1408 results about "Network congestion" patented technology

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention relates to a multi-protocol transmission text data monitoring and warning method and system, and the method comprises the steps: generating a multi-source protocol transmission instance based on dynamic authorization and hardware security verification, and collecting and analyzing text data; a network connection state, a data backlog amount and sensor numerical value content parameters are monitored in real time through multiple threads, and a transmission state and content exception event queue is generated; learning a causal relationship among network congestion, equipment faults and alarm events by using a Bayesian network algorithm, calculating a root cause probability in combination with a dynamic weight distribution strategy, and generating a comprehensive alarm list of priority ranking; on the basis of user feedback data, protocol weights and alarm strategies are adaptively updated, abnormal early warning triggering, data snapshot binding and closed-loop optimization of alarm logs are achieved, and the problems that in a multi-protocol mixed transmission scene, safety adaptability is poor, the monitoring dimension is single, root cause analysis depends on static rules, and strategy updating lags are solved. And the real-time performance, the accuracy and the self-adaptability of data transmission of the industrial Internet of Things are improved.

The invention relates to the technical field of traffic management, in particular to a video monitoring traffic intelligent optimization system which comprises a time sequence fragment evaluation module, a path priority adjustment module, a traffic scheduling optimization module and a buffer strategy adaptation module. According to the invention, the transmission efficiency and quality of the video monitoring data are improved by analyzing the video stream in real time and adjusting the frame rate and the compression rate, the importance of each time sequence segment in the video stream is evaluated, the frame rate is adjusted according to the importance, more resources are ensured to be obtained by important events, unnecessary data transmission is reduced, network congestion is effectively reduced, and the transmission efficiency of the video monitoring data is improved. The network transmission path is monitored in real time, and the data transmission proportion is adjusted, so that the video stream passes through the network path with higher quality more preferentially, the stability and speed of video transmission are enhanced, the priority and bandwidth allocation are dynamically adjusted, and the video stream with high priority is protected to ensure that the video quality of a key monitoring area is not influenced by network fluctuation.

The invention relates to a deterministic network congestion avoidance flow routing scheduling method, which belongs to the technical field of industrial internet, and comprises the following steps: S1, sensing network congestion based on IFIT flow detection and queue state, and calculating a congestion coefficient; s2, performing planning decision on a traffic routing path based on deep reinforcement learning to avoid congestion; and S3, sinking congestion coefficient calculation and strategy mapping calculation logic to switch hardware by using a P4 programmable data plane to realize localized closed-loop control. Through dynamic path optimization and localization execution, the throughput is remarkably improved, the time delay and jitter are reduced, the intelligent routing mechanism effectively balances the load, and a high-reliability dynamic scheduling solution is provided for a large-scale deterministic network in combination with the deterministic guarantee capability.

The invention relates to the technical field of data transmission processing, in particular to a data transmission method and system based on bird dynamic perception. According to the invention, the group perception vector obtained through the multi-dimensional perception fusion algorithm can comprehensively capture the complex features of the network environment, and compared with a traditional single-dimensional network state monitoring method, the method can significantly improve the accuracy and comprehensiveness of network environment perception. Therefore, the data transmission system can identify network congestion, link quality change, abnormal node state and other conditions more accurately, thereby providing more reliable basic information for subsequent transmission decisions. The group coordination information formed through group coordination analysis processing can effectively solve the problem that all paths in traditional multi-path transmission are lack of coordination, mutual interference and resource conflicts among the paths are remarkably reduced, and the utilization efficiency of overall network resources is improved.

The invention provides a monitoring video transmission method and system based on wireless communication, and relates to the technical field of monitoring video transmission, and the method comprises the steps: obtaining and preprocessing network state data, the network state data comprising bandwidth, time delay and packet loss rate; establishing a network state prediction model based on the LSTM neural network, and predicting a future network state according to the network state prediction model; based on the future network state, a multi-objective optimization algorithm is adopted to perform transmission path selection; and for the selected transmission path, video coding parameters are adjusted, the coding parameters comprise a code rate, a resolution ratio and a frame rate, and a self-adaptive transmission protocol is adopted to dynamically adjust the transmission rate according to a real-time network state. According to the method, different paths can be dynamically selected, network congestion or degradation is avoided, and the video transmission quality and rate are adaptively and flexibly adjusted according to the current network state, so that stable transmission of the video quality under different network conditions is ensured.

