17 results about "Virtual queue" patented technology

The application discloses an edge resource allocation method and device based on long-term value fairness and overlapping coalition game, a medium and equipment, and belongs to the technical field of mobile edge computing and resource scheduling. The method builds a distributed crowdsourcing system architecture, establishes an ESP multi-dimensional resource capability, a dynamic task flow and a reputation evaluation model, models the resource collaboration problem as an overlapping coalition game, builds a value loss virtual queue, converts it into a single time slot optimization problem by using a Lyapunov drift plus penalty framework, and designs a distributed potential game algorithm guided by rights and interests to realize the collaborative optimization of system efficiency and long-term fairness.

The application discloses an edge intelligent body flexible scheduling method based on Lyapunov optimization and Stackelberg game. The application constructs a cloud-edge-end three-layer edge intelligent body federated learning network architecture to complete distributed collaborative training under the premise of protecting data privacy. In view of the problems of heterogeneous edge intelligent body network nodes, non-independent and identically distributed data and limited communication resources, a long-term joint optimization model of client selection and bandwidth allocation is established, and model accuracy, time delay and energy consumption are considered. Lyapunov optimization is used to decouple long-term random optimization into real-time decision-making per time slot, and to convert constraints into virtual queue stability control. Dynamic client selection is realized based on Stackelberg game, and high-quality nodes are adaptively selected. Adaptive bandwidth allocation is completed through an evolutionary algorithm, and communication requirements of key clients are preferentially guaranteed. The application can significantly improve model accuracy, reduce time delay and energy consumption, guarantee long-term stability of the system, and is suitable for high-dynamic edge intelligent body federated learning scenes.

The present application relates to the technical field of intelligent sorting and industrial process control in coal mine, and particularly relates to a coal gangue intelligent rejection control method based on asynchronous data flow compensation. The technical scheme comprises asynchronous data acquisition, time stamp virtual queue establishment, dynamic space-time mapping, rejection advance calculation, rejection control execution, end feedback measurement, error self-correction, and the asynchronous data acquisition acquires a first data flow and a second data flow respectively, the first data flow is the target category, target coordinates and corresponding time stamp output by an X-ray identification device, and the second data flow is the speed, displacement or beat information and corresponding time stamp output by a belt or cloth device running state detection device. The present application solves the technical problems of asynchronous data registration, space-time dislocation in identification and execution, and time-varying delay compensation of the execution chain in the coal gangue rejection control, and realizes stable, high-precision and self-correctable rejection control of the online coal gangue sorting equipment.

The invention discloses a radiation sensing multi-cell cooperative transmission method and system supporting sustainable URLLC service, and relates to the technical field of wireless communication, and the method comprises the following steps: constructing a system model; based on a system model, a long-term stochastic optimization problem oriented to multiple time slots is constructed by taking minimization of the long-term time average transmitting power of the system as a target; introducing a virtual queue to convert a long-term stochastic optimization problem into a single-slot deterministic optimization sub-model in each scheduling period; determining an incidence relation between each cellular user and the multi-antenna base station through centralized global cooperative matching; and each multi-antenna base station independently solves the self beam forming and subcarrier allocation strategy in a distributed mode, and dynamically and repeatedly executes in a continuous scheduling period to realize multi-cell cooperative data transmission. According to the method, cross-cell joint resource scheduling and long-term stability guarantee are realized aiming at the problems of strong burstiness, interference sensitivity and high centralized optimization cost of the URLLC service.

This invention relates to the field of traffic control technology, and particularly to a multi-vehicle cooperative intelligent parking scheduling method integrating V2X communication. The method acquires a real-time parking space occupancy grid map through roadside units and receives parking intention information sent by multiple vehicles waiting to park via V2X. It predicts the travel paths of each vehicle and their arrival times at the intersection of paths. When the estimated arrival time difference of at least three vehicles is less than a preset threshold, they are incorporated into a dynamic scheduling alliance. A virtual queue is generated for the dynamic scheduling alliance, and the spatiotemporal resources of the intersection area are sliced ​​to allocate exclusive passage time windows and speed curves to each vehicle. During the journey, the state is shared in real-time via V2V, and the vehicle speed is adjusted based on dynamic priority to achieve cooperative passage through the intersection area without stopping. After passing through the intersection area, the alliance is disbanded, and each vehicle completes parking. This invention effectively eliminates conflicts at path intersection points and improves parking lot traffic efficiency through multi-vehicle cooperative scheduling.

The application belongs to the technical field of cross-linked polyethylene insulated cables, and particularly relates to a copper core cross-linked polyethylene insulated cable multi-layer twisting control method and system, which comprises the following steps: collecting multi-source heterogeneous data of key stations of a twisting machine, using a virtual queue mapping mechanism to perform space-time alignment on the data collected at different physical positions; based on a material work hardening mechanism and a thermal softening effect, a thermal hardening coupled cumulative factor is constructed, and a dynamic springback correction coefficient is constructed in combination with a die wear trend; the above features are input into a random forest model to predict the springback increment of a conductor, and the traction tension and the twisting speed are adjusted in a closed loop according to the prediction; and the model failure drift index is used to monitor the prediction residual, and the model is triggered to adaptively update. Through the fusion of physical mechanism features and machine learning, the application realizes adaptive and robust control in the whole life cycle, and eliminates the hidden danger of insulation eccentricity.

