JPH0451475B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an elevator group management device, and particularly to elevator group management control using a computer.

[Conventional technology]

With conventional group management devices, it was difficult to perform fine-grained group management control that adapted to the specific traffic volume of each building.
It had the disadvantage of not being able to respond to changes in building structure, such as when a complex building becomes a building occupied by a single company, or when a passageway is established to communicate with an adjacent building.

[Problem to be solved by the invention]

An object of the present invention is to calculate group management operation control parameters adapted to building-specific traffic in a short time in an elevator group control device equipped with in-building traffic collection means for capturing building-specific traffic. The purpose of this invention is to provide a group management device that can perform the following tasks.

[Means to solve the problem]

A feature of the present invention is that the present invention includes means for allocating a hall call to at least one of the plurality of elevators in accordance with an evaluation function using variable operation control parameters for a plurality of elevators operating between multiple floors of a building. In the elevator group management device,
For each of a plurality of predetermined traffic volumes, a plurality of values of at least one type of driving control parameter and a value of at least one type of service performance corresponding one-to-one to each value of the driving parameter are provided. a storage means for storing in advance, a collection means for collecting the traffic volume in the building, a means for externally setting the target value of the service performance, and a corresponding to the collected traffic volume based on the stored contents of the storage means. A plurality of values of the operation control parameters to be determined are determined, and among the plurality of values,
The present invention further includes means for determining, as an optimum operation control parameter, a value of the corresponding service performance that satisfies the target value.

〔Example〕

Hereinafter, the present invention will be explained in detail with reference to a specific embodiment shown in FIGS. 1 to 16. In addition, the explanation of the embodiment will first describe the hardware configuration that realizes the present invention, then describe the overall software configuration and its control concept, and finally explain the software that realizes the above control concept using a table configuration diagram and a flowchart. Explain using.

FIG. 1 is an overall hardware configuration diagram of an embodiment of the present invention.

The elevator group management device MA has an elevator operation control function, an in-building traffic volume data collection function, and a function to calculate the variable parameters described above by comparing the in-building traffic data and a plurality of preset traffic data. There is a microcomputer M.

The microcomputer M has a circuit PIA that inputs and outputs the call signal HC from the hall call device HD in parallel.
(Peripheral Interface Adapter) and control microcontrollers E1 to En for each elevator, such as door opening/closing and car acceleration/deceleration commands (Here, it is assumed that there is an elevator number n) ) is the serial communication processor SDA1.
- Connected to SDAn via communication lines CM1 to CMn.

On the other hand, the signal P _M from the setting device PD, which provides commands necessary for determining the variable parameters described above, is inputted via a circuit PIA that inputs and outputs signals in parallel.

In addition, the car control microcontrollers E1 to En are equipped with car call information necessary for control, car load change information on each floor, and control input/output elements EIO1 to EIO1, which are comprised of elevator safety limit switches, relays, and response lamps. Circuit that inputs and outputs signals in parallel with EIOn
It is connected to PIA via signal lines SIO1 to SIOn.

The present invention will be generally explained using FIG.

The microcomputer M has a built-in operation control program mainly for call assignment, and this operation control program takes in information necessary for control from the microcomputers E1 to En for controlling each machine and the hall call HC. Further, the operation control program performs call assignment using the variable parameters described above. For example, this parameter includes a weighting coefficient that indicates the relationship between waiting time and power consumption evaluation value in the call assignment evaluation function, a time coefficient that determines the door opening/closing time, and a call assignment control logic, that is, a call assignment algorithm. There are control parameters etc. to select.

These variable parameters are determined by the microcontroller for each machine.
Information on the number of passengers getting on and off each floor obtained from E1 to En, multiple preset traffic volumes, and a setting device
Calculated using PD command P _M. This operation is
It is processed in real time at regular intervals and outputs the optimal operation control parameters for elevator group management at each time.

