1801 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 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 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 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 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.

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.

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.

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 relates to a power line network topology estimation method and device and computer equipment. The method comprises the following steps: sending a modulation signal, and receiving a plurality of reflection signals corresponding to the modulation signal; determining a path length set and a current topological structure according to the signal propagation time corresponding to each reflected signal; the signal propagation time is a time difference between the sending time of the modulation signal and the receiving time of the reflection signal; selecting an expansion node from the current topological structure, and updating the current topological structure according to the expansion node and the path length set; the extension nodes are unselected nodes in the current topological structure. According to the topology estimation method, the complexity and cost of system deployment are reduced through a single modem implementation method, the topology structure is updated by analyzing the expansion nodes, dynamic network changes are supported, the applicability of the topology estimation method is improved, and the efficiency of topology network management and maintenance is improved.

The invention relates to the technical field of intelligent management of oilfield water injection systems, and particularly discloses a water injection pipe network operation situation analysis and energy consumption evaluation method and system. According to the method, real-time data and pipe network parameters of an oilfield water injection system are collected, and after anomaly detection and data cleaning treatment, a node pressure matrix and pipe section flow distribution are generated; performing residual analysis and time sequence modeling by combining real-time data and a simulation result to realize accurate positioning of operation abnormity; a fuzzy analytic hierarchy process for dynamic weight adjustment is adopted to construct a four-stage energy efficiency evaluation system of water injection station-pipe network-water injection well-overall system, and an energy consumption bottleneck thermodynamic diagram is generated; and finally outputting a strategy rehearsal report and a fusion cockpit. According to the method, the change of the water injection pipe network from'black box 'to'transparent' management is realized, the technical problems of single energy efficiency evaluation, scheduling dependence on experience and the like in a traditional method are solved, the system operation efficiency can be remarkably improved, and the energy consumption is reduced.

The invention relates to the technical field of intelligent city management, in particular to an intelligent drainage pipe network management system which comprises a drainage pipe network data monitoring module, a local health assessment module, an intelligent flow adjustment module, an abnormal fault recognition module and an operation state optimization module. According to the method, through comprehensive monitoring and time sequence analysis of the water flow speed, the pipeline pressure, the fluid temperature, the pipe wall vibration frequency and the data transmission state, the accuracy of data integrity evaluation is improved, the stability and reliability of data are enhanced, and the analysis can accurately reflect the pipe network state; the early warning capability of the health state of a local pipeline is improved, the pertinence of flow regulation and control is optimized by calculating the flow distribution ratio and recognizing the flow difference ratio of adjacent pipelines, and meanwhile, the refinement of fault diagnosis is improved by comprehensively analyzing abnormal characteristics and fault data, so that the operation state of a pipe network is optimized, and the fault diagnosis efficiency is improved. And the overall management precision and the operation stability are enhanced.

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

The invention discloses a municipal pipe network overflow detection and intelligent repair method, and relates to the technical field of municipal pipe network management, and the method comprises the following specific steps: data fusion and basic analysis: employing a plurality of sensors to collect various data in real time, and carrying out the fusion and preprocessing in a data processing center, comparing and analyzing historical and real-time data to set a threshold value, and finding out an external environment change factor influencing the operation of the pipe network to provide basic data; through deep integration of meteorological, hydrological and municipal pipe network related data and by means of an intelligent hydrodynamic simulation technology, high-precision simulation of water flow in a pipe network can be realized, the occurrence position, time and influence range of overflow can be accurately predicted in advance, and the risk of overflow can be accurately evaluated based on accurate overflow risk evaluation. The system can automatically formulate and adjust a pipe network operation parameter regulation and control strategy in real time, parameters of the drainage pumping station and the opening degree of a pipe network valve are intelligently adjusted according to different overflow risk grades, and therefore the pipe network operation parameters can be effectively optimized.

The invention discloses a traffic data security encryption and circulation system and method based on a block chain, belongs to the technical field of traffic control, and aims to solve the problems of security, privacy and trust in traffic data sharing. The system comprises an edge computing node which is deployed near a data source and is used for performing encryption or privacy enhancement processing on original traffic data; a block chain network with a smart contract is deployed, node identities are managed, verification information is recorded, an access strategy is stored, and data circulation is controlled; and the data processing unit is used for aggregating the processed data to generate a circulatory data product. The method comprises the steps of edge security processing, block chain management verification, product generation by the data processing unit and management and control circulation of the block chain according to a strategy. Through edge security processing and a block chain trust mechanism, data security privacy is guaranteed, safe, transparent and credible circulation and utilization of traffic data are realized, and reliable data support is provided for intelligent upgrading and digital transformation of the traffic industry.

