CN112811273A - Probability parallel planning based elevator scheduling method based on real-time additivity heuristic method - Google Patents

Probability parallel planning based elevator scheduling method based on real-time additivity heuristic method Download PDF

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CN112811273A
CN112811273A CN202110111498.9A CN202110111498A CN112811273A CN 112811273 A CN112811273 A CN 112811273A CN 202110111498 A CN202110111498 A CN 202110111498A CN 112811273 A CN112811273 A CN 112811273A
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heuristic
additivity
state
action set
reward
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饶东宁
曾进
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Guangdong University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/30—Details of the elevator system configuration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B2201/00—Aspects of control systems of elevators
    • B66B2201/40—Details of the change of control mode
    • B66B2201/403—Details of the change of control mode by real-time traffic data

Abstract

The invention discloses a probability parallel programming-based real-time additivity heuristic elevator scheduling method, which abstracts elevator scheduling problems into the field of parallel probability programming and obtains scheduling results through a probability parallel programming-based real-time additivity heuristic algorithm. The invention can efficiently realize elevator dispatching.

Description

Probability parallel planning based elevator scheduling method based on real-time additivity heuristic method
Technical Field
The invention relates to the technical field of intelligent planning, in particular to an elevator scheduling method based on probability parallel planning and a real-time additivity heuristic method.
Background
Each elevator has a number to facilitate monitoring and maintenance. Each elevator is provided with a real-time monitor which is responsible for monitoring the up and down of the elevator and sending signals for starting, braking, accelerating, decelerating and opening and closing the elevator door to the elevator lifting box. If the elevator is in failure, a distress signal is also sent to the corresponding elevator responsible person.
In a high-rise building with a large number of elevators and complex passenger demands, the current scheduling algorithm is not efficient, and the problems of no load and long-time waiting of passengers often occur.
Therefore, an efficient elevator dispatching method is urgently needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an efficient elevator dispatching method based on probability parallel programming real-time additivity heuristic.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a probability parallel programming real-time additivity heuristic elevator scheduling method abstracts elevator scheduling problems into the field of parallel probability programming, and obtains scheduling results through a probability parallel programming real-time additivity heuristic algorithm.
Further, the abstracting of the elevator scheduling problem into the field of parallel probabilistic planning specifically includes:
the object includes: elevators and floors;
the parameters include: the probability parameter of the passenger expecting to reach a certain floor is obtained by counting the frequency;
the state flow includes: the distribution of passengers, the state of the elevator;
the actions include: the elevator moves up and down, and the door is opened and closed;
the reward is as follows: the passengers are correctly transported to the destination, the more passengers are correctly transported, the higher the reward.
Further, the specific process of obtaining the scheduling result by the real-time additive heuristic algorithm based on the probability parallel programming is as follows:
s1, inputting initial state x0Total unit time h;
s2, initializing an additivity heuristic list;
s3, setting h1H/phase _ ratio, phase _ ratio being a phasing superparameter; motion set size estimation vector si＝0,i∈[0,h1]If the trace tr is empty, the state count nr is 0;
s4, judging whether nr is less than or equal to h1If yes, acquiring the state xnrThe set of all available actions in the set is called action set AnrOtherwise, go to step S7;
s5, calculating x through action set size estimation algorithmnrBest action set Abest，SnrOptimal action set A calculated for action set size estimation algorithmbestSize of (2), followed by AbestX is to benrEvolution to the next state xnr+1And a reward r to be paid for,adding the data into a history database and a track tr;
s6, updating the heuristic list through an additive numerical value regression analysis method, and returning to the step S4;
s7, predicting the optimal action set size ═ Avg (Σ S)i),i∈[0,h1]；
S8, judging whether nr is less than or equal to h, if yes, acquiring the state xnrSet of all available actions in Anr(ii) a Otherwise, outputting the trace tr;
s9, calculating the optimal action set A by the optimal action set algorithmbest；
