27 results about "Turnaround time" patented technology

A system for determining a turnaround time for an aircraft at an airport includes one or more processors, one or more geospatial databases storing geospatial data, and a memory coupled to the one or more processors and the one or more geospatial databases. The memory stores data into a database and program code that, when executed by the one or more processors, causes the system to monitor a wireless data stream indicating aircraft location data. In response to determining the aircraft is idle and is located within an area where a unique parking stand is located, the system establishes a current position timestamp as an on-block time. In response to determining the aircraft is moving out of the unique parking stand, the system determines a position timestamp when the aircraft was last idle, and sets the position timestamp collected when the aircraft was last idle as an off-block time.

The invention relates to the technical field of intelligent manufacturing and supply chain collaborative management of woven garments, in particular to a garment production process management system based on artificial intelligence, and the system comprises a production network management center which is used for obtaining a node execution entropy value; the demand dynamic analysis unit is used for carrying out feature extraction analysis on the collected unstructured design data to obtain an order complexity coefficient; the strategy game matching unit is used for obtaining a steady-state scheduling signal or a recovery scheduling signal; when a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used for generating a global optimal scheduling instruction, and minimizing turnover time is taken as an optimization target; when a recovery scheduling signal is generated, the collaborative scheduling execution unit is used for generating a risk recovery scheduling instruction, taking a minimum node execution entropy value as an optimization target, and actively inhibiting pursuit of turnover time; according to the method, the problem that the hidden risk of the node cannot be sensed in the prior art is effectively solved, and an accurate quantitative basis is provided for subsequent preventive scheduling.

The invention provides an interval redundancy time distribution method, device and equipment and a medium, and the method comprises the steps: obtaining train operation control system data, and extracting the allowed shortest operation time of each interval and the turn-back time of the two ends of an operation line; obtaining train transportation capacity supply demand information and train bottom allocation information, and determining train operation total redundancy time according to the train transportation capacity supply demand information, the train bottom allocation information, the shortest operation time allowed by each interval and turn-back time at two ends of an operation line; determining an elastic coefficient based on the obtained interval attribute information of each interval; based on the elastic coefficient and the total train operation redundancy time, constructing an interval redundancy time distribution optimization model; and solving the interval redundant time distribution optimization model by using a preset solver to obtain optimized interval redundant time, and adding the optimized interval redundant time with the shortest operation time to obtain an interval operation time configuration result. According to the scheme, the interval operation time configuration result can be determined more comprehensively and accurately.

The application discloses a process scheduling test method of an embedded operating system and a related device. The method comprises the following steps: obtaining a process scheduling algorithm to be tested of an embedded operating system to be tested, and writing the process scheduling algorithm to be tested into a pre-set process scheduling algorithm library; determining a corresponding typical test case set and a random test case set according to the process scheduling algorithm to be tested, and executing the typical test case set and the random test case set in a simulated embedded operating system by using the process scheduling algorithm in the process scheduling algorithm library to determine an average turnaround time set; taking the scheduling process algorithm with the minimum average turnaround time in the average turnaround time set as an optimal process scheduling algorithm, and recording first process scheduling information of the optimal process scheduling algorithm; executing the optimal process scheduling algorithm in the embedded operating system to be tested, recording second process scheduling information of the optimal process scheduling algorithm, and determining a test report according to the first process scheduling information and the second process scheduling information.

A system and a method directed to a monitoring system of semiconductor processing chambers is provided. In particular, monitoring of any chemical formation on a chamber and a wafer of a semiconductor processing chamber using in-situ laser induced fluorescence is provided. The monitoring system and method detect issues before they become a problem for the semiconductor processing chambers by providing diagnosis on chamber health and mechanisms for associated process shifts with a faster turnaround time.

A method performed by an electronic device for predicting a turnover time of a container is disclosed. The method includes obtaining shipping data associated with shipping of one or more containers. The method includes generating a turnaround time parameter indicative of a turnaround time of the container by applying a predictive model to the shipping data based on the shipping data. The method includes providing an output based on the turnaround time parameter.

Randomization methods for healthcare scheduling optimization using perioperative stages. Poor scheduling of surgical appointments and procedures in operating rooms can lead to unnecessary downtime, and therefore loss of efficiency. The randomization methods include various probability models, Monte Carlo simulations, and stochastic optimization is used to optimize procedure scheduling in operating rooms. The optimized schedule may be based on estimated procedure duration, estimated turn-around-time, estimated cancellation frequency, forecasted emergency operating room usage, estimated surgeon utilization, and hospital site configuration. A probabilistic machine learning model may be trained based on historic data and ongoing performance data to automate the optimization process and increase accuracy based on up-to-date information and statistics.

