302 results about "Global network" patented technology

The invention relates to the technical field of wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention provides a network slice resource allocation method oriented to an industrial internet and related equipment. Comprising the following steps: designing an SDN-based network slice resource allocation management and control architecture which comprises a control plane and a data plane; an intelligent agent is deployed for each switch node in the control plane, and each switch node is provided with an IT slice queue and an OT slice queue; designing a path planning and network slice resource allocation algorithm; modeling joint path planning and network slice resource allocation as a multi-agent Markov decision process; defining a state space and an action space; designing a reward function; constructing and training a multi-agent deep reinforcement learning model; and inputting a network global view collected in the SDN-based network slice resource allocation management and control architecture and demand information of each slice into the deep reinforcement learning agent obtained by training, and outputting a traffic forwarding path and a network slice resource allocation scheme. And the bearable flow quantity of the network can be improved to the maximum extent.

The invention belongs to the technical field of network security, and particularly relates to an intelligent association and global situation risk monitoring method based on multi-source data, which comprises the following steps: acquiring a multi-source heterogeneous data set; the multi-source heterogeneous data set is preprocessed, and preprocessed multi-source data is obtained; obtaining an attack behavior association graph according to the multi-source data, and performing anomaly detection on the association graph by using a graph neural network to obtain an explicit attack link and a potential attack link; obtaining a network security situation dynamic graph based on the explicit attack link and the potential attack link; and generating a risk assessment report according to the network security situation dynamic graph, triggering a corresponding security policy, and performing risk monitoring according to the security policy. According to the method, the accuracy and response speed of threat detection are remarkably improved, the global network security situation awareness capability is enhanced, and an intelligent solution is provided for security protection in a complex network environment.

The invention belongs to the field of Internet of Vehicles network communication, and discloses an Internet of Vehicles federated learning dynamic sparse training method based on cluster expansion. Constructing a hierarchical federated learning training architecture under the scene of the Internet of Vehicles, wherein the hierarchical federated learning training architecture under the scene of the Internet of Vehicles is of a client-server two-layer structure and comprises an edge server and a road system; performing federated dynamic sparse training based on a hierarchical federated learning training architecture in an Internet of Vehicles scene: training a sparse network structure based on local data by a training node under coordination and management of an edge server, and periodically updating the sparse network structure according to a predefined reconstruction updating standard; and finally, aggregating and reconstructing a global network structure meeting the target sparseness at the edge server.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention discloses a network communication dynamic optimization method based on multi-module collaboration, and relates to the technical field of network communication, a dual-mode communication module is deployed at each network node, and the network communication dynamic optimization method comprises the following steps: each network node broadcasts own existence information and power line channel characteristics through an HPLC (High Performance Liquid Chromatography) channel of the dual-mode communication module; each network node scans surrounding wireless networks through an HRF channel of the dual-mode communication module and reports own wireless channel quality information to the gateway; the global state sensing module collects all information and constructs a global network view containing physical topology and a channel quality map. A dual-mode cooperation mechanism, a machine learning prediction model and a dynamic decision strategy can adapt to complex dynamic environments such as power line noise fluctuation and wireless interference change, and communication parameters can be autonomously optimized without manual intervention; and meanwhile, the modular design is convenient to expand to a multi-mode communication scene, and has a wide application prospect.

The invention relates to the technical field of supply chain risk early warning, in particular to a supply chain risk early warning method based on deep learning, and the method comprises the steps: obtaining supply chain data, extracting a material circulation relation between supply chain nodes, constructing a node relation graph, and employing a graph neural network to achieve the aggregation of the features of the nodes and adjacent nodes. And space correlation characteristics in the global network are extracted, and a multi-stage transmission and diffusion path of the risk is effectively modeled. And then, splicing node space features and historical time sequence features, inputting the spliced features into a long-short-term memory network, dynamically capturing the evolution trend of node risks along with time, identifying periodic fluctuations and sudden anomalies, and improving the prediction precision of the risk trend. And finally, a multi-layer perceptron is adopted to carry out nonlinear mapping and feature fusion on risk time sequence features output by the long-short-term memory network, node risk scores are generated, real-time early warning of high-risk nodes is realized accordingly, and the accuracy and timeliness of supply chain risk monitoring are greatly improved.