The invention provides a live broadcast visitor flow prediction and route scheduling method and system based on a space-time sequence, and relates to the technical field of flow management and scheduling, and the method comprises the steps: building a live broadcast flow prediction network through collecting the flow data of a live broadcast room, and calculating the flow correlation degree through a deep learning model. When the flow change of the live broadcast room exceeds a threshold value, calculating propagation characteristics of the flow change based on the graph neural network, and generating a flow ripple propagation range matrix and a flow surge probability distribution matrix; and constructing a block chain network in the target area, and generating a data packet routing strategy by using an intelligent contract and lightning network technology. And the edge acceleration node receives the routing strategy to forward the data packet, and adjusts the transmission rate of the data packet based on an online reinforcement learning algorithm when the network is congested.

The invention discloses an air-space-ground network congestion control method based on deep reinforcement learning. The method comprises the following steps: acquiring data information of a target air-space-ground network; setting an optimization target and carrying out modeling; setting a state space, an action space and a reward function of the decision process; constructing a state prediction module, a reward redistribution module and an optimization strategy module of the decision process; and performing congestion control of the space-air-ground network based on deep reinforcement learning according to the constructed module. According to the invention, through obtaining the data information of the plurality of target air-space-ground networks, based on the setting of the state space, the action space and the reward function, and the realization of state prediction, reward redistribution and optimization strategies, the congestion control of the air-space-ground network based on deep reinforcement learning is realized, the reliability is higher, and the method is more suitable for large-scale popularization and application. The precision is better, and the effect is better.

The invention relates to an LLM-enhanced intelligent micro-service migration method and system in a cloud edge environment, and the method comprises the steps: analyzing and judging whether a current exception needs to be subjected to micro-service migration or not according to a historical exception event needing to be migrated through a to-be-migrated micro-service positioning method based on LLM and LSTM, and accurately positioning a to-be-migrated micro-service causing the exception if the micro-service needs to be migrated; a multi-target micro-service migration decision-making method based on dynamic GCN and BO is adopted, and appropriate nodes are intelligently selected for each micro-service to be migrated for migration according to the long-term performance and operation cost of the micro-service. According to the method, the accuracy of judging whether the current exception needs to be migrated or not is remarkably improved, and the to-be-migrated micro-service causing the exception can be accurately positioned. The problems of network congestion, service interruption, performance reduction, unbalanced resource utilization and the like possibly caused by frequent migration are effectively avoided, so that the long-term stability of the system performance after micro-service migration is realized, and the operation cost is remarkably reduced.

