2506 results about "Network control" patented technology

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

The invention belongs to the technical field of mobile communication, and particularly relates to a method for automatically switching a network channel and an SIM card according to signal strength, which comprises a sensing module, a decision module, an execution module and an optimization module, and automatically switches the network channel and the SIM card through a progressive cooperation process. The sensing module is used for acquiring data and identifying a user scene mode, and the data acquisition comprises acquisition of environment characteristic data and equipment state data; the decision module is used for intelligent analysis, selecting an optimal network access scheme according to a user scene type, and ensuring the security of network switching; the execution module is used for network control, and seamless switching of network channels is carried out through cooperation of hardware and software. And the optimization module is used for self-learning of the decision-making module, establishing a database and learning according to user behaviors, recommending an optimal network access scheme to a user in a similar scene, and assisting the Internet of Things terminal in electric quantity control.

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 invention provides a time division duplex multi-carrier synchronous transmission method based on 5G, which is used for solving the problems of inflexible carrier time slot allocation and inaccurate time delay compensation in synchronous transmission. By dynamically adjusting time slot allocation and working frequency bands of carriers in a network control center, the method optimizes network loads and reduces cross interference; the time delay of each carrier is compensated in real time by adopting a synchronous signal calibration algorithm, so that strict alignment of transmission time slots is ensured; the transmitting end dynamically adjusts the transmitting power according to link state information, and the receiving end reduces interference and signal attenuation through a multi-carrier cooperative interference management and multi-path delay compensation mechanism; and a receiving end performs joint decoding based on channel estimation and integrates demodulation signals of all carriers, so that the transmission efficiency and the signal quality of the 5G network are improved. The method is suitable for a base station system and a base station and repeater extension coverage system, and is especially suitable for a distributed wireless system including an optical fiber remote distributed system, a cable remote frequency shift repeater system and the like.

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 automatic control method and system for an electrostatic precipitation system. The method combines a neural network control algorithm with a PID regulation control algorithm, and uses the neural network control algorithm to carry out preprocessing on the control of the electrostatic precipitation system and uses the PID regulation control algorithm to carry out initialization processing when the electrostatic precipitation system is out of control, thereby avoiding the uncontrollable state of the electrostatic precipitation system caused by PID control overshoot or oscillation; moreover, the PID regulation control algorithm is used to carry out real-time tracking regulation on the electrostatic precipitation system, so that the real-time response speed of intelligent control of the electrostatic precipitation system can be effectively increased. Therefore, the energy conservation and efficiency improvement of the electrostatic precipitation system are better realized while the operation stability and response rapidness of an electric precipitator are ensured and controlled. By combining the two control algorithms, once intelligent control is used, the automatic control method effectively prevents the electrostatic precipitation system from being temporarily out of control due to excessive overshoot, thereby ensuring the stable operation of the electrostatic precipitation system, and implementing intelligent control.

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 self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

A computer implemented method performed in a network node (120), the method comprising configuring an advanced antenna system, AAS, of the network node (120) to generate a relay antenna beam associated with transmission to and from a repeater node (150) of the network node (120), the method further comprising generating at least two different first synchronization signals, and transmitting the at least two different first synchronization signals via the relay antenna beam towards the repeater node (150).

The invention provides a satellite-borne computing center system, in particular to a satellite-borne computing center system with high performance and high computing power, which comprises a data interaction module, a task management module, an artificial intelligence AI module M1 and an artificial intelligence AI module M2. The data interaction module is an input and output interface for high-speed data processing; the task management module serves as a task scheduling center and distributes calculation tasks to the AI module; the artificial intelligence AI module M1 and the artificial intelligence AI module M2 adopt domestic and imported AI chips, and the artificial intelligence AI module can work in an RC mode and an EP mode, provides computing power guarantee for satellite-borne high-computing-power computing tasks, and has the characteristics of flexible expansion and agile deployment. All the modules are interconnected through a serial port management plane, a gigabit network control plane, a PCIE data plane and a power supply plane, and control and data interaction between processors are achieved.

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.

Systems, methods, and instrumentalities are provided for a network control node for connecting a wireless transmit / receive unit (WTRU) to a network slice in a network. The network control node may receive service information associated with a service being requested by the WTRU, wherein the service information comprises one or more of a service class, a quality of service (QoS) requirement, or a mobility characteristic. The network control node may determine slice information associated with a plurality of network slices, wherein the slice information comprises one or more of an identifier of the network slice, priority of the network slice, a service class served by the network slice, a target device class, a QoS target, mobility support, security service, charging information, and / or performance information. The network control node may determine subscriber information for the WTRU. The network control node may select at least a first network slice of the plurality of network slices to serve the WTRU, wherein the network control node selects the first network slice based on the service information, the slice information associated with the first network slice, and the subscriber information for the WTRU. The network control node may determine whether the network control node or another network control node should service the WTRU for accessing the first network slice. On condition that the network control node determines that the network control node should service the WTRU for accessing the first network slice, the network control node may provide at least one network service associated with the first network slice to the WTRU. Or, on condition that the network control node determines that the another network control node should service the WTRU for accessing the first network slice, the network control node may send a request to the another network control node that indicates that the another network control node should service the WTRU for accessing the first network slice.

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.

Provided herein are methods and systems for radio access network (RAN) control including receiving, at a base station controller deployed at a base station of the RAN, a data input corresponding to a real-time operational status of the base station; determining a locally optimal signal configuration of the base station; controlling the base station to change a configuration of a signaling component to implement the locally optimal signal configuration; receiving, at an edge controller deployed at an edge of the RAN, a plurality of the locally optimal signal configurations corresponding to a plurality of the base stations within the RAN; determining a coordinated optimal signal configuration of each base station; and instructing, by the edge controller, the base station controller to change a configuration of the signaling component of the base station from the locally optimal signal configuration to the coordinated optimal signal configuration.

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.

The invention provides a self-discovery ad-hoc network control method based on a swan-gap operating system, and relates to the technical field of data processing, and the method comprises the steps: based on the swan-gap operating system, extracting the identification information and communication parameters of equipment from all to-be-networked equipment; carrying out compatibility judgment on the communication protocol version and the maximum support bandwidth between the devices; determining a control relationship between the master device and the slave device; dynamically allocating communication resources to each device by adopting a distributed channel allocation algorithm in combination with task requirements of the devices and network bandwidth information; periodically synchronizing the resource state of each device; when the slave device sends a networking quitting request, the master device updates the networking candidate data and re-determines the networking control relationship after releasing the corresponding communication resources; when the master device exits, the device with the highest priority is selected from the slave devices to automatically take over the control role and complete the reconstruction of the networking control relation; according to the invention, the autonomy and accuracy of networking are improved.