9 results about "Traffic optimization" patented technology

The present application relates to heavy load AGV cooperative control technical field, especially in a kind of heavy load AGV group's cooperative control method, the motion state parameter of each heavy load AGV is collected to construct motion state vector, local traffic congestion parameter is predicted, and then local congestion index is calculated and dynamic coordination trigger threshold is determined;Distributed speed coordination is triggered based on local congestion index and dynamic coordination trigger threshold, to determine the reference coordination speed of each heavy load AGV;The reference coordination speed is passed to relevant heavy load AGV, to generate transmission coordination speed;Based on ground state parameter, the motion state vector of each heavy load AGV and transmission coordination speed, the traffic optimization sorting of each heavy load AGV is determined, to modify transmission coordination speed and determine modified coordination speed;Based on the modified coordination speed of each heavy load AGV, the timing and number of subsequent local traffic congestion are predicted, to determine the effective duration of coordination control.The present application improves the running efficiency and safety of heavy load AGV group in complex traffic environment.

This invention discloses a high-efficiency network traffic optimization and control method based on a switch, belonging to the field of network traffic optimization technology. By introducing a micro-time detection window on the output port side and identifying synchronous burst time intervals, the traditional judgment logic based on long-term congestion thresholds is refined to a micro-time scale. This allows the switch to identify the independent phenomenon of short-term concentrated arrivals, rather than relying solely on whether the queue has grown for a long time as a trigger condition. This identification mechanism clearly locates the instantaneous traffic aggregation behavior that was previously difficult to distinguish individually, providing a targeted processing basis for subsequent scheduling actions. By constructing a set of micro-time slices and combining them with the offset budget of the service flow, a micro-time slice offset table is generated in the control plane. This allows multiple synchronously arriving service flows to be actively split into different time slices for dequeueing in terms of transmission timing, changing the traditional method of directly releasing queues in order.

This invention discloses a global collaborative prediction method for traffic signals based on quantum entanglement states. The method includes: establishing a quantumized model of the dynamic evolution path of the target traffic network within a future time window; constructing a spatiotemporal graph model; mapping the signal phase states of intersections to spatiotemporally integrated quantum states using a hierarchical quantum encoding scheme; constructing a Hamiltonian containing spatial coupling, temporal evolution, and cost terms based on a preset traffic optimization objective; solving for its ground state using a hybrid quantum-classical computation method to determine the optimal dynamic evolution path; encoding the current actual state of the traffic network as an initial state; decoding the collaborative signal control strategies for future time points using the optimal evolution operator and sequential measurements; and executing these strategies through a rolling time-domain control framework. This invention utilizes quantum parallelism for global optimization, enabling the acquisition of the optimal collaborative strategy across the entire spatiotemporal range in a single step, thereby improving the overall operational efficiency, predictability, and robustness of the traffic network.

The present application relates to the technical field of traffic informationization. Specifically relates to a kind of dynamic traffic optimization management method, device, equipment and medium, its method includes using sensing credibility information to the traffic deviation degree information is weighted fusion processing, obtains fusion deviation degree information, obtains candidate signal control scheme set according to fusion deviation degree information and preset phase configuration record information, for each candidate signal control scheme in candidate signal control scheme set, according to traffic state alignment sequence and the timing parameter information corresponding to candidate signal control scheme Queuing Growth Estimation processing is executed, obtain scheme evaluation information, according to scheme evaluation information and preset constraint rule information, determine target signal control scheme from candidate signal control scheme set, according to target signal control scheme to target intersection executes signal timing control, obtains target timing parameter information.The present application has the effect of improving traffic operation state analysis accuracy and control strategy adaptability in traffic management process.

