1529 results about "Network configuration" patented technology

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention discloses an information physical security defense method, system and device based on a distribution network digital twin simulation platform and a medium, and belongs to the technical field of information security and control defense of the power distribution Internet of Things, and the method comprises the steps: constructing a three-dimensional channel for interaction of a physical power grid, an information model and simulation data; on the basis of a three-dimensional channel, a three-state dynamic conversion model among a steady state, a transient state and a recovery state is constructed, and multi-state automatic switching simulation is achieved through sub-region division and parallel computing; a multi-level risk modeling system oriented to various risks is constructed based on simulation results, complex attack scenes are identified, and mapping of risk types and response paths is achieved; security risk assessment and defense strategy generation verification are completed, and intelligent collaboration and continuous learning of cross-domain defense strategies are achieved through collaborative optimization. According to the method, the construction efficiency of a complex risk scene is improved, quantitative analysis of cross-domain risks such as circuit breaker mis-tripping caused by network attacks is realized, and the limitation of traditional single-domain risk analysis is broken through.

The embodiment of the invention provides a network fault diagnosis method, a network fault diagnosis system, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining multi-dimensional data of network equipment, the multi-dimensional data comprises equipment configuration information, network topology information, performance index data and historical fault records; constructing a knowledge graph according to the multi-dimensional data, wherein the knowledge graph comprises nodes representing the network equipment, equipment links, network events and equipment configuration items, and edges representing a physical connection relationship, a logic dependency relationship, an equipment dependency relationship, network configuration information and a causal relationship among the nodes; performing map reasoning based on the knowledge map to obtain a map reasoning result; and generating diagnosis information for a target fault according to the map reasoning result. According to the embodiment of the invention, through multi-dimensional data integration, knowledge graph construction and automatic reasoning, the diagnosis efficiency, accuracy and adaptability are remarkably improved.

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

The invention relates to a TSN network configuration automatic generation method and system based on an anti-Agent. The system is composed of a network state perception and intention reflection module, a YANG model configuration parameter and structure decision module and a YANG model intelligent generation module. The network state sensing and intention reflecting module is responsible for structurally sensing the network state and resource information, the YANG model configuration parameter and structure decision module fuses multi-source knowledge and context information, intelligently generates deployable TSN scheduling parameters and a YANG configuration structure for service requirements, and sends the TSN scheduling parameters and the YANG configuration structure to the network state sensing and intention reflecting module. And the YANG model intelligent generation module automatically generates a corresponding YANG configuration model according to an optimized result, and issues the YANG configuration model to target equipment after completing grammar and semantic verification, so that automatic deployment of network configuration is realized. According to the method, the intellectualization level of TSN network configuration and the deployment automation capability are remarkably improved by introducing the reflection reasoning and feedback optimization technology.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention relates to the technical field of computer hardware and network communication, in particular to a high-speed I / O (input / output) virtualization server based on an FPGA (field programmable gate array) and a remote forwarding method.The high-speed I / O virtualization server comprises an FPGA chip, a network processing unit, an I / O protocol processing unit, a protocol conversion and scheduling unit, a time sequence and transaction management unit and a data buffering and flow control unit are integrated in the FPGA chip; the optical fiber Ethernet interface module comprises a physical layer interface and an MAC layer processing and network configuration interface; the local I / O interface module comprises a USB interface circuit, a serial port interface circuit, a PCIe interface circuit and an interface expansion connector; the power supply and monitoring module comprises a multi-path power supply management system, a state monitoring and diagnosis system and an LED indication system; according to the self-adaptive Baud rate detection circuit, the Baud rate can be accurately locked under the condition that data with less than two characters is received, the number of data bits, the type of a check bit and the length of a stop bit are automatically identified, and compared with a traditional microcontroller scheme, the self-adaptive Baud rate detection circuit has higher precision and higher detection speed.

The invention provides a general electromechanical transient modeling method and system for a network-building converter, and relates to the technical field of electromechanical transient modeling, and the method comprises the steps: simplifying the electromagnetic transient models of network-building converters of different topologies into a grid-connected equivalent circuit of an electromechanical transient time scale; uniformly representing by using an internal potential series reactance model controlled by a power outer ring so as to establish an electromechanical transient general grid-connected interface model of the grid-forming converter; combining mathematical transformation of active power control with a reactive power control mode to form a general power outer loop control model of the network construction type converter; through a method based on internal potential reconstruction, reconstructing a new internal potential satisfying a current limiting constraint, and establishing a general current limiting model suitable for a network construction type electromechanical transient model; according to the method, various topological structures, various power outer loop control and various current limiting strategies can be simulated, flexible use of models in large-scale power system simulation is facilitated, and the complexity of model development and maintenance is reduced.