A method that includes obtaining a minimum deactivation time for each processing device (203) of a processing system configured to produce one or more materials and operate in accordance with a deactivation schedule. The deactivation schedule includes a calendared deactivation period for each processing device (203). The method further includes obtaining a maximum turnaround time for the processing system, obtaining a set of conditions (311) for the deactivation schedule and determining, subject to the set of conditions (311), an optimum deactivation schedule (317) by optimizing a production objective. The conditions require that the calendared deactivation period of each processing device lasts at least the minimum deactivation time, and a total duration of the deactivation schedule does not last longer than the maximum turnaround time. The conditions also seek to reduce overlap between calendared deactivation periods. The method further includes performing a system maintenance (319) of the processing system according to the optimum deactivation schedule (317).

This application discloses a train timetable optimization method for urban rail transit networks considering transfer coordination, belonging to the field of urban rail transit technology. The method includes: acquiring network topology, transfer station information, train operation parameters, and passenger flow data; constructing a timetable optimization model and generating an initial timetable, considering constraints such as departure intervals, station dwell times, travel times, and turnaround times, with the objective of minimizing the total travel time and operating costs of passengers on the line; identifying the set of transfer stations and direction mappings based on the network topology, and extracting train arrival and departure events from the timetable; optimizing the train time windows at each transfer connection point with the objective of minimizing the total waiting time for transfers across the entire network, forming a time window contract; adding this contract to the optimization model and resolving it to obtain a new timetable; and outputting the final timetable by judging whether the improvement in transfer waiting time has converged. This method effectively improves the overall service level and economic benefits of multi-line networks while reducing computational complexity.

The present application relates to the technical field of intelligent manufacturing and supply chain collaborative management of clothing, in particular to a clothing production process management system based on artificial intelligence, comprising: a production network management center for obtaining node execution entropy; a demand dynamic analysis unit for performing feature extraction analysis on collected unstructured design data to obtain an order complexity coefficient; a strategy game matching unit for obtaining a steady-state scheduling signal or a recovery scheduling signal; when the steady-state scheduling signal is generated, a collaborative scheduling execution unit is used to generate a global optimal scheduling instruction with the optimization objective of minimizing the turnaround time; when the recovery scheduling signal is generated, the collaborative scheduling execution unit is used to generate a risk repair scheduling instruction with the optimization objective of minimizing the node execution entropy and actively suppressing the pursuit of turnaround time; the present application effectively solves the problem that the prior art cannot perceive the hidden risks of nodes, and provides accurate quantitative basis for subsequent preventive scheduling.

The invention provides a material conveying process control method and device of a raw material field, a medium and equipment, and belongs to the technical field of automatic control. The method comprises the following steps: extracting multi-process collaborative scheduling parameters of a raw material field; generating a planning strategy of a material pile and an equipment scheduling instruction set based on the collaborative scheduling parameters, and enabling the operation duration predicted based on the planning strategy to meet a turnover time window constraint in a demand plan of a downstream production unit; the material piling and taking equipment is driven to execute material conveying operation; collecting material distribution state data of a material pile in actual operation of the material piling and taking equipment, and identifying a deviation value between the material pile in actual operation and the material pile in the planning strategy; dynamically adjusting an equipment scheduling instruction set according to the deviation value, so that material distribution formed by material conveying operation executed by the material piling and taking equipment based on the dynamically adjusted equipment scheduling instruction set meets a planning strategy; and feeding back the material distribution state data to the MES system. The stability and reliability of the material conveying process of the raw material field can be improved.

The invention relates to a task scheduling method and device based on cloud edge collaboration and electronic equipment, and the method comprises the steps: responding to execution indication information for a target task, determining an action space corresponding to the target task, and enabling the action space to comprise a plurality of edge nodes; attribute information of the target task is obtained, resource information of each edge node is obtained, and the resource information comprises computing resources and network resources; predicting time information of executing the target task by each edge node according to the attribute information and the resource information, wherein the time information is average turnover time of the target task under a task processing chain to which the target task belongs; and determining a target edge node from the plurality of edge nodes based on the time information, and executing the target task through the target edge node. The target task processing time of each edge node is comprehensively predicted by combining the computing resources of the edge nodes, the network resources and the attribute information of the tasks, and the average turnover time of the tasks and the average consumed time of the task chain can be shortened.

