JPH07106845B2 - Elevator group management control device - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to an elevator group management control device and a group management control method suitable for realizing various user requests for elevator operation.

[Conventional technology]

Elevator group management control devices have undergone numerous improvements with the goal of reducing waiting time by improving operational efficiency. In particular, after the use of the microcomputer for the group supervisory control device, the waiting time for the generated hall call is predicted and calculated, and based on the predicted value, the allocation is performed by the evaluation function called "allocation evaluation formula". The method to be performed has become a general method, and the waiting time has been shortened to a considerable extent.

Further, there is a group management control device which automatically creates a control method corresponding to a traffic demand that changes from moment to moment by combining traffic demand learning and simulation.
It is disclosed in the publication. Further, Japanese Patent Application Laid-Open No. 62-79179 discloses a group management control method for predicting and modeling the future demand of each floor by using in-building information.

[Problems to be Solved by the Invention]

In the above-mentioned conventional technology, since the control target is to shorten the waiting time, it is "shortening the time in the car (boarding time)" or "reducing the congestion degree in the car" issued by the user. There is a problem in that a control target that has a trade-off relationship with is not taken into consideration, and a method of setting a unique control method for controlling such multiple targets has not yet been developed. For example, even in a group management control device that automatically creates a control method corresponding to traffic demand, there has been a demand for a control method with the shortest waiting time while satisfying the energy saving instruction. Alternatively, in the group management control method using the prediction model of each floor, since the transportation capacity is used as the standard for group management control, the degree of congestion in the car is sacrificed. In addition, a huge amount of in-building information was required to create a prediction model for each floor. Further, since modeling is performed for each floor, there is a problem that it cannot be used to perform group management control that covers the entire building, particularly group management control with multiple goals.

An object of the present invention is to provide a group management control device and a group management control method for determining group management control according to a user's request in a generalized form.

Another object of the present invention is to provide a group management control device and a group management control method in consideration of a specific floor in a building that strongly affects multi-target group management control.

[Means for Solving the Problems]

In order to achieve the above object, a traffic flow comparison means for comparing traffic flow data to be controlled with a traffic flow model focusing on a specific floor, a result compared by the traffic flow comparison means, and a user's request Control method determining means for determining the control method,
A group management control executing means for executing group management control according to the control method determined by the control method determining means is provided.

[Action]

The traffic flow comparison means compares the traffic flow data of the operation learning result of the actual group management control targeted for the group management control, or the predicted traffic flow data and the traffic flow model focusing on the specific floor, It operates to output the region of the traffic flow model corresponding to the traffic flow data.

The control method determination means operates to determine a control method according to the result of comparison by the traffic flow comparison means and the user's request.

The group management control execution means operates to execute the group management control according to the control method determined by the control method determination means.

Thereby, it is possible to determine a group management control method that is more general and more versatile according to the user's request.

〔Example〕

Description of an Embodiment of the Present Invention An embodiment of the present invention will be described below with reference to FIGS. 1 to 40.

FIG. 1 is a principle system block diagram showing the basic configuration of the present invention. As described below, the present invention can be implemented in various forms. However, as a configuration that is common to all of them: -Traffic flow comparison unit 1 that compares the target traffic flow data with a traffic flow model that focuses on a specific floor-Results compared by the traffic flow comparison unit 1 and user requests And a group management control execution unit 4 for executing group management control according to the control method determined by the control method determination unit 3. ing.

An embodiment of the present invention in accordance with the structure of the principle system block diagram of FIG. 1 is shown in FIGS. 2 to 40.

FIG. 2 is a schematic hardware configuration diagram of an embodiment of the present invention.

The hardware is roughly classified into a group management control device MA that is connected to an elevator machine E and a hall call device HD to perform group management control, and a personalization support device that is provided offline with the group management control device MA to determine a control method. Composed of SP and.

The group management control device MA has a group management control microcomputer M1 that executes group management control system software SF1, a learning microcomputer M2 that executes learning system software SF2, and an IC card input / output device MC. HD1-MDm are hall call devices,
It is connected to the group control microcomputer M1 via the signal line l _1. Here, in the following examples, the number of building service floors is represented as m floors. Further, the group management control microcomputer M1, each via a signal line l ₂ Elevator Unit microcomputer E1~En
Connected to. Here, in the following embodiments, it is assumed that there are n elevator cars.

Further, the personalization support device SP includes a control method determination microcomputer S1 that executes the support system software SF3, an IC card input / output device SC, an output device SD such as a display, and various user group management controls. It is composed of an input device SK such as a keyboard for inputting a request.

In this embodiment, the data between the group management control device MA and the personalization support device SP is passed through the IC card, but a magnetic medium such as a floppy disk or a communication line such as RS-232C may be used.

First, FIGS. 3 to 14 show the software configuration of the embodiment and the data table configuration thereof. The software is roughly classified into a program SF0 for the machine control executed by the microcomputer E for controlling the elevator machine, and a microcomputer M1 for the group management control.
The group management control system software SF1, executed by the learning microcomputer M2, and the learning system software SF2 executed by the learning microcomputer M2, and the support system software SF3 executed by the control method determination microcomputer S1.

FIG. 3 is a configuration diagram of the machine control program SF0 and the group management control system software SF1.

Group management control software SF1 is the operation control program S
Focusing on F11, elevator control data table ST11, hall call table ST12, elevator specification table ST13
Consists of. The operation control program SF11 directly commands and controls elevator group management control such as call assignment processing and elevator standby processing. The group management control microcomputer M1 and the learning microcomputer M2 are connected by a signal line l ₃ as shown in FIG. 2, and the contents of the elevator control data table ST11 and the hall call table ST12 are the fifth.
Program for collecting operation data of learning microcomputer M2 shown in the figure
Referenced in SF21. Further, the operation control program SF11 uses the prediction parameter table ST24 and the control parameter table ST25 of the learning microcomputer M2 shown in FIG.

FIG. 4 is a table configuration diagram in the group management control system software SF1.

The data table includes three kinds of tables: elevator control data table ST11 of current elevator status and various predicted values, hall call table ST12 showing the status of hall calls, and elevator specification table ST13 such as the number of vehicles, speed, etc. The inside of each table is divided into a plurality of parts. The details of each table will be described below as needed.

Next, a configuration diagram of the learning software SF2 will be described with reference to FIG.

Learning software SF2 consists of five programs (driving data collection program SF21, traffic demand classification program SF22,
Operation data learning program SF23, optimum parameter setting program SF24, input / output control program SF25), five tables (sampling data table ST21, traffic demand classification table ST22, learning result data table ST23, prediction parameter table ST24, control parameter table ST25) ,
And two databases (learning result database ST2
1, Control method database SD22).

The operation data collection program SF21 samples and collects data indicating the current operation state. The traffic demand classification program SF22 determines the current traffic demand classification (attendance, normal work, lunch, quiet time, etc.) from the collected traffic flow data.
The driving data learning program SF23 corrects and learns traffic flow data learned in the past with traffic flow data collected online. The optimum parameter setting program SF24 sets the prediction parameter and the control parameter corresponding to the determined traffic demand classification from each database in each parameter table. I / O control program SF25 is IC
Control I / O using a card.

