CN109062674A - Cloud manufacture energy management method, storage medium and the terminal of complex decision driving - Google Patents
Cloud manufacture energy management method, storage medium and the terminal of complex decision driving Download PDFInfo
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- CN109062674A CN109062674A CN201810750077.9A CN201810750077A CN109062674A CN 109062674 A CN109062674 A CN 109062674A CN 201810750077 A CN201810750077 A CN 201810750077A CN 109062674 A CN109062674 A CN 109062674A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/466—Transaction processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
A kind of cloud manufacture energy management method, storage medium and the terminal of complex decision driving, the described method includes: the received complicated manufacturing operation of institute is mapped as corresponding multiple sub-services, and using each sub-services as an information, corresponding multiple informations are obtained；Obtained multiple informations are integrated, corresponding multiple component kernel structures are obtained；Using obtained multiple component kernel structures, service distribution is carried out to the complicated manufacturing operation instruction.Above-mentioned scheme, can be improved the service allocative efficiency of cloud manufacturing operation, and save computing resource.
Description
Technical field
The present invention relates to cloud manufacturing technology fields, manufacture energy management side more particularly to a kind of cloud of complex decision driving
Method, storage medium and terminal.
Background technique
Cloud manufacture is the service-oriented, efficient low-consume of one kind and Knowledge based engineering network-enabled intelligent manufacture new model, is pair
The extension and change that existing network manufacture and service technology carry out.All kinds of manufacturing recourses and manufacturing capacity are virtualized, are taken by it
Businessization constitutes manufacturing recourses and manufacturing capacity pond, and intelligent management being unified, concentrating and operation, realization intelligence,
All-win, generalization and efficient shared and collaboration are that manufacture lifecycle process mentions by network and cloud manufacture system
For can obtaining at any time, using as needed, safe and reliable, high-quality cheap service.
The integration of resource and service is realized by the combination of service under cloud manufacturing mode, it is necessary first to will be unable to be had
It services met complex task request and is decomposed into a series of subtask, for each subtask, distribute suitable tool for it
Body service realizes that complex task request has the mapping of specific service into cloud manufacture system, in next step by executing service
It may to complete the service request offer of user.
But the cloud of existing complex decision driving manufactures energy management method, is carrying out service distribution to service request
When, the problem of there is computationally intensive, inefficiency.
Summary of the invention
Present invention solves the technical problem that being how to improve the service allocative efficiency of cloud manufacturing operation, and save calculating money
Source.
In order to solve the above technical problems, the embodiment of the invention provides a kind of clouds of complex decision driving to manufacture energy management
Method, which comprises
The received complicated manufacturing operation of institute is mapped as corresponding multiple sub-services, and is believed each sub-services as one
Grain is ceased, corresponding multiple informations are obtained；
Obtained multiple informations are integrated, corresponding multiple component kernel structures are obtained；
Using obtained multiple component kernel structures, service distribution is carried out to the complicated manufacturing operation instruction.
Optionally, described to integrate obtained multiple informations, corresponding multiple component kernel structures are obtained, are wrapped
It includes:
Calculate the grain compatibility numerical value between information；
The identical information of grain compatibility numerical value is included into same granulosa, to the multiple information is divided into multiple
Granulosa；
Calculate the granulosa compatibility numerical value of obtained granulosa；
Granulosa compatibility numerical value is located at the granulosa in same value range and is integrated into an initial kernel structure, is corresponded to
Multiple initial kernel structures；
Calculate the grain compatibility numerical value of obtained initial kernel structure, and by the smallest two initial grains of grain compatibility numerical value
Structural integrity is one-component kernel structure, and using other remaining initial kernel structures as one-component kernel structure, is obtained
Corresponding multiple component kernel structures.
Optionally, the grain compatibility numerical value between information is calculated using following formula:
Wherein, CfitIndicate that the grain compatibility numerical value of two informations, k indicate the sum of the flag property of information, XiTable
Show the variance of i-th of flag property of two informations.
