CN1270138A - Control method for elevator system - Google Patents
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- CN1270138A CN1270138A CN 00106709 CN00106709A CN1270138A CN 1270138 A CN1270138 A CN 1270138A CN 00106709 CN00106709 CN 00106709 CN 00106709 A CN00106709 A CN 00106709A CN 1270138 A CN1270138 A CN 1270138A
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Abstract
To provide a control method of an elevator system capable of avoiding the crowding in an elevator, shortening waiting time and increasing the degree of satisfaction of users. In this elevator system including a plurality of elevators, an imaginary circulation passage is defined that the elevating direction of each floor is discriminated from the lowering direction, the elevators are advanced to upper floors with an arbitrary floor as a starting point, and returned at the highest floor from the elevating direction to the lowering direction and advanced to lower floors, and then, returned at the lowest floor and advanced to upper floors to form the staring point. A cage allotment is controlled to a call at a landing floor in the elevator system, and the uniform distance between cages to unify the distance between cages in the imaginary circulation passage is also controlled in this allotment control.
Description
The present invention relates to the control method of an elevator device.The present invention is specifically related to a kind of control technology that elevator (car) can not be crowded, and can improve passenger's satisfaction.
Habitual elevator group control technology is to manage quantity limitation with elevator (car) at maximum range, and shortens passenger's wait time.
For example, can shorten wait time by the control method of regulating elevator according to the specific date and time cycle.According to this technology, the rush hour in the morning (up peak) is called out the platform that preceence gives from hall's floor to high-rise floor.On the other hand, the rush hour in the afternoon (lowering peak) is called out the platform that preceence gives from high-rise floor to hall's floor (first floor).
The problem that the present invention need solve is that if a plurality of lift cars are arranged in operation, lift car might crowd.For example, when all cars all up and take place when crowded, near the low layer floor intermediate floor will become very long to the wait time of high-rise floor.This is because crowded the causing of elevator (car).
Purpose of the present invention is exactly that a kind of group control method that can solve the problems referred to above in the prior art will be provided, and it can avoid the crowded of elevator (car), shortens wait time, and improves passenger's satisfaction.
The means of dealing with problems are such, in order to address the above problem, consider to prevent the problem of clustering in control of the present invention.In this case, prevent that clustering from being a kind of method of controlling elevator.When giving car, avoid the car in the elevator device that crowded (clustering) takes place with a platform call distribution.
Compare with the system that does not consider the clustering problem,, just can reduce clustering as long as take to prevent the measure of clustering.In order to carry out the distribution of considering the clustering problem, and judge with clustering and to improve the degree of distributing, need to limit a kind of standard of judging clustering, and combine with the control of elevator device.
Clustering is meant a kind of like this state, and promptly the position of Bian Zu a plurality of lift cars is in fact close to each other and move on same direction.In theory, car should be in whole access to elevators rectangular distribution call out so that reply the platform of input at short notice.Yet, during up peak, car often can not be in whole access to elevators rectangular distribution.Judge car whether should to distribute equidistantly in free be another target that will pursue.For example, this judgement can realize with software in the stage after a while.After considering the service direction of car, the distance between the car is near more, and the degree of clustering is just high more.For example, when two cars were in same floor, if their service direction is reciprocal, only the clustering grade for these two cars was minimum.On the other hand, be in same floor and during to the operation of same direction, the clustering grade of these two cars is the highest when two cars.
For all cars, the highest clustering grade correspondence the situation that all cars all are in same floor and move to same direction.On the other hand, minimum clustering grade or say that the state that does not have clustering is meant the state that car distributes in the access to elevators moderate distance, this wherein will consider position and direction.The clustering measurement standard is defined as a kind of method of calculating that the clustering grade of system is handled, and is defined as the distance between car when system is in minimum clustering grade about the ideal distance of clustering.
Below will be with reference to the description of drawings embodiments of the invention.
