4266 results about "Intelligent Network" patented technology

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention discloses a power failure monitoring system, and relates to the technical field of failure monitoring. During operation of the system, multi-dimensional data acquisition is carried out on key equipment of a transformer substation and a power distribution room, the operation state of the equipment is comprehensively covered through real-time acquisition of data of various sensors, and data acquired by a data acquisition module is preprocessed; the intelligent gateway equipment is deployed, real-time data analysis and preliminary fault diagnosis are carried out locally, the running state of the equipment is monitored in real time through set rules and threshold values, potential faults are detected, preliminary alarm is given out, and the system automatically judges the severity of the faults and decides whether to take automatic control measures including load switching or equipment power failure or not. Meanwhile, alarm information is generated and sent to operation and maintenance personnel, data, fault diagnosis results and automatic control measures in system operation are fed back, and a deep learning model and an edge calculation strategy are periodically updated through online learning and model updating.

The invention discloses an intelligent network connection automobile safety control method and system based on active and passive safety information fusion. The system comprises an environment detection module, a risk prediction module and a cooperative control module. Wherein the environment detection module comprises a passenger state monitoring part, a vehicle system monitoring part and a traffic environment sensing part, the risk prediction module comprises a vehicle collision risk prediction part and a passenger injury risk prediction part, and the cooperative control module comprises an in-vehicle intelligent restraint system, a vehicle steering system and a control execution part of a braking system. The vehicle collision risk probability and the passenger injury probability are calculated in real time according to the established vehicle risk prediction model and the passenger injury risk prediction model by fully utilizing the early warning capability of an active safety system for potential risks. When the system encounters unavoidable dangerous working conditions, the system adjusts the state of the restraint system to enable the restraint system to reach the optimal state of current passenger protection, and extra risks, caused by non-cooperation of the active safety system and the restraint system, of passengers are avoided.

The invention relates to the technical field of vehicle control, and provides a new energy vehicle operation optimization method and system based on intelligent networking, and the method comprises the steps: collecting a first operation parameter of a vehicle at a current moment and a road parameter at a next moment through a vehicle-mounted sensor network; wherein the first operation parameter comprises a tire movement value corresponding to vehicle suspension deformation variable data based on a current pavement coefficient, and the road parameter is road feature data of a road to be driven in; according to the first operation parameters and the road feature data, a load mean value model and an acceleration safety threshold value of the current vehicle in the road to be driven in are constructed; according to the load mean value model, a damping adjusting instruction of the vehicle suspension is generated; and generating a driving torque vector distribution scheme according to the acceleration safety threshold.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

The invention discloses a power equipment intelligent gateway fault detection system based on the Internet of Things, relates to the technical field of power equipment intelligent gateways, and is used for solving the problem that an existing power equipment monitoring system is inaccurate in fault detection. Comprising a power equipment access module, a data preprocessing and edge calculation module, a data storage and analysis module and a fault diagnosis and alarm module which are in signal connection. The power equipment access module is used for collecting operation state data of power equipment; the data preprocessing and edge calculation module performs cleaning, format conversion and preliminary calculation on the collected data, so that data redundancy is reduced, and the processing efficiency is improved; the data storage and analysis module stores the processed data and extracts feature information by using a big data analysis method; and the fault diagnosis and alarm module carries out fault identification based on an analysis result and triggers an alarm signal so as to realize accurate detection and efficient maintenance of the intelligent gateway of the power equipment.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention belongs to the technical field of Internet of Vehicles, and particularly provides a federated learning optimization system and method for an intelligent network connection automobile scene, which improves an asynchronous federated learning framework, divides a plurality of vehicles into different clusters due to the isomerism of computing power, and adopts a cloud-edge-end three-layer federated learning framework, so as to realize the optimization of the federated learning of the vehicles. An edge server is deployed on a roadside unit RSU, received model parameters are subjected to pseudo-asynchronous aggregation in a self-adaptive dynamic time window, the received model parameters are subjected to real-time aggregation at a cloud end, the system model training efficiency and quality are improved, meanwhile, the data security is ensured, the user privacy is protected, and the user experience is improved. And the efficiency of the federated learning optimization system and the quality of the global model in the intelligent network connection automobile scene are ensured.

