公开(公告)号：US5022497A

公开(公告)日：1991-06-11

申请号：US318295

申请日：1989-03-03

Applicant: Kandasamy Thanagavelu

Inventor： Kandasamy Thanagavelu

IPC: B66B1/18 ,  B66B1/20 ,  B66B1/24

CPC classification number: B66B1/2458 ,  B66B2201/102 ,  B66B2201/211 ,  B66B2201/213 ,  B66B2201/214 ,  B66B2201/215 ,  B66B2201/222 ,  B66B2201/235 ,  B66B2201/243 ,  B66B2201/402 ,  B66B2201/403

Abstract: An elevator control system employing a micro-processor-based group controller (FIG. 2), which communicates with the cars (3, 4) of the system to determine the conditions of the cars, and responds to hall calls registered at a plurality of landings in the building serviced by the cars under control of the group controller, assigning hall calls to cars based on the summation for each car, relative to each call, a weighted summation of a plurality of system response factors, some indicative, and some not, of conditions of the car irrespective of the call being assigned, assigning varying "bonuses" and "penalties" to them in the weighted summation. "Artificial intelligence" techniques are used to predict traffic levels and any crowd build up at various floors to better assign one or more cars to the "crowd" predicted floors, either parking them there, if they were empty, or more appropriately assigning car(s) to the hall calls. Traffic levels at various floors are predicted by collecting passengers and car stop counts in real time and using real time and historic prediction for the traffic levels, with single exponential smoothing and/or linear exponential smoothing. Predicted passenger arrival counts are used to predict any crowd at fifteen second intervals at floors where significant traffic is predicted. Crowd prediction is then adjusted for any hall call stops made and the number of passengers picked up by the cars. The crowd dynamics are matched to car assignment, with one or more cars being sent to crowded floor(s).

Abstract translation: 一种使用基于微处理器的组控制器（图2）的电梯控制系统，其与系统的轿厢（3,4）进行通信，以确定轿厢的状况，并响应于在多个 在由集团控制器控制的汽车服务的建筑物中的着陆，基于每个汽车相对于每个呼叫的总和，向轿厢分配门厅呼叫，多个系统响应因子的加权和，一些指示，一些不是 无论电话被分配的情况如何，在加权求和中给他们分配不同的“奖金”和“罚款”。 “人造智能”技术用于预测交通水平，并且任何人群聚集在不同的楼层，以更好地将一辆或多辆汽车分配到“人群”预测的楼层，或者将其停放在那里，如果它们是空的，或者更适当地分配汽车（ s）到大厅呼叫。 通过采集单个指数平滑和/或线性指数平滑的方式，实时收集乘客和停车点数，并对流量水平进行实时和历史预测，预测各层楼层的交通状况。 预计乘客到达人数用于预测任何人群，以十五秒的间隔预测明显的交通流量。 然后，对于任何大厅通话站和乘坐车辆的乘客人数，对人群预测进行调整。 人群动态与汽车分配匹配，一辆或多辆汽车被发送到拥挤的地板。