48 results about "Traffic allocation" patented technology

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention discloses a message issuing speed control method and system based on an AI large model. The method comprises the steps of collecting multi-source data in real time and constructing a multi-dimensional fusion feature vector; based on the BERT-CNN distillation architecture, detecting abnormity and executing hierarchical fusing; based on the fusion feature vector, using a reinforcement learning model to predict message issuing rates of a plurality of time windows in the future and dynamically adjusting priorities; in the offline stage, a mixed integer programming model is adopted to generate minimum cloud resource cost, and in the online stage, a deep reinforcement learning model is adopted to carry out real-time resource fine tuning; based on the node health degree, calculating a traffic allocation weight through a smooth weighted polling algorithm so as to carry out load balancing; different business scenes are adapted through domain adversarial training and multi-task learning, and different languages are adapted through multi-language BERT coding. The message rate is accurately controlled to improve the system stability and the resource utilization rate; low-cost scheduling fine tuning of cloud resources is realized; various business scenes and languages can be quickly adapted, and the development cost and time are reduced.

The invention discloses a multi-satellite communication network dynamic beam scheduling and power control method, and belongs to the field of resource allocation in satellite communication. The implementation method comprises the following steps: constructing a multi-satellite communication network model, constructing a multi-satellite communication network data queue model based on the network model, and constructing a multi-satellite communication network energy queue model; constructing a queue control model based on a Lyapunov equation; constructing a joint optimization problem of hopping beam scheduling and power control based on a drift and penalty function; reconstructing and approximating the joint optimization problem of the hopping beam scheduling and the power control by using an SCA algorithm; decomposing an approximated joint optimization problem by adopting a Benders decomposition framework, wherein the joint optimization problem is divided into a power control sub-problem and a beam scheduling main problem; solving the power control sub-problem and the beam scheduling main problem to obtain an optimal solution; and iterative optimization is carried out, and dynamic beam scheduling and power control of the multi-satellite communication network are realized according to the optimal solution, so that a multi-satellite network dynamic system reaches long-term stability, and user fairness is realized on flow distribution.

This invention proposes a time-synchronized real-time bandwidth analysis method for SDH / PDH payloads, relating to the field of transmission network technology. The method includes: acquiring network status information of each network working node in the optical transmission network; preprocessing the original data frame set to obtain the average payload corresponding to each data frame, and calculating a dynamic estimate of the payload based on the average payload and historical average payloads; obtaining the network transmission resource allocation strategy for the optical link between any two adjacent network working nodes and the corresponding network transmission resource parameters according to preset constraints and the dynamic payload estimate; obtaining a comprehensive evaluation index for the current data frame based on the network transmission resource parameters and the payload bandwidth performance function, and adjusting the traffic allocation path of the optical transmission network according to the comprehensive evaluation index. This invention helps to achieve dynamic adaptive adjustment of network traffic.

The invention discloses a multi-path traffic distribution method based on node parallel computing, and relates to the technical field of traffic flow distribution and computer application, and the method comprises the steps: modeling a traffic network into a computer directed graph composed of a node set and a segmental arc set, and loading a traffic origin-destination demand matrix; constructing an improved Logit multi-path random traffic distribution model to reduce the sensitivity of a path travel time unit and a network scale to a distribution result; a node-based multi-path flow distribution model is formed, and traffic flow iterative distribution is realized by updating the effective subsequent road section flow of the node; taking each terminal point as an independent unit, and executing a node-level multi-path traffic distribution calculation task; a parallel computing framework is designed, a global flow distribution problem is decomposed into node-level sub-problems, and distribution tasks are executed on a plurality of computing units at the same time. According to the method, efficient multi-path flow distribution of a large-scale traffic network is realized, and the calculation efficiency of traffic distribution is remarkably improved.

The invention discloses a traffic distribution method based on public transportation super network construction, which relates to the technical field of road traffic, and comprises the following steps: acquiring all bus station information, bus lines and traffic zone centroid point information in a target distribution area; generating real nodes and virtual nodes according to the bus station information and the bus route information; constructing a driving arc according to the virtual nodes; constructing a co-station transfer arc according to the real nodes and the virtual nodes; constructing a different-station transfer arc according to the real nodes; constructing an upper / lower network arc according to the real node and the centroid point of the traffic zone; according to the real node, the virtual node, the driving arc, the same-station transfer arc, the different-station transfer arc and the upper / lower network arc, constructing a bus super network; according to the method, the traffic flow is distributed to each arc section of the bus super network by adopting a multi-path-capacity limiting method, so that the bus distribution can comprehensively consider the influence of the transfer behavior, the distribution result of the traffic flow is closer to the reality, and the reasonability of the bus distribution is improved.