FIG. 6 is a table configuration diagram of a traffic flow sampling data table ST21 within a fixed time in the learning system software SF2. Here, the single quotation mark (') attached to each symbol indicates that the data is collected online.

FIG. 7 is a table configuration diagram of the traffic demand classification table ST22. The observed traffic conditions are classified into traffic demands such as attendance at work, normal times, lunch, and off-hours. Transportation demand classification table ST
22 stores data for classifying the traffic demand. In the following examples, the traffic demand is circled (~).
). The contents of these traffic demand categories Δ will be described later with reference to FIG. Further, in the embodiment, the traffic demand is divided into seven, but this division is not limited to seven.

FIG. 8 is a configuration diagram of the learning result data table ST23 showing the learning result of the traffic demand.

FIG. 9 is a table configuration diagram of a prediction parameter table ST24 for calculating various predicted values and a control parameter table ST25 for executing call assignment control.

FIG. 10 is a configuration diagram of a learning result database SD21 that stores the contents of the learning result data table ST23 according to traffic demand, and FIG. 11 is a control that stores the contents of the control parameter table ST25 according to traffic demand. Method database SD22
It is a block diagram of.

Next, the support system software SF3 will be described with reference to FIG.

Next, the support system software SF3 will be described with reference to FIG.

The support software SF3 has five programs (IC card input / output program SF31, traffic flow comparison program SF32, control method determination program SF33, keyboard input control program SF34, screen output control program SF35), three tables (learning traffic flow The data table ST31, the predicted traffic flow data table ST32, the control method data table ST33), and the control method decision knowledge base SK31.

The IC card input / output program SF31 controls input / output using the IC card. The traffic flow comparison program SF32 is a traffic flow data ST31 learned by the group management control device MA, or a predicted traffic flow data ST32 input from the input device SK,
The area corresponding to the traffic flow model is determined by comparing with the traffic flow model. The control method determination program SF33 searches the control method determination knowledge base SK31 and determines the control method corresponding to the determined area and the input user request.
The keyboard input control program SF34 controls the input of the keyboard which is the input device KS. The screen output control program SF35 controls the output of the display which is the output device SD.

FIG. 13 is a table configuration diagram in the support system software SF3.

The data table is a learning traffic flow data table ST31, a predicted traffic flow data table ST32, a control method data table S.
It consists of 3 types of T33. Here, the double quotation mark (") attached to each symbol of the predicted traffic flow data table ST32 indicates that the data is data input by predicting traffic demand.

FIG. 14 is a block diagram of a control method determination knowledge base SK31 showing the relationship between the knowledge area division based on the traffic flow model for a specific floor and the knowledge base corresponding thereto.

A control method determination knowledge base SK31, an area division table SK31T indicating area divisions, and knowledge bases SK311 to SK315 storing control methods corresponding to respective areas. In the examples below,
The area division is represented using Roman numerals (IV). Further, in the embodiment, the area is divided into five, but this division is five.
It is not limited to one.

Next, the hardware configuration shown in FIG. 2 and FIGS.
How the group management control is performed by the respective software SF0 to SF3 and various tables shown in the figure will be described with reference to FIGS. 15 to 40 showing the programs and the like of the embodiment.

The programs described below are managed and executed under a system program that divides the program into a plurality of tasks and performs efficient control, that is, a real-time operating system. Therefore, the program can be started or suspended freely by the system timer or another program.

The description of the program begins with the group management control system software.
SF1 is followed by learning software SF2 and finally support software SF3.

In the embodiment, the demands of many items of users for the elevator group management control, that is, the control targets, "shortening the waiting time", "shortening the boarding time", and "reducing the congestion degree in the cage" will be explained. Even if other goals such as “reduction of reservation change rate”, “achievement of energy saving”, and “reduction of long waiting rate” are taken, the present invention can be realized with substantially the same configuration.

First, general group management control system software SF1 which has been conventionally known will be described with reference to FIGS. 15 to 22.

FIG. 15 is a flow of the program A110 for calculating the estimated arrival time to an arbitrary floor of the elevator, which is the basic data for the waiting time evaluation calculation and the boarding time evaluation calculation. This program is periodically activated, for example, every one second, and the predicted arrival time from the current position of the elevator to any floor is calculated for all floors and elevators.

In FIG. 15, steps A111 and A119 show looping for all elevators. Here, k represents the machine number and n represents the maximum machine number. Also, Cont
inue indicates that the parameter (k) is increased by 1.

First, in step A112, an initial value is set in the time table T in the work table in the elevator control data table ST11 shown in FIG. 4, and the contents are set in the estimated arrival time table in the elevator control data table ST11 shown in FIG. . Depending on the open / closed state of the door, how many seconds can be taken as the initial value, and the time required for the elevator to start during the downtime can be considered. Next, the floor is advanced one step (step A113), and it is compared whether or not the floor is at the same position as the elevator (step A114). If they are the same, since the estimated arrival time table has been calculated for one elevator car, the process is jumped to step A119 and the same process is repeated for other elevator cars. On the other hand, if No in step A114, the first floor traveling time Tr is added to the time table T (step A1).
15). Then, this time table T is set in the estimated arrival time table (step A116). Next, it is determined whether or not there is a car call or assigned hall call, that is, a call to be serviced to the elevator of interest (step A117), and if there is, one elevator stop time Ts is added to the time table T. Yes (step A118). Then jump to step A113 and repeat the above process for all floors.

The first floor running time Tr in step A115 and the single stop time Ts in step A118 are stored in the prediction parameter table ST24 in FIG.

FIG. 16 is a flow chart of the program A210 for calculating the predicted number of people in the car at any floor of the elevator, which is the basic data of the congestion degree evaluation calculation in the car and the satisfaction prediction evaluation calculation.
Similar to the program shown in FIG. 15, this program is also periodically activated every second, for example, and calculates the predicted number of people in the car from the current elevator position to any floor for all floors and elevators.

In FIG. 16, steps A211 and A220 show looping for all elevators.

At step A212, first, an initial value is set in the work number table V, and its contents are set in the predicted car number table in the elevator control data table ST11 in FIG. As the initial value, the current number of people in the car can be considered. The number of people in the car can be obtained by converting 60 kg into one, based on the data measured by the load cells installed in each elevator. Therefore, the number of people in the car may be in units of 0.1, but this is not a problem. Next, the floor is advanced one step (step A213), and it is compared whether the floor is at the same position as the elevator (step A214). If they are the same, it means that the predicted car number table could be calculated for one elevator car.
And repeat the same process for other elevators. On the other hand, if No in step A214, it is determined whether there is a car call (step A215). If there is a car call, the number of people expected to descend from the people table V
Subtract Cij. (Step A216). Next, it is determined whether or not there is an assigned hall call (step A217), and if there is an assigned hall call, the predicted boarding person number Hij is added to the number table V (step A218). Then, this number table V is set in the predicted number of people table (step A219). Then jump to step A213 and repeat the above process for all floors.

