45 results about "Queue time" patented technology

The invention discloses an electric vehicle man-machine cooperative scheduling strategy for multiple scenes of an electric power traffic coupling network, and aims to solve the problem of electric vehicle charging optimization scheduling caused by deep coupling of an electric power system and a traffic system in different scenes. The method comprises the steps that firstly, topological information and behavior characteristics are fused through a graph generative adversarial network, a graph structured model is constructed, and an electric power traffic coupling operation scene is generated; secondly, establishing a multi-objective optimization mechanism by utilizing hierarchical reinforcement learning, constructing an electric vehicle charging optimization scheduling finite Markov decision model in a conventional scene and a fault scene, and designing an algorithm based on knowledge distillation to solve a scheduling strategy; and finally, realizing strategy migration of charging redistribution and path emergency adjustment in a fault scene by combining a man-machine cooperative regulation and control technology and fusing a user instruction. Experimental results show that the strategy can effectively improve the toughness of the power grid, relieve traffic congestion, reduce charging queuing time and increase user satisfaction.

The invention provides a network communication device and method with a dynamic packet transmission sorting mechanism. The packet classification circuit sets each low-latency packet as a selected low-latency packet for storage in a corresponding selected low-latency queue circuit. The transmission sorting circuit calculates a queue time parameter for the selected low-delay packet to sort the selected low-delay packet with other low-delay packets so as to update a sorting list. Wherein the queue time parameter is the proportion of the total queue time of the selected low-delay packet stored in the selected low-delay queue circuit exceeding the dynamic service time threshold value set corresponding to the selected low-delay queue circuit. The communication circuit sequentially transmits the low-latency packets according to the sorting list. The dynamic adjustment circuit calculates the difference between the total packet transmission time of the selected low-delay packet and the target delay threshold value so as to dynamically adjust the dynamic service time threshold value according to the difference.

The embodiment of the invention discloses a congestion control method and system for real-time audio and video transmission of a cloud terminal. Comprising the following steps: a receiving end accurately predicts a bandwidth and a queue time delay gradient by using an improved Kalman filter which stably reduces gain by adjusting a process noise covariance matrix in stages, and determines a first target sending rate candidate according to the bandwidth and the queue time delay gradient; the sending end determines a second target sending rate candidate based on the packet loss rate, and performs a big or small decision on the two candidate rates according to the level of the packet loss rate so as to determine a final sending rate; in addition, when the packet loss rate exceeds a threshold value, the source end data flow is actively interrupted to prevent user misoperation. According to the method, the bandwidth prediction precision is improved, misjudgment speed reduction when the network condition is good is avoided, and the user experience in a severe network is improved.

The present application relates to the technical field of film production, and specifically relates to a film production control method and system, comprising receiving film production work order information; determining a production line node according to the film production work order information; configuring an initial moving path of a tray according to the production line node; constructing a first operation time prediction model of an operating device of the first production line node according to historical operation data, and obtaining a first operation prediction time according to the first operation time prediction model; obtaining a production delay rate of the first production line node according to the first operation prediction time, a moving time and a queuing time; adjusting the initial moving path of the tray according to the production delay rates of all production line nodes to obtain a dynamic moving path; and controlling finished product roll production according to the dynamic moving path. The method and system of the present application guide the movement of finished product rolls based on the dynamic moving path, adapt to changes in production line load, improve production efficiency, and ensure orderly and efficient operation of large-batch and multi-specification production.

The invention is suitable for the technical field of experimental equipment management, and provides a chemical laboratory equipment full-process management method and system, and the method comprises the following steps: receiving an experimental equipment application form, and analyzing the experimental equipment application form to determine an experimental project, an applicant, an application equipment sequence and an emergency degree; an audit passing instruction is received, and the corresponding experimental equipment application form enters a queuing mode; calling all the experimental equipment application forms at intervals of set time values, and calculating the earliest available time of all the experimental equipment in the laboratory; calculating the time concentration degree of application equipment corresponding to each experimental equipment application form according to the earliest available time; and calling the queuing time length and the emergency degree of each experimental equipment application form, and carrying out experimental equipment distribution on the experimental equipment application forms based on the time concentration degree, the queuing time length and the emergency degree. According to the method, factors of time concentration, queuing duration and urgency are comprehensively considered, and quantitative balance of efficiency and fairness is realized through dynamic adjustment.

