1927 results about "Network control" patented technology

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention provides a power network intrusion analysis method and system based on data fusion, and the method comprises the steps: obtaining a multi-source monitoring data set in a power network, carrying out the dynamic feature fusion processing of the multi-source monitoring data set, and generating a multi-dimensional fusion feature set related to a power equipment node, inputting the multi-dimensional fusion feature set into a preset abnormal behavior recognition model for intrusion analysis processing, generating an abnormal behavior data set of the power equipment nodes, and generating a network security defense strategy set according to the abnormal behavior data set, the network security defense strategy set comprises a real-time blocking instruction and a node state repairing instruction for different abnormal behavior types, feeding back the network security defense strategy set to the power network control center to trigger defense response operation, and adjusting a collection strategy of the multi-source monitoring data set according to an execution result of the defense response operation. According to the invention, the system resource consumption is reduced while the intrusion detection precision is improved.

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and / or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and / or claimed.

The pattern generation method based on diffusion model fine tuning comprises the following steps: constructing a data set; the generation model comprises an edge detection model ControlNet and a stable diffusion model, the stable diffusion model comprises a text encoder CLIP, a diffusion model U-Net and an image decoder VAE, the CLIP text encoder converts an input natural language into word vector features and inputs the word vector features into a U-Net network in a submerged space, and the diffusion model U-Net and the image decoder VAE are connected with the edge detection model ControlNet and the diffusion model U-Net; the U-Net network is controlled to carry out iterative denoising on pure noise in a low dimension to generate a compressed image, and finally the compressed image is restored to an original pixel image through a decoder VAE, so that text content control image generation is realized; carrying out fine tuning training on the generative model by adopting a LoRA parameter efficient fine tuning technology; and intelligently generating a batik style pattern. The method has the advantages that the texture precision of the generated image can be improved, and the personalized style image can be generated.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

An apparatus may be configured to: receive, from a first user equipment, at least one real-time transport protocol header extension configured to handle at least one protocol data unit, wherein the at least one real-time transport protocol header extension comprises, at least, one or more application controlled bits; determine whether the one or more application controlled bits are valid; and set values for one or more network controlled bits of the at least one real-time transport protocol header extension based, at least partially, on a determination of whether the one or more application controlled bits are valid, wherein the one or more application controlled bits comprise bits that do not overlap with the one or more network controlled bits.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to the technical field of data analysis, and provides a power network data-driven optimization method and system based on dynamic authority modeling, which are used for improving the self-adaptability and anti-risk capability of a power network in a complex operation environment. The method comprises the steps of obtaining a power network operation data set, performing dynamic permission modeling processing on the power network operation data set, generating a user permission feature set and an equipment permission feature set, and generating a power resource dynamic allocation strategy according to the user permission feature set and the equipment permission feature set, and feeding back the power resource dynamic allocation strategy to the power network control system to activate a permission configuration updating operation. Therefore, through deep coupling of the permission model and resource scheduling, a technical path giving consideration to both elasticity and reliability is provided for intelligent upgrading of a power system, so that the self-adaptability and anti-risk capability of a power network in a complex operation environment can be improved.

The embodiment of the invention relates to the technical field of robot control, in particular to a robot control method and system combining fuzzy control and a neural network. The method comprises the steps of obtaining a multi-dimensional state data set of a robot in a target operation scene, performing fuzzy control feature extraction processing on the multi-dimensional state data set, and calling a pre-trained neural network control model to perform hierarchical decision processing on the fuzzy control feature set, and generating a real-time control instruction set of the robot, adjusting the operation state of the robot in the target operation scene based on the real-time control instruction set, and feeding back the adjusted operation state to the multi-dimensional state data set to execute loop control processing. In this way, the robot can be controlled more accurately, intelligently and dynamically, and the problems that in the prior art, control is not accurate under a complex scene, and adaptability is poor are effectively solved.

