CN103149839A - Operational control method for electrical equipment based on Kuhn-Munkres algorithm - Google Patents

Operational control method for electrical equipment based on Kuhn-Munkres algorithm Download PDF

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CN103149839A
CN103149839A CN2013101226053A CN201310122605A CN103149839A CN 103149839 A CN103149839 A CN 103149839A CN 2013101226053 A CN2013101226053 A CN 2013101226053A CN 201310122605 A CN201310122605 A CN 201310122605A CN 103149839 A CN103149839 A CN 103149839A
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limit
resource
summit
task
subgraph
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CN103149839B (en
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张寒
张震宇
孙凌卿
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Jiangsu Electric Power Information Technology Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Jiangsu Electric Power Information Technology Co Ltd
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Abstract

The invention discloses an operational control method for electrical equipment based on a Kuhn-Munkres algorithm. The method comprises the following steps of: carrying out weighted average on various influencing factors when various resources are distributed to various tasks, and obtaining an appropriate degree when some resource is distributed to some task; setting the number of the resources and the number of the tasks to be equal to form a resource demand network of the tasks; and obtaining the optimal equipment operational control method. According to the operational control method for the electrical equipment based on the Kuhn-Munkres algorithm, the resources are reasonably dispatched and distributed during the operation of the electrical equipment, so that the problem that the resources distribution is low in efficiency during the operation of the electrical equipment can be solved, the efficiency of the electrical equipment during the operation is high, and the resource distribution ratio is high.

Description

A kind of power equipment progress control method based on the Kuhn-Munkres algorithm
Technical field
The invention belongs to field of electrical equipment, relate to a kind of power equipment scheduling of resource distribution method, specifically say a kind of power equipment progress control method based on the Kuhn-Munkres algorithm.
Background technology
Along with constantly putting into operation of large capacity machine in production run and improving constantly that state-of-the-art facility control requires, the importance that operation is controlled in work in every seems more and more outstanding.Except the operational management personnel must know the equipment operation condition GPRS, also should coordinate the problem that resource is distributed in order to raise the efficiency this moment between each ensemble machine.For this reason, select sedate effective control method, very important to the efficient that improves device resource allocation.Traditional control method to device resource can not meet the demands.
Summary of the invention
The problem that exists in order to overcome prior art, the object of the invention is to provide a kind of power equipment progress control method based on the Kuhn-Munkres algorithm, the method makes resource carry out rational management and distribution at the power equipment run duration, has solved the resource allocation problem of power equipment run duration poor efficiency.
The present invention seeks to be achieved through the following technical solutions:
A kind of power equipment progress control method based on the Kuhn-Munkres algorithm, it is characterized in that: the various influence factors when the method is distributed to each task with each resource are weighted on average, appropriate level when obtaining certain resource and distributing to certain task, the quantity of setting resource and task equates, form the resource requirement network of task with this, thereby obtain optimum equipment progress control method; Specifically comprise the following steps:
1) resource and the facilities and equipments operational management task according to the equipment operation forms matrix, scheduling of resource fitness matrix
Figure 873593DEST_PATH_IMAGE001
Be defined as follows:
(1)
Wherein For element is the matrix of a certain plurality entirely, be provided with
Figure 2013101226053100002DEST_PATH_IMAGE004
Individual resource to be allocated and
Figure 445836DEST_PATH_IMAGE005
Individual task to be completed requires a resource only can distribute to a task, and a task can only be assigned to a resource, in formula (1)
Figure 2013101226053100002DEST_PATH_IMAGE006
Expression is the
Figure 2013101226053100002DEST_PATH_IMAGE007
Figure 2013101226053100002DEST_PATH_IMAGE008
Individual resource distributes to
