997 results about "Waiting time" patented technology

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

The present invention discloses a third party registration processing method based on the IMS network, which comprises that a UE completes the registration on a S-CSCF entity; the S-CSCF entity starts a waiting timer; the UE and a P-CSCF entity initiate and complete a registration status subscribing respectively to the S-CSCF entity; the S-CSCF entity initiate a third party registration to an AS when the waiting time of the waiting timer is enough. Through the present invention, the S-CSCF entity performs a waiting after the UE is successfully registered, to make the subscribing success of the registration status of the P-CSCF and the UE take precedence over the third party registration and avoid the situation of conflicting data of the UE, the P-CSCF, the S-CSCF and an HSS, to make the UE, the P-CSCF and the S-CSCF effectively process the abnormal circumstance occurred when the third party registration fails.

The invention relates to the field of data processing, in particular to a task concurrent scheduling method and a related device. The method comprises the following steps: analyzing task information of a target task to obtain a task demand of the target task; comparing the task demand with a system resource state monitored in real time; if the system resource state meets the task requirement, allocating the target task to an execution queue to start execution of the target task, and marking system resources allocated to the target task in a resource management table; if the system resource state does not meet the task requirement, the target task is allocated to a waiting queue, and a resource matching period corresponding to the target task is set; and according to the resource matching period corresponding to the target task in the waiting queue, re-comparing whether the system resource state monitored in real time meets the task requirement or not. According to the method, the resource utilization efficiency can be improved, the task waiting time is shortened, and the tasks can be scheduled and executed orderly and efficiently in a complex resource environment.

The invention belongs to the technical field of hospital triage systems, and provides an outpatient and emergency triage system, comprising: an information collection module collecting basic information and symptoms of a patient as original data, and preprocessing the data; the intelligent triage module classifies and predicts symptom data input by a patient through a medical knowledge graph and a machine learning algorithm; the triage result output module allocates departments to the patients according to the classification and prediction results of the intelligent triage module, and predicts the waiting time of the patients based on historical data; the interaction module displays and informs the patient according to the distribution information of the triage result output module and the predicted waiting time; through the intelligent triage module, the severity of the illness state of a patient can be rapidly judged, critical patients can be processed preferentially, so that distribution of medical resources is optimized, the waiting time of the patient in a hospital can be shortened through prediction and scheduling of waiting time of the patient, and the treatment efficiency is improved.

The invention discloses an intelligent scheduling method and system for port trailers, and the method comprises the steps: obtaining the state information of a trailer and the information of a target operation region when the trailer arrives at a port fork, and generating a plurality of feasible paths based on the position of the fork and the target region, and obtaining the real-time state of each path trailer, substituting the real-time state into the congestion index prediction model to calculate a congestion index value, screening out an actual path set overlapped with the feasible path, excluding paths with high-priority trailers and congestion indexes exceeding a threshold value according to a trailer operation priority rule, and selecting the path with the minimum congestion index value from the residual paths as a scheduling path. According to the invention, accurate congestion prediction is realized through multi-dimensional data and the model, emergency operation traffic is guaranteed in combination with a priority rule, the path is dynamically optimized, the waiting time is reduced, the road load is balanced, the port trailer scheduling efficiency and safety are improved, and the method adapts to fine management requirements of intelligent ports.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention discloses a service scheduling system based on artificial intelligence, and belongs to the technical field of service scheduling. Comprising a data acquisition layer, an artificial intelligence engine layer, a scheduling execution layer, a monitoring management layer and a function module. The data acquisition layer acquires and preprocesses data related to insurance services; the artificial intelligence engine layer integrates natural language processing and machine learning technologies, and identifies and analyzes customer demands; the scheduling execution layer realizes access of customer service staff through a scheduling algorithm according to customer requirements and customer service staff information; and the monitoring management layer monitors and optimizes the service scheduling process. According to the invention, customer demands are intelligently analyzed, appropriate professional service personnel are accurately matched, and zero-delay docking between demands and services is realized; service personnel are intelligently dispatched, the waiting time of clients is greatly shortened, and the satisfaction degree is improved; tasks are deployed according to the insurance business rhythm, the potential of service personnel is mined, idleness is reduced, and the overall service capacity is improved.