Aspects of the subject disclosure may include, for example, generating network configuration data for a network device according to device information of a vendor; providing the network configuration data to an Artificial Intelligence (AI) analysis platform that applies an AI model to the network configuration data based at least in part on topology data resulting in a configuration analysis, wherein the configuration analysis evaluates the network configuration data and generates at least one of rating information, adjustment information or a combination thereof. The rating information can include a security rating, a risk rating and / or an efficiency rating. The adjustment information can include a recommendation for changing at least a portion of the network configuration data for the network device.

Methods and apparatus for biometric data maintenance, access and distribution across two or more experiential and / or network domains. In one embodiment, a 5G NR-based network architecture is provided which allows ultra-low latency and effectively user-imperceptible biometric data use for e.g., authentication and maintenance of user state across multiple domains via multiple constituent user devices (e.g., UEs). The network architecture includes both (i) a distributed biometric database (BDB) model wherein relevant biometric data for individuals / UEs is intelligently cached in various portions of the distributed database, and (ii) centralized and local BAEs (biometric analytics entities) which manage the aforementioned intelligent caching, as well as network configuration using one or both of 5G NR network “slicing” and CU / DU split options to optimize end-user biometric-related applications such as those providing identification / authentication, AR functions, VR functions or yet others.

The invention relates to the technical field of monitoring and intelligent decision analysis, and discloses a monitoring and intelligent decision system for the influence of holographic blasting vibration on slope stability, and the system comprises a data collection module, a data processing module, an intelligent decision module, and a feedback control module. The method comprises the following steps: dynamically deploying a three-dimensional vibration sensor array based on topographic features; collecting and preprocessing blasting vibration signals and geological data; fusing the physical model and the data driving model to predict vibration propagation; calculating a dynamic safety coefficient and generating a multi-objective optimization scheme; and outputting an early warning instruction and feeding back and adjusting the sensor network configuration. Through a three-dimensional dynamic monitoring network, real-time data processing, a hybrid drive prediction model, multi-target quantitative evaluation and hierarchical early warning regulation and control, the problems of incomplete sensor coverage, early warning delay, prediction deviation, evaluation deficiency and poor scene adaptability are solved, and accurate monitoring and intelligent decision making of blasting vibration influence are realized.

This disclosure is generally directed to wireless communication systems and methods and relates particularly to a mechanism for implementing an artificial intelligence framework for adaptively configure the over-the-air communication interfaces of the wireless communication systems. AI network configuration functions may be provided as services for AI-assisted network configuration (AI configuration services, or AICS). Such AICS may be requested and configured via various messaging and signaling mechanisms. The AI model life cycle management including training, delivery, activation, inference, deactivation, switching, performance evaluation, and the like may be configured, triggered, and otherwise provisioned via control and data messages and signaling communicated between the various network elements in the wireless communication system.

The invention belongs to the field of stability analysis of a grid-connected system of a grid-building converter, and particularly relates to a grid-building converter grid-connected resonance risk analysis method considering virtual impedance, which comprises the following steps: S1, establishing a three-phase time domain differential equation and simplifying the equation into a single-phase equivalent time domain model; converting into a harmonic state space model of the main circuit; s2, constructing a harmonic state space model of a control system comprising an active power control loop and a reactive power control loop; s3, introducing a virtual impedance control link, and correcting the voltage amplitude reference phasor output by the reactive power control loop; deriving an expression of a modulation signal disturbance phasor; s4, obtaining a complete frequency domain admittance model YGSC of the network construction converter considering the influence of the virtual impedance; an open loop gain TGFM of the grid converter grid-connected system is constructed; and S5, drawing a Nyquist curve based on TGFM, and carrying out resonance risk determination. According to the method, the interaction dynamic characteristics of the network building converter and the power grid under the influence of the virtual impedance can be accurately represented, and the resonance risk of the grid-connected system is evaluated.

Provided in the present invention is a method for CLI measurement report transmission performed by a user equipment, including: when the user equipment is configured by a network to transmit a CLI measurement report on the basis of a CLI measurement event, determining, on the basis of a CLI measurement result acquired by performing CLI measurement, whether there is a CLI measurement resource satisfying an entering condition of the CLI measurement event among CLI measurement resources corresponding to the CLI measurement result; and when the number of CLI measurement resources satisfying the entering condition of the CLI measurement event reaches a numerical threshold for reporting, transmitting a CLI measurement report to the network, the CLI measurement report comprising indication information related to the CLI measurement resources satisfying the entering condition of the CLI measurement event.

