774 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.

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 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 relates to the technical field of multi-modal behavior anomaly detection scheme design, in particular to a multi-modal behavior anomaly detection method and system under the condition that encrypted traffic is not decrypted. The method comprises the following steps: capturing a network encrypted traffic data packet in real time; on the premise that decryption is not carried out, multi-mode non-decryption features such as flow layer statistics, time sequence interaction, encryption handshake and context association are extracted in parallel; establishing a dynamic normal behavior baseline model of each feature based on historical data; the real-time features are compared with the baseline model, comprehensive judgment is carried out through a multi-mode correlation analysis algorithm, and an anomaly detection result is output; and when the abnormal condition is judged, the network control equipment is automatically linked for blocking. The method thoroughly gets rid of dependence on traffic decryption, realizes high-precision and self-adaptive detection and rapid automatic response to abnormal behaviors in encrypted traffic through multi-dimensional feature fusion and dynamic baseline technologies, and effectively solves the problem of failure of a traditional detection technology in an encrypted environment.

The invention provides a network topology configuration method, a communication system and related equipment, and the method comprises the steps: enabling a network controller to analyze a training task before an AI model starts distributed training, determining the traffic demand information of the training task, and transmitting the traffic demand information to the AI model; the traffic demand information comprises bandwidth demand quantity among different processors in a training process of an AI training task, network topology information of the photoelectric hybrid networking can be determined based on the traffic demand information, and then configuration information of the optical switching equipment is generated based on the network topology information. According to the invention, the configuration information of the optical switching equipment is acquired, so that the optical switching equipment can perform link switching based on the configuration information to complete topology reconstruction of photoelectric hybrid networking, the network controller can actively perform flow planning before AI model training, and the problem of long network delay caused by slow link switching speed of the optical switching equipment in the training process can be avoided.

A repeater device receives a first configuration that associates reference signal (RS) resources and receive / transmit (Rx / Tx) beam pairs configurable at the repeater device for repeating one of: (i) a downlink (DL) radio frequency (RF) signal received on a backhaul link from the base station to a user equipment (UE) over an access link; and (ii) an uplink (UL) RF signal from the UE to the base station. The device: receives a second configuration for measuring and reporting self-interference of a first list of the RS resources; configures the repeater device with Rx / Tx beam pairs identified in the first list of RS resources; measures self-interference received on an Rx beam of first Rx / Tx beam pairs; and generates and forwards, to the base station, a self-interference measurement report comprising a second list of Rx / Tx beam pairs from the first list of Rx / Tx beam pairs that cause self-interference above a threshold value.

A system and method for a wireless device to derive a channel state information (CSI) codebook based on a decoder and a vector quantization codebook, receive a reference signal from a base station, derive an estimated channel from the reference signal, select an entry from the CSI codebook based on the estimated channel and a selection criterion, and report an index of the selected entry to the base station. The wireless device may receive a subset indication, derive therefrom a second CSI codebook, and select the CSI codebook entry from the second CSI codebook. A CSI compression machine learning (ML) system and vector quantization codebook may be obtained by a network controller. The decoder may be a part of the CSI compression ML system, the vector quantization codebook may be based on an encoder of the CSI compression ML system, and they may be sent to the wireless device.

The invention discloses a network resource allocation method, electronic equipment and a storage medium, and the method comprises the steps: taking a current excitation parameter issued by a global decision model in a cloud platform as a weighted item of a reward function of a domain-level decision model; the decision model in a single domain is controlled to realize the optimal local network resource and consider the optimal global network performance of the cross-domain network at the same time; according to the global decision model, quantizing the global income index after each candidate operation is executed so as to determine a global resource allocation strategy; and finally, the network resource allocation state of each data center is adjusted through the network controller deployed in each data center, and the domain-level decision model is deployed in a plurality of data centers in different regions. Through the method, the technical problem that cross-domain network resources in a cloud environment cannot be reasonably allocated in related technologies is solved, and the technical effect of intelligently and cooperatively allocating local network resources and global network resources in a domain is realized.

