18 results about "Congestion prediction" patented technology

This invention discloses a method, device, and medium for predicting highway congestion based on big data, relating to the field of road traffic control technology. The method includes: analyzing the traffic status of road segments and identifying the traffic relationships between road segments from multi-source traffic data, outputting road segment relationship status data; identifying bottleneck road segments in the highway from the road segment relationship status data, outputting bottleneck structure data; organizing the bottleneck road segments into paths according to their temporal impact relationships, outputting a congestion propagation path map; performing temporal extrapolation of the influence extension direction and expansion range of the bottleneck road segments along the continuous path relationships in the congestion propagation path map, outputting congestion trend data; and determining the congestion development status and propagation path direction of the highway based on the congestion trend data, outputting highway congestion prediction information. This invention, through the synergy of bottleneck structure data construction and congestion propagation path map generation, achieves clear identification of the transmission path and expansion range of highway congestion impacts.

This invention provides a method, system, and medium for predicting waterway vessel congestion based on multi-agent collaboration. The prediction method includes: at each time step, dynamically calculating the connectivity weights between nodes based on the vessel traffic flow characteristics, waterway environmental characteristics, and historical congestion characteristics of the current node, generating an adjacency matrix for the current time step, and constructing a dynamic waterway topology graph; inputting the dynamic waterway topology graph and corresponding node features into a two-branch temporal graph convolutional network, outputting short-term and long-term feature vectors; using a deep reinforcement learning-driven adaptive gating fusion module to generate a fusion weight vector in real time, and weighting and fusing the short-term and long-term feature vectors according to the fusion weight vector to generate multi-scale spatiotemporal fusion features; inputting the multi-scale spatiotemporal fusion features into a full-cycle prediction output head, and outputting short-term, medium-term, and long-term waterway congestion prediction results in parallel. This invention significantly improves prediction accuracy and adaptability.

The application discloses a low-altitude airspace multi-source data fusion congestion prediction and dynamic scheduling system, which solves the problems of incomplete data fusion, low congestion prediction accuracy, lack of coordination and real-time in existing low-altitude airspace traffic management. The system includes a perception layer, a data fusion layer, a prediction layer, a decision-making and scheduling layer, a control execution layer, and a blockchain storage layer. The perception layer collects multi-source data, the data fusion layer realizes data fusion through an improved deep belief network, the prediction layer adopts a mixed model of LSTM and graph neural network to output congestion prediction results, the decision-making and scheduling layer generates a dynamic scheduling scheme based on a multi-objective optimization algorithm, the control execution layer executes the scheme and feeds back data, and the blockchain storage layer guarantees data security and privacy. The application improves the accuracy of congestion prediction and the adaptability of the scheduling scheme, and realizes efficient, safe and coordinated operation of the low-altitude airspace.

A device includes one or more processors configured to generate an operational congestion metric associated with a particular airport, wherein the operational congestion metric is based at least on one or more usage metrics for the particular airport. The one or more processors are configured to generate an impact congestion metric non-linearly based at least on the operational congestion metric. The one or more processors are configured to determine based on the impact congestion metric, a congestion prediction for the particular airport. The one or more processors are configured to communicate a congestion alert for the particular airport, wherein the congestion alert is based at least on the congestion prediction

The application discloses a congestion prediction and DRL-based intelligent electric vehicle fleet queue optimization method, and steps include: (1) modeling the queue position optimization problem of the electric vehicle fleet as a mathematical model with energy balance as the target and subject to traffic environment constraints; (2) solving the mathematical model in step (1) by using a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle fleet. In step (2), congestion state information in the external traffic environment is obtained by using an LSTM traffic prediction and an FCM method, and the remaining electric quantity and the cumulative driving distance of each vehicle in the vehicle fleet are used as state inputs of the TRPO algorithm; the TRPO algorithm uses a strategy network and a value network to respectively obtain a strategy for adjusting the queue position and evaluate the expected return under the current state. The TRPO algorithm dynamically adjusts the strategy update step length by constraining the KL divergence of the strategy update, so that the stability and convergence in the strategy update process are ensured.

