JPH0780638B2 - Group management control method for elevators - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Industrial field of application) The present invention relates to elevator group management control, and in particular, the application of knowledge engineering makes it possible to achieve favorable target values in various group management controls. The present invention relates to an elevator group management control method that can be made to follow.

(Prior Art) In recent years, when a plurality of elevators are installed side by side, in order to improve the operation efficiency of the elevators and the service to the elevator users, a response machine to the hall call on each floor is a small computer such as a microcomputer. Allocation is performed reasonably and promptly using a computer.

That is, this is a control method called group management control,
When a hall call occurs, select the most suitable elevator to deal with the hall call, assign an elevator that responds to the hall call early, and prevent other elevators from answering the hall call, or Efficient while switching to a preset operation mode according to the traffic change unique to the building, such as when traffic demand increases such as when going to work or leaving the office, lunch, etc., and when traffic decreases, such as at night. It controls each elevator in order to operate it.

By the way, due to the cost reduction of small computers accompanying the remarkable development of small computers in recent years, small computers have come to be used not only for group management devices but also for elevator control devices for controlling a single elevator.
A serial transmission method is becoming the mainstream as a method of transmitting information to these computers. And
The group management control device and the elevator single control device can freely exchange group management data with each transmission device simply by wiring (or optical cable connection) by a certain procedure by software.

In addition, even in a large-scale building in which a large number of elevators are group-controlled, a building management computer or OA (office automation) computer manages the entire building and exchanges information between floors. There is. Many of these information are related to building traffic. In addition, the information of the computer for group management control also includes hall data (for example, passenger detection in the hall) necessary for pill management. However, such building management data and group management control data are hardly exchanged. For this reason, there are cases in which sensors and notification devices for obtaining information are attached to the respective cases.

However, in general, elevator group management control is centered on control of allocation for hall calls and special operation control for high traffic demand.

The allocation control for hall calls was conventionally performed by evaluation using various calculation data such as arrival time. For this reason, the allocation may have failed due to the prediction failure.

In order to eliminate such a situation, it was necessary to learn the daily traffic flow and to request the reliability of the prediction data.

However, in the elevator system, it is impossible to perfectly predict the stochastic hall call and the derived car call, no matter how much learning is performed as described above, and the reliability of the data is low. However, even if the reliability of the data is low, a value of some order is obtained by the prediction calculation, and it is unavoidable to incorporate it into the evaluation calculation.

This means that there are many possible failures in the evaluation decision. This also indicates that there is a discrepancy between the conditions set using the predicted values and the conditions that the true designer thinks, and the designer's condition settings are more stringent and complete. When I aim, I tend to have a light movement and a contrary movement as the designer intended.

Further, when incorporating the designer's idea into the evaluation operation of the allocation, it is limited to the change of the function value and the change of the weight, and it is indirectly incorporated and cannot be sufficiently reflected in the control.

Elevator group management control is centered on allocation control for hall calls and special operation control for high demand. To perform these controls, control instructions were determined by the following two elements. In some cases, one was used.

Basic control rules based on conservative and deterministic conditions For uncertain ones, control instructions are determined by evaluation calculation. An example of allocation control is shown. In part, it is possible to respond to the hall call by hardware. It is a basic rule such as the condition (in-group condition), and for those that pass, the evaluation calculation of is performed and the allocation instruction is given to the best one.

In these evaluations, predicted values were deterministically determined by predictions such as predicted non-response time and predicted loads, and were used as if they were 100% accurate. Moreover, in some cases, the uncertainties were weighted worse.
Others learned the daily traffic flow so that the determined value could be as accurate as possible.

[Problems to be Solved by the Invention] The conventional method has the following three problems.

In (a), since the condition is a definite rule, it is conservative and it is not possible to set fine-grained rules.

In (b), when the state of the system is made to follow, it is not possible to cope with a large state change or a short-time change due to the level of the coefficient change. In addition, control failure occurs due to the uncertainty of the data.

(C) Even if a rule is added to cope with a large change in state or a change in a short time, if the condition is uncertain (for example, predicted non-response time), there is a high possibility that the instruction is erroneous.

The above is due to the uncertainties of the group management data and the fact that the uncertain data is used as it is for the evaluation calculation for determining the final instruction.
In addition, this is because the detailed rules cannot be used and the instruction must be determined by a rough evaluation calculation,
As a result, it is difficult to follow the target by fine control.

Therefore, an object of the present invention is to provide an elevator group management control method capable of precisely following a target based on uncertain and ambiguous data and further improving service to users. To do.

[Configuration of the Invention] (Means for Solving Problems) In order to achieve the above object, the present invention is as follows. That is, in a group management control for elevator operation control in an elevator system in which a plurality of elevators are activated for a plurality of service floors, as a means for following each control target, the generated control request On the other hand, when determining various control instructions that meet the requirements, control instruction contents under various conditions prepared based on empirical rules to achieve the intended control purpose and expert control strategy conditions and instructions are used. The degree of satisfaction of the condition of the control rule when the control instruction is tentatively given using the series of control rules shown is calculated, and the degree of conformity of this temporary control instruction is synthesized, and the one with the best degree of conformity is optimized. It should be decided as a control instruction.

(Operation) When determining various control instructions satisfying the generated control request, the method requires control instructions under various conditions based on empirical rules in order to achieve the intended control purpose, and an expert. The degree of satisfaction of the condition of the control rule when the control instruction is temporarily given is obtained by using a series of control rules expressing the control strategy of 1. Then, the degrees of conformity of the temporary control instructions are combined, and the one with the highest degree of conformity is determined as the optimum control instruction. According to this, the average of the best control instructions at that time is selected as the probability distribution, the possibility of good control is increased, and the possibility of deterioration of the situation is reduced. It is possible to improve the service to elevator users.

