17 results about "Queuing network" patented technology

The invention discloses an unmanned aerial vehicle complex airspace cooperative passage method based on a graph neural network, and the method comprises the steps: collecting the airspace data of an unmanned aerial vehicle complex airspace, and generating standardized airspace data; constructing a mixed queuing network, and forming arrival, queue length and service capability state of each service node and service link; mapping to obtain graph nodes, graph edges and graph features, and constructing an airspace graph; inputting the airspace graph into the graph neural network, and carrying out constraint updating on the mixed queuing network; integrating the service capability state, the queue length state and the congestion propagation parameters to form a network passing cost relationship; and executing a Frank-Wolfe algorithm, and generating a cooperative passing instruction for each unmanned aerial vehicle. According to the invention, by introducing the mixed queuing network and the Frank-Wolfe algorithm, efficient and stable cooperative passage scheduling of multiple unmanned aerial vehicles in a complex airspace environment is realized.

The invention provides a performance evaluation method of a four-way shuttle robot system based on path optimization, which comprises the following steps of: modeling path nodes in a two-dimensional coordinate system, and on the basis of considering the motion characteristics of acceleration, deceleration and the like during operation of a shuttle robot, obtaining a path with shortest operation time of the shuttle robot by improving a Dijkstra algorithm; on the basis, a semi-open-loop queuing network model considering path optimization is constructed, and system performance indexes including a task period, an equipment utilization rate and throughput are solved by adopting an AMVA algorithm. Simulation verification shows that the method is high in calculation precision and excellent in efficiency; sensitivity analysis further reveals an influence rule of structural parameters such as the number of roadways, the number of columns and the number of layers on system performance, and indicates that when the roadways are close to the number of columns and the number of layers is adaptive to the total quantity of goods positions, the throughput capacity of the system is optimal, and a quantitative basis is provided for storage system configuration.

The invention discloses a collaborative environment-oriented service deployment and request scheduling optimization method and device, and relates to the technical field of edge-cloud collaborative mobile edge computing in the power industry, and the method comprises the steps: constructing a multi-instance queuing network model and a multi-dimensional hierarchical soft actor-commentator algorithm; based on the multi-instance queuing network model, modeling a multi-instance execution relationship in a power station task processing process, and providing a performance evaluation basis for a request scheduling strategy by quantifying task transmission delay, queuing delay and calculation delay; and based on the multi-dimensional hierarchical soft actor-commentator algorithm and in combination with the current state of the power information system, determining the deployment number and distribution position of each micro-service instance, and formulating a request scheduling strategy. According to the method, the dependency relationship and the multiplexing characteristic between the micro-services are fully considered, the request response delay can be effectively reduced, the network load and the system energy consumption can be balanced, and the service quality and the resource utilization efficiency of the electric power station are improved.

A spatial-temporal predictive analytics system and method for time-series generation, forecasting, and predicting future resource needs across multiples forecast timescales and multiple physical locations. Examples of the system and method use a tiered integration approach to integrate one or more queuing structures with a generative artificial intelligence engine based on a given forecast timescale. The generative AI engine captures the highly complex, nonlinear spatial-temporal correlations often present in time-series data. Queuing network containing the queuing structures captures the underlying physical system dynamics and, at the various integration tiers, refines to varying degrees the generative AI engine with enhanced generation and prediction power, explainability, and interpolation. This tiered integration approach utilizes the strengths of both the generative AI engine and queuing theory and adapts predictions to ensure that the most relevant model drives the forecasting process at the various forecast timescales.

Preventive medical services are crucial as they can intervene in a timely manner at the early stage of diseases, saving a large amount of medical expenditure and enhancing social welfare. Recognizing the importance of preventive medical services, the present invention proposes a software for the design of a preventive medical facility network considering the congestion effect to maximize the total social utility. Due to the two-level decision-making structure between the system manager and facility users, the present invention constructs a two-level non-linear integer programming model. The upper level is the problem of medical facility location and service capacity planning under budget constraints, while the lower level is the user choice equilibrium problem considering the congestion effect. To solve this two-level programming model, the present invention uses the genetic algorithm (GA) to solve the upper-level problem and the method of successive averages (MSA) to solve the lower-level problem. The software is developed using the free and open-source R language. Experiments show that the software can be used for the design of a preventive medical facility network under budget constraints and can also provide decision-making references for budget preparation.

The invention provides an intelligent collaborative workshop equipment configuration optimization method based on a queuing network, and relates to the technical field of resource configuration optimization. The method comprises the following steps: firstly, decomposing each device in an intelligent collaborative workshop into a plurality of types of nodes, describing the operation process of each type of nodes as a transfer process among different states, and respectively calculating the state transfer rates of the AGV and the mechanical arm unit nodes in the transfer process, so as to construct and solve a state transfer balance equation set of the AGV and the mechanical arm unit nodes; obtaining an average production cycle and an average output rate of the intelligent collaborative workshop; constructing a multi-resource equipment collaborative configuration optimization model, and obtaining an intelligent collaborative workshop equipment configuration optimization result by solving the multi-resource equipment collaborative configuration optimization model; according to the method, the configuration of the mechanical arm of the intelligent workshop, the configuration of the AGV, the production cycle and the output rate are brought into the optimization model for collaborative optimization by constructing and solving the multi-resource equipment collaborative configuration optimization model, so that the processing efficiency and the productivity of the intelligent workshop are improved.

The invention provides an urban rail transit station passenger flow simulation method and system based on a queuing network, and the method comprises the steps: obtaining the related information of an urban rail transit station, carrying out the passenger demand analysis based on the related information, and obtaining a passenger demand result; establishing an urban rail transit station passenger flow simulation system, and constructing a station facility instance, a facility network and a passenger instance in the passenger flow simulation system based on the related information and the passenger demand result; and according to the station facility instance, the facility network and the passenger instance, simulating the flow of passengers among the urban rail transit station facilities in the passenger flow simulation system. The invention provides an urban rail transit station passenger flow multi-agent simulation method based on a queuing network, which is used for providing a guidance basis for operation management of urban rail transit so as to improve the service level of the urban rail transit.

The present invention discloses an optimization method for a human-machine collaborative picking system, comprising the following steps: S1. Modeling the three human-machine collaborative picking modes ("bundling," "picker-led," and "robot-led") based on a semi-open queuing network embedded in a Fork-Join queuing network to construct a queuing network model; S2. Based on the queuing network model, converting the Fork-Join queuing network into a load-based composite node; S3. Obtaining an analytical solution for performance indicators based on the composite node; and S4. Optimizing the picking system layout based on this. The present invention can effectively predict the performance of a human-machine collaborative picking system and help managers better optimize, select, and make scientific decisions about the human-machine collaborative picking system, thereby improving work efficiency and reducing operating costs.

1. The name of the product under this design: Cash register (queuing network S300). 2. Purpose of this design product: Shenzhen Pai Dui Network Technology Co., Ltd. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention provides an AGV configuration optimization method and system based on a queuing network, and relates to the technical field of resource configuration optimization. The method comprises the following steps: firstly, constructing a queuing network, decomposing each intelligent manufacturing unit in an intelligent manufacturing workshop into a plurality of types of nodes, decomposing the motion process of each AGV node into transfer processes of different state spaces, obtaining an AGV node state transfer rate, constructing and solving an AGV node state transfer balance equation set, and obtaining performance indexes of the intelligent manufacturing workshop; and based on the performance indexes, constructing an AGV number configuration optimization model, and solving the AGV number configuration optimization model by using a particle swarm algorithm to obtain an optimization result. The AGV configuration optimization problem can be expanded to a multi-layer workshop or large-scale flexible operation scene through establishment of the queuing network, the expansibility and adaptability of the method are improved, a state space decomposition method is introduced, and the modeling complexity and the calculation complexity are reduced.

The application discloses a battery swap station optimization scheduling method and system, relates to the technical field of electric vehicle battery swap station operation optimization, and clearly defines the decision relationship between a power distribution network operator and a battery swap station operator through a two-stage stochastic programming model, provides a theoretical framework for the coordination and interaction between the two, can comprehensively consider the mixed queuing network service characteristics, demand uncertainty and service quality constraints, and realizes a computable robust optimal scheduling through a physical information neural network model, so that an effective battery swap station scheduling method which can effectively cope with demand uncertainty and collaboratively optimize economic benefits and service quality is provided.

The invention discloses a mixed aggregation mode federated learning optimization method based on a queuing network. The method comprises the following steps: firstly, constructing a federated learning model; k clients are selected to participate in the current round of training, and the average time needed by the selected clients to complete local training in the synchronous aggregation mode is calculated; calculating the average time required for completing local training by the selected client in the asynchronous aggregation mode by using an approximation algorithm of a closed queuing network theory; and comparing the average time in the synchronous and asynchronous aggregation modes, if the former is not greater than the latter and the number of completed training rounds is greater than 80% of the preset total number of training rounds, selecting the synchronous aggregation mode to carry out the training round, otherwise, selecting the asynchronous aggregation mode. And repeating the steps until a preset total training round number is reached. According to the method, under the reasonable communication overhead, the convergence speed and training precision of the global model are effectively improved, meanwhile, the calculation overhead is small, the result is accurate, and the method has wide universality and applicability.

This invention relates to a performance evaluation method for robotic sorting systems that considers regional congestion control. This method divides the system's grids into zones, controls the number of robots admitted to each zone, and establishes a queuing network model for the robotic sorting system. This queuing network model is then analyzed to quickly and accurately determine the throughput of a system of a given scale. This proposed method for evaluating robotic sorting system performance based on regional congestion control provides a new theoretical approach and practical tool for evaluating the operational efficiency of complex robotic sorting systems, ensuring that the design of robotic sorting systems meets practical application requirements.

This invention provides a parallel I / O write consistency guarantee method based on state preheating, belonging to the field of computer technology. This invention collects the I / O operation timestamp sequence of MPI processes and the inter-process communication topology, and uses a risk assessment model fused with temporal causal convolution and graph attention to output risk scores for each variable's write operation. It then uses an adaptive preheating timing estimation algorithm based on a queuing network to calculate the expected completion time variance of the I / O path, determining whether to trigger the preheating process. Finally, it uses a preheating intensity adjustment function to fuse the risk scores, MPI process size, and historical write success rate, triggering policy upgrades and retrying when deviations are detected. The execution results are updated with reinforcement learning reward signals to update the parameters of the artificial intelligence model and the weights of the policy decision model, achieving closed-loop adaptive adjustment. This solves the technical problem that parallel I / O write consistency guarantee mechanisms cannot adaptively adjust the synchronization strategy strength according to the real-time system load status and the risk of abnormal propagation between processes.

The present invention discloses a method for estimating the cycle time of a robotic warehouse based on a dynamic priority queuing network. The steps include: S1. Establish a coordinate system for the robotic warehouse system; S2. Calculate the travel time of the autonomous transfer robot from the stopping point to the target shelf; S3. Calculate the travel time of the autonomous transfer robot from the target shelf to the picking wall; S4. Calculate the picking time of the autonomous transfer robot; S5. Calculate the throughput rate of the queuing network and the expected waiting time for the autonomous transfer robot to transport the container to the front of the picking wall for unloading based on the approximate average value estimation method; S6. Calculate the total time for the automatic transfer robot to serve one order; S7. Synchronous steady-state analysis; S8. Analysis of the expected waiting time of the dynamic priority queue; S9. Calculate the cycle time of an order of category k. Through the above steps, the present invention can be effectively used to evaluate the cycle time of different types of orders in the robotic warehouse system.

The invention discloses a heterogeneous robot intelligent warehousing system and a layout design optimization method thereof, and the method comprises the steps: building a Fork-Join queuing network model, determining the service time, calculating system performance indexes, carrying out the error analysis, and combining different robot stay point strategies, charging region layout strategies and robot configuration proportions, and comparing and identifying the parameter combination with the optimal system operation performance. The system comprises a transportation robot, a table type platform, a picking robot, a work station, a charging area and a control unit for executing the optimization method. The method has the advantages of being accurate in modeling, high in adaptability and high in decision-making performance, and a theoretical basis and practical guidance are provided for design and scheduling optimization of the intelligent warehousing system.

The invention discloses an optimal dispatching method and system for a battery swap station, and relates to the technical field of operation optimization of electric vehicle battery swap stations. The decision relation between a power distribution network operator and a battery swap station operator is clearly defined through a two-stage stochastic programming model, and a theoretical framework is provided for coordination interaction between the power distribution network operator and the battery swap station operator; service characteristics, demand uncertainty and service quality constraints of the mixed queuing network can be comprehensively considered, and computable robust optimal scheduling can be realized through a physical information neural network model; therefore, the battery swap station scheduling method which can effectively deal with the demand uncertainty and collaboratively optimize the economic benefit and the service quality is provided.