1654 results about "Maintenance management" patented technology

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 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 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 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 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 discloses a pipe network operation and maintenance management system based on data analysis, which relates to the technical field of municipal infrastructure operation and maintenance management and comprises an equipment feature modeling module, a group anomaly identification module, a common-mode risk tracing module, a dynamic risk modeling module, a regulation and control strategy optimization module and a closed-loop self-adaptive updating module. Based on historical operation data and real-time acquisition parameters, feature coding is performed on the model, batch, position and time sequence of the sensor, and an equipment attribute mapping matrix is constructed. According to the method, the equipment attribute mapping matrix and the group anomaly identification mechanism are constructed, and external induction factor positioning and the dynamic risk weight map are combined, so that intelligent adjustment of regulation and control parameters and anomaly interference avoidance are realized, a closed-loop optimization process is constructed, and the identification accuracy, the control safety and the operation toughness of a pipe network system in a multi-disturbance scene are improved.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

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 an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention relates to the technical field of substation operation and maintenance management, in particular to a three-dimensional visual substation operation and maintenance management method and system based on digital twinning. The method comprises the following steps: acquiring real-time working condition data of equipment; acquiring equipment distribution data and space electric field intensity distribution; inputting the equipment distribution data and the historical operation and maintenance data into a physical information neural network model to obtain a health index and a residual service life prediction value; when the real-time working condition data is abnormal or the health index is lower than a preset threshold value, message transmission and node updating are carried out in the substation graph neural network model, a fault source is positioned, and the cascading fault probability is evaluated; and generating an operation and maintenance work order based on the fault source, the health index and the cascading fault probability, and planning a safe operation and maintenance path by using an A-star algorithm. According to the scheme, the limitation that traditional operation and maintenance depend on surface monitoring and experience judgment can be overcome, the depth and the breadth of fault diagnosis are improved, and the safety of field operation is enhanced.

The invention provides an automatic inspection method and device and a readable storage medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: automatically logging in a plurality of devices through a pre-written automatic login script of terminal simulation software, and executing an inspection command; obtaining an original log output by the plurality of devices after executing the inspection command, and storing the original log in a local database; preprocessing the stored original log, and extracting a key index in the preprocessed original log by using a preset index extraction rule; and carrying out anomaly detection on the extracted key indexes to obtain the anomaly indexes and cross-device association anomaly results of the corresponding devices. According to the method, the device and the readable storage medium, the problems that the manual inspection efficiency is low, the log analysis intelligence degree is insufficient, intelligent assistance is lacked and potential safety hazards exist in data circulation due to the fact that an existing inspection technology mainly depends on manual operation or a basic script tool can be solved.

The invention relates to the technical field of vehicle charging station operation and maintenance management, and particularly discloses a new energy vehicle charging station operation and maintenance management system and method based on the Internet of Things, and the method comprises the steps: collecting high-frequency harmonic characteristic data in a charging process in real time, and generating harmonic-impedance spectrum combined monitoring data; extracting dynamic attenuation gradient data of the line characteristic impedance; when the dynamic attenuation gradient data is lower than a preset critical safety threshold value, triggering a charging mode switching instruction; the charging piles are controlled to be switched into a charging mode, the phase difference parameters of the multiple charging piles are dynamically optimized, and a phase difference dynamic adjustment instruction set is generated; and finally, self-adaptive correction of the impedance compensation amount is carried out through the MEC equipment, and by self-adaptive correction of the impedance compensation amount, the system can maintain that the overall impedance is constantly greater than a critical safety threshold value, and power loss and equipment damage caused by impedance mismatch are avoided. The real-time feedback mechanism is helpful for improving the energy efficiency of the system, reducing the operation cost and ensuring the long-term reliability of the charging infrastructure.

The invention provides a smart park power distribution equipment intelligent operation and maintenance method and system based on big data, and relates to the technical field of smart park operation and maintenance management.The method comprises the steps that firstly, a structured expression mode of operation behaviors of a multi-park power distribution system is constructed, and the relationship between common operation characteristics of parks and equipment behaviors is extracted; rapid modeling and migration prediction of the new park are realized; secondly, an intelligent layered response mechanism based on abnormal event triggering is constructed, and an adaptive analysis and diagnosis strategy can be dynamically selected according to the type and grade of an abnormal event; meanwhile, a closed-loop process from event triggering to fault tracing, equipment positioning and response strategy making is realized in combination with a data driving and structured reasoning method; in addition, various heterogeneous data sources are fused, an information barrier is broken through, the comprehensive judgment of the system is improved, the output of the AI model is verified by introducing electrical physical constraint conditions, and it is ensured that the result logic is reasonable.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention discloses a charging pile remote operation and maintenance system based on the Internet of Things, and the system comprises a data collection and edge calculation module which is used for collecting multi-mode operation data of a charging pile, and generating standardized operation data; the health degree dynamic evaluation module is used for executing multi-modal feature coupling analysis on the standardized operation data; the fault root cause analysis module is used for starting a time-space coupled fault root cause analysis process to obtain a root cause confidence score and a high-risk component; the self-healing strategy execution module is used for generating a self-healing strategy of the charging pile; the resource dynamic scheduling module is used for realizing dynamic optimal distribution of operation and maintenance resources according to the health degree score and the geographic position; the intelligent operation and maintenance management module is used for updating the weight of the dynamic pruning random forest model and generating a hierarchical maintenance report; and the data full-process control module is used for completing automatic closed-loop management from data acquisition to operation and maintenance decision. According to the invention, remote intelligent diagnosis and self-healing operation and maintenance of the charging pile can be realized.

