JPH0790997B2 - Group management control method for elevators - Google Patents

Description

Description: [Object of the invention] (Field of industrial application) The present invention relates to a group management control of an elevator, and relates to a group management control method for making various target values favorably follow in various group controls. It is a thing.

(Prior Art) Elevator group management control is centered on control of allocation for hall calls and special operation control for high demand. In the allocation control for hall calls, conventionally, allocation was performed by evaluation using various predictive calculation data such as arrival time. Therefore, the failure of the prediction sometimes caused the failure of the allocation. In order to eliminate such a situation, there have been studies such as learning the daily traffic flow and demanding the reliability of the prediction data.

(Problems to be Solved by the Invention) It is impossible to completely predict a stochastic Hall call or a derived car call even in group management control for learning. Although it is impossible to completely predict a hall call or a derived car call, conventionally, the prediction calculation of the hall call or a derived car call is made 100% correct, and the prediction calculation such as arrival time is performed. An error occurs in the evaluation decision in various controls based on the prediction calculation.

Further, even if the usage prediction data is important data, it may not be used due to the ambiguity due to the prediction, and the usage of the data is restricted.

An object of the present invention is to provide a user of an elevator by performing control to change the strength of a control command given to various control targets in the elevator group management control, depending on the degree of satisfaction of the condition that is the basis of the control command. To provide better service to users by using the probability distribution of waiting time as important predictive data in consideration of the ambiguity of the data, and to provide an elevator group management control method It is a thing.

[Structure of Invention]

(Means for solving the problem) In a group management control method in which a plurality of elevators are activated for a plurality of service floors to collectively control the operation of the elevators, a control strategy of a specialist is a condition in which a waiting time is considered. By using at least one of the plurality of control rules represented by and the instruction, the degree of satisfaction of the condition of the control rule and the weighting of the instruction are determined for each control rule by the inference function, and the weighting is performed for each control rule. The control instruction is determined based on the given instruction.

(Operation) The expert control strategy is a practical knowledge of 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 control by assigning hall calls,
The control strategy of the expert is expressed as one of the control rules 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 condition shown in the control rule is satisfied when a control command based on the control rule is given to the controlled object is predicted, and the instruction shown in the control rule is weighted from this degree. For example, suppose that the control rule is "if long wait, do not allocate." At that time, when "assign a hall call." Is given as a control command to the elevator No. 1 which is one of the controlled objects, the degree to which the condition "long waiting", which is the condition shown in the control rule, is established is predicted. . 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”.

The inference function described above determines the degree to which the condition of the control rule is satisfied and the weighting of the instruction for each control rule, and finally determines the control command from the weighted instruction.

(Embodiment) 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.

Reference numeral 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 and 100mse
Rebooting is performed every about c. 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 determines to be Unit A by the method described later, it will be 0bit and 6 of HS11. The bit becomes 1. When the above-mentioned 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 the number set of each elevator, 6 bits indicate whether elevators are assigned to hall calls, and 7 bits indicate the presence of hall calls.

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 “0001” “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 evaluate the damage such as the predicted non-response time and fullness, and determine the best evaluation machine.

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 assignment is changed for hall calls that are long waited, full, or predicted. Reference numeral 28 denotes each single elevator communication task. In addition to cyclic data transmission, control output and data requests are allocated as necessary, allocation cancellation, etc., number of passengers, number of people alighting, new active car call And so on. These are performed using the buffer, and the 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 after 10 ms, 100 ms, one 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 information is updated by the floppy disk of the second I / O task 31 and the CRT of the first I / O task 30. To be done.

CRT transmission input output of the first I / O task 30,
The task for the character display terminal is used to transfer information with an external terminal or another computer. This task 30 is time-sliced and activated at a low level so as not to damage other group management rusks.