The application discloses a kind of operation vehicle in train station waiting to promote the efficiency of scheduling method and system, it is related to intelligent traffic scheduling technical field, including, when getting into roundabout queue, entrance vehicle license plate number is obtained;Vehicle state and reservation information are determined according to license plate number, whether vehicle enters express channel is judged, vehicle queuing time is recorded, and shunt decision is generated and judged;The position and queue time of all roundabout queue vehicles are obtained, and the integral starting threshold and the integral demand of entering express channel are calculated;After the waiting time of online car-hailing exceeds the integral starting threshold, passenger gets on, map service is called to determine passenger destination, and the obtained integral is calculated;After driver submits express channel reservation application, application verification is carried out, and the next day parameter optimization is carried out after a day operation;Abnormal operation is classified, and maintenance personnel are linked to carry out offline processing.The application effectively improves the efficiency of train station operation vehicle waiting.

The application relates to the technical field of computing power scheduling, and discloses a data processing computing power scheduling method based on an intelligent agent, which comprises the following steps: acquiring basic data and real-time running data of each computing power device in an operation cluster, and acquiring real-time characteristic elements of each computing power device according to the basic data and the real-time running data; identifying each task based on the intelligent agent, acquiring task characteristic elements and priorities, determining a processing sequence according to the queue time and the priorities of current tasks and assigned tasks; and performing coincident matching according to the task characteristic elements of each task and the real-time characteristic elements of each computing power device, and assigning corresponding computing power devices to each task according to the coincident matching result. The real-time characteristic elements of each computing power device are acquired according to the basic data and the real-time running data of each computing power device, so that the computing power tasks and the computing power devices are accurately matched, and the performance optimization and the cost optimization of the computing power scheduling are realized.

The invention relates to a self-adaptive capacity expansion and contraction system and method based on deep reinforcement learning and sequence prediction. The method comprises the following steps of: constructing a queue; recording a generalized queue length of a request by adopting the queue; acquiring a time-based request arrival number; preprocessing the generalized queue length of the request, a GPU utilization rate, a service response delay and request queuing time to generate a high-dimensional multi-dimensional state vector; the method comprises the following steps: performing modeling on a high-dimensional and multi-dimensional state vector of a historical load sequence by adopting a Transform model, predicting a future load trend, constructing a deep reinforcement learning environment based on a prediction result of the future load trend and a current system state, and performing construction of a capacity expansion and contraction strategy model and optimization of a capacity expansion and contraction strategy by adopting a PPO algorithm; a system supply and demand balance state is quantified through a generalized queue length, the limitation that a traditional queue can only reflect request backlog is solved, and a closed loop is formed through prediction, decision making, feedback and re-prediction.

A method for managing a queue in a contact center that includes: receiving a previous call from a first customer and determining a unique identifier; tracking a previous queue time; detecting the previous call ending without being connected to an agent; determining the previous wait time accrued; saving in a database a previous call record that includes the previous queue time and the unique identifier; receiving a present call from the first customer and determining a unique identifier; tracking a present queue time; using the unique identifier to check the database to determine the related previous call record; determining the previous queue time; calculating an adjusted total queue time that is the present queue time plus previous queue time; and positioning the first customer in the queue based on the adjusted total queue time.

The application relates to the fields of CAE simulation technology and distributed architecture, and discloses a service-oriented CAE simulation platform micro-service architecture construction method and system, which comprises the following steps: constructing a micro-service architecture, wherein the micro-service architecture comprises a core micro-service cluster, a unified service bus, a dynamic resource scheduling center and a distributed data layer; data generated by each micro-service is stored through the distributed data layer; the distributed data layer cuts unstructured data into data shards and stores the data shards in the form of multiple copies into an object storage cluster, stores structured data into a relational database, stores business metadata into a cache cluster, listens to change logs of the relational database by using a change data capture technology, and synchronizes change events to the cache cluster. The traditional monomer platform needs 3 hours to complete the same task, the application only needs 1 hour and 30 minutes, the efficiency is improved by 50%, and the task queuing time is shortened from 1 hour to 10 minutes.

Scheduling requests of multiple processing-in-memory threads and requests of multiple non-processing-in-memory threads is described. In accordance with the described techniques, a memory controller receives a plurality of processing-in-memory threads and a plurality of non-processing-in-memory threads from a host. The memory controller schedules an order of execution for requests of the plurality of processing-in-memory threads and requests of the plurality of non-processing-in-memory threads based on a priority associated with each of the requests and a current operating mode of the system. Requests are maintained in queues at the memory controller and are individually assigned a priority level based on time enqueued at the memory controller. Requests of a different mode than a current operating mode of the system are delayed for scheduling until at least one different mode request is escalated to a maximum priority value, at which point the memory controller initiates a system mode switch.

