737 results about "Network congestion" patented technology

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 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 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 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.

The invention relates to the technical field of artificial intelligence, and discloses an internet television regulation and control method and device, a storage medium and electronic device.The method comprises the steps that network topology data, real-time performance parameters, user behavior data and environment data of nodes of an internet television are collected in real time through a space-time hypergraph convolutional network, a dynamic hypergraph model is constructed, and the network topology data of the nodes of the internet television, the real-time performance parameters of the nodes of the internet television and the environment data of the nodes of the internet television are obtained; extracting a spatial feature matrix and a time feature matrix of the nodes of the Internet television; performing feature fusion on the spatial feature matrix and the time feature matrix to generate a congestion probability prediction result of each node of the Internet television; and based on a congestion probability prediction result, combining historical flow data, generating a resource allocation optimal strategy by using multi-target Bayesian optimization, and training and updating a congestion prediction model by using a lightweight federated incremental learning framework. The Internet television regulation and control method provided by the invention is further improved in the aspects of network congestion prediction precision, real-time performance, energy consumption and the like.

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.

Described herein are systems and methods for programmable, hardware-accelerated congestion monitoring and / or control. At least one circuit can configure a plurality of hardware circuits with one or more rules that, when satisfied, cause the plurality of hardware circuits to generate one or more congestion events indicative of congestion in a network. The at least one circuit can receive the one or more congestion events generated by the plurality of hardware circuits based on one or more network signals in the network satisfying the one or more rules. In response to receipt of the one or more congestion events from the plurality of hardware circuits configured with the one or more rules to detect the congestion in the network, the at least one circuit can analyze the one or more congestion events to address the congestion in the network. Various other methods, systems, and computer-readable media are also disclosed.

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 relates to the technical field of intelligent traffic, in particular to a subway traffic operation decision adjustment system and method based on a large visual screen, and the system comprises a perception data module, a data processing center, a large visual screen interaction platform, an intelligent instruction decision engine, and a dynamic adjustment unit. Wherein the sensing data module collects train positioning data, platform monitoring video streams, gate passing data and environment sensor data in real time; integrating and analyzing the data by the data processing center, and mining potential modes, association relationships and abnormal conditions in the data; the visual large-screen interaction platform presents a line network congestion thermodynamic diagram, equipment fault positioning and passenger flow prediction trend in real time; the intelligent instruction decision engine is used for dynamically generating a train shift adjustment strategy and an emergency dispersion scheme; and the dynamic adjusting unit dynamically adjusts the electronic guide screen and the platform broadcast, and dynamically dispatches the train. Therefore, the problems of lagging abnormal response, unreasonable resource allocation, low efficiency and the like in the prior art are solved.

The invention discloses an electric vehicle charging load power grid operation real-time scheduling optimization method and system, belongs to the technical field of electric vehicles, and solves the problems of insufficient coordination of new energy consumption and network congestion regulation and weak real-time dynamic response capability in electric vehicle charging load power grid operation real-time scheduling optimization. Comprising the following steps: constructing a vehicle-road-network coupling three-domain knowledge graph fusion framework, bidirectionally coupling electric vehicle charging and discharging behavior dynamic sensing and charging pile load sensing, outputting a final optimization scheme, and pushing the optimization scheme to a user side, a power grid side and a road network side; according to the method, the coupling relation among the vehicle, the network and the power grid is considered, the power grid blocking peak regulation problem is solved through coupling of the three knowledge maps and the charging and discharging characteristics of the electric vehicle, the collaboration of new energy consumption and network blocking regulation and control is enhanced, the real-time dynamic response capability of the electric vehicle is improved, and the power grid blocking peak regulation and control efficiency is improved. And technical reference is provided for power grid operation management personnel.

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 optical communication, in particular to an algorithm service system and method for an optical transport network, and the system comprises an optical network sensing module which is used for collecting optical layer performance parameters, service flow data and node equipment operation states of the optical transport network; the deep algorithm decision center predicts service traffic based on a space-time diagram convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the strategy adaptation module is used for dynamically generating an operation and maintenance strategy and an energy-saving scheduling instruction according to the prediction result and the optimization scheme; the risk assessment unit is used for assessing a link fault risk, a service interruption risk and a network congestion risk in real time, quantifying a risk weight through an analytic hierarchy process, and optimizing an operation and maintenance strategy in combination with historical fault data; and the intelligent resource reconstruction unit is used for performing optical channel reconstruction according to the optimized operation and maintenance strategy, dynamically allocating optical layer resources or switching redundant transmission paths. Therefore, the problems of poor strategy adaptability, low resource scheduling efficiency, insufficient fault-tolerant capability and the like in the prior art are solved.

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 relates to the field of data transmission, and particularly discloses a data bandwidth guarantee and encryption transmission system based on a tunnel technology, which comprises the following steps: through an intelligent preprocessing module, before a message enters a tunnel encapsulation process, carrying out strategy matching so as to identify a target flow, and accurately capturing an original length without any tunnel overhead; based on the original length, the system not only can perform fair and accurate bandwidth measurement and solve the problem that the actual available bandwidth of a user is damaged due to tunnel encapsulation overhead, but also can perform dynamic and probabilistic intelligent access decision by further combining the service priority of the message with the current network resource state, so that the network access efficiency is improved. Therefore, the communication quality of the high-value service is guaranteed preferentially when the network is congested. Finally, only the message which passes through the layer of accurate and intelligent screening can be sent to be encrypted and packaged.

