JPH0557192B2 - - Google Patents

Description

[Detailed description of the invention]

[Field of Application of the Invention] The present invention relates to an elevator group management and control device, and more particularly to an elevator group management and control device suitable for enabling efficient elevator service in response to variously changing traffic demands. be. [Background of the Invention] Recently, microcomputers (hereinafter referred to as microcomputers) have been applied to various industries, and in the field of elevators, they have been used as group management control devices to efficiently manage multiple elevators, and to control individual elevators. It is applied to the unit control equipment. These efforts have made a significant contribution to elevator control equipment due to microcontrollers' small size, high functionality, high reliability, and low cost. For example, in the case of group management control, it is possible to monitor each hall call that occurs online, take into account the service status of all hall calls, and select and allocate the most suitable elevator, which greatly reduces waiting time. Contributing. In addition, it has become possible to perform priority service control, such as having multiple elevators serve a hall with a large number of passengers, or having an elevator with short waiting time serve an executive floor, making it possible to perform fine-grained control. ing. By the way, there are various methods for elevator call allocation, but regarding the waiting time variance minimum call allocation method shown in Japanese Patent Publication No. 55-21709, which uses the difference between the predicted waiting time and the reference time parameter as the evaluation value. In other words, it is difficult to set an optimal reference time parameter, and therefore various methods related to this have been proposed. For example, (a) a method in which the average value of predicted waiting times for calls is used as a reference time parameter (Japanese Patent Application Laid-open No. 165672/1983). (b) A method of setting by calculating from a higher-order function of the average value of call unanswered time (Japanese Patent Application Laid-Open No. 1983-47780). (c) A method of setting according to the duration of hall calls on each floor at that time (Japanese Patent Application Laid-Open No. 58-52163). (d) A method in which the relationship between the standard time parameter and the average waiting time is obtained through offline simulation, and the optimal value for that building is set in advance. etc. However, the above-mentioned systems are not necessarily adapted to the ever-changing building environment. [Object of the Invention] The present invention has been made in view of the above, and its object is to provide elevator group management control that can enable efficient elevator service that quickly responds to variously changing traffic demands. The goal is to provide equipment. [Summary of the Invention] The present invention provides a group of elevators that performs call assignment by selecting an optimal elevator from among a plurality of elevators operating between floors based on the deviation between the predicted waiting time and the standard waiting time. In the management control device, a means for collecting usage information during elevator operation and a reference waiting time or a power (power of the deviation of the predicted waiting time and the reference waiting time) are variable parameters,
The traffic demand to be simulated is set based on the information from the usage information collection means when the elevator is in operation, the simulation is executed multiple times while changing the variable parameters, and the variable parameters that give the best simulation result are output. The present invention is characterized by comprising a simulation means and a means for allocating calls based on the reference waiting time obtained by the simulation means or the exponent of the deviation between the predicted waiting time and the reference waiting time. [Embodiment of the Invention] Hereinafter, the present invention will be explained in detail with reference to a specific embodiment shown in FIGS. 1 to 12. In the description of the embodiment, first, the hardware configuration for realizing the present invention will be described, then the overall software configuration and its control concept will be described, and finally, a flowchart for realizing the above control concept will be explained. FIG. 1 is an overall hardware configuration diagram of an embodiment of the present invention. The elevator group management control device MA has a microcomputer M1 that controls elevator operation and a microcomputer M2 that controls simulation.
Between 1 and M2 is a serial communication processor SDA _c .
Data is communicated via communication line CM _c . Note that detailed configuration and operation explanation regarding this SDA _c are disclosed in Japanese Patent Application Laid-Open No. 56-37972 and Japanese Patent Application Laid-Open No. 56-37973. Furthermore, although the microcomputer M2 is also used in this embodiment, it is also possible to configure the entire system using the microcomputer M1. A call signal HC from a hall call device HD is connected to the microcomputer M1 that controls elevator operation via a parallel input/output circuit PIA.
