JPS641389B2 - - Google Patents

Description

[Detailed description of the invention]

[Field of Application of the Invention] The present invention relates to an elevator group management control device, and particularly to an elevator group management control device suitable for realizing appropriate group management control. [Background of the Invention] In group management control of elevators, if a car that has finished its service is made to wait on the floor where the service has ended until a new hall call is received, the cars are concentrated on a specific floor band and are not able to respond to the new hall call. We may not be able to provide immediate service to you. For this reason,
Conventionally, the following car distributed waiting system has been proposed. (1) Divide the service floor of a building or elevator into multiple blocks, and have one or two cars waiting in each block (Unexamined Japanese Patent Application Publication No. 1983-1982-1).
73755, JP-A-55-56958). (2) Either have one car wait at the longest intermediate point of the distance between the cars (see Japanese Patent Publication No. 57-17829), or move the car to the least crowded floor zone (Japanese Patent Publication No. 17829). -Refer to Publication No. 45854). (3) A predetermined value is set for each car interval or the distance between each car stop floor when the service is completed, and the car is moved so that the car interval or stop floor interval becomes less than the predetermined value (see Japanese Patent Laid-Open No. 156411/1983). ). (4) One elevator car is placed on standby at a specific floor or the floor with the most calls on average, and the other elevators are placed on standby at the floor where service is completed (see Japanese Patent Laid-Open No. 57-62176). However, in these methods, the idle elevator on the floor closest to the distributed floor is often selected as the elevator for distributed waiting, and also when the distance between the front and rear cars after distributed movement becomes large. This does not necessarily result in appropriate decentralized control. [Object of the Invention] The present invention has been made in view of the above, and an object thereof is to provide an elevator group management control device that can perform optimal movement control. [Summary of the Invention] The present invention is characterized by a means for setting a plurality of bands divided by the positions of the top floor, the bottom floor, and a plurality of elevators, and a means for setting a plurality of bands divided by the positions of the top floor, the bottom floor, and a plurality of elevators, and a means for setting a plurality of bands divided by the positions of the top floor, the bottom floor, and a plurality of elevators, and the number of passengers on each floor included in the band during a predetermined period. means for calculating a distributed requested bandwidth evaluation value for each band by summing the product of the minimum predicted arrival time of each elevator to the relevant floor for all floors within the relevant band, and a means for calculating a distributed requested bandwidth evaluation value for each band; The present invention has a configuration including means for selecting elevators to be distributed in a band exceeding a predetermined value, and means for moving the elevators selected by the selecting means to the band. [Embodiments of the Invention] The present invention will be described below with reference to FIGS. 1, 2, and 7 to 17.
A detailed explanation will be given using the embodiment shown in the figure and FIGS. 3 to 6. In the explanation of the embodiment, first, the hardware configuration for realizing the present invention will be explained, then the overall software configuration and its control concept will be explained, and finally, a flowchart for realizing the control concept will be explained. . FIG. 1 is an overall configuration diagram of the hardware of a group management control device according to the present invention. The elevator group management control device MA includes a microcomputer M1 that controls elevator operation and a microcomputer M2 that controls simulation, and the microcomputers M1 and M2 communicate data via a communication line CM _c using a serial communication processor SDA _c . It's like this. In addition,
The detailed configuration and operation of the serial communication processor SDA _c are known from Japanese Patent Laid-Open No. 56-37972 and Japanese Patent Laid-Open No. 56-37973.
The explanation will be omitted here. The microcomputer M1 is connected to the call signal HC from the hall call device HD via the parallel input/output circuit PIA, and is also used to control individual elevators, such as door opening/closing and car acceleration/deceleration commands. The microcomputers E ₁ -E _o (in this case, it is assumed that there is an n-th elevator) are connected to serial communication processors SDA ₁ -SDA _o similar to those described above via communication lines CM ₁ -CM _o . On the other hand, a signal PM from the target setting device PD, which provides information necessary for determining the optimum operation control parameters for the simulation, is input to the microcomputer M2 via the parallel input/output circuit PIA. In addition, the control input/output elements EIO ₁ to EIO _o , which are composed of car call information necessary for control, various elevator safety limit switches and relays, and response lamps, are connected to the communication line SIO ₁ to the microcontrollers E ₁ to E _o for controlling the units. ~
Connected via SIO _o . FIG. 2 is an overall configuration diagram showing one embodiment of the software of the group management control device of the present invention. The software is roughly divided into operation control software SF1 and simulation software SF2. The operation control system software SF1 is an operation control program that directly commands and controls elevator group management control such as call assignment processing and elevator distribution (movement) standby processing, which is the object of the present invention.
