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METHOD AND SYSTEM FOR A PRELIMINARY DETERMINATION OF WEIGHT SHIPPING career excavating equipment

机译:初步确定职业运输设备重量的方法和系统

摘要

1. A method for preliminary load weight determination career excavating equipment, the method comprising the steps of: scanning the surface of excavation for development profile excavation surface excavation plan chosen for the profile of excavation surface excavation plan operate for excavation surface profile on a surface excavation using the algorithm Pre-weighing of cargo, based on a set of drive signals of career excavating equipment; and determining the volume of material to be excavation via career excavating equipment, on the basis of at least the plurality of drive signals and profile poverhnosti.2 excavation. . A method according to claim 1, wherein the scanning surface to develop excavation excavation surface profile comprises the steps of: scanning the medium surrounding the excavation surface using a plurality of sensors; ianaliziruyut data generated by a plurality of sensors, using the algorithm obrazov.3 classification and recognition. . A method according to claim 2, in which scanning the environment surrounding the excavation surface, using a plurality of sensors comprises the steps of: determining a topography that can inhibit the activity of career excavating equipment; and determining one or more items that may hinder the activities of career excavating oborudovaniya.4. . A method according to claim 2, wherein the classification algorithm and the pattern recognition comprises at least one of a decision tree, a Bayesian network; neural network; Gaussian
机译:1.一种用于初步确定职业重量的挖掘设备的方法,该方法包括以下步骤:扫描挖掘表面以寻找开发轮廓挖掘表面挖掘计划,该挖掘表面挖掘计划选择为挖掘表面挖掘计划的轮廓,用于在表面挖掘中挖掘表面轮廓使用基于职业挖掘设备的一组驱动信号的货物预称重算法;至少基于多个驱动信号和轮廓poverhnosti.2开挖,确定通过职业开挖设备开挖的物料量。 。 2.根据权利要求1所述的方法,其特征在于,所述用于形成挖掘基坑轮廓的扫描表面包括以下步骤:使用多个传感器扫描围绕所述挖掘基坑的介质; ianaliziruyut数据由多个传感器生成,使用obrazov.3算法分类和识别。 。 3.根据权利要求2所述的方法,其中,使用多个传感器扫描所述挖掘表面周围的环境包括以下步骤:确定可以抑制所述职业挖掘设备的活动的地形;并确定一项或多项可能阻碍职业发掘oborudovaniya活动的项目。4。 。 3.根据权利要求2所述的方法,其中,所述分类算法和所述模式识别包括决策树,贝叶斯网络和决策树中的至少一个。神经网络;高斯型

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