The invention discloses an equipment scheduling method and system based on dynamic priority and multi-objective optimization, and the method takes the minimization of a waiting time mean square error, the minimization of total waiting time and the minimization of maximum completion time as optimization objectives. A task dependency relationship, an equipment maintenance time window, a time window constraint, a student learning progress priority and a delay compensation mechanism constraint condition are comprehensively considered, a dynamic priority calculation mechanism is introduced, the priority is dynamically adjusted in real time according to task waiting time, deadline and urgency, and a high-quality initial population is generated through CMA-ES; a non-dominated sorting framework optimized by NSGA-II is combined, crossover variation of a traditional genetic algorithm is replaced by PSO updating, and an optimal solution is searched by using an elitism mechanism. The method can effectively improve the utilization rate of equipment resources, reduce the task completion time and optimize the fairness of task allocation, is suitable for equipment sharing and dynamic scheduling scenes, and has high practicability and popularization value.

The invention discloses an intelligent scheduling system integrating cooperative work of multiple external self-developed plug-ins. The intelligent scheduling system comprises a data information acquisition unit, a data information preprocessing unit, a storage database, a decision analysis unit, a task scheduling distribution unit and an execution scheduling unit. According to the method, the load and the resource of the sub-node server are analyzed according to the load model and the scheduling model, and the calculation task is allocated according to the load prediction and resource prediction conditions of the sub-node server, so that the overall working efficiency is improved, the waiting time and the processing time are reduced, and the task is completed more quickly; task scheduling allocation is carried out based on the resource utilization rate, tasks are allocated according to the resource utilization rate, obtained through resource prediction, of the sub-node servers and scheduling optimization of data information tasks, the priority of the tasks with large loads is high, it is ensured that the tasks with the high priority are executed firstly, and resource use is balanced as much as possible.

The invention relates to the field of data processing, and discloses an alarm task execution method and system, computer equipment and a storage medium. According to the alarm task execution method, when an alarm task request is received, the request is analyzed to generate an alarm task identifier, and an execution result of an alarm task is obtained based on the identifier; if the execution result does not meet the preset condition, the alarm task is inserted into a task queue according to the priority attribute of the alarm task, the alarm task with the highest priority is obtained from the task queue, then the alarm task is disassembled into alarm sub-tasks, a task dependency graph is constructed according to the dependency relationship between the sub-tasks, the alarm sub-tasks are dispatched and executed, and the alarm task with the highest priority is obtained. In the sub-task execution process, part of execution results are generated and sent to the task request end in real time, the task processing efficiency and user experience are remarkably improved through the alarm task execution method, sudden tasks can be flexibly responded, key tasks can be preferentially scheduled, and the waiting time of a user is shortened by feeding back the part of results in real time.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention discloses an elevator dispatching method and device based on passenger number perception and a medium, and relates to the technical field of elevator control. The method comprises the steps that a button on a floor elevator calling panel is triggered in response to a passenger, so that a face image of an operator is captured through preset image collecting equipment; the passengers are prompted to input the number of elevator passengers in the same row, and an elevator waiting queue is constructed; calculating the residual available load of each elevator, and comparing the residual available load with the predicted load increment to obtain a candidate elevator set with feasible load; selecting an optimal elevator from the candidate elevator set by using a preset multi-target scheduling algorithm, and running the optimal elevator to the floor where the passenger is located; and after the passengers successfully climb the elevator, the actual elevator climbing condition of the optimal elevator is obtained, and the state of the elevator waiting queue is updated. By sensing the number of the waiting passengers in real time and calculating the optimal elevator scheduling based on the multi-target scheduling algorithm, the elevator running efficiency is remarkably improved, and the waiting time of the passengers is shortened.

The invention discloses an intelligent scheduling method based on task optimization. According to the method, firstly, the task load of each time period is predicted through historical data, and then an M / M / c queuing model is established to quantify a task queuing duration expected value as an optimization target. A two-stage optimization strategy is adopted: in the first stage, the optimal number of service desks in each time period is determined by applying dynamic planning, and accurate distribution of human resources is realized; in the second stage, specific employees are distributed to shifts through constraint planning, and meanwhile, complex constraints such as total working time fixing, continuous working duration, shift intervals and skill matching are met. According to the system, a queuing theory, constraint optimization and a prediction model can be combined, and on the premise of ensuring the feasibility of a scheduling scheme, the task processing waiting time is effectively shortened, and the service quality and the human resource utilization rate are improved.

This disclosure describes, in part, systems and methods for dynamic allocation, monitoring, and reallocation of work machines (108) at a worksite (100) by using a controller (112) to monitor ranked production circuits (102) and dynamically reallocate work machines (108) based on their energy consumption, energy availability, machine operating parameters, flow rates, delays, wait times, and other such factors to provide compliance with the ranked production plan (204). The controller (112) is used to determine slow-downs, delays, queues, or other potential delays, including insufficient energy availability for a particular work machine allocation (110), and to dynamically adjust to provide for work machines (108) to reduce empty travel time and to maintain material flow rates or other metrics of the production plan (204).