Various aspects of the present disclosure relate to a device and method for a network-controlled repeater (NCR) device to detect, indicate, and recover from a backhaul failure. The method includes communicating, by the NCR device with: (i) base station(s) via a control link or a backhaul link and (ii) a user equipment (UE) via an access link. A beam association is determined of the control link and the backhaul link. The method includes determining that the control link is communicated on a first beam and the backhaul link is communicated on a second beam of the base station(s). In response, the method includes monitoring a reference signal received on the backhaul link for an indication of failure of the backhaul link. The method includes transmitting a request for backhaul beam recovery on the control link in response to detecting the indication of failure of the backhaul link.

The invention discloses a water supply pipe network dynamic pressure control method and system based on multi-parameter fusion, and relates to the technical field of water supply pipe network control, and the method comprises the steps: carrying out the standardization processing and topological optimization of pipe network geographic information system data, and constructing an urban pipe network topological graph; identifying a pressure sensitive node and a distance sensitive node based on a hydraulic calculation result of the mechanism calculation model; performing association coding on historical monitoring data of the key nodes and a topological connection relationship, and training a machine learning model fusing pipe network topological constraints; collecting real-time monitoring data, inputting the real-time monitoring data into the mechanism calculation model and the machine learning model, and respectively obtaining offline adjustment parameters and online adjustment parameters; and determining a final adjusting instruction through consistency verification to control water supply pump set equipment. According to the method, the calculation precision of the mechanism model and the response speed of the machine learning model are fused, organic unification of accuracy and real-time performance is achieved, and the control stability and reliability under the abnormal working condition are improved.

The invention provides an energy storage station trusted supply chain and command physical consistency verification method and device. The method comprises the following steps: setting a trusted platform module or a security element in a key device of the energy storage station, and generating a device trusted state identifier; adding a time quality identifier and a source signature to the measurement and state data; identity authentication, protocol analysis, object address and parameter range verification and rate constraint verification are carried out on the remote control instruction; generating a feasible region according to state estimation and equipment constraint, and performing executable judgment on the instruction; when any link does not meet the preset condition, switching to a verifiable security state and blocking a remote write-in path; and when the command firewall and the physical consistency check are both passed, a control instruction is issued to the converter control device and the battery management system, and power, reactive power and grid-connected and off-grid control of the energy storage station is executed. According to the application, supply chain credible closed loop, metering linkage access and double-loop instruction verification can be realized, and failure safety and verifiable evidence obtaining are supported.

The invention relates to the technical field of industrial automation control, in particular to a boiler combustion stability intelligent control method based on a self-adaptive neural network, and the method comprises the steps: collecting multi-source heterogeneous data of a boiler system, the multi-source heterogeneous data comprises a high-frequency vibration data flow and a low-frequency process data flow, carrying out space-time alignment preprocessing on the multi-source heterogeneous data; and calculating a self-adaptive penalty factor based on the current load characteristics and the vibration energy, and performing variational mode decomposition on the high-frequency vibration data stream by using the self-adaptive penalty factor to obtain a combustion characteristic component. According to the method, the gain of the neural network controller is dynamically adjusted through the comprehensive stability risk index, smooth switching of a control strategy between steady-state fine adjustment and fault strong control is achieved, and therefore the fast inhibition capacity for combustion instability is remarkably improved while the steady-state precision of a combustion system is guaranteed.

A method for wireless communication at a network node and related apparatus are provided. In the method, the network node transmits, to a wireless device, a downlink (DL) signal for amplification and transmission of an amplified DL signal to a user equipment; determine an operating region of the wireless device for amplifying the DL signal. The operating region is one of a power-limited region in which a DL output power of the wireless device reaches a maximum DL output power, and a linear region, in which the DL output power of the wireless device is less than the maximum DL output power. The network node further adjusts the DL output power of the wireless device to cause the wireless device to operate at the linear region when amplifying the DL signal. The method improves the energy efficiency of the wireless device when transmitting the signals.

The invention provides an intelligent gate control method based on deep reinforcement learning, and relates to the technical field of hydraulic engineering, and the method comprises the steps: obtaining the operation state information of a gate control system at the current moment, carrying out the preprocessing, obtaining a system state vector, inputting a deep network control model, and obtaining a state action value function value through calculation; selecting a target gate control action based on the state action value function value to obtain a gate opening value, and updating the state of the gate control system by using a hydraulic physical model to obtain a state transition sample; using the state transition sample to train and update the deep network control model to obtain a trained deep network control model; and analyzing the system state vector at the next moment by using the trained deep network control model to obtain an optimal gate control action. The problem that in the prior art, it is difficult to autonomously learn a control strategy and improve the water level control precision in a complex, dynamic and uncertain environment is solved.