Fig. 1 is a scheme drawing of the elevator device of one embodiment of the invention.A plurality of access to elevators are arranged in this elevator device.A lift car is arranged in rising/decline in each access to elevators.Be transmitted to the control unit (300) of elevator device from the car call input that is arranged on the platform button (120) on each floor or the car call button (110) from elevator is sent.As mentioned below, have the clustering evaluation unit (310) that is used for determining elevator (car) clustering grade in the control unit (300), and be used for car is distributed to the allocation units (320) that platform is called out.In addition, in the allocation units (320) of present embodiment, also be provided with fuzzy logic processes unit (321), to allow fuzzy logic be used for the distribution of car.Yet,, also can adopt habitual direct control although adopted fuzzy logic in the present embodiment.
In addition, use service aid (200) to carry out interface listed in the following table (I/O) in the present embodiment with respect to control unit (300).
Table 1
Input by input block (220):
Is clustering mechanism in ON?
Fuzzy parameter about clustering
Calculate the cycle of clustering standard of appraisal
Go up demonstration at display unit (210):
The aviation value of clustering measurement standard
The maxim of clustering measurement standard
About the fuzzy parameter of clustering, the computation period of clustering standard of appraisal, the aviation value of clustering measurement standard, and the maxim of clustering measurement standard remains to be explained hereinafter.
At first to explain the general rule that prevents clustering mechanism of the present invention.For concrete explanation all sidedly prevents that the mechanism of clustering from must provide necessary conditioned disjunction is prerequisite.Carry out thisly when preventing clustering machine-processed concrete, this operation may be had the more operation of high priority and interrupt or postpone in the preceence order.Thereby, restart this operation after more the operation of high priority order is finished having.
In addition, the fuzzy logic of carrying out in the present embodiment calculate can be with embed the group control device in fuzzy logic account form identical (platform is called out reallocation or the like for variable MIT for example, platform call distribution).Adopted the conventional process of a kind of MIT of being called as in addition, distributed car according to Moderate Incoming Traffic (medium passenger flow volume).This method has been applicable to that a lot of passengers need upstream case.In above-mentioned MIT, all cars are all according to the MIT mode operation.In habitual MIT, the preceence order (instruction) that gives the passenger in the hall is than the passenger's height on other floor.In above-mentioned variable MIT, not all elevator (car) all is according to the MIT mode operation, but has at least a part wherein like this.When determining elevator (car) ratio of employing MIT pattern, adopted fuzzy logic.
Prevent the mechanism of clustering
Below to explain the measurement standard of clustering.In clustering (Bunching) evaluation unit (310) of group control device, periodically estimate the passenger flow state, to judge clustering degree at that time.This clustering measurement standard is to determine according to the position and the direction of each car.Determine to respond the nearest car and the distance of its car call by being estimated as each floor from this car to this floor.Calculate the summation of above-mentioned distance at each floor, derive the clustering degree of carving at this moment in the system thus.Calculate the ratio between clustering degree in the system and the maximum clustering (all clusterings), with it as the clustering measurement standard.
To specify this mode with reference to the flow process of Fig. 2 hereinafter.In explanation, represent " step (Step) " with " S ".In addition, with the uppermost storey in the colony (group), the lowest floor in the colony, the number of elevator in the colony, and the position of the car in the colony and initial launch direction are input to group's (group) control setup (S101).
Program below in the clustering evaluation unit of group control device, carrying out.At first derive number of floor levels in the colony as the border with uppermost storey and lowest floor.Going out all platforms that can import with the following derivation of equation then calls out.
[formula 1]
Possible platform is called out [quantity]=(number of floor levels-1) * 2
That is to say that possible platform is called out and had only a kind of (platform of up direction is called out) on lowest floor, and possible platform calling also is to have only a kind of (down direction) on the uppermost storey.Yet, for intermediate floor, but have two kinds of possible platforms to call out (up direction and down direction).Then determine the quantity that platform is called out according to aforesaid method.
In the ideal distance calculating unit (311) of clustering evaluation unit (310), calculate the ideal distance I between (S102) car.Ideal distance herein is meant a kind of like this distance, just calls out the service that provides in this chance that all has equality apart from the car the car that is in shutoff OFF state except those for all platforms.Adopt following formula to calculate this ideal distance, and the part of arithmetic point back is rounded up.
[formula 2]
Platform call number/number of elevator that ideal distance I=[is possible]+1
[] expression Gauss symbol wherein.