(Example) Hereinafter, an example of the present invention will be described, but before that, a basic idea of the present method will be described. This method uses the ambiguity of the data as it is with the ambiguity of the condition.

A control instruction is output according to a set of detailed control rules.

These two points are made possible, and the target is tracked finely,
An object of the present invention is to provide an elevator group management method capable of improving services to elevator users.

Therefore, for many control goals, the method of following them is made into a series of control rules, and the expert's control strategy is transplanted there. They are in the form of condition-instruction, and the weight of the instruction is determined by the degree to which the condition is satisfied,
Outputs for tracking each target are output. The final control instruction is determined by the optimum inference method from the values output by many of these control rules.

For this reason, the data that determines the condition is used in the form of a probability distribution, and the final instruction is an output that leaves a good possibility and reduces the possibility of deterioration.

Also, since the control strategy of the expert is directly ported as a rule, fine control is possible. Also, for use in the optimal inference method for many rules,
It can be effectively used for control.

Here, the expert's control strategy is a practical knowledge of highly efficient group management control empirically obtained by an elevator group management expert who is familiar with elevator group management.

Next, the control rule expressed by the condition and the instruction is a rule expressed in a general form of "if A, then B". For example, in hall call allocation control,
The control strategy of the expert is expressed as one rule of the control rule expressed by the condition and the instruction such as "If long waiting, do not allocate." In this way, a plurality of rules expressing the expert's control strategy in the form of "if A then B" are prepared.

In the inference function, the degree to which the conditions shown in the control rule are satisfied when a control command based on the control rule is given to the control target is predicted, and the instruction shown in the control rule is weighted from this degree, and most of them are calculated. Determine the most appropriate instruction from the output of. For example, suppose that the control rule is "if long wait, do not allocate." At that time, if "allocate a hall call." Is given as a control command to the elevator No. 1 which is one of the controlled objects, it predicts the degree to which the condition "long wait", which is the condition shown in the control rules, will be satisfied. To do. That is, the degree of long waiting due to the hall call assignment is predicted. Based on this predicted degree, weighting is determined for the instruction to “assign”.

In the above, the degree of satisfaction of the condition of the control rule and the weighting of the instruction are determined for each of many control rules, and the worst and the best range are narrower than the finally weighted instruction.
The average determines the best control command.

As described above, it is used for determining the control output by an inference method that is optimal for many control rules.

An elevator group management control method according to an embodiment of the present invention will be described below with reference to FIG.

In FIG. 1, reference numeral 1 denotes a group management control device, which includes a group management control unit 1A, a knowledge engineering application unit 1B, and an auxiliary storage unit 1C.
The elevator controller 2, the transmission controller 3, and the elevator monitor 4 are connected to each other via a system bus formed of a transmission-dedicated LSI by serial transmission. The hall gate, the lamp, the sensor, and the display are connected to the I / O controller 5 by serial transmission using a transmission-dedicated LSI and general-purpose transmission software. The in-car controller 6 and the elevator control device 2 are also connected by serial transmission. The data of the building management computer 7, the data of the computer 8 for OA, the data input device 7 of the time recorder 9A, the I / O controller 10 of the notification data and the data of the entrance counter 10A are connected by the interface of the transmission controller 3, and the serial system bus. Be transmitted to.

The present system is an example close to the maximum specifications, and therefore, the present invention can be used even for a system without a part (performs what is input). In addition, 7A, 8A, 4A
It is an operation display system for CRT terminals and key inputs.

Next, the software configuration will be described with reference to FIG.

In FIG. 2, after the group management control device 1 (FIG. 1) is started (S), the task management program 20 determines which task is to be activated. A task is a software module for each function and is activated by a condition.

Here, each task will be briefly described.

Reference numeral 32 denotes an initialization task for initializing RAM and CPU registers and each LSI.
It is activated when the initial state or operation mode is switched.

21 is an external input task for setting external inputs such as CCT (car condition table), KCT (car condition table), and HCT (hole condition table) on the RAM. This external input task 21 has high priority, 100ms
A reboot is performed every ec. Here, HCT is an abbreviation for the hall condition table, and hall call registration is performed by the elevator control device, and the data is input.

Assuming that there are 4 units A to D in the group, and 1 to 8 floors, the above HCT, CCT, and KCT are shown in FIG. 3, respectively.
The bit configuration is as shown in FIGS. That is, in the HCT showing the hole state shown in FIG. 3, 8 for the hole sub-index (HS) of 0 to 13
8 bits of information are stored from the floor down (8D) to the floor 7 up (7U). The state of the hall on each floor will be specifically described. For example, if the up switch is pressed in the elevator hall on the 5th floor, the 7th bit of HS11 (5U) becomes 1, and if the service elevator corresponding to this hall call decides to be Unit A by the method described later, HS11's 0 bit and 6
The bit becomes 1. When Unit A arrives at the 5th floor, all bits 0, 6, 7 of HS11 are reset to 0. That is, 0 to 3 bits indicate each elevator machine set, 6 bits indicate whether elevators are assigned to hall calls, and 7 bits indicate whether hall calls are present.

In the CCT showing the car state in Fig. 4, each of elevators A to D is indexed from 0 to 3
16-bit information is stored. That is, the load state of the car is shown in the binary system in bits 0 to 3. The meaning of these 0 to 3 bits is “0011” “0010” “0011” “010
0 "" 0101 "" 0110 "" 0111 "" 1000 "" 1001 "" 1010 "" 101
1 "to 1100" 0-10%, 11-20%, 21-3
0%, 31-40%, 41-50%, 51-60%, 61-70%, 71-8
0%, 81-90%, 91-100%, 101-110%, 111% or more.