For example, if you command the setting device PD to minimize the waiting time, first compare the traffic data in the building from that point until a predetermined time with multiple preset traffic data, and then The traffic data closest to the in-building traffic data is selected from the traffic data, and the parameter that achieves the shortest waiting time is selected from the list of waiting time data in the selected traffic data. This parameter is assumed to be the optimal motion control parameter for the current traffic volume in the building. Therefore, according to the present invention, elevator group management control can respond to the ever-changing building environmental conditions, and greatly contributes to improving elevator group management performance.

Next, the specific hardware configuration of each microcomputer will be shown, but these microcomputers can be easily configured as shown in FIGS. 2 and 3. The MPU (Micro Processing Unit), which is the core of the microcomputer, uses 8 bits, 16 bits, etc. In particular, since the microcomputers E1 to En for machine control do not require much processing power, an 8 bit MPU is appropriate. On the other hand, since the microcomputer M for controlling elevator operation requires complex calculations, it is a 16-bit computer with excellent calculation performance.
MPU is appropriate.

Now, as shown in Figures 2 and 3, each microcomputer has a ROM (Read Only Memory) that stores control programs, etc. on the bus line BUS of the MPU, and a RAM that stores control data, work data, etc.
(Random Access Memory), a circuit PIA that inputs and outputs signals in parallel, and a dedicated processor SDA (Serial Data Memory) that performs serial communication with other microcontrollers.
Adapter (for example, Hitachi HD43370) is connected.

In addition, for each microcomputer M, E1 to En,
RAM and ROM are composed of a plurality of elements depending on the size of the control program, etc.

In FIG. 2, the setting device PD is composed of a setting volume VR and an A/D converter that converts the analog output voltage of this VR into a digital value, and this output P _M is taken into the RAM from the PIA.

In FIG. 3, as elevator control data, for example, a car call button CB, a safety limit switch SW _L , a relay contact SW _Ry , and a car weight Weight are loaded into the RAM from the PIA. On the other hand, the data calculated by the MPU is output from the PIA to control output elements such as the response lamp Lamp and the relay Ry.

Here, the hardware configuration of the processor SDA for serial communication between microcomputers used in Figures 2 and 3 is as follows:
As shown in Figure 4, the transmission buffer is
TX _B , reception buffer RX _B , data parallel/
It is composed of a P/S that performs serial conversion, an S/P that performs inverse conversion, and a controller CNT that controls their timing. The above transmitting buffer TX _B and receiving buffer RX _B can be freely accessed by the microcomputer, and data can be written and read. On the other hand, SDA is the controller CNT
Therefore, it has a function of automatically transmitting the contents of the transmission buffer TX _B to the reception buffer RX _B of another SDA via the P/S. Therefore, the microcomputer does not need to perform any transmission/reception processing, so it can concentrate on other processing. Detailed configuration and operation explanation regarding this SDA can be found in Japanese Patent Application Laid-Open No. 56-37972 and Japanese Patent Application Laid-Open No.
No. 56-37973.

Next, a software configuration according to an embodiment of the present invention will be described.First, the overall software configuration will be explained with reference to FIG.

As shown in FIG. 5, the software is roughly divided into operation control system software SF1 and optimum operation control parameter calculation system software SF2, both of which are processed by the microcomputer M.

The operation control system software SF1 includes an operation control program SF14 that directly commands and controls elevator group management control such as call assignment processing and elevator distributed standby processing. The input information for this program includes elevator control table SF11 such as elevator position, direction, car call, etc. sent from the machine control program (Fig. 1: built in microcontrollers E1 to En), hall call table SF12, and elevator management. The elevator specification table SF13 including the number of elevators and the optimum operation control parameter calculation system software SF2 are used to calculate and output the optimum operation control parameters, etc. as input data.

On the other hand, the optimal operation control parameter calculation system software SF2 is composed of the following processing program.

(1) In-building traffic data collection program SF21
...This is a program that samples the contents of hall calls and elevator control tables online at predetermined intervals, and mainly collects the traffic volume within the building.

