公开(公告)号：US5354957A

公开(公告)日：1994-10-11

申请号：US49091

申请日：1993-04-16

Applicant: Euan Robertson

Inventor： Euan Robertson

IPC: B66B1/18 ,  B66B1/20 ,  B66B1/24 ,  B66B3/00 ,  G05B13/02 ,  G06F15/18 ,  G06N99/00

CPC classification number: B66B1/2408 ,  B66B1/2458 ,  B66B2201/102 ,  B66B2201/211 ,  B66B2201/235 ,  B66B2201/402 ,  B66B2201/403

Abstract: A system for allocating hall calls in a group of elevators includes a plurality of neural network modules to model, learn and predict passenger arrival rates and passenger destination probabilities. The models learn the traffic occurring in a building by inputting to the neural networks traffic data previously stored. The neural networks then adjust their internal structure to make historic predictions based on data of the previous day and real time predictions based on data of the last ten minutes. The predictions of arrival rates are combined to provide optimum predictions. From every set of historic car calls and the optimum arrival rates, a matrix is constructed which stores entries representing the number of passengers with the same intended destination for each hall call. The traffic predictions are used separately or in combination by a group control to improve operating cost computations and car allocation, thereby reducing the travelling and waiting times of current and future passengers.

Abstract translation: 一组用于在一组电梯中分配门厅呼叫的系统包括多个神经网络模块，以模拟，学习和预测乘客到达率和乘客目的地概率。 模型通过输入到先前存储的神经网络交通数据来学习在建筑物中发生的交通。 然后神经网络调整其内部结构，根据前一天的数据和基于最近十分钟数据的实时预测进行历史预测。 结合到达率的预测来提供最佳预测。 从每组历史车厢呼叫和最佳到达率，构建一个矩阵，其存储表示每个门厅呼叫具有相同预期目的地的乘客人数的条目。 流量预测单独使用或组合使用，以改善运营成本计算和汽车分配，从而减少当前和未来乘客的行驶和等待时间。