116 results about "Balancing network" patented technology

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm / model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and / or claimed.

The invention relates to the technical field of network security, in particular to an intelligent network security detection system and method based on big data analysis, and the method comprises the steps: generating adversarial data through a historical network data set, training a network security detection model through the adversarial data, and dynamically adjusting an adversarial training objective function in training. The network security detection model can balance normal loss and adversarial loss, and the robustness and accuracy of the network security detection model are balanced; the network flow data flow is analyzed through the machine learning model, the network state index is output, the network security detection model is optimized according to the output network state index, and the detection capability of the network security detection model to the antagonistic attack is improved; according to the method, traditional network attack data is extracted from a historical network data set, the traditional network attack data and adversarial disturbance generated through adversarial training are combined, and a network security detection model is trained, so that the network security detection model can detect traditional network attacks at the same time.

In an aspect, a system for an optimally balanced networked system is disclosed. The system includes a fabric adapter communication system communicatively coupled to a plurality of network ports and a plurality of controlling hosts. The fabric adapter communication system is configured to receive a network packet from, or transmit a network packet to, a network port of the plurality of network ports. The fabric adapter communication system is configured to separate the network packet into different portions, each portion including a header or a payload. The fabric adapter communication system is configured to forward the headers of the different portions to one or more controlling hosts. The fabric adapter communication system is configured to forward multiple payloads of the different portions in parallel through a bundled interface to multiple memory buffers of a global memory pool based on one or more scatter gather lists (SGLs).

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

The invention discloses a link bandwidth allocation method and system for multilink aggregation of a multimodal mobile network. The method comprises the following steps: acquiring a mobile network base station, an equipment position and real-time environment data; an intelligent power control algorithm is adopted, and transmission power is dynamically adjusted according to environment data and network loads; acquiring real-time signal intensity data and historical data from the network equipment to the base station; predicting a minimum transmission point of signal transmission based on real-time and historical data; adjusting the direction and position of an antenna according to the position of the base station, and designing connection by adopting a beam forming antenna; setting a threshold value according to the real-time weather and the minimum transmission point data, and deploying a signal enhancer; according to the link bandwidth allocation method and system for multi-link aggregation of the multi-modal mobile network, the link quality is monitored in real time, the network is dynamically adjusted, the network load is balanced through an intelligent algorithm, the signal transmission environment is evaluated in combination with real-time weather data, the signal enhancer is deployed, and the signal coverage range is expanded.

The invention discloses a self-adaptive dynamic routing method and system for an NoC (Network on Chip), and relates to the technical field of routing management. Comprising the steps that the local congestion degree of each routing node is collected through a communication link, the global congestion degree is generated through a weighted average mechanism, each routing node integrates delay counters in the east direction, the west direction, the south direction and the north direction according to a local router, the delay counters are used for monitoring the time interval when adjacent data packets leave a buffer area in real time, and the global congestion degree is generated; taking a time interval of leaving a buffer area of adjacent data packets as output delay, spreading the output delay to neighbor routing nodes, calculating local congestion degree and global congestion degree of the routing nodes, generating all possible shortest paths and a candidate direction set corresponding to output directions according to addresses of a source routing node and a destination routing node, and sending the candidate direction set to the destination routing node; screening an optimal path through the local congestion degree and the global congestion degree in sequence; and when a plurality of equivalent paths exist, the routing direction is randomly selected to balance the network load.

The system includes a wireless network that combines the functionality of the sensors with the formation and maintenance of the network, while balancing the network with the minimization of the energy consumed by the entire system. The system may include nodes, gateways, coordinators, extension sensors and boosters. The system may perform a method comprising applying a known load to a battery in a first node; measuring a voltage drop in the battery; determining an internal resistance of the battery; estimating a remaining life in the battery based on the internal resistance; and in response to the remaining life being below a remaining life threshold, re-directing, by a first gateway, signals in a mesh network away from the first node and to one or more of a plurality of nodes interfacing with the first node, wherein the first gateway interfaces with the first node and a network.

The invention discloses a satellite network service routing deployment method based on Transform combined with swarm intelligence, and the key points of the technical scheme are as follows: the method realizes optimized routing path selection of a service function chain (SFC) request and embedded deployment of a virtual network function (VNF) by combining a Transform encoder, a decoder module and a swarm intelligence algorithm; comprising the following steps of S1, satellite network state construction, S2, service function chain request modeling, S3, initial global resource state coding, S4, SFC request feature coding, S5, heuristic information generation, S6, path search based on ant colony optimization, S7, VNF embedding deployment decision making, S8, path resource state updating and S9, multi-round iterative optimization. According to the method, routing and deployment optimization of the SFC request can be efficiently completed in the dynamic satellite network, network delay and resource consumption are remarkably reduced, load balancing and efficient resource utilization are achieved, routing and deployment decisions of the SFC request can be effectively optimized, network loads are balanced, and the resource utilization rate is increased.