The invention discloses a bus intelligent travel service dynamic recommendation transfer method, and relates to the technical field of bus services, and the method comprises the steps: S1, building a bus route set; s2, matching the overlapped stations with adjacent stations; s3, acquiring a starting point and an ending point of a user and generating candidate paths; s4, calculating transfer efficiency density, and performing path pruning; s5, calculating a departure time rhythm staggering coefficient between the transfer nodes; s6, calculating a transfer chain type risk accumulation function; s7, estimating total consumed time and waiting time; and S8, constructing a path comprehensive score and a recommendation sequence. According to the bus smart travel service dynamic recommendation transfer method provided by the invention, based on the public traffic operation path and station information, in combination with adjacent station distance calculation and departure rhythm feature construction, the structural association between transfer nodes can be identified more accurately, and path screening is realized through the rhythm interleaving factor, so that the transfer efficiency is improved. The recommendation frequency of the high-risk transfer path is effectively reduced, and the stability of the transfer process is improved.

The invention relates to the field of deep learning, in particular to a container task scheduling method and system based on the reinforcement learning technology and a storage medium, and the method comprises the steps: obtaining container task scheduling data; constructing a scheduling optimization problem based on the minimum task waiting time and the minimum cluster resource imbalance degree; constructing a reinforcement learning model, and training the reinforcement learning model according to the scheduling optimization problem and the container task scheduling data to obtain a scheduling model; the method specifically comprises the following steps: filtering data obtained in a training process during training, and then training to obtain a scheduling model; and carrying out container task scheduling. According to the method, a scheduling optimization problem based on the minimum task waiting time and the minimum cluster resource imbalance degree is constructed; filtering the data obtained in the training process, and then training to obtain a scheduling model; therefore, the training effect is improved, and finally the container task scheduling effect is improved.

The invention provides a high-speed rail emergency train number matching optimization method and system based on timeliness guarantee, and belongs to the field of logistics distribution scheme optimization. According to the method, the set business process comprises customer delivery, goods pickup and delivery, inter-station transportation and goods pickup and delivery; according to the order data and the rider data, the taking and delivering service time distribution of the two links of taking goods to the station and taking goods and delivering goods is constructed, and then the goods waiting time, the latest arrival time of the day and the goods arrival time meeting the maximum delay time are determined; taking the smaller value of the latest arrival time of the day and the cargo arrival time meeting the maximum delay time as the latest arrival time of the cargo; determining a train alternative set according to cargo time limit requirements and cargo OD stations; and based on the timeliness guarantee and the operation cost, constructing a high-speed rail emergency train number matching optimization model based on the timeliness guarantee, and solving the model to obtain an optimized train number matching result. According to the invention, the punctuality and reliability of transportation are improved, and the overall operation efficiency and economic benefits are improved.

The invention discloses a method and a device for determining queuing waiting time of a user, and relates to the field of financial science and technology. The method comprises the following steps: under the condition of user authorization, obtaining to-be-handled service types and age information of N queued users; according to the to-be-handled service type of the user, detecting whether the user has handled a historical service corresponding to the to-be-handled service type; when the business has been handled, determining a predicted business handling duration according to the duration of handling the historical business; when the to-be-handled business is not handled, determining a predicted business handling time length according to a preset handling time length and age information corresponding to the to-be-handled business type; and determining the queuing waiting time of the jth user according to the predicted business handling time of all queuing users before the jth user. According to the method and the device, the technical problems of influence on user service experience and low user service processing efficiency caused by excessive queuing personnel due to difficulty in judging queuing time length by a user because only a queuing serial number is provided during financial institution network service in the prior art are solved.

The invention is suitable for the technical field of traffic control, and provides a road section pedestrian crossing signal control method and system based on human and vehicle postures. The method comprises the following steps: acquiring instantaneous speed and position data of a head vehicle on each lane in front of a pedestrian crossing in real time, and calculating the maximum crossing time by combining the length of the pedestrian crossing and the type of a pedestrian; determining the length of the upstream and downstream judgment area of the pedestrian crossing based on the road design speed and the street crossing time; monitoring the number of pedestrians and the longest waiting time in the waiting area, and judging whether a first triggering condition is met or not by combining a reasonable waiting time threshold value and a capacity threshold value; judging whether a second triggering condition is met or not by comparing the theoretical driving time of the vehicle with the pedestrian crossing time in the judgment area; and when the first and second trigger conditions are met simultaneously, starting a pedestrian green light phase, otherwise, maintaining the current signal state. According to the invention, the waiting time of pedestrians and vehicles can be effectively reduced, and the intersection passing efficiency and safety are improved.