(2) Optimum traffic volume calculation and optimal operation control parameter calculation program SF25... Input the in-building traffic data collected for a predetermined period of time, a plurality of preset traffic volume data, and a target value arbitrarily set by the building manager. First, a traffic volume whose difference with the in-building traffic volume is within a reference value is selected from among a plurality of preset traffic volumes. If the difference between all the preset traffic volumes and the traffic volume inside the building is not within the predetermined standard value, the traffic volume obtained by adding or subtracting multiple preset traffic volumes Select the traffic volume that has the smallest difference from the traffic volume within the building. This selected traffic volume is set as the current optimal traffic volume. Next, operating control parameters that achieve the target value set by the building manager within a predetermined reference value under the optimal traffic volume are selected from the optimal traffic volume data. If there is no driving control parameter in the optimal traffic volume data that achieves the target value within a predetermined reference value, one of the driving control parameters obtained by interpolating multiple driving control parameters in the optimal traffic volume data. Then, the operation control parameter that minimizes the difference from the target value is selected. This selected operation control parameter is set as the optimum operation control parameter. As explained above, this program outputs optimal operation control parameters.

Next, a table configuration used in an embodiment of the present invention will be explained with reference to FIGS. 6 and 7. Figure 6 shows the table configuration of the operation control system software.
Broadly divided, elevator control table SF11,
It consists of blocks of hall call table SF12 and elevator specification table SF13. The tables within each block will be described each time the operation control program is explained below.

FIG. 7 shows the table configuration of the optimal operation control parameter calculation system software, including an in-building traffic data table SF22, a plurality of preset traffic data tables SF23, and a target value table SF2.
4. Consists of blocks of optimal operation control parameter table SF26.

In this embodiment, the in-building traffic data table SF23 includes three types of service performance values (i.e., average waiting time T, long waiting rate P, and power consumption value PW) for each of traffic volumes 1 to m, and 3 It is a table of values of various operation control parameters (namely, area priority parameter K, door opening/closing time control parameter D, and fullness prediction parameter F). Here, n combinations of three types of driving control parameter values are prepared for each traffic volume, and each value of three types of service performance obtained according to each combination is associated. .

Next, an embodiment of the software of the present invention will be described.

First, the program for the operation control system will be explained, and then the program for the optimum operation parameter calculation system will be explained. Note that the programs described below are managed under a system program that divides programs into multiple tasks and performs efficient control, that is, an operating system (OS). Therefore, the program can be started freely from the system timer or from other programs.

Now, FIGS. 8 to 11 show the flow of the operation control program. Two particularly important operation control programs, the elevator arrival prediction time table calculation program and the call assignment program, will be explained.

FIG. 8 is a flowchart of a program that calculates the predicted arrival time of an elevator to an arbitrary floor, which is the basic data for waiting time evaluation calculations. This program is activated periodically, for example, every second, and calculates the predicted arrival time from the current position of the elevator to an arbitrary floor for all floors and for all elevators.

In FIG. 8, steps E10 and E90 indicate loop processing for all elevator numbers. In step E20, an initial value is first set in the work time table T, and its contents are set in the predicted arrival time table of FIG. As an initial value, it is possible to consider how many seconds it will take to depart based on the open/closed state of the door, or the predetermined time until activation when the elevator is stopped.

Next, advance one floor (step E30) and compare whether the floor is the same as the elevator position (step E40). If they are the same, 1
Now that the predicted arrival time table for each elevator has been calculated, we jump to step E90.
Repeat the same process for other elevators. On the other hand, if "NO" in step E40, the first floor running time T _r is added to the time table T (step E50). Then, this time table T is set as a predicted arrival time table (step E60). Next, it is determined whether there is a car call or an assigned hall call, that is, a call that should be serviced by the elevator of interest, and if there is,
Since the elevator stops, one stop time T _s
is added to the time table (step E80). Next, the program jumps to step E30 and repeats the above process for all floors.

Figure 9 shows the flow of the call allocation program.
This program is activated when a hall call occurs. In this program, the call allocation algorithm is a long-waiting call minimization call allocation algorithm (described later in FIG. 10) as shown in step A50.

When a hall call occurs, the first step is step A10.
Reads the hall call generated from outside. and,
The following processing is performed in a loop at steps A20 and A80, and steps A30 and A70. In other words, if there is a hall call, this call is assigned to the elevator that is most suitable in terms of minimizing long-waiting calls (step
A60).