Fig. 3 (A) is the scheme drawing that has the elevator device of three elevators (car) in a 8-floor.Each floor and the probability that contingent platform is called out in a kind of annular diagram in Fig. 3 (B), have been represented, from ideal distance as can be seen wherein.Certainly, single and idle elevator (car) can be arranged in each access to elevators.Yet as shown in FIG., when calculating ideal distance, this distance is to calculate under the condition of mono-circulation route operation at the hypothesis car.That is to say that in the cyclic program of supposition that with the hall is departure point, car is up to ground floor in the cyclic program of this supposition, the second layer ....On uppermost storey (among the figure the 8th layer), turn to then and go downwards to the 7th layer, the 6th layer ... and get back to hall.Suppose that hereinafter car is the cyclic program operation along this supposition.For convenience of explanation and with hall as departure point.Yet other floor arbitrarily can not had the difference of any essence as departure point yet.
Under the state shown in the figure, number of elevator is 3, and 8 floors are arranged in the building, and therefore, the quantity that platform is called out is exactly (8-1) * 2=14, and ideal distance is 5, because 14/3=4.66.
In calculating unit (312), calculate the distance D that (S301) goes out to respond the nearest car of low layer calling in inter-car distance then for each floor.Be not the absolute distance that adopts between car and each floor during computed range in this case, but will consider the service direction of car, and adopt the distance of in the cyclic program of above-mentioned supposition, on the cage operation direction, measuring.For example, resting in a car of the second layer and the distance D between the 3rd layer (when being up to the 3rd layer) under uplink state is exactly 1, and is 13 resting in car on the second layer and the distance D between the hall under the uplink state.
In addition, in the present embodiment, the car of stop can move on both direction.Fig. 4 is the scheme drawing of an example of computed range.In Fig. 4, car A rests in the 4th layer and be in uplink state, and car B rests in the 6th layer and be in downstream state, and car C rest in the 2nd layer and be not up neither downstream state.That is to say there is not car call in this state, and the platform that does not need to distribute is called out.
When computed range, also considered one be not in that uplink state (having under the state that a relative car calls out from the platform of high-rise floor or having to the state of the car call of high-rise floor under) also is not in downstream state and the car stopped as effective car.Yet, be not directly to calculate the actv. car in the method for calculating of ideal distance.For example,,, may be on the same floor, also reflect a kind of cybotactic state at the car that moves on the same direction if they have been stopped even car distributes ideally.Below to illustrate in an embodiment of the present invention how the car of stopping is handled in order to calculate ideal distance.Because the complexity of this processing can be got rid of this processing from the theme that calculates ideal distance, or will arrive next circulation to the processing delay of stopping car.
In this state, in clustering record (Bunching score) calculating unit (313) at each floor come comparison it with the distance D of car whether (S104) less than (ideal distance I-1), and calculate the clustering of calling out in the following manner and write down B with respect to platform.
If the distance that arrives nearest car is less than (ideal distance-1), the clustering record is=0 (S105) just.If greater than (ideal distance-1), just calculate the clustering record with following formula to the distance of nearest car: distance-(ideal distance-1) of the nearest car of clustering record=arrive (S106).
Specifically, can calculate the clustering record that each platform is called out in the following manner.
Hall: stopped when neither up also not descending at car C, it might respond the calling of hall and be descending.Ideal distance is 5.On the other hand, because hall and car C be across a floor, their distance is 1.Since 1＜(5-1), clustering record=0.
In this case, when car C is up (having one go the car call of high-rise floor or have under the state of calling out from the platform of high-rise floor), just be defined in the clustering record that calculates it when it stops.In this case, can respond nearest car that hall calls out be with hall only at a distance of 5 layers car B.Because 5＞(5-1), the clustering record is 5-(5-1)=1.
The 2nd a layer of up side: because car C rests in the 2nd layer and just up, distance is 0.Therefore, the clustering record is 0.
The 3rd a layer of up side: because car C is just up, it might respond the up call on the 3rd layer.Distance is 1.Because 1＜(5-1), the clustering record is 0.
The 4th layer up: because car A is positioned at the 4th layer and be in uplink state, the clustering record is 0.
The 5th layer up: distance is 1, and the clustering record is 0.
The 6th layer up: distance is 2, and the clustering record is 0.
The 7th layer up: distance is 3, and the clustering record is 0.
The 8th layer (up): distance is 4, and the clustering record is 0.