The invention belongs to the technical field of intelligent production scheduling of clothing production enterprises, particularly relates to an intelligent production scheduling system and method for clothing production enterprises, and solves the technical problems of unreasonable resource allocation and difficulty in handling emergencies. The intelligent production scheduling system can perform priority analysis and sorting on clothing orders, preferentially process emergency orders, reasonably arrange production plans and reduce waiting time and resource waste in the production process, thereby improving the production efficiency and reducing the production cost. The unit resource allocation amount is calculated according to the clothing order information and the historical data, resources such as equipment, manpower and raw materials are reasonably allocated, idle or overuse of the resources is avoided, the utilization rate of the resources is improved, the production process is monitored in real time, and when the situation of production lag occurs, proper adjustment information can be selected from the historical data, so that the production efficiency is improved. The resources are redistributed and adjusted, and resource configuration is further optimized.

The invention provides a timed task execution optimization method and system. The method comprises the following steps: forming a timed task execution optimization system architecture; determining a corresponding calculation factor to obtain a dynamic priority score; predicting the to-be-predicted task data by using the task prediction model to obtain a task execution time prediction value; analyzing the to-be-executed task queue, calculating the average waiting time, determining a multiple factor according to the task priority and the system load state, and identifying the task with the task execution time prediction value larger than the dynamic threshold value in the to-be-executed task queue as a long task; performing automatic fragmentation processing on the long task, generating a fragmentation result, determining a scheduling sequence based on the dynamic priority score and the task execution time prediction value, and generating a scheduling decision; and performing resource availability evaluation on the resource use data to obtain a node resource evaluation result, and allocating execution resources for the selected task. According to the method, the problems of efficiency and reliability of the timed task execution system in a high-load scene are solved.

The invention provides a multi-factor collaborative dynamic priority task scheduling method based on waiting time feedback, which is characterized by comprising the following steps of: initializing waiting time, task criticality and weight values of queue length, initializing a mixed task set and a task waiting queue, and initializing the task waiting queue into an empty set; entering a task waiting queue; updating the priorities of all tasks in the task waiting queue; sorting the tasks in the task waiting queue according to the priority; monitoring whether a new task arrives in real time; judging whether the newly arrived task meets a preemption condition or not; updating the historical average waiting time of the task; updating weight values of task waiting time and queue length; and judging whether all tasks in the mixed task set are scheduled. According to the method, long-term hunger of low-priority tasks is avoided, and the average waiting time of the tasks is shortened; the preemption threshold value is dynamically adjusted, and the context switching overhead is reduced; a dynamic weight mechanism is designed, and the self-adaptive capacity of the system is improved.

The invention belongs to the technical field of text-to-speech conversion, and particularly relates to a solution for TTS (text-to-speech) in a high-concurrency scene through tone cloning. According to the method, the text can be generated and returned to the client by segmenting and parallelizing the text and reducing the delay of the first segment, compared with a traditional serial whole segment synthesis mode, the waiting time of a user is greatly shortened, the audio is returned after being generated, the waiting time of the user can be effectively shortened under high concurrency, the network bandwidth and the delay pressure are reduced, and the user experience is improved. The real-time performance is guaranteed, reconnection disasters are avoided, underlying model services can be fully utilized, the influence of the serial processing characteristic of a CosyVoice2 model is avoided, a client can immediately start to play a first segment of audio, even if subsequent segments are still synthesized, the continuous playing experience of previous content is not influenced, and when the concurrency is increased, the continuous playing experience of the previous content is not influenced. The model gateway can dynamically disperse requests to a plurality of GPU instances, and long queue waiting after a single-card video memory is fully occupied is avoided.

The invention belongs to the technical field of order management, and discloses an order management method based on cloud computing service. The method comprises the steps that the cloud platform receives a passenger position sent by a passenger terminal; acquiring real-time traffic data, defining a screening radius, and generating an order dispatching area for passenger positions; driver positions sent by all driver ends in the order sending area are received; road traffic information is obtained, the road traffic information, the passenger position and all driver positions are fused, the shortest driving time from each driver position to the passenger position is calculated, and a time sorting table is generated; the order is sent to the driver end with the shortest receiving time, and if the driver end does not receive the order, the order is sent to the driver end in sequence according to the time sorting table; according to the invention, cloud computing and big data analysis technologies are fully utilized, intelligent optimization and dynamic adjustment of order distribution are realized, the waiting time of passengers and the unloaded driving rate of drivers can be effectively reduced, the user experience is effectively improved, and the overall operation efficiency is improved.