When a queue time satisfies a queuing threshold for a facility (e.g., a lounge, a venue, merchant location, any defined area, etc.), a notification of the queue time may be sent to a user device. A user interface of the user device may be used to provide credential information, and based on the credential information, display an indication of a digital instrument. A selection of the digital instrument may cause the user device to display a requirement for accessing the facility. Based on a confirmation of the requirement for accessing the facility and an indication that the queue time is less than the queuing threshold, the user device may display image data (e.g., a barcode, a matrix barcode, a quick response (QR) code, Radio Frequency Identification (RFID) tag, a marker, etc.) encoded with an access identifier that enables access to the facility.

The invention relates to the technical field of computing power scheduling, and discloses a data processing computing power scheduling method based on an intelligent agent, and the method comprises the steps: obtaining basic data and real-time operation data of each computing power device in an operation cluster, and obtaining a real-time feature element of each computing power device according to the basic data and the real-time operation data; identifying each task based on an intelligent agent, and obtaining task feature elements and priorities; determining a processing sequence according to the queue time and priority of the current task and the allocated tasks; and according to the task feature element of each task and the real-time feature element of each computing power device, carrying out coincidence matching, and according to a coincidence matching result, allocating a corresponding computing power device to each task. According to the method, the real-time characteristic elements of each computing power device are acquired according to the basic data and the real-time operation data of each computing power device, so that the computing power task is accurately matched with the computing power device, and performance optimization and cost optimization of computing power scheduling are realized.

In a method of processing a substrate having a copper line in the substrate wherein an Ru metal cap layer is formed on a copper line disposed in the substrate and an ashless carbon oxygen barrier layer is deposited on the Ru metal cap layer to provide a substrate having a protected capped copper line. The substrate having the protected capped copper line is queued before carrying out subsequent process steps, and the ashless carbon oxygen barrier layer is removed before carrying out subsequent process steps. Protecting the capped copper line in this manner provides greater flexibility in managing substrate processing operations by permitting longer queue times before subsequent substrate processing steps are carried out, without fear that the copper line will be adversely affected by oxidation during the queue.

A method of streaming data elements from an industrial site to a remote server, including: receiving data elements from industrial devices on the industrial site, storing the data elements in a queue, periodically extracting a number N of data elements from the queue and aggregating the same into one data frame whose size does not exceed a maximum size, storing each data frame in a buffer, and periodically extracting a data frame from the buffer and sending the same to the remote server. A data element is removed from the queue and stored in a database if the data element's time in the queue exceeds a maximum queuing time. A data frame is stored in the database instead of being stored in the buffer if the buffer is full. The maximum queuing time and the number N are then optimised.

The application discloses a task processing method based on a quantum-classical hybrid architecture, a server for the quantum-classical hybrid architecture and a task processing system based on the quantum-classical hybrid architecture. The method comprises the following steps: establishing a session connection with a client through a preset communication protocol, and setting a session identifier; receiving a first task processing request sent by the client; determining session computing power resources according to the first task processing request; associating the session computing power resources with the session identifier; and processing the first task processing request according to the association between the session identifier and the session computing power resources to obtain a first task processing result. In this way, the session computing power resources are associated with the set session identifier, so that the session computing power resources cannot be used by other clients before the session connection is ended, and are only used for processing the task request sent by the client, thereby reducing the queuing time of the to-be-processed tasks and improving the overall efficiency of the quantum-classical hybrid architecture.

When a queue time satisfies a queuing threshold for a facility (e.g., a lounge, a venue, merchant location, any defined area, etc.), a notification of the queue time may be sent to a user device. A user interface of the user device may be used to provide credential information, and based on the credential information, display an indication of a digital instrument. A selection of the digital instrument may cause the user device to display a requirement for accessing the facility. Based on a confirmation of the requirement for accessing the facility and an indication that the queue time is less than the queuing threshold, the user device may display image data (e.g., a barcode, a matrix barcode, a quick response (QR) code, Radio Frequency Identification (RFID) tag, a marker, etc.) encoded with an access identifier that enables access to the facility.

