2792 results about "Network management" patented technology

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

The invention relates to the technical field of network security, in particular to a network security situation awareness method and system, and the method comprises the following steps: extracting a source IP address and a target IP address based on a network behavior record, carrying out the statistics of the number of used ports, the transmission direction and the time interval value, analyzing the direction change times and the time interval difference, and screening abnormal communication pairs. And generating an abnormal communication pair set. According to the invention, through analyzing port usage, transmission direction and time interval value, deeply mining communication features, screening abnormal communication pairs and improving identification accuracy, dividing a time sequence window, analyzing rate fluctuation and frequency distribution, locking an unstable time window, combining with a multi-dimensional data classification behavior mode, and extracting a switching path and priority, a multi-dimensional data classification behavior mode is combined. Potential threat paths are identified independently, global threat situations are identified through node interaction relation statistics and correlation analysis and an expansion range, threat assessment precision and efficiency are enhanced, and comprehensive and reliable risk protection capability is provided for network managers.

The invention discloses an optical transmission network hidden danger detection method and device and a computer program product, which can realize dynamic adjustment of differentiated bandwidth requirements by acquiring optical network state information in real time, evaluating service priority and establishing an optical channel resource allocation model. And when a topology anomaly is detected, constructing a service quality evaluation model by using a long-short-term memory neural network, and quantitatively analyzing the influence of the anomaly on the service. When a fault occurs, distributed link fault detection and a fault diagnosis engine based on a decision tree are adopted, and fault hidden dangers are quickly positioned and classified. And then, through an elastic recovery mechanism oriented to service sensitivity, the service quality level of a standby optical path is adjusted, and an optical layer topology connection relationship is optimized. According to the method, the resource utilization efficiency, the fault recovery capability and the service quality of the optical network are remarkably improved, and an innovative solution is provided for intelligent optical network management.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

A low earth orbit satellite (LEO)-based communication system and method. The system comprises a low-orbit satellite constellation, a ground station, a user terminal and a network management and control center. The network management and control center comprises a track and link prediction module, a link quality prediction and evaluation module, a predictive resource pre-allocation and dynamic adjustment module and a space-ground cooperative control module. According to the communication system, active resource optimization and dynamic adjustment before actual switching are realized through track and link prediction, link quality prediction, predictive resource pre-allocation and dynamic adjustment and air-ground cooperative control. According to the method, the switching time delay can be greatly reduced, the packet loss rate is remarkably reduced, and the system throughput and the access success rate are also remarkably improved. Through predictive management, the performance, reliability and user experience of LEO communication are comprehensively improved.

The invention relates to the technical field of network management, in particular to an Internet of Things gateway control method and system based on AI computing power, and the method comprises the following steps: obtaining channel real-time signal intensity, noise level and occupancy proportion, monitoring protocol type and transmission condition, analyzing the relationship between retransmission times and interference, deducing protocol combination interference and throughput, and obtaining a real-time channel signal intensity, noise level and occupancy proportion. And configuring an optimal scheduling scheme, evaluating resource consumption and load states, packaging and requesting permission entrustment, and judging permission information in combination with an interaction record and an access control list. According to the method, different protocol distribution combinations are simulated and deduced, the data throughput of the whole network is further estimated and optimized, potential protocol conflicts are reduced, the use efficiency and the overall performance of network resources are effectively improved, consumption of a local computing unit and network bandwidth is comprehensively considered, packaging processing of request information is optimized, and the network performance is improved. And the real-time response capability and manageability of the network state are obviously improved.

A network management system (NMS) generates a hierarchical attribution graph representing different scopes at different hierarchical levels of a wide area network (WAN); obtains logical path down data indicative of operational behavior including failure events associated with logical paths of network devices over the WAN; obtains total path data indicative of a historical number of active logical paths between the network devices; and determines a scope of a logical path down issue by, for a time period of the logical path down issue, determining a score for each scope of the different scopes based on the logical path down data aggregated across the respective scope and the total path data, and determining the scope of the logical path down issue as a particular scope of the different scopes having a highest score. The NMS may identify the particular scope as a root cause of the logical path down issue.

The invention provides an urban underground cable pipe network management method and system based on digital twinning, and the method comprises the steps: firstly integrating basic attributes and real-time perception data of an urban underground cable pipe network, and constructing a pipe network digital twinning mapping model which comprises the digital reproduction of physical entities and the dynamic association relationship between the entities; and continuously inputting real-time operation state data of the physical pipe network through a preset data interaction protocol to drive the model to update, carrying out association coupling analysis based on the updated digital twin mapping model of the pipe network, and identifying key nodes and abnormal conduction links of the abnormal pipe network. Simulating the state diffusion process of the abnormal conduction link in different environments by using the model, generating a risk evolution simulation result, generating a pipe network maintenance instruction containing a priority order and a resource allocation scheme according to the risk evolution simulation result and key node position information, and issuing the pipe network maintenance instruction to an operation and maintenance execution system to trigger a maintenance response, and intelligent management is realized.

