584 results about "Network congestion" patented technology

The invention relates to a three-in-one network Internet of Things card automatic switching method. The switching method comprises the following steps: collecting state parameters of a plurality of operator networks; constructing a signal space topology model of the target area, predicting each operator network according to the signal space topology model, and calculating a comprehensive quality score of each operator network according to a preset multi-dimensional weighted evaluation model so as to establish standby communication links of other to-be-switched networks; calculating a corresponding switching income index according to the comprehensive quality score and a historical switching record of the currently used network, and generating a corresponding network switching instruction; and calculating a communication performance change result before and after switching of the network switching instruction so as to perform adaptive adjustment on weight parameters in the multi-dimensional weighted evaluation model. The method can predict and complete switching in advance before signal attenuation or network congestion, avoids communication interruption, dynamically optimizes a decision according to the use history and switching cost, and achieves a network switching control effect with both performance stability and economical efficiency.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

A scheduling method for managing network congestion includes: determining, in response to link congestion in a network, a plurality of queue pairs (QPs) on a congested link in the network; determining, from the plurality of QPs on the congested link, a target QP for scheduling out from the congested link; determining a target traffic-forwarding path based on traffic of links in the network, the target traffic-forwarding path being configured for forwarding traffic of the target QP; determining a changed route of the target QP based on a target address of the target QP and the target traffic-forwarding path; and delivering the changed route to a target source local area network access (LA) device of the target QP, the target source LA device being configured to output, based on the changed route, the traffic of the target QP through the target traffic-forwarding path.

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 relates to an intelligent scheduling method and device for communication network resources, equipment and a medium. The method comprises the following steps: performing IP packet header analysis on a service data packet to obtain an initial service priority identifier, and performing adjustment in combination with a preset priority rule, an equipment alarm state and a network congestion rate to generate a dynamic service label; based on the dynamic service label, the real-time link state and the energy consumption coefficient, generating a resource allocation strategy and a path scoring weight parameter through a preset deep reinforcement learning network; and calculating a path score based on the resource allocation strategy and the path score weight parameter, generating a path mapping table, generating a port suppression instruction based on the dynamic service label and the resource allocation strategy, and extracting a base station power regulation instruction. According to the method, the dynamic adaptability, the resource utilization rate and the energy consumption management and control capability of communication network resource scheduling are improved by means of dynamically adjusting the service priority, comprehensively modeling a resource allocation strategy and the like, and the transmission guarantee level of high-priority services is enhanced.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The invention relates to the technical field of distributed computing, and discloses a distributed computing modeling system design method for high-dimensional discrete data, which comprises the following steps: acquiring a feature identifier access request in a high-dimensional discrete data stream, counting an access frequency, and marking a feature identifier of high-frequency access as a high-frequency access state; constructing a local parameter copy on a non-master node physical computing unit of the distributed cluster; routing gradient update data aiming at the feature identifier to a local parameter copy closest to the network and generating a local residual tensor; calculating the direction cosine similarity of the local residual tensor and the global gradient update vector; calculating a real-time access entropy based on the access source distribution and converting the real-time access entropy into a dynamic synchronous threshold value; according to the method, through a dynamic synchronization judgment mechanism based on semantic information gain, high-frequency but invalid redundant communication is effectively inhibited, and network congestion under long-tail distribution is reduced.

The invention is applicable to the technical field of power distribution network monitoring, and provides a power grid monitoring-oriented power adaptive congestion control method, which comprises the following steps of: acquiring a network performance index and a power grid operation parameter of a power grid monitoring data transmission network, and determining a congestion state of a current network through a preset congestion evaluation model; a 5G ad hoc network node connection strength coefficient, a new energy monitoring data priority label, a west-to-east transmission channel power flow fluctuation coefficient and a regional vulnerability index are used as decision variables, and the minimum weighted transmission delay, the packet loss rate and the maximum new energy consumption responsivity are used as objective functions. Generating a control strategy corresponding to the current network congestion state according to the hybrid intelligent optimization model fusing reinforcement learning and a genetic algorithm; executing the control strategy to dynamically adjust the network resources; monitoring the execution effect of the control strategy, and feeding the result back to the model to update the parameters; and therefore, the requirements of multi-target optimization and differential service quality in a complex power grid monitoring scene can be met.

