41 results about "Traffic load" patented technology

Disclosed are a network traffic load balancing method and apparatus for a data center, which are applied to the data center including a switch, a host, and a server. The method includes the following steps: collecting connection information between the switch, the host, and the server, and constructing a network topology structure of the data center according to the connection information; monitoring working information about each network link under the network topology structure, and correspondingly determining a congestion condition of each network link according to the working information about each network link; and allocating pre-transmitted data traffic packets to each network link according to the congestion condition to balance data traffic on each network link.

This invention relates to a method, system, device, and storage medium for creating Virtual Machines (VFs). It monitors the throughput data of physical network interface cards (NICs) in a network interface card (NIC) resource pool. The throughput data includes the number of bytes received, bytes sent, and cumulative time. The network utilization of each physical NIC in the resource pool is calculated using the throughput data and the physical NIC interface speed. The number of VFs that can currently be created for each physical NIC in the resource pool is determined. The physical NIC with the largest product of its network utilization and the number of VFs it can currently create is selected to create the VF. This method automatically selects the physical NICs for VF creation based on both network utilization and the number of VFs that can be created, eliminating the need for manual intervention and balancing the number of VFs configured across different physical NICs, thus facilitating the balancing of traffic load across physical NICs.

Spectrum allocation in non-terrestrial wireless networks is efficiently and intelligently managed by predicting traffic loads for beam cells using historical network traffic data and a trained model. Beam cells with higher predicted traffic loads are assigned larger spectrum bandwidths, while other beam cells are assigned smaller, non-overlapping bandwidths that dynamically change frequencies according to a predefined hopping sequence. Example solutions disclosed herein optimize bandwidth utilization and minimize interference, enhancing service quality and coverage in satellite communication systems.

This invention relates to a vehicle-mounted communication method and system based on multi-point network priority awareness, comprising: initializing and configuring a vehicle-mounted multi-point communication network; each node in the network selecting the target to be sent according to the priority order of the messages to be sent, extracting runtime information variables and encapsulating them in the message to be sent, and transmitting them to the vehicle-mounted multi-point communication network within a sending opportunity window; each node real-time parsing of the runtime information variables of the received messages to obtain the distribution status of priority messages across the entire network, and deciding whether to send or remain silent within the sending opportunity window based on the global priority relationship; during the operation of the vehicle-mounted multi-point communication network, statistical analysis of the traffic load of each node is performed, and the node identifier of each node is dynamically adjusted accordingly, so that the nodes complete message transmission under the PLCA mechanism according to the updated node identifier order. Compared with the prior art, this invention significantly improves the determinism of high-priority message transmission while maintaining standard compatibility.

The present disclosure provides a method and a system for handling MAP signaling traffic in a network (106). The method includes adding one or more instances of HSSFE (308-1, 308-2) by assigning at least one VLAN IP address to each instance. The method includes performing a whitelisting operation in the added instances and an existing HLGMAPGW and establishing an internal connection between the existing HLRMAPGW and the one or more HSSFE instances (308-1, 308-2). The method includes distributing incoming MAP signaling traffic load received at the existing HLRMAPGW across the one or more HSSFE instances (308-1, 308-2). This approach eliminates the need for additional HLRMAPGW, optimizes traffic handling, and improves overall system efficiency.

A device includes a plurality of digital signal processors (DSPs) and analog crossbars in communication with the DSPs. The DSPs and crossbars dynamically adjust power consumption based on traffic load and / or a signal quality, leveraging traffic prediction models to optimize resource allocation. The system supports features such as powering down unused lanes, asymmetrically deactivating transmission or receiving lanes, and preemptively scaling resources during predicted traffic increases. Dynamic voltage and frequency scaling (DVFS) optimizes power usage based on link utilization and traffic priority, minimizing latency impacts during transitions. The switch controller integrates traffic prioritization algorithms, including weighted round-robin (WRR), to allocate power and bandwidth efficiently to high-priority traffic flows. Redundant crossbars handle overflow traffic or failover scenarios, transitioning between standby and active states dynamically.