The invention discloses a cross-platform authority unified management method and system based on a multi-WEB system, and the key point of the technical scheme is that the method comprises the steps: obtaining organization structure data of an enterprise system, analyzing the organization structure data, constructing a multi-level tree topology network, forming organization entity topology parameters, analyzing the organization entity topology parameters, and storing the analyzed organization entity topology parameters into a database; according to the scheme, post authority baseline parameters are generated, dynamic optimization of channel selection is achieved by constructing a dual-channel transmission decision maker and combining real-time network state data and an inter-system interaction characteristic score table, and compared with a traditional static configuration mode, the mechanism can achieve dynamic optimization of channel selection according to parameters such as department communication strength, system transmission performance and safety scores. According to the method, the optimal transmission path is automatically selected, network congestion or high-delay nodes are avoided, cross-system adaptation rules are generated through mapping calculation of an authority interface strategy and post authority baseline parameters, the problem of authority granularity difference between different systems is solved, and the accuracy and consistency of authority distribution are ensured.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention discloses a cross-regional vehicle-mounted network switching method and system based on digital twinning, and a medium. The method comprises the following steps: constructing a cloud side end digital twinning network architecture; acquiring vehicle state data and traffic environment sensing data through the terminal sensing layer; carrying out data aggregation on the vehicle state data and the traffic environment perception data through edge computing nodes to obtain a local digital twinborn model; meteorological data, operator base station data and a local digital twinborn model are integrated through a cloud digital twinborn center to obtain a global digital twinborn model, and the traffic congestion condition and the network congestion condition of each road network region in a future time period are predicted according to the global digital twinborn model. Determining a network switching strategy according to the traffic congestion condition and the network congestion condition; and performing a network switching decision on the target vehicle through the edge computing node. According to the method, the timeliness and accuracy of cross-regional vehicle-mounted network switching are improved, so that the stability and reliability of the vehicle-mounted network are improved, and the method can be applied to the technical field of the Internet of Vehicles.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of data exchange, in particular to a TCP-IP protocol-based application program data interaction method, which comprises the following steps of: collecting a signal quality index of application program data in a physical layer, capturing signal quality data of each node through real-time network monitoring, and performing time sequence analysis; and extracting a signal quality trend and an abnormal point, and generating an initial signal analysis result. According to the invention, through real-time analysis and trend prediction, potential problems in the network path can be effectively predicted, network path optimization is realized, the data are analyzed through the reinforcement learning model, the network path is predicted and optimized in real time, the adaptive adjustment capability of the network is enhanced, and the data transmission efficiency is greatly improved. Through simulation testing and real-time monitoring, delay reduction of data transmission and bandwidth utilization maximization are ensured, and then through dynamic adjustment of the size of a TCP window, adjustment is performed according to real-time network congestion conditions, and the overall performance of the network is greatly optimized.

The invention discloses a dynamic traffic scheduling method and system based on an SD-WAN technology, which can timely discover potential problems in a network by monitoring performance parameters of network links and nodes in real time, and can effectively relieve network congestion and improve the overall network performance based on analysis of the performance parameters and dynamic adjustment of network routing and bandwidth allocation strategies. According to the method, a high-flow time period, a low-flow time period and key service flow are identified, so that the distribution of network resources is more reasonable and efficient, the adjusted network performance is evaluated in real time, the expected effect of each adjustment can be ensured, and if the performance does not reach a preset standard, the scheduling strategy is continuously optimized, and the scheduling efficiency is improved. According to the technical scheme, the method is beneficial to adapting to rapidly changing network environments and service requirements, the stability and reliability of the network are enhanced, the network states, scheduling strategies and performance evaluation results before and after adjustment are recorded in logs, and operation and maintenance personnel can rapidly position network problems by analyzing log data, so that the operation and maintenance efficiency and accuracy are improved.

The invention relates to the technical field of power distribution network operation optimization, and discloses an intelligent power distribution network distributed power scheduling system based on edge computing, which comprises a data acquisition and preprocessing module, an edge computing and local scheduling module, an SDN network control module, an application layer global scheduling module and a scheduling execution and control module, according to the invention, by introducing the edge computing node, local preprocessing of data and generation of a preliminary scheduling strategy are realized, delay of data transmission to the central server is significantly reduced, and the system can dynamically adjust the scheduling priority in combination with the improved particle swarm optimization algorithm, so that the scheduling efficiency is improved. Three core indexes of renewable energy utilization rate, power distribution network loss and voltage stability are comprehensively considered, and a more accurate and efficient local scheduling strategy is generated. And meanwhile, the SDN network control module ensures that high-priority data can still be efficiently transmitted when the network is congested by calculating a link priority coefficient, so that the real-time performance and the reliability of the system are further improved.

The invention provides a data acquisition method and device based on an industrial internet identification analysis system, which are applied to the technical field of industrial internet, and are used for realizing data grading processing by acquiring data and priority parameters and distributing transmission queues according to the priority parameters; the method comprises the following steps: compressing and coding data, evaluating the quality of a wireless channel, selecting a transmission path, avoiding an interference channel, determining a retransmission strategy by combining a data priority and a network congestion state when transmission fails, receiving a data packet at an edge computing node according to the retransmission strategy, and uploading the data packet after local processing. The pressure of a core system is dispersed by utilizing edge computing power, and the overall data processing time delay can be reduced. Therefore, according to the method, through the steps of data priority distinguishing, data compression, channel quality evaluation, intelligent retransmission strategy, edge computing application and the like, the beneficial effect of anti-interference and low-delay data transmission in a wireless communication factory environment is achieved.