Figure 214947DEST_PATH_IMAGE009
Figure 2013101226053100002DEST_PATH_IMAGE010
The appropriate level of individual task; Establish herein
Figure 91636DEST_PATH_IMAGE011
2) model conversion
Equipment is moved control problem be converted into and ask bipartite graph Optimum Matching problem, namely every of bipartite graph sideband has weights; Ask a coupling to make weights and the maximum of mating on the limit; General X is identical with Y set number of vertices, and Optimum Matching is also a Perfect matching, and namely each summit is mated; If number is unequal, puts by benefit and add 0 limit and realize to transform; Top fitness matrix is converted into the bipartite graph model, model wherein: X 1, X 2... X mBelong to set X; Y 1, Y 2... Y nBelong to set Y; Appoint one that gets in a some formation M from set X, Y;
3) the feasible top of initialization target value
Give one, each summit label asking the problem of maximum weight matching to be converted into the problem of asking Perfect matching; If summit X iTop mark be A[i], summit Y iTop mark be B[i], summit X iWith Y jBetween limit power be w[i, j]; Arbitrary moment in the algorithm implementation is for arbitrary limit (i, j), A[i]+B[j]=w[i, j] set up all the time; If by in the bipartite graph model, all satisfy A[i]+B[j]=w[i, j] the subgraph that consists of of limit (i, j) Perfect matching is arranged, this Perfect matching is exactly the maximum weight matching of bipartite graph so; When initial in order to make A[i]+B[j]=w[i, j] the permanent establishment, make A[i] be all and summit X iThe authority on related limit, B[j]=0;
4) seek Perfect matching with Hungary Algorithm
(41) for a nodes X of not mating i, seek its every limit, if it satisfy A[i]+B[j]=w[i, j] on the limit another node Yi also no coupling show and found a coupling, directly turn step 6);
(42) if another node Yi on nodes X i its limit mates, turn to so the node with the Yi coupling, suppose it is w, and then to w repeating step (41), (42), namely seek augmenting path;
(43) if find an augmenting path in step (41), (42) process, revise so each self-corresponding match point, go to step 6), if without augmenting path, go to step 5);
5) if do not find Perfect matching to revise feasible top target value
Asking the Perfect matching failure of current equal subgraph, is because for certain X summit, can not find one from the staggered road of it; At this moment obtained an alternating tree, its leafy node is all the X summit; The top mark on X summit in alternating tree is all reduced certain value d, and the top mark on Y summit all increases same value d:
1. limit (i, j) in alternating tree all, two ends, A[i]+B[j] value do not change; That is, it belonged to equal subgraph originally, still belonged to now equal subgraph;
2. limit (i, j) in alternating tree not, two ends, A[i] and B[j] all do not have to change; That is, it belonged to or did not belong to equal subgraph originally, still belonged to now or do not belong to equal subgraph;
3. X end is in alternating tree, the limit (i, j) of Y end in alternating tree, its A[i]+B[j] value increase to some extent; It did not belong to equal subgraph originally, did not still belong to now equal subgraph;
4. X end is in alternating tree, and the Y end is the limit (i, j) in alternating tree not, its A[i]+B[j] value reduce to some extent; That is, it did not belong to equal subgraph originally, may enter equal subgraph now, thereby made equal subgraph obtain expansion;
In order to make A[i]+B[j]=w[i, j] set up all the time, and have at least a limit to enter equal subgraph, d equals min{A[i]+B[j]-w[i, j] | Xi is in alternating tree, and Yi is not in alternating tree };
6) repeating step 4), 5) until find the Perfect matching that equates subgraph, namely get the power equipment progress control method.
In the present invention, various influence factors comprise distribution time, unit exception situation, plant efficiency.
Various influence factors when the present invention distributes to each task with each resource (such as various factorss such as distribution time, unit exception situation, efficient) are weighted on average, (quantity of hypothetical resource and task equates appropriate level when obtaining certain resource and distributing to certain task here, if unequal, then take different solutions according to particular problem).Form the resource requirement network of task with this, thereby obtain optimum equipment progress control method.
The present invention makes resource carry out rational management and distribution at the power equipment run duration, and power equipment run duration efficient is high, resource allocation rate is high.
Description of drawings
Fig. 1 is the bipartite graph illustraton of model that the fitness matrix changes into.
Fig. 2 is based on the process flow diagram of Kuhn-Munkres algorithm.
