3208 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.

Disclosed is a method for evaluating the fatigue damage and life of a bridge structure under a multi-factor coupling effect. The method comprises the following steps: S1, reproducing complex service environment action values; S2, analyzing performance values of a bridge structure; S3, calculating coupling fatigue damage of a vulnerable part; and S4, predicting a remaining life of the bridge structure. By adopting the method, the fatigue damage condition and remaining life of an in-service bridge under a multi-factor coupling effect can be accurately evaluated, and the effect of each factor / action and the coupling effect the factors / actions in fatigue damage of a vulnerable part are quantitatively analyzed, thereby providing data support and decision-making reference for the operation and maintenance management, reinforcement and repair, and optimal design of bridges.

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 a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention provides a photovoltaic module temperature monitoring method and system, and relates to the technical field of temperature monitoring, and the method comprises the steps: obtaining the standardized temperature data of a photovoltaic module through infrared thermal imaging and visible light image collection in combination with a space registration algorithm and edge calculation. And a temperature prediction model is constructed by using wavelet transform and a graph convolutional neural network, and recognition of a temperature anomaly region is realized. And performing fault analysis by adopting hierarchical clustering and a graph neural network, generating early warning information including fault positioning, early warning levels, fault types and processing guidance, and sending the early warning information to an operation and maintenance management platform, thereby realizing intelligent temperature monitoring and fault early warning of the photovoltaic module.

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 a building operation and maintenance management method and system based on big data, and relates to the technical field of building intellectualization, and the method comprises the steps: collecting multi-source heterogeneous data, executing data standardization processing, and generating a standard data stream; constructing a virtual building model based on a digital twinning technology, mapping the standard data stream to a corresponding device node of the virtual building model, and generating a device feature matrix; inputting the equipment characteristic matrix into a deep learning network, training to generate an energy consumption prediction model, and outputting an energy consumption prediction sequence; a multilayer perceptron health scoring model is constructed based on the energy consumption prediction sequence, and a fault early warning priority list is generated in combination with an equipment weight coefficient; and automatically generating a maintenance work order according to the fault early warning priority list. Through the maintenance effect evaluation and secondary maintenance mechanism, a complete closed-loop management process is formed, the maintenance quality is ensured, the maintenance strategy is continuously optimized, and the overall operation and maintenance management level is improved.

The invention discloses a building intelligent operation and maintenance management system and method based on big data analysis, and relates to the field of building intelligent operation and maintenance, and the system comprises a data collection layer including high-precision multi-type sensors and adaptive collection and transmission, a data storage layer including distributed storage and high-redundancy cache, and a big data analysis layer including data cleaning and data storage. The operation and maintenance decision-making layer is used for generating a strategy according to analysis and performing dynamic adjustment; the user interaction layer is used for performing visual interface and convenient feedback; the intelligent early warning module is used for performing dynamic threshold and accurate early warning; the analog simulation optimization module is used for performing model simulation and VR / AR auxiliary decision making; and the operation and maintenance management level and the energy utilization efficiency are improved. The system integrates building data, monitors equipment in real time, accurately predicts faults and energy consumption, optimizes operation and maintenance decisions, improves the management efficiency and the energy utilization rate, reduces the cost, and improves the user experience.

The invention discloses a shield machine operation and maintenance management system based on data analysis, which relates to the technical field of shield equipment monitoring and comprises a data acquisition unit, a data transmission unit, a data processing and analysis unit, an anomaly detection unit, a verification unit and an early warning unit. The data acquisition unit is used for installing various sensors at the cutter part of the shield tunneling machine, acquiring cutting force, vibration, temperature and displacement data and providing basic information for subsequent analysis; according to the invention, through integration of various sensors and a deep learning algorithm, real-time monitoring and anomaly detection of the state of the shield tunneling machine cutter are realized, compared with a traditional manual inspection method, the system can provide more continuous and accurate monitoring, and errors and omission caused by manual inspection are reduced; through automatic data acquisition, transmission, processing and analysis, the efficiency and accuracy of operation and maintenance management of the shield tunneling machine are remarkably improved, and the construction risk and economic loss caused by tool abnormity are reduced.