FIG. 10 is a processing flow of the long-waiting call minimum call allocation algorithm. Steps A50-1 and A50 to determine which elevator is best
-6 performs loop processing based on the number of elevators.
In the process in the loop, first, in step A50-2, the maximum predicted waiting time Tmax of the allocated hall calls on the front floor including the generated hall call is calculated. Note that the predicted waiting time is the sum of the hall call elapsed time (see FIG. 6), which indicates the elapsed time from the occurrence of the hall call to the present, and the predicted arrival time (see FIG. 6). In the next step A50-3, a stop call evaluation value Tc is calculated from the stop calls on predetermined floors before and after the generated hall call, and an evaluation function φ is calculated using this evaluation value and the maximum predicted waiting time Tmax described above ( Step A50-4). Then, the smallest elevator in this evaluation function φ is selected (A50-4).
If the above process is executed for all elevators, the elevator with the optimum evaluation value will be selected by the calculation in step A50-5.

Above, we have explained the processing flow of the predicted arrival time table calculation program and the call assignment program, which are operation control programs. There are distributed standby processing programs that cause elevators to wait at predetermined floors, but their explanations will be omitted.

Next, the optimal operation control parameter calculation system software program will be explained using FIGS. 11 to 16.

FIG. 11 is a flowchart of the in-building traffic data collection program.

First, in step SA10, the number of passengers getting on and getting off the stopped elevator is measured.
Next, in step SA20, the elevator direction and the stop floor are detected in SA30. From the above measurement data, the traffic volume within the building can be collected by updating the traffic volume G(i, j, k) within the building (step SA40).

FIG. 12 to FIG. 16 are programs that are the key points of the optimum operation control parameter calculation system.

This program is activated every time a predetermined period of time (for example, 10 minutes) has elapsed, and extracts the trend of the current in-building traffic volume by comparing and interpolating with a plurality of preset traffic volumes. Next, the optimal driving control parameters are calculated based on this extracted trend.The optimal driving control parameters for each of the multiple preset traffic volumes are calculated in advance using a simulation method, etc. The data are stored in a plurality of preset traffic data tables SF23. Operation control parameters include crowd prediction parameters that greatly affect service performance due to traffic demand, and stoppages for each floor or direction that are necessary to accurately determine the predicted arrival time required for the elevator to arrive at the hall call floor. A probability parameter, a lockout parameter for preferentially allocating the generated hall call to an elevator that has a car call on the generated hall call floor, and a lockout parameter for preferentially assigning the generated hall call to an elevator that has a car call on the floor where the generated hall call occurs; Area priority parameters for preferentially allocating hall calls that occur on floors near the floor, door opening/closing time control parameters for controlling door opening/closing times, and priority services for preferentially servicing congested floors. There are level parameters etc. The driving control parameters described above vary greatly depending on not only traffic demand but also patterns such as commuting to work and normal times.

Next, the basic concept of optimal operation parameter calculation will be explained.

First, the traffic volume within the building is compared with multiple preset traffic volumes, and the traffic volume with the most similar traffic demand and parameters is selected from among the multiple preset traffic volumes, and the similarity is calculated. is within a certain standard value, the current traffic volume in the building is considered to be the same as the traffic volume selected above, and the operation control parameters for the selected traffic volume are set to the optimal one for the current traffic volume in the building. Use as an operation control parameter.
However, if the similarity is not within a certain standard value, multiple traffic volumes within the vicinity of the traffic volume that is considered to be most similar to the traffic demand and pattern selected above are interpolated and Create a traffic flow similar to a traffic flow. The driving control parameters for the traffic volume created above are also created by interpolation calculations similar to those described above.

As mentioned above, by interpolating not only multiple preset traffic volumes but also operational control parameters for multiple preset traffic volumes, the optimal operational control parameters for the current in-building traffic volume are determined. This finding is an important point of this embodiment.

FIG. 12 will be explained.