This application discloses a method, apparatus, computer device, and storage medium for allocating channel traffic, relating to the fields of computer and internet technology. The method includes: acquiring historical traffic records and constraints corresponding to at least two channels, where the constraints indicate the conditions that traffic allocated to the channel must meet; for a first channel among the at least two channels, determining a first predicted traffic volume for the first channel in the next prediction period based on the historical traffic records corresponding to the first channel; determining a first dynamic decision weight for the first channel in the next prediction period based on the first predicted traffic volume and the constraints corresponding to the first channel, where the first dynamic decision weight is used to adjust the weights of key parameters for traffic allocation in the next prediction period; and adjusting the traffic allocated to the first channel in the next prediction period based on the first dynamic decision weight. This application can effectively improve the efficiency of traffic allocation in multi-channel scenarios.

The invention discloses an electric power-traffic network optimal scheduling method based on a graph attention agent model, and the method comprises the steps: constructing a second-order cone DistFlow optimal power flow model of a power distribution network by taking the electricity purchase quantity and the output of a distributed power supply as control variables; on the basis of the semi-dynamic traffic distribution model, the flexibility capability of the charging demand of the electric vehicle is brought into play through the dynamic charging service fee, and a semi-dynamic traffic distribution model considering dynamic charging service fee scheduling is constructed; on the basis of a multi-scene solving result, traffic network travel demands, traffic network basic parameters and charging service charges are used as inputs, multi-period traffic flows of all charging stations are used as outputs, a graph-attention agent model is trained, nonlinear mapping of charging prices and charging flows is learned, and the nonlinear mapping is used for rapid scheduling. According to the method, the user balance response can be approximately obtained in a multi-period and uncertain scene, and the charging load and the traffic flow are optimized.

This invention discloses a data communication scheduling system for intelligent manufacturing production lines based on edge computing, belonging to the field of industrial IoT edge computing data scheduling technology. The system synchronously acquires the real-time load current discrete sequence of the target device node and the real-time signal-to-noise ratio sequence of the main communication path, extracting the second-order derivative zero-crossing discrete component and the first-order attenuation discrete component of the envelope. A cross-correlation algorithm is used to perform coherence analysis to derive a cross-domain healthy coupling index. Using this index as the independent variable, a weighted mapping of the real-time packet loss rate is performed through an exponential gain function to generate a virtual impedance factor. It determines whether the index exceeds a threshold to trigger backup gateway context pre-synchronization and calculates the positive offset. Finally, a normalization function is used to calculate the traffic allocation weight and configure path scheduling labels to achieve dynamic scheduling of multi-path traffic splitting actuators. This solution achieves a priori prediction of industrial communication link quality and refined multi-path traffic splitting scheduling.

The invention relates to the technical field of lane deployment, and provides a charging lane deployment method and system based on dual-objective optimization, and the method comprises the steps: initializing a plurality of particles, and coding whether each candidate road segment is deployed with a wireless charging lane or not and the deployment length through each particle; for each particle, executing a deployment-traffic distribution iteration process, and calculating an operation income and a user comprehensive cost; iterative selection and updating of particles are carried out by taking maximization of operation income and minimization of user comprehensive cost as targets to finally obtain an optimal solution, and the optimal solution is decoded into a wireless charging lane deployment scheme. The vehicle charging can be more effectively induced without excessively increasing congestion or detouring distance, and the practical feasibility and the overall social benefit of the scheme are improved.

The invention discloses an AI training service-oriented intra-network computing intelligent arrangement method and device, and the method comprises the steps: obtaining a system state, and constructing a mixed integer linear programming model MILP according to the system state; inputting the system state into the LLM, generating a new heuristic algorithm by adopting a computing resource layout heuristic algorithm based on the LLM, and iteratively selecting an optimal layout heuristic algorithm; an optimal layout heuristic algorithm is adopted to select a plurality of switches to deploy an intra-network computing function, namely, the switches serve as programmable switches to form a computing resource layout; after the MILP is relaxed, a solver is adopted to solve a flow distribution rate and an ideal sending rate of a working node of the AI training task for sending a gradient to a programmable switch and a PS in the computing resource layout; according to the flow distribution rate, the AI training task is distributed to a programmable switch and a PS in the computing resource layout, and a gradient routing path is formed; and sending the computing resource layout and the gradient routing path to a physical network device where the AI training service cluster is located for execution.