The microcomputers E ₁ to _{E o} (here, it is assumed that there is elevator number n) that control individual elevators, such as door opening/closing and car acceleration/deceleration commands, are serial communication processors SDA ₁ similar to those described above. ~
It is connected to SDA _o via communication lines CM ₁ to _{CM o} . On the other hand, the microcomputer M2 receives a signal PM from the power saving target setter PD, which provides information necessary for determining the optimum operation control parameters for the simulation, through the parallel input/output circuit PIA. In addition, the control input/output elements EIO ₁ to EIO _o , which consist of car call information necessary for control, various elevator safety limit switches, 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 . Figure 2 is an overall configuration diagram of the software.
Software can be broadly divided into operation control software
It consists of SF1 and learning software SF2. The operation control system software SF1 consists of an operation control program SF14 that directly commands and controls elevator group management control such as call assignment processing and elevator distributed standby processing. Elevator control data table SF1 including elevator position, direction, car call, etc. sent from
1. Hall call table SF12, elevator specification table SF1 for managing number of elevators, etc.
There is also data such as optimal operation method selection parameters and call assignment control parameters calculated and output using the learning software SF2. On the other hand, the learning software SF2 is composed of the following processing programs. (1) Elevator usage information collection program SF2
0 This is a program that samples the number of people getting on and off, hall calls, and the contents of elevator control data tables SF12 and 11 online at regular intervals, and collects data for selection of call allocation method and simulation. Mainly collect. (2) Traffic demand learning calculation program SF22 This calculates traffic demand data by taking into account the contents of the online sampling data in the sampling data table SF21 collected by the elevator usage information collection program SF20 and the contents in past time periods. The result is a transportation demand data table
Output to SF23. (3) Traffic demand classification program SF24 This program inputs the traffic demand by destination floor and time information obtained from the traffic demand data table SF23, and calculates the traffic volume in the building for work, before lunch, during lunch, etc.
After lunch, normal, normal congestion, clocking out, closing, etc., the program divides traffic demand into characteristics that affect the operation control efficiency of elevator group management, and the results are divided into traffic demand classification table SF
Output to 25. (4) Driving program generation system SF3 This is a configuration that uses a combination of individual functions in items (5) to (8) explained below, and the driving program generation system SF3 as a whole can be used to respond to traffic demand with certain characteristics. Generate an optimal driving program. (5) Simulation execution management program SF2
6 This inputs the contents of the traffic demand data table SF23, the traffic demand classification table SF25, and the elevator specification table SF27, executes the simulation, and outputs the result to the statistical processing data table SF28 based on the simulation. (6) Various curve calculation program SF29 using simulation This program inputs the contents of statistical processing data table SF28 using simulation, performs simulation for each predetermined plurality of parameters, calculates various curves, and displays the results in various curve data tables. Output to SF30. The various curve data tables SF30 include, for example, an average hall call duration (or average waiting time) curve table, a power consumption curve table, a long waiting occurrence probability curve table, a first-come-first-serve rate curve table, and the like. (7) Calculation program for optimal operation control parameters
SF31 This is a power saving target value table SF3 set by various curve data cables SF30 and an external target setting device PD (see Figure 1).
2 is input, the optimum group management operation control parameter corresponding to the power saving request is calculated, and the optimum operation control parameter SF33 is output. In addition,
If there is no power saving request, the system outputs group management operation control parameters that are optimal for the characteristics of traffic demand.
It is also possible to input target values for the first-come-first-served rate and the probability of long waiting. (8) Statistical processing calculation program SF34 This calculates the stop probability, fullness prediction, etc. based on the contents of the statistical processing data table SF28 by simulation, and calculates the statistical processing table SF35.
