OPERATIONAL-TASK-ORIENTED SYSTEM AND METHOD FOR DYNAMICALLY ADJUSTING OPERATIONAL ENVIRONMENT
    1.
    发明申请
    OPERATIONAL-TASK-ORIENTED SYSTEM AND METHOD FOR DYNAMICALLY ADJUSTING OPERATIONAL ENVIRONMENT 有权
    基于操作的面向任务的系统和动态调整操作环境的方法

    公开(公告)号:US20160103705A1

    公开(公告)日:2016-04-14

    申请号:US14541377

    申请日:2014-11-14

    CPC classification number: G06F9/5061 G06F9/4856 G06F9/5005 G06F9/5027

    Abstract: The present invention provides an operational-task-oriented system and method for dynamically adjusting operational environment applicable to a computer cluster. Each operational node of the computer cluster has two or more operational systems installed. After receiving the operational task, the control node estimates the time required for completing different tasks requiring different operational systems by appropriate operational nodes and compares the estimated finish time and the assigned finish time for judging how to adjust the operating system running in the operational nodes. Thereby, the operational task can be completed in the assigned finish time. Another method is to use the control node to analyze the proportions of the tasks requiring different operational systems in an operational task and hence adjusts the operational system running in an operational node according to the proportion of requirement. Thereby, the operational task can be completed in the shortest time.

    Abstract translation: 本发明提供了一种用于动态调整适用于计算机集群的操作环境的面向操作任务的系统和方法。 计算机集群的每个操作节点都安装了两个或多个操作系统。 在接收到操作任务之后,控制节点估计完成由适当的操作节点完成不同任务需要不同操作系统所需的时间,并比较估计的完成时间和所分配的完成时间,以判断如何调整在操作节点中运行的操作系统。 因此,可以在指定的完成时间内完成操作任务。 另一种方法是使用控制节点来分析在操作任务中需要不同操作系统的任务的比例,并因此根据需求比例来调整在运行节点中运行的操作系统。 从而可以在最短的时间内完成操作任务。

    METHOD FOR CORRECTING ABNORMAL POINT CLOUD

    公开(公告)号:US20220172327A1

    公开(公告)日:2022-06-02

    申请号:US17354188

    申请日:2021-06-22

    Abstract: A method for correcting abnormal point cloud is disclosed. Firstly, receiving a Primitive Point Cloud Data set by an operation unit for dividing a point cloud array into a plurality of sub-point cloud sets and obtaining a plurality of corresponding distribution feature data according to an original vector data of the Primitive Point Cloud Data set. Furthermore, recognizing the sub-point cloud sets according to the corresponding distribution feature data for correcting recognized abnormal point cloud. Thus, when the point cloud array is rendered to a corresponding image, the color defect of the point cloud array will be improved or decreased for obtaining lossless of the corresponding image.

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