There are provided measures for inter-network congestion reporting. Such measures may comprise (e.g. at a network gateway entity providing access to a network for a network external entity external to said network) receiving, from an entity external to a first network, a report reporting congestion indication in a downlink direction from said first network towards a terminal device, and transmitting information corresponding to said congestion indication towards a network entity in said first network.

The embodiment of the invention provides a data transmission method and device of Internet of Things equipment, electronic equipment and a storage medium, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining business data collected by the Internet of Things equipment, and determining the target business priority of the business data; acquiring network index data of communication equipment connected with the Internet of Things equipment; performing data volume prediction according to the service data to obtain a corresponding predicted data volume; performing transmission mode selection according to the predicted data volume and the network index data to obtain a first transmission strategy for the service data; and issuing the first transmission strategy to the Internet of Things equipment, so that the Internet of Things equipment transmits the service data according to the first transmission strategy, thereby optimizing the data transmission mode, realizing dynamic adjustment of the transmission strategy, improving the network adaptability, improving the success rate of data transmission, and reducing the probability of network congestion.

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 provides an electric energy load real-time data acquisition and analysis method based on a low-cost scheme, and the method comprises the following steps: constructing a distributed data acquisition network, employing a combined hardware architecture of a current / voltage sensor and a low-power-consumption MCU, and achieving the data acquisition optimization through a dynamic sampling rate adjustment mechanism, when the load fluctuation exceeds a set threshold value, the sampling rate is automatically increased, when the load is stable, the sampling rate is reduced to a calibration sampling rate, and a Kalman filtering algorithm is adopted to carry out real-time data preprocessing; a three-level data transmission system is established, the three-level data transmission system comprises an edge acquisition node layer, a convergence gateway layer and a cloud processing layer, an optimized AODV routing protocol is adopted among nodes to construct a star-shaped and net-shaped hybrid network topology, and an optimal transmission path is dynamically selected according to the signal strength and the network congestion condition; a differentiated QoS transmission strategy is designed, and data is divided into three priorities of fault alarm data, real-time monitoring data and historical data.

The invention relates to the technical field of remote control, in particular to an intelligent device remote cooperative control method and system based on the Internet of Things, and the method comprises the following steps: monitoring and adjusting the CPU utilization rate and memory usage of an intelligent device based on an Internet of Things platform, optimizing the data collection and network resource distribution, and improving the intelligent device remote cooperative control efficiency. By analyzing equipment performance and a network load balance index, equipment connection priority ranking and task load management are realized, and task execution and connection frequency is dynamically adjusted, so that real-time task requirements are met, and system efficiency is improved. According to the method, the performance index of the equipment is monitored in real time, the data transmission interval is dynamically adjusted, the data processing speed is remarkably increased, resource waste is reduced, efficient use of network resources is ensured through real-time load analysis and optimization of network nodes, the problem of data delay caused by network congestion is avoided, and in the aspect of equipment connection management, the data transmission efficiency is improved. An intelligent priority decision mechanism allows the key device to quickly access the network in an emergency.

The invention belongs to the technical field of communication, and particularly relates to a directional traffic service guarantee method of a PCC self-intelligence network based on an AI agent. Comprising the following steps: an AI identifies a live broadcast application and issues a policy control and charging PCC rule; service experience detection: the NWDAF receives user flow data, and the NWDAF continuously monitors the experience quality of the live broadcast service; establishing a service awareness baseline: establishing the service awareness baseline for different live broadcast applications through AI learning: based on a user level signing protocol, analyzing experience class data of a service flow in combination with a network element; according to the experience class data analysis result of the service flow, when the service experience index is lower than the service awareness baseline, the definite positioning analysis is carried out, then a dynamic guarantee mechanism is triggered according to needs, and oriented dynamic QoS optimization is carried out on a user network. According to the invention, the problems of network congestion and network resource waste of the PCC self-intelligence network are solved.