Output to. An embodiment of the overall software configuration of the present invention has been described above. Next, a method of calculating various parameters forming the optimum operation control program SF14 by simulation will be explained. First, a general explanation of the main terms used in the following explanation will be given. (b) Power consumption (F _P ) Refers to the power consumed by the entire group control elevator during a specified period. Here, the estimated power consumption value is calculated by calculating the number of times the elevator is activated, the running time, and the average number of passengers in the car for each direction of operation, as well as the car light lighting time and elevator drive preparation circuit energization time by simulation. (b) Service index ( _FT ) This is an index for evaluating the service performance of the elevator group management system. For this purpose,
The average waiting time has generally been used, but since it is not possible to measure the average waiting time in actual operating conditions, we use the hall call duration time (the waiting time for the first arriving passenger) to manage the simulation results and the actual service results. This enables manual verification and automatic correction of various simulation constants. Note that when the long waiting rate is used instead of the average as the target value for energy saving control, this index is also used as the long waiting rate. (c) Minimum waiting time variance call assignment method The evaluation function φ _B of the minimum waiting time variance call assignment method is basically expressed by the following equation. φ _B = |T ⁱ _k −T _n | ...(1) Here, T ⁱ _k ; k of the floor (i) with the allocation request
Predicted waiting time T _n of machine number; reference time parameter Also, considering the allocated hall calls,
The evaluation formula φ _T becomes φ _r = 〓 ⁿ |T(n) ⁱ _k −T _n | ...(2). By further adding an exponent parameter β, it is generally expressed by the following equation. φ _T = 〓 ⁿ ｜T(n) ⁱ _k −T _n ｜〓 ……(3) Here, T(n) ⁱ _k ; Predicted time for the allocated hall call and the allocated hall call of car No. k Newly registered The hall call received is assigned to the machine whose evaluation formula above is the minimum. Next, FIG. 3 shows the waiting time distribution of this method.
When the waiting time is the standard time parameter value, it is characterized in that the probability of occurrence is almost at its maximum value. FIG. 4 is a diagram showing the relationship between the reference time parameter and the average waiting time, in which the exponent β is shown as a parameter. The average waiting time can be minimized by combining the optimal reference time parameter value T _n and the exponent β. Figure 5 is a diagram when traffic demand is taken as a parameter in Figure 4,
This shows that there is an optimal reference time parameter T _n depending on the traffic demand at that time. FIG. 6 is a diagram showing the influence of the power parameter β on the waiting time distribution. This shows that by increasing β, it is possible to concentrate on a predetermined waiting time. From the above, it can be seen that in order to minimize the average waiting time, it is necessary to set the optimal reference time parameter T _n and exponent parameter β according to the traffic demand at that time. Figure 7 shows the operation control system software SF in Figure 2.
1 table configuration diagram, roughly divided into elevator control table SF11 and hall call table SF1.
2. It is composed of the blocks of elevator specification table SF13. Regarding the tables in each block, please refer to the operation control program SF1 described below.
When explaining 4, I will mention it each time. First, the operation control system program will be explained, and then the learning system program will be explained. The program described below is a system program that divides a program into multiple tasks and performs efficient control, that is, an operating system (OS).
shall be managed under the Therefore, programs can be started freely from the system timer or from other programs. Now, Figures 8 and 9 show the operation control program.
The flowchart of SF14 is shown. Note that the elevator arrival predicted time table calculation program and call assignment program, which are particularly important in the operation control program SF14, 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 to be used as basic data for calculating the waiting time evaluation value. 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, first, an initial value is set in the work time table T, and its contents are set in the predicted arrival time table of the control data table SF11 in 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 (step
E70), if any, the first floor stop time T _s is added to the time table for the elevator to stop (step E80). Next, the process jumps to step E30 and the above process is repeated for all floors. In addition, 1 in step E50 and step E80
The floor travel time T _r and one stop time T _s are the optimal operation control parameters from the learning software SF2.