A communication control apparatus is configured to calculate a turnaround time per unit data amount between a server to be controlled and each of a plurality of servers including an other server, based on a required time from when the server to be controlled starts transmitting a signal to such other server until the server to be controlled receives a response to the signal from such other server, and a data size of the signal; determine, based on the turnaround time, a priority for selecting from among the plurality of servers a connection destination server to which the server to be controlled is to be connected; and select the connection destination server from among the plurality of servers based on the priority.

Randomization methods for healthcare scheduling optimization using perioperative stages. Poor scheduling of surgical appointments and procedures in operating rooms can lead to unnecessary downtime, and therefore loss of efficiency. The randomization methods include various probability models, Monte Carlo simulations, and stochastic optimization is used to optimize procedure scheduling in operating rooms. The optimized schedule may be based on estimated procedure duration, estimated turn-around-time, estimated cancellation frequency, forecasted emergency operating room usage, estimated surgeon utilization, and hospital site configuration. A probabilistic machine learning model may be trained based on historic data and ongoing performance data to automate the optimization process and increase accuracy based on up-to-date information and statistics.

A memory includes interface circuitry for receiving at least one write command. Multiple memory banks are organized into multiple memory bank groups and operate according to a corresponding memory bank group timing sequence. Each of the multiple memory banks includes a memory cell array. Multiple write buffers temporarily store write data associated with at least one write command. Write buffer selection circuitry egresses the temporarily stored write data from a selected buffer among the multiple write buffers based on the memory bank group timing sequence and directs the egressed write data to a given array of memory cell arrays.

A system includes an intermediate mortgage integration platform as a service (MiPaas). Specifically, an MiPaas layer may facilitate integration between a loan origination system (LOS) and supporting services. The MiPaas layer may provide integration flexibility by enabling integration of the supporting services with each other and with different LOSs. Indeed, a number of MiPaas integration formats (e.g. REST API, OpenAPI, GRPC, GraphQL, Industry Standard interface, etc. may be provided to facilitate ease of integration between the LOS and the supporting services. Further, the MiPaas may provide processing efficiencies by adapting workflows based upon particular retrieved data. In this manner, efficient processing may be performed in an intermediate data processing layer, resulting in more efficient turnaround times and reduced processing requirements at traditionally over-burdened systems.

The present application provides a material conveying process control method, device, medium and equipment for a raw material yard, which belongs to the field of automatic control technology. The method includes: extracting multi-process collaborative scheduling parameters of the raw material yard; generating a planning strategy and equipment scheduling instruction set for the material pile based on the collaborative scheduling parameters, so that the operation duration predicted based on the planning strategy meets the turnaround time window constraint in the downstream production unit demand plan; driving the stacking and reclaiming equipment to perform material conveying operations; collecting the material distribution status data of the material pile where the stacking and reclaiming equipment is actually operating, and identifying the deviation between the actual operating material pile and the material pile in the planning strategy; dynamically adjusting the equipment scheduling instruction set according to the deviation, so that the material distribution formed by the material conveying operation performed by the stacking and reclaiming equipment based on the dynamically adjusted equipment scheduling instruction set meets the planning strategy; and feeding back the material distribution status data to the MES system. The present application can improve the stability and reliability of the material conveying process of the raw material yard.

Disclosed herein a system having an artificial intelligence model, which is executed to generate and display valuation reports on an interactive graphical user interface. The valuation reports include valuation information of companies. The valuation reports include multiple variables associated with the valuation information of the companies whose values are dynamic, and the values may be updated in real-time. The swift turnaround time of the valuation reports on the interactive graphical user interface may allow the client user to trade swiftly and efficiently.

The application discloses an airport flight scheduling method and device based on big data, and a medium, to solve the technical problem that the existing airport flight scheduling is greatly different from the actual scheduling. It includes: determining the transit duration of each transfer of the to-be-scheduled flight of the flight schedule according to the historical scheduling information of the flight schedule, and determining the average duration corresponding to the to-be-scheduled flight according to the transit duration of each transfer, taking the average duration as the reference duration of the to-be-scheduled flight; determining the influence coefficient corresponding to each influence factor that has an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information, and determining the expected turnaround time corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration; arranging the flight time corresponding to all to-be-scheduled flights of the day based on the expected turnaround time corresponding to all to-be-scheduled flights of the day, and scheduling the flight schedule of the to-be-scheduled flight in the airport according to the arranged flight time.