Systems and methods for configuring and managing a network device with a transformer model under control of a network control function (NCF) are disclosed. A processor of the NCF receives a request that identifies a network management task and associated service targets. The processor forms a token set of schema-defined tokens that represent network context, applies positional encodings to generate an ordered token sequence, and invokes the transformer model to produce a configuration patch. The configuration patch is validated against a schema-constrained decoder that enforces device grammar and is applied to the target network device. Device state and telemetry are read back to obtain a read-back state, which is evaluated against the service targets. When telemetry deviates, the processor generates a further configuration patch that modifies a bounded subset of parameters relative to the read-back state to restore compliance

The invention belongs to the technical field of power equipment power supply, and relates to a dual-power fast switching redundant system CCV power supply device, which comprises a dual-power module, a fast switching device, a UPS system, a redundant system, an intelligent monitoring module and an operation and maintenance management module according to a functional structure, and all system modules are electrically communicated in an electric information structuring manner to form an integrated power supply technical scheme. Rapid switching, stable operation and a safe and reliable power supply environment are realized; a power supply, a communication network, a control three-dimensional redundant system and an ATS rapid switching device are matched to be differentially matched with an online / backup UPS, zero interruption of load power supply is achieved, the single-point fault risk is effectively resisted, and the high-reliability power supply requirements of precision manufacturing, data centers and the like are met; the system is scientific and comprehensive in design, reliable and flexible in architecture, convenient and efficient in operation and maintenance, wide in application scene and remarkable in advantages of guaranteeing power supply stability and reducing comprehensive cost.

Methods and apparatus are provided for configuring a dynamic hybrid automatic repeat request acknowledgement (HARQ-ACK) codebook for a network-controlled repeater (NCR). A first HARQ-ACK group is configured for physical downlink control channel (PDCCH) transmissions from a base station to the NCR. The PDCCH transmissions include first side control information. A second HARQ-ACK group is configured for scheduled physical downlink shared channel (PDSCH) transmissions from the base station to the NCR. The scheduled PDSCH transmissions include second side control information. A third HARQ-ACK group is configured for downlink (DL) channels or signals from the base station for the NCR to forward to a user equipment (UE). The dynamic HARQ-ACK codebook includes a combination of the first HARQ-ACK group, the second HARQ-ACK group, and the third HARQ-ACK group.

The invention discloses an industrial control operation and maintenance instruction real-time risk detection method and system based on double-engine cooperation, relates to the technical field of industrial control system security, and aims to solve the problem of low security of industrial control network control. The method comprises the following steps: collecting an operation and maintenance instruction in an industrial control network in real time, and performing protocol analysis on the operation and maintenance instruction to perform semantic reduction to obtain an operation event; analyzing the operation event by utilizing a rule inference engine, and judging whether the operation event violates a predefined rigid rule or not; if yes, outputting the highest risk level and terminating the process; if not, turning to the next step; analyzing the operation event by utilizing a dynamic behavior analysis engine, and calculating an abnormal deviation score of a next operation event of the target equipment corresponding to the operation event; and mapping the abnormal deviation degree score into a risk level, and outputting a final detection result. Rapid compliance judgment and deep behavior analysis of the industrial control operation and maintenance instruction can be realized, and both the detection efficiency and the detection accuracy are considered.

Methods and apparatus are provided for wireless communication performed by a network-controlled repeater (NCR) configured to forward downlink (DL) channels or signals from a base station to a user equipment (UE). The NCR determines a duration for which hybrid automatic repeat request acknowledgement (HARQ-ACK) feedback is configured. The NCR determines a number of measurements of the DL channels or signals for which a single HARQ-ACK bit is reported. The NCR determines, based on the number of measurements and a comparison with a success threshold for each of the measurements, an acknowledgement (ACK) or negative acknowledgement (NACK) for each configured HARQ-ACK bit in a first sequence of HARQ-ACK bits. The NCR constructs a dynamic HARQ-ACK codebook to send to the base station. The dynamic HARQ-ACK codebook includes at least the first sequence of HARQ-ACK bits.