It is given as one of SF33. FIG. 9 is a flowchart of a call assignment program, which is activated when a hall call occurs or periodically. When a hall call occurs, first, in step H05, the type of hall call is specified. Next, in step H10, the generated hall call is read from the outside. And steps H30 and H70
Loop processing is performed for floors and directions. In step H40, it is determined whether there is a hall call that has occurred or a call that requires reallocation. If not, jump to step H70 and check all floors,
Process about direction. Step H40 is “YES”
If so, the call assignment elevator is selected using the waiting time variance minimum call assignment algorithm in step H50, and the call is assigned to the optimal elevator (step H60). After that, in step H80, it is determined whether all types of hall calls have been processed. (Hall call). Note that the call allocation algorithm in step H50 can be changed depending on the type of hall call. In other words, the parameters in equation (3)
This can be realized by having T _n and β for each type of hall call. Figure 10 shows the table configuration of the learning software SF2, with the optimal operation control parameters SF33,
Various curve data table SF30, target value table SF32, sampling data table SF2
1. Traffic demand data table SF23, elevator specification table SF27 (not shown as it is the same as SF13 in Figure 8), traffic demand classification table SF2
5. The configuration of the statistical processing data table SF28 and the statistical table SF35 by simulation is shown. Next, the program of the learning software SF2 will be explained. First, the elevator usage information collection program SF20 is run at regular intervals (for example, once every
2 seconds) and collects data for a certain period of time (for example, 10 minutes), it is stored in the sampling data table SF21 in FIG. There are various data collection items, but in the program of the present invention,
In particular, data such as destination traffic volume Q, first floor travel time t _r of elevators, and one standard stop time t _s are collected. First floor running time t _r and 1 of the above elevator
To calculate the standard number of stops, _ts , after the sampling time is over, divide the running time by the number of floors traveled to calculate the running time for the first floor. The standard stopping time for the first floor can be calculated. When the sampling time ends, the collected data is subjected to the above-mentioned calculation and stored in the online measurement table and the time-based table of the sampling data table SF21 in FIG. 10, respectively. This online measurement data table has a new subscript added to the item name, such as Q _oew , t _roew , and t _soew , and an old subscript is added to the time zone table, such as Q _pld , t _rpld , and t _spld . It is written as. The traffic demand learning calculation program of SF22 is activated periodically, and predicts and calculates traffic demand data by adding an appropriate coupling variable γ to online measured data and past data. For example, the destination traffic volume is calculated using the following formula. Q _pre= 〓 _Qoew + (1-γ) Q _pld Therefore, the larger the coupled variable γ, the greater the weight of the destination traffic volume data measured online. Note that the subscript "pre" is added to the predicted data. Similarly to the above, predicted data t _rpre and t _spre of the first floor running time and the first floor standard stopping time are also calculated. Further, the data of t _rpre and t _spre are set in the table of Tr _and T _s of the optimum operation control parameter SF33 shown in FIG. Then, the simulation execution management program SF26 is activated based on the predicted data calculated by this program. Furthermore, based on the above forecast data, the forecast data of destination traffic volume is further divided into traffic demand by characteristics such as attendance at work, before lunch, during lunch, after lunch, normal, normal congestion, leaving work, and quiet. This is the transportation demand classification program SF24. FIG. 11 is a flowchart of the simulation execution management program SF26. First, in step SC10, traffic demand, control target,
Set simulation management specifications that specify permitted driving methods, etc. Next, step
At SC20, the reference waiting time T _n and the power number β are specified as parameters, and at step SC30, the number of each parameter is specified. Next, the first parameter is specified in step SC40, and a simulation is executed in step SC50. Next, step
At SC60, it is determined whether all the specified parameters have been completed, and the simulation is performed sequentially using the specified number of parameters. Next, from the simulation results for each case, a sex evaluation curve is determined in step SC70, optimal parameters are calculated in step SC80, and the results are recorded. In this way, the optimal driving control program is generated for each traffic demand learned for each time period or characteristic mode. Next, the simulation execution at step SC50 in FIG. 11 will be explained in detail using the flowchart in FIG. 12. First, input processing of the reference time parameter T _n and exponent β 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 a hall call table. In step A30, statistical processing variables are initialized. Here, initial settings of the statistical table, etc. are performed. In step A40, the time is set to zero, and in step A90, the time is added to a predetermined value (here, it is set to 1), and it is determined whether this time exceeds the predetermined time (step A100). Processing from step A50 to step A90 is performed until the above-mentioned time exceeds a predetermined time. Step A50 is a process to process the occurrence of passengers, step A60 is a group management process to allocate a hall call when a hall call occurs, and step A70 is a group management process to allocate a hall call when a hall call occurs. It is processing. Step A80 is a statistical data collection process for collecting statistical data. Here, steps A50 to A70 will be explained in more detail. The passenger generation process in step A50 is performed using the traffic demand learning calculation program SF22.