The invention discloses a mixed sampling network traffic sample balancing method, which comprises the following steps of: firstly, carrying out data preprocessing on network traffic in a CIC IDS-2017 data set, and dividing the network traffic into a training set and a test set; secondly, the training set is subjected to SMOTE oversampling and ENN undersampling processing, Gaussian noise processing is introduced after SMOTE oversampling, and a mixed sampling sample is obtained; and finally, carrying out CNN training on the mixed sampling sample and verifying a training result through a test set to obtain a classification result. According to the method, Gaussian noise is added after SMOTE processing, so that the complexity of data is increased, the generated sample can better reflect various characteristics in real network traffic, and the risk of overfitting is remarkably reduced. According to the method, most of important information is reserved in the ENN, and unnecessary redundant data is reduced, so that the problem of information loss is avoided, and the adaptability of the model to complex network traffic data is improved. And the function of balancing the network flow data is realized.

The invention discloses a dynamic topology ad hoc network method based on environmental perception and adaptive optimization, and relates to the field of wireless sensor networks. Parameters (such as temperature, obstacle distribution and node energy consumption) of nodes are collected in real time; a multi-dimensional quantization parameter matrix including an obstacle coverage matrix, an energy consumption distribution matrix and a signal attenuation coefficient matrix is constructed, and path loss, communication delay and node energy consumption are fused and calculated to generate a node weight value by adopting an adaptive weight calculation mechanism, so that the priority of a communication link is dynamically adjusted. Compared with the prior art, the method has the advantages that the self-healing capability and the real-time performance of the network in a complex environment are remarkably improved, the global calculation overhead is reduced, the real-time performance and the expansibility of the system are improved, the node layout and the dynamic optimization of a communication path are realized, and the network load and the stability are balanced.

The invention relates to the technical field of Internet of Vehicles data transmission, in particular to an intelligent networked automobile data transmission method and system.The intelligent networked automobile data transmission method comprises the steps that firstly, the interaction frequency degree of a user and an edge server is analyzed, and a demand user needing to be provided with a mobile server is screened; then analyzing the similarity between any two demand users, and grouping the demand users; setting a mobile server matched with the user group and planning a moving path of the mobile server according to the quantity distribution condition of the demand users in the user group and in combination with the matching index; the distribution of the demand users in each user group is adjusted by analyzing the moving trend difference of all the demand users in each user group in real time; and replacing the mobile server according to the residual electric quantity and the residual memory. According to the method and the system, the mobile server is used for assisting the edge server to process the vehicle data in each user group, so that the load of the server in the network is balanced, and efficient and stable data transmission is ensured.

The invention discloses a simulation method, device and system and a storage medium. The network simulation service consumption entity can propose the expectation for achieving different simulation performance requirements, so that the network simulation service providing entity can obtain the model parameters according to the expectation and obtain the simulation model for simulation based on the model parameters instead of considering the simulation model with better comprehensive dimension for simulation in any scene, and the simulation efficiency of the network simulation service providing entity is improved. Simulation performance requirements of network simulation service consumption entities can be balanced, and simulation efficiency can be improved.

The invention provides a network attack traffic category balancing method and system based on an improved ACGAN, and the method comprises the steps: carrying out the preprocessing of a network traffic data set, and obtaining a preprocessed network traffic data set; wherein the category of the network traffic data set comprises normal traffic data and attack traffic data; carrying out preliminary enhancement on the preprocessed attack traffic data by using an oversampling technology SMOTE to obtain a preliminary balanced data set; and enhancing the attack traffic data in the preliminary balanced data set by using a generative adversarial network ACGAN to obtain a final balanced data set, and completing balancing of network attack traffic categories. According to the technical scheme, the problem that extreme categories of data samples are unbalanced in the prior art is solved.