S10, use AbestX is to benrEvolution to the next state xnr+1And a reward r, which is added to the history database and the track tr;
and S11, updating the heuristic list through an additive numerical value regression analysis method, and returning to the step S8.
Further, the step S2 initializes the additivity heuristic list specifically as follows: in an initial state x0For reference value, all state flow propositions in the field are extracted, and then the initial reward value of the state flow propositions is counted as the value of the item in the initial additivity heuristic list.
Further, the motion set size estimation algorithm compiles the motions including ascending, opening and closing of the elevator into a set, tries are carried out by using different numbers of motions, obtains a motion set with the maximum reward value as a state result, and estimates the optimal motion set size according to the algorithm results of a plurality of states; the specific process is as follows:
a1, input state xnrAnd action set Anr, Ai∈Anr,i∈[0,n]N is the size of the action set Anr;
a2, determining whether i is less than n, if yes, using action AiEvolution State xnrTo xnr'; otherwise, action AiAccording to corresponding xiSorting in descending order to obtain action group Asorted(ii) a Then jumping to step A4;
a3, calculating x by using additive heuristic calculation algorithmnr' reward riI ═ i +1, return to step a 2;
a4, initial value, reward r 0, ntry＝0；
A5, judging whether incrase is true, if yes, entering the step A6, otherwise, calculating an optimal action set through an optimal action set algorithm;
a6, judgment Aj∈Asorted,j∈[0,ntry]If yes, then action set Atry＝U(Aj),j∈[0,ntry]Using AtryEvolution State xnrTo xnr'; otherwise, ntry＝ntry+1, return to step a 5;
a7, calculating x by an additive heuristic calculation algorithmnr' s reward
Figure BDA0002919333350000031
A8, determining the reward
Figure BDA0002919333350000032
If there is an increase, then the optimal action set Abest＝AtryOptimum state xbest＝xnr', reward
Figure BDA0002919333350000033
Otherwise, increment is false;
a9, j ═ j +1, return to step a 6.
Further, the specific process of calculating the optimal action set by the optimal action set algorithm is as follows:
b1, input State xnrAction set AnrTarget action set size;
b2 enumerating all action sets A of sizenrSubset A ofi；
B3, judging whether enumeration is completed or not, if yes, outputting AbestIs the best action set; otherwise, use AiX is to benrEvolution to xnr′；
B4 generalOver-additive heuristic calculation algorithm calculation xnr' s reward
Figure BDA0002919333350000034
B5, determining the best reward rbestWhether or not less than or equal to
Figure BDA0002919333350000041
If so, then Abest＝Ai，xbest＝xnr'，
Figure BDA0002919333350000042
Returning to the step B2; otherwise, go directly back to step B2.
Further, the specific process of the additive heuristic calculation algorithm is as follows:
c1, input State xnrThe reward r is 0;
c2 enumerating all state streams x in state xi；
C3, judging whether enumeration is completed or not, if yes, outputting r as a heuristic value; otherwise, r is increased by x in the heuristic listiCorresponding value and returns to step C2.
Further, the additive numerical regression analysis method updates the heuristic list through a data analysis method.
Further, the data analysis method includes multiple regression or deep learning.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
the elevator scheduling problem is abstracted into the field of parallel probability planning, and the scheduling result is obtained through a real-time additive heuristic algorithm based on the probability parallel planning. The scheme can efficiently realize elevator dispatching.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a probability-based parallel programming real-time additivity heuristic algorithm (Algorithm 1) according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an initialization algorithm (Algorithm 2) in an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an action set size estimation algorithm (Algorithm 3) in an embodiment of the present invention;
FIG. 4 is a schematic flow chart of the optimal action set algorithm (Algorithm 4) in an embodiment of the present invention;
FIG. 5 is a schematic flow chart of an additive heuristic calculation algorithm (Algorithm 5) in an embodiment of the present invention;
FIG. 6 is a schematic flow chart of an additive numerical regression analysis method (Algorithm 6) in an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
the elevator scheduling method based on the probability parallel programming real-time additivity heuristic method abstracts elevator scheduling problems into the field of parallel probability programming, and obtains scheduling results through the probability parallel programming real-time additivity heuristic algorithm.
Wherein, abstract the elevator scheduling problem for parallel probability planning field specifically includes:
the object includes: elevators and floors;
the parameters include: the probability parameter of the passenger expecting to reach a certain floor is obtained by counting the frequency;