This application relates to a congestion control method and a traffic splitter system for lossless networks. The method includes: collecting the queue depth of each output port; comparing the queue depth with a congestion threshold to determine the congestion state, where the congestion state includes multiple congestion levels corresponding to a set of linked response parameters; obtaining cache depth configuration parameters and traffic splitting weights from the linked response parameter set based on the current congestion level, with the two jointly generated within the same decision cycle and inversely correlated for the same output port; performing cache depth adjustment, scheduling priority adjustment, and traffic allocation based on the linked response parameter set; statistically analyzing the packet loss rate change within the evaluation window after the adjustment action, and upgrading the congestion level and re-jointly deciding when the adjustment is ineffective. This application couples the decisions of caching, scheduling, and traffic splitting into an inseparable joint generation process through the linked response parameter set, and drives inter-stage jumps with closed-loop feedback, reducing packet loss caused by micro-burst traffic in high convergence ratio scenarios.

The application discloses a traffic network bottleneck identification method based on traffic cascade in the technical field of traffic information control, which comprises the following steps: acquiring road data and commuting behavior data in a target area, extracting the midpoint of each road in the road data, connecting the start and end points of the commuting behavior data to the midpoint of the nearest road, and recording the corresponding index relationship between the start and end points and the midpoint of the road. The application uses an incremental traffic assignment algorithm to assign the trip matrix of the spatial position information, simulates the congestion effect generated by the network traffic cascade by iteratively updating the network edge weight, identifies the network bottleneck under the network traffic reconstruction, can systematically depict the cascade conduction mechanism of the traffic network traffic under the disturbance condition, identify the network bottleneck which has a key influence on the system stability, and improve the response ability and structural resilience of the city risk.

The invention relates to the technical field of traffic-power optimization scheduling. The invention discloses an electric vehicle traffic-power collaborative optimization method, device and equipment and a medium. The method comprises the following steps: constructing a battery capacity and energy consumption model of an electric vehicle along with temperature change; constructing a road traffic capacity and driving speed correction model changing along with the weather state; constructing a charging efficiency model of the electric vehicle battery along with temperature change; according to the battery capacity and energy consumption model, the road traffic capacity and driving speed correction model and the charging efficiency model, a traffic-electric power double-layer optimization model is constructed, and the traffic-electric power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct-current optimal power flow model; and based on a preset solving model, solving the traffic-power double-layer optimization model to obtain electric vehicle traffic flow space-time distribution. Mutual feedback adjustment of traffic and a power grid can be realized in a space-time dynamic framework, estimation deviation is reduced, and system behaviors in extreme weather in winter are effectively reflected.

The invention relates to the technical field of computers, in particular to a rate control method and device for a Docker container virtual network. The method comprises the following steps: acquiring event data corresponding to a Docker internal event, and extracting network event characteristics in the event data; judging whether the Docker internal event is a virtual network creation event or a virtual network abnormal event based on the network event characteristics; if the Docker internal event is a virtual network creation event or a virtual network abnormal event, obtaining a network flow control parameter corresponding to the virtual network creation event or the virtual network abnormal event; and performing traffic control on the Docker internal event in a preset mode according to the network traffic control parameter. According to the invention, fairness and flow distribution of the container in the same network environment can be ensured, and an individual task container is prevented from occupying excessive bandwidth resources.

This application discloses a traffic allocation method, a file download method, a controller, an electronic device, a chip, and a computer-readable storage medium. The traffic allocation method is applied to a controller and includes: acquiring a first download request sent by a first edge site to a central cloud; extracting first download information for a first target file from the first download request; acquiring a first flow table and a first group table issued by the central cloud; forwarding the first download request to a first target edge site using the first flow table and the first group table to obtain the first target file; and sending the first target file to the first edge site. This application's solution extracts download information from the request using the controller and then allocates the request using flow table rules and group tables, allowing P2P traffic to bypass the central cloud. This enables traffic control, supports segmented downloads, and ensures security and compliance.

The invention provides a transaction flow allocation method, relates to the technical field of network communication, and can be applied to the technical field of finance. The transaction traffic allocation method comprises the following steps: acquiring traffic information of at least one transaction in a target network; determining transaction type traffic of a first type to which at least one transaction belongs according to the traffic information; in response to the fact that the transaction type flow does not fall into the first proportion range of the reference maximum network flow of the first type, carrying out capacity expansion and shrinkage on the reference maximum network flow; after waiting for a preset time interval, obtaining the transaction category flow of the first category again; and if the transaction type flow obtained again does not fall into the first proportion range of the reference maximum network flow of the first type, continuing to perform capacity expansion and shrinkage on the reference maximum network flow until the transaction type flow falls into the first proportion range. The invention further provides a transaction flow allocation device and equipment, a storage medium and a program product.