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 an offline consumption method under a network interruption condition, and belongs to the technical field of electronic payment. In order to solve the problems of payment failure and low efficiency caused by network congestion or interruption in people-intensive scenes such as canteens and the like, the scheme realizes offline consumption through a local transaction verification and asynchronous order reporting mechanism: firstly, TCP connection is established between a consumption machine and an offline server through an intranet, a user swipes a card / scans a code to trigger a transaction after a worker inputs an amount, and the payment efficiency is improved; the offline server verifies the account balance and the transaction limitation in real time, if the verification is passed, an order is generated and stored in a local database, payment success is returned, and external network communication is not needed in the transaction process; the problem of transaction paralysis during network interruption is effectively solved, the stability and efficiency of a high-frequency payment scene are improved, and the method is suitable for canteens, convenience stores and other scenes needing offline payment support.

The invention discloses an intelligent computing cluster parallel training performance optimization method oriented to large model training, and relates to the technical field of artificial intelligence computing. Hardware feature indexes of heterogeneous computing nodes in an intelligent computing cluster are collected in real time, a hardware topological graph is constructed, and an optimal computing power combination suitable for the hardware topological graph is calculated; the method comprises the following steps: selecting an optimal communication path by adopting a self-adaptive routing algorithm, coding and decoding transmission data in combination with a mixed precision compression technology to realize efficient transmission of the transmission data, constructing a fault prediction model based on a deep learning algorithm, and inputting real-time data in a hardware monitoring log into the fault prediction model to obtain a prediction result. Prediction results are classified and stored according to a preset result classification standard, check points are trained based on the prediction results to achieve rapid recovery, the super-node computing power utilization rate is improved, meanwhile, a communication path can be dynamically adjusted according to the network congestion state, and the fault recovery speed is improved.

The invention relates to the technical field of edge computing, in particular to a load balancing computing method and system for an electric power internet of things. The invention discloses a load balancing calculation system for an electric power internet of things. The load balancing calculation system comprises a data reissuing operation triggering module, a data reissuing operation module and an updating module. According to the invention, the data reissuing priority of the local acquisition data and the power monitoring terminal equipment is determined through the stability, predictability and proximity correlation of the local acquisition data, and the data reissuing operation is executed in a polling manner according to the data reissuing priority of the local acquisition data and the power monitoring terminal equipment. In this way, only one power monitoring terminal device executes the data reissuing operation in each scheduling period, the situation of network congestion caused by the fact that multiple devices execute data reissuing at the same time is avoided, and the load situation of the power Internet of Things is balanced.

The invention relates to the technical field of mobile communication, and discloses a prediction-based network congestion avoidance system, which comprises a data sensing end, a space-time prediction end, a strategy generation end and an execution feedback end, and is characterized in that the data sensing end, the space-time prediction end, the strategy generation end and the execution feedback end are jointly integrated with a voice alarm module; a data sensing end is arranged, when network state monitoring is carried out, a network topological graph with weights is constructed in real time, a timestamp index feature matrix is generated, the time delay defect of a traditional passive response mechanism is eliminated, and an active sensing mechanism constructed at the end captures link dynamic features before service damage; the real-time performance and the first-onset performance of congestion risk identification are ensured, the problem of service interruption caused by post-detection is overcome, and the core defect of insufficient prediction model precision is solved by setting a space-time prediction terminal and based on a collaborative modeling architecture of a graph convolutional neural network and an attention mechanism during congestion risk analysis.

The invention discloses a partition network architecture, a routing method, equipment, a medium and a program product, and relates to the technical field of routing, a target network is divided into a plurality of network partitions, first nodes of the network partitions are connected through configured second ports, and therefore interconnection between the network partitions is achieved; when a multicast message needs to be transmitted, transmission between nodes in the same network partition can be realized through the first port and the second port; and meanwhile, the cross-region transmission of the multicast message is realized by using the second port. Each network partition can independently transmit messages in parallel in the partition, cache competition is reduced, the synchronization of multicast routing is optimized, a special multicast message transmission channel between the network partitions is realized by independently arranging a second port for a first node, delay caused by long-distance transmission of the messages is reduced, and the service life of the network partitions is prolonged. The message transmission delay and the blocking risk of the target network are significantly reduced, and the energy efficiency and throughput of the target network are improved.

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 relates to the technical field of data processing, in particular to a data diffusion method and a data diffusion system. A wireless network comprises a plurality of network nodes, when a sending node determines that sensor data needs to be sent, a wake-up frame is broadcasted to wake up all neighbor nodes in a single-hop range, the woken node is kept active within a preset active time and broadcasts first link state information, and after the sending node receives the information, a local neighbor table is updated according to the information, and the sensor data is sent to the wireless network. And after the local neighbor table is updated, the sending node sends the sensor data to the awakened node. According to the method and the device, the neighbor node is accurately awakened, the link state information is acquired, and the neighbor table is updated, so that targeted data transmission is realized, the problems of overlarge data transmission quantity, network congestion, energy consumption increase and the like caused by blind forwarding in a traditional mode are avoided, the network performance is effectively improved, and the node energy consumption is reduced.

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.