Based on the destination traffic forecast data obtained from
Passenger generation floor i ₁ and passenger destination floor i ₂ are determined by uniform random numbers.
Determine. 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 made.
i Generate on _{the first} floor. Next, in the group management process of step A60, if the hall call is generated, call assignment is performed. The call assignment method is the same as that described in the operation control program above. Step A70
The machine number processing processes the elevator running status, stopped status, door opening/closing, car call generation, etc. [Effects of the Invention] As explained above, according to the present invention, data on the ever-changing building environment is collected online, and based on this data, the variable parameters of the waiting time variance minimum call allocation method are determined. This has the effect of making it possible to provide efficient elevator service that can quickly respond to variously changing traffic demands.

[Brief explanation of the drawing]

FIG. 1 is an overall configuration diagram of the hardware showing an embodiment of the elevator group management and control device of the present invention, and FIG. 2 is an overall configuration diagram of the software of the elevator group management and control device of the present invention. Fig. 3 is a waiting time distribution diagram of the waiting time variance minimum call assignment method, and Fig. 4 is a diagram showing the relationship between the standard waiting time and the average waiting time of the waiting time variance minimum call assignment method.
Figure 5 is a diagram when traffic demand is used as a parameter in Figure 4, Figure 6 is a diagram showing the relationship between power parameters and waiting time distribution, and Figure 7 is the operation control system software shown in Figure 2. FIG. 8 is a flowchart showing an example of a program for calculating the predicted arrival time of the operation control program of FIG. 2; FIG. 9 is a flowchart showing an example of a call assignment program; Fig. 10 is a table configuration diagram of the learning software shown in Fig. 2, Fig. 11 is a flowchart showing an example of the simulation execution management program shown in Fig. 2, and Fig. 12 is a process in the simulation execution shown in Fig. 11. This is a flowchart. MA...Elevator group management control device, HD...
...Hall calling device, M1...Microcomputer for elevator group management control device, M2...Microcomputer for simulation, _E1 to E _o ...Microcomputer for controlling machine number,
PD...Goal setting device.

Claims (1)

[Claims] 1. In an elevator group management control device that selects and allocates calls to an optimal elevator based on the deviation between a predicted waiting time and a reference waiting time from among a plurality of elevators operating between floors. , a means for collecting usage information while the elevator is in operation, and a reference waiting time or an exponent (an exponent of the deviation of the predicted waiting time and the reference waiting time) as a variable parameter, and information from the means for collecting usage information while the elevator is in operation. a simulation means that sets the traffic demand to be simulated, executes the simulation multiple times while changing the variable parameter, and outputs the variable parameter that gives the best simulation result; a reference waiting time obtained by the simulation means;
Alternatively, an elevator group management control device comprising means for allocating calls based on a power of the deviation between the predicted waiting time and the standard waiting time. 2. In claim 1, the simulation means calculates a standard waiting time or a predicted waiting time and a standard based on the information obtained by the usage information collecting means by time period or characteristic mode during elevator operation. An elevator group management control device characterized by simulating the optimum standard waiting time for each time period or characteristic mode or the power of the deviation between the predicted waiting time and the standard waiting time by changing the power of the waiting time deviation. .