Optionally, the granulosa compatibility numerical value of granulosa is calculated using following formula:
Wherein, Cfit′Indicate that the granulosa compatibility numerical value of granulosa, k indicate the sum of the flag property of information, YiIndicate letter
Cease the variance of i-th of flag property of all informations in granulosa.
Optionally, grain compatibility numerical value the smallest two initial kernel structures are integrated by one-component using following formula
Kernel structure:
F:MSi×MSj→MSk(k=1,2,3 ..., N), and meet following operation rule:
f(MSi, MSj)=(f1(LAi, LAj), f2(LRi, LRj))；
MSi=(LAi, LRi) (i=1,2,3 ..., k)；
MSj=(LAj, LRj) (j=1,2,3 ..., k, and i ≠ j)；
LA={ L1, L2, L3..., Lm}；
LR=α | α (Lj, Lk)}；
Wherein, MSi、MSiRespectively indicate the smallest two initial kernel structures of compatibility numerical value, MSkIt indicates grain compatibility
Numerical value the smallest two initial kernel structure MSi、MSiIntegrate obtained component kernel structure, LAiIt indicates in initial kernel structure MSiMiddle grain
The set of layer L, LRiIt indicates in initial kernel structure MSiTransformational relation collection between middle granulosa, LiIt is a grain in kernel structure
Layer, LA indicate the set of granulosa, and LR indicates certain two granulosa LjWith LkInformation between transformational relation collection, f1、f2Table respectively
Show binary mapped function relation respectively, α indicates the partial ordering relation with granulosa or between the information across granulosa.
Optionally, the method also includes:
When the manufacturing recourses used in the complicated manufacturing operation change, the component kernel structure is carried out more
Newly.
It is optionally, described that the component kernel structure is updated, comprising:
Obtained multiple component kernel structures are decomposed, merged and deleted.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, described
Computer instruction executes the step of cloud manufacture energy management method of complex decision driving described in any of the above embodiments when running.
The embodiment of the invention also provides a kind of terminal, including memory and processor, energy is stored on the memory
Enough computer instructions run on the processor, the processor execute any of the above-described when running the computer instruction
The step of cloud of the complex decision driving manufactures energy management method.
Compared with prior art, the technical solution of the embodiment of the present invention has the advantages that
Above-mentioned scheme, by each sub-services in multiple sub-services for mapping of received complicated manufacturing operation make
For an information, obtained multiple informations are integrated, obtain corresponding multiple component kernel structures, and use gained
The multiple component kernel structures arrived carry out service distribution to the complicated manufacturing operation instruction, can be to guarantee data value
Under the premise of, by the way that data scale is become smaller, i.e. Information Granulating, to convert more granularities, multi-level problem for problem, therefore can be with
The service allocative efficiency of cloud manufacturing operation is improved, and saves computing resource.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of the cloud manufacture energy management method of complex decision driving of the embodiment of the present invention；
Fig. 2 is the process signal of the cloud manufacture energy management method of another complex decision driving of the embodiment of the present invention
Figure；
Fig. 3 is a kind of structural schematic diagram of cloud manufacture energy cognition management system of the embodiment of the present invention.
Specific embodiment
Technical solution in the embodiment of the present invention by by multiple sub- clothes for mapping of received complicated manufacturing operation
Obtained multiple informations are integrated as an information, obtain corresponding multiple points by each sub-services in business
Kernel structure is measured, and uses obtained multiple component kernel structures, service distribution is carried out to the complicated manufacturing operation instruction, it can be with
I.e. under the premise of guaranteeing data value, by the way that data scale is become smaller, i.e. Information Granulating, to convert more for problem
Degree, multi-level problem, therefore the service allocative efficiency for improving cloud manufacturing operation can be improved, and save computing resource.
It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, with reference to the accompanying drawing to this
The specific embodiment of invention is described in detail.
Fig. 1 is a kind of flow diagram of the cloud manufacture energy management method of complex decision driving of the embodiment of the present invention.
Referring to Fig. 1, a kind of cloud manufacture energy management method of complex decision driving, which comprises
Step S101: being mapped as corresponding multiple sub-services for the received complicated manufacturing operation of institute, and by each sub-services
As an information, corresponding multiple informations are obtained.
