The present invention relates to an elevator operation management and control system.
FIG. 7 is an explanatory diagram showing the basic concept for estimating a traffic flow of a prior art traffic control system described in JP-A-7-309546 (corresponding to U.S. Pat. No. 5,459,665) when the object controlled is a plurality of elevators.
In FIG. 7, reference numeral 11 denotes traffic amount data which is composed of quantitative information, such as number of persons who get into an elevator and number of persons who get off the elevator at each floor, 13 denotes a traffic flow indicative of the generation and movement of elevator users, including elements such as number of passengers, time period, elevator direction, and the like, and 12 denotes a multi-layered neural network (controlling neural network) for estimating the traffic flow 13 from the traffic amount data 11 based on a relationship between a preset traffic amount and a traffic flow pattern.
When the number of elevator users who get in at the i-th floor and get off at the j-th floor within a predetermined time period in a particular building, i.e., a number of elevator users who move from the i-th floor to the j-th floor, is assumed to be TFij, the traffic flow within the building in that time period may be expressed as follows:
Traffic Flow: TF=(TF12, TF13, . . . , TFij, . . . )xe2x80x83xe2x80x83(1)
Then, traffic amount data, which is generated by that traffic flow and is observable, may be expressed as follows:
Traffic Amount Data: G=(p, q)xe2x80x83xe2x80x83(2)
where, p is a number of persons who get into the elevator and q is a number of persons who get off the elevator at each floor.
Thus, the traffic flow is the actual flow of traffic and the traffic amount is a readily observable amount which can be found from the traffic flow.
Further, when an observable control result is set as E, the control result E may be expressed as follows:
xe2x80x83Control Result: E=(r, y, m)xe2x80x83xe2x80x83(3)
where, r is a distribution of response times to hall calls, y is a distribution of the number of times of prediction miss of each floor, and m is a distribution of the number of times when a car is full and passes a floor.
Because it is difficult to find the traffic flow TF accurately and directly from the traffic amount data G, which includes no information on moving directions of elevator users in a target time period, the traffic flow is found by an approximation.
At first, a large number of traffic flow patterns assumed for a building are prepared and the traffic amount data G and the control result E are generated for each traffic flow pattern, and control parameters are determined by simulation. Thereby, several relationships between xe2x80x9ctraffic amount data and traffic flow patternsxe2x80x9d and between xe2x80x9ctraffic flow patterns and control resultsxe2x80x9d are obtained.
Next, the relationship of the xe2x80x9ctraffic amount data and traffic flow patternxe2x80x9d is expressed in a neural network. The multi-layered neural network 12 as shown in FIG. 7 is prepared and the traffic amount data 11 is supplied to the input side. The traffic flow data 13, which has generated the traffic amount data 11, is supplied to the output side as teacher data so the neural network learns the relationship between the input and output.
As a result, when certain traffic amount data is input, the neural network 12 outputs a traffic flow pattern most resembling a traffic flow pattern that generates the input traffic amount data from the traffic flow patterns prepared in advance.
Accordingly, by teaching the neural network 12 enough traffic flow patterns, the neural network 12 can select and output a traffic flow pattern generating any arbitrary traffic amount data, or at least a traffic flow pattern very close to that traffic amount data, from the relationships of xe2x80x9ctraffic amount data and traffic flow patternxe2x80x9d learned so far.
When the same traffic amount data is generated from a plurality of different traffic flow patterns, the neural network 12 can select a traffic flow pattern which allows a specific control result to be obtained from the traffic flow patterns generating the same traffic amount data, utilizing the relationship between xe2x80x9ctraffic flow pattern and control resultxe2x80x9d. The control results differ from each other, depending upon a fixed control parameter, when the traffic flows are different.
Further, the neural network 12 can set the optimum control parameter when it is possible to estimate the traffic flow data from the traffic amount data because it is possible to set a control parameter which produces the optimum control result by simulation and the like, for the traffic flow pattern prepared in advance.
The precision of the traffic flow estimation depends on the number of combinations between traffic flow patterns and traffic amount data obtained from the traffic flow patterns prepared in advance. However, it is not practical to prepare and store in advance combinations of all kinds of traffic flow patterns and traffic amount data obtained from the traffic flow patterns because an enormous amount of memory capacity is required. Further, this prior art technology cannot allot an appropriate car efficiently with respect to a particular elevator hall call and elevator user to be served.
In addition, the technology described in JP-B-62-36954 cannot allot an appropriate car efficiently because it cannot estimate what kind of traffic flow is occurring at the current point of time in real-time, while managing the elevator operation, although it can analyze what kind of traffic flow has occurred in the past.
Accordingly, it is an object of the present invention to solve such problems by providing an elevator operation management and control system which can estimate traffic flow from observed traffic amount data in real-time and can manage and control elevator operation corresponding to the estimated traffic flow.
The present invention provides an elevator operation management and control system for managing the control of at least one elevator. The system includes a traffic data collecting section that collects traffic data regarding users of the elevator; a traffic amount calculating section, responsive to the traffic data collecting section, that calculates traffic amount data for the elevator; a traffic flow estimating section, responsive to the traffic amount calculating section, that calculates an estimated traffic flow of elevator users who move between respective floors of the elevator, based on the traffic amount data, and including a neural network having an input side to which the traffic amount data is supplied and an output side at which the estimated traffic flow was obtained; a control parameter setting section that sets control parameters for controlling operation of the elevator, based on the estimated traffic flow calculated by the traffic flow calculating section and the traffic amount data; an operation control section that controls operation of the elevator based on the control parameters set by the control parameter setting section; a teacher data creating section for creating teacher data for learning by the neural network, based on the traffic data collected by the traffic data collecting section; and an estimating function constructing section, responsive to the teacher data creating section and connected to the traffic flow estimating section, for constructing a function for calculating the estimated traffic flow, supplied to the traffic flow estimating section, by the learning of the neural network, based upon the teacher data created by the teacher data creating section.
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