The invention relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

The invention relates to the technical field of operation and maintenance management, and discloses a cloud operation and maintenance management method, system and equipment for a charging station, and the method comprises the steps: monitoring the WiFi signal strength, 5G network quality and wired network delay parameters of each floor of a stereo garage in real time, and building a network environment database; when the charging pile detects that the SOC change amplitude exceeds a set threshold value, an optimal target transmission path set is screened out; carrying out differential bit coding on the collected charging data to generate a charging data frame, and sending the charging data frame to a cloud server in a multi-path concurrent mode; the cloud server receives and gathers the charging data frames from different floors of the stereo garage, and analyzes the charging load distribution condition of the whole garage; and calculating a power distribution scheme of the charging pile of each floor based on the whole garage charging load distribution condition, and issuing a scheduling instruction to the charging pile of the corresponding floor, thereby improving the intelligent operation and maintenance management level and resource utilization efficiency of the stereo garage charging station.

The invention discloses an intelligent computing center automatic operation and maintenance management method based on computing power resource allocation, and relates to the technical field of resource management, and the method comprises the steps: obtaining real-time computing power load data and a to-be-processed task queue of an intelligent computing center; constructing a resource state matrix according to the real-time computing power load data, performing historical data backtracking analysis on the resource state matrix by using a sliding window algorithm, and calculating a resource load predicted value and a resource availability score of each computing node; based on the resource availability score, an improved genetic algorithm is adopted to carry out optimal allocation solution on the constructed task-resource matching matrix, and an optimal task allocation scheme is generated; and converting the optimal task allocation scheme into a resource allocation instruction set, sending the resource allocation instruction set to each target computing node to execute task scheduling, and updating the resource occupation state of the corresponding node in the task-resource matching matrix. According to the invention, the technical jump from passive response type management to active prediction type management is realized, and the resource configuration efficiency of an intelligent computing center is improved.

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 relates to the technical field of intelligent operation and maintenance management of urban water supply pipe networks, in particular to a double-model urban water supply pipe explosion risk prediction method based on multi-dimensional dynamic indexes. The method comprises the following steps: collecting multi-dimensional data of a water supply pipe network, and combining with gas-containing water hammer simulation analysis to form a standardized index feature set; establishing a dual-model prediction system comprising a subjective and objective weight fusion model and a machine learning model, and dynamically adjusting and setting a risk assessment weight proportion of the model according to the change of model prediction precision in the dual-model prediction system; and calculating the risk probability score of the pipe section according to the dynamically adjusted weight ratio, marking the pipe section when the prediction result difference of the double-model prediction system exceeds a preset threshold value, and further dividing the risk level of the pipe network. According to the method, closed-loop management from prediction to decision making is realized based on multi-dimensional data, dual-model prediction and operation efficiency evaluation, and the scientificity and efficiency of operation and maintenance of the water supply network are improved.

The invention provides a smart park operation and maintenance management method and system based on CIM and a medium, and belongs to the technical field of smart park management. The method comprises the following steps: constructing a CIM digital twinborn model based on a park building group, a geographic position, Internet of Things equipment and business data, and synchronously extracting equipment state and energy consumption information; analyzing the state data by using the equipment fault prediction model and generating a repair work order to realize predictive maintenance; performing gridding processing on the energy consumption data, generating a thermodynamic diagram and identifying an abnormal energy consumption area; and positioning a high-energy-consumption grid by combining an energy consumption analysis model, and generating a comprehensive operation and maintenance scheme of equipment regulation and control, energy scheduling and system optimization through matching of an energy management strategy library. According to the invention, the digital twinning technology and the CIM platform are fused, so that full-life-cycle management and dynamic energy consumption optimization of the equipment are realized, the operation and maintenance efficiency is remarkably improved, the failure rate and energy waste are reduced, and both economy and environmental protection 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 intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.