The invention relates to the technical field of power distribution network operation optimization, and discloses an intelligent power distribution network distributed power scheduling system based on edge computing, which comprises a data acquisition and preprocessing module, an edge computing and local scheduling module, an SDN network control module, an application layer global scheduling module and a scheduling execution and control module, according to the invention, by introducing the edge computing node, local preprocessing of data and generation of a preliminary scheduling strategy are realized, delay of data transmission to the central server is significantly reduced, and the system can dynamically adjust the scheduling priority in combination with the improved particle swarm optimization algorithm, so that the scheduling efficiency is improved. Three core indexes of renewable energy utilization rate, power distribution network loss and voltage stability are comprehensively considered, and a more accurate and efficient local scheduling strategy is generated. And meanwhile, the SDN network control module ensures that high-priority data can still be efficiently transmitted when the network is congested by calculating a link priority coefficient, so that the real-time performance and the reliability of the system are further improved.

The invention relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include generating a digital twin network simulation of a physical computer network controlled through a software-defined-network (SDN) control system. The acts also can include training a routing agent model on the digital twin network simulation using a reinforcement-learning model on traffic that flows through nodes of the digital twin network simulation. The routing agent model includes a machine-learning model. The acts additionally can include deploying the routing agent model, as trained, from the digital twin network simulation to the SDN control system of the physical computer network. Other embodiments are described.

The invention belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

The invention relates to the technical field of alarm network control, and discloses an alarm controller remote linkage method, device and equipment and a storage medium, and the method comprises the steps: obtaining the multi-level monitoring layout information of a target monitoring area, carrying out the self-linkage and interactive linkage analysis, forming a linkage layout total mode, collecting and analyzing the data of each monitoring unit, and carrying out the remote linkage of the alarm controller. Obtaining regional monitoring feature distribution; the method comprises the steps of carrying out alarm live analysis based on historical data, analyzing response measures according to a total mode, issuing a remote instruction, switching a linkage object to a response mode, obtaining expansion information and scheduling subsequent measures, and can realize intelligentization and quick response, improve the emergency processing capability and efficiency of a safety monitoring system, and improve the safety of the safety monitoring system. The problems that in the prior art, the alarm form is fixed, and adaptability is not meticulous and comprehensive enough are solved.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention provides an optical storage parallel network control method, which comprises the following steps: calculating the current load rate percentage of each residual unit according to the connection relation and electrical path information of the residual units after isolation; evaluating the available margin capacity and the frequency regulation bearing capacity of the remaining units, matching the available margin capacity and the frequency regulation bearing capacity with a specific power value and a frequency regulation requirement which need to be compensated, and determining a feasible unit combination scheme for fault recovery; generating a power redistribution list and a frequency adjustment responsibility distribution table according to the direct current bus connection mode of the optimal unit recombination, and obtaining an output power set value and a frequency adjustment parameter of each unit after adjustment; and extracting the continuous power supply time value of the optical storage parallel network, updating the total power supply capacity value of the optical storage parallel network, and maintaining the continuous power output and frequency stability after the fault of the optical storage parallel network.

The invention relates to an afflux area main road vehicle driving behavior modeling method based on deep inverse reinforcement learning. The method comprises the following steps: constructing a main road vehicle expert trajectory set; constructing a simulation environment model, wherein the input of the environment model comprises a current state feature provided by a main road vehicle expert track set D and an action feature provided by a main road vehicle strategy network control main road vehicle in a PPO algorithm; the state transfer function is used for returning the state feature of the next time step according to the current state feature and the action feature; the reward network is used for giving an instant reward for each time step according to the state characteristics of the main road vehicle; constructing a framework formed by a maximum entropy depth inverse reinforcement learning algorithm and a PPO algorithm; then training is carried out, and a PPO algorithm is used for training to generate a prediction track of the main road vehicle in a simulation environment; and inputting the predicted trajectory and the main road vehicle expert trajectory into a maximum entropy depth inverse reinforcement learning algorithm, calculating a loss function, and updating reward network parameters until convergence, thereby solving the problem of strategy misalignment existing in a traditional single method.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention provides a network traffic scheduling optimization method based on deep learning. The method is applied to the technical field of communication networks, and comprises the following steps: S1, collecting flow data, link state, delay and packet loss rate indexes deployed at network nodes in real time, and constructing a sample database; s2, according to the sample database, constructing a traffic prediction model fusing a retrieval enhancement diffusion model and a mixed linear expert model to perform multi-modal traffic prediction; s3, constructing a state space of a reinforcement learning agent according to a real-time network state and the future traffic prediction result; s4, performing strategy optimization based on a multi-objective optimization mechanism; and S5, deploying the trained and optimized model to a network controller or an edge computing node to realize real-time sensing and dynamic scheduling control of network resources. According to the method, the traffic prediction precision is remarkably improved, the scheduling strategy is updated and optimized in time according to the network environment change, and the comprehensive performance of the network is improved.