The second (flexible) floppy disk control of the I / O task 31 is started when the learning data and the like are stored in an external floppy disk. First
Like the I / O task 30, it is started at a low level. The learning data processing task 27 is a task that sets data in the state at that point in time according to external input or data from a single unit, and replaces that 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, the learning data is shown in FIG. 8 (a) (b) (c).
(D) As shown in (e), it is classified into several equivalent traffic modes according to factors such as month, date, day of the week, six days of the week, holidays, and time zones (time bands). It has the data shown in Figure 10.

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.

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

Also, when the demand is high, those changes are learned at a detailed interval. This is AV $ MEN $ P (HS ・ t) and each HS
And t. However, t is time.

As other tasks, in FIG. 2, a task 34 that is activated every 1 second and performs data input / output data communication with an external building management computer and data collection by it, and the data is used. There is a communication demand forecasting task 33 that anticipates demand, forecasts traffic demand, and determines a driving model.The tasks 33 and 34 are 100 msec.
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 the flowchart of split equalization 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 flow on each floor is in the vance state will be described.

In allocation control, there are the following control goals.

(1) Reduce long waiting time.

(2) Increase good waiting time.

(3) Maintain good service on high demand floors.

(4) 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 time” is “do not allocate if long waiting”. “Increases good waiting time.”
Is assigned if it is a good waiting time, etc., and similarly for other control strategies, a control rule of the form "if A is B" is prepared.

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-mentioned control command strength is determined for each elevator, and the control command with the strongest control command is assigned.

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 elevators that meet the target to the highest degree are assigned.

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 1, the elevators that cannot perform allocation control due to failure, out-of-group operation, etc. are excluded from allocation control. In the next step 2, the prediction calculation of the data necessary for the inference calculation in the subsequent step 4 is performed. In step 3, preprocessing is performed to convert the various data obtained in step 2 into a form suitable for performing the inference operation in step 4. In step 4, 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 2 to step 4 described above is performed for each machine. Hereinafter, for convenience of description, the degree of instruction weighting is referred to as instruction strength. In step 5, the control command strengths of the respective units are compared with each other, and a command for allocating the newly generated hall call to the unit with the strongest control command is output. With the above, it is decided which machine to allocate to the newly generated hall call, and the allocation control is completed.

In step 4 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. The determination of the degree of satisfaction of the condition and the strength of the instruction will be described. Therefore, in accordance with the above-mentioned control rule, in step 2, the predicted non-response time of each unit,
The minimum predicted non-response time, the maximum predicted non-response time, and the certainty factor of the non-response time are calculated. 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 2a, 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 is '11'.
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 car is on the 7th floor and the driving direction is descending. The value of the hole sub-index HS indicates the starting point of the calculation of the predicted non-response time for each floor, which is performed in the following steps.

Then step 2b is performed. In step 2b, 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, the hall call is 9
Assuming they are on the 4th and 4th floors, their derived basket calls are
It is the 7th floor for hall calls on the 9th floor and the 2nd floor for hall calls on the 4th floor. After the generation of the derived car call is completed as described above, step 2c is performed.

In step 2c, the predicted non-response 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 non-response time Tx for each floor. Here, the time required to rise or fall between floors is 1 second,
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 and off the floor. This is a simple example, and is actually determined by a more accurate calculation.

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'). Also, it takes 2 seconds from the 11th floor to the 9th floor (Hall Sub Index HS is '3'). On the 9th floor, there is a hall call, so 10 seconds are spent as lost time. Therefore 11
Floor to 8th floor (hall sub index HS is '4')
It will take 13 seconds. 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, then 1
It took 62 seconds to climb from the first floor to the 12th floor and return to the 11th floor again. This time is when there are hall calls on the 9th and 4th floors, and as the number of hall calls increases, the predicted non-response time to each floor also increases accordingly.

Predicted unanswered time RESPT (HS) for floors with hall car calls
Can be expressed by the following equation.

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 it is ignored. l indicates the number of calls to the floor for which the predicted unanswered time is obtained. (Including the call of the floor for which the predicted unanswered time is to be obtained.) In the case of FIG. 14, for example, the predicted unanswered 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 (sec) There is no call between the 11th floor and the 9th floor, so the car does not stop on the 10th floor, so the loss time is '0'. Similarly, the predicted non-response time RESPT (8) on the fourth floor is expressed by the following equation.