The invention discloses an intelligent network transaction credit risk assessment system and method based on multi-modal data fusion and deep learning, and particularly relates to the technical field of network transaction risk assessment, and the system comprises a multi-modal data collection, fusion and deep reinforcement learning agent, and a large model interaction and risk assessment and supervision execution module. The multi-modal data acquisition module acquires various data in real time; the fusion module realizes semantic alignment and interactive learning through multiple strategies; the deep reinforcement learning agent module optimizes a monitoring and supervision strategy based on an Actor-Critic framework; the large model interaction module provides multi-aspect support by means of various technologies; and the risk assessment and supervision execution module identifies illegal behaviors and executes supervision actions. The evaluation method comprises the steps of data acquisition and preprocessing, feature vector generation, agent training, large model deployment optimization, real-time monitoring decision and the like. The system can improve the monitoring and supervision efficiency, protects the rights and interests of consumers, constructs a healthy transaction environment, and promotes the development of related technologies.

Systems, methods, and computer-readable media for autonomous agents are disclosed. The autonomous agent can include one or more processing circuits configured to receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations. The processing circuits can register the autonomous agent with a decentralized network, centralized network, or data source (DNCNDS). The processing circuits can receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data. The processing circuits can perform, in real-time, the at least one cyber resilience operation of the plurality of cyber resilience operations based at least on selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data.

The invention provides a multi-protocol communication intelligent switch gateway control method and device, and the method comprises the steps: constructing a knowledge graph according to a network topological graph and protocol information of an intelligent switch, and obtaining a switch networking knowledge graph; determining an inference rule according to the switch networking knowledge graph to obtain an inference rule engine; acquiring a historical data packet of the intelligent switch in a preset time period, and analyzing and converting the historical data packet to obtain a data conversion module; integrating the inference rule engine and the data conversion module to obtain a protocol conversion module of the intelligent gateway; dynamically designing the protocol conversion module according to a preset implementation framework to obtain a dynamic protocol adaptation mechanism; evaluation and performance optimization are carried out on the dynamic protocol adaptation mechanism, an optimized protocol conversion system is obtained, and the optimized protocol conversion system is used for assisting the intelligent switch gateway to control the intelligent switch.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention relates to the technical field of data processing, and discloses a communication transmission management method and system for a heterogeneous network and a storage medium. The method comprises the following steps: processing a signal strength parameter and a data priority level in a data hierarchical configuration table to obtain a network resource allocation scheme; performing real-time monitoring processing on a 4G / 5G cellular network base station, a WiFi hotspot and a satellite communication node to obtain a network switching control instruction; performing transmission quality monitoring processing on the engine parameters, the navigation data, the airborne equipment state data and the environment monitoring data to obtain a transmission quality parameter table; and performing data mining analysis processing on the network switching control instruction and the transmission quality parameter table to obtain a transmission strategy optimization parameter containing network resource optimization configuration. In a heterogeneous network environment, intelligent network switching and resource allocation based on data priorities are realized, so that transmission requirements of data with different priorities are ensured, and meanwhile, the utilization efficiency of network resources is improved.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention discloses a network intrusion intelligent detection system based on a 5G network, and relates to the technical field of network security. Comprising a traffic anomaly detection and analysis module, a data encryption tampering detection module, an anomaly analysis and evaluation module, an attack prediction and prevention module, a dynamic isolation and protection module, a reverse tracking and tracing module and a redirection and isolation protection module. According to the system, more efficient and more accurate intrusion detection and threat defense can be realized in a 5G network environment by integrating a plurality of advanced technologies and intelligent modules. Compared with a traditional rule-based detection method, the system can adaptively learn and identify a new attack mode, and the detection capability for zero-day attacks, variant attacks and advanced persistent threats (APT) is enhanced. Besides, by combining deep learning and reinforcement learning algorithms, the system can predict potential attacks in a dynamically changing network environment and automatically optimize a defense strategy, and the response speed and accuracy of the defense system are improved.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention provides a network intrusion intelligent monitoring method and system based on deep learning, relates to the field of network security, and solves the technical problem of response lag of an existing defense method. The method comprises the following steps: collecting multi-source data; preprocessing the multi-source data to generate a spatial-temporal feature map; inputting the spatial-temporal feature map into a first model and a second model constructed based on a deep learning algorithm for anomaly detection to obtain a detection result; wherein the first model is used for detecting a known attack mode, and the second model is used for detecting an unknown attack mode; and carrying out hierarchical risk level division on the detection result, and carrying out active defense according to the defense strategy of each risk level. The method is used in the network intrusion monitoring and defense process, intelligent monitoring and active defense of network intrusion are realized through multi-source data acquisition, spatial-temporal feature map generation, dual-model cooperative detection and layered defense strategy implementation, and the real-time performance and initiative of network security protection are improved.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention relates to the technical field of new energy vehicle-mounted data processing, and discloses a new energy vehicle vehicle-mounted data processing method and system based on intelligent networking, and the method comprises the steps: collecting and preprocessing data through a multi-source sensor, carrying out the slicing fusion of the data based on time-space correlation, and extracting features through a lightweight convolutional neural network. Constructing an inter-vehicle communication topology model to realize collaborative feature learning, adopting a federal learning framework to train the model and protect privacy, applying a reinforcement learning algorithm to make a decision, establishing a multi-objective optimization model, deploying a dynamic priority scheduling module to manage resources, and optimizing the model based on an incremental learning algorithm. The system covers a plurality of modules such as data acquisition and preprocessing, data dynamic slicing and fusion and the like. According to the method, the data processing efficiency and accuracy are improved, intelligent decision and multi-objective optimization are realized, data privacy is guaranteed, computing resources are reasonably distributed, and the overall performance of the new energy automobile in an intelligent network connection environment is improved.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention relates to an intelligent network attack surface prediction method and system based on deep learning, and belongs to the technical field of network security and information, and the method comprises the steps: obtaining network asset information, vulnerability distribution information and external threat intelligence data in a target network environment, and carrying out the preprocessing to generate a standardized data set; inputting the standardized data set into a pre-trained deep learning model to extract a feature vector related to the network attack; reasoning and analyzing a potential attack link based on the feature vector and the knowledge graph, and combining an association relationship among a network asset node, a vulnerability node and a threat intelligence node in the knowledge graph; and finally, according to a reasoning analysis result, evaluating an intrusion path possibly utilized by an attacker, outputting an attack surface prediction result, and presenting the attack surface prediction result in the form of an attack path list. According to the scheme, a potential attack link can be subjected to deep reasoning analysis, an intrusion path possibly utilized by an attacker can be accurately predicted, and the effectiveness of network security protection is improved.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention discloses an intelligent network traffic anomaly detection and automatic isolation system, which comprises a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module and a visual monitoring and alarm platform, by introducing a deep learning algorithm and a multi-dimensional anomaly detection algorithm, deep mining and comprehensive analysis of network traffic can be realized, feature information is automatically learned and extracted from mass data, an accurate traffic behavior model is constructed, and abnormal behaviors in the traffic are detected in real time. And full-process automatic processing from data acquisition, preprocessing, intelligent analysis to automatic isolation is realized. When abnormal traffic is detected, the system can automatically execute isolation measures and monitor the isolation purpose in real time, and compared with a traditional method based on a rule base or threshold judgment, the method is higher in detection accuracy and real-time performance.