The predicted number of passengers Cij in step A216 and the predicted number of passengers in step A218 Hij are
It is stored in the prediction parameter table ST24 in the figure. Although omitted in FIG. 16, when the predicted number of people in the car exceeds the number of people, the number of people table V is set to the number of people, when it is less than 0, the number of people table V is set to 0, or when the direction is reversed, the number of people table V is set. Needless to say, it is necessary to reduce the number of people to 0.

FIG. 17 is a flow of the call assignment program B110. This program is, for example, periodically activated every 0.1 seconds, but may be activated at any time when a hall call occurs.

In FIG. 17, steps B112 and B117 rise (j =
1) It shows that loop processing is performed in both directions (j = 2). Also, steps B113 and B116 are at the bottom floor (i =
From 1), it shows that loop processing is performed for all floors of the top floor (i = m).

In the program B110, first, in step B111, an unassigned hall call generated after the previous cycle is read. Step B112
It is determined whether there is a hall call on the floor set in the direction set in step B113 and in the step B113 (step B114). If not, the process proceeds to the next step (B116). If there is a hall call at step B114, the process proceeds to subroutine B210 (described later) of the multi-target control allocation algorithm. Then step B1
In 15, the subroutine B210 compares the total evaluation values of the elevators stored in the total evaluation value table Φ [k] for each machine in the work table, and assigns the hall call as the optimum elevator machine with the machine having the smallest value. When the loop concerning the floor and direction ends, the processing of B110 is completed.

Then, the multi-target control algorithm B210 in FIG.
It will be described with reference to the drawings.

In the embodiment, the multi-target control algorithm will be described by taking an example of controlling the boarding time and the congestion degree in the car in addition to the waiting time, but as described above, in addition to this, the reservation change rate, energy saving, and long The waiting rate etc. may be taken up. Further, in the embodiment, the evaluation of the stop call for improving the operation efficiency and the evaluation of the full capacity are also performed.

Now, in FIG. 18, steps B211 and B213 indicate that loop processing is performed for all elevators.

In the multi-target control, the evaluation value of each target is calculated, and the total table value is assigned. Waiting time evaluation value φw in subroutine B310, boarding time evaluation value φt in subroutine B320, stop call evaluation value αa in subroutine B330, subroutine B3
At 40, the evaluation value φc of the degree of congestion in the car and the evaluation value αf of the prediction of fullness
Is calculated. In step B212, the result is multiplied by the weighting factors kw, kt, kc stored in the control parameter table ST25, and Φ [k] = kwφw + ktφt + kcφc + αa is stored in the work table total evaluation value Φ [k]. After finishing all elevators,
Fig. 17 Return to step B115.

Subsequently, each evaluation subroutine will be described.

FIG. 19 is a flow chart of the waiting time evaluation subroutine B310.

The method of calculating the waiting time evaluation value φw can be switched by the waiting time control method parameter Wmth of the waiting time control parameter of the control parameter table ST25 in FIG. That is, in step B311, the waiting time control method parameter Wmth
If is 1, proceed to step B312. In Step B312,
The call generation floor waiting time minimum algorithm (Min allocation) is used. In the minimum call generation floor waiting time algorithm, the predicted waiting time Wcall of the generated hall call is substituted into the evaluation value φw by referring to the predicted arrival time table in FIG. Also, wait time control method parameter in step B311.
If Wmth is 2, proceed to step B313. In Step B313, the maximum waiting time minimum algorithm (Min-Max allocation)
Is used. The maximum waiting time minimum algorithm refers to the predicted arrival time table and the hall call elapsed time table in FIG. 4 and predicts the sum of the elapsed time up to the present time and the future predicted arrival time from the forward assigned hall calls. Substitute for the waiting time Wmax evaluation value φw of the floor where the waiting time is maximum. Further, if the waiting time control method parameter Wmth is 3 in step B311, the process proceeds to step B314. Step B314
Uses the waiting time average minimum algorithm (Mean allocation). In the latency average minimum algorithm, the fourth
By referring to the predicted arrival time table and the hall call elapsed time table in the figure, the average value Wmean of the predicted waiting times of the forward-assigned hall calls is substituted into the evaluation value φw. When the assignment to the waiting time evaluation value φw is completed, the process proceeds to the next process of the subroutine B210.

FIG. 20 is a flow of the boarding time evaluation subroutine B320.

First, referring to the estimated arrival time table and the car call elapsed time table in Fig. 4 in step B321, the estimated boarding time, which is the sum of the elapsed time up to the present and the estimated arrival time in the future, from the car calls in the front Maximum boarding time on the floor
Calculate Tmax. At step B322, Tmax and the set value Tinst of the boarding time control parameter of the control parameter table ST25 shown in FIG. 9 are compared in magnitude, and if Tmax ≦ Tinst, step B323 substitutes 0 for the boarding time evaluation value φt. Tmax> Ti
If it is nst, at Step B324, the boarding time evaluation value φt is set to Tma.
x−Tinst) is substituted. After the above processing is completed, the processing proceeds to the next processing of the subroutine B210.

FIG. 21 is a flow of the stop call evaluation subroutine B330.

Stop call evaluation is to eliminate the dumpling operation by performing an evaluation that makes it easy to assign a hall call stop on the floor near the newly generated hall call or a car call scheduled to stop the car call to a new hall call, The idea is to save energy and improve operating efficiency.

First, in step B331, the stop call evaluation value αa is initialized. Using the neighborhood floor constant αd of the stop call evaluation parameter table of the control parameter table ST25 of FIG. 9, a loop of step B332 and step B335 is performed with the current floor i as the center ±.
Perform processing on the αd floor. In step B333, it is checked whether there is an assigned hall call or a car call in the same direction, and if there is, an evaluation value α [iw] corresponding to the floor difference is added to αa in step B334. After the above processing is completed, the processing proceeds to the next processing of the subroutine B210.

FIG. 22 is a flow of the subroutine B340 for the evaluation of congestion degree in the basket and the evaluation of full capacity.

First, referring to the predicted car number table in FIG. 4 at step B341, the maximum value Cmax of the predicted car number on each floor in front is calculated.
Is calculated. In step B342, Cmax is compared with the set value Cinst of the congestion degree control parameter in the car of the control parameter table ST25 shown in FIG. 9, and if Cmax ≦ Cinst, 0 is assigned to the in-car congestion degree evaluation value φc in step B343. To do. Cmax
If it is> Cinst, the congestion evaluation value C ₁ of the in-car congestion degree control parameter in the control parameter table ST25 in FIG. 9 is substituted into the in-car congestion degree evaluation value φc in step B344. Furthermore,
At step B345, Cmax is compared with the capacity Ccap of the elevator specification table ST13 shown in FIG. 4, and if Cmax ≦ Ccap, at step B346, 0 is substituted for the full capacity estimation evaluation value αf.
If Cmax> Ccap, M step B347 will predict full capacity evaluation value α
The fullness evaluation value C ₂ of the fullness estimation evaluation control parameter in the control parameter table ST25 of FIG. 9 is substituted into f. After the above processing is completed, the processing proceeds to the next processing of the subroutine B210.