This invention relates to a method for monitoring the deflection and deformation state of bridges. The method includes: acquiring real-time UWB radar data, time-series environmental parameter data, and time-series traffic load data; preprocessing to form a timestamp-aligned multimodal dataset; based on an online-deployed, pre-trained deflection and deformation state prediction model, using the real-time multimodal dataset as input, outputting real-time bridge deflection prediction values ​​and deformation state assessment results; periodically collecting actual bridge deflection annotation data using a high-precision ranging device, and periodically optimizing the deflection and deformation state prediction model using this actual deflection annotation data as a standard. This invention, based on UWB radar monitoring, can non-contactly and with high precision capture minute displacements on the bridge surface. Combined with multi-source environmental and load data, and through deep learning models for deep feature extraction and state assessment, it achieves a leap from simple data collection to intelligent diagnosis and early warning.

The present disclosure relates to the technical field of wireless communication, and relates to a licensed and unlicensed controller (LUC) configured to coordinate different traffic load transmissions in a multi-connectivity mode for a user equipment (UE) based on real-time measurement reports, a method for traffic control, a computer readable storage medium, and an electronic device. The LUC of the present disclosure can support licensed and unlicensed multi-connectivity and spectrum aggregation usage in coordination with data packets of unlicensed networks and licensed networks.

This application discloses a bridge traffic early warning system and method for intelligent transportation, belonging to the field of intelligent transportation and bridge structural health monitoring technology. The method includes: calculating a single load event risk score; constructing a short-term analysis window, performing cumulative damage calculation and trend analysis on the risk score sequence within the short-term analysis window to obtain a short-term trend risk score; calculating a structural health index and dynamically correcting a preset risk judgment threshold; weightedly fusing the single load event risk score, the short-term trend risk score, and the structural health index to obtain a dynamic risk index; determining whether the dynamic risk index exceeds the corrected risk judgment threshold: if it exceeds, a graded early warning judgment is made based on the dynamic risk index, triggering the corresponding level of early warning signal and implementing corresponding traffic control measures; if it does not exceed, the current traffic operation status is maintained, and bridge structural response data and traffic load data continue to be collected.

A device includes a plurality of digital signal processors (DSPs), analog crossbars in communication with the DSPs, and a switch controller. The switch controller facilitates control signals using in-band signaling within payload traffic or out-of-band signaling over a dedicated management interface. The system supports dynamic traffic prioritization based on real-time metrics, such as latency and traffic load, ensuring efficient resource allocation and seamless operation during congestion or reconfiguration. Redundant crossbars and failover mechanisms enhance fault tolerance, while granular backpressure selectively throttles traffic flows to prevent bottlenecks. Integrated diagnostic tools monitor performance and optimize traffic flow, enabling high-speed and reliable communication.

This invention discloses a method for dynamic control of virtual lane lines for intelligent vehicle-road interaction, relating to the field of lane line dynamic adjustment technology. The method includes: obtaining pavement deflection data for each lane of a target road segment using a non-destructive lane detection method, and calculating the relative damage index for each lateral position of each lane; establishing a load distribution model to calculate the load pressure exerted by traffic flow on each point of the pavement; establishing an overall damage risk model after virtual centerline offset, and solving for the optimal overall offset; determining the final virtual centerline position of each lane based on the optimal offset, and propagating the optimized lanes to intelligent driving vehicles in the target road segment; and actively and dynamically allocating traffic loads from structurally weak areas to robust areas.

The application relates to a kind of integrated multi-source monitoring data road surface condition degradation trend evaluation method, belong to road evaluation technical field.It includes the following steps: collecting road surface performance detection, traffic load and environmental meteorological multi-source monitoring data, by road surface structure type benchmark normalization, spatial position and cumulative damage embedding construction, environment-load coupling fusion processing, obtain coupling feature vector;Through the spatial dependence graph convolution module, the spatial neighborhood features are extracted, and the interactive performance features are obtained through the performance index interaction module, and the comprehensive fusion features are obtained after fusion;Finally, the response vector modulation characteristics are constructed through the double-branch gate, and the next period road surface condition index prediction value is output by combining residual aggregation. The application can depict the nonlinear degradation law of road surface, improve the prediction accuracy and physical consistency, and provide quantitative support for road preventive maintenance.

