Elevator – industrial lift truck – or stationary lift for vehicle – With call registration means – Shared by plural load supports
Reexamination Certificate
2000-12-04
2001-12-04
Salata, Jonathan (Department: 2837)
Elevator, industrial lift truck, or stationary lift for vehicle
With call registration means
Shared by plural load supports
C187S384000, C187S387000
Reexamination Certificate
active
06325178
ABSTRACT:
TECHNICAL FIELD
The present invention relates to an elevator group managing system for managing and controlling efficiently a plurality of elevators in a group.
BACKGROUND ART
In general, in the system in which a plurality of elevators go into commission, group management control is carried out. There are carried out therein various types of controls such as the assignment control for selecting an optimally assigned elevator in response to a call which has occurred in a hall. A forwarding operation is carried out in a peak time for a a specific floor differently from the occurrence of the call, and service zone may be divided.
In recent years, for example, as disclosed in Japanese Patent No. 2664766 or Japanese Patent Application Laid-open No. Hei 7-61723, there has been proposed a method of predicting the control result of the group management, i.e., group management performance such as waiting time and the like to set the control parameters.
In accordance with the above-mentioned two prior art publication, there is stated a system in which a neural network for receiving as its input traffic demand parameters and evaluation arithmetic operation parameters when carrying out the call assignment to output group management performance is employed, and the output result of the neural network is evaluated to set the optimal evaluation arithmetic operation parameter.
However, in the above-mentioned two articles relating to the prior art, a parameter which is set on the basis of the group management performance prediction result is limited to the single evaluation arithmetic operation parameter when carrying out the assignment. Thus, carrying out the arithmetic operation employing such a single evaluation arithmetic operation parameter when carrying out a call assignment leads in the limitation to the enhancement of the transport performance. That is, the various rule sets such as the forwarding operation and the zone division needs to be utilized depending on the traffic situation and hence excellent group management performance can not be obtained.
In addition, while the neural net has the advantage that its accuracy of arithmetic operation can be enhanced by learning, at the same time, it has also the disadvantage that it takes a lot of time for the accuracy of the arithmetic operation to reach a practical level.
In the system which is disclosed in the above-mentioned two articles relating to the prior art, it is impossible to obtain the expected group management performance unless the learning of the neural net is previously carried out in the factory. In addition, in a case where the traffic demand is abruptly changed due to a change of tenants in an associated building, it is possible to cope speedily with the change.
In the light of the foregoing, the present invention has been made in order to solve the above-mentioned problems associated with the prior art, and it is therefore an object of the present invention to provide an elevator group managing system which can select the optimal rule set in accordance with the performance prediction result to provide excellent service at all times.
DISCLOSURE OF THE INVENTION
According to an elevator group managing system of one aspect of the present invention, an elevator group managing system for managing a plurality of elevators in a group, includes: traffic situation detecting means for detecting the current traffic situation of a plurality of elevators; a rule base for storing therein a plurality of control rule sets; performance predicting means for predicting the group management performance which is obtained when applying an arbitrary rule set stored in the rule base to the current traffic situation; rule set selecting means for selecting the optimal rule set in accordance with the prediction result obtained from the performance predicting means; and operation control means for carrying out the operation control for each of the elevator cars on the basis of the rule set which has been selected by the rule set selecting means.
In addition, an elevator group managing system further includes a weight database for storing therein weight parameters of a neural net corresponding to an arbitrary rule set stored in the rule base, and the system is characterized in that the performance predicting means, for the specific rule set stored in the rule base, fetches the weight parameters of the neural net corresponding to the specific rule set from the weight database to carry out the prediction of the group management performance by the neural net using the weight parameters thus fetched.
In addition, an elevator group managing system further includes performance learning means for comparing the prediction result provided by the performance predicting means with the actual group management performance after having applied the specific rule set to carry out the learning of the neural net to correct the weight parameters stored in the weight database in accordance with the learning result, and the system is characterized in that the performance predicting means carries out the prediction of the group management performance by the neural net using the corrected weight parameters.
In addition, an elevator group managing system is characterized in that the performance predicting means, on the basis of the mathematical model, predicts the group management performance which is predicted when applying an arbitrary rule set stored in the rule base to the current traffic situation.
Furthermore, according to an elevator group managing system of another aspect of the present invention, an elevator group managing system for managing a plurality of elevators in a group, includes: traffic situation detecting means for detecting the current traffic situation of a plurality of elevators; a rule base for storing therein a plurality of control rule sets; first performance predicting means for on the basis of a neural net, predicting the group management performance which is obtained when applying an arbitrary rule set stored in the rule base to the current traffic situation; a weight database for storing therein weight parameters of the neural net corresponding to the arbitrary rule set stored in the rule base; and performance learning means for comparing the prediction result provided by the first performance predicting means with the actual group management performance after having applied the specific rule set to carry out the learning of the neural net to correct the weight parameters stored in the weight database in accordance with the learning result, wherein the first performance predicting means carries out the prediction of the group management performance by the neural net using the corrected weight performance, and wherein the system further includes: second performance predicting means for on the basis of the mathematical model, predicting the group management performance which is predicted when applying an arbitrary rule set stored in the rule base to the current traffic situation; performance prediction accuracy evaluating means for comparing the prediction results provided by the first and second performance predicting means with the actual group management performance to determine which of the first or second performance predicting means is employed in accordance with the comparison result; rule set selecting means for selecting the optimal rule set in accordance with the prediction result, from either the first or second performance predicting means, which has been determined by the performance prediction accuracy evaluating means; and operation control means for carrying out the operation control for each of the elevator cars on the basis of the rule set which has been selected by the rule set selecting means.
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pate
Hikita Shiro
Tajima Shinobu
Leydig , Voit & Mayer, Ltd.
Mitsubishi Denki & Kabushiki Kaisha
Salata Jonathan
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