SF14 and distributed evaluation value calculation program SF15
An elevator control data table SF11 containing elevator positions, directions, car calls, etc. transmitted from the machine control programs E' to _Eo ' as input information sources of the operation control program SF14;
Hall call table SF12 and elevator specification table SF1 such as the number of elevators to manage
3 is available, and there is also a distributed evaluation value calculation program SF
Reference numeral 15 uses the optimum dispersion parameters calculated and outputted by simulation software SF2 as input data, and outputs the distributed floor and distributed moving machine SF16 by the calculation. On the other hand, simulation software SF2,
It consists of the following processing programs. (A) Data collection program SF20 This is a program that samples the contents of the hall call table SF12 and elevator control data table SF11 online at regular intervals and collects simulation data.
In particular, we will mainly collect traffic demand by destination floor. (B) Simulation data calculation program
SF22 This is the sampling data table SF21
This is a program that calculates simulation data by taking into account the contents of online sampling data collected by the data collection program SF20 and the contents of past time periods, and the results are as follows.
Output to simulation data table SF23. (C) Traffic Demand Program SF24 This is a program that creates traffic demand by destination, and outputs the results to the traffic demand table SF25. (D) Traffic demand classification program SF26 This inputs the contents and time information of the traffic demand table SF25 and calculates the traffic volume in the building for work, before lunch, during lunch, after lunch, normal, normal congestion, leaving work, and closing. This is a program that divides the traffic demand into eight traffic demands, and outputs the results to the traffic demand classification table SF27. (E) Simulation execution program SF28 This is the simulation data table SF
23, the traffic demand classification table SF27, and the contents of the elevator specification table SF29 are input to execute the simulation, and the results are output to the statistical processing data table SF30 based on the simulation. Details of this program will be described later. (F) Various performance curve calculation program SF31 using simulation This program inputs the contents of statistical processing data table SF30 using simulation, performs simulation for each predetermined plurality of parameters, calculates various performance curves, and uses the results as performance curves. Output to data table SF32. Performance curve tables include average waiting time curve tables, power consumption curve tables, and the like. Details of this program will be described later. (G) Optimal dispersion parameter calculation program SF33 This inputs the contents of the performance curve data table SF32 and the target value table SF34 of the target values set by the external target setting device PD (see Figure 1). The optimal dispersion parameter corresponding to power saving is calculated using the optimal dispersion parameter SF35. (H) Statistical processing calculation program SF36 This is the statistical processing data table SF30 based on simulation and the current car position table SF
17 is input, minimum values minTS, minTS', etc. of predicted waiting time for arrival at each floor are calculated and outputted to statistical table SF37. An embodiment of the overall software configuration of the present invention has been described above. Next, the concept of distributed control according to the present invention will be explained. The variance evaluation value φi for floor i is expressed by the following equation, taking into account the learned number of people boarding the elevator from floor i. Here, Ci (m): Number of people boarding the elevator from floor i at a learned traffic demand time m TSki: Predicted arrival time of car k to floor i L: Number of group-controlled elevators Also, φi is Although the calculation can also be performed using the following equation (2), here, for convenience of explanation, equation (1) will be used. Here, α: Predetermined coefficient (number of passengers x predicted arrival time of the car that can reach the floor earliest) Next, the elevators are assigned I to L in ascending order from the one closest to the bottom floor, and the distributed request bandwidth evaluation is performed. Let's explain the value FA. Now, if the floor where machine k is located is a, and the floor where machine (k+1) is located is b, then Here, FA(O) is the distributed requested bandwidth evaluation value between the lowest floor and the first car, and FA(L) is the distributed requested bandwidth evaluation value between the Lth car and the top floor. Therefore, a band with a large distributed request bandwidth evaluation value is a band that includes many floors with a large number of passengers and no nearby elevators, or a band that has a large predicted arrival time for each floor. This is a band with a large floor difference. Next, the evaluation value of the distributable elevator is FB
Then, if the floor where the (k-1) car is located is c, FB is expressed by the following formula. Here, L': group control elevator excluding machine k. Dispersion parameters include the number of distributed vehicles, the distance traveled by distributed vehicles, the floors through which distributed vehicles pass, etc.