The invention discloses an inter-satellite link load balancing method, system and device and a storage medium, relates to the field of satellite communication, and is used for solving the problem of network congestion of a core network user plane. In the application, an SMF network element receives inter-satellite link state information from each UPF network element; calculating a routing path from the source satellite node to the target satellite node according to the inter-satellite link state information under the limitation of inter-satellite link time delay and bandwidth; and distributing the routing path decision to the UPF network element of the corresponding satellite node. According to the invention, the load balancing of inter-satellite links can be realized, the user plane network congestion of the satellite-borne core network is avoided, and the service quality of the satellite-borne core network is improved.

The invention discloses a streaming telemetry data plane forwarding method and device, and belongs to the technical field of communication. According to the method disclosed by the invention, stream telemetering is realized by processing the header of a service message through multi-level cache, and satellite nodes and paths in a spatial network are measured and monitored in a high-fineness manner; according to the method, the proportional sampling of the stream telemetering is realized through the stream classification list, so that the resource overhead of the stream telemetering is saved, and the occurrence of network congestion possibly caused by the stream telemetering is reduced; the end-to-end sending of the telemetering message is realized by pre-configuring the telemetering message output interface, and the telemetering message uploading process is simplified. Based on the streaming telemetry data plane forwarding method, the streaming telemetry data plane forwarding device comprises a service message processing subsystem which is used for service message analysis, processing and stream classification; the telemetering message processing subsystem is used for constructing and uploading a telemetering message; and the message forwarding subsystem is used for forwarding the service message and the telemetering message.

The invention discloses an optical network dynamic resource allocation method fused with an AI large model, which relates to the technical field of real-time scheduling of network resources, and comprises the following steps: constructing a service prediction model based on terminal behavior characteristics, calculating bandwidth sensitivity Bs and burst probability Pt, generating priority weight Wp, and improving burst service scheduling response; the T-CONT allocation rate and period are adjusted in a self-adaptive mode through a resource allocation efficiency function Ef and a buffer area demand index Bh, and priority scheduling of key services is guaranteed; the scheduling pressure is judged by introducing double thresholds of a signaling overhead index So and a network congestion degree Oc, AI reestimation and resource reallocation are triggered when abnormity occurs, and meanwhile, a bandwidth recovery parameter Br is recorded to optimize a subsequent strategy, so that minute-level intelligent scheduling and resource utilization maximization are realized.

This application relates to the field of wireless communication technologies, and discloses a random access method, a terminal, a network device, and a communication system. In this application, an RRC establishment cause may be determined in a random access procedure based on a network congestion status, and an uplink scheduling message including the RRC establishment cause is sent to the network device.

The invention discloses a computer hardware monitoring system based on a network, and relates to the technical field of hardware monitoring. The computer hardware monitoring system based on the network comprises a monitoring data acquisition module used for acquiring monitoring data of set computer hardware, a load situation analysis module used for analyzing a load situation feature set of the set computer hardware based on the monitoring data of the set computer hardware, comprising an operation bearing characteristic value, a network congestion characteristic value and an energy consumption load characteristic value; and the load risk analysis module is used for analyzing the load risk characteristic value of the set computer hardware based on the load situation characteristic set of the set computer hardware, and the load risk characteristic value in the monitoring alarm feedback module is used for performing alarm processing on the set computer hardware. Therefore, stable operation of computer hardware in a complex operation environment is guaranteed, risk omission or misjudgment is avoided, and the safety of a hardware system is improved.

The invention discloses a traffic shaping scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target service type of a first data stream at a first time, and obtaining a second data stream at a second time corresponding to the target service type; the second time is before the first time and comprises flow data of a plurality of time periods; determining flow characteristic data of a third data stream at a third time according to the second data stream; the flow characteristic data is used for describing the flow rate and the burst degree of the third data flow; and determining a control instruction of a token bucket shaper of the flow queue based on the flow characteristic data, and adding a token to the token bucket shaper based on the control instruction to meet the flow transmission of the third data flow. According to the invention, through real-time adjustment of the token bucket parameters, accurate control of network congestion is realized.