The invention provides a dynamic multi-link intelligent management and scheduling system, and relates to the technical field of network communication. According to the system, real-time monitoring, dynamic management and transmission data scheduling of a network link can be realized, and the problems of low bandwidth utilization rate and insufficient transmission efficiency in the prior art are solved. The method comprises the following steps: firstly, acquiring and monitoring state information of each link in real time through an intelligent agent technology below a link layer; and secondly, the system performs link admission control according to real-time monitoring data by using a dynamic regulation rule method, and dynamically judges whether a new relay link needs to be established and provides an optimal relay node selection when the data transmission bandwidth between the two nodes is insufficient. And finally, multi-link transmission data scheduling is carried out by adopting a mode of combining deep reinforcement learning and a heuristic scheduling algorithm, and the bandwidth utilization rate is improved to the maximum extent. On the whole, an efficient and intelligent network link management scheduling scheme is provided through combination of methods of intelligent proxy, dynamic regulation rules, deep reinforcement learning and the like, and the utilization rate of network resources and the quality of data transmission are effectively improved.

The invention relates to the technical field of combination of communication network management and artificial intelligence, in particular to an intelligent network traffic anomaly monitoring system fused with artificial intelligence, which comprises a data acquisition unit, a feature extraction and processing unit, an intelligent anomaly detection unit, a fault prediction and health management evaluation unit, a decision response unit and a continuous model optimization unit. Through multi-modal intelligent fusion, lightweight modeling and hybrid modeling technologies, real-time accurate identification and potential threat positioning of network flow abnormal behaviors are realized, network equipment faults and health states are predicted based on interpretable AI and physical law fusion, a transparent and prospective decision basis is provided for network operation and maintenance, and the network flow abnormal behavior prediction method has the advantages of high reliability and high reliability. The anomaly detection accuracy is greatly improved, the fault prediction advance exceeds 24 hours, and the network security and reliability are remarkably enhanced.