RESPT (8) = RANT (Sta ₁ , End ₁ ) + RANT (Sta ₂ , En
d ₂ ) + RANT (Sta ₃ , End ₃ ) + LOST (End ₁ ) + LOST (En
d ₂ ) RANT (Sta ₂ , End ₂ ) is the traveling time from 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,
RANT (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. This is the first
The predicted non-response time for each intervention shown in Fig. 5 is obtained.

Next, step 2d and step 2e are performed. here,
Minimum predicted unanswered time Tm only for floors with hall calls
In, the maximum predicted non-response time Tmax and the probability distribution mode of the non-response time are determined.

The predicted unanswered 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 predicted unanswered time Tmin for this predicted unanswered time Tx is the unanswered time of each floor when only actual hall calls and car calls are considered. Further, the maximum predicted unanswered time Tmax is the unanswered time of each floor when the hall call is generated on the high demand floor and the derived car call lengthens the predicted unanswered time. In the present embodiment, as an example, the high demand floors are the 5th floor and the 1st floor, and their derived car calls are the 1st floor and the 12th floor, respectively. The minimum predicted non-response time Tmin and the maximum predicted non-response time Tmax are obtained by the above equation (1).

Next, the probability distribution mode of the non-response 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 probability distribution modes of unanswered time for floors with hall calls Fig. 16 (a) and (b)
Indicates. This distribution mode is distributed around the predicted non-response time Tx. In addition, this distribution mode is
The distribution state varies depending on the values of in, the predicted non-response time Tx, and the maximum predicted non-response time Tmax. However, this distribution mode does not fail with minimum predicted unanswered time Tmin and maximum predicted unanswered time Tmax.
And the areas of S ₁ and S ₂ are equal, the sum of the areas of both is 1 and TL 1 and TL 2 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, if the derived car call generated by learning data etc. is unlikely to match the car call that actually occurs, as shown in Fig. 16 (a), the probability of a long pattern of TL1 and TL2 The distribution mode is selected. Fig. 17 (a) 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 non-response time to the 8th floor and the 7th floor of the car on the 3rd floor 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,
As shown in FIG. 16 (b), TL1 and TL2 are shown in FIG. 16 (a).
The probability distribution mode with a smaller value pattern is selected. TL1 and TL2 change depending on the certainty, and the longest value is determined by the certainty. However, TL1 and TL2 are limited by the minimum predicted non-response time Tmin and the maximum predicted non-response time Tmax.

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 non-response time is obtained from the probability distribution of the non-response 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 non-response 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 probability distribution mode is normalized so that the area becomes 1, the certainty factor of each non-response time can be obtained by finding the area. This certainty factor is expressed as a probability.

As described above, the predicted non-response time and the certainty factor thereof are obtained by step 2c, step 2d and step 2e shown in FIG.

Then step 2f is executed. In this step 2, the probability distribution of waiting time is determined. This step 2f
As shown in Fig. 19, 2fb and 2fb
Of the waiting time probability distribution for the predicted person. These are obtained by using the probability distribution of the non-response time obtained previously. A description will be given below with reference to FIGS. 20 to 26. First, the case of normal times will be described using an example. It is assumed that the probability distribution of the response time obtained in step 2e is shown in FIG. T1 in Fig. 20
min is the minimum predicted non-response time, T1max is the maximum predicted non-response time, and TImd is the median value.

In step 2fa, the number of waiting passengers in each hall is predicted. In the time zone shown in FIG. 20, the predicted occurrence interval a of waiting passengers in the hall is calculated from the average number HCT $ RAT of calls generated in the hall and the average value IN $ RAT of the number of passengers. First
Figure 21 shows the change in the predicted number of people over time. That is, at time T1md, it is predicted that there will be three passengers in the hall.