Step S102: obtained multiple informations are integrated, and obtain corresponding multiple component kernel structures.
Step S103: using obtained multiple component kernel structures, carries out service point to the complicated manufacturing operation instruction
Match.
Above-mentioned scheme, by each sub-services in multiple sub-services for mapping of received complicated manufacturing operation make
For an information, obtained multiple informations are integrated, obtain corresponding multiple component kernel structures, and use gained
The multiple component kernel structures arrived carry out service distribution to the complicated manufacturing operation instruction, can be to guarantee data value
Under the premise of, by the way that data scale is become smaller, i.e. Information Granulating, to convert more granularities, multi-level problem for problem, therefore can be with
The service allocative efficiency of cloud manufacturing operation is improved, and saves computing resource.
Below in conjunction with Fig. 2 in the embodiment of the present invention complex decision driving cloud manufacture energy management method carry out into
The detailed introduction of one step.
Fig. 2 is a kind of flow diagram of the cloud manufacture energy management method of complex decision driving of the embodiment of the present invention.
Referring to fig. 2, a kind of cloud of complex decision driving manufactures energy management method, which comprises
Step S201: being mapped as corresponding multiple sub-services for the received complicated manufacturing operation of institute, and by each sub-services
As an information, corresponding multiple informations are obtained.
In specific implementation, meet because complicated manufacturing operation can not be had service, therefore need the complicated system first
Making Task-decomposing is a series of subtask, for each subtask, suitable specific service is distributed for it, to realize complexity
Manufacturing operation has the mapping of specific service into cloud manufacture system.Then, then by using each sub-services as one
Information, so that the corresponding information on services of the received complexity manufacturing operation of institute is granulated.
Step S202: the grain compatibility numerical value between information is calculated, and the identical information of grain compatibility numerical value is returned
Enter same granulosa, so that the multiple information is divided into multiple granulosas.
In an embodiment of the present invention, by statistical analysis technique, according to including material composition similitude CS, the stage is neighbouring
Degree DS, functional relationship influence the flag property classification of the sub-services including AS etc., using following formula calculate information it
Between grain compatibility numerical value:
Wherein, CfitIndicate that the grain compatibility numerical value of two informations, k indicate the sum of the flag property of information, XiTable
Show the variance of i-th of flag property of two informations.
When grain compatibility numerical value in multiple informations are calculated between every two information, by by grain compatibility
The identical information of numerical value is included into same granulosa, so that the multiple information is divided into multiple granulosas.In other words, each grain
Layer includes at least two informations.
Step S203: the granulosa compatibility numerical value of obtained granulosa is calculated, and by granulosa compatibility numerical value positioned at same
Granulosa in value range is integrated into an initial kernel structure, obtains corresponding multiple initial kernel structures.
It in specific implementation, is multiple granulosas when multiple informations to be passed through to the grain compatibility numerical division between information
When, granulosa is integrated to obtain corresponding multiple initial kernel structures by the compatibility numerical value of granulosa.
In specific implementation, granulosa is integrated to obtain corresponding multiple initial by the compatibility numerical value by granulosa
Kernel structure refers to that the information of maximum two granulosas of compatibility is integrated into one by the compatibility numerical value that granulosa is obtained by calculation
A initial kernel structure, the information of other remaining granulosas is respectively as corresponding first initial kernel structure, to obtain institute
State multiple initial kernel structures.
In an embodiment of the present invention, the granulosa compatibility numerical value of granulosa is calculated using following formula:
Wherein, Cfit′Indicate that the granulosa compatibility numerical value of granulosa, k indicate the sum of the flag property of information, YiIndicate letter
Cease the variance of i-th of flag property of all informations in granulosa.
Step S204: the grain compatibility numerical value of obtained initial kernel structure is calculated, and grain compatibility numerical value is the smallest
Two initial kernel structures are integrated into one-component kernel structure, and using other remaining initial kernel structures as one-component grain
Structure obtains corresponding multiple component kernel structures.
In specific implementation, when the granulosa compatibility numerical value by calculating granulosa, granulosa is integrated into corresponding initial grain
When structure, by calculating the grain compatibility numerical value of initial kernel structure, and by the maximum two initial kernel structures of grain compatibility numerical value
Further it is integrated into one-component kernel structure.In an embodiment of the present invention, initial kernel structure can be calculated using formula (2)
Grain compatibility numerical value.
In an embodiment of the present invention, using following formula that grain compatibility numerical value the smallest two initial kernel structures are whole
It is combined into one-component kernel structure:
F:MSi×MSj→MSk(k=1,2,3 ..., N) (4)
And meet following operation rule:
f(MSi, MSj)=(f1(LAi, LAj), f2(LRi, LRj)) (5)
MSi=(LAi, LRi) (i=1,2,3 ..., k) (6)
MSj=(LAj, LRj) (j=1,2,3 ..., k, and i ≠ j) (7)
LA={ L1, L2, L3..., Lm} (8)
LR=α | α (Lj, Lk)} (9)
Wherein, MSi、MSjRespectively indicate the smallest two initial kernel structures of compatibility numerical value, MSkIt indicates grain compatibility
Numerical value the smallest two initial kernel structure MSi、MSiIntegrate obtained component kernel structure, LAiIt indicates in initial kernel structure MSiMiddle grain
The set of layer L, LRiIt indicates in initial kernel structure MSiTransformational relation collection between middle granulosa, LiIt is a grain in kernel structure
Layer, LA indicate the set of granulosa, and LR indicates certain two granulosa LjWith LkInformation between transformational relation collection, f1、f2Table respectively
Show binary mapped function relation respectively, α indicates the partial ordering relation with granulosa or between the information across granulosa.
Step S205: using obtained multiple component kernel structures, carries out service point to the complicated manufacturing operation instruction
Match.
In specific implementation, when obtaining corresponding multiple component kernel structures, by for obtained multiple component burls
Structure distributes corresponding service respectively, to complete to carry out service distribution to the complicated manufacturing operation instruction.
In specific implementation, the method can also include:
Step S206: when the manufacturing recourses used in the complicated manufacturing operation change, to the component burl
Structure is updated.
In specific implementation, manufacturing recourses used in the complicated manufacturing operation change namely the complexity is made
Make task subtask actual use manufacturing recourses data source D incremental update result (such as equipment update bring material
It expends and reduces, change etc. of the new process to material), the component kernel structure can be updated.
Wherein, obtained multiple component kernel structures are updated, to delete and update being reflected according to the merging of sub-services
It is mapped to the merging, decomposition and deletion of kernel structure.
In an embodiment of the present invention, multiple component kernel structures are decomposed, merged and is deleted by following mode.
Wherein, the merging of component kernel structure is carried out using weighting algorithm；The deletion of component kernel structure, then using periodically deletion and i.e. opportunity
System, that is, default every 30min and check whether there is completed sub-services, have, delete, and avoids all checking that calculation amount is excessive stuck,
Using every 100 sub- service inspections are randomly selected, when specific sub-services are deleted in instant request, recalls label sub-services and delete；
The update of component kernel structure carries out in such a way that setting caches expired time, database is re-read if expired, updates slow
It deposits, it is ensured that the consistency of network-caching information and database.
The above-mentioned method in the embodiment of the present invention is described in detail, below will be to the above-mentioned corresponding device of method
It is introduced.
Fig. 3 shows the structural schematic diagram of one of embodiment of the present invention cloud manufacture energy cognition management system.Referring to
Fig. 3, a kind of cloud manufacture energy cognition management system 30 may include map unit 301, integral unit 302 and configuration unit 303,
Wherein:
The map unit 301, suitable for the received complicated manufacturing operation of institute is mapped as corresponding multiple sub-services, and will
Each sub-services obtain corresponding multiple informations as an information.
The integral unit 302 obtains corresponding multiple component grains suitable for integrating obtained multiple informations
Structure.
The configuration unit 303 is suitable for using obtained multiple component kernel structures, instructs to the complicated manufacturing operation
The service of progress distribution.