In the present invention, considering the top floor and the bottom floor, the weighting coefficient of the average value F _AVR of the distance between each car is β
shall be. Then, F _AVR is F _AVR = 1/L + 1 _L 〓 ^k=0 FA(k) ...(5), and the predetermined dispersion value F _L for determining the presence or absence of distributed control is F _L = β・F _AVR ...(6). FIG. 3 is a diagram showing the relationship between the dispersion parameter β (weighting coefficient of the average value of the distance between each car) and the average waiting time and power consumption, where the broken line a indicates the average waiting time,
A solid line b indicates power consumption. If the dispersion parameter β is small, the dispersion movement will be active and the power consumption will be large, but the average waiting time will be small.If the dispersion parameter β is large, the dispersion movement will be small and the power consumption will be small. , it can be seen from Fig. 3 that the average waiting time increases. Next, a specific example of distributed control according to the present invention will be explained. However, the building will have 13 floors and 4 elevators. Also, for the sake of explanation,
The distance between each floor is constant, and the travel time between each floor is 2
It is assumed to be constant in seconds. Figure 4 shows the number of people boarding the elevator from each floor during a certain traffic demand period that has been learned, and Figure 5 shows the number of people boarding the elevator from each floor during a certain traffic demand period. This shows the car being abandoned on the 5th floor. Distributed request bandwidth evaluation value FA(k) in this case
FA (0) = 30 (persons) x 0 (seconds) = 0 (persons/seconds) Here, FA (O) is the evaluation value between the lowest floor and Unit 1. FA(1)=30×0=0(person/second) FA(2)=30×0+9×2+13×2+7×0 =44(person/second) FA(3)=7×0+9×0=0(person)・Seconds) FA(4)=9×0+7×2+10×4+21×6 +22×8+15×10+19×12+18×14+20
×16 = 1306 (person/second) Here, FA(4) is the top floor (13
This is the evaluation value up to (floor). The average value F _AVR between each car is calculated as follows: F _AVR = 1/5 (0 + 0 + 44 + 0 + 1306) = 270 (person-seconds). Next, the evaluation value FB(k) of the dispersible elevator
(In this case, k is 1 to 4). FB(1) is the evaluation value from the bottom floor up to Unit 2 excluding Unit 1. FB(1)=30×0=0(person/second) FB(2)=30×0+9×2+13×2+7×0 =44(person/second) FB(3)=30×0+9×2+13×4+7×2 +9×0=84 (person/second) FB(4)=7×0+9×2+7×4+10×6 +21×8+22×10+15×12+19×14 +18×16+20×18=1588 (person/second) Now, the variance parameter β If F L is 3, the predetermined dispersion value F _L in equation (6) is F _L = 3 x 270 = 810 (people/seconds), and FA(4) (= 1306 people/seconds) is larger than this F _L.
It can be seen that it is sufficient to move one unit approximately to the center of the second). Now, assuming that the elevators are distributed to the 10th floor (the elevator numbers to be selected will be described later), we calculate the evaluation value again: FA(4) = 9 x 0 + 7 x 2 + 10 x 4 + 21 x 4 + 22 x 2 + 15 x 0 = 182 (person/second) If the elevator on the 10th floor is k′,
Since k′ is the elevator on the highest floor, its evaluation value is the value from the floor where k′ is located to the top floor, FA (k′) = 15 × 0 + 19 × 2 + 18 × 4 + 20 × 6 = 230 (persons/seconds), which is below the predetermined value of 810 persons/seconds. The elevator that moves distributed to the 10th floor has an evaluation value.
It is sufficient to specify the elevator with the smallest evaluation value among FB(1) to FB(4), that is, the elevator No. 1. Above, for convenience of explanation, we have explained that the one with the smallest evaluation value FB(k) is selected, but of course, the second and third units with distributable elevator evaluation values smaller than the predetermined value of 810 people-seconds are also selected. It can be moved. Therefore, the evaluation value FB smaller than the predetermined value F _L
It is also possible to select elevators from among (k) by performing min-max processing and control the elevators to move in a distributed manner. FIGS. 6a and 6b each show the state of an example of distributed control. Next, when the dispersion parameter β is 1, the predetermined dispersion value F _L in equation (6) is F _L =1×270=270 (person/second). In this case, the 5th floor where Unit 4 is located and the top floor
Assume that there is a request to move 2 or 3 cars to the 13th floor.