Systems and methods are disclosed for optimizing network routing and data processing through dynamic route optimization. The invention features a dynamic route optimization engine that uses real-time performance metrics such as latency, bandwidth availability, memory utilization, CPU load, and throughput capacity. It allows for rule-based configurations, enabling custom parameters for transaction processing, data retrieval, and user authorization. Integration with network monitoring tools provides continuous visibility into network performance. The system supports multi-cloud and hybrid cloud environments, dynamically selecting optimal paths based on real-time conditions and regulatory compliance. Predictive analytics anticipate network congestion, allowing proactive routing adjustments. Adaptive algorithms learn from past conditions to optimize performance continuously. Scalability is achieved through automated updates to routing tables and security groups. The invention reduces latency and ensuring efficient data transfer, providing a cost-effective and reliable solution for modern network management, addressing a long-standing need for adaptability and efficiency in network routing.

A first ratio of a sending rate limit to a full line rate for a link in the computing network is accessed. A second ratio of a sending window size to W_max for the link is accessed. W_max is the maximum allowed window size or the window size that utilizes an end-to-end path for the link. One or more of the first or second ratio is used to determine an amount to reduce the sending rate or window for the link in response to an indication of network congestion in the link.

The invention discloses a credit control method and device for a data message service and a medium, and relates to the technical field of intelligent scheduling, and the method comprises the following steps: when it is detected that the current network congestion level reaches a congestion deterioration threshold in the execution process of an authorization overdraft sending instruction, automatically skipping to a prepaid instruction execution process; when the prepaid instruction is required to identify that a semantic priority label is a high-priority emergency message in a payment verification process, immediately terminating a payment verification process, switching to an authorization overdraft sending instruction execution process, starting a rapid transmission channel, and applying a delayed charging mode to complete data message forwarding; and when the temporary storage message of refusing to send the instruction monitors that the current network congestion level is recovered to a smooth state, reactivating any one of the authorized overdraft sending instruction and the required prepayment instruction. According to the invention, through the established instruction execution state pool, the dynamic state conversion among the authorized overdraft sending instruction, the prepayment requesting instruction and the sending refusal instruction is realized.

Methods and systems for operation of an endpoint device of a deployment are disclosed. The endpoint device may obtain a network data united directed to a destination. The endpoint device may select a channel over which to forward the network data united based on a forwarding policy and a state of an optical link between the endpoint device and an optical service point. The forwarding policy may be keyed, at least in part, on the state of the optical link. The endpoint device may forward the network data unit over the selected channel to direct the network data unit towards the destination in order to facilitate provisioning of desired computer-implemented services.

The invention relates to the technical field of network scheduling, and discloses an agricultural heterogeneous perception and intelligent network scheduling method and system, and the method comprises the following steps: S01, a system initialization stage; step S02, a resource scheduling and strategy learning stage; step S03, a sensing data fusion and processing stage; s04, a security mechanism and life cycle management stage: adopting an end-to-end encryption and identity authentication mechanism in a data transmission process, and collecting feedback parameters for updating a scheduling strategy and releasing resources after a task is completed; the system comprises a multi-mode agricultural sensing terminal, intelligent gateway equipment, a 5G and LPWAN hybrid access network, a resource scheduling module and a scheduling strategy learning engine. According to the method, the multi-dimensional state characteristics such as the network congestion level, the edge node load, the data timeliness and the task priority are sensed in real time through the deep reinforcement learning model, and the optimal allocation decision of communication and computing resources is dynamically generated.