Disclosed are methods, systems, devices, and other implementations, including a method that includes obtaining measurement data from at least part of a wireless communication network during a first interval of time, with the measurement data including measurements indicating whether there are potential disturbances to operation of the wireless communication network. The method further includes determining, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time, and managing resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval.

The invention provides a network security assessment method and system based on threat intelligence, and the method comprises the steps: obtaining a threat intelligence data set and a to-be-assessed network data set, and carrying out the feature association extraction processing, thereby obtaining a target security feature set; the target security feature set covers threat and component association matching, threat and behavior influence conduction and network component protection capability features, calling a pre-training security assessment model to carry out risk situation analysis on the target security feature set, and generating a network security assessment result including a potential threat triggering probability and a threat influence propagation path, and determining a to-be-evaluated network risk level and key risk point distribution information according to the network security evaluation result, generating a network security evaluation report containing a risk positioning identifier, and outputting the network security evaluation report to the network management terminal to support security policy adjustment operation, so that the network security condition can be comprehensively and accurately evaluated, and the user experience is improved. And the pertinence and effectiveness of safety protection are improved.

Techniques are disclosed for sending downloadable software packages that may be executed to perform tests of a network device without requiring the network device to update firmware. In one example, the network device obtains a software package comprising instructions for simulating a network instance and stores the software package in a volatile memory of the network device. The network device simulates, based on the instructions, the network instance to obtain data indicative of a performance of the network instance. The network device sends, via a communication channel established with a network management system (NMS) based on the software package, the data indicative of the performance of the network instance. In some examples, the NMS uses the data indicative of the performance of the network instance to identify a root cause of a failure of the network device or a remedial action to be performed upon the network device.

A method of initiating actions based on analyzing automated fix actions in a telecommunication network management system, the method comprising obtaining, by an incident management application executing on a computer system, prior incident reports associated with a plurality of historical incidents from a database, wherein the prior incident reports comprise one or more recommended corrective actions taken or to be taken to resolve the historical incidents; evaluating, by the incident management application, the prior incident reports to determine a severity level of the prior incident reports and / or an effectiveness value of the one or more recommended corrective actions; prioritizing actions to be taken automatically or by NOC personnel based on the severity level and / or the effectiveness value; and initiating a corrective action to be taken automatically or by the NOC personnel to resolve an active incident report based on the prioritization.

Some embodiments of this disclosure include systems, apparatuses, methods, and computer-readable media for use in a wireless network for deploying User Plane Function (UPF) and edge computing virtualized network functions (VNF). For example, some embodiments are directed to a network management system. The network management system includes processor circuitry. The processor circuitry can be configured to transmit a request to an edge computing management system to instantiate an edge computing virtualized network function (VNF). The request can include an identifier of a Network Service (NS) where a User Plane Function (UPF) VNF supporting the edge computing VNF is located. The processor circuitry can be further configured to receive, from the edge computing management system, a response indicating that the edge computing VNF is instantiated.

The invention discloses an intelligent underground drainage pipe network management method and system, and relates to the field of drainage pipe network management, and the method comprises the steps: obtaining the real-time liquid level and real-time flow of an upstream monitoring point of a target pipe section; dividing the monitoring data into water rising time period data and water recession time period data; two-way resistance characteristic analysis is conducted on the liquid level-flow model, resistance release characteristics and resistance recovery characteristics are recognized, and the resistance release characteristics are that when the liquid level at the water rising time period or the water rising and recession junction reaches a first threshold value, the flow is sharply increased, and the liquid level is stagnated in rising amplitude or suddenly changed in falling; the resistance recovery characteristic is that the liquid level rebounds and rises when the flow is reduced to a second threshold value in the water recession period; when the two types of features are detected at the same time, it is preliminarily judged that soft intermittent blockage abnormity exists in the target pipe section; and generating a flexible dredging management work order based on a judgment result, and updating an asset state mark in a pipe network operation and maintenance database. The method solves the problem that it is difficult to timely and accurately identify the soft intermittent blockage abnormity and carry out linkage operation and maintenance disposal.