Next, in step 2fb, the probability distribution of the waiting time for each of the persons predicted in step 2fa is obtained. The one shown in FIG. 21 means that the number of waiting customers becomes two after a seconds and the number of waiting customers becomes three after 2a seconds. Therefore 3
The probability distribution of the predicted waiting time for each person waiting is 22
As shown in the figure, the predicted occurrence interval is delayed by a. As shown in the explanatory view of FIG. 23, these have a pointer of the next person behind the table of the first person, and the data of the second person and the data of the third person are combined at the same time according to the pointer. An END mark is added behind the last person's data.

Next, the case of peak time will be described. The peak time will be explained with reference to FIGS. 24 to 26. The twenty-fourth shows the load change in the car over time. Also, the 25th
The figure shows the change in the predicted number of people over time. When the number of waiting passengers in the hall increases and becomes saturated, there is a situation that the load is left unloaded and the load becomes full. For this reason, the waiting time increases due to the remaining load. From the full state of the load data, the waiting passenger occurrence interval b in the hall is predicted. This generation interval b is usually an arrival time interval for 1 to 1.5 vehicles. FIG. 26 (a) shows the probability distribution of the non-response time. The probability distribution of the predicted waiting time shown in FIG. 26 (b) is the probability distribution of the non-response time delayed by the occurrence interval b.

However, the number of waiting passengers is large, and the waiting passengers generated later have less loss. (Occurrence interval b
Is reduced by the amount of occurrence time. ) This concludes the explanation of the measures to be taken when the product is left unloaded during peak hours. With the above steps
2f ends.

By repeating the above steps 2c to 2f for each floor, the certainty factor of the predicted non-response time of each floor and the predicted non-response time of a floor with a hall call is obtained. This completes the calculation of the predicted non-response time in step 2 of FIG.

The inference operation of allocation control in step 4 shown in FIG. 11 will be described.

In the inference calculation of step 4, calculation of allocation control is performed. In this allocation control, with the goal of "reducing the long waiting time", that is, to set the long waiting time of 60 seconds or more to "0", when a newly generated hall call is temporarily allocated, the long waiting time is set. It is difficult to allocate to a machine that is more likely to become, that is, the deviation from the target of "reducing the long waiting time" by making a temporary allocation (hereinafter,
Say an error. ) E and the error increment ΔE are used to numerically represent the strength of the instruction “assign to hall call”. Determine the strength of this instruction for each elevator.

The inference operation of allocation control will be described using the flowchart shown in FIG. What is shown in FIG. 27 is a general flowchart, and each step is performed for each target rule.

At step 4a, a control command "temporarily assign newly generated hall call" is given to the elevator A, which is the control target.

Then step 4b is performed. The error E from the target and the error increment ΔE are calculated from the following equations.

Confidence: Confidence that a car will arrive in 60 seconds or more TP ₂ n; Number of waiting persons for all hall call numbers including tentative allocation According to the above formula (2), tentative allocation was made before and after tentative allocation to Unit A. increment Δ values and error E _a2 error E _a2 later
Find E _a . If the value of the error E before the temporary allocation is E _a1 and the error E after the temporary allocation is E _a2 , the increment ΔE _{a of the} error E is calculated by the following equation.

ΔE _a = E _a2 −E _a1 (4) Next, using the degree of membership function, error E _a2 and error increment ΔE
to 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. 28, the horizontal axis represents the error E and the error increment ΔE, and the vertical axis represents the membership degree. FIG. 28 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 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 in the error E and the error increment ΔE. A set that is "positive and large" is shown. Each membership function has all errors E
Alternatively, the degree of inclusion of each value in the set ZO, the set PM and the set PB is given to the error increment ΔE. This degree indicates the degree of belonging to a set, and is called the degree of membership, which is indicated by 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, and 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. 28, 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 PM “positive and medium” with the degree of belonging of 0.3.

The membership function also changes depending on the total confidence K. When the data is accurate, it is a membership function with clearly separated intervals as shown in Fig. 29, and when the data is incorrect, it is a smooth function as shown in Fig. 30.