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 information management, and comprises a distribution automation operation and maintenance work log intelligent recording platform based on a CS architecture, and the platform comprises a log field analysis module, a task chain construction module, a task logic verification module, and a log storage tracing module. According to the method, missing and abnormal fields are effectively marked and corrected through meticulous analysis and comparison of uploaded log records, the accuracy and integrity of data are improved, task scheduling is optimized and the operation and maintenance management efficiency is improved through association of device numbers and task types and adjustment of execution time in sorting and grouping of task sequences, and the task scheduling efficiency is improved. Task chains are recombined to solve conflicts and adjust priorities, smooth execution of operation and maintenance tasks is guaranteed, delay is reduced, logic verification enhances task reliability through rule verification and error marking, unassociated tasks are deduced, analyzed and corrected, the exception processing capacity is enhanced, the data tracing and storage efficiency is improved, and the tracing capacity of data management is enhanced.

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 provides a bridge structure fatigue crack propagation prediction method and system, and relates to the technical field of artificial intelligence. According to the method, structural parameter information of a bridge is collected, a three-dimensional model of the bridge structure is established through finite element analysis, and a fatigue crack propagation prediction model is established based on the fracture mechanics theory and a fatigue damage accumulation model; performing expansion simulation on preset cracks in the three-dimensional model of the bridge structure, calculating a crack expansion rate and predicting a crack expansion path, correcting a crack expansion simulation result in combination with an actual working environment and a load condition of the bridge structure, and calculating a crack expansion simulation result according to the corrected crack expansion simulation result. And predicting crack propagation information of the bridge structure in the preset time period. The method can accurately predict the expansion path, rate and future state of the crack under a complex working condition, thereby providing a scientific basis for maintenance and repair of the bridge, realizing real-time feedback and dynamic adjustment, ensuring high efficiency and accuracy of a prediction result, and providing a scientific basis for maintenance management of the bridge.

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 a signal processing and intelligent algorithm fused main helium fan residual life prediction method, and relates to the technical field of mechanical equipment state monitoring and maintenance management, and the method comprises the steps: collecting equipment operation data through multiple sensors, carrying out the feature fusion through principal component analysis, and achieving the fault mode recognition through a deep learning algorithm; and the residual life of the equipment is predicted by utilizing transfer learning and an XGBoost algorithm, and a diagnosis and prediction model is integrated into an online monitoring system, so that real-time perception and dynamic prediction of the health state of the equipment are realized, and decision support for maintenance and spare part management is provided. According to the method, the accuracy of fault diagnosis and life prediction of the primary helium fan is effectively improved, and a new thought is provided for safe operation and intelligent maintenance of equipment.

The invention relates to the technical field of structural health monitoring, in particular to a wharf breakwater intelligent monitoring system which comprises a multi-modal data acquisition module, a data preprocessing module, a stress prediction module, a structural health assessment module, a self-adaptive early warning module and a remote monitoring and maintenance management module. A plurality of data such as sound waves, vibration, pressure, temperature and corrosion are collected in real time, data cleaning, denoising and standardization processing are carried out, a sparse auto-encoder model is used for extracting low-dimensional sparse features, the breakwater stress change trend is analyzed, and a comprehensive report of stress distribution, crack propagation, corrosion loss and structural fatigue is generated through structural health assessment. The early warning threshold value is dynamically adjusted through a self-adaptive early warning mechanism, an early warning notice is sent to a manager, the remote monitoring and maintenance management module supports real-time monitoring and remote configuration and scheduling of maintenance management of the breakwater, the structural safety of the breakwater is improved, the service life is prolonged, and the management efficiency is improved.

The invention discloses a transformer monitoring method and system based on cloud edge fusion, and relates to the technical field of transformer monitoring. The method comprises the following steps: acquiring a historical fault database of a target transformer, and carrying out statistical recognition to obtain fault occurrence parameter information; positioning and acquiring a plurality of transformer monitoring points, and deploying edge sensors and edge computing devices on the plurality of transformer monitoring points respectively; multiple point position sensor monitoring data streams are obtained, mapping anomaly analysis is carried out, and multiple point position anomaly edge feature information is obtained; performing abnormal fusion analysis to obtain abnormal fault parameters of the transformer; and performing cluster parameter optimization based on the transformer abnormal fault parameters to obtain operation and maintenance optimization control parameters, and performing fault operation and maintenance management and control on the target transformer based on the operation and maintenance optimization control parameters. The technical problem of insufficient transformer monitoring real-time performance and accuracy in the prior art is solved, and the technical effect of improving the transformer monitoring real-time performance and accuracy is achieved through the cloud edge fusion technology.

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 a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

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.