Embodiments of the invention provide an MPP cluster exception handling method and apparatus, and a storage medium. The method comprises the steps of obtaining a to-be-executed job; when the to-be-executed jobs are dominant faults, whether the queuing time of the to-be-executed jobs exceeds a first threshold value or not is judged, and if the queuing time of the to-be-executed jobs exceeds the first threshold value, the jobs are submitted and removed out of a to-be-distributed job queue; if the first threshold value is not exceeded, the to-be-executed job is queued continuously; when the to-be-executed job is a hidden fault, whether the queuing time of the to-be-executed job exceeds a second threshold value or not is judged, and if the queuing time of the to-be-executed job exceeds the second threshold value, the to-be-executed job is submitted and removed out of the queuing queue; if the number does not exceed a second threshold value, judging the number of job instances which are associated with the metadata cluster in the to-be-executed job and are in operation, and if the number of the job instances does not exceed a third threshold value, submitting the to-be-executed job and removing the to-be-distributed job queue; and if so, continuing to queue the to-be-executed jobs. According to the method, invalid scheduling submission is reduced, and the automation level is improved.

The invention discloses a deadline-oriented mobile edge computing task joint scheduling and unloading method, and relates to the technical field of mobile edge computing. In a system comprising a plurality of terminals and edge servers, the method is realized through end-side cooperation: each edge server maintains a queue in an ascending order according to absolute deadline, operates a maintenance algorithm, and deletes a task with the maximum processing duration when accumulated processing time is overtime, so that a feasible execution sequence with the minimum overtime task number is maintained; the server periodically broadcasts queue state summary information containing queue head task absolute deadline and processing duration to the terminal; the terminal carries out feasibility judgment and queuing time estimation on each server through a simulation insertion and early stop mechanism based on the information; and the terminal screens a feasible server set, selects the server with the minimum total experience as an unloading target according to a predefined cost function and a lexicographical order optimization principle, and completes a decision in combination with a jitter suppression strategy.

The embodiment of the invention relates to a model scheduling method and device, equipment and a medium, and the method is applied to a model scheduling server, and comprises the steps: obtaining at least one first model node address in an idle state in response to obtaining a model calculation request sent by a model user; performing occupation operation on the at least one first model node address by using the centralized memory to obtain a second model node address which is successfully occupied; sending a model calculation request to a corresponding target model node based on the second model node address, so that the target model node calculates through a target model in the graphics processor to obtain a calculation result; and obtaining a calculation result, and returning the calculation result to the model user. According to the embodiment of the invention, the request is processed by the model in the idle model node as far as possible, the queuing time of the request is reduced, the fluctuation of response time sensed by a model user is reduced, and the load fluctuation of graphics processors of different model nodes is further reduced.

This invention discloses a method, apparatus, and system for determining queuing time. The invention divides a battery swapping station into sequentially connected vehicle entry, battery swapping, and vehicle exit areas. Probe components are deployed in each area of ​​the battery swapping station. A central control device acquires vehicle data collected by multiple probe components. Based on this vehicle data, it determines the passing vehicle records corresponding to the vehicle entry, battery swapping, and vehicle exit areas. Upon receiving a queuing time query request, it determines and feeds back the target queuing time based on the passing vehicle records. The passing vehicle records are used to record the vehicle identifiers of battery swapping vehicles located in the corresponding battery swapping station areas at different times. Therefore, this invention can provide queuing time query services without requiring data reporting from the user side.

The application provides a task load balancing scheduling method and device based on big data, which acquires the task load state on the big data platform in real time, constructs a task load state sequence based on the real-time acquired task load state, updates the resource competition heat map based on the task load information sequence when reaching the preset update time point, and then makes a dynamic decision based on the current resource competition heat map and the historical resource utilization rate of the to-be-scheduled task when receiving the to-be-scheduled task, obtains the recommended running time period of the to-be-scheduled task, and schedules the to-be-scheduled task based on the recommended running time period, which can effectively identify the resource usage trend, accurately predict the future resource competition state, and make the optimal running time recommendation combined with the historical running data of the to-be-scheduled task, so as to realize the balanced use of system resources and the maximization of scheduling efficiency, can significantly alleviate the task resource conflict problem, reduce the task queuing time, and improve the system throughput capacity and task completion rate.

The invention discloses a queuing team adjustment method and device, electronic equipment and a storage medium. The method comprises the following steps: determining first information of each first object in a current stage; determining the position of each first object in the queue according to the first information of each first object and a predetermined first weight of the current stage; and adjusting the queuing team according to the position of each first object in the queue. By adopting the technical scheme of the invention, the influence factors influencing whether the first object continues to queue or not can be acquired as far as possible, so that the first object can be reserved as far as possible after the queuing team is adjusted, and the business loss is reduced; when the position of each first object is adjusted, the emergency degree, the object type and the queuing time of the first object are comprehensively considered, so that the adjusted queue can retain the first object as far as possible, and the reduction of the business handling amount caused by too long queuing time is reduced.