First, in step SB1, an in-building traffic volume table G (i, j, k) (here, i...indicates the number of passengers table or number of people getting off the table, j...indicates the elevator direction, and k...indicates the floor at which the elevator is stopped) is set in advance. m traffic volumes H (i, j, k, l) (l
= 1,...m) to find the optimal traffic volume H(i, j,
k, l*). In step SB2, the optimum traffic volume H(i, j, k, l*) table and the target value table are compared to determine the optimum driving control parameters.

Next, FIG. 13 is shown.

FIG. 13 is a program explaining step SB1 in FIG. 12.

First, in step SB1-1, the in-building traffic volume G (i, j, k) and m preset traffic volumes H (i, j, k, l) (l = 1, ~, m) are calculated. Difference E
Calculate the square of (i, j, k, l). step
In SB1-2, 2 of E (i, j, k, l) for variable I (indicating the table of number of passengers or number of passengers disembarking)
Calculate the multiplication, and in step SB1-3, set E for variable J (elevator direction UP or Down).
Calculate the square of (i, j, k, l) and step SB
1-4, the variable K (elevator stop floor 1~
The square of E(i, j, k, l) is calculated for N), and in step SB1-5, the traffic volume in the building G
(i, j, k) and the preset traffic volume H(i,
j, k, l) (l=1, m) sum of squares of differences S
(l) (l=1,...0...,m) is being found. next,
In step SB1-6, the minimum value Min(l*) is found among m S(l), and the l*th traffic volume H(i,
j, k, l*) is determined to be closest to the traffic volume within the building. In step SB1-7, Min(l*) is
It is determined whether it is smaller than the reference value ST. As a result,
If it is within the standard value, the preset traffic volume H(i,
j, k, l*) are equated with the traffic volume within the building (step SB1-9), and step SB1 is ended.
However, if Min(l*) is larger than the standard value ST, the preset traffic volume H(i, j, k, l*)
is not equated with the traffic volume inside the building, and step SB1
-8, a plurality of preset traffic volumes are interpolated to create a traffic volume within the reference value ST.

FIG. 14 illustrates a program for interpolating the preset traffic volume in steps SB1-8 of FIG. 13.

In step SB1-8-1, the traffic volume in the building G
(i, j, k) and the preset H(i, j, k,
There are differences E(i, j, k, l1) and E(i, j, k,
l2) exists. If it exists, the building traffic volume G(i, j, k) is determined by the preset traffic volume H(i, j, k, l1) and H(i,
j, k, l2), and using an appropriate positive number d, the traffic volume H(i,
j, k, l*) as shown in the following equation (1).

H(i, j, k, l*) = α・H(i, j, k, l1) +(1-α)・H(i, j, k, l2) ...(1) (Step SB1-8-3).

In addition, the difference E(i,
j, k, l) (l=1 to m), if there is no difference (i, j, k, l) with a different sign,
The traffic volume H(i,
j, k, l*) cannot be created, so the 13th
2 of the difference E, which is the calculation result of step SB-6 in the figure.
A preset traffic volume H(i, j, k, l*) that gives S(l*), which is the minimum value of the sum of the products SCl)
is the same as the traffic volume inside the building. However, since the traffic volume within a building is examined in detail when the building is completed, the upper and lower limits of the traffic volume within the building can be predicted.

From this result, if the above upper and lower limits of traffic volume are also set in advance, the method according to this embodiment can be used to accurately calculate the traffic volume within the building G(i, j, k).
It can be created by comparing or interpolating the traffic volume that can be equated with .

In step SB1-8-4, the average waiting time T _l * ^q , long waiting rate P l * ^q , and power consumption _for the traffic volume H (i, j, k, l*) created by interpolation using equation (1) are calculated. value
PW _l * ^q (q = 1 to n) is the average waiting time T ₁ q , ^T ₂ at preset traffic volumes H (i, j, k, l1) and H (i, j, k, l2) ^q , long waiting rate P ₁ ^q , P ₂ ^q ,
Power consumption values PW ₁ ^q , PW ₂ ^q (q=1 to n) and positive number α
It can be found by interpolating as shown in the following equations (2) to (4).