5 bits indicate the running state of the car, "1" is running, "0"
Indicates that the vehicle is decelerating. 7-bit indicates the open / closed state of the door, "1"
Indicates open and “0” indicates closed. Bits 8 to 13 represent the car position in binary notation. Bits 14 and 15 indicate the moving direction of the car. “10” indicates rising, “01” indicates falling, and “00” indicates no direction, that is, stopped.

In the KCT showing the car call state in Fig. 5, H in Fig. 3
Similar to CT, bits 0 to 3 indicate the presence or absence of a car call for elevators A to D.

As a result, the elevator and hall call states have been entered.

In FIG. 2, reference numeral 22 is an allocation task for allocation.
This allocation task 22 checks the newly-generated hall call every 100 msec, and if there is any occurrence, it predicts the unanswered response time calculation subroutine 24, fills up, etc.
25 and the evaluation subroutine 23 perform composition for damage such as predicted non-response time and fullness, and determine the machine with the best composite value.

26 is an assignment review task, and this assignment review task 26
Is a low-level task that is activated about once a second,
The allocation is changed for hall calls that are expected to last a long time, are full, or are expected. 28 is each single elevator communication task, and in addition to cyclic data transmission, allocation of control output and data request as necessary, allocation cancellation, number of passengers, number of people getting off, new call These are carried out using a buffer, and data having the contents shown in FIG. 6 are transmitted in the format shown in FIG.

Reference numeral 29 denotes an annual timer and various timers, which are routines of various interval timers such as 10 ms, 100 ms and 1 second, and an annual timer combined with them. Also, these data are corrected by an external timer.

The yearly timer has information about the month, date, day of the week, holidays, six days, and other events, and the second I / O task 31 for the floppy disk (flexible disk) and the second task for the CRT. The information is updated by the I / O task 30 of 1 and the like.

First I / O task 30 which is a task for CRT transmission input / output and character display terminal
Is used for transmitting information to an external terminal or another computer. This task 30 is time-sliced and started at a low level so as not to harm other group management tasks.

Second task for floppy disk control
I / O task 31 is started when learning data and the like are stored in an external floppy disk. First I / O task 3
It is activated at a low level like 0. The learning data processing task 27 is a task for setting the data table of the state at that point in time by external input or data from a single unit, and replacing the data when changing to the next state. It is activated when there is a change or state change. It is also a low-level task that is activated so as not to hurt high-level group management tasks. However, it changes when a special flag or priority order is changed. here,
As shown in FIGS. 8 (a), (b), (c), (d), and (e), the learning data has some equivalents depending on factors such as month, date, day of the week, six days of the week, holiday, and time zone. It is classified into the following traffic modes and has data as shown in Figs. 9 and 10 for each mode.

Examples of these are shown in FIGS. 9 and 10. The symbols in FIGS. 9 and 10 are as follows.

HCT & RAT: Average number of hall calls generated in 15 minutes.

KCT & RAT: Average number of car calls generated.

IN & RAT: Average number of passengers.

OUT & RAT: Average number of people getting off.

KCT & SET: Car call generation rate for each floor.

HCT & RAT to OUT & RAT are indicated by the index HS (hall sub-index) of the directional floor. KCT & RAT is shown by the matrix of A and B from A floor to B floor.

In addition, when the demand is high, those changes are learned at small intervals. This is shown for each HS and t in AV & MEN & P (HS · t). However, t is time.

As other tasks, in FIG. 2, task 34 is started every 1 second and performs data input / output data communication with an external building management computer and data collection by it, and there is a demand using the data. There is a traffic demand forecasting task 33 that predicts the traffic demand and determines the driving model.
Start each time. Further, there are various driving tasks 35 as tasks of the driving model activated by these.

Next, allocation control for hall calls in the group management control method according to the present invention will be described. The allocation control is performed by the group management actual control unit 1A and the knowledge engineering application unit 1B in the group management control device 1 shown in FIG. 11th
The figure shows a flowchart of allocation control. Further, for convenience of explanation, four elevators (Unit A, Unit B, Unit C and D
(Unit No.) shall be group controlled.

In the above description of the configuration, the case of the 8th floor has been described, but in the following description, the case of the 12th floor will be described for convenience.

Allocation control for hall calls in the elevator group management control will be described. The allocation control method differs depending on the operation mode of the elevator.

As an example, allocation control in the operation mode when the traffic flows on each floor are in a balanced state will be described.

In the allocation control, it is assumed that the following control targets are given as an example.

(1) Reduce long waiting calls.

(2) Increase good calls.

(3) Make the maximum long wait good.

(4) Maintain good service on high demand floors.

(5) Reduce the number of people who are full.

The above-mentioned goals are based on the knowledge possessed by experts and are expert control strategies in allocation control. Control rules represented by conditions and instructions are prepared corresponding to the respective control strategies described above. For example, the control rule corresponding to "reduce long waiting calls" is "do not allocate if long waiting". For "increase good calls", "if good calls are assigned.", Etc., and similarly for other control strategies, control rules of the form "if A then B" are prepared ( However, these are synthesized by a more detailed set of control rules).