The invention provides a medical resource dynamic allocation method and system based on patient state data driving. The method comprises the following steps: receiving a patient state data set of a target user; generating target recommended medical resources; generating a candidate hospital sequence; acquiring a first adjustable medical resource of the first hospital in a first preset time; generating a first adaptation index; and when the first adaptation index is greater than or equal to a preset adaptation index, triggering a resource allocation instruction, and sending the target recommended medical resource to a first hospital. The technical problem that the medical resource preparation time is long due to the fact that data cannot be transmitted to a hospital in real time and medical resource scheduling analysis is lacked in the prior art can be solved, the vital sign data of the patient is transmitted in real time, the hospital is matched through intelligent analysis, the medical resource allocation scheme is generated, and the medical resource allocation efficiency is improved. Before the patient arrives at the hospital, the hospital is prepared correspondingly, so that the technical effect of shortening the waiting time of the patient is achieved.

The invention discloses a trunk bus dynamic speed optimization method based on time headway equalization, and a dynamic optimization model is constructed by taking the minimum total deviation of the time headway on a bus line as a target. According to the method, factors such as departure time, driving speed and signalized intersection delay of intelligent networked buses are comprehensively considered, and the departure time of the buses at stations and the driving speed of the buses when the signalized intersection delay is not considered are optimized by establishing a calculation method for the bus delay at the signalized intersection. Meanwhile, according to a time headway balance principle, a calculation method based on the waiting time of passengers is provided so as to coordinate the operation efficiency of the motorcade and the experience of the passengers. According to the method, the fluctuation of the time headway is effectively reduced, the bunching effect is avoided, the overall operation efficiency and the service level of the bus route are remarkably improved, and theoretical support and technical guarantee are provided for design and implementation of an intelligent network connection bus system.

Apparatuses, methods and computer programs for elevator call allocation with adaptive multi-objective optimization are disclosed. At least some of the disclosed embodiments may allow adaptively and smoothly changing an objective function according to passenger traffic. This in turn may allow minimizing waiting times in all traffic situations compared to using a fixed objective function. Furthermore, at least some of the disclosed embodiments may allow adaptively and smoothly changing the objective function according to the traffic while taking user preferences into consideration via a single transit time target parameter.

A robot system including a robot, an image capturing device, and a controller. The controller and the image capturing device have clocks, the controller conducts transmission of, to the image capturing device, an image-capturing instruction including a predetermined waiting time, the predetermined waiting time being a time between a time of the transmission and an image capturing time, or a required time obtained by subtracting, from the waiting time, a difference time between the time of the transmission and receiving time of the image-capturing instruction, the image capturing device conducts image capturing of an image of the workpiece at time when the required time elapses from the receiving time and transmits image data, and the controller controls the robot based on position information of the robot at time when the waiting time elapses from the time of the transmission and the image data.

The invention relates to the technical field of customer management, in particular to a cross-channel customer interaction management system and method based on data fusion. The method comprises the following steps: acquiring a customer communication demand, analyzing the customer communication demand, and determining customer basic information and customer communication content; based on multi-channel data, according to the basic information of the customer and the communication content of the customer, determining a consultation intention of the customer; and determining a reply thought according to the customer consultation intention, and responding to the customer according to a preset reply logic and the reply thought. User requirements are quickly responded, and the waiting time of customers is shortened. The personalized service is based on multi-channel data and communication history of the customer, and can provide services better meeting customer requirements, thereby improving customer satisfaction. And by utilizing machine learning and natural language processing technologies, the system can more accurately identify the consultation intention of the customer, and misunderstanding and repeated consultation are reduced. The accuracy of consultation response is improved, and the customer is ensured to obtain correct information and suggestions.

The invention discloses an intelligent public transportation transfer recommendation system based on multi-source data fusion, and relates to the technical field of intelligent transportation systems, and the system comprises a transfer recommendation management center which is in communication connection with the following modules: a data acquisition module which is used for collecting and preprocessing traffic data from a plurality of traffic data sources, and transmitting the traffic data to a data processing module; various traffic modes of metros and buses are covered; and the multi-source data fusion analysis module is used for carrying out fusion processing on the collected multi-source heterogeneous data. By fusing multi-source traffic data and combining simulation analysis and real-time traffic states, the transfer path is dynamically optimized, the transfer waiting time and total consumed time of the user are remarkably shortened, the user can obtain optimal path recommendation based on real-time traffic information, long-time waiting caused by sudden delay is avoided, the travel efficiency and experience are improved, and meanwhile, the user experience is improved. Double constraints of walking distance and transfer waiting time are supported, it is ensured that path planning meets actual requirements of users, and travel experience is further optimized.