74 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 discloses a task scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: determining task information of a to-be-allocated first task and first task time sequence information of at least one to-be-executed second task of each processor core in a plurality of processor cores; and determining at least one first processor core in which the first task can be inserted from the plurality of processor cores based on preset end time and preset execution duration in the task information of the first task and the first task time sequence information corresponding to each processor core. According to the preset end time and the preset execution duration of the first task, the target processor core for executing the first task can be determined from the multiple processor cores. According to the method, the appropriate processor core can be rapidly determined through the preset end time and the preset execution duration of the first task, the task queuing time is shortened, and meanwhile it is ensured that the first task can be started in time and completed smoothly.

The embodiment of the invention provides a flow management method, device and system and a storage medium. In the embodiment of the invention, the bandwidth flow control is firstly carried out on the service process based on the throughput threshold of the service process, so that the network flow sent by the service process does not exceed the throughput threshold of the service process, and the flow control is carried out at the home terminal, so that the flow sent by the home terminal does not occupy all resources of the home terminal; and thus, the traffic of the home terminal can be prevented from occupying all resources of the opposite terminal. Afterwards, for a plurality of second requests read in unit time, the plurality of second requests are sent to the destination end in batches according to the scheduling priorities and / or delay sensitivities of the plurality of second requests, and the requests with high scheduling priorities and / or delay sensitivities can be preferentially sent to the destination end, so that the requests with high scheduling priorities and / or delay sensitivities can be preferentially sent to the destination end. The queuing time of the requests with the high scheduling priority and / or the high delay sensitivity is shortened, the processing efficiency of the requests with the high scheduling priority and / or the high delay sensitivity is preferentially guaranteed, and the service quality can be improved.

The embodiment of the invention discloses a resource scheduling method and device, electronic equipment and a storage medium. The method comprises the steps that it is determined that idle resources do not meet the operation requirement of a current job; the idle resource is the idle resource of the target heterogeneous partition indicated by the scheduling request, and the scheduling request is generated according to the current job; according to scheduling parameters carried in the scheduling request, calculating a first node number threshold value of a first type of resources required for executing the current job; the first type of resources are any type of resources required for executing the current job; comparing the first node number threshold value with the node number of the first type of resources in the idle resources, and determining a resource reservation strategy of the first type of resources; the reserved resource indicated by the resource reservation strategy is used for executing the current job. According to the embodiment of the invention, the resource utilization rate in a heterogeneous partition scene can be improved, the job queuing time is shortened, and the user experience is improved.

The invention discloses a GPU sharing scheduling method and device supporting heterogeneous job scale, and the method comprises the steps: carrying out the interference portrait analysis of a newly submitted job, obtaining the interference feature representation of the job, and adding the interference feature representation into a job queue; collecting cluster operation information and job information; determining a job scheduling interval; on the basis of the job runtime information, the job priority is updated, and the jobs are sorted through a maximum sharing efficiency priority method; traversing jobs in the job queue, and determining jobs to be run and jobs to be preempted; calling an optimal packaging strategy and job co-located interference prediction model to determine a placement position of a to-be-operated job; placing jobs and updating a cluster state; and monitoring a new job submission condition and updating a scheduling interval, if a new job is submitted, skipping to carry out interference portrait analysis on the newly submitted job, otherwise, skipping to the step of determining the job scheduling interval. According to the method, the job completion time and the queuing time can be effectively reduced, and the resource scheduling efficiency of the GPU cluster is remarkably improved.

A method for analysis of in-person attendant interactions according to an embodiment includes determining a location of a person within a monitored area based on sensor data generated by one or more sensors, determining a start queue time associated with a time at which the person is located at a start queue position within the monitored area, determining an end queue time associated with a time at which the person is located at an end queue position, recording interaction data of an interaction between the person and an attendant when the person is located at the end queue position, determining a wait time of the person in the queue based on the start queue time and the end queue time, determining an interaction time of the interaction based on the interaction data, and adjusting an attendant schedule for the monitored area to improve the wait time or the interaction time.