In the present invention, the degree of satisfaction of the conditions shown in the above control rules and the weighting of instructions are determined for each control rule by the inference function. The degree to which the above-mentioned condition is satisfied is the degree to which a long wait occurs if the condition is “if long wait”. A high degree of this means a very long wait, and a low degree of this means a long wait. In the above reasoning, the degree of long waiting is predicted by temporarily assigning a certain elevator machine, and the instruction is weighted based on the degree. That is, if a long wait is likely to occur when the temporary allocation is performed, the weighting of the instruction is performed in the direction in which the allocation is not performed. Comprehensively evaluate the weighting of the instructions determined for each control rule, and "allocate" in allocation control
Determines the strength of the control command.

The above determination of the control command output strength is performed for each of a series of control rules for each target and each elevator machine. In addition, we use a method that seems to be particularly effective in using many output values by the control rule. This is called optimal inference from rules, and is due to the following decision method. This is a method of assigning control command machines that have a small difference between the worst and the best output and have a good average value.

In deciding the allocation, if you try to allocate to an elevator that completely meets all the above goals,
No allocation can be made. Therefore, the optimal reasoning is used to allocate elevators that satisfy all the goals as evenly as possible.

Therefore, a control rule is prepared based on the control strategy described above, and the strength of the control command is determined by obtaining the degree to which the condition is satisfied and the strength of the instruction.

Next, allocation control will be described. Determination of allocation in allocation control will be described using the flowchart shown in FIG. In step S1, the elevators that cannot perform allocation control due to failure, out-of-group operation, or the like are excluded from allocation control. In the next step S2, a prediction calculation of data required for the inference calculation in the subsequent step S4 is performed. In step S3, preprocessing is performed to convert the various data obtained in step S2 into a form suitable for performing the inference operation in step S4. In step S4, an inference operation is performed. This inference operation weights the degree to which the condition of each control rule, which represents the expert's control strategy by conditions and instructions, and the instructions are weighted. The strength of the control command is determined from the weighted instruction for each control rule. This control command is to "assign to hall call." Each operation from step S2 to step S4 described above is performed for each unit. Further, in step S4, a series of control rules for many goals are performed respectively. Hereinafter, for convenience of description, the degree of instruction weighting is referred to as instruction strength. In step S5, the strengths of control commands for many targets of each car are compared, and a command for finally allocating a newly generated hall call to the car having a good average with a narrow worst and best width is output (many Optimal reasoning for rules). With the above, it is decided which machine to allocate to the newly generated hall call, and the allocation control is completed.

In step S4 described above, the degree to which the condition is satisfied and the strength of the instruction are determined for each control rule, but one of the control rules will be described below: "If long wait, allocation is not performed." The determination of the degree of satisfaction of the condition and the strength of the instruction will be described. Therefore, in step S2, the predicted arrival time, the minimum arrival time, the maximum arrival time, and the certainty factor of the arrival time of each vehicle are calculated in accordance with the above-mentioned control rule. The certainty factor indicates the possibility that the car will arrive within a predetermined time.

The above prediction calculation will be described with reference to the flowchart shown in FIG.

In step S2, the hole sub index HS where the car is currently set is set. Hole sub-index in Fig. 13
Indicates HS. The hall sub-index HS takes into consideration the floor where the car is currently located and the driving direction of the car. Explaining the elevator on the 12th floor, if you have a car on the 12th floor and you want to perform descending operation, the hall sub index HS of this car will be "0". As you descend from the 12th floor
As shown in Fig. 13, the value of the hole sub index HS becomes large. In addition, if there is a car on the first floor and the car is going up, the hall sub index HS for that car will be “11” and 1
It gets bigger as you go up from the floor. For example, if the car of Unit A descends on the 11th floor, the hall sub index HS of this car will be "1". In addition, the fact that the hall sub-index HS of the car is "5" means that the driving direction is downward for the car on the 7th floor. The value of this hall sub-index HS indicates the starting point of the calculation of the predicted arrival time at each floor, which is performed in the following steps.

Next, step S2b is performed. In step S2b, a derived car call for the already registered hall call is generated based on the learning data or the like. This derived car call is a call generated imitatively to a hall call and may be different from an actual call. Figure 14 shows the hall call and its derivative calls. For example, if the hall calls are on the 9th and 4th floors, their derivative car calls are 7th floor for the 9th floor hall calls and 2nd floor for the 4th floor hall calls. After the generation of the derived car call is completed as described above, step S2c is performed.

In step S2c, the predicted arrival time Tx of each floor is calculated. For convenience of explanation, the calculation is performed assuming that the call is focused only on the hall call.

Figure 15 shows the predicted arrival time Tx for each floor. Here, the time required to rise or fall between floors on the first floor is 1 second, and when a car arrives at a floor with a hall call or a car call, the loss time is 10 seconds due to passengers getting on or off the floor. .

Consider the case where the car descends on the 11th floor. It takes 1 second from the 11th floor (Hall sub index HS is "1") to the 10th floor (Hall sub index HS is "2"). 11th to 9th floors (Hall Sub Index HS
It takes 2 seconds to reach "3"). On the 9th floor, there is a hall call, so 10 seconds are spent as lost time. Therefore, it takes 13 seconds from the 11th floor to the 8th floor (Hall sub index HS is "4"). Similarly, the 11th to 4th floor hall sub-index HS is "8". ) Takes 27 seconds. The car on the 11th floor descends to the 1st floor,
Next, climb up from the 1st floor to the 12th floor and return to the 11th floor again 62
Takes seconds. This time is when there are hall calls on the 9th and 4th floors, and as the number of hall calls increases, the expected arrival time to each floor also increases accordingly.

The predicted arrival time RESPT (HS) of the floor with a hall car call can be expressed by the following formula.

RANT (Sta _i , End _i ) is the travel time required to travel from one stop floor to the next stop floor.