the state flow includes: the distribution of passengers, the state of the elevator;
the actions include: the elevator moves up and down, and the door is opened and closed;
the reward is as follows: the passengers are correctly transported to the destination, the more passengers are correctly transported, the higher the reward.
As shown in fig. 1, the specific process of obtaining the scheduling result by the heuristic algorithm based on the probability parallel programming real-time additivity is as follows:
s1, inputting initial state x0Total unit time h;
s2, initializing an additivity heuristic list ahl through an initialization algorithm, specifically: in an initial state x0Extracting all state flow propositions in the field as reference values, and then counting the initial reward value of the proposition as the value of the item in the initial additivity heuristic list, as shown in FIG. 2;
s3, setting h1H/phase _ ratio, phase _ ratio being a phasing superparameter; motion set size estimation vector si＝0,i∈[0,h1]If the trace tr is empty, the state count nr is 0;
s4, judging whether nr is less than or equal to h1If yes, acquiring the state xnrThe set of all available actions in the set is called action set AnrOtherwise, go to step S7;
s5, calculating x through action set size estimation algorithmnrBest action set Abest，
In the step, the motion set size estimation algorithm compiles the motions including ascending, opening and closing of the elevator into a set, tries are carried out by using different numbers of motions, obtains the motion set with the maximum reward value as the result of the state, and estimates the size of the optimal motion set according to the algorithm results of a plurality of states; as shown in fig. 3, the specific process is as follows:
a1, input state xnrAnd action set Anr, Ai∈Anr,i∈[0,n]N is the size of the action set Anr;
a2, determining whether i is less than n, if yes, using action AiEvolution State xnrTo xnr'; otherwise, action AiAccording to corresponding xiSorting in descending order to obtain action group Asorted(ii) a Then jumping to step A4;
a3, calculating x by using additive heuristic calculation algorithmnr' reward riI ═ i +1, return to step a 2;
a4, initial value, reward r 0, ntry＝0；
A5, judging whether incrase is true, if yes, entering the step A6, otherwise, calculating an optimal action set through an optimal action set algorithm;
a6, judgment Aj∈Asorted,j∈[0,ntry]If yes, then action set Atry＝U(Aj),j∈[0,ntry](U represents union), using AtryEvolution State xnrTo xnr'; otherwise, ntry＝ntry+1, return to step a 5;
a7, calculating x by an additive heuristic calculation algorithmnr' s reward
Figure BDA0002919333350000061
A8, determining the reward
Figure BDA0002919333350000062
If there is an increase, then the optimal action set Abest＝AtryOptimum state xbest＝xnr', reward
Figure BDA0002919333350000063
Otherwise, increment is false;
a9, j ═ j +1, return to step a 6.
SnrOptimal action set A calculated for action set size estimation algorithmbestSize of (2), followed by AbestX is to benrEvolution to the next state xnr+1And a reward r, which is added to the history database and the track tr;
s6, updating the heuristic list (as shown in FIG. 6) by an additive numerical regression analysis method, and returning to the step S4;
s7, predicting the optimal action set size ═ Avg (Σ S)i),i∈[0,h1]；
S8, judging whether nr is less than or equal to h, if yes, acquiring the state xnrSet of all available actions in Anr(ii) a Otherwise, outputting the trace tr;
S9calculating an optimal action set A through an optimal action set algorithmbestAs shown in fig. 4, the specific process includes:
b1, input State xnrAction set AnrTarget action set size;
b2 enumerating all action sets A of sizenrSubset A ofi；
B3, judging whether enumeration is completed or not, if yes, outputting AbestIs the best action set; otherwise, use AiX is to benrEvolution to xnr′；
B4 calculating x by additive heuristic calculation algorithmnr' s reward
Figure BDA0002919333350000071
As shown in fig. 5, the process includes:
c1, input State xnrThe reward r is 0;
c2 enumerating all state streams x in state xi；
C3, judging whether enumeration is completed or not, if yes, outputting r as a heuristic value; otherwise, r is increased by x in the heuristic listiCorresponding value and returns to step C2.
B5, determining the best reward rbestWhether or not less than or equal to
Figure BDA0002919333350000072
If so, then Abest＝Ai，xbest＝xnr'，
Figure BDA0002919333350000073
Returning to the step B2; otherwise, go directly back to step B2.
S10, use AbestX is to benrEvolution to the next state xnr+1And a reward r, which is added to the history database and the track tr;
s11, updating the heuristic list through an additive numerical regression analysis method (as shown in FIG. 6), and returning to the step S8.
In this embodiment, the heuristic list is updated by an additive numerical regression analysis method through a data analysis method, which includes, but is not limited to, deep learning, multiple regression analysis, and the like.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.