A system includes a virtual queue controller configured to receive an indication of a reduced capacity event from an amusement park attraction, determine a reduction in capacity of the attraction, identify each guest having a return time in a virtual queue of the attraction that is affected by the reduced capacity event, remove guests having affected return times from the virtual queue, and generate a reaccommodation time slot for the guests removed from the virtual queue, select two or more updated return times within the reaccommodation time slot for each of the guests removed from the virtual queue, provide a notification to each guest removed from the virtual queue requesting guest input to select a single updated return time from the two or more updated return times, and return each guest to the virtual queue upon receiving a corresponding selection of the single updated return time.

The application discloses a one-stop large model intelligent agent development operation and maintenance platform fusing computing power scheduling and model management, relates to the technical field of artificial intelligence, and comprises: a heterogeneous computing power resource pool containing multiple inference nodes and generating a node-level computing power state summary record; a computing power scheduling control node used for collecting the node-level computing power state summary records of all the inference nodes to obtain a global computing power state table, and taking the difference between a scheduling throughput component and a queue depth value punishment component of a resource constraint virtual queue as an instant reward signal; and a self-adaptive shape-preserving prediction confidence test module used for selecting a task distribution scheme to be issued or executing a deterministic rollback scheduling based on the global computing power state table according to whether a confidence interval radius exceeds a preset radius threshold. The application can effectively relieve a memory bandwidth bottleneck in an ultra-long context inference scene and improve the adaptive response capability of scheduling decisions to real-time load changes and environmental drift.

The application discloses a kind of highway micro-grid dynamic energy control instruction generation and control method, comprising: according to the environmental data and traffic flow data of real-time acquisition, respectively through photovoltaic output model and turbulent wind power output model calculation photovoltaic output data and turbulent wind power output data.Combining traffic flow data, photovoltaic output data, turbulent wind power output data and energy state data, update the feasible region of each partition current control period.Combined with traffic flow data and energy state data, the objective function is solved by rolling optimization based on the feasible region, and the control instruction of highway micro-grid in current control period is obtained.Traffic flow is embedded in energy scheduling state space, and traffic flow-energy double-state vector is formed for highway micro-grid control decision, which realizes the deep coupling of traffic information and energy decision, and adopts the adaptive dynamic penalty coefficient mechanism based on virtual queue backlog, which overcomes the defects of fixed weight method priority rigidity and insufficient robustness.

The virtual queue system includes a virtual queue controller comprising a processor and a memory, wherein the memory stores instructions executable by the processor and is configured to receive a request, the request being associated with an individual guest, for a position in a virtual queue of an attraction, assign the individual guest to the position in the virtual queue in response to the request, receive ride schedule data for the attraction comprising information about a change in status of individual rides of the attraction, and determine a wait time for the individual guest for the attraction based at least on the position of the individual guest in the virtual queue, the ride schedule data, and historical guest throughput at the attraction. The virtual queue system is further configured to output a signal to a guest-associated device indicating the wait time for the attraction.

The system according to the present embodiment includes a plurality of virtual queue stations (530) associated with respective attractions within a theme park, and a virtual queue control system (502) configured to maintain respective virtual queues for the respective attractions and to communicate with the plurality of virtual queue stations (530). The virtual queue control system (502) is configured to receive communications from the plurality of virtual queue stations (530) and to add guests to the respective virtual queues based on the communications.

The application discloses a kind of based on the scheduling domain delineation method of micro-grid gateway node of micro-grid planning, steps include: obtaining the operating parameter of micro-grid and grid topology structure parameter, establishes the micro-grid operation model with the minimum operating cost as target;For the energy storage constraint of micro-grid operation model, time is decoupled;Virtual queue of energy storage device is constructed, and energy storage constraint after decoupling is relaxed as stability problem of virtual queue and is solved using Lyapunov function, and the solution result is added to objective function to obtain decoupled micro-grid operation model and be expressed as compact form multi-parameter programming model with gateway node power as parameter, and the critical domain of gateway node and the segmented analytical function of scheduling cost are obtained by using multi-parameter programming method to solve the multi-parameter programming model;According to segmented analytical function, the visualized graphics of micro-grid gateway node scheduling domain is drawn in parameter feasible region.The application can efficiently and accurately delineate the feasible operation range and economic characteristics of micro-grid.

A controller for performing command scheduling, a storage device including the same, and an operating method of the same are provided. The storage device communicates with a host device that generates an original data packet. The storage device includes a memory device and a controller, the memory device including a die having a plurality of planes. The controller includes an operation checker that receives the original data packet indicating a simultaneous parallel work request occupying at least two planes among the plurality of planes, wherein the operation checker sets a simultaneous parallel identifier associated with the original data packet to generate a modified data packet, an operation allocator that stores the modified data packet in at least one virtual output queue in a virtual queue cluster, and a scheduler that transmits the modified data packet to at least one plane occupied by the simultaneous parallel work request among the plurality of planes according to a scheduling policy.

This application relates to a collaborative charging scheduling method and system for heterogeneous unmanned platforms. Based on a charging behavior model constructed from the charging data of each unmanned platform, a charging feature vector is determined for each platform, including charging urgency and charging duration. Based on these feature vectors, multiple unmanned platforms are divided into at least one cluster. A scheduling objective function is set to minimize the time-averaged error between the actual total load of the cluster and the grid reference load. Simultaneously, a first virtual queue representing the backlog of charging demand and a second virtual queue representing priority based on charging urgency are constructed. Based on the first and second virtual queues, the scheduling objective function, and the Lyapunov drift penalty algorithm, the charging scheduling strategy for the current scheduling slot is solved. Thus, this application achieves real-time tracking of the grid reference load without requiring precise prediction of future charging demand and load changes, while prioritizing the charging needs of unmanned platforms with high urgency.