The invention relates to the technical field of manufacturing data acquisition, and discloses a distributed manufacturing data acquisition system based on NB-IoT. The system comprises a heterogeneous node sensing layer, a narrowband signaling scheduling layer, an edge fusion processing layer, a cloud collaborative decision-making layer and a closed-loop optimization feedback layer. The heterogeneous node sensing layer obtains multi-source data through a sensor array, generates an equipment operation state thermodynamic diagram and divides region priorities; the narrowband signaling scheduling layer deploys NB-IoT nodes, transmits time division multiplexing signaling and generates a node load distribution diagram; the edge fusion processing layer synchronizes the clock, fuses the multi-source data and the load distribution map, and generates fusion confidence; the cloud collaborative decision-making layer converts the fusion confidence into an executable instruction and issues the executable instruction; and the closed-loop optimization feedback layer monitors the load change of the system, generates an optimization index and dynamically optimizes an acquisition strategy. The system can penetrate through network congestion, improves the real-time performance and accuracy of data acquisition, dynamically adapts to the change of a manufacturing environment, and is suitable for a distributed manufacturing scene.

The application discloses a chip based on a reconfigurable neuromorphic interconnection architecture, comprising a layered dynamic reconfigurable interconnection network layer and a memristor-CMOS reconfigurable interconnection unit, and the layered dynamic reconfigurable interconnection network layer and the memristor-CMOS reconfigurable interconnection unit are dynamically adjusted in a routing path through a dynamic routing algorithm. The application has the beneficial effects that: the dynamic routing algorithm is combined with the layered dynamic reconfigurable interconnection network layer and the memristor-CMOS reconfigurable interconnection unit, thereby performing dynamic prediction of network congestion and dynamic adjustment of a routing path, and the CMOS switch and the memristor switch are connected in parallel, avoiding the limitation of traditional single devices or static interconnections, so that the network performance and reliability are improved, and the delay and power consumption of data transmission are reduced.

The invention discloses a minimum-time-delay routing path generation method applied to a multi-channel unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle communication network routing path optimization generation. Wherein in the link attribute calculation process, external channel input information is received to calculate network link rate information; according to the route path generation process, information such as a link connection relation and media time in an unmanned aerial vehicle cluster network is generated based on OLSR basic ad hoc network protocol broadcast, a route path with the minimum media time is selected, information transmission delay is reduced, the network throughput is improved, and the network congestion degree is reduced. According to the method, the routing path with the minimum media time can be actively selected in a high-dynamic and multi-channel unmanned aerial vehicle cluster communication environment, the information transmission reliability of the network is improved, the network delay is reduced, the throughput of the whole network is improved, and rapid control and reliable guarantee of the unmanned aerial vehicle cluster network are guaranteed.

The present application relates to a kind of multi-protocol transmission text data monitoring and alarm method and system, method includes: based on dynamic authorization and hardware security check generates multi-source protocol transmission instance, collection and analysis text data;Through multithreading real-time monitoring network connection state, data accumulation and sensor numerical content parameter, generate transmission state and content abnormal event queue;Utilize bayesian network algorithm to learn the causal relationship of network congestion, equipment failure and alarm event, in combination with dynamic weight distribution strategy calculates root cause probability and generates priority-ordered comprehensive alarm list;Based on user feedback data adaptive update protocol weight and alarm strategy, realize abnormal early warning trigger, data snapshot binding and alarm log closed-loop optimization, solve the poor safety adaptability in multi-protocol hybrid transmission scene, monitoring dimension single, root cause analysis relies on static rule and strategy update lag problem, improve the real-time performance, accuracy and adaptability of industrial internet of things data transmission.

A novel dynamic bandwidth allocation system integrates an intelligent traffic prediction module and an on-demand token replenishment module in a quality-of-service (QoS)-based media access control (MAC) layer controller, which is able to dynamically allocate data transmission capacity (i.e., bandwidth) to multiple incoming service flows of data packets for multiple subscribers more efficiently with less network congestions and service inconsistencies than conventional MAC layer management methods. The novel dynamic bandwidth allocation system utilizes network traffic predictions to identify potentially sudden or rapid changes in near-term network traffic, and if necessary, adjusts bandwidth allocations preemptively to service flows to minimize service quality degradations while also improving network resource usage efficiencies, compared to conventional static bandwidth allocation methods. The novel dynamic bandwidth allocation system also minimizes unnecessary packet droppages, network congestions, and / or speed degradations during bursty and oscillating network traffic, and improves QoS satisfaction rates for network subscribers.