Embodiment
A kind of power equipment progress control method based on the Kuhn-Munkres algorithm, various influence factors when the method is distributed to each task with each resource (such as various factorss such as distribution time, unit exception situation, efficient) are weighted on average, appropriate level when obtaining certain resource and distributing to certain task, the quantity of setting resource and task equates, form the resource requirement network of task with this, thereby obtain optimum equipment progress control method; Specifically comprise the following steps:
1) resource and the facilities and equipments operational management task according to the equipment operation forms matrix, scheduling of resource fitness matrix
Figure 914099DEST_PATH_IMAGE001
Be defined as follows:
Figure 181132DEST_PATH_IMAGE002
(
Figure 2013101226053100002DEST_PATH_IMAGE012
)
Wherein For element is the matrix of a certain plurality entirely, be provided with
Figure 924277DEST_PATH_IMAGE004
Individual resource to be allocated and
Figure 917641DEST_PATH_IMAGE005
Individual task to be completed requires a resource only can distribute to a task, and a task can only be assigned to a resource, in formula (1)
Figure 671970DEST_PATH_IMAGE006
Expression is the
Figure 723496DEST_PATH_IMAGE007
Figure 246881DEST_PATH_IMAGE008
Individual resource distributes to
Figure 739043DEST_PATH_IMAGE009
Figure 980668DEST_PATH_IMAGE010
The appropriate level of individual task; Establish herein
Figure 68841DEST_PATH_IMAGE011
For
Figure 712312DEST_PATH_IMAGE013
Situation, can take different solutions according to particular problem.
2) model conversion
For top resource scheduling, it is converted into asks bipartite graph Optimum Matching problem, claim again the cum rights maximum matching problem, namely every of bipartite graph sideband has weights.Ask a coupling to make weights and the maximum of mating on the limit.General X is identical with Y set number of vertices, and Optimum Matching is also a Perfect matching, and namely each summit is mated.If number is unequal, can puts by benefit and add 0 limit and realize to transform.Top fitness matrix is converted into bipartite graph model such as Fig. 1: wherein: X 1, X 2... X mBelong to set X; Y 1, Y 2... Y nBelong to set Y.Appoint one that gets in a some formation M from set X, Y.
3) the feasible top of initialization target value
By coming the problem of asking maximum weight matching is converted into the problem of asking Perfect matching for the label in each summit (being called top mark).If summit X iTop mark be A[i], summit Y iTop mark be B[i], summit X iWith Y jBetween limit power be w[i, j].Arbitrary moment in the algorithm implementation is for arbitrary limit (i, j), A[i]+B[j]=w[i, j] set up all the time.If by in bipartite graph, all satisfy A[i]+B[j]=w[i, j] the subgraph (being called equal subgraph) that consists of of limit (i, j) Perfect matching is arranged, this Perfect matching is exactly the maximum weight matching of bipartite graph so.When initial in order to make A[i]+B[j]=w[i, j] the permanent establishment, make A[i] be all and summit X iThe authority on related limit, B[j]=0;
4) seek Perfect matching with Hungary Algorithm
41: for a nodes X of not mating i, seek its every limit, if it satisfy A[i]+B[j]=w[i, j] on the limit another node Yi also no coupling show and found a coupling, directly turn step 6);
42: if another node Yi on nodes X i its limit mates, turn to so the node with the Yi coupling, suppose it is w, and then to w repeating step 41,42, namely seek augmenting path;
43: if find an augmenting path in step 41,42 processes, revise so each self-corresponding match point, go to step 6), if without augmenting path, go to step 5).；
5) if do not find Perfect matching to revise feasible top target value
Asking the Perfect matching failure of current equal subgraph, is because for certain X summit, can not find one from the staggered road of it.At this moment obtained an alternating tree, its leafy node is all the X summit.Now the top mark on X summit in alternating tree is all reduced certain value d, the top mark on Y summit all increases same value d, can find so:
1. limit (i, j) in alternating tree all, two ends, A[i]+B[j] value do not change.That is to say, it belonged to equal subgraph originally, still belonged to now equal subgraph;
2. limit (i, j) in alternating tree not, two ends, A[i] and B[j] all do not have to change.That is to say, it belonged to (or not belonging to) equal subgraph originally, still belonged to now (or not belonging to) equal subgraph;
3. X end is in alternating tree, the limit (i, j) of Y end in alternating tree, its A[i]+B[j] value increase to some extent.It did not belong to equal subgraph originally, did not still belong to now equal subgraph;
4. X end is in alternating tree, and the Y end is the limit (i, j) in alternating tree not, its A[i]+B[j] value reduce to some extent.Just say, it did not belong to equal subgraph originally, may enter equal subgraph now yet, thereby made equal subgraph obtain expansion;
In order to make A[i]+B[j]=w[i, j] set up all the time, and have at least a limit to enter equal subgraph, d should equal min{A[i]+B[j]-w[i, j] | Xi is in alternating tree, and Yi is not in alternating tree };
6) repeating step 4), 5) until find the Perfect matching that equates subgraph, namely find optimum scheduling of resource allocative decision, complete the power equipment progress control method.