The embodiment of the invention relates to an analog simulation method based on electric power spot transaction. The analog simulation method comprises the steps of obtaining static structure data such as power grid topology and unit parameters and dynamic time sequence data such as load and new energy prediction; performing preprocessing and multi-time scale division on the dynamic data; constructing a multi-agent model with differentiated characteristics based on the processed data; each agent generates and reports energy block information such as a time-sharing block, a continuous block, a curve block or a variable block according to a perceived market environment; constructing a market clearing model considering system power balance, network power flow, unit operation and energy block characteristic constraint based on all declarations, and solving to obtain clearing electricity price and electric quantity; a result is fed back to the intelligent agent, and the strategy is updated through deep reinforcement learning; and performing rolling co-simulation between the day-ahead market and the real-time market, wherein a day-ahead clearing result is used as a boundary condition of the real-time market. The authenticity, accuracy and practicability of market simulation are improved.

The invention relates to the technical field of computer vision, in particular to an unmanned aerial vehicle aerial small target detection method fusing variable kernel convolution and recursive gating, and is suitable for accurate detection of small targets in unmanned aerial vehicle aerial images. The method includes: acquiring an unmanned aerial vehicle aerial image; preprocessing the image; the optimized YOLOv8 model is utilized to perform small target detection, and the model fuses variable kernel convolution, recursive gating convolution and lightweight convolution structures, adapts to different target size changes, and balances network overhead and performance; and outputting a detection result. By introducing the variable kernel convolution, convolution kernel parameters and sampling shapes are flexible and variable, target diversity in aerial images is effectively dealt with, efficient and accurate small target detection is realized, and powerful support is provided for unmanned aerial vehicle aerial photography technology application.

The present invention relates to methods of sparsifying signals over time in multi-bit spiking neural networks, methods of training and converting these networks by interpolating between spiking and non-spiking regimes, and their efficient implementation in digital hardware. Four algorithms are provided that encode signals produced by nonlinear functions, spiking neuron models, supplied as input to the network, and any linear combination thereof, as multi-bit spikes that may be compressed and adaptively scaled in size, in order to balance metrics including the desired accuracy of the network and the available energy in hardware.

The invention discloses a RedCap terminal access shunting system and method based on a decision tree model, and the system comprises a central control unit which is used for collecting network state information reported by each base station, and the network state information comprises key indexes including the number of users, the radio bearer RB utilization rate and the RRC access success rate; the plurality of base stations are used for executing the instruction of the central control unit and adjusting the on or off states of RedCap functions of the base stations and adjacent cells so as to optimize network resource allocation; and the decision tree model is arranged in the central control unit and is used for intelligently analyzing the network state information data and sending an instruction to the base station according to an analysis result so as to adjust the starting state of the RedCap function. By monitoring the network state in real time and intelligently evaluating and dynamically adjusting the RedCap function enabling state of the cells near the congested cell, the network load is balanced, the resource allocation is optimized, the network congestion can be effectively relieved, and the overall performance of the network is improved.

The invention discloses a whole-network integrated power balance method, which comprises the following steps of: firstly, constructing a whole-network integrated balance mathematical model, deducing an integrated balance network equation for representing network structure constraint on the basis of the model, and secondly, introducing a key balance target of a novel power system on the basis of the network equation, the method comprises the following steps of: firstly, deducing an integrated balance power equation formed by four process forms, namely, supply-preserving natural mutual aid, supply-preserving starting-up mutual aid, absorption natural mutual aid and absorption starting-up mutual aid, and then substituting an engineering boundary condition into the integrated balance power equation to deduce nine integrated balance configurations for engineering practice so as to realize whole-network integrated power balance. According to the integrated balance model and mechanism of the novel power system, the basic principle problem of whole-network integrated balance is effectively solved. By providing an integrated balance engineering practice basic mode and an eight-seed configuration, the problems of various engineering practice scenes and multiple targets are effectively solved.

The invention provides a hybrid routing method and device based on congestion awareness, and the method comprises the steps: determining a source node and a target node of a data packet, the source node representing a starting router node which initiates data transmission, the target node representing a termination router node which finally transmits data, and the destination node representing a termination router node which finally transmits the data; acquiring an input buffer occupation state of a target adjacent node corresponding to the source node, if the input buffer occupation state of the target adjacent node is an idle state, transmitting the data packet through a target shortest path corresponding to the target adjacent node, and if the input buffer occupation state of the target adjacent node is a congestion state, transmitting the data packet through a target shortest path corresponding to the target adjacent node. If yes, other nodes of the source node in the second target direction are determined, other shortest paths corresponding to the other nodes are obtained, and the data packet is transmitted to the target node through the other shortest paths. After the output port with the lightest load is selected for the data packet to bypass the congestion area near the source node, the algorithm is simplified while the network load is balanced, and overhigh routing overhead is avoided.