The 7th layer descending: because car B is just in descending operation, this car is difficult to the calling of the 7th layer of up-downgoing of response.Nearest car is car A, and it only is separated by 5 layers with the 7th layer on down direction.Because 5＞(4-1), the clustering record is 5-(4-1)=1[sic].
The 6th layer descending: distance is 0, and the clustering record is 0.
The 5th layer descending: distance is 1, and the clustering record is 0.
The 4th layer descending: distance is 2, and the clustering record is 0.
The 3rd layer descending: distance is 3, and the clustering record is 0.
The 2nd layer descending: distance is 4, and the clustering record is 0.
Derive total clustering record (S107) after will writing down addition at the above-mentioned clustering that each car calculates.
Be provided with when preventing clustering machine-processed initial, suppose that all cars all are in same position and move on same direction, and in each circulation, set maximum clustering that (group setting) allow and write down and calculated by the colony on above-mentioned steps 1-5 basis.Preserve this maximum clustering record in order to using.
Use following formula to calculate clustering estimated value (S108) then.
[formula 3]
Clustering estimated value=total clustering record/maximum clustering record
Fig. 5 is a scheme drawing, is illustrated in the state when reaching maximum clustering record.In following table 2, enumerated an example that calculates this clustering estimated value.
[table 2]
Hall: distance=4, clustering record=0
The 2nd layer up: distance=5, clustering record=1
The 3rd layer up: distance=6, clustering record=2
The 4th layer up: distance=7, clustering record=3
The 5th layer up: distance=8, clustering record=4
The 6th layer up: distance=9, clustering record=5
The 7th layer up: distance=10, clustering record=6
The 8th layer: distance=11, clustering record=7
The 7th layer descending: distance=12, clustering record=8
The 6th layer descending: distance=13, clustering record=9
The 5th layer descending: distance=0, clustering record=0
The 4th layer descending: distance=1, clustering record=0
The 3rd layer descending: distance=2, clustering record=0
The 2nd layer descending: distance=3, clustering record=0
Maximum clustering record=45
Under the state of Fig. 4, total clustering record=1
In this state, in clustering estimated value calculating unit (314), calculate the clustering estimated value according to above-mentioned each clustering recording gauge of deriving.In this case, the clustering estimated value is defined as total clustering record/maximum clustering record.Obtain 1/4.5=0.022=2.2% in this case.
Under state shown in Figure 4, the clustering estimated value is 0.022, and the cybotactic state of system is 2.2%.
The clustering estimated value of the clustering degree on this time point is represented in output.Afterwards this result is used for the team control of elevator.
Periodically clustering estimation
Below to describe periodic clustering evaluation method in detail.
For example, in order to measure the clustering degree in the predetermined period of time of passenger's situation of a kind of expectation in correspondence, need and calculate the clustering estimated value regularly.Subsequently a plurality of cycle calculations in the predetermined period of time are gone out the clustering estimated value.The aviation value of all clustering estimated values of calculating and the maximum clustering estimated value of calculating are preserved together.So just can the whole bag of tricks that help to prevent clustering be compared.
It below is detailed explanation.
At first to import the cycle that is used for setting the clustering estimated value.Can utilize service aid to set this setting cycle.
In the above-described embodiment, the minimum value of setting cycle is 1 second, and maxim is 5 seconds, and the unit of input is second.In addition, when initial setting prevents clustering machine-processed, average clustering estimated value and maximum clustering estimated value must be set at zero.
When the time in cycle is estimated in clustering of experience, just calculate the clustering estimated value on this time point according to the method described above.Refresh the average clustering estimated value of preservation then with up-to-date clustering estimated value.If up-to-date clustering estimated value is bigger than the maximum clustering estimated value of preserving, just replace old maximum clustering estimated value with up-to-date clustering estimated value.
Then average estimated value and maximum clustering estimated value are presented on the service aid.Export this average estimated value and maximum clustering estimated value in the present embodiment.
In the distribution of considering under the situation of clustering lift car.
To be described in detail under the situation of considering clustering distribution hereinafter to lift car.In the present embodiment, in order to reduce the clustering of internal system, in platform call distribution logic, increased the clustering factor.When carrying out the platform call distribution, need reflect with the quantitative up-to-date cybotactic state of calculating of clustering estimated value.