This invention provides a computer vision-based system and method for monitoring the spatiotemporal distribution of bridge traffic loads. The system includes a toll station vehicle dynamic weighing and capture unit, a bridge traffic video acquisition unit, a traffic flow group identification and tracking unit, and a traffic load spatiotemporal distribution information processing unit. The traffic flow group identification and tracking unit acquires bridge traffic features through a YOLO-based vehicle identification module, fuses toll station vehicle information streams and multi-level associated vehicle particle groups, and processes this information through a vehicle appearance feature matching and tracking module. This achieves multi-level matching and trajectory tracking of individual and group vehicles on the bridge. Finally, the traffic load spatiotemporal distribution information processing unit merges the traffic flow spatiotemporal distribution information (composed of trajectory and time information) with the traffic flow moving load information (composed of vehicle weight, speed, and road surface unevenness) and displays it in real time on a display terminal. This invention enables real-time monitoring of the spatiotemporal distribution of traffic loads, providing a guarantee for the safe operation of bridges.

This invention discloses an inter-satellite routing method and system based on hop count priority and load awareness, primarily addressing the problems of unreliable long-distance service transmission latency and unbalanced inter-satellite service load in existing technologies. The method includes: dividing satellite operating time into consecutive equal-length slices and executing tasks on a single time slice period; predicting ground traffic for the next time slice and generating a transmission demand matrix for ground traffic services in the next time slice; predicting the topology map of the next time slice based on satellite orbit ephemeris, and calculating the minimum hop count between the source and destination nodes of all services on this map; sorting the service transmission demands according to the minimum hop count and service transmission traffic to obtain a service transmission demand queue, and sequentially extracting the service with the maximum hop count to calculate its optimal path, updating the link weights of the topology map according to the traffic load of this service; and converting the calculated path set into a routing instruction set, which is then sent to the routing control layer to guide service forwarding. This invention offers low long-distance service transmission latency, balanced inter-satellite service load, and high inter-satellite service transmission reliability, making it suitable for large-scale inter-satellite network routing scenarios.

The application relates to the technical field of electric power communication, and discloses a digital converter station distributed fusion terminal time synchronization compensation method and device, which comprises the following steps: S1: according to the type of a service, priority of the service of a digital converter station is divided; S2: a preset routing and spectrum allocation algorithm is used to initialize a path and a time delay of the service after the priority is divided; S3: through adaptive setting of a to-be-forwarded waiting real-time delay in a pending node, differential compensation is carried out, time synchronization of a receiving end is realized, and a path and spectrum are reconstructed according to a minimized compensation routing spectrum; S4: whether there is a licensed spectrum slot is judged, if yes, step S5 is executed, and if not, step S2 is executed through re-routing; and S5: transmission delay of the service is recorded, and a blocking probability under a traffic load to which the service belongs is calculated. The method provided by the application guarantees time synchronization of a receiving end of a digital converter station, and has better performance in terms of transmission delay and blocking probability.

The present disclosure provides an information transmission method and apparatus, a ground gateway, a satellite node, a storage medium, and a computer program product. A method for a ground gateway side comprises at least one of the following: sending first information to each satellite node among all satellite nodes in a first area, the first information being used for indicating a change in available bandwidth of the corresponding satellite node within a first time range; receiving second information sent by a satellite node in the first area, the second information being used for indicating a change in traffic load of the corresponding satellite node within the first time range; and sending third information to each satellite node among all the satellite nodes in the first area, the third information being used for indicating a change in network topology of the corresponding satellite node within the first time range. The ground gateway establishes a neighbor relationship with each satellite node among all the satellite nodes in the first area, and the ground gateway at least has a route reflector (RR) function and / or a controller function.