The invention relates to the field of airport management and aviation services, and discloses an airport check-in consignment counter configuration planning method and device, and the method comprises the following steps: S1, collecting real-time or historical data, and obtaining a passenger arrival rate, a counter service rate, and system limitation condition parameters; s2, based on the collected data, constructing a queuing theory model, and performing preliminary evaluation on check-in counter configuration through an M / M / c queuing model; and S3, establishing a target function by using an optimal control theory, optimizing a configuration scheme, and minimizing a comprehensive target among queuing time, passenger waiting time and resource configuration cost. According to the dynamic counter configuration scheme based on the real-time data acquisition and feedback mechanism, the technical effect of dynamically adjusting the number of counters in the peak period is achieved, so that the problem that passengers queue for a long time in the peak period is effectively avoided, and resource waste in the valley period is also reduced.

The present invention provides a service optimization management system for telemedicine services, which relates to the field of data processing technology. The system includes: retrieving multiple queue information sets based on the reception window, extracting multiple queue personnel characteristics for differential identification of consultation time, generating multiple consultation time prediction data, monitoring queue conflicts based on the appointment queue information set and the online queue information set, building a queue time prediction model, generating a consultation queue time data set, formulating multiple exclusive channels, performing feedback response through the user terminal, optimizing the multiple consultation time prediction data according to the feedback response results, updating the consultation queue time data set, and realizing the optimization management of telemedicine services. The technical effects of more accurate resource allocation and service optimization, improving service efficiency and quality, reducing patient waiting time, and improving patient satisfaction are achieved.

The invention discloses a task scheduling method and device, equipment, a storage medium and a program product, and relates to the technical field of cloud computing. The task scheduling method comprises the following steps: constructing a third-order hypergraph by utilizing an obtained task set, a task data set and a computing platform set, the third-order hypergraph comprises a computing platform layer constructed by taking each computing platform in the computing platform set as a node, a task layer constructed by taking each task in the task set as a node, a data layer constructed by taking each task data in the task data set as a node and a hyperedge; determining task completion time of a scheduling scheme corresponding to each hyperedge in the third-order hypergraph according to the time corresponding to each hyperedge connection task, the computing platform and the task data; according to the sequence of the task completion time of the hyperedges, task scheduling schemes of the tasks in the third-order hypergraph are selected in sequence, and a task scheduling scheme of the task set is obtained. According to the embodiment of the invention, the technical problem of overlong task queuing time is solved, and the task completion efficiency can be improved.

The government affair service data processing method comprises the following steps: a user logs in through a mobile terminal to carry out business pre-handling; obtaining a business pre-handling progress of a mobile terminal login user; if the service pre-handling time of the user logged in by the mobile terminal exceeds the preset duration of the service type, the formal handling is confirmed; the mobile terminal logs in the user to complete business pre-handling and confirms to enter formal handling; and determining a formal handling sequence according to the formal handling time sequence of the mobile terminal login user. The invention aims to provide the government affair service data processing method, the government affair service system and the government affair service intelligent terminal, the service speed of the government affair service intelligent terminal is improved through user business pre-handling and evaluation, calculation and sorting according to the user business pre-handling progress, the queuing time of the user is further shortened by adopting parallel sorting, and the service efficiency of the government affair service intelligent terminal is improved. And the efficiency of serving users by the government affair system is improved.

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 invention provides a task load balancing scheduling method and device based on big data, and the method comprises the steps: obtaining task load states on a big data platform in real time, and constructing a task load state sequence based on the task load states obtained in real time; the resource competition thermodynamic diagram is updated based on the task load information sequence when a preset updating time point is reached, and then when a to-be-scheduled task is received, a dynamic decision is made based on the current resource competition thermodynamic diagram and the historical resource utilization rate of the to-be-scheduled task to obtain a recommended operation time period of the to-be-scheduled task. And the to-be-scheduled task is scheduled based on the recommended operation time period, so that the resource use trend can be effectively identified, the future resource competition state can be accurately predicted, and the optimal operation time recommendation is made in combination with the historical operation data of the to-be-scheduled task, thereby realizing the balanced use of system resources and the maximization of the scheduling efficiency. The task resource conflict problem can be obviously relieved, the task queuing time is shortened, and the system throughput and the task completion rate are improved.