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 obtained by obtaining the degree of belonging of the error E and the increment ΔE of the error.

The degree of membership of the error E _a2 after provisional allocation and the increment ΔE _{a at} that time is obtained from the membership function shown in FIG. From FIG. 28, the degree of membership of the error E _a2 with respect to the set PM is 0.9, and the degree of membership with respect to the set ZO is 0.1. Also, the set P of error increments ΔE _a
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 and PB the values of the error E and the increment ΔE of the error belong is determined in consideration of the degree of membership. This membership function has error E and error increment Δ
An evaluation of whether the value of E is large or small is given to the value of E. That is, the values of error E are
Belonging to 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. 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. Therefore, a large error E and an error increment ΔE indicate a large degree of long waiting.

As shown in FIG. 28, the error E _a2 belongs to the set PM with a degree of belonging of 0.9, and the error increment ΔE _a has _a degree of belonging to the set PM and the set ZO.
Since it belongs to 0.5, it means that it will be a long wait.

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

The condition-instruction table of step 4d in FIG. 27 is shown in FIG. FIG. 31 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, PM and set NE, and also three types in the case of the error increment ΔE, and 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 “not assigned”, and the same applies to rule 2 and thereafter.

As shown in FIG. 31, the condition-instruction table indicates an instruction Δ on the condition that the error E and the set to which the error value belongs are increments ΔE.
It determines U. Therefore, the condition-instruction table shown in FIG. 31 is a matrix of the production rules represented by the "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, from FIG. 31, the instruction ΔU is obtained by evaluating the error E _a2 and the error increment ΔE _a . Error E _a2 is set PM and set Z
Belongs to O and the error increment ΔE _a belongs to the sets PM and ZO. Therefore, when the instruction ΔU is obtained as the condition and the set to which the error E _a2 and the increment ΔE _a belong, there are the following four ways.

(B) Error E _a2 belongs to set PM and increment ΔE _a is set PM
If it belongs to, the instruction ΔU becomes NE (not assigned).

(B) Error E _a2 belongs to set PM and increment ΔE _a is set ZO
If it belongs to, the instruction ΔU becomes ZO (normally assigned).

(C) Error E _a2 belongs to set ZO and increment ΔE _a is set PM
If the instruction belongs to, the instruction ΔU is NS (it is not necessary to allocate).
Becomes

(D) Error E _a2 belongs to set ZO and increment ΔE _a is set Z
If it belongs to O, the instruction ΔU becomes PO (assign).

As described above, four rules are extracted from the above-mentioned nine types of rules by the combinations (b) to (d) of the set to which the error E _a2 belongs and the set to which the increment ΔE _a belongs. The extracted rules are rule 5, rule 6, rule 8 and rule 9 shown in FIG. 4 shown in four rules for error E _a2 and increment ΔE _a
We got four instructions ΔU, but we 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 strength of the instructions obtained by weighting and weighted averaging the instructions of each rule will be described with reference to FIG. 33rd
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. 33, for error E the set PM is satisfied with a degree of 0.9 and for error increment ΔE the set PM is satisfied with a degree of 0.5. The degree of satisfaction of rule 5 is the smallest value of the degrees of satisfaction of the two sets. Therefore, Rule 5 will be satisfied with a degree of 0.5. The set indicating the instruction ΔU is limited by the degree of 0.5. Hereinafter, similarly, a set indicating the instruction ΔU is obtained for the rules 6, 8 and 9. This is the end of step 4c.

Next, step 4e is performed. In step 4e, the logical sum of the set of instructions for each rule obtained in step 4c is taken, and the weighted average is calculated by the degree of belonging to the set to finally obtain the strength U of the instruction. Here, as shown in FIG. 34, the instruction strength U is -0.69. 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 4e is completed and step 4 shown in FIG. 11 is completed.

Next, in step 5, it is determined which vehicle is finally assigned based on the strength of the control command obtained for each vehicle in step 4. After deciding which car number to allocate to the newly generated hall call, a control command "allocate" is output to that car number.