LOST (End _i ) is the time lost at the scheduled stop floor.
KEIKAT (HS) indicates the elapsed time since the allocation was set for the hall sub index HS where the hall call occurred. KEIKAT (HS) can be considered as "0", so ignore it. l indicates the number of calls to the floor for which the predicted arrival time is to be obtained. (Including the call of the floor for which the predicted arrival time is to be obtained.) In the case of FIG. 14, for example, the predicted arrival time RESPT (3) of the 9th floor where there is a hall call is expressed by the following equation.

RESPT (3) = RANT (Sta ₁ , End ₁ ) ≈ 2 (seconds) Since there is no call between the 11th floor and the 9th floor, the car does not stop on the 10th floor, so the loss time is "0". Similarly, the predicted arrival time RESPT (8) on the fourth floor is expressed by the following equation.

RESPT (8) = RANT (Sta ₁ , End ₁ ) + RANT (Sta ₂ , End ₂ ) + RANT (Sta ₃ , End ₃ ) + LOST (End ₁ ) + LOST (End ₂ ) RANT (Sta ₂ , End ₂ ) is the 9th floor To the 7th floor. RANT (Sta ₃ , End ₃ ) is the traveling time from the 7th floor to the 4th floor. In this case, RANT (Sta ₂ , End ₂ ) is 2 seconds, and R
ANT (Sta ₃ , End ₃ ) is 3 seconds. LOST (End ₁ ) is the lost time on the 9th floor, and LOST (End ₂ ) is the lost time on the 7th floor. Therefore, RESPT (8) is 27 seconds. With the above, the 15th
The estimated arrival time of each floor shown in the figure is obtained.

Next, step S2d and step S2e are performed. Here, the minimum arrival time only for the floor with the hall call
Determine T _min , maximum arrival time T _max, and probability distribution mode of arrival time.

The predicted arrival time Tx is the arrival time of each floor when a derivative call is generated for an actual hall call and the actual hall call, its derivative call, and the car call are considered. The minimum arrival time T _{min with} respect to the predicted arrival time Tx is the arrival time of each floor when only actual hall calls and car calls are considered. The maximum arrival time T _max is the arrival time of each floor when hall calls are generated on all floors. Minimum arrival time T
_{The min} and the maximum arrival time T _max are obtained by the above equation (1).

Next, the probability distribution mode of arrival time is obtained. In order to determine the possibility that a car with a hall call will arrive within a predetermined time, the probability distribution mode of each floor with a hall call is set.
FIG. 16 shows two types of modes as probability distribution modes of arrival times on floors with hall calls, FIGS. 16 (a) and 16 (b). This distribution mode is distributed around the predicted arrival time Tx. The distribution state of this distribution mode differs depending on the values of the minimum arrival time T _min , the predicted arrival time Tx, and the maximum arrival time T _max . However, this distribution mode always guarantees a minimum arrival time T
It exists between _min and the maximum arrival time T _max , the areas of S ₁ and S ₂ are equal, and the sum of the areas of both is 1 and TL1 and TL2
Are set to be equal. The choice of the two probability distribution modes shown in Fig. 16 is determined by the possibility of matching the derived car call generated by learning data etc. for the hall call on a certain floor with the actual car call. .
That is, when the derived car call generated by learning data or the like is unlikely to match the car call actually generated, the probability distribution mode of the pattern shown in FIG. 17A is selected. Figure 17 (b) shows the case where a derived car call is generated on the 12th floor in response to an ascending hall call on the 5th floor. In this case, the probability distribution mode of the arrival time of the car on the third floor to the eighth and seventh floors is the mode shown in FIG. 16 (a).

On the other hand, if the derived car call generated by learning data etc. is likely to match the car call actually generated,
The probability distribution mode of the pattern shown in FIG. 16 (b) is selected.

That is, in the case shown in FIG. 17 (b), there is a high possibility that a car call will actually occur on the first floor of the reference floor with respect to the hall call, so the possibility that the car will go to the first floor is high. Therefore, the probability distribution mode of the arrival time of the car on the 10th floor on the 3rd floor is the distribution mode shown in FIG. 16 (b).

The certainty factor of the arrival time is obtained from the probability distribution of the arrival time obtained as described above. The certainty factor indicates the possibility that the car will arrive within a predetermined time. In FIG. 18, the horizontal axis represents the estimated arrival time and the vertical axis represents the probability value. Basket
The probability of arrival within 30 seconds, that is, the certainty factor TP0 is obtained by determining the area of the A portion. Similarly, the confidence TP1 that a car will arrive in 31 seconds or more and less than 60 seconds can be obtained from the area of the B part, and the confidence TP2 in the case of 60 seconds or more TP2
Can be obtained from the area of the C portion. Since the certainty distribution mode is normalized so that the area becomes 1, the certainty factor of each arrival time can be obtained by obtaining the area. This certainty factor is expressed as a probability.

As described above, the predicted arrival time and the certainty factor thereof are obtained by steps S2c, S2d, and S2e shown in FIG. By repeating steps S2c to S2g for each floor, the confidence level of the predicted arrival time of each floor and the predicted arrival time of the floor with the hall call is obtained. Above,
The calculation of the predicted arrival time in step S2 in FIG. 11 is completed.

The inference operation of allocation control in step S4 shown in FIG. 11 will be described.

In the inference operation of step S4, allocation control operation is performed. In this allocation control, with the aim of "reducing the number of long waiting calls", that is, "0" for long waiting calls of 60 seconds or more, when a newly generated hall call is tentatively allocated, long waiting It is difficult to allocate to a machine that is more likely to become, that is, the deviation from the target of “reducing long waiting calls” by tentative allocation (hereinafter,
Say an error. ) E and the error increment ΔE represent the strength of the instruction "assign to hall call." Determine the strength of this instruction for each elevator.