Now, in the case of two elevators, if we calculate the distributed request bandwidth evaluation value FA(k) assuming that the elevators are distributed between the 9th and 12th floors, FA(4) = 9 x 0 + 7 x 2 + 10 x 4 + 21 x 2 + 22 x 0 = 96 (people/seconds) FA (k') = 22 x 0 + 15 x 2 + 19 x 2 +18 x 0 = 68 (people/seconds) (k' is the elevator on the 9th floor) FA (k'') = 18 x 0 + 20 ×2 = 40 (people/seconds) (k″ is the elevator on the 12th floor) In this case, the first elevator with the lowest evaluation value of the dispersible elevator is first distributed to the 9th floor, and then the other three Recalculate the dispersible elevator evaluation value. As a result, FB(2)=30×6+9×4+13×2+7×0 =242(person/second) FB(3)=30×0+9×2+13×4+7×2 +9×0=84(person/second) FB( 4) = 7 x 0 + 9 x 2 + 7 x 4 + 10 x 4 + 21 x 2 + 22 x 0 = 128 (people/seconds) (There will be one unit on the 9th floor, and Unit 3 with the lowest evaluation value will be dispersed and moved to the 12th floor. If the distributed requested bandwidth evaluation value FA(k) and the dispersible elevator evaluation value FB(k) are determined as shown in FIG. The diagram shows the operation control system software SF in Figure 2.
1, the elevator control data table SF11, hall call table SF12,
It is composed of blocks such as an elevator specification table SF13 and a current car position table SF17. The tables in each block will be described each time the operation control program SF14 is explained later. Figure 8 is a table configuration diagram of the simulation software SF2 shown in Figure 2, including the optimal dispersion parameter SF35 and various performance curve data tables SF3.
2. Target value table SF34, sampling data table SF21, simulation data table SF23, elevator specification table SF
29 (Elevator specification table SF1 in Figure 7)
3), a traffic demand classification table SF27, a traffic demand table SF25, a statistical table SF37, and a statistical processing table SF30 based on simulation. The program is a driving system program (Unexamined Japanese Patent Publication No. 56-
158739) and a simulation program, these programs are divided into a plurality of tasks and managed by a system program that performs efficient control, that is, an operating system. Therefore,
Programs can be started by the system tire or by other programs. Next, simulation software SF2
Each program will be explained. First, the data collection program SF20 runs at a certain period (for example,
1 second) and for a certain period of time (for example,
After collecting the data (for 10 minutes), it is stored in the sampling data table SF21 as shown in FIG. In addition,
The collected data is subjected to the above-mentioned calculation when the sampling time ends, and is stored in the online measurement data table and the past time period data table of the sampling data table SF21, respectively. As shown in Figure 8, the online measurement data table includes Q _oew , t _roew , t _soew
Add a new subscript to each item name like this, and
The past time period data table includes Q _pld , t _rpld ,
Each item name is displayed with the subscript "old", such as t _spld . Simulation data calculation program SF
22 is activated periodically, and the simulation data is predictively calculated by combining online measured data and past data with an appropriate coupling variable γ.
For example, the destination traffic volume is calculated using the following equation. Q _pre = γQ _oew + (1 − γ) Q _pld (7) Therefore, the larger the coupling coefficient γ, the greater the weight of destination traffic volume data measured online. Note that the subscript "pre" is added to the predicted data. In the same way as above, the predicted data t _rpre and t _spre of the first floor running time and one standard stopping time are also calculated,
The data of t _rpre and t _spre are stored in each table of the simulation data table SF23 in FIG.
Also set it on the Ts table. Then, the simulation execution program SF28 is activated based on the predicted data calculated by this program. The traffic demand program SF24 is executed based on the above forecast data, and the traffic demand classification program SF26 uses the forecast data of the destination traffic based on the above forecast data and time information for work, before lunch, during lunch, etc.
After lunch, traffic demand is divided into eight categories: normal, normal congestion, leaving work, and closed traffic. Figure 9 shows the simulation execution program SF.
28 is a flowchart showing an example of No. 28. There is a dispersion parameter as a simulation parameter, and a simulation is executed for each parameter case. First, simulation data such as destination traffic volume is set (step SC10). Next, in step SC20, the dispersion parameter α is set, and a simulation is executed (step SC30). Note that the dispersion parameter α is, for example, 0, 1, 2, 3, 4, or 5.