As described above, by using the programs of FIGS. 15 to 22,
The group management control system software SF1 that executes multi-target control has been described.

Next, regarding general learning software SF2,
This will be described with reference to FIGS. 11 to 11 and FIGS. 23 to 28.

FIG. 23 is a flow of the operation data collection program SF21.

This program is started every fixed period (for example, 1 second), and after collecting data for a fixed time (for example, 5 minutes), the program is written to the sampling data table ST21 in FIG.

First, in Step C111, data on the running time and the number of running floors of all units after the previous cycle is collected. This data is used to determine the first floor running time Tr. At Step C112, the data of the stop time and the stop count of all the units after the previous cycle are collected. This data is used to determine the once stop time Ts. Next, in step C113, the direction reversal is detected, and if there is reversal, the number of times of direction reversal d'is counted up in step C114. In step C115, a stop state is detected, and if there is a stop, step C116 counts up the number of stop times e'ij of the stop state generation floor. In Step C117,
Detect hall call, and if there is hall call, step C118
Then, the number of hall calls f'ij at the hall call generation floor is counted up. Step C119 detects a car call, and if there is a car call, the number of car calls at the car call generation floor is detected by step C120.
Count up g'ij. At step C121, the boarding passengers are detected, and if there is boarding, the number of passengers h'ij on the boarding occurrence floor is counted up at step C122. Step C123 detects passengers getting off, and if there is an exit, step C123
At C124, count down the number of people c'ij who have descended on the floor where the descending flight occurred. When the data collection is completed, the sampling time is detected in step C125, and if it is the end timing, the sampling data table ST21 is written in step C126. The data collected by this program are the traffic demand classification program SF22 and the driving data learning program SF23.
Used in.

Next, the traffic demand classification program SF22 will be described.

First, the concept of traffic demand classification will be described with reference to FIG.

Elevator traffic demand is balanced during uptime, such as when going to work, and during climbing and down, as in normal times. There are times when traffic is very light. Therefore,
This traffic demand classification can be divided by measuring the number of passengers boarding within a certain period of time, with the horizontal axis representing DN traffic (number of passengers descending within a certain period of time) and the vertical axis representing UP traffic (constant in rising direction). Fig. 24 shows the number of passengers boarding in time. For example, in Fig. 24, it corresponds to a quiet time, and corresponds to a normal time. Since the traffic demand classification is characterized by the total traffic volume and the UP / DN ratio, the traffic demand classification table ST22 stores the maximum and minimum (the UP / DN ratio is converted to an angle by taking the arctangent). ing.

The actual program flow is shown in Fig. 25.

This program is started after writing to the sampling data table ST21 by the operation data collection program SF21. As a pre-process for the traffic demand classification, the number of passengers hsum [j] for each direction is calculated in a loop from step C211 to step C216. At step C217, set the traffic demand to the variable δ, and at step C218 check whether the total traffic volume is within the traffic demand δ range or at step C218 whether the UP / DN ratio is within the traffic demand δ range. If not satisfied, the traffic demand δ is changed and the process is repeated. If both conditions are satisfied, the variable δ is substituted for the traffic demand classification Δ at step C220, and the process is ended. Through the above processing, the traffic demand classification Δ shown in FIG. 7 corresponding to the collected sampling data can be obtained.

Next, the driving data learning program SF23 will be described with reference to FIGS. 26 and 27.

Learning is performed by modifying the contents of the past learning result database using the data ST21 collected online.

This program is written to the sampling data table ST21 by the operation data collection program SF21,
It is activated following the calculation of the traffic demand classification Δ by the traffic demand classification program SF22. First, Fig. 26 Step C311
Then, the sampling data table ST21 is read. In the subroutine C320, various parameters are calculated using the sampling data table ST21. In other words, the first floor running time Tr at Step C321 in FIG. Ask in. One stop time Ts at step C322, Ask in. Further, parameters are calculated for each direction and each floor by a loop relating to the directions of steps C323 and C331 and a floor relating to the floors of steps C324 and C330. First, at step C325, stop establishment Pij, Ask in. At Step C326, the predicted number of passengers hij Ask in. At Step C327, the predicted number of people Cij Ask in. In Step C328, the door time control parameter Dij is Ask in. In step C329, the service priority level Sij
To Ask in. Here, from step C325 to step C329
a ₁ , ..., A ₆ represent a certain constant. The parameter groups such as Tr, Ts, Pij, Hij, Cij, Dij and Sij obtained by the above processing are collectively expressed as Dnew. Returning to FIG. 26, in step C312, the previously learned data corresponding to the traffic demand classification Δ is read from the learning result database. This data is collectively referred to as Dold. In Step C313, this Dn
Data corresponding to the learning result data table ST23 by combining ew and Dold with the following equation using the connection coefficient γ (0 ≦ γ ≦ 1)
Calculate Dpre.

Dpre = γDnew + (1-γ) Dold Here, the closer γ is to 1, the more important is the data collected online, and the closer γ is to 0, the more important is the previous learning result. Data Dpre at step C314
Is written to the learning result database SD21, and the process ends.

Further, referring to FIG. 28, the optimum parameter setting program SF
Explain 24.

This program is written to the sampling data table ST21 by the operation data collection program SF21,
It is activated following the calculation of the traffic demand classification Δ by the traffic demand classification program SF22.

Step C411 reads the traffic demand classification Δ. Step C4
At 12, the control parameter corresponding to the traffic demand classification Δ is read from the control method database SD22 and set in the control parameter table ST25. At Step C413, the prediction parameter corresponding to the traffic demand classification Δ is read from the learning result database SD21 and set in the prediction parameter table ST24.

Further, the input / output control program SF25 controls processing such as writing the contents of the learning result database SD21 to the IC card or writing the contents of the IC card to the control method database SD22. (The flow is omitted.) The above is the embodiment of the group management control system software SF1 and the learning system software SF2 in the present invention, and the processes up to this point are the group management control execution unit of the principle system block diagram of FIG. Equivalent to 4.

Next, regarding the support system software SF3 that determines a group management control method by a traffic flow model focusing on a specific floor,
This will be described with reference to FIG. 12, FIG. 12 to FIG. 14, and FIG. 29 to FIG.

This support system software SF3 is started when a specific floor is changed, it seems that the group management control does not match the traffic flow, or automatically every predetermined time.

The support system software SF3 was compared by the traffic flow comparison unit that compares the data of the target traffic flow and the traffic flow model focusing on a specific floor in the Fig. 1 principle system block diagram, and the traffic flow comparison unit. It corresponds to a control method determination unit that measures and determines the control method according to the result and the user's request from the control method determination knowledge base.

First, the traffic flow comparison unit will be described.

The traffic flow data used here is the group management controller MA.
The result of learning in, that is, the traffic demand classification Δ shown in Fig. 24.
The data related to the learning traffic flow data table ST31 passed to the personalization support device SP through the IC card is used. If the building (elevator) is not in operation, the one input as the predicted traffic flow data table ST32 from the input device SK is used.