The invention discloses a network optimization method based on an online conference, which relates to the technical field of real-time audio and video transmission, and comprises the following steps: in a transnational network, deploying monitoring probes at nodes participating in the online conference for monitoring network parameters such as network delay, bandwidth, packet loss rate and jitter in real time; the method comprises the following steps: processing network parameters and predicting abnormity by using a statistical analysis algorithm and a time sequence model to obtain network condition information, and then combining global network topology and using a reinforcement learning Q-Learning model and a graph neural network GNN topology prediction algorithm; according to the method, the network condition is monitored and predicted in real time, the optimal transmission path is output by using the reinforcement learning Q-Learning model and the GNN topology prediction algorithm, the bandwidth fluctuation is predicted through the LSTM, the packet loss rate is predicted based on the Bayesian network, the data transmission strategy is optimized, the network delay, the packet loss rate and the jitter are effectively reduced, and the network performance is improved. And the smoothness and the stability of the conference are improved.

The invention provides a network fault node automatic detection and roundabout path planning method, which comprises the following steps of: acquiring optical power and bit error rate of an optical fiber link, and recording abnormal time point and position information; obtaining a strong electromagnetic interference influence weight map; generating a deterioration trend curve; obtaining a health degree score of each optical fiber segment, and generating a health degree distribution map; probability distribution of fault nodes is determined, whether the nodes are fault points is judged according to the probability distribution, and a fault node position set is generated; obtaining a roundabout path planning scheme; and calculating a global network health state perception score to obtain an optimized global network health state diagram. The invention further discloses a corresponding storage medium. According to the invention, the reliability and stability of the optical fiber network can be effectively improved, and the efficiency and accuracy of fault detection and processing can be improved.

The present invention relates to the technical field of supply chain scheduling, and discloses a multiple supply chain scheduling method and system based on a global Critic multi-agent algorithm. The method includes: constructing a multi-agent supply chain environment; introducing a global Critic network, and using the multi-agent deep deterministic policy gradient algorithm to perform reinforcement learning on the multi-agent supply chain environment to construct a multi-agent decision-making model; inputting supply chain demand data into the multi-agent decision-making model for optimized scheduling processing to generate a scheduling strategy that maximizes the overall profit of the supply chain. The present invention introduces a global Critic network to reduce multi-agent competition and optimize the overall performance. The multi-agent deep deterministic policy gradient algorithm is used to construct a decision-making model to adapt to complex supply chain environments. With the goal of maximizing the overall profit, it solves the problems of low efficiency and low returns. The environment has strong adaptability and can handle scheduling problems of different scales. By continuously learning to optimize decisions, the reliability is improved, and the efficiency and returns are significantly enhanced.

The invention provides a hierarchical deep reinforcement learning routing protocol method based on a multi-link ad hoc network, relates to the technical field of communication routing, and solves the problem that the calculation efficiency in the current multi-link ad hoc network is degraded. The method comprises the following steps: firstly, constructing and dynamically updating a global network view for all nodes in a network, and further constructing a route selection model which is divided into a high-level controller and a low-level controller; a high-level controller plans a route from a global perspective and balances a global load, and a low-level controller selects a target node according to a planning result of the high-level controller and distributes an optimal communication link for the target node in combination with link state information; training and optimization of the network of each controller are carried out independently, external rewards are introduced to a high-level controller, and internal rewards are introduced to a low-level controller; the external reward is used for optimizing the overall performance of the high-level controller; the internal reward is used as an index value of the low-level controller for feeding back the link state in real time, and collaborative optimization of the high-level controller and the low-level controller is achieved.