This invention discloses a dynamic load balancing method and system based on Redis and memory collaboration, belonging to the field of intelligent resource scheduling technology. It includes obtaining complete profile data and generating a hot group list and link matrix through unified collection of multi-level indicators and synchronous monitoring of node links; obtaining a link migration execution report by detecting the link health of migration commands and node parameters; verifying and replaying the migration execution report using contradictory verification correction to generate an adoption report and an anomaly list; and dynamically adjusting the order of different levels and nodes and updating the migration execution report based on the adoption report and the anomaly list using an adaptive parameter tuning method. This invention achieves hot / cold zone perception and congestion prediction of migration commands by combining a cross-layer remapping candidate list with a link matrix for bandwidth hot zone projection and cold zone priority sorting, effectively improving the accuracy of resource scheduling under Redis and memory collaboration.

The application relates to the technical field of data prediction, and provides a traffic jam prediction method for an accident high-risk area, which comprises the following steps: extracting the connection relationship between different detection points, the width of a road and the traffic flow of the detection points, determining the connection characteristic value of the detection points, and establishing the traffic flow vector and the traffic flow matrix of the detection points; establishing a connection characteristic matrix, a selectable path matrix and a traffic flow similarity matrix, and determining the fusion traffic characteristic matrix at a collection time; using a sine function to encode the collection time, obtaining the time point encoding value of the collection time, and obtaining traffic flow prediction data; and according to whether the collection time is within the rush hours of a workday, the traffic flow prediction data and the width of the road where the detection point is located, realizing traffic jam prediction. The application can improve the accuracy of traffic jam prediction.

The application relates to the technical field of intelligent traffic, and provides an intelligent traffic congestion governance method and system based on multi-modal data fusion, which comprises the following steps: preprocessing basic traffic data and associated traffic data; performing multi-modal fusion on the pretreated basic traffic data and associated traffic data based on a data fusion unit to obtain traffic state fusion data; obtaining road network space data and historical congestion time series data, and constructing a traffic state space-time feature map according to the road network space data and the historical congestion time series data; taking the traffic state fusion data as the input of a congestion prediction model, dynamically capturing congestion key influence factors of different road sections and different time periods through an attention mechanism, and obtaining a congestion prediction result; obtaining multiple groups of candidate traffic governance strategies according to the congestion prediction result, and obtaining an optimal governance strategy according to the multiple groups of candidate traffic governance strategies. The application can improve the accuracy of congestion prediction.

The application discloses a network-on-chip data transmission method and device, equipment and storage medium, and relates to the technical field of data transmission, and comprises the following steps: collecting traffic data of each routing node in the network-on-chip in real time; based on current to-be-transmitted data and corresponding source routing node and target routing node, combining the traffic data, the congestion of each routing node is predicted to obtain a corresponding prediction result; a node array containing a plurality of node serial numbers is generated according to the prediction result; the order of the node serial numbers in the node array corresponds to the order of the routing nodes through which the current to-be-transmitted data is transmitted from the source routing node to the target routing node; and the current to-be-transmitted data is transmitted from the source routing node to the target routing node based on the node array. Thus, the congestion is predicted in combination with real-time traffic data, the appropriate routing node can be accurately selected for data transmission; and the transmission path of the data is represented in the form of an array, so that the efficiency and transmission reliability of the data transmission can be ensured.

This invention relates to the field of overhead conveying and discloses an intelligent overhead conveying system for garment production. The system proactively screens and isolates abnormal hangers at the source by comparing weight characteristics through a workstation operation trigger module. A global scheduling analysis module generates initial target addresses and process time windows, and a congestion prediction module uses this information, combined with historical data, to predict future road congestion. A path pre-simulation evaluation module and a dynamic path planning module work together to construct a multi-objective evaluation function based on congestion index and energy consumption, generating the optimal conveying path and achieving a comprehensive balance between efficiency and energy consumption. A fault response processing module dynamically blocks faulty road sections and automatically replans the path when a fault is detected. The monitoring and feedback module forms a data closed-loop optimization prediction algorithm. This system effectively solves the problems of abnormal conveying, path congestion, and delayed fault response, significantly improving conveying efficiency and intelligence.

This application discloses a traffic congestion relief method and apparatus, belonging to the field of traffic technology. The method includes: acquiring information on M vehicles; M being an integer greater than 1; predicting traffic congestion based on the information of the M vehicles; and sending traffic guidance information to N of the M vehicles based on the traffic congestion situation. This method can predict traffic congestion by acquiring information from multiple vehicles in advance, thereby achieving congestion prediction, preventing delayed traffic intervention, and allowing sufficient time for traffic congestion relief.