A traffic flow model focusing on a specific floor used here will be described with reference to FIGS. 29 to 31. The traffic flow in the building corresponds to the traffic demand, boarding demand, or
There are specific floors and other general floors that are especially in demand
Affects multi-target control. Therefore, paying attention to the specific floor and the general floor, the elevator use of each passenger can be classified into any of the following three modes.

1. Boarding from a specific floor and getting off at the general floor.

2. Boarding from the general floor and getting off at a specific floor.

3. Boarding from the general floor and getting off at the general floor.

This individual passenger is the basic element that forms the traffic flow,
Every traffic flow is a collection of individual passengers. Therefore,
Each of the above three forms is: 1. A divergent element traffic flow model (Spread-out Traffic Model: ST model) shown in Figure 29. 2. A concentrated element traffic flow model (Gathering Traffic Model: GT model) shown in Figure 30. It is modeled as the inter-level elemental traffic flow model (Intrefloor Traffic Model: IT model) shown in Fig. 31. Here, the ST model is a central component of the traffic flow at the time of going to work, and the movement of the visitor from the lobby floor to the destination floor during normal times can also be represented by the ST model. The GT model is used for going to the dining room floor during lunch, or for using the elevator to go out of the building during normal times. The IT model is a model used between floors, which is often used in normal times of a company-owned building.
Elevator usage that cannot be classified as a GT model can be considered as an IT model component.

By using these elemental traffic flow models, the SGT model is a combination of the ST model and GT model as shown in Fig. 32, and the SGIT model with the IT model as shown in Fig. 33 and the It can be synthesized. As a composite traffic flow model, ST models and GT models with multiple specific floors, and IT models for only certain parts of the building can be considered. A combination of three-element traffic flow models in this composite traffic flow model,
Alternatively, since the component ratios can be set arbitrarily, it becomes possible to treat all traffic flows as a composition in which the component ratios of the three elemental traffic flow models are changed.

The component ratios of the three elemental traffic flow models in the target traffic flow can be illustrated by using the “ternary state diagram” of FIG. 34 (a). The ternary phase diagram is used to illustrate the composition ratios of the three elements contained in the characteristics in the field of metal materials and the like, but in the present invention, three elements contained in the traffic flow are included. It is used to illustrate the composition ratio of the traffic flow model.

In the ternary diagram, the ST vertex shows the traffic flow with 100% ST model component, and conversely, the bottom of GT-IT shows the traffic flow with 0% ST component.
If the ST component is x%, it is expressed as a straight line parallel to the GT-IT base and at a position of x% of the height from the GT-IT base to the ST vertex.
The same applies to the GT and IT components. By using this ternary state diagram, all traffic flows can be represented by the constituent ratios of their elemental traffic flow models. For example, ST component
The traffic flow of 30%, GT component 45%, IT component 25% is expressed as the intersection of the straight lines corresponding to each component.

Furthermore, the control method for realizing the multi-target control corresponding to the traffic flow changes according to the component ratio of the three elemental traffic flow models. FIG. 34 (b) shows an example of the experimental result showing the situation.

In Fig. 34 (b), the traffic volume (total number of passengers) is fixed, and the IT model component ratio at that time is 0% (SGT), 50% (SGIT), 100.
% (IT) generated traffic flow, and the result of a simulation experiment on the result of group management control for the traffic flow using the set value Cinst of car congestion control in Fig. 22 as a parameter Is. The degree of congestion in the car on the vertical axis is represented by the ratio of passengers who encounter congestion (multiplication by 50% or more of the capacity). From Fig. 34 (b), in the SGT model (IT component 0%), the stronger the control of congestion degree in the car, the better the result. However, when the IT component is included in 50% (SGIT model), the The improvement tends to be minimal or saturated. Furthermore, in the IT model (IT component 100%), the results cannot be improved even if the control is strengthened.
From this, as a control method when improvement of the congestion degree in the car is required: -In the region V where the IT component is small, Cinst is set small.

-In the area IV where the IT component increases, Cinst is set near the minimum point.

-In the region III where the IT component is particularly large, control is performed by adjusting the control amount C ₁ regardless of Cinst.

And it is necessary to make settings.

Therefore, the configuration of the knowledge base for setting the control method that realizes the multi-target control corresponding to the traffic flow is performed for each region divided on the ternary diagram according to the component ratios of the three elemental traffic flow models. It is effective to use the method of the present invention in which a control method is prepared.

That is, Fig. 34 (a) shows the specific traffic flow of the traffic demand classification Δ detected in Fig. 24.
The group management control is performed by determining the group management control method suitable for processing the obtained traffic flow model by the ternary state diagram shown in FIG.

An embodiment of the support system software SF3 will be described with reference to FIGS. 35 to 40.

Fig. 35 shows the flow of the traffic flow comparison program SF32.

The program is a subroutine D210 that determines a specific floor from the input traffic flow, a subroutine D310 that calculates the element traffic flow model component ratio of the input traffic flow, and a subroutine D4 that determines the area division Λ from the element traffic flow model component ratio.
Composed of 10.

Figure 36 shows a program that automatically determines a specific floor from the number of people getting on and off the traffic flow. In FIG. 36, when the parameter k = 1, it means boarding, and when k = 2, it means getting off.

Loop about getting on and off of steps D211 and D220, step D2
In the loop about the direction of 12 and D219, first, step
At D213, the average μ and standard deviation ρ of the number of people getting in or out of the train are calculated. After that, in step D215 in the loop regarding the floors of steps D214 and D218, it is determined whether the number of people getting in or out of the i-th floor in the j direction is (μ + 2ρ) or more. 1 is set to the bit of the specific floor flag corresponding to. If the result of the determination at step D215 is No, step D217 is set to 0 for the bit of the specific floor flag corresponding to the i floor. When all the above processing is completed, the specific floor determination result regarding getting on / off and the direction is obtained in the specific floor flag.

By using this method, it is possible to automatically determine the specific floor in the target time zone.

Next, the subroutine D310 for calculating the element traffic flow model component ratio of the input traffic flow will be described with reference to FIG.

The divergence traffic flow ratio STR is the ratio of the number of passengers from a specific floor to the total number of passengers, so in step D311, Calculate with. Concentrated traffic flow ratio GTR is step D312
so, Calculate with. Inter-floor traffic flow ratio ITR is from all (100%) to ST
Since it is a value excluding R and GTR, in step D313, ITR = 100-STR-GTR is calculated.

Through the above processing, the component ratio of each elemental traffic flow model can be calculated.

Next, a subroutine D410 for determining the area classification Λ from the element traffic flow model component ratio will be described with reference to FIG. 38.

At step D411, the area division is set to the variable λ. At step D412, the divergent traffic flow ratio STR is in the range of region λ, at step D413 the concentrated traffic flow ratio GTR is in the range of region λ, or at step D413 the inter-level traffic flow ratio ITR is in the range λ. It is checked whether it is within the range, and if either is not satisfied, the area division λ is changed and the processing is repeated. If all the conditions are satisfied, the region segment Λ is obtained at step D415.
Substitute the variable λ for and end the process.