The application discloses an environment-friendly traffic distribution method considering carbon emission cost, determines traffic demand, road capacity and road resistance coefficient in a road network, calculates time consumed by vehicle travel on a road ij and running speed of the vehicle on the road ij, and determines a carbon emission cost function and a maximum value of carbon emission minimization speed based on the running speed of the vehicle on the road ij; the application determines a constraint condition based on the carbon emission cost function, constructs an environment-friendly traffic distribution model considering carbon emission cost, solves the environment-friendly traffic distribution model considering carbon emission cost, and obtains an environment-friendly traffic distribution scheme considering carbon emission cost.

The invention discloses a flow distribution method and device based on behavior data. A server side method comprises the following steps: collecting historical behavior data of each user in an automobile financial platform; obtaining multiple types of system records of each user to obtain current user state information; inputting the historical behavior data and the current user state information into a preset flow distribution plug-in, and outputting a probability value of each preset intention label corresponding to each user; the preset flow distribution plug-in comprises a pre-trained customer intention prediction model generated based on a machine learning algorithm; and taking the preset intention label corresponding to the maximum probability value as a final intention label of each user, distributing each user to a preset flow pool corresponding to the final intention label of each user, and continuing to execute the step of collecting the historical behavior data of each user in the automobile financial platform. According to the invention, the method can accurately recognize the customer with the vehicle purchase financing intention, reduces the invalid flow distribution, and improves the resource utilization efficiency.

A burst traffic allocation method based on deep learning and cooperative game theory, belonging to the field of communication network traffic engineering technology, solves the problems of resource optimization and user experience fairness under burst traffic in dynamic networks. The core of this invention lies in deeply coupling network-side traffic engineering (QoS optimization) with user-side resource allocation (QoE optimization) to form a closed-loop collaborative optimization framework. In burst traffic scenarios, this method first allocates traffic to alternative paths according to the path allocation ratio calculated by the model, reducing maximum link utilization, avoiding network congestion, and enhancing the network's robustness to burst traffic. Then, the available bandwidth on the allocated paths is used as a resource and distributed among multiple users, aiming to maximize the overall experience of all users while ensuring fairness among users, achieving collaborative optimization of service quality and user experience quality in a dynamic network environment.

This application provides a multi-dimensional, dynamically coupled traffic allocation decision-making method and system. The method includes acquiring user attribute data, business attribute data, channel performance data, and channel cost data; determining channel anomaly indicator data, calculating anomaly weighting coefficients, performing circuit breaker status detection, and generating anomaly management strategies; extracting basic channel cost information for each channel, and determining the dynamic premium factor and effective cost coefficient for each channel based on channel performance data; calculating user weights, business weights, and performance scores to determine the channel score for each channel; converting the channel score into traffic allocation probability; determining the budget ratio based on the real-time call count and effective cost coefficient for each channel; performing a status assessment on the budget ratio to obtain the budget status; and determining cost control parameters based on the budget status. This application addresses the problems of lack of real-time adaptability, crude anomaly handling mechanisms, and a single decision-making dimension.

The invention discloses a visitor flow regulation and control method, device and equipment based on Internet live broadcast, and the method comprises the steps: introducing a user behavior classification mechanism, firstly calculating a better proportion of different types of users in each live broadcast scene based on historical data as a reference, then carrying out the real-time statistics of the actual proportion of different types of users in each live broadcast scene in a current time period, and carrying out the real-time statistics of the actual proportion of different types of users in each live broadcast scene; and finally, the traffic quota of each live broadcast scene is dynamically adjusted according to the deviation, so that the user structure change in each live broadcast scene is accurately sensed, the traffic distribution is dynamically optimized, the requirements of different user groups are better met, the traffic resource is inclined to the user groups with higher value, and the user experience is improved. User experience and platform business income are improved, and healthy development of live broadcast content ecology is promoted.

This invention belongs to the field of communication automation technology and discloses a multi-protocol adaptation system and method for traffic scheduling and quality monitoring in communication automation. The invention proposes six unique innovations by constructing three core modules: intelligent traffic scheduling, dynamic communication quality monitoring, and multi-protocol adaptive compatibility, covering the entire process of communication automation from traffic scheduling and quality monitoring to multi-protocol adaptation. Employing core technologies such as dynamic traffic allocation, accurate quality assessment, and adaptive protocol parsing, it overcomes the industry bottlenecks of traditional communication automation, including rigid traffic scheduling, lagging quality monitoring, and difficulties in multi-protocol compatibility. It achieves accurate traffic scheduling, real-time communication quality monitoring, and adaptive compatibility with multiple protocols, significantly improving the traffic utilization efficiency, quality controllability, and multi-scenario adaptability of communication automation. It adapts to the needs of various scenarios such as industrial heterogeneous communication, multi-terminal collaborative communication, and cross-domain converged communication, and particularly solves the technical problems of uneven traffic distribution, difficulty in timely detection of quality anomalies, and lack of seamless multi-protocol compatibility.