The simulation results for each case are stored for each parameter (step SC50).
The simulation results include firstly an average waiting time, secondly a power consumption value, and thirdly a value obtained from the average waiting time and the power consumption value. After completing the simulation for all the above cases (step SC40), the distribution parameters and service performance are calculated (step SC60). FIG. 10 is a flowchart showing an embodiment of the simulation execution of step SC30 in FIG. First, input processing of the dispersion parameter α is performed (step A10). Next, simulation variables are initialized (step A20). For example, this includes the initial setting of random numbers for passenger generation processing, which will be described later, and the initial setting of the hall call table SF12.
In step A30, statistical processing variables are initialized. Here, initial settings of the statistical table SF37, etc. are performed. In step A40, the simulation start time is set to zero, in step A90 a predetermined value (here, 1) is added to the time, and in step A100 it is determined whether this time exceeds the predetermined time. Then, the process continues from step A50 to step A until the above-mentioned time exceeds a predetermined time.
Process up to 90. In step A50, a passenger generation process is performed, and in step A60, a group management process is performed to allocate a hall call when a hall call occurs. Further, in step A70, elevator processing such as running and stopping of the elevator and opening/closing of doors is performed, and in step 80, statistical data collection processing is performed to collect statistical data. Here, the processing from step A50 to step A70 will be explained in more detail. In the passenger generation process at step A50, the passenger generation floor i ₁ and the passenger destination floor i ₂ are determined by uniform random numbers based on the destination traffic volume prediction data obtained by the simulation data calculation program SF22. Furthermore, the number of passengers generated from the i _1st floor to the i _2nd floor is determined using the above uniform random number, and a hall call is generated on the i _1st floor. Next, in step A60, the group management process performs call assignment if the hall call occurs.
The car processing in step A70 includes processing such as the running state and stopped state of the elevator, door opening/closing, and car call generation. Next, the statistical data collection process in step A80 will be explained with reference to the flowchart shown in FIG. From steps A80-1 to A80-4, the elevator direction j, the dispersion parameter α,
This is the number of loops for traffic demand classification M and floor i, and in steps A80-6 to A80-9, the loop completion determination for each of the above-mentioned j, α, M, and i is performed. In step A80-5, statistical data (number of elevator stops, number of hall calls, number of car calls,
number of people boarding, number of people getting off, etc.) above j, α, M, i
Collect separately. Next, each program of the operation control system software SF1 will be explained. FIG. 12 is a flowchart showing an embodiment of the distributed evaluation value calculation program SF15 that calculates the distributed request band evaluation value and the evaluation value of the distributable elevator. Step B10 is an initial process in which the optimum dispersion parameter β from the optimum dispersion parameter SF35 and the current car position table SF1 are
Enter the current car position etc. from 7. In step B20, the distributed requested bandwidth evaluation value FA between each car is calculated.
(k), and in step B30, the predetermined variance value
In step _B40 , the dispersible elevator evaluation value FB(k) is calculated, and in step B5
0, the distributed floors and distributed moving elevators are calculated and determined. FIG. 13 is a flowchart showing details of the process in step B20 of FIG. Enter the number of people entering the elevator Ci (m) from each floor i learned in Step B20-1, and proceed to Step B.
20-2 is the minimum value of the predicted arrival time to each floor i.
Enter minTS(i). In step B20-3,
Let the sum SFA of evaluation values FA(k) be 0. Step B
From 20-4 to B20-15, the process is repeated, and for each machine [FA (0) is from the bottom floor to machine 1, FA (KMAX) is from KMAX (machine 4 in Figure 5) to the top floor. ) is performed to obtain the evaluation value. First, B20 determines whether the k-th machine is KMAX.
-5. If it is KMAX, enter the floor where KMAX is located in A and the top floor in B20-7.
If k is not KMAX, k is 0 (lowest floor)
B20-6 determines whether. If k is 0, enter the lowest floor in A and the floor where the first car is located in B20-8. If k is not 0, the floor where the kth car is located is inputted into A, and the floor where the (k+1)th car is located is inputted into B20-9. In step B20-10, all evaluation values
Clear FA(k). Steps B20-11 to B20-13 are repetitive processing for calculating the evaluation value FA(k) for each car. Step B20-1
In step 4, calculate the sum of evaluation values FM(k) and find out that machine k is
Calculate up to KMAX and step B20 in Figure 12
The calculation process for the distributed requested bandwidth evaluation value FA(k) is completed. FIG. 14 is a flowchart showing details of the process in step B30 of FIG. In step B30-1, the optimal dispersion parameter β is input, and in step B30-2, the sum SFA of the evaluation values FA(k) is input. In step B30-3,
The average value F _AVR of the distributed requested bandwidth evaluation values between each machine is calculated, and then, in step B30-4, the distributed predetermined value is calculated.