T _l * ^q = αT _l1 ^q + (1-α)T _l2 ^q (q = 1 to n)
...(2) P _l * ^q = αP _l1 ^q + (1 - α) P _l2 ^q (q = 1 to n)
...(3) PW _l * ^q = αPW _l1 ^q + (1 - α) PW _l2 ^q (q = 1 to n)
...(4) Similarly, the above T _l * ^q , P _l * ^q , PW _l * ^q (q=
The operation control parameters for realizing (1 to n) are also determined by interpolation using the positive number α as shown in the following equations (5) to (8). Area priority parameter K _l * ^q (q=1 to n)
is, K _l * ^q = αK _l1 ^q + (1-α) K _l2 ^q ... (5) Door opening/closing time control parameter D _l * ^q (q = 1 ~
n) is D _l * ^q = αD _l1 ^q + (1-α) D _l2 ^q ...(6) The full occupancy prediction parameter F _l * ^q (q = 1 to n) is F _l * ^q = αF _l1 ^q +(1-α)F _l2 ^q ...(7) The stop probability parameter S _l * ^q (q = 1 to n) is S _l * ^q = αS _l1 ^q + (1-α)S _l2 ^q ...( 8) In step SB1-8-6, step
Traffic volume H(i,
j, k, l*) and the traffic volume in the building G(i, j, k)
It is determined whether the square of the difference between ST and ST is smaller than the reference value ST.
If it is small, the optimal traffic volume and positive number α have been found. However, if it is larger than the standard value ST,
In step SB1-8-7, 0.1 is added to the positive number α, the above equation (1) is repeated, 0.1 is added to the new positive number α, and the above equation (1) is repeated to obtain a new traffic volume H(i,
j, k, l*) and repeat the above process.
In this way, the optimal traffic volume H for the traffic volume within the building is determined.
(i, j, k, l*) and operation control parameters were determined.

FIGS. 15 and 16 are program flows for interpolating and calculating optimal operation control parameters in order to realize the target values in the target value table SF24 set from the outside by a building manager or the like.

FIG. 15 is a flowchart of an operation control parameter calculation program for achieving the target value with high accuracy when the target value is the average waiting time.

This program is a program for when the target value is the average waiting time, but it is possible to create a program that can calculate the optimal operation control parameters even when the target value is the long waiting rate or power consumption value using a similar configuration. It is.

In step SB2-1, it is determined whether the target value is the average waiting time. Otherwise, it ends, and if it is the average waiting time, in step SB2-2, the average waiting time and the optimal traffic volume H(i,
The average waiting time T _l * ^q (q=1
~n) Calculate the square of the difference A(q) A ² (q) (A ²
(q) = (Target value - T _l * ^q ) ² , q = 1 to n), and in the next step SB2-3, A ² (q) (q = 1 to n)
Find the minimum value Min(q*) among them. Step SB
In 2-4, it is determined whether Min(q*) is smaller than the reference value TU. If smaller, the average waiting time T _l * ^q*
is considered to be equal to the target value, and the operation control parameters that realize T _l * ^q* are the optimal operation control parameters (area priority parameter K _l * ^q* , door opening/closing time control parameter D _l * ^{q *} , fullness prediction parameter F _l * ^q* , stop probability parameter S _l * ^q*, etc.) (step
SB2-6).

If it is not small, T _l * ^q (q=1~n)
is interpolated to create optimal operation control parameters (step SB2-5).

Figure 16 shows the optimal traffic volume H(i, j, k, l*)
If the square of the difference between the average waiting time T _l * ^q (q = 1 to n) and the average waiting time target A ² (q) (q = 1 to n) are all greater than the reference value TU, then T _l * ^q (q
= 1 to n), and the operation control parameter K _l
* ^q , D _l * ^q , F _l * ^q , S _l * ^q (q = 1 to n) are also interpolated to obtain the average waiting time T _l * ^q* and the operation control parameter K _l * ^q that are smaller than the reference value TU. ^* , D _l * ^q* , F _l * ^q* ,
This is a program to find S _l * ^q* .