Claims (9)

1. A probability parallel programming based real-time additivity heuristic elevator scheduling method is characterized in that elevator scheduling problems are abstracted into the field of parallel probability programming, and scheduling results are obtained through a probability parallel programming based real-time additivity heuristic algorithm.
2. The elevator scheduling method based on the probabilistic parallel programming real-time additivity heuristic method of claim 1, wherein abstracting the elevator scheduling problem into the field of parallel probabilistic programming specifically comprises:
the object includes: elevators and floors;
the parameters include: the probability parameter of the passenger expecting to reach a certain floor is obtained by counting the frequency;
the state flow includes: the distribution of passengers, the state of the elevator;
the actions include: the elevator moves up and down, and the door is opened and closed;
the reward is as follows: the passengers are correctly transported to the destination, the more passengers are correctly transported, the higher the reward.
3. The elevator scheduling method according to claim 2, wherein the specific process of obtaining the scheduling result by the probabilistic parallel programming real-time additivity heuristic based on the probabilistic parallel programming real-time additivity heuristic algorithm is as follows:
s1, inputting initial state x0Total unit time h;
s2, initializing an additivity heuristic list ahl through an initialization algorithm;
s3, setting h1H/phase _ ratio, phase _ ratio being a phasing superparameter; motion set size estimation vector si＝0,i∈[0,h1]If the trace tr is empty, the state count nr is 0;
s4, judging whether nr is less than or equal to h1If yes, acquiring the state xnrThe set of all available actions in the set is called action set AnrOtherwise, go to step S7;
s5, calculating x through action set size estimation algorithmnrBest action set Abest，SnrOptimal action set A calculated for action set size estimation algorithmbestSize of (2), followed by AbestX is to benrEvolution to the next state xnr+1And a reward r, which is added to the history database and the track tr;
s6, updating the heuristic list through an additive numerical value regression analysis method, and returning to the step S4;
s7, predicting the optimal action set size ═ Avg (Σ S)i),i∈[0,h1]；
S8, judging whether nr is less than or equal to h, if yes, acquiring the state xnrSet of all available actions in Anr(ii) a Otherwise, outputting the trace tr;
s9, calculating the optimal action set A by the optimal action set algorithmbest；
S10, use AbestX is to benrEvolution to the next state xnr+1And a reward r, which is added to the history database and the track tr;
and S11, updating the heuristic list through an additive numerical value regression analysis method, and returning to the step S8.
4. The elevator dispatching method based on the probabilistic parallel programming real-time additivity heuristic, according to claim 3, wherein the step S2 initializes the additivity heuristic list ahl specifically as: in an initial state x0For reference value, all state flow propositions in the field are extracted, and then the initial reward value of the state flow propositions is counted as the value of the item in the initial additivity heuristic list.
5. The elevator dispatching method based on the probability parallel programming real-time additivity heuristic method of claim 3, characterized in that the action set size estimation algorithm compiles actions including ascending, opening and closing of an elevator into a set, tries by using different numbers of the actions, obtains an action set with the largest reward value as a result of a state, and estimates the size of the optimal action set by the algorithm results of a plurality of states; the specific process is as follows:
a1, input state xnrAnd action set Anr, Ai∈Anr,i∈[0,n]N is the size of the action set Anr;
a2, determining whether i is less than n, if yes, using action AiEvolution State xnrTo xnr'; otherwise, action AiAccording to corresponding xiSorting in descending order to obtain action group Asorted(ii) a Then jumping to step A4;
a3, calculating x by using additive heuristic calculation algorithmnr' reward riI ═ i +1, return to step a 2;
a4, initial value, reward r 0, ntry＝0；
A5, judging whether incrase is true, if yes, entering the step A6, otherwise, calculating an optimal action set through an optimal action set algorithm;
a6, judgment Aj∈Asorted,j∈[0,ntry]If yes, then action set Atry＝U(Aj),j∈[0,ntry]Using AtryEvolution State xnrTo xnr'; otherwise, ntry＝ntry+1, return to step a 5;
a7, calculating x by an additive heuristic calculation algorithmnr' s reward
Figure FDA0002919333340000031
A8, determining the reward
Figure FDA0002919333340000032
If there is an increase, then the optimal action set Abest＝AtryOptimum state xbest＝xnr', reward
Figure FDA0002919333340000033
Otherwise, increment is false;
a9, j ═ j +1, return to step a 6.
6. The elevator dispatching method based on the probabilistic parallel programming real-time additivity heuristic method of claim 5, wherein the specific process of calculating the optimal action set through the optimal action set algorithm is as follows:
b1, input State xnrAction set AnrTarget action set size;
b2 enumerating all action sets A of sizenrSubset A ofi；
B3, judging whether enumeration is completed or not, if yes, outputting AbestIs the best action set; otherwise, use AiX is to benrEvolution to xnr’；
B4 calculating x by additive heuristic calculation algorithmnr' s reward
Figure FDA0002919333340000034
B5, determining the best reward rbestWhether or not less than or equal to
Figure FDA0002919333340000035
If so, then Abest＝Ai，xbest＝xnr'，
Figure FDA0002919333340000036
Returning to the step B2; otherwise, go directly back to step B2.
7. The elevator dispatching method based on the probability parallel programming real-time additivity heuristic method of claim 6, wherein the concrete process of the additivity heuristic calculation algorithm is as follows:
c1, input State xnrThe reward r is 0;
c2 enumerating all state streams x in state xi；
C3, judging whether enumeration is completed or not, if yes, outputting r as a heuristic value; otherwise, r is increased by x in the heuristic listiCorresponding value and returns to step C2.
8. The elevator dispatching method based on the probabilistic parallel programming real-time additivity heuristic method of claim 3, wherein the additivity numerical regression analysis method updates the heuristic list through a data analysis method.
9. The elevator dispatching method based on the probabilistic parallel programming real-time additivity heuristic method of claim 8, wherein the data analysis comprises multivariate regression or deep learning.
CN202110111498.9A 2021-01-27 2021-01-27 Probability parallel planning based elevator scheduling method based on real-time additivity heuristic method Pending CN112811273A (en)

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Application publication date: 20210518