The invention discloses a communication method and device, a readable storage medium and a computer program product, which are used for balancing network load and improving communication quality. In the application, a source network device sends first information to a first target network device. The first information is used for requesting switching of the terminal device from the source network device to the first target network device. The first information comprises indication information of the first resource. The first resource is used for the relay network device to transmit information associated with a handover process. Since the source network device configures resources for the relay network device, the resources are used for the relay network device to transmit information associated with the handover process. Therefore, the resources of the relay network device for transmitting the information associated with the terminal device can be dispersed and do not need to be excessively concentrated, so that the network load can be balanced, the peak value of the network load can be reduced, and the communication quality can be improved.

The present invention relates to the technical field of wireless communication networks, and particularly to a V2V interaction method for vehicle ad-hoc networks based on intelligent connected vehicle WiFi. It collects vehicle motion state and network load data through in-vehicle sensors, dynamically switches between AP / STA modes by combining the Deep Q-Network (DQN) model and the ε-greedy strategy, and constructs an adaptive topology structure. It extends the OLSR protocol packet to embed a path delay field, optimizes multi-hop routing selection by combining pheromone gradient calculation, and realizes message priority classification and differential privacy protection through a federated learning model. Based on the particle swarm algorithm, it dynamically adjusts the AP / STA ratio threshold to trigger node role reconfiguration to balance network load. The present invention reuses existing WiFi hardware to reduce deployment costs, combines cross-layer routing optimization and a distributed decision-making mechanism, and improves network real-time performance, scalability, and group collaboration ability in dynamic scenarios.

The application relates to the technical field of high-frequency inverter output current sharing, and discloses a high-frequency inverter parallel current sharing method and system. According to the application, for multiple parallel high-frequency inverters, the output currents of the high-frequency inverters flow into a parallel confluence node and subsequent circuits after being balanced by a preset impedance balancing network. The impedance balancing network is modular, and comprises multiple modular current sharing branches. One end of each current sharing branch is connected to the output end of each high-frequency inverter in one-to-one correspondence, and the other end of each current sharing branch is connected to be suspended. The parameter design of the impedance balancing network is determined based on the connection between the output end of each high-frequency inverter and the parallel confluence node and the subsequent circuits. The balancing method has the characteristics of few devices, wide frequency range and strong extensibility of multi-inverter current balancing.

The invention provides a radio frequency switch circuit, which is characterized in that a voltage-sharing network is connected to a body region of a switch transistor, the voltage-sharing network comprises a first branch and a second branch, and the first end of the first branch and the first end of the second branch are both connected to the body region of the switch transistor; when the radio frequency switch circuit is in a turn-off state, the first branch circuit and the second branch circuit are conducted, the second end of the first branch circuit is connected to a source-drain connection node or a source electrode of another switch transistor, and the second end of the second branch circuit is connected to a source-drain connection node or a drain electrode of another switch transistor. The first branch leads the leakage current on the body region of the switch transistor to the source-drain connection node or the source electrode of other switch transistors, and the second branch leads the leakage current on the body region of the switch transistor to the source-drain connection node or the drain electrode of other switch transistors. The phenomenon of uneven voltage division of the switch transistor caused by the fact that the voltage of the body region of the switch transistor is raised is improved.

The application discloses a kind of anonymous routing methods of double-layer satellite network for delay-sensitive service, it is applied to VLEO / LEO double-layer satellite network, this network is based on software-defined network SDN architecture implementation, including data plane, control plane and application plane;Data plane includes VLEO layer, VLEO / LEO cross layer and LEO layer;Application plane is deployed with the routing method of the application upper;The method comprises: constructing layered decoupling routing calculation framework, to divide routing decision into including LEO layer inner routing, VLEO / LEO cross layer routing and VLEO layer inner routing multilayer path selection;Using the collaborative computing capability of SDN controller and satellite node, multilayer path selection is reconfigured and weight disturbance in real time, generates optimal routing path.The application can effectively reduce end-to-end delay, balance network load, improve the real-time performance, robustness and security anonymity of double-layer satellite network.