The inference calculation of allocation control will be described with reference to the flowchart shown in FIG.

In step S4a, a control command "temporarily assign a newly generated hall call" is given to the elevator A, which is the control target.

Next, step S4b is performed. The error E and the error increment ΔE are calculated from the target by the following equations.

Confidence; Confidence that a car will arrive in 60 seconds or more TP ₂ n; Number of all hall calls including temporary allocation From the above formula (2), before temporary allocation to Unit A and after temporary allocation. the value of the error Ea ₂ and incremental error Ea ₂ △
Ask for Ea. When the value of the error E before the temporary allocation is Ea ₁ and the error E after the temporary allocation is Ea ₂ , the increment ΔEa of the error E is calculated by the following equation.

ΔEa = Ea ₂ −Ea ₁ (3) Next, using the degree of membership function, error Ea ₂ and error increment ΔE
Evaluate the value of a. Here, the membership function will be described.

In general, when considering whether or not a certain object is an element of the set A, a membership function is used to consider the degree of being an element of the set A, rather than strictly dividing it. In the membership degree function shown in FIG. 20, the horizontal axis represents the error E and the error increment ΔE, and the vertical axis represents the membership degree. FIG. 20 shows the respective membership functions of the set ZO, the set PM, and the set PB as a set. The set ZO is a set in which the error E and the error increment ΔE are “approximately zero”, the set PM is a set in which the error E and the error increment ΔE are “positive and medium”, and the set PB is the error E and the error increment. A set in which ΔE is “positive and large” is shown. Each membership function has a set of ZO and a set of values for all errors E or error increment ΔE.
Gives the degree of inclusion in PM and set PB. This degree indicates the degree of belonging to the set, and is called the degree of membership.
It is shown as a number between 0.0 and 1.0. A degree of membership of 1.0 indicates that the target is a complete element of set A,
A degree of membership of 0.0 indicates that the target is not a complete element of set A.

For example, when the error E is e, consider the degree of belonging. As can be seen from FIG. 20, the degree of membership of the set PB with respect to the error e is 0.7, and the degree of membership of the set PM is 0.3. That is, the error e belongs to the set PB “positive and large” with the degree of belonging of 0.7, and belongs to the set PB “positive and medium” with the degree of belonging of 0.3.

Next, the degree to which the condition of the control rule is satisfied is obtained. That is, since this condition is "if long wait", the degree of long wait is calculated. This degree is represented by the degree of belonging of the error E and the error increment ΔE. Therefore, the degree to which the condition is satisfied is determined by determining the degree of membership of the error E and the error increment ΔE.

The degree of membership of the error Ea ₂ after provisional allocation and the increment ΔEa at that time is obtained from the membership function shown in FIG. From FIG. 20, the degree of belonging of the error Ea _{2 to} the set PM is 0.9, and the degree of belonging to the set ZO is 0.1. Also, the set P of the error increment ΔEa
The degree of belonging to M is 0.5, and the degree of belonging to the set ZO is
It is 0.5.

As described above, to which of the sets ZO, PM, PB the values of the error E and the error increment ΔE belong was determined in consideration of the degree of membership. This membership function gives an evaluation of the value of the error E and the error increment ΔE as to whether the value is large or small. That is, the fact that the value of the error E belongs to the set PB means that the value is large, and that the larger the degree of belonging to the set PB is, the larger the value of the error E is. The same applies to the value of the error increment ΔE. Since the degree of long waiting is represented by the set to which the error E and the error increment ΔE belong and the degree of membership thereof, a large error E and an error increment ΔE indicate a large degree of long waiting.

As shown in FIG. 20, the error Ea ₂ belongs to the set PM with a degree of membership of 0.9, and the error increment ΔEa is 0 to the set PM and the set ZO.
Since it belongs at 5, it means that you will have to wait a little longer.

The strength of the instruction shown in the above control rule is determined from the evaluation result of the error E and the error increment ΔE described above. With this evaluation result, error E and error increment ΔE are set ZO, PM, PB
That is, what degree of membership belongs to which set.

The condition-instruction table of step S4d in FIG. 19 is shown in FIG. FIG. 21 shows the instruction ΔU corresponding to the error E and the error increment ΔE. For example, the error E is “approximately zero” (set ZO), and the error increment ΔE is “positive and large”.
In the case of (set PB), “no need to allocate” is obtained as the instruction ΔU. There are five types of instructions ΔU: PO is “allocated”, PS is “allocated”, ZO is “normal”, NS is “not required to be allocated”, and NE is “not allocated”. The number of sets as error E is set PB, set
There are three types of PM and set NE, and similarly in the case of the error increment ΔE, there are nine types of rules depending on the combination of the error E and the error increment ΔE. Therefore, nine kinds of rules are considered, and the rules are shown in FIG. Rule 1 means that when the error E is “positive and large” and the error increment ΔE is “positive and large.”, The instruction ΔU is set to “not assigned”, and the same applies to rule 2 and the subsequent steps.

As shown in FIG. 21, the condition-instruction table indicates the condition Δ on the set to which the error E and the value of the error increment ΔE belong.
It determines U. Therefore, the condition-instruction table shown in FIG. 21 is a matrix of the production rules represented by "if A, then B" type. Further, the instruction for the condition is artificially determined, and is based on the control strategy based on the expert knowledge.

Next, referring to FIG. 21, the instruction ΔU is obtained by evaluating the error Ea ₂ and the error increment ΔEa. Error Ea ₂ is set PM and set Z
Belongs to O and the error increment ΔEa belongs to the sets PM and ZO. Therefore, when the instruction ΔU is obtained as a condition and the set to which the error Ea ₂ and the increment ΔEa belong, there are the following four ways.