The invention discloses a self-adaptive and self-coordinated unified communication system and method based on an SDN, and relates to the field of coordinated communication, and the method comprises the steps: collecting network state data of a switch, extracting traffic chaos features through a lightweight chaos analysis algorithm, generating a traffic feature vector, generating a network management threshold based on the traffic feature vector, and carrying out the dynamic adjustment, updating a flow table, generating a resource state log, evaluating cross-domain network congestion according to the resource state log, calculating a congestion index, generating a key through a quantum key distribution unit, dynamically adjusting a key update frequency and distributing the key update frequency to a cross-domain switch, recording a key distribution log, and using the key distribution log and a real-time network state to determine the congestion of the cross-domain network. Detecting network abnormity, defining as an antigen, running an antibody generation algorithm to generate a repair strategy, and storing as an antibody record; according to the method, the traffic feature vector is generated through the chaos analysis algorithm, potential congestion or abnormity can be detected earlier, and the dynamic adaptability of a cross-domain network is improved.

The invention provides a wireless communication method based on a cellular Internet of Vehicles technology, a communication terminal and a vehicle, and relates to the technical field of vehicle communication. The method comprises the following steps: adding a first vehicle-mounted communication terminal and a second vehicle-mounted communication terminal of a target following vehicle in preset formation vehicles to a communication network of a target multicast group, and generating a target multicast routing table, so that a leading vehicle adopts a multicast communication mode, the multicast data can be sent to the second vehicle-mounted communication terminal of each target following vehicle in a targeted manner through the first vehicle-mounted communication terminal, so that the data is prevented from being sent to other vehicles. Due to the adoption of the multicast communication mode, the vehicle-mounted communication terminal of any vehicle in the target multicast group sends the multicast data, and the vehicle-mounted communication terminals of the other vehicles can receive the multicast data, so that unnecessary repeated transmission is avoided, the risk of network congestion is remarkably reduced, the information spreading efficiency is improved, and the user experience is improved. And meanwhile, all target following vehicles can receive the latest instruction in time, and synchronization and stability of a motorcade are maintained.

The invention belongs to the technical field of data center network congestion, and discloses a congestion control parameter adaptive adjustment method based on collaborative machine learning. According to the invention, training of a machine learning performance prediction model and a reinforcement learning agent is carried out through historical traffic data, prediction of a port-level global ECN threshold matrix is carried out through the machine learning performance prediction model of an integrated controller, and reasoning of the reinforcement learning agent of a switch port is carried out to obtain a local ECN threshold matrix. A parameter decision module of a switch port performs weighted summation on the global ECN threshold matrix and the local ECN threshold matrix to obtain an optimal ECN threshold and configures the optimal ECN threshold, so that the method can adapt to flow characteristics changing along with time and user requirements, and provides optimal transmission performance for data center network services; the method can effectively deal with the heterogeneity of the data center cluster network traffic of a multi-tenant architecture, and reduces the damage of traffic burst to the network performance.

A communication method and apparatus. The method comprises: sending first information to a terminal device, the first information instructing the terminal device to identify a data packet discarded when no network congestion occurs; sending data packets to the terminal device; and receiving first packet loss information from the terminal device, wherein the first packet loss information comprises packet loss information when no network congestion occurs, and / or packet loss information when network congestion occurs. Thus, an application server can know packet loss when no network congestion occurs, and / or packet loss when network congestion occurs, and the impact of packet loss of base stations on media services can be more accurately obtained.

The invention relates to the technical field of flow scheduling, in particular to a deterministic network interconnection method and system oriented to distributed computing power nodes, and the method comprises the steps: obtaining resource information of the distributed computing power nodes and basic parameters of a deterministic network, and building a node-network basic data set based on the resource information and the basic parameters; and constructing a network topology structure model of the deterministic network based on the node-network basic data set, and determining connection relation features and computing power matching degree features among distributed computing power nodes in the network topology structure model to obtain a topology-computing power correlation graph. According to the method, the network topology, the computing power matching degree and the flow types are accurately calculated, connection and data transmission between the distributed computing power nodes are optimized, and particularly, proper time slots and path selections are distributed for different types of flows, so that network congestion and resource waste are avoided, and the overall transmission performance of the network is improved.