The invention discloses a network processing system based on artificial intelligence, and the system comprises an intelligent sensing layer which is responsible for collecting the flow, delay and topology original data of network equipment in real time, carrying out the initial processing at an edge node, generating structured network state information, and outputting the structured network state information to a cognitive analysis layer; the cognitive analysis layer is used for performing four-dimensional modeling and intelligent analysis on the network state after receiving the structured data of the intelligent perception layer; then generating an optimization decision and outputting the optimization decision to an adaptive execution layer; the self-adaptive execution layer is responsible for converting an optimization decision into specific network configuration and operation according to the decision of the cognitive analysis layer, feeding back an execution result to the man-machine collaboration layer, and receiving an administrator confirmation or adjustment instruction from the man-machine collaboration layer at the same time; the man-machine collaboration layer is responsible for interaction between the system and an administrator and displaying a real-time network state and an AI decision causal chain; and an administrator monitors the network state through the man-machine collaboration layer, confirms or rejects AI suggestions, and feeds back an operation log to the cognitive analysis layer.

The invention provides an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: determining an energy scheduling target node and a node interaction strategy through a block chain network according to the real-time operation data of energy storage equipment collected by an Internet of Things platform, and generating a decentralized scheduling instruction; docking a target node edge calculation unit according to the instruction, and outputting network configuration parameters; global state monitoring is carried out through an end-edge-cloud collaborative architecture, and data of charging and discharging abnormity of a physical layer and communication abnormity of a service layer are acquired; generating an exception handling strategy based on an adaptive deep reinforcement learning model, recalculating an energy scheduling target node, forming a closed-loop control instruction, and feeding back the closed-loop control instruction to the block chain network; according to the invention, processing delay easily caused by dependence on a master-slave block chain structure in a shared energy storage scene is avoided; the adaptability to the dynamic environment is enhanced, the sudden electricity price change or equipment abnormity is handled through automatic adjustment, and the effect of decision making in the complex power grid environment is improved.

In some implementations, a network test device may obtain a labeled dataset based on a radio access network intelligent controller (RIC) test simulation. The network test device may generate an artificial intelligence or machine learning (AI / ML) model that is trained using the labeled dataset. The network test device may receive, via a user interface, a network configuration associated with a user application deployed on an RIC. The network test device may predict, using the AI / ML model running on the network test device, a key performance indicator (KPI) based on the network configuration. The network test device may provide, via the user interface, an indication of the KPI.

A remote user equipment (UE) configured to access a network via one of a direct link with a base station or a relay link with a relay UE performs a method including receiving link selection criteria, the link selection criteria configured by the network, identifying that the relay UE is available for camping, selecting the relay UE based on the link selection criteria, camping on the relay link corresponding to the relay UE and initiating a network access procedure via the relay link.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

The invention discloses a micro-grid off-grid operation distributed control method based on consistency cooperation, belongs to the field of new energy micro-grid control, and aims to solve the problems of high single-point fault risk and single control target of existing centralized control. The method is operated in a three-layer architecture formed by a distribution network master station control layer, a micro-grid coordination control layer and an equipment distributed coordination control layer, a distributed communication network is constructed through edge agents, and weight coefficients and security constraints are configured; the edge agent receives a master station total power target, collects and interacts equipment data, calculates and limits amplitude to obtain a final power instruction based on a consistency updating law fusing global power difference distribution, energy storage SOC balance and system loss minimization, and then decomposes the final power instruction into equipment-level instructions to be issued and executed. According to the method, a single-point fault is eliminated, multi-target collaborative optimization is realized, control is simplified, expansibility is high, and power balance and stable and efficient operation when the micro-grid is off-grid are effectively guaranteed.

The invention relates to a measurement adjustment method, terminal equipment, network equipment, a chip, a computer readable storage medium, a computer program product and a computer program. The measurement adjustment method comprises the following steps: the terminal equipment adjusts SMTC and / or first information corresponding to the SMTC for measurement of a plurality of satellites or / cells under the condition that a first condition is met; wherein the first condition is related to a plurality of satellites or cells, and the adjustment comprises autonomous adjustment of the terminal equipment or adjustment according to network configuration. The embodiment of the invention can be suitable for measuring the movement characteristics of different satellites.