With the above processing, the area segment Λ corresponding to the target traffic flow data can be obtained. The upper and lower limit values of the area in the processing of D410 are stored in the area classification table SK31T as the values of the control method determination knowledge base SK31 in FIG. The area division can be changed by rewriting the upper and lower limit values of the area division table SK31T.

Continuing on, the control method decision program SF33 is shown in Figs.
It will be described with reference to the drawings.

Control method decision program SF33 is a traffic flow comparison program
The program is a program for deciding a control method by searching a control method decision knowledge base according to the area segment Λ determined by SF32 and the user's request input from the input device SK.

First, the user's request is accepted in step D511 in FIG.
This input is performed on the screen as shown in FIG. 40, for example.
The input user's request is assigned points at step D512. The points assigned to each input target are
The order of importance is 3 (important) → 2 → 1 (similar) → 0 → 0. In the embodiment, the total score of each input target is 5 (= 3 + 2) waiting time, 0 (= 0 + 0) boarding time, and 2 (= 0 + 2) congestion level in the car. At step D513, the important goal is determined from the scoring result. When deciding, a goal of 4 points or more is important, and when all goals are 3 points or less, each goal is the same. As a result, the user's request is "waiting time is important (inpW)", "boarding time is important (inpT)", "car congestion is important (inpC)", and "each goal is the same (inpW)".
It can be divided into four ways. At step D514, the control method according to the request is retrieved from the knowledge SK311, ..., KS315 corresponding to the area segment Λ determined by the above-described traffic flow comparison program SF32. The area correspondence knowledge is in the form of if (desired condition) -then (control method parameter).

For example, the knowledge about the area segment I is if {inpW} -then {kw = 0.6, Wmth = 1, kt = 0.2, Tinst = 40, kc = 0.2, Cinst = Ccap × 0.8, C ₁ = 300, ...} if {inpT} -then {kw = 0.3 , Wmth = 2, kt = 0.5, tinst = 20, kc = 0.2, Cinst = Ccap × 0.4, C 1 = 600, ...} if {inpC} -then {kw = 0.2, Wmth = 2, kt = 0.3, Tinst = 20, kc = 0.5, Cinst = Ccap × 0.3, C ₁ = 600, ...} if {inpA} −then {kw = 0.4, Wmth = 2, kt = 0.3, Tinst = 40, kc = 0.3, Cinst = Ccap × 0.6, C ₁ = 300, ...}, and the control method parameters can be determined when the area section Λ and the request are given. At step D515 the control method parameters are presented to the output device and at step D516 a confirmation is requested. If the confirmation is No, the request is corrected in step D517 and the process is repeated. If Yes at step D516, the control method parameter is written to the control method data table ST33, and the processing is ended.

In addition, the IC card input / output program SF31 of the support software SF3 uses the contents of the IC card as a learning result data table.
Write to ST31 or control method data table ST33
It controls the processing such as writing the contents of the to the IC card.
(The flow is omitted.) According to the present embodiment, there are the following effects.

The configuration that compares the traffic flow to be targeted for group management control with the traffic flow model focusing on a specific floor facilitates the determination of the traffic flow that can be generalized and handled. Also, the characteristics of traffic flow can be clarified.

The influence of the specific floor can be more accurately reflected in the determination of the control method by adopting a configuration in which the specific floor is determined by getting on and off. Therefore, even if the specific floor changes, it is not necessary to reset the control data by obtaining it, and the group management control that automatically matches the traffic flow for the new specific floor is performed.

By constructing the knowledge base for each traffic flow model corresponding area, the construction and maintenance of the knowledge base can be facilitated.

The traffic flow can be easily recognized by expressing the traffic flow in the area on the ternary diagram by the component ratio of the three basic traffic flow models.

By configuring the traffic flow comparison unit and the control method determination unit off-line with the group management control execution unit, the device scale of the group management control device can be reduced. Also, the knowledge base can be easily updated.

The support device can be simplified by determining the control method only with the knowledge of the knowledge base. In addition, the immediacy of the control method determination can be improved.

Description of Other Embodiments of the Present Invention Next, other embodiments of the present invention will be described. In the following description, the description of the common part with the above-described embodiment is omitted,
Only the difference will be described.

First, an embodiment in which the control method determination knowledge base is provided with supplementary knowledge will be described.

In this embodiment, the table structure of FIG. 41 in which the supplementary knowledge SK31C is added to the table structure diagram (FIG. 14) of the control method decision knowledge base SK31 in the FIG. 12 support software SF3 is used. The supplemental knowledge is if {environmental condition} -then (control method change point) or if {environmental condition} -then {control method parameter}. Environmental conditions include: -Conditions related to the location of specific floors (specific floors are end floors, several floors from the end floors, central floors, etc.)-Conditions related to the number of specific floors (specific floors are two floors in one direction and ride in the same direction) There are special floors and special floors). Whether these conditions are satisfied can be checked by the specific floor flags HSj and CSj created by the specific floor determination program D210.

Supplementary knowledge is, for example, if {Descent specific floor is the end floor} -then {Cinst = Cinst × 1.1} if {Descent specific floor is the central floor} -then {Cinst = Cinst × 0.8} if {Descent specific floor is two Both floors} -then {Cinst = Cinst × 1.2} and so on. The supplementary knowledge is used when the control method is searched in step D514 in FIG.

According to the present embodiment, by adding the supplementary knowledge to the control method decision knowledge base, there is an effect that the control method can be decided in accordance with the actual condition of the building environment. Further, there is an effect that the knowledge that cannot be stored in the knowledge base corresponding to the ternary state can be utilized for determining the control method.

Next, an embodiment in which a simulation program for simulating group management control is added to the support system software SF3 will be described.

In this embodiment, the support system software SF3 is changed as shown in FIG. The change in FIG. 42 is the addition of the simulation program SF36.

Program changes of the control method determination program SF33 that accompany this will be described with reference to FIGS. 43 to 45.

In FIG. 43, steps D611, D612, D613, D617, D618, D
619 and D620 are the steps D511, D512, D513 and D51 shown in Fig. 39, respectively.
It is the same as 6, D517, D518, D519. However, step D614
The method to be searched in the, if {inpW} -then {kw = 0.6, Wmth = 1, kt = 0.2, Tinst = 40, kc = 0.2, Cinst = Ccap × 0.8, C 1 = 300, ...} or { kw = 0.6, Wmth = 1, kt = 0.1, Tinst = 40, kc = 0.2, Cinst = Ccap × 0.8, C ₁ = 300, ...} if {inpC} −then {kw = 0.2, Wmth = 2, kt = 0.3, Tinst = 20, kc = 0.5, Cinst = Ccap × 0.2, C ₁ = 600, ...} or {kw = 0.2, Wmth = 2, kt = 0.3, Tinst = 20, kc = 0.5, Cinst = Ccap × 0.3 , C ₁ = 600,…} or {kw ＝ 0.2, Wmth ＝ 2, kt ＝ 0.3, Tinst ＝ 20, kc ＝ 0.5, Cinst ＝ Ccap × 0.4, C ₁ ＝ 600,…} if {Request Condition} -then {control method parameter} or {control method parameter}, and outputs a plurality of control method parameters for one request. When the control method parameter is retrieved, the target traffic flow (learning traffic flow data table ST31 or predicted traffic flow data table ST32) and the retrieved plurality of control method parameters are passed to the subroutine D710, and a simulator (described later) is activated. . From the simulator, the simulation result and its norm Lp are received. In step D615, the smallest received norm Lp is determined as the optimal control method,
The simulation result is output at step D616.
The subsequent processing is the same.