The application discloses a queuing number calculation method, device and equipment and a storage medium. The queuing number calculation method comprises the following steps: acquiring first queuing queue and time information of current queue query of a user; determining a first priority of the first queuing queue and a second priority of other queuing queues; and determining a queuing number result of the current queue query based on the first priority, the second priority and the time information. The application belongs to the technical field of computers, considers the first priority of the queuing queue allocated by the user, the second priority of other queuing queues and the time information of the current queue query, two influencing factors, calculates the queuing number result of the current queue query, makes the waiting number displayed by the ticket number consistent with the actual number of people to be waited for, the expected queuing time of the user is small in difference with the actual queuing time, and thus the user experience is improved.

The invention provides a mine card scheduling method and device for a strip mine, and the method comprises the steps: training a mine card travel time prediction model through employing the obtained historical mine card scheduling data, and obtaining a target mine card travel time prediction model; in response to the scheduling request, obtaining scheduling operation information; the scheduling operation information comprises road network information, task information and mine card information; based on the road network information, the task information and the mine card information, establishing a multi-target scheduling model taking minimum transportation cost, minimum mine card total queuing time and minimum ore grade deviation as target functions; a multi-target scheduling model is solved by adopting a multi-target optimization algorithm and combining with the target mine truck travel time prediction model, and a Pareto optimal scheduling scheme set is obtained; and selecting an optimal scheduling scheme from the Pareto optimal scheduling scheme set by using a weighted summation method. Therefore, the mine card travel time is accurately predicted, the reliability of mine card scheduling decisions is improved, and an efficient and low-cost scheduling scheme fitting an actual scene is obtained.

This invention provides an attack detection method, apparatus, and electronic device, applied in the field of vehicle network security technology. The invention acquires anchor messages configured with deadlines, collects the number of arbitration failures, queuing time, and actual transmission time during the transmission process, calculates the queuing waiting time, and generates a deadline violation indicator based on the deadline. Multiple transmission samples are aggregated within a specified sliding window, and a fingerprint vector reflecting the bus resource contention status is generated based on the above parameters. This fingerprint vector is compared with a pre-built adaptive baseline to obtain an anomaly score. When the anomaly score exceeds a preset threshold, a disguised high-priority flooding attack is identified. This invention improves the accuracy and robustness of attack detection by monitoring underlying resource contention characteristics and accurately identifying anomalies caused by disguised high-priority attacks.

The invention provides a check-in queuing model index calculation method based on backlog carry-over stability approximation, and relates to the technical field of check-in services. Firstly, in each time period of check-in queuing time, the passenger arrival rate and the service efficiency of each business are calculated, the business overstock rate is obtained based on the passenger arrival rate and the service efficiency, and the actual total arrival number of the time period is calculated by using the calculated overstock rate of the previous time period and the new arrival number of the current time period; and further calculating the total expected waiting time and the expected waiting queue length of the time period on the basis of the actual number of people accumulated in the previous time period and the handling efficiency, and finally configuring and adjusting check-in service resources according to the prediction indexes. According to the technical scheme, a dynamic and refined resource allocation mechanism is realized, the instantaneous arrival rate is considered, and the influence of historical backlog on the current queuing state is also considered, so that the service capability and the actual passenger flow fluctuation can be more accurately matched, the check-in service resource allocation is convenient to optimize, and the service quality is improved.

Methods and apparatuses for signaling and handling priority information for groups of protocol data units, PDUs, or PDU sets. An example method, as performed by by a communication device configured for use in a communication network comprises determining (1110), for each of one or more groups of PDUs to be transmitted to the communication network, a priority and / or a time parameter indicative of a queueing time or a remaining delay budget time for the group of PDUs. The example method further comprises transmitting (1120), to the communication network, a report comprising, for each of the one or more groups of PDUs, the priority and / or time parameter.

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.

The invention provides a slow disk detection method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring first read-write event queuing time of a to-be-tested disk in a preset content distribution network in a first time period; obtaining a first read-write queuing time reference value; obtaining historical read-write event queuing time, and calculating a disk evaluation value corresponding to the first time period according to the first read-write event queuing time, the first read-write queuing time reference value and the historical read-write event queuing time, the historical read-write event queuing time comprises second read-write event queuing time of the to-be-tested disk in a plurality of second time periods before the first time period; and performing slow disk detection on the to-be-detected disk according to the disk evaluation value to obtain a detection result. The slow disk detection method can improve the efficiency of slow disk detection.