, and the method is of great significance in guaranteeing service continuity and reducing operation and maintenance cost.