In general, techniques are described for a SDN architecture system that implements an integrated gateway service. In an example, network controller comprises processing circuitry and memory and is configured to: configure a virtual network in a cluster of nodes of a compute infrastructure, the cluster of nodes managed in part by the network controller; receive a manifest for a gateway service instance that abstracts a transit gateway resource configurable in a plurality of different types of compute infrastructures, wherein the manifest specifies an intended state of a transit gateway object of the gateway service instance; and reconcile the intended state of the transit gateway object for the gateway service instance by sending configuration data, generated based on the transit gateway object, to an interface for the compute infrastructure to configure a transit gateway to forward network packets between a compute infrastructure node of the compute infrastructure and the virtual network.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

Embodiments herein disclose methods and systems for providing network control for MBSR behaviour in wireless communication networks. Embodiments herein disclose methods and systems for the HPLMN / EHPLMN or the serving / registered PLMN or any PLMN in any of the UE's preferred lists to selectively control the UE behaviour to either act as a normal UE or as a UE (MBSR) based on either the PLMN serving the UE or the location of the UE. Embodiments herein disclose methods and systems for the HPLMN to control / prioritize / restrict certain PLMN(s) for UE, to operate as UE (MBSR), over other PLMN(s). Embodiments herein disclose methods and systems for determining which UEs are allowed or not allowed to operate as a MBSR UE (with mobile relay operation) in specific areas / PLMNs / geographical locations / TACs / TAIs.

A system, method and apparatus to control memory devices over computer networks. For example, a server system establishes a secure authenticated connection with a client computer system. Over the connection, the server receives from the client computer system a request identifying a memory device and determine, based on data stored in the server system, that the client computer system is eligible to control the memory device. In response to a request from the client computer system, the server system generates a digital signature for a command using at least a cryptographic key stored in the server system in association with the memory device. The client computer system receives the digital signature from the server system and submits the command with the digital signature to the memory device. The memory device validates the digital signature prior to execution of the command.

The invention relates to a situation awareness network vulnerability defense method and system, and the method comprises the steps: extracting a multi-mode traffic feature, inputting the multi-mode traffic feature into a fusion twin network model, and outputting security event information; constructing a preliminary control strategy matrix based on the security event information, and performing optimization adjustment to generate an access control strategy matrix; converting the access control strategy matrix into a network control instruction set, and sending the network control instruction set to each execution device; performing network situation monitoring on the execution equipment, performing evaluation through a game theory evaluation model, and dynamically adjusting and optimizing parameters based on an evaluation result; in conclusion, abnormal traffic behavior pattern recognition is realized by fusing the twin network model, the problems of static strategy stiffness and low resource scheduling efficiency in the traditional defense technology are effectively solved by combining multi-objective optimization and a dynamic strategy adjustment mechanism, accurate recognition of the abnormal traffic behavior pattern and optimization of resource scheduling configuration are realized, and the method is suitable for the implementation of the defense technology. The method has the effect of improving the network protection adaptivity.

The invention discloses an asymptotic tracking control method for a mobile double-flexible mechanical arm network. The method comprises the following steps: constructing the mobile double-flexible mechanical arm network; the uncertainty of a leader is considered, and a self-adaptive distributed switching observer is constructed; a servo system is introduced, transverse displacement is generated, and a tracking error model is constructed; an adaptive neural network controller is constructed by considering unknown gain faults and parameter uncertainty; on the basis of a distributed switching observer and a neural network controller, asymptotic consistency tracking control over a mobile double-flexible-mechanical-arm network is achieved. According to the method, asymptotic fault-tolerant consistency tracking control of the mobile double-flexible mechanical arm network can be effectively realized under heterogeneous linear leader and denial of service attacks, and the problems of unknown gain faults and unknown parameters are solved by utilizing a self-adaptive method and a neural network technology; the moving position of the moving double-flexible mechanical arm and the angle position of the two flexible mechanical arms reach the specified transient performance, and asymptotic consistency tracking is achieved.