By ending step 5, the allocation control for hall calls is completed.

The relationship between the membership function shown in FIG. 28 and the error E shown in FIG. 31, the increment ΔE and the instruction ΔU used in the inference operation of the allocation control is artificially determined.

That is, the degree-of-attribute function is determined by using an expert's rule of thumb. Further, which instruction to use for the error E and its increment ΔE shown in FIG. 31 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.

Also, in allocation control, the "confidence" of the predicted non-response time
Therefore, the number of long-waiting calls can be reduced because it can be assigned to a machine with a high “confidence” of the same predicted unanswered time.

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.

(1) Reduce long waiting time.

(Reduce waiting time of 60 seconds or more.) (2) Increase good waiting time.

(Increase the waiting time within 30 seconds.) (3) Reduce the longest waiting call.

(4) Maintain good service on high demand floors.

(The arrival time at the high demand floor should be within 30 seconds.)

(Eliminate cases where the load inside the car exceeds 80%.) (6) Reduce the number of first-come-first-served cars.

(7) Increase early calls.

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.

In group control of elevators, the transportation capacity is increased by determining the operation model according to the traffic demand.
The operation model includes a divergence model, a concentrated model, a composite model, and the like, and the inference operation according to the present invention can be used in the determination of switching of these operation models.
Predetermined split equalization control is performed on each of the above-described operation models. Inference operations are also used in this allocation control, but the goals of each split control are (1) to (7) above.
Chosen from the goals of.

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.

(Other Embodiments) Using the waiting time in consideration of the unpaid load at the peak of the morning or the daytime, the high-transport capacity allocation control peculiar to the peak can be realized by using the target and the rule for that as well.

Further, in order to correspond to the waiting time in speeding up the algorithm of the present invention, it can be simply performed by [prediction occurrence number] × [prediction non-response time].

〔The invention's effect〕

The degree of satisfaction of the conditions of a plurality of control rules, which represents the expert's control strategy by conditions and instructions, and the weighting of the instructions are determined for each control rule, and the control in the group management control is performed from the weighted instructions for each control rule. By determining the command, highly efficient group management control can be performed, and the service to the elevator users can be improved.

Further, since the control for always leaving the possibility of making the service good is performed, the average waiting time and the maximum waiting time can be shortened. Since the probability distribution and ambiguity of the waiting time are predicted and used as data in the control, the above-mentioned effect can be further enhanced.

[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.
18 to 26 are diagrams for obtaining the certainty factor of the predicted arrival time, Fig. 27 is a flow chart of inference operation, and Figs. 28 to 28.
Figure 30 shows the membership function, and Figures 31 and 32 show the conditions-
FIG. 33 is a diagram showing an instruction, FIG. 33, and FIG. 34 are diagrams for obtaining a control command. 1 ... Group management control device, 2 ... Elevator control device, 3 ... Transmission controller, 4 ... Elevator monitoring monitor, 5 ... Hall gate, lamp, sensor, display I / O controller, 6 ... In-car controller.

Claims (1)

[Claims] 1. A group management control method for elevators, which allocates an optimum elevator from a plurality of elevators to a generated hall call, wherein the hall is considered by considering only an already assigned hall call and an actual car call. The minimum unanswered time of the call origination floor is obtained, and the maximum unanswered time of the hall call origination floor is obtained by considering the already assigned hall call, the car call derived from this hall call and the actual car call, and the minimum unanswered time is obtained. A certainty factor of the unanswered time is obtained from the time and the maximum unanswered time, and a plurality of control rules represented by conditions and instructions prepared corresponding to the control target in the elevator allocation control are used, Tentatively assigning the hall call to each elevator, and using the certainty factor, the degree to which the conditions in the plurality of control rules are satisfied and the strength of the instruction are determined for each elevator. The control command strength of each elevator is calculated by synthesizing the strength of the command calculated for each control rule, and the optimum elevator is determined from this control command strength and assigned to the hall call. An elevator group management control method characterized by: