15 results about "Queueing theory" patented technology

The invention relates to a large-throughput low-delay access and scheduling method for industrial internet platform equipment data, which comprises the following steps of: separately processing real-time data and historical data reported by edge equipment, and respectively processing the real-time data and the historical data through a real-time data processor and a historical data processor according to the data volume characteristics of the two types of data; and dividing different computing resources to the real-time data processor and the historical data processor. For a real-time data processor, a multi-stage feedback queue task scheduling mechanism is adopted, so that real-time data with smaller data packets can be processed preferentially, real-time data with larger data packets can be processed in time, and data loss caused by overlong waiting time of the smaller data packets is avoided. For a historical data processor, an M / M / C / infinity / infinity / FCFS queuing model in a queuing theory (stochastic service system theory) is adopted to dynamically adjust the size of computing resources, and it is ensured that when queuing is too long, historical data can be processed in time.

This invention provides a dynamic network routing optimization method and system based on deep learning prediction, comprising: constructing a graph network with heterogeneous nodes based on network topology and traffic; generating a large number of network traffic performance indicators based on queuing theory algorithms based on the heterogeneous graph information; pre-training a deep learning model based on the heterogeneous graph network and network traffic data with pseudo-labels; fine-tuning the model by combining a small amount of network traffic data with real labels; selecting a better-performing routing optimization scheme using the trained network prediction model; and a dynamic network routing system in which a global monitoring device calculates and distributes routing optimization results to different forwarding devices based on the current network topology, traffic data of different forwarding devices, and the performance prediction model. This invention uses weakly supervised learning and pre-training fine-tuning methods to build a reliable network performance prediction model with low data cost and designs a routing optimization method to meet real-time dynamic routing strategies.

The application discloses a kind of based on vacation queuing application service dynamic deployment and updating method.The method considers an edge computing system consisting of mobile device, edge server, cloud server, the key performance indicators of system are analyzed by queuing theory related model, deduce closed form expression including but not limited to edge server average delay, the probability that application is deployed on edge server, busy period probability and other related indexes, efficient dynamic application deployment and updating strategy is proposed.Optimization problem is constructed with the goal of minimizing average system delay, where storage space and energy consumption are constrained.The method described in the application constructs an interior point convex approximation optimization method to determine user task offloading probability, application response threshold, application waiting duration, edge server computing resource allocation, so as to minimize the average delay of the entire system.

This invention relates to the field of text processing technology and discloses a method for structuring unstructured information based on multi-agent systems. The method includes: outputting candidate data definition languages ​​and multi-table distribution samples from a generator, statistically projecting them to obtain discrete statistics; constructing a query plan cost phase transition surface based on the statistics, and calculating the steady-state probability of each plan using a Markov steady-state model combined with a plan caching mechanism; calculating the high-quantile tail delay using a queuing theory model, and solving for the second derivative of the tail delay with respect to the distribution perturbation in the minimum transport direction in Wasserstein space to obtain the optimal transmission bifurcation buffer congestion curvature; finally, updating the generator with minimizing this curvature as a constraint and outputting the table structure. This invention effectively reduces the risk of performance avalanche caused by data perturbations and significantly improves database robustness.

The invention relates to the technical field of text processing, and discloses a multi-agent-based unstructured information structured extraction method, which comprises the following steps of: outputting a candidate data definition language and a multi-table distribution sample through a generator, and carrying out statistical projection on the candidate data definition language and the multi-table distribution sample to obtain discrete statistics; constructing a query plan cost phase change surface based on the statistics, and calculating the steady-state probability of each plan in combination with a Markov steady-state model of a plan cache mechanism; calculating a high-quantile tail delay by using a queuing theory model, and solving a second derivative of the tail delay about distributed perturbation in the minimum carrying direction of the Warisstein space to obtain an optimal transmission bifurcation cache congestion curvature; and finally, a generator is updated by taking the minimum curvature as a constraint, and a table structure is output. According to the method, the performance avalanche risk caused by data perturbation can be effectively reduced, and the robustness of the database is remarkably improved.

This invention discloses an optimized scheduling method for electric vehicle (EV) charging based on queue theory. The EV charging process is divided into an input process, a queuing process, and a service process. When an EV connects to the power grid with a discrete Poisson probability distribution, it enters the queuing process. When the number of EVs connected to the grid is small or the grid load is low, EVs can charge directly without waiting. However, when the number of EVs connected to the grid is large or the grid load is at its peak, available grid power resources are limited, requiring queuing. This invention optimizes the queuing process, effectively balancing efficiency and fairness in the EV charging process, significantly reducing queue length and waiting time at charging stations.

The invention discloses a joint optimization method for DNN partition and resource allocation, which realizes collaborative reasoning and resource optimization of a deep neural network in an environment of a single edge server and a plurality of mobile devices, and comprises the following steps of: determining DNN hierarchy segmentation points of each device and the number of computing resource blocks and wireless resource blocks purchased from the edge server; decoupling modeling is performed on a DNN structure and an edge environment through a heterogeneous graph attention network, an optimal partition point and a resource deployment strategy are determined through Actor-Critic under the constraint of an M / M / 1 queuing theory model, and an edge-end cooperative reasoning scheme with price-demand-time delay closed-loop optimization is formed.

The invention discloses a server-free platform-oriented random workflow performance optimization method, which comprises a task random workflow sorting stage: according to a basic principle of a queuing theory model, performing arrival sorting on tasks of random workflows on the basis of an arrival sequence and a topological structure of the random workflows; in the instance parameter calculation stage, the blocking probability is calculated through an Erland B formula, the cold start probability is deduced, modeling is conducted through the semi-Markov process in the queuing theory, and performance indexes under the steady state condition are calculated through the parameters so as to evaluate the performance of the platform; and a system performance analysis stage: according to different task parameters, comprehensively analyzing the influence of the different task parameters on the performance of the function task, and based on the influence, giving out estimated resource overhead so as to realize cost minimization. According to the method, a processor distribution mode and constraint conditions are studied, and a demand processor combination prediction model is constructed by using a queuing theory model M / N / m / infinity for the problem of inaccurate prediction caused by strong uncertainty of a cloud center.

The invention relates to the technical field of edge computing and data distribution transmission, in particular to an information processing board card multi-channel data distribution transmission method based on edge computing. The system comprises a data portrait acquisition module, a queuing theory simulation module, a risk assessment module and a strategy optimization module. The system constructs prediction delay distribution by using a queuing theory simulation function by acquiring a static business portrait, predicting an arrival rate and a channel state; the core of the method is that a timeliness failure probability is calculated by combining prediction delay and a failure benchmark, and a quantitative task decision risk score is constructed; when the risk score exceeds a preset threshold value, the system starts a constraint optimization process, and generates and issues an optimal shunting strategy; according to the invention, through digital twinborn simulation, conversion from passive response to congestion to active prediction of delay distribution and risk decision is realized.

The application relates to the technical field of elevator module updating, and discloses an elevator updating control method and system based on AI, which comprises the following steps: obtaining an adjustment factor by prediction based on running state data, combining a pre-constructed waiting time model to calculate elevator waiting time, enabling the elevator service state to dynamically reflect running differences, dividing the elevator coverage area of a target elevator group through the elevator waiting time, evaluating a migration load value based on a queuing theory model, constructing a dynamic upgrading path graph, enhancing the identification capability of the task transfer capability difference between elevators, and finally determining an elevator upgrading sequence based on a graph search algorithm, taking minimizing the migration load value as the target, so that the upgrading tasks are distributed in the order of minimizing the overall system service influence when multiple elevator units need to be updated, thereby avoiding the problems of overload, task backlog or service blind area caused by improper scheduling.

The application relates to the field of airport management and aviation services, and discloses an airport check-in and consignment counter configuration planning method and device, which comprises the following steps: S1, collecting real-time or historical data to obtain a passenger arrival rate, a counter service rate and system limitation condition parameters; S2, constructing a queuing theory model based on the collected data, and preliminarily evaluating the check-in counter configuration through an M / M / c queuing model; and S3, using optimal control theory to establish a target function, optimizing the configuration scheme, and minimizing the comprehensive target among the queuing time, the passenger waiting time and the resource configuration cost. The dynamic counter configuration scheme based on the real-time data acquisition and feedback mechanism achieves the technical effect of dynamically adjusting the counter quantity during the peak period, thereby effectively avoiding the problem of long passenger queues during the peak period and reducing the resource waste during the trough period.

This invention relates to a reliable transmission method for RDMA cross-WAN transmission, comprising the following steps: Step 1: Detecting and obtaining the basic physical packet loss rate and the average duration of micro-burst packet loss of the WAN link; Step 2: Constructing a probability-based lower bound model for packet loss recovery theoretical requirements; Step 3: Constructing a nonlinear congestion penalty model based on queuing theory; Step 4: Multiplying the ideal number of redundant packets by the congestion resistance penalty coefficient to obtain a deterministic analytical solution for the theoretically optimal number of redundant packets; Step 5: Deriving the safe time interval during which redundant packets must be delayed; Step 6: After the sending end continuously sends k original data packets on the underlying physical link, time-domain interleaving is achieved; This scheme enables the redundant packets to be physically isolated from the original data stream on the time axis, effectively avoiding the long-tail coverage effect of micro-burst packet loss.

The embodiment of the invention provides a load space-time prediction method in a power distribution network-traffic network coupling system and related equipment, and belongs to the technical field of cooperation of a smart power grid and a traffic system. According to the method, firstly, based on an improved queuing theory model, user charging behavior randomness is quantified by using truncated Gaussian distribution, a complete estimation chain from dynamic traffic flow to charging load is established, and the load is fused to a power distribution network node; then, a spatio-temporal prediction model based on Mama is constructed, the model integrates a dynamic graph learning module, an LSTM module and a Mama module, and spatio-temporal evolution characteristics of the load can be captured at the same time; wherein the Mama module realizes efficient modeling of a long sequence with linear calculation complexity. The method effectively solves the problems that a traditional method is insufficient in description of uncertainty of user behaviors and the calculation complexity of a prediction model is too high, improves the precision and efficiency of load prediction in a coupling system, and is suitable for operation optimization and planning support of a large-scale power distribution-traffic network.

This invention discloses a site selection and capacity determination method for charging and battery swapping stations based on entropy weighting and queuing theory. By acquiring and processing multi-source spatial data to construct an object dataset, a candidate index set is formed, and a retained index set is obtained after redundancy detection. After defining upper and lower quantiles, proportional coefficients and annual entropy weights are calculated, combined with preset time smoothing weights to obtain smoothing weights. Based on the national electric vehicle ownership and average daily power consumption per vehicle, the national daily power demand is calculated, and the daily power demand for each station is derived. Waiting time and probability thresholds are set, the minimum station cost is calculated, and recommended station types and capacities are output. A candidate station set is constructed, and a comprehensive score is calculated. New stations are selected based on the comprehensive score to build a new station set. The annual and station-by-station power demand, station type, capacity, and new construction plans are integrated to generate an annual construction suggestion list and multi-scenario comparison results for rolling decision-making and scheme evaluation. This invention improves the stability, comparability, and applicability of annual station evaluation results.

This invention relates to a microservice and distributed database collaborative deployment system for edge computing networks, comprising: a database replica number dynamic optimization unit that obtains a dynamic optimization strategy for the number of database replicas based on a queuing-based gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit that calculates end-to-end latency based on microservice routing paths and database routing paths; calculates data inconsistency metrics based on the number of each database replica; and dynamically adjusts the deployment strategy based on resource constraints between end-to-end latency and data inconsistency metrics, as well as the dynamic optimization strategy for the number of database replicas. The deployment strategy includes: microservice instance deployment, database replica deployment, and optimal routing path selection. This invention provides a distributed database design and fine-grained collaborative deployment optimization method for MEC scenarios, integrating message queues, queuing theory, and the Canal database incremental update mechanism to improve microservice application performance and data query efficiency / reliability.