78 results about "Traffic allocation" patented technology

The invention belongs to the technical field of power system optimization scheduling, and discloses a mathematical model dual-drive-based port intelligent scheduling method, which comprises the following steps of inputting real-time operation data of a port logistics system and an energy system; on the basis of a deep reinforcement learning model, operation data is adopted for training, a state-action-reward mapping relation is established, and a data-driven preliminary scheduling strategy is generated; based on the operation data, constructing a model-driven traffic distribution-user balance optimization model, and obtaining an energy pricing strategy capable of minimizing the operation cost; constructing a multi-agent collaborative decision framework to coordinate a preliminary scheduling strategy and a traffic distribution-user equilibrium optimization model, and realizing iterative optimization of dual-drive strategy collaboration; dynamically selecting an optimal sub-heuristic strategy by adopting a dual deep Q network structure based on an iterative optimization result; the problems of insufficient strategy generalization ability and poor dynamic environment adaptability in the prior art are solved.

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.

A virtualized cellular access node includes a control plane interface coupled to a physical cellular access node, a first virtualized network function that receives a first portion of data, a second virtualized network function that receives a second portion of data, and a third virtualized network function that receives a third portion of data. The control plane interface receives data related to a cellular network having cellular terminals. The first virtualized network function analyzes the first portion of data and generates a first instruction related to traffic allocation within the cellular network. The second virtualized network function analyzes the second portion of the data and generates a second instruction related to bandwidth allocation within the cellular network. The third virtualized network function analyzes a third portion of the data and generates a third instruction related to quality of service assignment to a first cellular terminal within the cellular network.

The invention provides an SDH / PDH payload real-time bandwidth analysis method based on time synchronization, and relates to the technical field of transmission networks, and the method comprises the steps: obtaining the network state information of each network working node in an optical transmission network; preprocessing the original data frame set to obtain an average effective load corresponding to each data frame in the data frame set, and calculating an effective load dynamic estimation value based on the average effective load and a historical average effective load; obtaining a network transmission resource allocation strategy of an optical link between any two adjacent network working nodes and a network transmission resource parameter corresponding to the network transmission resource allocation strategy according to a preset constraint condition and the effective load dynamic estimation value; and obtaining a comprehensive evaluation index of the current data frame according to the network transmission resource parameter and the load bandwidth performance function, and adjusting a flow distribution path of the optical transmission network according to the comprehensive evaluation index. According to the invention, the dynamic self-adaptive adjustment of the network flow can be realized.

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.

This invention provides a network adjustment method, apparatus, electronic device, and storage medium, relating to the field of data processing technology. The method includes: acquiring network performance information of each routing port at the current moment; wherein the network performance information includes priority flow control message information of the routing ports; inputting the network performance information of each routing port into a network congestion assessment model, and outputting first real-time network congestion level information of each routing port; and calculating network weight information of each routing port based on the first real-time network congestion level information of each routing port, so as to adjust the port traffic allocation of each routing port according to the network weight information.

The invention discloses a traffic network bottleneck identification method based on flow cascading in the technical field of traffic information control, and the method comprises the steps: obtaining road data and commuting behavior data in a target area, extracting the midpoint of each road in the road data, connecting the origin-destination space of the commuting behavior data to the midpoint of the nearest road, and obtaining the traffic network bottleneck identification result. And recording a corresponding index relationship between the origin-destination point and the midpoint of the road. The incremental traffic distribution algorithm is adopted to distribute the travel matrix of the spatial position information, the congestion effect generated by network flow cascade is simulated by iteratively updating the network edge weight, the network bottleneck under the network flow reconstruction is identified, the cascade conduction mechanism of the traffic network flow under the disturbance condition can be systematically described, and the traffic flow reconstruction efficiency is improved. Network bottlenecks having key influences on system stability are identified, and the response capability and structural toughness of urban risks are improved.

The present invention discloses a method for analyzing the total travel distance of multiple modes of transportation based on traffic distribution, which belongs to the technical field of calculation, estimation or counting. The method includes the following steps: clarifying the research area and time period; dividing traffic areas; constructing a road network and a bus line network; calculating the line network density and average station spacing of buses in the traffic area; conducting a questionnaire survey on residents' transportation travel; constructing a traffic distribution matrix and performing traffic distribution; calculating the non-linear coefficient of residents' transportation travel within the traffic area; constructing a calculation model for the distance from passengers to bus stops; and calculating the total travel distance of various transportation modes. The present invention comprehensively analyzes all aspects of the integrated transportation system, organically combining travel on the road network, travel within the traffic area, and travel by passengers to bus stops. It can systematically analyze the entire process of residents' transportation travel and accurately calculate the total travel distance of multiple transportation modes.

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.

1. The name of the design product: test service configuration graphical user interface of electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: the graphical user interface displayed on the electronic device. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the present graphical user interface is used for test service configuration. The front view is the test service configuration home page. The user can jump to the interface change state diagram 1 by clicking the "traffic allocation mode" box of the topology diagram. The user can jump to the interface change state diagram 2 by clicking "next step" of the interface change state diagram 1 after setting the relevant parameters according to the interface prompt. The user can jump to the interface change state diagram 3 by clicking "submit" of the interface change state diagram 2 after setting the relevant parameters according to the interface prompt. 6. Other circumstances that need to be explained: "XX" in the interface represents replaceable text and / or numbers and / or letters and / or symbols.