The invention relates to the technical field of urban drainage pipe network detection and evaluation, in particular to an intelligent pipeline defect evaluation method based on a multi-modal large model. The evaluation method comprises the following steps: firstly, carrying out defect identification on a pipeline detection video based on a deep learning model to obtain a series of defect images containing defect types and positions, and carrying out statistics and segmented scoring on the positions of the defects and the number and distribution of pipe section defects; then, the obtained images with defects are transmitted to a multi-modal large model for deep analysis, scoring is performed from multiple aspects such as the position and size of the defects, the influence on the pipeline structure and the use stability, and the scoring standard and rule are unified by adopting fixed cue words; and finally, weighting by using the two scoring results to obtain the comprehensive score of each pipe section, and synthesizing the scores of each pipe section into the comprehensive score of the pipeline. The evaluation objectivity and accuracy are remarkably improved, the detection and evaluation efficiency is greatly improved, the maintenance decision is more scientific and accurate, and the pipe network management level and safety are improved.

Techniques for a service provider network to manage a meeting between individuals located in a conference room with one or more other individuals remote from the conference room are discussed herein. An Enterprise system can implement one or more machine learned models to generate tiles that represent different individuals associated with a meeting. A same or different machine learned model can automatically arrange a series of tiles for display that, when presented collectively, promotes inclusiveness and attention for both in-room and remote participants of the meeting. The meeting management techniques can include determining which content to include on a display device based on evaluating changes in behavior of the meeting participants over time.

Techniques are described for onboarding a plurality of devices using a network management system (NMS). The NMS has memory and one or more processors coupled to the memory, the one or more processors configured to obtain onboarding information of a first device of a plurality of devices; assign, based on obtaining onboarding information of the first device, the first device to a group of devices to be managed by the network management system; and based on assigning the first device to the group of devices to be managed by the NMS, automatically assign a second device of the plurality of devices to the group of devices to be managed by the network management system.

The invention belongs to the technical field of network management, and particularly relates to a road toll collection system network security task dynamic allocation and load balancing method, which comprises a road toll collection system, and the road toll collection system comprises a network security task dynamic allocation system and a load balancing system. The network security task dynamic allocation system serves as an intelligent security protection scheduling center, is used for coping with continuous evolution threats, and comprises a real-time threat perception and response scheduling integration, a resource elastic scaling system and a strategy self-adaptive adjustment system. The load balancing system serves as a foundation stone of service continuity and performance, ensures stable operation of high-concurrency transactions, and comprises an intelligent flow distribution system, an automatic fault isolation and recovery system and a load balancing system. The method can deal with sudden security events, optimize the resource utilization rate, improve the protection precision, guarantee high availability, improve the user experience and support the elastic expansion of the system.

The invention discloses a hierarchical authority control method and platform for a network management platform oriented to cloud side-end collaboration. The method comprises the following steps: constructing a topological structure by collecting equipment resources, user identities and authority level information of all levels of platforms; based on a task scheduling and resource management algorithm and an edge-cloud collaboration model, factors such as task types and resource loads are comprehensively considered, and initial allocation and dynamic adjustment of tasks between cloud edge ends are achieved; verifying a user request by using the permission level information, recycling resources after a task is completed, and feeding back a result; and regularly collecting and analyzing data, and optimizing an algorithm, a model and an authority control strategy. The platform comprises a resource information acquisition unit, a task initial allocation unit, a task cooperative processing unit, an authority verification unit, a resource recovery feedback unit, a strategy optimization unit and the like, and the units are mutually connected and cooperated. According to the method, efficient task scheduling, accurate authority control and reasonable resource utilization of the cloud side-end collaborative network are realized, and stable operation of a network management platform is guaranteed.

An embodiment method for managing at least one vehicle network including at least one electronic control unit (ECU) among a plurality of ECUs in a vehicle network management apparatus is provided, which may include, based on information of at least one default partial network cluster including at least one ECU among the plurality of ECUs according to at least one default partial network function provided by default in a mapping database, configuring a new partial network cluster by adding an ECU according to user customization to the at least one default partial network cluster, and storing information of the new partial network cluster in the mapping database.

To perform channel monitoring, a user equipment monitors (1104) a physical downlink control channel (PDCCH) using a radio network temporary identifier (RNTI), during a small data transmission (SDT) procedure and when the UE operates in an inactive state (1102). The UE transitions from the inactive state to a connected state in response to a command from a radio access network (RAN) (1110); and monitors (1110) the downlink control channel using the RNTI after the transitioning, when the UE operates in the connected state.