Compute F _L and end this process. FIG. 15 is a flowchart showing details of the process in step B40 of FIG. In step B40-1, the predetermined variance value F _L and the number of people Ci (m) boarding the elevator from each floor i are input. Next, in step B40-2, the minimum predicted arrival time minTS'(i) of each car excluding car k to each floor i
Enter. Steps B40-3 to B40-1
Steps up to 5 are repeated calculation processes to obtain the evaluation value FB(k) of the distributable elevators of all units. In step B40-4, the KMAX of the car No. k is determined. If k is KMAX, the top floor is entered in B and the floor where the car No. (KMAX-1) is located is input in B40.
-5. If it is not KMAX, it is determined in step B40-6 whether car k is the first car, and if it is the first car, in step B40-7, the lowest floor is input into C and the floor where the second car is located is input into B. If machine number k is neither machine number 1 nor machine number KMAX, proceed to step B4.
0-8, floor with unit k-1 on C, k-1 on B
Enter the floor where the machine is located. In step B40-9, all evaluation values FB
Clear (k). Steps B40-10 to B4
From 0 to 15, the evaluation value FB(k) for each car is calculated. That is, steps B40-4 to B40
Calculate the sum of the product of the number of people boarding the elevator from the C floor to the B floor, Ci (m) defined in the process up to -8, and the predicted arrival time minTS'(i) of each unit, and FB(k). In step B40-13, the evaluation value FB(k) and the predetermined dispersion value F _L are compared, and if the evaluation value FB(k) is less than or equal to the predetermined value F _L , in step B40-14, the k-th elevator is set to the dispersible elevator. , and find the number of units BNLTM. Step B40-16 is step B40-1
This is a well-known process of sorting the evaluation values of the distributable elevators obtained in step 4 in order from the smallest to the smallest, and with this process, the process of step B40 in FIG. 12 is completed. FIG. 16 is a flowchart showing details of the process of calculating distributed elevators and floors in step B50 of FIG. Step B50
-1, distributed predetermined value F _L , distributed requested bandwidth evaluation value FA
(k), and input the evaluation value FB(k) of the dispersible elevator. Next, in step B50-2, the total number of distributed requested devices BN is cleared. Steps B50-3 to B
Up to 50-9, the evaluation value FA(k) and the predetermined value F _L are compared, and the band larger than the predetermined value F _L and the required number of devices are determined.
This is the process of finding the BN. In step B50-4, the requested number N of devices in one band is cleared. In step B50-5, the evaluation value FA(k) and the predetermined value F _L are compared, and if the predetermined value F _L is less than the evaluation value FA(k), the process returns to step B50-3 and the machine number k is updated. do. Also, in step B50-5, the evaluation value FA(k)
is larger than the predetermined value F _L , the requested number N in one band (meaning between cars) is updated and FA(k)/
(N+1) is compared with a predetermined value F _L , and this process is repeated until F _L becomes larger. Then, N is added to the total number of distributed request units BN (step B50-
5-B50-8). In step B50-10, the total distributed requested number
Compare BN and the number of elevators that can be distributed BNLMT, and if BN is larger, proceed to step B.
At 50-11, BNLMT is selected in order of magnitude from among FA(k)/(N+1). If BN is smaller, the process of step B50-12 is performed. In step B50-12, FA(k) larger than the predetermined value F _L is input. Step B50-13
Then, let A be the floor where machine k is located, and B be (k+1)
Enter the floor where the machine is located. Step B50-1
In step 4, the product sum value S of one band is cleared. Steps B50-15 to B50-20 are processes for determining the distribution request floor(s) for one band.