This invention provides a speed optimization method for uncertainty perception in intelligent vehicle cyber-physical systems, belonging to the field of intelligent transportation technology. The method includes: collecting vehicle and traffic flow operational status data and transmitting it to the information layer for fusion analysis; constructing multi-temporal and spatial scale traffic state models at the vehicle, road, and regional levels; establishing speed optimization models for fuel consumption, congestion uncertainty, and travel time risks; using a swarm intelligence optimization algorithm to solve for recommended speeds for different road segments and distributing them; vehicles responding to the recommended speeds based on an information-guided intelligent driver model to achieve real-time control; and transmitting the vehicle's response data back to the information layer to update the model and re-execute optimization. This invention, through multi-temporal and spatial scale modeling and uncertainty feature quantification, constructs a closed-loop feedback mechanism for macro-level traffic optimization and micro-level vehicle behavior control, effectively improving traffic system operating efficiency, reducing fuel consumption, and shortening travel time.

This invention discloses a cross-cluster traffic optimization scheduling method and system for data processing workflows. It provides a strategy for optimizing job scheduling of cross-cluster data processing workflows by analyzing bottleneck points and migrating jobs across clusters at those bottlenecks. The main steps include: constructing a branching-merging directed acyclic graph of the data processing workflow; performing a depth-first post-order traversal of the graph; analyzing the bottleneck points and bottleneck traffic of each job during the traversal, and determining the execution cluster for that job at the bottleneck point. This invention can reduce cross-cluster data traffic in data pipelines and container workflows.

The application provides a cooperative path planning method and system of an intelligent connected vehicle, relates to the technical field of traffic optimization, determines a plurality of groups of initial planning paths of a plurality of intelligent connected vehicles in a preset area, identifies a first connected vehicle set located at a first intersection at a first prediction time, performs a traditional vehicle shunting feature prediction at the first intersection, performs intersection connected vehicle shunting optimization in combination with a first shunting prediction information, a driving purpose certainty label, and an initial planning path corresponding to the first connected vehicle set, returns an optimization result to a corresponding intelligent connected vehicle control terminal, solves the problem that intelligent connected vehicles and traditional vehicles form a decision conflict, cause path conflict and low shunting efficiency, and cause traffic hazards and congestion, and achieves the effects of improving the traffic flow and overall traffic efficiency of the intersection, reducing traffic congestion and conflict, and improving traffic safety and efficiency.

The present application relates to static traffic optimization technical field, for a kind of vehicle in three-dimensional parking building shift and charging scheduling optimization method and system.The method includes: establishing multiple data sets;Introduce obstruction matrix and distance matrix;Settable scheduling indicator, decision variable and constraint condition, to maximize continuous parking duration and minimize charging cost as joint target, vehicle scheduling charging problem is modeled as mixed integer linear programming model;The initial scheduling solution of model is solved by branch and bound method;Design large neighborhood search algorithm, identify high interference vehicle by structural disturbance influence function and generate disturbance subset, generate multiple candidate scheduling schemes, comprehensively measure transposition distance, path obstacle cost and charging cost, update solution using greedy repair strategy;Through multiple rounds of disturbance and update, output optimal parking scheduling and charging scheme.The present application can effectively improve parking space utilization, scheduling efficiency and system operation robustness.

The application relates to the technical field of traffic optimization, in particular to a city traffic optimization system, which comprises a sensor comprehensive module, a camera monitoring module and a comparison and optimization module, wherein: the sensor comprehensive module collects vehicle traffic sensing data by using various sensors, and the vehicle traffic sensing data is subjected to vehicle quantity prediction by using a decision tree algorithm model; the camera monitoring module collects vehicle traffic picture data by using a camera, and the vehicle traffic picture data is subjected to vehicle quantity prediction by using a YOLO neural network model; the comparison and optimization module collects a vehicle quantity threshold range stipulated by traffic flow management, accepts the vehicle quantity predicted by the sensor comprehensive module and the camera monitoring module, carries out summation and averaging, compares the summation and averaging result with the threshold range, determines a final result according to the comparison result, and optimizes the methods in the sensor comprehensive module and the camera monitoring module.