A compute device includes a compute die disposed on a package substrate, and an on-chip cache disposed on the package substrate and coupled to the compute die. The on-chip cache includes volatile and non-volatile memory dies and a memory controller which is coupled between the compute die and the on-chip cache. The memory controller is configured to perform operations including receiving a request to write data to the on-chip cache, determining respective access bandwidths for the one or more volatile memory dies and the one or more non-volatile memory dies based on respective characteristics of the one or more volatile memory dies and the one or more non-volatile memory dies, and writing respective portions of the first data to the one or more volatile memory dies and the one or more non-volatile memory dies, wherein respective sizes of the respective portions are based on the respective access bandwidths.

Embodiments of the present application provide a CSI-RS configuration method and device, a satellite base station and a storage medium. The method comprises: determining the number of activated UEs and the traffic load of multiple wave positions of a satellite base station in a target detection period; determining the CSI-RS configuration mode corresponding to the multiple wave positions based on the number of activated UEs and the traffic load of the multiple wave positions, wherein the CSI-RS configuration mode comprises periodic CSI-RS configuration and CSI-RS transmission accompanying traffic; and determining the target CSI-RS configuration mode of the satellite base station according to the CSI-RS configuration mode corresponding to the multiple wave positions of the satellite base station. With dynamic CSI-RS configuration, the CSI-RS configuration mode suitable for the multiple wave positions is determined according to the number of activated UEs and the traffic load in the target detection period, and then the target CSI-RS configuration mode suitable for the satellite base station is determined more reasonably by comprehensively considering the CSI-RS configuration mode corresponding to the multiple wave positions of the satellite base station, thereby reducing resource waste when the load is light and reducing the occurrence of resource increase when the load is heavy.

The present disclosure relates generally to wireless communication systems, and more particularly to an apparatus and method for controlling operation and configuration of radio access network components for AI / ML-based energy saving in a wireless communication system. An operation method of an intelligent controller according to the present disclosure configures a dataset including performance data of a plurality of cells constituting a network for training an AI / ML model, trains an AI / ML model for traffic load prediction using the dataset, obtains traffic load prediction values for each of the plurality of cells using the trained AI / ML model, sets thresholds for each of the plurality of cells based on statistical values of the dataset, and determines switch on or switch off operation of capacity cells by comparing the traffic load prediction values with the thresholds.

The application provides a kind of advertisement information service-oriented traffic load monitoring method, system and medium, by obtaining the service node load state record sequence corresponding to the time stamp of advertisement request stream data sequence in the preset monitoring period;Advertisement request stream data sequence is decoupled and rhythm extraction is carried out to request source, and the request impact conduction time sequence description is generated by cascading propagation in combination with advertisement position request propagation topological structure, and the time sequence distribution feature representation is extracted, while the load change feature representation is extracted to load state record sequence;Call traffic load collaborative analysis network to carry out collaborative interaction mapping to two kinds of feature representations, generate associated response characteristic representation;Associated response characteristic representation is carried out load bearing situation deduction, and load bearing capacity boundary description is obtained;According to the boundary description, generate traffic scheduling guide instruction and push to advertisement request distribution component to trigger request shunt adjustment.The application can improve the accuracy of load bearing capacity boundary determination.

A compute device includes a compute die disposed on a package substrate, and an on-chip cache disposed on the package substrate and coupled to the compute die. The on-chip cache includes volatile and non-volatile memory dies and a memory controller which is coupled between the compute die and the on-chip cache. The memory controller is configured to perform operations including receiving a request to write data to the on-chip cache, determining respective access bandwidths for the one or more volatile memory dies and the one or more non-volatile memory dies based on respective characteristics of the one or more volatile memory dies and the one or more non-volatile memory dies, and writing respective portions of the first data to the one or more volatile memory dies and the one or more non-volatile memory dies, wherein respective sizes of the respective portions are based on the respective access bandwidths.