The invention relates to the technical field of network communication and route management, in particular to an intelligent router flow management method for a home scene, which comprises the following steps that: a home router collects network activity data of equipment, including data flow size, connection frequency and equipment type, performs cleaning and formatting processing, and stores the data flow size, the connection frequency and the equipment type; formatted device behavior data is generated. According to the method for analyzing the behavior data of the home network equipment based on the long and short-term memory network, the prediction precision of the periodicity and dependency of user behaviors is improved, and the allocation of bandwidth resources is more prospective and dynamic. Specifically, resources can be automatically adjusted according to predicted data requirements, and particularly in the peak period of requirements, it is ensured that key applications such as video conferences and online education preferentially obtain necessary network resources. According to the flow management technology, the overall operation efficiency of the network is improved by optimizing bandwidth utilization and reducing network congestion.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention relates to a cross-domain clock synchronization method in a time sensitive network, and belongs to the technical field of time synchronization. The method comprises the following steps: establishing a multi-domain clock synchronization network topology based on a transit clock; the transit clock obtains main clock time according to the main domain synchronization message, carries out link delay measurement message interaction with adjacent equipment, records a corresponding timestamp and obtains link delay; transmitting the acquired main clock time, each timestamp and link delay to a synchronization error compensation prediction module in the transfer clock, calculating a clock time offset result by the synchronization error compensation prediction module, and predicting error compensation; frequency and phase adjustment is carried out on a local clock according to the calculated error compensation predicted value, a slave domain synchronization message is sent to adjacent slave clock equipment, and forwarding of the slave domain synchronization message and clock correction are carried out in sequence. According to the invention, accumulated errors such as jitter, time delay change and network congestion can be eliminated as much as possible.

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 an intelligent operation and maintenance management system and method based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance management. Comprising an operation and maintenance acquisition module, a storage management analysis module, a delay management analysis module and an execution module. In an existing system, redundant data processing efficiency is low, and redundant data cannot be effectively identified and optimally managed, so that resource waste and low data analysis efficiency are caused; in the prior art, delay processing is not intelligent enough, a traditional system does not comprehensively consider multiple delay factors, adjustment in the network congestion or data transmission process is not flexible enough, delay in the transmission process is high, and real-time performance and user experience are affected, in the prior art, through optimization of a storage management analysis module and a delay management analysis module, real-time performance and user experience are improved. The key problems of redundant data processing efficiency and data transmission delay in the intelligent operation and maintenance management system of the Internet of Things are solved, and the overall performance and response speed of the system are improved.

The invention relates to an address resolution protocol message sending method and device, computer equipment, a readable storage medium and a program product, relates to the technical field of networks, and can maintain the stability of de-stacking network communication. The method comprises the following steps: determining a plurality of member interfaces aggregated through a server logic interface; the plurality of member interfaces correspond to different message receiving devices; determining a network congestion index corresponding to the logic interface according to the network monitoring data of the plurality of member interfaces, and adjusting an initial index threshold according to the network monitoring data to obtain a current index threshold; if the network state is determined to be a normal state according to the network congestion index and the current index threshold, sending an address resolution protocol message to a plurality of message receiving devices according to a normal processing mode; and if the network state is determined to be a congestion state according to the network congestion index and the current index threshold, sending address resolution protocol messages to a plurality of message receiving devices for multiple times according to a preset time interval through a plurality of member interfaces.

The invention relates to the technical field of power system control, solves the problems of network attack, limited bandwidth and transmission delay of a power system in a signal transmission process and conservative DET parameter selection and controller design, and particularly relates to a power system dynamic event triggered load frequency control method under Dos attack and random delay. Comprising obtaining a continuous time system parameter matrix; setting an event triggering threshold function; state parameters facing Dos attack and random time delay conditions are set; constructing a state feedback controller; and collaborative design optimization is carried out by applying a GA optimization algorithm. Compared with a traditional control method, the method has the advantages that on the premise of ensuring the stability of the system, by optimizing event triggering conditions, the communication requirement and bandwidth occupation are remarkably reduced, the utilization efficiency of communication resources is improved, network congestion and delay possibly caused by frequent communication are reduced, and the service life of the system is prolonged. And the operation efficiency of the whole power system is improved.