In step SB2-5-1, the difference A(q) (q=
A(q1) and A(q2) with different signs in 1 to n)
is determining whether it exists. If it does not exist, the average waiting time target value is the optimal traffic volume H
(i, j, k, l*) means that it is larger or smaller than all the average waiting times T _l * ^q (q=1~n), so the optimal operation control parameters are determined by step SB2 in FIG. - Set the operation control parameters selected in step 6 as the optimum operation control parameters. However, if it exists, the average waiting time target value is found to exist between the average waiting times T _l * ^q1 and T _l * ^q2 . Therefore, step SB-5-2
First, let β = 0, and in step SB-5-3, use T _l * ^q1 and T _l * ^q2 to calculate T _l * ^q as shown in the following equation.
Find ^* .

T _l * ^{q *} = βT _l * ^q1 + (1 - β) T _l * ^q2 Next, in step SB2-5-4, the operation control parameters to realize the newly created average waiting time T _l * ^q* are determined. is calculating. That is, the area priority parameter K _l * ^{q *} , the door opening/closing time control parameter
D _l * ^q* , the fullness prediction parameter F _l * ^q* , etc. are K _l * ^{q *} = βK _l * ^q1 + (1 - β) K _l * ^q2 D _l * ^q* = βD _l * ^q1 + (1 −β)D _l * ^q2 F _l * ^q* = βF _l * ^q1 + (1-β)F _l * ^q2 . In step SB2-5-5, the average waiting time T _l * ^q* obtained in step SB2-5-3
It is determined whether the square of the difference between the average waiting time target value and the average waiting time target value is smaller than the reference value TU. If it is not smaller, 0.1 is added to β in step SB2-5-6 and the above process is repeated. If it is smaller, then T _l * ^{q *} at that time is equated with the average waiting time target value, and the operation control parameters K _l * ^{q *} , D _l * ^{q *} , F _l * ^q*, etc. are set as the optimal operation control parameters. . With the above, this program has ended.

By performing group management control based on the optimal operation control parameters determined by the above-mentioned optimal operation control parameter calculation system software, it is possible to perform elevator operation control as desired by the building manager. It was found that the system can be adapted to different traffic volumes.

In the optimal operation control parameter calculation system software, only the average waiting time is given as an example as the target value, but the long waiting rate may also be used, or if energy saving is desired, power consumption may be used.

The above-described embodiments have a drawback that group management control parameters suitable for the building traffic volume cannot be determined if the difference between the collected traffic volume and the preset traffic volume is greater than a predetermined value. However, in that case, if the group management control device is equipped with means for simulating group management control, it is possible to use the collected traffic volume to calculate operation control parameters suitable for any traffic volume in the building using the simulation means. becomes possible.

An embodiment of the present invention has been described above, and the effects of the embodiment of the present invention will be described below.

The first effect is that multiple traffic volumes and optimal operation control parameters for the above traffic volumes (for example, area priority parameters that minimize waiting time, area priority parameters that enable energy-saving driving, and There are set door opening/closing time control parameters, fullness prediction parameters, stop probability parameters to accurately calculate the predicted arrival time required for the elevator to arrive at the corresponding hall call floor, etc.), and the number of elevator users is a constant value. Service performance evaluation values and economic evaluation values such as the average waiting time, long waiting rate, and power consumption values when performing group management operation under the above operation control parameters are set in advance, and the collected current values are set in advance. The difference between the traffic volume inside the building and the traffic volume in the building is below a certain standard value by selecting from among the plurality of preset traffic volumes or by performing interpolation calculations on the plurality of preset traffic volumes. In addition to calculating the traffic volume, the optimal operation control parameters, service performance evaluation values, and economic evaluation values are also interpolated, so that the optimal operation control parameters for the current traffic volume within the building can be determined.
As a result, the group management device can easily adapt to changes in the building environment, which reduces the average waiting time and
This greatly contributes to reducing long waiting times, reducing overcrowding, and reducing power consumption.

The second effect is that since the optimal operation control parameters are found for the current traffic volume in the building, the control delay is small and changes in the building environment can be quickly responded to, making it possible to reliably shorten waiting time.