A tunnel construction ground surface settlement monitoring and early warning method, system, medium and product relate to the technical field of data processing. In the method, by acquiring soil settlement, ground surface settlement and traffic load data, the ground surface point cloud data is obtained by spatial registration correction of the monitoring points; the traffic load characteristic parameters are analyzed, and a digital twin model is constructed; the potential settlement sensitive area is identified, and the monitoring point arrangement is optimized accordingly; the measured and simulated settlement data are compared, and when the deviation exceeds the threshold, the traffic disturbance influence factor is extracted to establish a correction function; a prediction model is established through correlation analysis, and the predicted settlement value of the unexcavated section is calculated in combination with the correction function; and finally a dynamic early warning cloud map is generated to provide a decision basis for construction safety. The technical scheme provided in the application can improve the accuracy of construction ground surface settlement monitoring and early warning.

This application provides a routing, routing path, and multicast routing path decision-making method and electronic device. The routing decision-making method includes acquiring first information of a source node satellite and second information of a destination node satellite in a routing path decision-making task; determining a satellite to be decided upon, and designating neighboring node satellites of the satellite to be decided upon as candidate routing node satellites; acquiring the real-time traffic load of the candidate routing node satellites and the predicted traffic load within a preset future time step; and, based on the real-time traffic load, the predicted traffic load, the first information, and the second information, determining the next routing node satellite from the candidate routing node satellites using a routing decision-making model pre-constructed based on a Markov decision process. The routing, routing path, and multicast routing path decision-making method and electronic device of this application can determine both real-time performance and accuracy, thereby effectively improving data transmission efficiency.

This application discloses an anomaly identification method, system, and related products. The method includes: acquiring traffic data to be detected, the traffic data including traffic behavior features and / or traffic load features; inputting the traffic data to be detected into a pre-trained large language model, so that the large language model extracts traffic behavior features and / or traffic load features from the traffic data to be detected, and outputs a business behavior identification result based on the traffic behavior features and / or traffic load features, the business behavior identification result including: normal business behavior or abnormal behavior type. It is evident that the large language model of this application embodiment can automatically learn the distinguishing features between different network behaviors (such as normal business behavior and abnormal behavior), thereby effectively determining whether abnormal behavior has occurred, improving the accurate identification capability and fast processing speed of abnormal behavior types, ensuring detection rate, reducing false alarm rate, and enhancing the protection level of network information security.

The application discloses a vehicle-mounted CAN network flow load prediction method and system and electronic equipment, and relates to the technical field of flow load prediction sources.The method comprises the following steps: acquiring vehicle-mounted CAN network flow load data of a current period of a vehicle to be predicted; inputting the vehicle-mounted CAN network flow load data of the current period into a flow prediction model to obtain predicted vehicle-mounted CAN network flow load data of a next moment of the current period.The application realizes vehicle-mounted CAN network flow load prediction, and improves the prediction accuracy and speed of the vehicle-mounted CAN network flow load.

The described technology is generally directed towards data clustering for network traffic modeling. Cellular network measurement data from different geographic areas can be separated into clusters based on similarities in network performance indicators, cell traffic load data, their changing pattern over time, and / or other metrics. A machine learning model can then be assigned to each cluster, and the machine learning models can be trained to make network traffic control decisions under conditions exhibited in their respective clusters. If the error rate of the trained machine learning models is acceptable, then the machine learning models can be deployed for use at network equipment. If the overall error rate is not acceptable, then the cellular network measurement data can be re-separated into a larger number of clusters, and machine learning models can again be trained for each cluster. The re-separation of data and re-training of machine learning models can repeat until the error rate is acceptable and the machine learning models can be deployed.

The technology described herein is directed towards near-instantaneous optimization of radio resource control (RRC) rejection wait times per user equipment, which can be per user equipment of a group of multiple user equipment. In inference mode, a trained wait time prediction model (e.g., an artificial intelligence / deep reinforcement learning (AI / DRL) model in a base station) adaptively determines the per-UE rejection wait times based on current (measured) network state data, including current centralized unit and distributed unit utilization levels. The technology described herein operates to decreases the overall RRC request rate, while maintaining very high utilization of gNodeB resources, which results in increased per-UE energy efficiency and decreased signaling traffic loads.