Techniques for providing a language model with dynamic configuration data for network devices in a network that is then used by the language model to generate network-contextual responses to prompts received at the language model. A network controller may collect configuration data from the network devices where the configuration data includes device configurations of the network devices. The network controller may further receive a prompt from a network operator to which the language model is to respond and identify a relevant device configuration that is contextually relevant to the prompt. Further, the network controller may provide the prompt and the relevant device configuration to the language model, generate, by the language model and using the relevant device configuration, a response to the prompt, and provide the network operator with an indication of the response to the prompt.

Apparatuses, systems, and methods for determining network-controlled repeater (NCR) behavior over flexible symbols in TDD schemes, e.g., in 5G NR systems and beyond. An NCR may receive, from a base station, side control information (SCI). The SCI may include an indication of time-domain resources. The NCR may be configured to determine, based, at least in part, on the SCI, a direction for one or more flexible symbols and / or flexible slots (flexible symbols / slots) on an access link with a user equipment device (UE). The NCR may be configured to treat the one or more flexible symbols / slots as uplink symbols / slots or downlink symbols / slots based on the determination. In other words, the NCR may determine that the flexible symbols / slots are uplink symbols / slots and / or downlink symbols / slots and forward data received on the flexible symbols / slots accordingly.

The invention relates to the technical field of lithium battery pole piece rolling, and provides a heat-based roller type regulation and control method and system for a lithium battery pole piece roller press. The method comprises the following steps: S1, establishing a roller finite element mechanism model; s2, performing simulation by using a roller finite element mechanism model; s3, performing thermocouple distribution preliminary screening based on the temperature comprehensive contribution degree; s4, redundant preliminary screening thermocouple distribution points are eliminated based on data clustering analysis; s5, determining an optimal thermocouple distribution point combination; s6, constructing a dynamic thermal field matrix; s7, constructing a data driving model; s8, obtaining a joint prediction thermal convexity through joint prediction; and S9, the power of the electromagnetic coil and the edge water cooling flow are adjusted. The system comprises a roller heating device, an edge water cooling device, a temperature sensing network, a control platform, a regulation and control execution unit and a lithium battery pole piece roller press. Through accurate prediction of the thermal crown of the roller, real-time dynamic compensation of the roller shape is realized, and the thickness stability of the pole piece is improved.

The embodiment of the invention provides a power distribution network transformer operation state monitoring and energy efficiency collaborative optimization method, which is applied to the technical field of power grid remote control and telemetering and network control systems, and is characterized in that the system synchronously acquires transformer state data through acoustics, vibration, load and other sensors, and performs feature fusion extraction by adopting deep learning; and a health reference library of the sub-load intervals is established. Through multi-dimensional difference calculation of real-time monitoring data and a health template, a health degree score is generated and state early warning is realized. An energy efficiency adaptive optimization module is innovatively introduced, an optimal load set value is solved based on a multi-objective optimization algorithm, and an optimization strategy is executed through closed-loop control. The system is suitable for various scenes such as an urban power grid, a new energy power station, an industrial environment and a remote area, realizes the conversion from fixed cycle maintenance to state-driven operation and maintenance, improves the operation reliability, the service life and the operation and maintenance efficiency of the transformer, and provides a solution for the full-life-cycle management of power equipment.

The invention provides a low-delay multi-channel video data processing method and system for remote driving, and is applied to the field of data processing. According to the invention, a low-delay multi-channel video data full-process processing system is constructed for remote driving of a target unmanned vehicle. And four types of core data including hardware, video, network and control are obtained. Aiming at multiple cameras, images are screened according to driving scene priorities, full-coverage dead-corner-free, core view-angle-first and vehicle-type adaptation constraints are applied, and preprocessed multiple video streams are generated. Encoding, slicing and decoding the preprocessed video stream to construct a low-delay processing mechanism; a customized WebRTC protocol is adopted, routing is optimized, direct connection between a vehicle end and a cockpit is achieved, meanwhile, the network state is monitored, and transmission stability and safety are guaranteed through multiple constraints when the network state is abnormal. Fusing multiple paths of decoded pictures, arranging and superposing multiple types of key information according to driving habits, and combining constraints to generate an offering cabin display result; and finally, cooperatively processing the control instruction and the video feedback to form an interactive monitoring visual interface.