Compare FA(k)/(N+1) and S, and if FA
If (k)/(N+1) is small, S has Ci(m)・
minTS(i) is added and floor i is updated (steps B50-16 and B50-17). And if,
If S is larger, the floor i is designated as the distributed request floor (BFL ₁ , BFL ₂ , ..., BFL _BNLMT ),
Clear S (steps B50-18, B50
-19). This process is performed with FA(k) larger than the predetermined value F _L
This process is repeated until the process is completed. Above, using Figures 12 to 16, the distributed request bandwidth evaluation value FA, the distributed elevator evaluation value FB
In search of distributed designated floors and elevators that can be distributed. In addition, in FIGS. 12 to 16, when determining the dispersible elevator, adjacent elevators are not described because the explanation would be complicated, but in this case, the evaluation value FB(k) is specified as small. It is necessary to recalculate the evaluation values FB(k) of other elevators assuming that there are no elevators. Figure 17 shows the operation control program SF1 in Figure 2.
4 is a flowchart showing an embodiment of a distributed standby control calculation program related to the present invention in No. 4.
In steps C10 and C20, the distributed request floor calculated by the distributed evaluation value calculation program SF15,
Enter dispersible elevator. Step C3
From 0 to C50, a virtual hall call is given to the distributed request floor, and min-max control processing (for example,
51-23932) and select a mobile elevator from among the dispersible elevators. Above, we have explained distributed control in which the elevators with movable elevator evaluation values are used as distributable elevators. It can also be applied to selecting an elevator when waiting in advance for a floor where the elevator is expected to arrive. Furthermore, for convenience of explanation, the explanation has been made for elevators that are inactive or stopped, but it can also be applied to elevators that are in service, and the present invention can be applied to elevators that are in service. The evaluation value of the movable elevator may be taken into consideration to select an elevator with a small evaluation value. [Effects of the Invention] As explained above, according to the present invention, the evaluation value of the movable elevator is obtained, and distributed waiting, waiting on crowded floors, and centralized service control are performed according to this evaluation value. , there is an effect that optimal movement control can be performed.

[Brief explanation of drawings]

FIG. 1 is an overall hardware configuration diagram showing an embodiment of the elevator group management control device of the present invention;
Fig. 2 is an overall configuration diagram showing one embodiment of the software of the elevator group management control device of the present invention, Fig. 3 is a relationship diagram between dispersion parameters, average waiting time, and power consumption, and Fig. 4 is a learning diagram. Figure 5 is a diagram showing the number of people boarding elevators by floor during a certain traffic demand, Figure 5 is a diagram showing the position of each elevator before distributed control,
Figure 6 is a diagram showing an example of distribution control, Figure 7 is a table configuration diagram of the operation control system software in Figure 2, and Figure 8 is a diagram showing an example of distribution control.
The figure is a table configuration diagram of the simulation software in Figure 2, Figure 9 is a flowchart showing an example of the simulation execution program in Figure 2, and Figure 10 is an example of step SC30 in Figure 9. 11 is a flowchart showing an embodiment of step A80 in FIG. 9; FIG. 12 is a flowchart showing an embodiment of the variance evaluation value calculation program in FIG. 2; FIGS.
FIG. 16 shows steps B20 and B20 in FIG. 12, respectively.
A flowchart showing an embodiment of B30 and B50, and FIG. 17 is a flowchart showing an embodiment of the distributed standby control calculation program of FIG. MA...Elevator group management control device, HD...
...Hall calling device, M1...Microcomputer for elevator group management control, M2...Microcomputer for simulation, _E1 to _Eo ...Microcomputer for controlling machine number, PD...
...goal setter.

Claims (1)

[Scope of Claims] 1. In an elevator group management control device that performs distributed standby control of a plurality of elevators operating between a plurality of floors, a plurality of elevators divided by the highest floor, the lowest floor, and the position of the plurality of elevators is provided. means for setting a band, the product of the number of people boarding each floor in a predetermined period included in the band and the minimum predicted arrival time of each elevator to the floor is summed for all floors in the band; means for calculating a distributed requested bandwidth evaluation value for each band; means for selecting an elevator to be distributed to a band whose distributed requested bandwidth evaluation value exceeds a predetermined value; 1. A group management control device for an elevator, comprising means for moving to the band. 2. The elevator group management control device according to claim 1, wherein the predetermined value in the means for selecting the elevators to be distributed is an average value of the distribution request band evaluation values of each of the bands. 3. The means for selecting the elevator to be distributed calculates for each elevator the distribution request band evaluation value in a new band created when the elevator leaves the floor on which it is located, as the distribution possible evaluation value of the elevator. 2. The elevator group management control device according to claim 1, further comprising means for specifying, as a distributed elevator, an elevator whose dispersibility evaluation value is equal to or less than a predetermined value.