The invention relates to the technical field of electric power system communication, in particular to a dynamic bandwidth allocation method and system for an electric power communication network, and the method comprises the steps: constructing a service traffic characteristic database, dividing service types, defining indexes such as a bandwidth threshold value, and predicting traffic fluctuation by adopting an LSTM-GARCH hybrid neural network; parameters such as a link bandwidth occupancy rate are obtained in real time, and a network state matrix is constructed; based on the prediction model and the network state, generating a bandwidth allocation scheme through a deep reinforcement learning algorithm optimized by a near-end strategy; a software-defined network controller is used to execute a strategy, and a gradient descent method is combined to optimize parameters to form a closed loop. By dynamically adapting the service flow and the network state, the bandwidth utilization rate is improved, the key service transmission quality is guaranteed, and efficient and stable operation of the electric power communication network is achieved.

The invention relates to the technical field of electric energy storage systems, in particular to an offshore wind-solar hydrogen storage ammonia-alcohol-based constructed network control system and a broadband oscillation prevention and control method. Comprising an offshore hydrogen-ammonia-alcohol base, an offshore wind power base, an offshore photovoltaic base, an inverter station, an offshore wind-light base energy storage power station, a booster station, a network-forming type energy storage phase modulation power station and a broadband oscillation control and prevention device, so that the network-forming type energy storage control research of the offshore wind-light hydrogen-ammonia-alcohol multi-energy complementary base can be carried out under low-short-circuit-ratio access and isolated network operation. Voltage, frequency and short-circuit capacity adjustment and inertia supporting are achieved, oscillation prevention and control can be carried out, and the purposes of power grid supporting optimization adjustment and oscillation treatment are achieved; meanwhile, multi-energy complementation is achieved, the energy utilization rate is increased, hydrogen ammonia alcohol can be prepared through green energy, and carbon emission is reduced. Therefore, the problems of poor frequency stability, weak voltage supporting capability, serious weak power grid oscillation and the like in the prior art are solved.

The invention relates to the technical field of wireless communication, and particularly discloses a high-voltage transformer remote metering monitoring system based on 5G network slicing, which senses network performance in real time by constructing a multi-dimensional network state matrix, performs path rehearsal and resource allocation calculation by using a digital twinning technology, and realizes remote metering monitoring of a high-voltage transformer. Generating a candidate transmission path set with optimal time delay consistency and a corresponding wireless resource block allocation scheme; calculating a path resource allocation factor and combining with network load prediction to generate an optimal resource scheduling strategy; and finally, converting the strategy into a specific network control instruction, establishing a special transmission channel with deterministic service quality, and implementing real-time monitoring and maintenance.

The invention belongs to the technical field of data circulation, and discloses a data circulation method and system based on a slice network and a trusted data space. Comprising the following steps: a connector obtains private network generation feedback to allow a user to access the connector through a corresponding slice private network on the basis of a pre-configured static route; obtaining a user credible voucher and an authentication message forwarded after addressing by the first router; verifying the user credible credential, analyzing the authentication message, and forwarding an obtained network attribute label to a network controller; and allowing the user to enter the trusted data space for data interaction through a second router when the user trusted certificate passes verification and the network attribute tag passes verification by a network controller. According to the invention, the security during data circulation is effectively improved.

Some embodiments provide a method for a first data compute node (DCN) operating in a public datacenter. The method receives an encryption rule from a centralized network controller. The method determines that the network encryption rule requires encryption of packets between second and third DCNs operating in the public datacenter. The method requests a first key from a secure key storage. Upon receipt of the first key, the method uses the first key and additional parameters to generate second and third keys. The method distributes the second key to the second DCN and the third key to the third DCN in the public datacenter.