The invention relates to the technical field of network security, in particular to a network security active security defense method and system based on vulnerability utilization, and the method comprises the steps: collecting multi-modal data through a sensing module deployed in a network, and constructing a prediction model of a high-risk region and an attack path in combination with a global network situation map; according to the path prediction data and the risk region division result, dynamically allocating defense resources; by adjusting the deployment density of trapping nodes and feedback chain logic, a trapping chain is formed to guide an attack behavior, the operation characteristic data of an attacker is captured in real time, and a defense strategy is dynamically adjusted. Through the above process, the method achieves the precision of resource distribution, the optimization of the synergistic effect of trapping nodes, and the real-time guidance and interference of attack behaviors, greatly improves the defense efficiency and flexibility of a network, reduces the security threats of high-risk nodes, and effectively protects the core assets of the network.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

Examples provide improved methods for managing wireless network connectivity for a mobile device. Examples include receiving a network map that defines geographic availability of a first wireless network; determining that the first wireless network is accessible within a travel segment of a travel plan based on comparing geographic location data associated with the travel segment to the geographic availability of the first wireless network from the network map; creating a network plan that includes the travel segment and an associated connectivity waypoint, the connectivity waypoint defining when to alter connectivity status with the first wireless network; and causing the mobile device to alter connectivity status with the first wireless network based on proximity to the connectivity waypoint.

The invention discloses a wireless data uploading method and system for an industrial internet of things, and relates to the field of wireless data processing, and the method comprises the steps: firstly, comprehensively considering the inherent service attributes of data to determine the initial service priority of the data; meanwhile, terminal side sensing data and global network state data obtained from the gateway are fused, and a more accurate and comprehensive network quality score is calculated through double-source information. Finally, the initial priority and the network quality are dynamically combined according to the two key dimensions, and a final transmission strategy is generated. According to the method, a data uploading decision is no longer static and one-dimensional, but a two-dimensional dynamic decision which can be adaptively adjusted according to a real-time network condition, so that the sending opportunity or mode of the high-priority data is intelligently adjusted when the network is congested, the blocking of key services due to blind sending is effectively avoided, and the service quality is improved. And the robustness and the high efficiency of data uploading in a complex industrial environment are ensured.

The invention discloses a machine learning enhanced real-time hydraulic modeling method for a water supply network. The method comprises the following steps: acquiring position numbers of sensors in the water supply network and correspondingly acquired historical hydraulic data; based on position numbers of sensors in the water supply network and time nodes in an acquisition period, constructing a sensor sampling matrix corresponding to each monitoring target, and filling historical hydraulic data into the corresponding sensor acquisition matrixes to form a training set; constructing an initial model; constructing a physical constraint loss function and training the initial model by using the training set to obtain a hydraulic prediction model for predicting a global hydraulic state; and inputting the sensor acquisition matrix of the monitoring target in the water supply pipe network into the hydraulic prediction model to output global pipe network hydraulic data. The invention further provides a real-time hydraulic monitoring system for the water supply network. The model constructed by the method provided by the invention can realize real-time estimation of the hydraulic state of the water supply network, and provides accurate data support for daily maintenance of the water supply network.

Systems and methods for connecting devices via a virtual global network are disclosed. In one embodiment the network system may comprise an endpoint device including a tunnel manager and a first virtual interface, an access point server including at least one tunnel listener and a second virtual interface. One or more tunnels are formed connecting the tunnel managers and tunnel listeners. The virtual interfaces provide a logical point of access to the one or more tunnels.

The invention relates to a congestion control method and device for network equipment, computer equipment and a medium. The method comprises the following steps: acquiring global network state information of the network equipment; and performing multi-dimensional congestion measurement calculation on the global network state information by adopting a preset weighting function to obtain a congestion measurement value of each node. And identifying one or more bottleneck nodes and / or one or more congestion paths from the network equipment based on a set congestion metric threshold value and each congestion metric value. And determining target path configuration from the plurality of available paths according to each bottleneck node and / or each congestion path. And performing flow distribution on each node on the one or more available paths according to the target path configuration. According to the method, the global network state information of various network devices is comprehensively considered, and multi-dimensional congestion measurement calculation and dynamic path selection are combined, so that cross-device end-to-end congestion control is realized, and the overall congestion response capability and reliability of the network devices are improved.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