(B) If the error Ea ₂ belongs to the set PM and the increment ΔEa belongs to the set PM, the instruction ΔU becomes NE (not assigned).

(B) If the error Ea ₂ belongs to the set PM and the increment ΔEa belongs to the set ZO, the instruction ΔU is ZO (normally assigned).
Becomes

(C) If the error Ea ₂ belongs to the set ZO and the increment ΔEa belongs to the set PM, the instruction ΔU becomes NS (it is not necessary to allocate).

(D) If the error Ea ₂ belongs to the set ZO and the increment ΔEa belongs to the set ZO, the instruction ΔU becomes PO (assign).

As described above, four rules are extracted from the above nine types of rules by the combinations (b) to (d) of the set to which the error Ea ₂ belongs and the set to which the increment ΔEa belongs. The extracted rules are rule 5, rule 6, rule 8 and rule 9 shown in FIG. 4 shown in 4 rules for error Ea ₂ and increment ΔEa
I got four instructions ΔU, but I can't give these four instructions ΔU to the elevator with the same strength. That is, among the four rules, there are rules that can be applied strongly and rules that can only be applied weakly. Therefore, the instructions given by each rule are compared according to the degree to which the conditions of the rule are satisfied. That is, weighting the instructions of each rule,
The weighted average of the weighted instructions is used to determine the strength U of the instruction.

The weighting of the instructions of each rule and the strength of the instructions obtained by weighted averaging will be described with reference to FIG. 23rd
In the figure, the horizontal axis of the graph of error E and increment ΔE for each rule is the value of error E or increment ΔE, and the vertical axis is the degree of membership. Further, the positive direction on the horizontal axis of the graph showing the instruction ΔU indicates the assigning direction, the negative direction indicates the non-assigning direction, and the vertical axis indicates the belonging degree.

With respect to rule 5 shown in FIG. 23, for error E the set PM is filled to a degree of 0.9 and for error increment ΔE the set PM is filled to a degree of 0.5. The degree to which Rule 5 is satisfied is the degree to which the two sets are satisfied.
It will be a small value. Therefore, Rule 5 will be satisfied with a degree of 0.5. The set indicating the instruction ΔU is limited by this degree of 0.5. Similarly, rule 6, rule 8, rule 9
A set indicating the instruction ΔU is obtained. Step by step
Exit S4c.

Next, step S4e is performed. In step S4e, the logical sum is calculated from the set of instructions for each rule obtained in step S4c, and the weighted average is calculated by the degree of belonging to the set to finally obtain the instruction strength U. Here, the strength U of the instruction is -0.69 as shown in FIG. The above-mentioned instruction strength U indicates the degree of weighting with respect to the instruction "not assign" indicated in the control rule "do not assign if long wait".

As described above, the determination of the degree of satisfaction of the condition and the weighting of the instruction has been described based on the control rule that “there is no allocation if waiting for a long time”. The degree to which the condition is satisfied and the weight of the instruction are determined. The weight of the instruction obtained for each control rule, that is, the strength of the control command is determined from the strength U of the instruction. The control command referred to here is "assigned to hall call". This control command is determined for each unit. With the above, step S4e is completed and step S4 shown in FIG. 11 is completed.

Next, in step S5, which machine is finally assigned is determined from the strength of the control command obtained for each machine in step S4. After deciding which car number to allocate to the newly generated hall call, a control command "allocate" is output to that car number.

The allocation control for hall calls is completed by ending step S5.

The relationship between the increment ΔE and the instruction ΔU with the membership degree function shown in FIG. 20 and the error E shown in FIG. 21 used in the inference operation of the allocation control is artificially determined.

That is, the degree-of-attribute function is determined using the expert's examination rule. Further, which instruction to use for the error E and its increment ΔE shown in FIG. 21 is also determined by using the empirical rule of the expert. Therefore, in assignment control, inference can be performed by a direct expression of an expert's empirical rule, and accurate assignment can be performed. Hall calls etc. occur probabilistically, and it is very difficult to perform the assignment operation accurately with a mathematical formula in consideration of the probability, but as shown in the above inference operation, various data are weighted. Accurate allocation control can be performed by using a direct expression of human experience.

In addition, since the "confidence" of the predicted arrival time is taken into account in the allocation control, it is possible to allocate to a machine with a high "confidence" of that value even with the same predicted arrival time, reducing the occurrence of long waiting calls. can do.

Since the expert's direct algorithmic expression is used when determining the control command without using a complicated evaluation expression, it is possible to easily improve the forecasting accuracy, and it is easy to add or change the rule that is the expression of the algorithm. Therefore, it can be easily and quickly adapted to various buildings with different traffic demands. The following goals can be considered in group control of elevators.

However, these are only examples, and in addition to this, in this system, there is a target and a series of rules that are automatically generated in order to eliminate this failure by collecting data of control failure.

Group management goals are: (1) Reduce long waiting calls.

(2) Increase good calls.

(3) Reduce the longest waiting call.

(4) Maintain good service on high demand floors.

(5) Reduce the number of people who are full.

(6) Reduce first-come-first-served basis.

(7) Increase early calls.

and so on.

The inference operation routine described above is represented in a list format for each target in the allocation control of the group management control. Therefore, it is possible to easily add or change each routine.

These targets and their set of control rules include special ones that the computer has automatically created from the damage list (a list of control instruction failures) that the computer has collected so far. Therefore, a large number of rules will be used depending on the allocation mode.