The present invention makes resource carry out rational management and distribution at the power equipment run duration, and power equipment run duration efficient is high, resource allocation rate is high.

Claims (3)

1. power equipment progress control method based on the Kuhn-Munkres algorithm, it is characterized in that: the various influence factors when the method is distributed to each task with each resource are weighted on average, appropriate level when obtaining certain resource and distributing to certain task, the quantity of setting resource and task equates, form the resource requirement network of task with this, thereby obtain optimum equipment progress control method; Specifically comprise the following steps:
1) resource and the facilities and equipments operational management task according to the equipment operation forms matrix, scheduling of resource fitness matrix
Figure 638944DEST_PATH_IMAGE001
Be defined as follows:
Figure 282415DEST_PATH_IMAGE002
(1)
Wherein
Figure 679898DEST_PATH_IMAGE003
For element is the matrix of a certain plurality entirely, be provided with Individual resource to be allocated and
Figure 487634DEST_PATH_IMAGE005
Individual task to be completed requires a resource only can distribute to a task, and a task can only be assigned to a resource, in formula (1)
Figure 47928DEST_PATH_IMAGE006
Expression is the
Figure 553996DEST_PATH_IMAGE007
Individual resource distributes to
Figure 67520DEST_PATH_IMAGE010
The appropriate level of individual task; Establish herein
Figure 75315DEST_PATH_IMAGE011
2) model conversion
Equipment is moved control problem be converted into and ask bipartite graph Optimum Matching problem, namely every of bipartite graph sideband has weights; Ask a coupling to make weights and the maximum of mating on the limit; General X is identical with Y set number of vertices, and Optimum Matching is also a Perfect matching, and namely each summit is mated; If number is unequal, puts by benefit and add 0 limit and realize to transform; Top fitness matrix is converted into the bipartite graph model, model wherein: X 1, X 2... X mBelong to set X; Y 1, Y 2... Y nBelong to set Y; Appoint one that gets in a some formation M from set X, Y;
3) the feasible top of initialization target value
Give one, each summit label asking the problem of maximum weight matching to be converted into the problem of asking Perfect matching; If summit X iTop mark be A[i], summit Y iTop mark be B[i], summit X iWith Y jBetween limit power be w[i, j]; Arbitrary moment in the algorithm implementation is for arbitrary limit (i, j), A[i]+B[j]=w[i, j] set up all the time; If by in the bipartite graph model, all satisfy A[i]+B[j]=w[i, j] the subgraph that consists of of limit (i, j) Perfect matching is arranged, this Perfect matching is exactly the maximum weight matching of bipartite graph so; When initial in order to make A[i]+B[j]=w[i, j] the permanent establishment, make A[i] be all and summit X iThe authority on related limit, B[j]=0;
4) seek Perfect matching with Hungary Algorithm
(41) for a nodes X of not mating i, seek its every limit, if it satisfy A[i]+B[j]=w[i, j] on the limit another node Yi also no coupling show and found a coupling, directly turn step 6);
(42) if another node Yi on nodes X i its limit mates, turn to so the node with the Yi coupling, suppose it is w, and then to w repeating step (41), (42), namely seek augmenting path;
(43) if find an augmenting path in step (41), (42) process, revise so each self-corresponding match point, go to step 6), if without augmenting path, go to step 5);
5) if do not find Perfect matching to revise feasible top target value
Asking the Perfect matching failure of current equal subgraph, is because for certain X summit, can not find one from the staggered road of it; At this moment obtained an alternating tree, its leafy node is all the X summit; The top mark on X summit in alternating tree is all reduced certain value d, and the top mark on Y summit all increases same value d:
1. limit (i, j) in alternating tree all, two ends, A[i]+B[j] value do not change; That is, it belonged to equal subgraph originally, still belonged to now equal subgraph;
2. limit (i, j) in alternating tree not, two ends, A[i] and B[j] all do not have to change; That is, it belonged to or did not belong to equal subgraph originally, still belonged to now or do not belong to equal subgraph;
3. X end is in alternating tree, the limit (i, j) of Y end in alternating tree, its A[i]+B[j] value increase to some extent; It did not belong to equal subgraph originally, did not still belong to now equal subgraph;
4. X end is in alternating tree, and the Y end is the limit (i, j) in alternating tree not, its A[i]+B[j] value reduce to some extent; That is, it did not belong to equal subgraph originally, may enter equal subgraph now, thereby made equal subgraph obtain expansion;
In order to make A[i]+B[j]=w[i, j] set up all the time, and have at least a limit to enter equal subgraph, d equals min{A[i]+B[j]-w[i, j] | Xi is in alternating tree, and Yi is not in alternating tree };
6) repeating step 4), 5) until find the Perfect matching that equates subgraph, namely get the power equipment progress control method.