The invention discloses a remote sensing image change detection method, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting a preprocessed dual-time image of a target region into a remote sensing image change detection model, and outputting a change probability graph of the target region; the remote sensing image change detection model is obtained by training a global network model by adopting a training set, and the global network model comprises an initial network model and a classifier; the initial network model is constructed on the basis of a transform model; performing thresholding processing on the change probability graph of the target area to obtain a binary change detection graph of the target area; the initial network model comprises a pyramid segmentation attention feature enhancement module, a cross-spatio-temporal feature interaction module, a cross-scale attention transformer module and a cross-level pyramid transformer module. According to the invention, the accuracy and reliability of remote sensing image change detection are improved.

The invention relates to the technical field of network slicing, and discloses a wireless network multi-link communication method based on dynamic link configuration. The method comprises the following steps: collecting state data of heterogeneous links such as cellular links, Wi-Fi links and satellites in real time, and constructing a global network state view; analyzing the network slice SLA and converting the network slice SLA into a quantitative performance constraint; predicting the future performance of the link by using a gating circulation unit neural network; generating a global optimal data flow routing and distribution strategy meeting the QoS (Quality of Service) requirements of the slices in the central controller based on reinforcement learning; and compiling the strategy into a configuration instruction and issuing the configuration instruction to the terminal and the access point for execution. The system comprises a state sensing module, a strategy analysis module, a prediction modeling module, a strategy generation module and a configuration execution module. By means of predictive planning and intelligent decision making, dynamic, fine and on-demand scheduling of heterogeneous resources is achieved, and the network resource utilization rate and the multi-service service quality guarantee capacity are remarkably improved.

The invention discloses a cross-domain time-sensitive satellite network traffic scheduling method based on a TSN and MF-TDMA, and the method comprises the steps: a cross-domain controller collects network topology information, builds a satellite cluster global network model, carries out the collection of data and task demands, and a user controller collects an intra-domain sending end traffic access request, the traffic demand is reported to the cross-domain controller and the in-satellite controller; the cross-domain controller performs flow characteristic analysis and establishes a flow model; the in-satellite controller evaluates the network load, constructs a dynamic network resource state information base, and determines the traffic scheduling granularity according to the long-time stable service; according to the positions of the source node and the destination node of the service flow, the cross-domain controller and the in-satellite controller respectively calculate the scheduling paths of the cross-domain flow and the single-domain flow; and according to the routing result of each flow, based on the scheduling decision variable, establishing a wireless and wired joint scheduling constraint and optimization target, generating an integer linear equation, and solving an optimal solution to generate a scheduling scheme.

The invention discloses a central air conditioner control method, system and device, a medium and a program product, and relates to the technical field of data processing and control systems.The method comprises the steps that a central air conditioner system control model is built; the method comprises the following steps: constructing an actor network, and embedding an optimization control model consisting of a global critic network and a local critic network of a GAT (Graphics Attention Transform); and training the optimization control model, and controlling the refrigeration temperature set value and the heating temperature set value of each area according to the current state observation variable of each area by using the trained actor network. A critic network is designed into a global critic network and a local critic network, the problem that regional thermal comfort awards and central air conditioning system total energy consumption awards conflict with each other in the training process is relieved, a graph attention network is fused into the global critic network, coupling information in the system is fully extracted, and the optimization control performance is improved.

The invention relates to the technical field of Internet of Things, and discloses an Internet of Things terminal intelligent connection system and method based on a global operator network, and the method comprises the steps: when equipment is powered on or restarted, automatically executing network environment scanning, detecting a currently available cellular network, and collecting signal intensity, network coverage condition, delay and bandwidth; the equipment identifies an available operator network based on a global network configuration file, and comprehensively evaluates signal strength, network coverage, delay and bandwidth by adopting an optimization algorithm; the equipment continuously monitors a current connection state, collects network quality parameters, analyzes a network fluctuation trend in combination with a machine learning algorithm, and judges whether to switch to other operator networks when monitoring that the network quality is lower than a dynamic threshold value; and when detecting that the current network quality is continuously reduced, the signal is weak or the connection is lost, automatically triggering a network switching mechanism. The method has the advantage of improving the connection efficiency and stability of the Internet of Things terminal.