FIG. 44 is a flow of the subroutine D710 that manages the simulator. Learned as data for simulation at step D711 From the traffic flow data table ST31 or the predicted traffic flow data table ST32, the first floor running time Tr (T ″ r if the predicted traffic flow data table is used) and one stop time
Ts (T "s) is set. In step D712, passengers are generated according to each floor boarding hij (h" ij) and each floor getting-off number cij (c "ij). Change the parameter.
In D713, it is checked whether there is a control method that is not simulated, and if there is a control method that is not simulated, the process proceeds to step D714. In step D714, the control method not yet simulated is set in the simulator. In subroutine D810, simulation is executed. In Step D715, the simulation result data (average waiting time Wsim, average boarding time Tsim, congestion degree in the car)
Csim), the score (pw,
pt, pc) as the weight, and the sum is the norm Lp
Calculate as.

Lp = pw.multidot.Wsim + pt.multidot.Tsim + pc.multidot.Csim This norm Lp indicates the excellence of the control result with respect to the request, and the shorter the norm, the more preferable the control method. As a method of calculating this norm, a control target value Xtarg and a standardized value Xstnd may be set, and (X represents each target item) Lp = Σpx · (Xsim−Xtarg) / Xstnd. When the simulation for all the control methods passed is completed, the judgment of step D713 is made.
No and return.

Next, the program of the simulation unit will be described with reference to FIG.

First, in step D811, elevator position, hall call,
Initialize the simulation variables such as car calls. In step D812, statistical variables such as waiting time, boarding time, and congestion degree in the car are initialized. At step D813, 0 is assigned to the simulation time Time and the simulation is started.
At step D814, processing such as passenger arrival is performed. Step D8
15 controls the movement of the machine. In step D816, hall call allocation processing is performed. Step D817 collects simulation data such as waiting time and number of hall calls. Time is updated in step D818, and the end of the simulation is determined in step D819. If it is not the end of the time, the return processing is repeated at step D814. When the processing is completed, the statistical processing for calculating the average waiting time and the like is performed in step D820.

According to this embodiment, there is an effect that the precision of the control method determination can be improved by using the control method determination simulation.

Next, an embodiment will be described in which a traffic flow comparison unit and a control method determination unit are incorporated in the group management control device online.

FIG. 46 is a hardware schematic configuration diagram of the embodiment. The hardware is roughly divided into an elevator machine E and a hall call device HD, which is composed of a group management control device MA for executing group management control, an input device SK, and an output device SD.
The input device SK and output device SD may be installed in the control panel or installed in the manager's room. Group management controller MA
Is a group management control microcomputer M1 that executes group management control system software SF1, a learning microcomputer M2 that executes learning system software SF2, and support and correction system software.
It has a microcomputer M3 for determining and modifying the control method that executes SF4.

Next, the software configuration of the embodiment is shown. The software is roughly classified into the program SF0 for controlling the machine executed by the microcomputer E for controlling the elevator machine, the microcomputer for group management control.
Group management control software SF1 executed by M1, learning system SF2 executed by learning microcomputer M2, and
It consists of support / correction software SF4 that is executed by the control method decision / support microcomputer M3. Unit control program S
The configurations of F0 and group management control system software SF1 are the same as in FIG.

FIG. 47 is a block diagram of the learning software SF2. Compared to the first embodiment described above, the input / output control program
SF25, learning result database SD21, control method database
The difference is that there is no SD22.

FIG. 48 is a block diagram of the support / correction system software SF4.

The support / correction system software SF4 consists of six programs (traffic flow comparison program SF42, control method determination program S
F43, keyboard input control program SF4, screen output control program SF45, simulation program SF46, control method modification program SF47), predicted traffic flow data table ST
42, control method decision knowledge base SK41, and two databases (learning result database SD41, control method database SD42). In Figure 48, SF42, SF43,
SF44, SF45, ST42, SK41 are SF32, SF33, SF3 shown in Fig. 12, respectively.
4, SF35, ST32, SK31, Fig.48 SF46, Fig.42 SF36, Fig.
SD41 and SD42 in FIG. 48 are almost the same as SD21 and SD22 in FIG. 5, respectively. Here, the contents of the control method decision knowledge base SK41 are
It can be changed from the keyboard input control program SF44. (The flow is omitted.) The control method modification program SF47 is a program that automatically modifies the control method parameters from the traffic flow learning result.

FIG. 49 is a flow of the control method modification program SF47. The control method modification program SF47 is started when the contents of the learning result database SD42 are changed, or 1
It is activated periodically such as once a day or once a week.

In Fig. 49, steps E111 and E116 are a loop concerning traffic demand. At Step E111, the target traffic demand for correction is set. At step E112, traffic flow data is read, and at step E113, control method parameters are read. In the subroutine E210, the correction item of the control method parameter is determined. In Step D710, activate the simulator (No. 44
Figure) At step E114, the simulation result and its norm Lp are received from the simulator, and the norm Lp is received.
Is determined as the optimum control method (parameter). In step E115, the modified control method is written in the control method database SD42.

FIG. 50 is a flow of the correction item determination subroutine E210.

In this embodiment, a method of determining a correction item based on the importance of the control target is used.

First, in step E211, it is checked whether the important target is the boarding time. If Yes, the set value Tinst of the boarding time of the control method pattern is set as a correction item in step E213. In this example, it is instructed to simulate the parameter in which the set value Tinst of the boarding time is changed by ± 10 and ± 20, respectively. If No at Step E211, check whether the important target is the in-car congestion degree at Step E212.If Yes, at Step E214 the set value Cinst of the in-car congestion degree of the control method parameter is a correction item. To do. If No in step E212, the stop call evaluation parameter α is set as a correction item in step E215. With the above processing, the correction item is determined, and the control method correction program is restored.

According to the present embodiment, the traffic flow comparison unit and the control method determination unit are configured to be online with the group management control execution unit, so that the setting of the control method can be changed at any time. Further, by providing the control method correction means and deciding the correction item based on the important control target, there is an effect that the control method parameter can be automatically corrected with the important target being emphasized with respect to the traffic flow change.

As another embodiment of the present invention, an example in which a control method decision knowledge base is provided corresponding to traffic volume will be described.

Since the control method for executing the multi-target control depends not only on the specific floor but also on the traffic volume, the control method must be changed depending on the traffic volume. Therefore, a ternary state diagram-corresponding knowledge base in the control method decision knowledge base is prepared for every 5,1,0,15,20,25,30 (person / five minutes / vehicle) according to the traffic volume. The target traffic flow for determining the control method is, for example, traffic volume.
If it is 12 (people / 5 minutes / vehicle), the control method is the element traffic flow model component ratio of the target traffic flow on the three-way diagram of 10 and 15 (people / 5 minutes / vehicle) that sandwich the traffic volume. It is selected from the corresponding area according to the user's request, and is determined by the simulation result. (The flow is omitted.) According to the present embodiment, there is an effect that the control method decision accuracy can be improved by preparing the control method decision knowledge base corresponding to the traffic volume.