As a third effect, by installing a target value setting device in the group management device, the operation desired by the building manager becomes possible. For example, if you want to operate with an average waiting time of 20 seconds, the optimal operation control parameters will be interpolated, and if you want to reduce energy consumption by 10%, the area priority parameters that will achieve 10% energy saving can be interpolated. Machine performance has been significantly improved compared to conventional group management devices.

〔Effect of the invention〕

As described above, according to the group management control of the present invention, it is highly efficient in responding to the traffic volume unique to a building, and is man-machine compatible in the sense that it achieves the service performance or economic efficiency desired by the building manager. able to provide exceptional elevator service.

[Brief explanation of drawings]

Figure 1 is an overall configuration diagram of the elevator system, Figure 2 is a configuration diagram of the group management control device, Figure 3 is a configuration diagram of the unit control device, Figure 4 is a block configuration diagram of the SDA, and Figure 5 is a diagram for explaining the overall configuration of the software, Figure 6 is a table diagram of the operation control system software, Figure 7 is a table diagram of the optimum operation control parameter calculation system software, and Figure 8 is a table of predicted arrival times. Calculation flowchart,
Figures 9 and 10 are flowcharts for long-waiting call minimization allocation calculations, Figure 11 is a flowchart for collecting in-building traffic volume, Figure 12 is a flowchart for the entire optimal operation parameter calculation system software, and Figure 13 The figure is a flowchart for selecting the traffic volume whose difference from the traffic volume inside the building is less than a certain standard value from the preset traffic volume. Figure 14 shows the preset traffic volume, average waiting time, long waiting rate, and consumption. Flowchart for interpolating power values and operation control parameters,
FIG. 15 is a flowchart for selecting the optimum operation control parameters to achieve the set target value, and FIG. 16 is a flowchart for interpolating the optimum operation control parameters for realizing the set target value. SB1...The part that calculates the optimal traffic volume from the traffic volume in the building and the preset traffic volume.SB2...The part that determines the optimal driving control parameters by comparing the traffic volume in the building and the target value.

Claims (1)

[Scope of Claims] 1. A means for allocating a hall call to at least one of the plurality of elevators in accordance with an evaluation function using variable operation control parameters for a plurality of elevators in service between multiple floors of a building. In the elevator group management device, for each of a plurality of predetermined traffic volumes,
a storage means for storing in advance a plurality of values of at least one kind of operation control parameter and at least one kind of service performance value in one-to-one correspondence with each value of the operation parameter; and collecting traffic volume in the building. a collecting means for externally setting the target value of the service performance; a means for determining a plurality of values of the driving control parameters corresponding to the collected traffic volume based on the stored contents of the storage means; An elevator group management device comprising means for determining, as an optimum operation control parameter, one of the values whose corresponding service performance value satisfies the target value. 2. The determining means determines, if the error between the collected traffic volume and any of the plurality of predetermined traffic volumes is within a standard value, the value of the driving control parameter corresponding to the traffic volume within the standard value. 2. The elevator group management device according to claim 1, wherein the optimum operation control parameter is determined based on the value of service performance. 3. If the error between the collected traffic volume and the plurality of predetermined traffic volumes is outside the standard value, the means for determining the amount of traffic corresponds to a plurality of traffic volumes similar to the collected traffic volume. The method is characterized in that the storage contents of the storage means are interpolated to determine values of driving control parameters and service performance values corresponding to the collected traffic volume, and the optimum driving control parameters are determined based on these determined values. An elevator group management device according to claim 2. 4. When none of the corresponding service performance values satisfy the target value, the determining means selects one of the plurality of values of the driving control parameter and the plurality of service performance values corresponding to the collected traffic volume, when none of the corresponding service performance values satisfy the target value. A plurality of service performance values similar to the target value and a plurality of corresponding operation control parameter values are interpolated, and the operation control parameter value corresponding to the service performance value within the tolerance determined by the interpolation is An elevator group management device according to claim 1, 2, or 3, characterized in that the optimum operation control parameter is used as an optimum operation control parameter. 5 The above service performance includes average waiting time, long waiting rate,
The elevator group management device according to any one of claims 1 to 4, characterized in that the elevator group management device includes at least one of the power consumption values.