It is the step S5 in FIG. 11 that makes a good inference based on these many rule outputs and determines the control instruction.
(See Figure 25).

In order to follow the targets of (1) to (7), the output value of a series of control rules is set as rule OUT (instruction number, target rule number). This is a matrix of instruction numbers and target rule numbers. In the case of allocation, the instruction number will be the number of the machine to be allocated. Also,
The target rule number is the target value number of (1) to (7).

In FIG. 25, for each instruction number, the minimum output target rule number value in S25a; the maximum output target rule number value in minruleOUT and S25b; the average output value in maxruleOUT and S25c; averuleOUT was obtained.

Next, in S25e, the maximum (maxruleOUT) and minimum (minruleO)
UT) width is less than a certain value, and the average (averuleOUT) has the best instruction number (S26a, S26b).

That is, for a large number of goals, all are selected to some extent on the order of good and overall best indication. The first
25. FIG. 26 is a detailed flowchart of the portion S25e in FIG.

If there is no maximum or minimum width that is equal to or less than a certain value, the instruction that gives the worst value is the best is selected (S26c).

As described above, one control instruction is determined from a large number of rules by the optimum inference method.

In addition, in group management control of elevators, by increasing the transportation capacity by determining the operation model in response to traffic demand,
Change the allocation method. This operation model is a divergence model,
Although there are a centralized model and a complex model of these, the inference operation according to the present invention can be used for switching between these driving models. Predetermined allocation control is performed for each of the above operation models. Although the inference operation is also used in this allocation control, the target of each allocation control is selected from the above-mentioned goals (1) to (7). There are also goals that are followed.

In addition, in response to high demand for periodic concentration and divergence that occur during uppeak and lunch, and temporary high demand for a floor such as a conference room, we model those micro and macro traffic flows, and The present invention can be applied to the determination of the operation mode that can meet the demand.

In addition, in the optimal inference method from multiple rules,
It is also possible to use only S26c in the figure.

As described above, the degree to which the conditions of a plurality of control rules, which represent the expert's control strategy by conditions and instructions, and the weighting of the instructions are determined for each control rule, and the instructions weighted for each of these multiple control rules are determined. By determining the control command in the group management control by the optimum inference from the output, the group management control with high efficiency can be performed, and the service to the elevator user can be improved.

In summary, the method of the present invention has the following features. That is, (1) In a group management control method that activates a plurality of elevators for a plurality of service floors and controls the operation of the elevators, a control strategy of a specialist is used as conditions and instructions as a means for following each control target. Using a series of control rules represented by, determine the degree to which the conditions of the control rules are met when a control instruction is given, and use the method of synthesizing the conformity of the temporary control instruction It is characterized in that a control instruction optimally set is set.

(2) In (1), the degree of conformity to the provisional control command from these series of control rules is used to finally determine the control instruction, and the maximum and minimum widths are narrower than a certain value, and the average value is It is characterized in that a provisional control command that becomes favorable becomes a final control command.

(3) In (2), in the case where the maximum and minimum widths of the degree of conformity are no narrower than a fixed value, the provisional control instruction that is the best among the minimum values of the degree of conformity in each temporary control instruction is selected. It is characterized in that it is a final control instruction.

(4) In addition, in the control command of (1), it can be used for an allocation control command for hall calls and a peak operation command for traffic demand. (5) Further, in obtaining the degree of satisfaction of the condition of (1),
The predicted probability distribution of those elements can be used.

(6) Furthermore, the predicted non-response time probability distribution and the predicted load probability distribution can be used as the predicted probability distribution of the element in (5).

[Effects of the Invention] As described in detail above, according to the present invention, in group management control using inference, there is provided an elevator group management control method capable of following a target finely and improving service to users. it can.

[Brief description of drawings]

FIG. 1 is a system configuration diagram of an elevator group supervisory control device according to an embodiment of the present invention, FIG. 2 is a software configuration diagram for realizing the same embodiment, and FIGS. 3 to 10 are the same embodiment. FIG. 11 is a flow chart of the allocation control of the same embodiment, FIG. 12 is a flow chart of the predictive calculation used for the allocation control, and FIG. 13 is a hall of the car used in the predictive calculation. Fig. 14 shows the sub-index, Fig. 14 shows the state of the derived car call with respect to the hall call, and Fig. 15 shows the calculation result of the predicted arrival time.
Figure 16 shows the probability distribution mode of predicted arrival time, and Figure 17 shows the state of derived car calls for hall calls.
FIG. 18 is a diagram for obtaining a certainty factor of predicted arrival time, FIG. 19 is a flowchart of inference operation, FIG. 20 is a diagram showing a membership function, and FIGS. 21 and 22 are conditions-instruction diagrams, 23rd
Figures and 24 are figures for obtaining control commands, Figures 25 and 26
The figure is a flowchart of a routine for determining a control instruction from a large number of rule outputs. 1 ... Group management control device, 2 ... Elevator control device, 3
...... Transmission controller, 4 …… Elevator monitor,
5 …… Hall gate, lamp, sensor, display I /
O controller, 6 ... Controller in the car.

Claims (1)

[Claims] 1. A group management control for controlling the operation of an elevator in an elevator system in which a plurality of elevators are activated for a plurality of service floors, which is generated as means for following each control target. When determining various control instructions that satisfy the control requirements, control instruction contents under various conditions prepared based on empirical rules to achieve the intended control purpose and expert control strategies are used as conditions. The degree of satisfaction of the condition of the control rule when a control instruction is tentatively given using a series of control rules represented by the instruction and the degree of conformity of the provisional control instruction is synthesized, and the degree of conformity is the best. Is determined as the optimum control instruction, and a group management control method for an elevator.