〔The invention's effect〕

As described above, according to the present invention, the traffic flow is determined by comparing the traffic flow with a traffic flow model focusing on a specific floor, so that the traffic flow can be generalized and handled. The flow can be easily determined. Since the traffic flow is determined by the model focusing on the specific floor, it is possible to perform group management control of a plurality of elevators in a form that matches the actual traffic flow.

[Brief description of drawings]

FIG. 1 is a system block diagram of the principle of the present invention, FIG. 2 is a schematic hardware configuration diagram of an embodiment of the present invention, FIG. 3 is a software configuration diagram of group management control system software, and FIG. 4 is a group. FIG. 5 is a table configuration diagram of management control software, FIG. 5 is a software configuration diagram of learning system software, and FIGS. 6 to 9 are table configuration diagrams of learning system software.
Figures and 11 are database configuration diagrams of learning software, Fig. 12 is a software configuration diagram of support software, and Fig. 13 is a table configuration diagram of support software.
Fig. 14 is a knowledge base configuration diagram of support system software, Figs. 15 to 22 are diagrams showing program flow of group management control system software, and Figs. 23 and 25 to 28 are learning system software. Fig. 24 shows the traffic flow classification diagram, Fig. 24 is a traffic demand classification diagram, Figs. 29 to 31 are elemental traffic flow model diagrams, Figs. 32 and 33 are synthetic traffic flow model diagrams, and Fig. 34 (a). ) Is a traffic flow model corresponding area division classification diagram, Fig. 34 (b) is a diagram showing an example of an experiment result when congestion control is performed for three traffic flow models, and Figs. 35 to 39 are support systems. FIG. 40 is a diagram showing a program flow of software, FIG. 40 is a diagram showing an example of a user request input screen, FIGS. 41 to 50 are explanatory diagrams relating to another embodiment of the present invention, and FIG. 41 is a support system software. Software knowledge base structure diagram, Fig. 42 is support software table structure diagram, Fig. 43 FIG. 45 is a diagram showing a program flow of support system software, FIG. 46 is a hardware schematic configuration diagram, FIG. 47 is a software configuration diagram of learning system software, and FIG. 48 is a support / correction system software. Software configuration diagram, Figure 49 and Figure 50 support
It is a figure which shows the program flow of correction type software. MA ... Group management control device, M1 ... Group management control microcomputer,
M2: Learning microcomputer, MC: IC card input / output device, E
1,…, En …… Microcomputer for controlling machine, HD1,…, HDm …… Hall call button, SP …… Individualization support device, S1 …… Microcomputer for determining control method, SC …… IC code input / output device, SD ...... Output device, SK …… Input device, SF0 …… Unit control program, S
F1 …… Group management control software, SF2 …… Learning software, SF3 …… Support system software, SF4 …… Support ・
Modification software.

Front page continued (72) Inventor Kiyoshi Nakamura 4026 Kujimachi, Hitachi City, Ibaraki Prefecture Hitachi Research Laboratory, Hitachi Ltd. (72) Inventor Kenji Yoneda 1070 Ige, Katsuta City, Ibaraki Hitachi Ltd. 72) Inventor Takaaki Uejima 1070 Ige, Katsuta City, Ibaraki Prefecture, Hitachi, Ltd. Mito Plant (56) References JP 59-48369 (JP, A) JP 62-79173 (JP, A)

Claims (4)

[Claims] 1. A group management control device for an elevator, comprising: a group management control execution unit for integrally controlling a plurality of elevators working between multiple floors; and a control method determination unit for determining a group management control method. , An input device that receives a user's request for group management control, and the control method determination unit determines the ratio of the divergent traffic flow that is the ratio of the number of passengers boarding a specific floor to the number of passengers who are using the elevator The three-way state diagram is used according to the ratio of the three components of the ratio of concentrated traffic flow, which is the ratio of the number of people descending from a specific floor to the number of people, and the ratio of inter-floor traffic flow, which is the overall ratio minus the divergent traffic flow ratio and the concentrated traffic flow ratio. A traffic flow comparison unit that classifies the traffic flow according to the classification result of the traffic flow comparison unit and the user's request input by the input device. A group management control device for an elevator, wherein the group management control execution unit executes group management control by a determined control method. 2. A group management control device for an elevator, comprising: a group management control device for integrally controlling a plurality of elevators working between multiple floors; and an individualization support device for determining a group management control method. The group management control device includes a learning result data table that collects and records the operation status of the elevator, and the personalization support device records the traffic flow data recorded in the learning result data table and the number of people on a specific floor with respect to the total number of people boarding. The ratio of divergent traffic flow, the ratio of concentrated traffic flow that is the ratio of the number of people getting off at a specific floor to the total number of people getting off, and the ratio of inter-floor traffic flow excluding the divergent traffic flow ratio and the concentrated traffic flow ratio from the overall ratio Traffic flow comparison unit that classifies by using a ternary phase diagram according to the ratio of the three components, a control method decision knowledge base that records knowledge about control methods, and group management control An input device that accepts the user's request, a traffic flow determined by the traffic flow comparison unit, and a control method according to the user's request input from the input device are searched from the control method knowledge base, and the traffic flow is searched. A control method determining unit for determining as a control method for, the group management control device, a control method database for recording the control method determined by the control method determining unit, and a group using the determined control method A group management control device for an elevator, comprising: a group management control execution unit that executes management control. 3. A group management control device for an elevator comprising a group management control execution unit for integrally controlling a plurality of elevators operating between floors on a multi-story floor, and a control method determination unit for determining a group management control method. In the above, a learning result database that collects and records the operation status of the elevator is provided, and the control method determination unit is the ratio of the traffic flow data recorded in the learning result database and the number of people boarding a specific floor to the total number of boarding passengers. According to the ratio of three components, the flow rate, the ratio of concentrated traffic flow that is the ratio of the number of people getting off at a specific floor to the total number of people getting off, and the ratio of inter-floor traffic flow that is the overall ratio excluding the divergent traffic flow ratio and the concentrated traffic flow ratio. A traffic flow comparison section that classifies using a ternary state diagram, a control method decision knowledge base that records knowledge about group management control methods, and user requirements for group management control. An input device that accepts a request, and the control method determination unit searches the control method determination knowledge base for a control method according to the traffic flow determined by the traffic flow comparison unit and the user's request input by the input device. Then
A control method database is provided that determines the control method for the traffic flow model and records the control method determined by the control method determination unit, and the group management control execution unit executes control using the determined control method. An elevator group management control device characterized in that 4. The elevator group management control according to claim 3 or 4, wherein the control method determination unit includes a simulator for simulating group management control, and determines the control method based on a simulation result of the simulator. apparatus.