The application relates to a kind of digital power energy storage system dynamic network reconfiguration methods in the technical field of power energy storage, S1. Build energy storage system;It is composed of intelligent layer, control layer and data layer;S2. Energy storage network modeling;Adopt graph capsule neural network to learn the optimal control strategy of energy storage network;S3. Dynamic network reconfiguration;Based on Markov decision process, generate fault emergency response model;By systematic optimization of the topological structure and optimal control strategy of energy storage network, the quick isolation of fault area is realized, and the dynamic adjustment and reconfiguration of the working mode of energy storage equipment and load equipment are realized, to ensure that the energy storage network can still operate stably under fault situation, improve the flexibility and elasticity (fault tolerance) of energy storage system in dynamic environment;By reducing the energy loss in the transmission process, balancing network load and preventing local failure, the technical problems existing in the prior art are solved, and the sustainable development of new renewable energy system is promoted.

The application discloses a resource allocation method of a hybrid space division multiplexing elastic optical network and belongs to the technical field of optical communication. The application constructs a hybrid network model containing a multi-fiber bundle link and a multi-core optical fiber link, and designs a load and crosstalk aware heuristic algorithm. By means of modulation format step degradation, spectrum window plane construction to implicitly satisfy spectrum continuity constraints, load aware routing to balance network load, crosstalk aware core selection to suppress inter-core crosstalk, and optical signal-to-noise ratio verification to ensure transmission quality, joint optimization of routing, spectrum and spatial channels is realized. The application solves the problem that the prior art is only directed to a single type of space division multiplexing network and is difficult to adapt to a hybrid scene, effectively reduces network blocking rate under the premise of ensuring transmission quality, takes into account crosstalk suppression and spectrum efficiency, and has good engineering practicability.

The invention discloses a flow load balancing network system and method, the system comprises a user autonomous domain and an operator autonomous domain, and the user autonomous domain comprises a core layer, a convergence layer and an access layer; the core layer comprises two core routers, a network management switch and a plurality of network management servers, is connected to a network of an operator and is used for being responsible for flow forwarding of a core network; the convergence layer comprises four convergence routers and is used for converging user traffic of the access layer and transferring the user traffic to the core layer; the access layer comprises a plurality of access switches and is used for forwarding access user traffic to the convergence layer; the core router communicates with the convergence router through an ISIS and communicates with the operation router through an EBGP protocol; the access switch is configured with transparent transmission and access of a VLAN (Virtual Local Area Network) and related ports; and designing a DMtwo traffic classification method to carry out classification and load balancing on traffic. According to the method, a DMtwo flow classification method is adopted, multi-outlet network flow load balancing is achieved, and the method has the advantages of dynamic adjustment and fine management.

The invention provides a dialogue reply method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining dialogue data, inputting the dialogue data into a trained large language model based on a deep learning network, and obtaining a dialogue reply feature, the deep learning network comprises at least one first module, each first module comprises a second module with at least one first basic unit and a second basic unit which are connected in sequence, or the second module with at least one second basic unit and the first basic unit which are connected in sequence, and the first basic unit and the second basic unit comprise a normalization layer and a sub-layer; the output of the normalization layer is used as the input of the sub-layer, the input of the normalization layer in the first basic unit and the output of the sub-layer are subjected to residual connection, the output of the normalization layer in the second basic unit and the output of the sub-layer are subjected to residual connection, and reply is carried out according to dialogue reply features. The stability and model performance of the network are improved by balancing the internal structure of the network.

Disclosed is a high-power resonant inverter system (100) for wireless power transfer. The system (100) includes a control block (102) that generates control input signals. A synchronous gate driving arrangement (104), isolated from the main power circuit, operates a series-parallel configuration of plurality of switching elements (106). The series connection of switching elements increases voltage handling capability, while the parallel connection increases current handling capability. Furthermore, a voltage balancing network (108) evenly distributes voltage stress across the plurality of switching elements (106). Additionally, a resonant tank circuit (110) is coupled to the voltage balancing network (108) for efficient wireless power transfer.

The present invention relates to the field of network security technology, and particularly to an intelligent network security detection system and method based on big data analysis. The method includes: generating adversarial data through a historical network dataset, training a network security detection model with the adversarial data, dynamically adjusting an adversarial training objective function during training so that the network security detection model can balance the normal loss and the adversarial loss, and balance the robustness and accuracy of the network security detection model; analyzing network traffic data streams through a machine learning model, outputting network status indicators, and optimizing the network security detection model according to the output network status indicators to improve the detection ability of the network security detection model against adversarial attacks; extracting traditional network attack data from the historical network dataset, combining the traditional network attack data with adversarial perturbations generated through adversarial training, and training the network security detection model so that the network security detection model can detect traditional network attacks simultaneously.