The invention discloses a multi-dimensional network intrusion behavior intelligent identification method based on deep learning. The method comprises the following steps: collecting network multi-dimensional data and generating a standardized network event set and a network control path input set; establishing a neural controlled differential equation model, and generating a continuous time context representation set through hidden state evolution; establishing a neuro-hox process identification model, performing intensity function modeling, and generating an event intensity prediction sequence set and an identification intermediate representation set; forming an intrusion behavior decision rule set and a reasoning configuration set through joint training; new network multi-dimensional data are collected, the reasoning configuration set operation model is loaded, and a new event intensity prediction sequence set and a new candidate trigger time set are output; and generating a network intrusion behavior recognition result set in combination with the intrusion behavior decision rule set. According to the method, time modeling and logical reasoning are fused, and high-precision intrusion identification is realized.

The invention belongs to the field of integrated circuits, and particularly relates to a high-Q-value quick-start crystal oscillator based on an MLP neural network. A neural network is used for replacing a complex phase synchronization hardware circuit in a traditional energy injection technology. Firstly, working state parameters of a crystal oscillator are collected in real time through a state collection module; then mapping the received real-time digital information into a corresponding output instruction through a trained MLP neural network model, and further controlling an energy injection module to inject an energy signal in the same phase as crystal oscillation; along with gradual increase of the oscillation amplitude, when the oscillation amplitude reaches a threshold value, the MLP neural network control model outputs an injection stopping instruction, switches the switch state, and submits the instruction to the negative resistance oscillation circuit to maintain steady-state oscillation and output a stable oscillation signal. According to the invention, the low complexity and high robustness are taken into account while quick starting is realized, and a solution is provided for the application of the high-Q-value crystal oscillator in a low-delay and low-power-consumption scene.

Systems and methods are provided for maximizing / optimizing the quality of experience (QoE) associated with applications. A network controller may receive telemetry data from an access point (AP). A resource manager operatively connected to the network controller may estimate, based on the telemetry data, a QoE value for individual application traffic flows of one or more application traffic flows passing through the AP. An access category and traffic priority based on the estimated QoE value may be calculated, and jointly assigned to a particular application traffic flow.

The invention provides a server cluster load balancing state tracking analysis method, and relates to the technical field of servers, and the method comprises the steps: collecting a load migration triggering moment, the number of control messages generated by single migration, and the number of active connections to be migrated of each server from each server in a cluster, meanwhile, the bandwidth bearing upper limit of a network control channel is obtained, migration triggering events are arranged according to the time sequence, and a list of the number of triggered migration servers is obtained; the migration scheduling strategy of each server in the load balancing system is updated by adopting the target migration coordination delay interval, a migration rhythm coordination confirmation signal is obtained by monitoring the network congestion change, and whether the cluster load balancing state is recovered to a safe range is verified by combining a high-risk time period list, so that the stability and efficiency of load migration are improved, and the load balancing efficiency is improved. And a reliable flow coordination guarantee is provided for a large-scale cluster system.

The embodiment of the invention provides a task scheduling method and device and related equipment, and relates to the technical field of computers.The method comprises the steps that a target task is obtained; determining a target computing domain in the computing power network according to a computing resource required by the target task and a first processing resource of each computing domain in the computing power network; determining a plurality of candidate transmission paths corresponding to the target task according to resource configuration information of each computing node in the computing power network; determining a target transmission path in the plurality of candidate transmission paths according to the data packet capacity corresponding to the target task and the bandwidth resource quantity occupied by each candidate transmission path; and scheduling the target task to the target computational domain based on the target transmission path. In the embodiment of the invention, deterministic computing control is realized by determining the target computing domain in the computing power network, and deterministic network control is realized by determining the target transmission path; and the task scheduling efficiency is improved by combining deterministic network control and deterministic calculation control.

A method for backhaul link beam indication includes: receiving by a network controlled repeater (NCR) signaling sent by a network device, in which the signaling is used for configuring a beam of a backhaul link, and the signaling includes beam indication information; and according to the beam indication information, determining by the NCR, from a plurality of beams configured by the network device for a control link of a network control repeater a target beam used by the backhaul link.