According to the distribution of each car being increased in system or the possibility that reduces the clustering degree is estimated each car.In order to determine a kind of allocative decision preferably, and cooperate the calculating of Remaining Response Time (RRT) (residual response time) and Predicted Waiting Time (PWT) (prediction latency time) to analyze clustering information by fuzzy logic.
This residual response time is a value that limits for each car.Its expression car is called out the remaining before time of response (car arrives this floor and allows the passenger enter car) of making to the platform on a certain floor.On the other hand, the wait time of prediction representative from the passenger press the platform call button the time be carved into time between the moment that the passenger enters car.It is last the calculating of time of pressing the moment experience of platform call button from the passenger by the above-mentioned residual response time is added in.Yu Ce wait time is used to prevent passenger's waits for too long like this.
In above-mentioned fuzzy logic, can determine to occur the floor that new platform is called out on it, and can determine that this calling is up or descending.
Then with at this moment the state (open mode of door of each car, close and lock-out state the intermediateness between the opening/closing), the position of car and service direction thereof, the platform that has distributed is called out and car call, and the other system status information all inputs to fuzzy logic.Also to use and prevent that clustering fuzzy logic subordinate degree function from carrying out fuzzy logic operation.
To explain an example in fuzzy logic processes, handling the clustering estimated value with reference to flow process shown in Figure 6 hereinafter.
At first to calculate the clustering estimated value (S201) of the state of the system on this time point.On the basis of above-mentioned " cycle clustering estimation " method, carry out this calculating.
Suppose each car is distributed.Dope position and the direction of each car after a schedule time.For example position and the direction of car after 5 seconds predicted (S202).If there are a plurality of platforms to call out, just have several different methods and make car respond each platform calling.Then calculate its clustering estimated value (S203) at this each method with above-mentioned method.
Then, under each state of prediction, between the estimated value of now and clustering estimated value, compare (S204).Judge the relation value of each following state with this fuzzy compiling then with respect to the allocation result of estimation.
Cybotactic state improves
Cybotactic state does not change
Cybotactic state worsens
In addition, at the cybotactic state of prediction, use with respect to following each state fuzzy and compile to determine this relation value.
Difference (Poor)
Moderate (Fair)
Good (Good)
With determining and the above-mentioned combined clustering standard (S205) of standard this fuzzy compiling.Then this clustering standard is also taken in, and adopt the distribution method (S206) of general type.If for example can adopt first the general fuzzy expression of (IF-then) form, and obtain:
(IF) if " RRT were low, and PWT is within the permissible range, and the clustering standard is suitable ", then (then) " this distribution would be a kind of good distribution ".
Then, not only to also to export the platform call distribution according to RRT and PWT according to the clustering factor.In this manner, the distribution of Zhi Hanging in the present embodiment is done in such a way that and adds the clustering standard as the passengers quantity in a parameter and the car in habitual distribution method, and RRT and PWT or the like parameter is carried out fuzzy logic operation together.It is crowded so just to prevent that car from taking place, and distributes car expeditiously.
Generally speaking, the present invention defines a kind of like this supposition calculation procedure for the elevator device that comprises a plurality of elevators (car): identify up direction and down direction; With any one floor is that initial point moves to high-rise floor; On uppermost storey, make operation redirect to down direction, and move to the low layer floor from up direction; On lowest floor, make operation redirect to up direction from down direction, and to high-rise floor operation, until the above-mentioned floor that arrives as initial point.
Distribute control with respect to carrying out from the calling of platform in the above-mentioned elevator device, this distribution control comprise inter-car distance from equilibrium control, be used for the distance between the balanced car of above-mentioned supposition calculation procedure.
Fig. 1 is a scheme drawing that is used for explaining the elevator system configuration of one embodiment of the invention;
Fig. 2 is a diagram of circuit that is used for representing calculating the method for clustering estimated value;
Fig. 3 is a scheme drawing of elevator device;
Fig. 4 is a kind of scheme drawing of car status;
Fig. 5 is a kind of scheme drawing of car status;
Fig. 6 is a scheme drawing of handling an example of clustering estimated value in the fuzzy logic processes computing.