Embodiments here in disclose a method and system for network configuration generation using a network digital twin (NDT) comprising a spatio-temporal graph neural network (STGNN) for performance-based simulation in a communication network system is disclosed. A simulation request is received from a management service (MnS) consumer, including network parameters related to live network nodes. Performance data is retrieved from a management server, and performance is assessed against a performance threshold. Multi-domain telecom data is processed using a spatio-temporal graph neural network (STGNN) by a feature fusion attention mechanism, trained via a hybrid feedback loop with real and synthetic data. Upon meeting performance thresholds, a configuration recommendation is generated by a configuration recommendation engine, including an explanation and confidence value. The configuration recommendation is transmitted to the MnS consumer, enabling network automation without altering the live network directly.

Methods, systems, and computer-readable media for automated packetless network reachability analysis are disclosed. An analysis is performed of network configuration data for a network comprising a host computer. Based at least in part on the analysis, one or more ports at the host computer that are reachable from another computer are determined. Based at least in part on the analysis, one or more routes to the one or more ports are determined. A report is generated that is descriptive of the one or more ports and the one or more routes.

Techniques are disclosed for validating a cloud region built at a prefab factory. A computing device of the cloud region can receive a network configuration from a manager service. The network configuration can correspond to a network topology of physical resources in the cloud region and can include a first identifier associated with a computing device, a second identifier associated with a neighboring computing device, and information associating the computing device with the neighboring computing device. The computing device can be configured for transmitting to a second data center and can boot into a test mode at the second data center and receive a new identifier from a server device. The computing device can verify the new identifier and send a validation request to the neighboring computing device. The computing device can validate a network connection to the neighboring computing device based on a response to the validation request.

The embodiment of the invention discloses a network configuration method and device, a data forwarding method and device, equipment and a storage medium, and the network configuration method comprises the steps: obtaining slice configuration information between source end equipment and sink end equipment, and the slice configuration information comprises a network slice ID, bandwidth information, a segment routing traffic engineering strategy and a target service identifier; generating a routing path between the source end device and the sink end device according to the bandwidth information and the segment routing traffic engineering strategy; generating a slice configuration command according to the slice configuration information and the routing path, and issuing the slice configuration command to each device in the routing path, the slice configuration command is used for instructing each device to transmit target service data corresponding to the target service identifier according to a network slice channel corresponding to the network slice ID. According to the embodiment of the invention, the data transmission efficiency can be improved, and the waste of bandwidth resources is reduced.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

A heterogeneous multi-hop wireless network is provided. The network consists of single-link data nodes, multi-link data nodes and data centers. The single-link data node supports one communication interface, the multi-link data node supports two communication interfaces and the data center is considered as multi-link node. The routes from all data nodes to data centers are given. The network is configured to schedule data transmissions from all data nodes to data centers without transmission interference and channel access delay. The routing link scheduling problem is formulated as an optimization problem with constraints. Due to the NP-Hard complexity of the formulated optimization problem, the scheduling policies are then parameterized for the application of graph neural network techniques. The parameterized optimization problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is provided to train the primal-dual problems.

In order to manage virtual private cloud (VPC) network configuration, a network topology and configuration of each network element are firstly obtained, and then a reachability intention including a target function and an intention attribute is defined. In one embodiment, a network representation is generated that describes how traffic is processed in a network element through an operable filter function, and a minimization problem is defined by converting the network representation into a MaxSAT formula that is used to minimize the number of decision variables to be modified. And according to a target function, defining a decision weight for the decision variable, and obtaining a solution of a minimization problem through a MaxSAT resolution algorithm weighted by the decision weight to generate an optimized network configuration.

An example method may include receiving first configuration data associated with a network device, the first configuration data comprising a Yet Another Next Generation (YANG) payload; determining a first subset of the YANG payload that is associated with a first network configuration database; determining a first database transaction, wherein the first database transaction represents one or more database updates to the first network configuration database that is determined based on a first state of the network device and one or more configuration requirements represented by the first subset; discarding the first database transaction; determining second configuration data based on the first database transaction, the second configuration data comprising a command line representation (CLI) of the YANG payload; and providing the second configuration data to a system, the system configured to display the command line representation to a user.

The invention discloses a network configuration automatic recovery method and device, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: acquiring a network configuration request for target network configuration, and calling a system command based on the network configuration request to execute a network configuration operation for the target network configuration; if the network configuration operation is successfully executed, persistently storing the structured data of the current target network configuration into a preset independent storage space; and when an operating system where the gateway software is located is restarted, executing a preset configuration recovery process so as to read the structured data of the stored network configuration from the preset independent storage space, and re-issuing the stored network configuration to the operating system through a preset multiplexing issuing logic so as to complete recovery of the network configuration. Therefore, the network configuration can be automatically recovered after the system is restarted, and the adaptability and manageability for different operating system versions are improved.