A destination station (STA) in a wireless network, comprising: a memory; and a processor coupled to the memory, the processor configured to: receive a plurality of packets from a source STA via a router, the router located between the destination STA and the source STA; determine a congestion prediction for the source STA based on a packet arrival time of the plurality of packets; compare the congestion prediction to a first threshold; generate a congestion prediction signal based on the comparison, wherein the congestion prediction signal indicates an operating mode of the source STA; and transmit the congestion prediction signal to the source STA via the router.

This invention relates to the field of path planning technology, and more specifically, to a multi-objective path planning method for intelligent warehousing based on a large model. The method includes: acquiring a state vector of an intelligent warehousing system, wherein the state vector represents the system's current timeliness pressure, energy consumption pressure, and throughput pressure; inputting the state vector into a pre-trained Actor policy network to obtain timeliness weights, energy consumption weights, and throughput weights corresponding to the timeliness pressure, energy consumption pressure, and throughput pressure. This invention, by constructing a state vector representing the system's timeliness, energy consumption, and throughput pressure in real time, drives a large model to dynamically generate the decision weights for these three factors. Combined with dynamic congestion prediction and cost calculation based on neural networks, it plans the globally optimal path for AGVs and introduces an adaptive replanning mechanism to cope with environmental changes, thus solving the problem of intelligent trade-offs among multiple conflicting objectives.

This application discloses a method, apparatus, device, and medium for predicting highway congestion based on an adaptive mixed bacterial foraging optimization algorithm, relating to the field of computer technology. The method includes: collecting highway traffic flow data to construct an imbalanced training set; initializing a bacterial population using the imbalanced training set and a hot-start mechanism based on a fuzzy C-means clustering algorithm; selecting and executing bacterial movement strategies based on the initialization results and a highway congestion predictor constructed using the adaptive mixed bacterial foraging optimization algorithm; evaluating fitness based on the strategy execution results and the hot-start rules; and training the predictor using the fitness evaluation results. After training, the unprocessed traffic flow data from the highway is input into the trained predictor to obtain congestion prediction results. The congestion prediction results include congestion status signals or smooth flow status signals. This application improves the accuracy, precision, and efficiency of classification prediction, as well as its robustness and adaptability in complex imbalanced environments.

The application provides a wiring congestion prediction method and device, equipment and a storage medium, and belongs to the chip design field. The method comprises the following steps: acquiring layout basic data of a chip, wherein the layout basic data comprises pin information, pin network information and wiring resource information; determining the rectangular uniform line density and the total pin wiring line length of each grid unit wiring of the chip according to the pin information and the pin network information; and determining the wiring congestion information of the chip according to the rectangular uniform line density, the total pin wiring line length and the wiring resource information of each grid unit wiring, wherein the wiring congestion information comprises the congestion level of each grid unit. According to the rectangular uniform line density, the total pin wiring line length and the wiring resource information, the wiring congestion information of the chip can be accurately determined.

The application relates to the technical field of traffic emergency guidance, and discloses a traffic warning device and an emergency guidance system, the device comprising a data acquisition unit, a data processing unit, a congestion warning unit and a path guidance unit; wherein: the data acquisition unit is used for collecting historical congestion data, environmental data and real-time traffic data; the data processing unit is used for identifying congestion risk road sections and congestion risk time periods, and calculating the traffic index of each road section; the congestion warning unit is used for carrying out congestion prediction, identifying congestion road sections and congestion time periods; and the path guidance unit plans alternative guidance paths for each congestion road section based on a digital map, and selects an emergency guidance path from the alternative guidance paths. The application improves the accuracy of congestion prediction, and can provide more scientific and effective detour suggestions for vehicles.

Apparatuses, systems, and techniques for bounding area planning using a congestion prediction model. Placement data associated with cells of an IC design is identified. A graph based on the identified placement data is generated. The graph is provided as input to a machine learning model. The machine learning model is trained to predict, based on a given graph associated with cells according to a respective IC design, a congestion level for cells at one or more bounding areas of a respective IC design. Outputs of the machine learning model are obtained. The outputs include congestion data indicating a congestion level for a first bounding area of the IC design. Cells are designated for installation at a region, of the IC design, corresponding to the first bounding area.

We provide the system. [Solution] A means of acquiring multiple types of location data, A means for analyzing abnormal pedestrian flow using a congestion prediction model based on the aforementioned location data, A means for generating the optimal detour route based on predicted congestion, Means for notifying the mobile device's terminal of the aforementioned detour route, A means for an autonomous vehicle in motion to monitor changes in the situation in real time and update the aforementioned route, A means for receiving route data in real time in an autonomous vehicle and providing guidance using a display device and an audible device, A system that includes this.