The invention relates to the technical field of medical image segmentation, and discloses a federated medical image segmentation method based on client consistency and adaptive disturbance, and the method comprises the following steps: constructing a supervised client image segmentation network, an unsupervised client image segmentation network, and a server global network; an interaction mechanism between clients is constructed, difference loss between the clients is obtained, the problem of model degradation during global model aggregation is reduced by reducing model distances between different types of clients, and a high-quality aggregated global model is completed; the noise intensity in the learning process of the unsupervised client is dynamically adjusted by using the local model performance of the unsupervised client so as to adapt to the learning difficulty of different client data sets, and the learning quality of the unsupervised client is enhanced so as to enhance the performance of the global model. The method is beneficial to more accurate medical image segmentation in a federal semi-supervised scene, and the segmentation capability of a global model is improved.

A predictive artificial intelligence (AI) engine is trained phase offsets measured between global network satellite system (GNSS) derived clocks and precision timing protocol (PTP) derived clocks and network parameters including network impairment metrics in a packet network. The predictive AI engine may predict which PTP input source should be selected by a network element, phase offset(s) to be applied to the PTP input source, and which network element(s) should apply phase offset(s). Other embodiments are disclosed.

The invention relates to the field of forged image detection, and provides a forged image detection method and device, a medium and a product, and the method comprises the steps: dividing an input image into a plurality of local regions, and constructing a region dependence graph of the local regions; extracting local style features from the region dependence graph by using a local network model; extracting global style features of the input image by using the global network model; performing attention fusion on the local style features and the global style features by using an attention model to obtain a fusion vector; performing image forgery detection on the fusion vector by using a classifier; wherein the local network model, the global network model, the attention model and the classifier are optimized through training. According to the invention, high identification accuracy and robustness can be maintained in detection scenes of scarce samples, local detail forgery and cross-domain forgery.

The invention belongs to the technical field of power networks, and particularly relates to a power network vulnerability analysis system and method based on virtual attack and defense deduction, and the system comprises a data collection module which is used for collecting various data of a power network, the various types of data comprise network topology structure data, equipment configuration data, operation state data and security log data; and the threat intelligence integration module is used for collecting and integrating global network security threat intelligence and establishing a data interface with a threat intelligence platform to realize automatic synchronization and updating of the threat intelligence. Through continuous virtual attack and defense deduction, an attack and defense strategy is optimized in combination with a machine learning algorithm, a novel attack means can be simulated, and the power network security state can be monitored in real time. Even if an unprecedented attack mode appears, potential threats can be quickly identified through attack and defense confrontation, the discovery capability of novel and unknown vulnerabilities is greatly improved, and network security risks are responded in time.

The invention belongs to the technical field of Internet of Vehicles communication, and relates to a digital twinning-based cognitive Internet of Vehicles connectivity clustering algorithm, each vehicle constructs a vehicle communication network through a Dijkstra algorithm according to a vehicle communication probability table, and a communication probability weight minimum path from the current vehicle to all vehicles in the network is calculated through path search; enabling the direct reachable neighbor vehicles and the indirect reachable neighbor vehicles on the path with the minimum communication probability weight to jointly form a reachable cluster, and enabling the direct reachable neighbor vehicles on the path with the minimum communication probability weight to form a conflict cluster; each vehicle adjusts own transmitting power according to the conflict cluster; and each vehicle perceives a global network state through a digital twin model according to the vehicles in the conflict cluster and the channel access probability of the corresponding vehicle, selects an optimal channel to complete a channel access request, and forms a connected cluster. According to the method, the communication probability between vehicles is combined in the clustering process, channel allocation is optimized, communication conflicts are reduced, and the data transmission efficiency is improved.