2. the power equipment progress control method based on the Kuhn-Munkres algorithm according to claim 1, it is characterized in that: described various influence factors comprise devices allocation time, unit exception situation, plant efficiency.
3. the power equipment progress control method based on the Kuhn-Munkres algorithm according to claim 1, it is characterized in that: described appropriate level comprises efficient, benefit, the time of cost, the cost that equipment is obtained.
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CN107241813A (en) * 2017-05-22 2017-10-10 大连理工大学 A kind of dispatching algorithm of the earning rate time correlation service data bag based on Kuhn Munkres algorithms
CN108399500A (en) * 2018-03-02 2018-08-14 江苏电力信息技术有限公司 A kind of bipartite graph Optimum Matching method of complex condition balance in the supply and demant of goods and materials Li Ku
CN111709597A (en) * 2020-04-24 2020-09-25 广东卓维网络有限公司 Power grid production domain operation monitoring system
CN111722209A (en) * 2020-04-16 2020-09-29 电子科技大学 MIMO radar transmitting antenna arrangement method based on extended Kuhn-Munkres algorithm
CN112183938A (en) * 2020-09-02 2021-01-05 浙江吉城云创科技有限公司 Logistics scheduling method and device
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Publication number Priority date Publication date Assignee Title
CN104573855A (en) * 2014-12-24 2015-04-29 南京理工大学 Bipartite graph based iterative increment type maximum dispatching method meeting timing sequence constraint
CN104573855B (en) * 2014-12-24 2018-01-23 南京理工大学 The maximum work dispatching method of the iterative and incremental for meeting temporal constraint based on bipartite graph
CN107241813A (en) * 2017-05-22 2017-10-10 大连理工大学 A kind of dispatching algorithm of the earning rate time correlation service data bag based on Kuhn Munkres algorithms
CN107241813B (en) * 2017-05-22 2020-04-07 大连理工大学 Scheduling algorithm of yield time-dependent service data packet based on Kuhn-Munkres algorithm
CN108399500A (en) * 2018-03-02 2018-08-14 江苏电力信息技术有限公司 A kind of bipartite graph Optimum Matching method of complex condition balance in the supply and demant of goods and materials Li Ku
CN111722209A (en) * 2020-04-16 2020-09-29 电子科技大学 MIMO radar transmitting antenna arrangement method based on extended Kuhn-Munkres algorithm
CN111709597A (en) * 2020-04-24 2020-09-25 广东卓维网络有限公司 Power grid production domain operation monitoring system
CN112183938A (en) * 2020-09-02 2021-01-05 浙江吉城云创科技有限公司 Logistics scheduling method and device
CN112668129A (en) * 2020-12-24 2021-04-16 福建永福电力设计股份有限公司 Power distribution network multi-level grid intelligent division method based on space load clustering
CN112668129B (en) * 2020-12-24 2023-10-27 福建永福电力设计股份有限公司 Space load clustering-based intelligent grid dividing method for power distribution network

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