The present invention provides an RTOS timer interrupt processing system, device, method, electronic device and readable storage medium. The device includes: an average task switching time calculation unit that calculates the average task switching time by pre-statistically calculating the average time-consuming of multiple task context switches; a timer task management and calculation unit that recalculates the remaining timeout times of all timers according to the timeout time of the head task in the timer task list; traverses the list to determine whether a task expires within the average task switching time, and cancels the suspended state of the timer task that expires and is within the average task switching time, and enters the ready state; a hardware timer configuration unit configures and restarts the hardware timer, and sets the timer overflow preload value according to the timer task timeout time. Through the present invention, invalid task context switches are reduced, the turnaround time of tasks is shortened, and the throughput of the system is improved, providing a more efficient and stable operating environment for real-time applications.

Disclosed are assay formulations and methods that enable ultra-fast (e.g., 5 min to 15 min turnaround time, depending on target concentration), one-pot detection of nucleic acids. The assay can implement an ultra-fast LAMP or RT-LAMP reaction with real-time fluorescent readout, pH-independent colorimetric readout, and / or pH-dependent colorimetric readout. The assay enables quantification of the target with fluorescent readout. The assay can be carried out in one-pot format with the reaction components in solution form, lyophilized form, or air-dried form. The assay enables a broader effective temperature range for the LAMP or RT-LAMP reaction as compared to conventional assay chemistries.

Systems and methods to receive a computing job from an Electronic Design Automation (EDA) software application, and dynamically determine at least one precedence or successor job constraint for the received computing job, are described herein. Further, an edge inference algorithm is used to determine edges of a Dynamic Acyclic Graph (DAG) representing the EDA software application computing jobs, along with jobs that are dependent on the received computing job. In this way, job dependencies are discovered and scheduled dynamically, reducing turnaround time, and increasing efficiency of computing resources.

A riserless marine package enables post-BOP utilization of both drill centers simultaneously using an offset riser system to significantly reduce turnaround time between drilling and casing operations and reduce associated costs. A sealing system of the riserless marine package, disposed above the subsea BOP, separates the drilling mud below the sealing system from the seawater above. Drillstrings and casing strings are run through the seawater and transit through the sealing device of the riserless marine package. The rig may assemble and operate a drillstring from the first drilling position and assemble and operate a casing or liner string in the second drilling position. Upon reaching total depth using the offset riser, the rig may trip the drillstring until the BHA clears the subsea BOP. Once the BHA clears the subsea BOP, the rig may then move to insert a casing string hanging from the second drilling position into the well.

The present invention discloses a cluster-based strategy-based joint planning method and system for container port berth and yard allocation. The method comprises: collecting information on ships, container trucks, and yards during a target time period, including the planned arrival time of ships, the number and type of containers on board, the stockpiling status of the yard, and the planned arrival time of external container trucks; inputting the collected information into a pre-established multi-objective mixed integer programming model to calculate an optimal planning strategy, including a berthing plan for all ships and a loading and unloading plan for container batches during the specified time period; wherein the pre-established multi-objective mixed integer programming model includes three objective functions, namely, minimizing the expected turnaround time for ship berthing, minimizing the total container transport distance between berths and container areas, and minimizing the workload imbalance in the container areas of the yard; and executing ship and yard operations according to the optimal planning strategy. The present invention can improve the overall operational efficiency of the terminal.

Disclosed are assay formulations and methods that enable ultra-fast (e.g., 5 min to 15 min turnaround time, depending on target concentration), one-pot detection of nucleic acids. The assay can implement an ultra-fast LAMP or RT-LAMP reaction with real-time fluorescent readout, pH-independent colorimetric readout, and / or pH-dependent colorimetric readout. The assay enables quantification of the target with fluorescent readout. The assay can be carried out in one-pot format with the reaction components in solution form, lyophilized form, or air-dried form. The assay enables a broader effective temperature range for the LAMP or RT-LAMP reaction as compared to conventional assay chemistries.

A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.

A battery management system includes multiple measurement circuits, a communication circuit performing communication with each of the multiple measurement circuits, and a battery manager managing each of multiple batteries. The battery manager acquires a turnaround time for each of the multiple measurement circuits, measures a communication delay time between each of the multiple measurement circuits and the communication circuit for each of the multiple measurement circuits based on the turnaround time, and aligns timings at which each of the multiple measurement circuits performs analog-to-digital conversion on measurement data of at least one of a voltage and a current transmitted, based on the turnaround time corresponding to each of the multiple measurement circuits.