The brief description of symbol
Fig. 1:
100 elevators
110 car call button
120 platform call buttons
200 service aids
210 display units
220 input blocks
300 control units
310 clustering evaluation units
311 ideal distance calculating units
312 inter-car distance are from calculating unit
313 clustering criterion calculation unit
314 allocation units
321 fuzzy logic processes unit
Fig. 2:
S101 input number of elevator or the like
S102 calculates ideal distance I
S103 calculates the distance D between each floor and car
S105 clustering record B=0
S107 calculates total clustering record
S108 calculates the clustering estimated value
A begins
B finishes
Fig. 3 car number=3
Floor number=8
Issuable platform number of calls=(8-1) * 2=14
Ideal distance=14/3=4.66＞5
Example: under uplink state, the distance between hall and the layer 6 is 5.
Fig. 6:
S201 calculates current clustering estimated value
5 seconds states afterwards of S202 prediction
S203 calculates the clustering estimated value under each predicted state
S204 compares between the clustering estimated value of now and the clustering estimated value under each predicted state
S205 determines the clustering standard
S206 carries out distribution with the clustering standard as a parameter
A begins
B finishes.
Claims (4)
1. the control method of an elevator device is characterized in that: in the control method of the elevator device that comprises a plurality of elevators (car),
A kind of supposition calculation procedure is defined as follows: identify mutual up direction and down direction at each floor; With any one floor is that initial point moves to high-rise floor; On uppermost storey, make operation redirect to down direction, and move to the low layer floor from up direction; On lowest floor, make operation redirect to up direction from down direction, and to high-rise floor operation, until the above-mentioned floor that arrives as initial point;
With respect to carry out distributing control from the calling on the platform in the above-mentioned elevator device, this distribution control comprise inter-car distance from equilibrium control, be used for the distance between the balanced car of above-mentioned supposition calculation procedure.
2. according to the control method of claim 1, it is characterized in that, in above-mentioned balanced control,
With in actual car state of living in (setting state) and the above-mentioned supposition calculation procedure about the degree of deviation of the best state of living in when balanced of car as the clustering estimated value, and with this clustering estimated value carry out inter-car distance from equilibrium control.
3. according to the control method of claim 2, it is characterized in that, in above-mentioned balanced control,
Derive the state that car can reach after a schedule time, and derive above-mentioned clustering estimated value at the various states of deriving.
4. according to the control method of claim 2 or 3, it is characterized in that, in above-mentioned distribution control, adopt fuzzy logic, and simultaneously with the parameter of above-mentioned clustering estimated value as fuzzy logic.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP104676/99 | 1999-04-13 | ||
| JP11104676A JP2000302343A (en) | 1999-04-13 | 1999-04-13 | Control method of elevator system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107235391A (en) * | 2016-03-28 | 2017-10-10 | 株式会社日立制作所 | The control method of lift appliance and lift appliance |
| CN115215169A (en) * | 2022-07-12 | 2022-10-21 | 日立楼宇技术（广州）有限公司 | Elevator group control method, elevator group control device, elevator group control equipment and storage medium |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4139819B2 (en) * | 2005-03-23 | 2008-08-27 | 株式会社日立製作所 | Elevator group management system |
| JP4567553B2 (en) * | 2005-08-31 | 2010-10-20 | 株式会社日立製作所 | Elevator group management system and control method thereof |
| JP4955323B2 (en) * | 2006-07-10 | 2012-06-20 | 株式会社日立製作所 | Data display method and apparatus for group management elevator |
| KR20120137372A (en) * | 2010-02-26 | 2012-12-20 | 오티스 엘리베이터 컴파니 | Best group selection in elevator dispatching system incorporating group score information |
-
1999
- 1999-04-13 JP JP11104676A patent/JP2000302343A/en active Pending
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2000
- 2000-04-12 CN CN 00106709 patent/CN1270138A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107235391A (en) * | 2016-03-28 | 2017-10-10 | 株式会社日立制作所 | The control method of lift appliance and lift appliance |
| CN107235391B (en) * | 2016-03-28 | 2019-06-14 | 株式会社日立制作所 | The control method of lift appliance and lift appliance |
| CN115215169A (en) * | 2022-07-12 | 2022-10-21 | 日立楼宇技术（广州）有限公司 | Elevator group control method, elevator group control device, elevator group control equipment and storage medium |
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