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 application provides an information interaction method, device and system for advertisement recommendation, so that a user can change from a passive receiver to a creator and sharer of advertisement content in the form of secondary creation of the content of the advertisement, and the enthusiasm of the user to participate in the advertisement activity is improved, so that the recommendation effect of the advertisement is improved. The method comprises the following steps: sending an advertisement and creation invitation information corresponding to the advertisement to each terminal, the creation invitation information being used to instruct the user to create an advertisement copy which is the same as the advertisement in terms of the purpose of publicity; obtaining each advertisement copy made by the user of the terminal according to the creation invitation information; screening target advertisement copies according to the content quality of each advertisement copy and recommending the target advertisement copies to the user; receiving feedback data corresponding to each target advertisement copy collected by the terminal, determining a traffic allocation ratio corresponding to each target advertisement copy according to the data, and recommending each target advertisement copy to the user of each terminal again according to the traffic allocation ratio.

The invention belongs to the technical field of traffic-electric power crossing, and discloses an electric vehicle traffic-electric quantity flow collaborative optimization method considering multi-complementary energy and V2G, and the method specifically comprises the following steps: obtaining traffic network data and power distribution network data, and constructing a traffic-electric power coupling network; constructing a double-layer optimization model based on the traffic-power coupling network, wherein the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model; the upper-layer optimization model is constructed based on a dynamic traffic distribution method, and the upper-layer optimization model comprising a road model, a dynamic energy complementing station model and an electric vehicle circulation flow model is formed through a mapping relation between electric vehicle traffic flow and electric quantity flow and is used for optimizing vehicle path selection and energy complementing decision; the lower-layer optimization model comprises a power distribution network alternating current optimal power flow model containing an electric vehicle load and is used for optimizing the operation state of the power distribution network; the problem that in the prior art, due to the fact that a traffic-power system is single in energy supplementing mode, vehicle network interaction and cooperation are insufficient is solved.

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 invention discloses a GIS data visual management system and method based on digital twinning, and belongs to the technical field of urban intellectualization, and the method comprises the steps: a data acquisition module collects complexity data (temperature, humidity, accident rate and abrupt slope number) of a route, maintenance difficulty data (construction time, heavy load traffic flow and bridge-tunnel ratio) and vehicle data (traffic flow and vehicle speed) of the route; the line complexity analysis module outputs a line complexity coefficient; the maintenance difficulty analysis module outputs a maintenance difficulty coefficient; the vehicle analysis module fuses the line complexity coefficient, the maintenance difficulty coefficient and the real-time traffic flow to generate a vehicle analysis coefficient; the traffic flow distribution module distributes the target traffic flow based on the vehicle analysis coefficient and the supply point distance index. According to the invention, road network state dynamic evaluation and traffic flow accurate distribution are realized through the multi-factor coupling model, and the urban traffic management efficiency is improved.

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.

This invention discloses a traffic allocation method based on multidimensional information fitting. Its characteristics are as follows: by analyzing input data and behavioral data within the service cycle, the relationship between input and traffic is explored. After processing the data into equal frequency bins and chi-square bins, the method of least squares is used to fit the data, establishing a mathematical model between industry traffic and satisfaction, thereby deriving a reasonable industry traffic allocation standard. This model can dynamically allocate traffic based on expected renewal rates and achieve real-time traffic monitoring. In this way, e-commerce platforms can more effectively manage and optimize traffic distribution, improve supplier satisfaction, and enhance the operational efficiency of the entire platform.

The invention discloses an electric vehicle dynamic traffic distribution and charging and discharging scheduling method and system, and relates to the technical field of electric vehicle traffic scheduling, and the method comprises the steps: receiving electric vehicle travel demand data and charging station electricity price data, the number of the electric vehicles and the initial battery energy states of the vehicles, and the charging station electricity price data comprise charging and discharging prices of different charging stations at different moments; the method comprises the steps that an electric vehicle dynamic traffic distribution model is established in advance, and the electric vehicle dynamic traffic distribution model is established based on electric vehicle travel demand data and charging station electricity price data in combination with a dynamic road model, a dynamic charging station model and a dynamic vehicle battery energy state model; and solving a pre-established dynamic traffic distribution model of the electric vehicle based on a linear programming problem optimization solution algorithm, and outputting and obtaining a distribution result of the flow of each road and the charging and discharging power of each charging station in a scheduling period.

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 application discloses a kind of track digital twin real-time online deduction method and system, belong to the traffic simulation technical field based on machine learning.Solve the problem that the conventional traffic simulation and track passenger flow dynamic allocation method in prior art is difficult to meet the demand of large-scale track traffic network dynamic passenger flow allocation due to low solving efficiency;The application constructs track network model;Generate road network passenger flow data and line schedule data;Further generate train and passenger simulation individual, carry out path trajectory matching based on time and space, complete passenger-train dynamic traffic allocation and passenger-train interactive operation deduction;According to real-time detection data and simulation result, adjust dynamic line schedule and passenger flow until the preset convergence condition is reached, complete dynamic traffic checking;Simulation is carried out, realize the restoration and prediction of passenger flow flow rate / flow direction of whole line network.The application effectively improves the efficiency of track passenger flow dynamic allocation, and can be applied to large-scale traffic network simulation.