The application discloses a path allocation method and device of a network flow, an electronic device and a storage medium. The traffic demand and a path capacity matrix are input into a path allocation model, so that the path allocation model can fully focus on a preconfigured transmission path, that is, a topology structure when processing. A predicted path allocation ratio is obtained through the path allocation model. When it is detected that a distribution of the predicted path allocation ratio meets a preset constraint condition, the predicted path allocation ratio is taken as a target path allocation ratio distribution of the network flow. The application combines the topology structure of network flow transmission and the relationship with other network flows. The obtained target path allocation ratio distribution can realize adaptive optimization of traffic allocation of the network, thereby improving the overall performance of the network and reducing the risk of network congestion.

This application discloses a traffic allocation method, apparatus, storage medium, and electronic device, relating to the field of cloud computing technology. The method includes: identifying target traffic flowing through a target gateway and determining a cluster for processing the target traffic, thus obtaining a target cluster; selecting any proxy container within the target cluster, thus obtaining a target proxy container; determining the failure rate associated with the target proxy container and the health rate of the target cluster; determining whether the failure rate and health rate are outliers; and, if either the failure rate or health rate is an outlier, controlling the target gateway to send the target traffic to the server corresponding to the candidate proxy container. This application solves the problem in related technologies where the traffic pressure of each service cluster cannot be perceived, leading to anomalies in the service clusters when processing business traffic.

This invention discloses a big data-based SMS sending optimization system, belonging to the field of big data marketing technology. The system includes: a compliance pre-verification module, a multi-version content generation module, a spatiotemporal test matrix construction module, a dynamic traffic allocation module, and a strategy decision feedback module. In terms of user grouping and traffic allocation, this invention uses user profile feature screening and Pearson correlation coefficient to accurately select features highly correlated with SMS response rate, dividing the target user group into K homogeneous groups and rigorously verifying the internal homogeneity and inter-group heterogeneity of the groups. On this basis, each group of users is mapped to a test unit matrix proportionally, and the consistency between the sample feature distribution and the target group is verified by calculating feature statistics. This ensures that the samples of the test units represent the target group to the greatest extent, making the data relied upon by the subsequent strategy decision feedback module more representative and scientific.

This application discloses an optimization method, system, and storage medium for CDN global service traffic allocation. The method involves obtaining an undirected graph of all traffic allocation points in a target area, using Dijkstra's single-source shortest path algorithm to find the shortest path distance between traffic sending points and traffic receiving points as a shortest path distance consideration factor; then, based on traffic sending point data and traffic receiving point data, parsing the consideration factors for traffic sending points and traffic receiving points; comprehensively analyzing and determining the sorted sets of traffic sending points and traffic receiving points; using the sorted sets of traffic sending points and traffic receiving points as optimization consideration factors, matching traffic sending points to traffic receiving points, and selecting the best matching result. Therefore, the embodiments of this disclosure can improve the rationality of global service traffic allocation in CDN.

A flight plan robustness evaluation system and method. The system comprises a network structure server, a simulation driving server, a first client, a second client, a third client and a fourth client connected with each other through a network. The flight plan robustness evaluation system and method provided by the application apply the dynamic traffic distribution concept to the air traffic network operation, realize the dynamic evaluation of the flight plan robustness, and help the civil aviation operation monitoring and management personnel to fine-tune the flight plan in advance when the robustness evaluation is low, so as to improve the robustness of the flight plan in the implementation process and avoid the occurrence of large-area flight delay.

The application relates to a traffic distribution method and system of a kubernetes cluster, wherein a pod and a plurality of services are deployed in the kubernetes cluster, the load balancing corresponding to each service corresponds to different charging modes one by one, and the addresses of the plurality of load balancers use the same domain name. The method comprises the following steps: calling a cloud service provider interface to obtain the traffic usage of the load balancing corresponding to each service; determining a target charging mode according to the usage; determining a current actual charging mode; enabling the load balancing corresponding to the target charging mode in the case that the current actual charging mode is not the target charging mode; and disabling the remaining load balancers in the case of enabling the load balancing. Through the application, the problem that the load balancing faces a short service unavailability in the process of changing the load balancing in the related art is solved, and the user experience is improved.