The invention discloses a congestion control method for an artificial intelligence training cluster, a chip and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing an acknowledgement data packet fed back by a data receiving end; when it is confirmed that the reference rate carried in the data packet is not changed and the current reference rate adjustment interval is not smaller than a transmission rate adjustment interval threshold value, according to the one-way queue delay and the target queuing time, the sending rate of the data packet is adjusted in a staged mode, and the target sending rate is obtained; and determining the number of congestion windows according to the target sending rate and the loopback time, and determining a data packet sending interval according to the number of congestion windows. According to the technical scheme of the embodiment of the invention, accurate congestion control can be carried out on the artificial intelligence training cluster, efficient and reliable communication of the training nodes can be ensured, and good expandability is achieved.

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 present invention discloses a docker-based testing method, including: obtaining a code test case from a master server, and based on a preset slave server management mapping table, selecting a target slave server according to the code test case, and sending the code test case to the target slave server; selecting an idle test docker container from a plurality of test docker containers constructed in the target slave server as a target test docker container; and finally running the code test case in the target test docker container to obtain a test result of the code test case. The present invention also discloses a docker-based testing device, equipment and storage medium. By adopting the embodiments of the present invention, reasonable allocation of slave server resources is achieved, code queuing time is shortened, and code testing efficiency is improved.

A method for analysis of in-person attendant interactions according to an embodiment includes determining a location of a person within a monitored area based on sensor data generated by one or more sensors, determining a start queue time associated with a time at which the person is located at a start queue position within the monitored area, determining an end queue time associated with a time at which the person is located at an end queue position, recording interaction data of an interaction between the person and an attendant when the person is located at the end queue position, determining a wait time of the person in the queue based on the start queue time and the end queue time, determining an interaction time of the interaction based on the interaction data, and adjusting an attendant schedule for the monitored area to improve the wait time or the interaction time.

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.

The invention belongs to the field of communication and traffic management, and particularly discloses a reserved arrival scheduling method and system.The method is applied to a scheduling system and comprises the steps that when the scheduling system is currently located on an application interface of a first client side, first input of a user is received, a departure port and a date of reserved arrival are determined in response to the first input, and the departure port and the date are sent to the first client side; determining initial shift information on the basis of the departure port and date, wherein the initial shift information comprises the departure time, the ship name and the predicted queuing time; generating indication information indicated by different colors according to the initial shift information, and displaying a queuing congestion degree corresponding to the predicted queuing time to a user based on the indication information; and receiving a second input of the user, and responding to the second input to complete port entering ship ticket reservation, the second input being an operation of clicking the initial shift information. According to the invention, the method can improve the efficiency of appointment arrival, and improves the user experience.

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.

The present application provides a task processing method, device and storage medium, which are applied to the field of big data, including: when the queuing time of the target task in the queue to be scheduled is greater than a threshold, N target computing nodes are determined from multiple computing nodes; when the target task is scheduled, the N target computing nodes are used to execute the target task. The method of the present application determines whether the target task needs to be scheduled first by judging the queuing time of the target task. When the target task needs to be scheduled, the target computing node is selected and the target task is executed based on the target computing node, thereby reducing the "queue jumping" of other tasks and also reducing the queuing time of the target task.

The invention discloses a crown block scheduling method, device and equipment, a medium and a product. The method comprises the following steps: acquiring state information, motion information and to-be-processed task information corresponding to each crown block; determining a task allocation strategy according to the state information, the motion information and the to-be-processed task information corresponding to each crown block; and scheduling each crown block according to the task allocation strategy. Through the technical scheme of the invention, the traffic conflict of the crown block at the intersection of a plurality of rails can be solved, the queuing time of the crown block can be reduced, the overall traffic efficiency can be improved, and particularly, the delay caused by the bottleneck phenomenon can be avoided and the smooth production operation can be ensured in the traffic peak period in a complicated scheduling environment.

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 embodiment of the invention provides a request processing method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a target queuing level of a target user in response to a queuing request of the target user; inputting the target queuing level into a pre-trained queuing model, and predicting to obtain queuing time information of the target user; and adding the target user to a queuing queue corresponding to the target level based on the queuing time information. By introducing a differentiated queuing mechanism based on user queuing levels and adopting an appointment ordering mode, the queuing time of players of each queuing level can be predicted by utilizing a pre-trained queuing model when a large number of users are queued in a centralized manner, and then the players are allocated to corresponding queues. Different from a traditional sorting mode, the appointment sorting mode arranges the queuing sequence of the players in advance, the situation that a large number of players queue at the same time is avoided, and therefore the high-concurrency load problem of a server is relieved, and the queuing experience of the target user is improved.

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.