A network management platform can include a configuration management database (CMDB), an IP address management (IPAM) network device, a network security platform, and a software-defined wide area network (SDWAN). The CMDB can be configured to store configuration data of endpoint devices. The IPAM can allocate IP addresses within the network. The network security platform can be configured to enforce policies and / or manage endpoint access. The SDWAN can be utilized to configure and / or control the endpoint devices.

A unified prompt-based network management system that involves an intelligent auto-prompt engine generating contextualized prompts for an artificial intelligence model. The artificial intelligence model generates instructions and / or solutions and adapts to different application scenarios based on an enterprise network knowledge and reverse inference(s). Specifically, methods are provided that involve obtaining input data related to a configuration or an operation of one or more assets in an enterprise network and generating a contextualized prompt based on the input data, network knowledge information of the enterprise network, and at least one reverse inference generated using an artificial intelligence model. The methods further involve providing the contextualized prompt to the artificial intelligence model for generating a tailored response to the input data, wherein the tailored response includes a set of actionable tasks to be performed with respect to the one or more assets of the enterprise network.

A secure communication method includes receiving, by a first network element, first information and a first signature from a network management network element. The first signature is generated based on a private key of the network management network element and the first information, and the first information is used to describe the first network element. The method also includes sending, by the first network element, the first information and the first signature to a certificate issuing network element. The first information is used to obtain a first certificate, and the first certificate is used to prove an identity of the first network element. The method further includes receiving, by the first network element, the first certificate from the certificate issuing network element.

The invention discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

The invention discloses an intelligent pipe network topological structure restoration analysis system, and relates to the technical field of intelligent pipe network management, the intelligent pipe network topological structure restoration analysis system comprises a design management platform, and the design management platform is in communication connection with a topological structure data acquisition module, a topological structure analysis module, a pipe network fault diagnosis module, a restoration scheme generation module and a pipe network operation monitoring module. According to the method, the pipe network topological structure model is constructed by integrating various sensors and external data sources, branches, loops or special-shaped connection structures in a complex pipe network are accurately recognized, the judgment capacity of the pipe network connection relation and the flow direction is improved, and the fault diagnosis efficiency is improved by combining the pipe network topological structure model and the flow direction analysis result and utilizing the pre-constructed fault diagnosis model. According to the method, the pipe network fault can be quickly and accurately diagnosed, the fault type and position can be quickly identified by integrating the multi-source data, extracting the fault features and matching the fault features with the preset fault mode, the misdiagnosis rate is reduced, and the fault diagnosis efficiency and accuracy are greatly 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.

To provide a method, an apparatus, a system, an architecture, and an interface for a wireless transmit / receive unit (WTRU) to discover available edge network (EN) management (ENM) servers associated with either an edge network (EN) service and an EN application.SOLUTION: A method may include sending a first message to a core network entity including information indicating a request for discovery of an ENM server, receiving a second message from the core network entity responding to the request for discovery of an ENM server, the second message includes ENM server information indicating (1) any number of available ENM servers and (2) respective available ENM server endpoint information, and performs communication with one or more of the available ENM servers.SELECTED DRAWING: Figure 2

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention relates to the technical field of hydropower engineering, and discloses a single-core self-healing type looped network networking method and system, and the method comprises the steps: dividing a whole network into a plurality of sub looped networks; each sub-ring network operates independently; the light switch in each sub-ring network adopts an A / B end connection mode; reversely connecting the B end of the last measuring point to the A end of the initial measuring station by using an optical fiber to form a closed physical structure; configuring a protocol on the switch, and controlling a data flow path through the protocol; different sub-ring networks are connected through RJ45 ports, and a CAT5E shielded twisted pair or an Ethernet interface is used; when a certain node or link fails, the protocol automatically triggers a switching mechanism; the master station optical transceiver monitors the states of all nodes in real time and displays fault points through a network management interface. The system comprises a sub-ring network module, a control data flow module and a fault point display module. The problems that a traditional networking method of hydropower engineering is poor in reliability, and self-healing cannot be achieved after an intermediate node is disconnected are successfully solved.

A cloud-based architecture includes a cloud-based network management system (NMS) that provides the management plane and one or more cloud-based NAC systems that provide NAC services, including end-point device authentication. The cloud-based NAC system may have multiple groups of authentication server instances. Each group may have multiple authentication server instances to allow the authentication service provided by the group to serve more client devices than a single authentication server instance alone. Different Groups can be configured differently to serve different tenant sets.

A network management system may collect processor usage statistics from one or more network devices. The network management system may determine, for each network device of the one or more network devices, aggregate processor usage statistics across a time window based on the processor usage statistics and, based on an aggregate overall processor usage for a given network device exceeding a baseline threshold, analyze aggregate per-process processor usage for the given network device to determine one or more processes as a root cause of anomalous behavior of the given network device. The network management system may generate a remedial action to remediate the root cause.

The invention discloses a navigation robot self-adaptive communication system and method based on multi-network collaboration, and relates to the technical field of network communication, the system comprises a network management module which is responsible for safely storing encrypted AP information and network topology data and providing a function of receiving AP configuration update through NFC; the signal detection module is responsible for detecting network parameters and generating a network quality evaluation report; the switching control module is responsible for predicting the signal intensity in future set time, realizing microsecond-level decision response by combining a dynamic threshold algorithm and a manufacturer protocol adapter, and providing a decision basis for optimization of network connection; and the dual-channel communication module is integrated with a WiFi chip and a cellular network modem, realizes operator link redundancy through dual SIM (Subscriber Identity Module) card slots, dynamically optimizes WiFi and cellular signal reception by utilizing a built-in intelligent antenna array, and executes network connection optimization and switching operation according to a decision of the switching control module. The navigation robot can automatically adapt to different network environments.

The invention provides a microwave network topology optimization method and system based on a graph neural network, and the method comprises the steps: firstly obtaining a real-time operation data set of a plurality of microwave nodes in a microwave communication network, covering a link quality index, a node load state parameter and an environment interference coefficient, and then carrying out the dynamic feature extraction of the real-time operation data set, the method comprises the steps of obtaining a dynamic propagation feature set containing inter-node signal attenuation features and the like, calling a preset graph neural network model to carry out topological relation modeling on the dynamic propagation feature set, generating an association weight matrix of microwave nodes, and carrying out iterative matching calculation based on the association weight matrix and a preset topological optimization constraint condition, generating a microwave network topology optimization strategy; and finally, the microwave network topology optimization strategy is sent to a microwave network management system to trigger link reconstruction operation, and the microwave network topology can be effectively optimized.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

Methods, systems, and devices for wireless communications are described. A person Internet of Things (IoT) network (PIN) architecture may support configuration and management of a PIN. The PIN architecture may include a first network node configured to establish the PIN, a second network node configured as a network gateway for the PIN, a third network node configured as a network management element for the PIN, and a PINE. The first network node may receive, via a user plane of a network, information from the second network node that triggers the first network node to establish the PIN. The first network node may transmit information that identifies and configures the second network node and the third network node as the network gateway and the network management element, respectively. The second network node may relay communications between the PINE and the network.

A processing system including at least one processor may obtain network operational data of a communication network, transform the network operational data into a text-based format, and train a generative machine learning model implemented by the processing system using the network operational data in the text-based format. The processing system may then receive a query pertaining to the network operational data, apply the query to the generative machine learning model implemented by the processing system to generate a textual output in response to the query, and present the textual output that is generated in response to the query.

The invention relates to the technical field of remote control, in particular to an electrical automation remote control system which comprises an energy receiving and converting module, a dynamic network management module, a node fault diagnosis module, an energy distribution control module and a remote control execution module. According to the method, communication quality is optimized through signal fluctuation analysis, data packet routing and bandwidth allocation are dynamically adjusted, accurate transmission of remote control instructions is ensured, network structure is optimized through real-time network flow analysis, congestion is reduced, transmission efficiency is improved, fault diagnosis is carried out according to a load adjustment result, accurate identification, automatic isolation and routing reconstruction are achieved, and the method is suitable for large-scale popularization and application. And energy management dynamically adjusts energy output based on node supply and demand, ensures stable operation of key nodes, optimizes energy utilization, combines remote control execution with real-time monitoring, ensures accurate instruction issuing, and improves reliability and safety of remote control.

Described herein are technique to enable the autonomous generation of configurations for a network environment, including but not limited to an edge network of a datacenter. Additional embodiments include prompt-based generation of network and device configurations and neural network based systems for adaptive network management.

The invention discloses a network management racing item question answering system based on a knowledge graph, and belongs to the technical field of knowledge graph teaching. In the system, the association relationship among massive network equipment, protocol configuration and fault cases is stored in a knowledge graph structuralization mode, and natural language processing and graph database retrieval technologies are combined; core knowledge points of complex problems can be quickly positioned, and strong scene adaptability and expansion potential are shown through modular design and a dynamic cooperation mechanism. The knowledge graph management module supports incremental updating and cleaning fusion of multi-source heterogeneous data, can integrate newest competition cases and technical specifications in time, and guarantees the frontier and authority of a knowledge base; and the interaction optimization module automatically identifies a knowledge blind area according to the score of the user on the answer and triggers graph completion. The self-feedback and self-iteration mechanism not only reduces the manual maintenance cost, but also provides a technical basis for cross-domain knowledge migration.

Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.

The invention discloses an eSIM service cost intelligent optimization method based on different operator information sources, and particularly relates to the technical field of operator network management. The method comprises the following steps: collecting eSIM tariff rules of a plurality of operators and real-time charging data of eSIM terminals, extracting tariff terms and conditional expense trigger mechanisms hidden in charging behaviors of different operators by using an association rule mining algorithm, and constructing a multi-level cost structure model; on the basis of a network registration behavior log of the eSIM terminal, predicting the probability that each operator signal source has an abnormal registration event, and evaluating the risk that each operator signal source triggers a hidden cost trap in combination with a multi-level cost structure model; and according to the real-time network signal parameters of the eSIM terminal, a signal source recommendation strategy is generated in combination with the risk assessment result, and the eSIM terminal is driven to execute a signal source switching operation, so that real-time adjustment and cost optimization control of communication network selection are realized, and the service stability of the eSIM in a cross-network use scene is improved.