In specific implementation, the integral unit 302, suitable for calculating the grain compatibility numerical value between information；By grain phase
The identical information of capacitive numerical value is included into same granulosa, so that the multiple information is divided into multiple granulosas；Calculate gained
The granulosa compatibility numerical value of the granulosa arrived；Granulosa compatibility numerical value is located at the granulosa in same value range to be integrated at the beginning of one
Beginning kernel structure obtains corresponding multiple initial kernel structures；Calculate the grain compatibility numerical value of obtained initial kernel structure, and by grain
Compatibility numerical value the smallest two initial kernel structures are integrated into one-component kernel structure, and other remaining initial kernel structures are divided
Not Zuo Wei one-component kernel structure, obtain corresponding multiple component kernel structures.
In an embodiment of the present invention, the integral unit 302, suitable for being calculated between information using following formula
Grain compatibility numerical value:
Wherein, CfitIndicate that the grain compatibility numerical value of two informations, k indicate the sum of the flag property of information, XiTable
Show the variance of i-th of flag property of two informations.
In an embodiment of the present invention, the integral unit 302, suitable for calculating the granulosa phase of granulosa using following formula
Capacitive numerical value:
Wherein, Cfit′Indicate that the granulosa compatibility numerical value of granulosa, k indicate the sum of the flag property of information, YiIndicate letter
Cease the variance of i-th of flag property of all informations in granulosa.
In an embodiment of the present invention, the integral unit 302, suitable for using following formula by grain compatibility numerical value most
The initial kernel structure of small two is integrated into one-component kernel structure:
F:MSi×MSi→MSk(k=1,2,3 ..., N), and meet following operation rule:
f(MSi, MSj)=(f1(LAi, LAj), f2(LRi, LRj))；
MSi=(LAi, LRi) (i=1,2,3 ..., k)；
MSi=(LAj, LRj) (j=1,2,3 ..., k, and i ≠ j)；
LA={ L1, L2, L3..., Lm}；
LR=α | α (Lj, Lk)}；
Wherein, MSi、MSjRespectively indicate the smallest two initial kernel structures of compatibility numerical value, MSkIt indicates grain compatibility
Numerical value the smallest two initial kernel structure MSi、MSjIntegrate obtained component kernel structure, LAiIt indicates in initial kernel structure MSiMiddle grain
The set of layer L, LRiIt indicates in initial kernel structure MSiTransformational relation collection between middle granulosa, LiIt is a grain in kernel structure
Layer, LA indicate the set of granulosa, and LR indicates certain two granulosa LjWith LkInformation between transformational relation collection, f1、f2Table respectively
Show binary mapped function relation respectively, α indicates the partial ordering relation with granulosa or between the information across granulosa.
In specific implementation, described device 30 can also include updating unit 305, in which:
The updating unit 305, when changing suitable for the manufacturing recourses used in the complicated manufacturing operation, to institute
Component kernel structure is stated to be updated.
In specific implementation, the updating unit 305, suitable for being decomposed, being closed to obtained multiple component kernel structures
And it and deletes.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, described
The step of cloud manufacture energy management method of the complex decision driving is executed when computer instruction is run.Wherein, described
The cloud manufacture energy management method of complex decision driving refers to elaborating for preceding sections, repeats no more.
The embodiment of the invention also provides a kind of terminal, including memory and processor, energy is stored on the memory
Enough computer instructions run on the processor, the processor execute the mixing when running the computer instruction
The step of cloud of decision driving manufactures energy management method.Wherein, the cloud of complex decision driving manufactures energy management side
Method refers to elaborating for preceding sections, repeats no more.
Using the above scheme in the embodiment of the present invention, by multiple sub- clothes for mapping of received complicated manufacturing operation
In business, each sub-services integrate obtained multiple informations as an information, obtain corresponding multiple components
Kernel structure, and obtained multiple component kernel structures are used, service distribution is carried out to the complicated manufacturing operation instruction, can be
Under the premise of guaranteeing data value, by the way that data scale is become smaller, i.e. Information Granulating, thus by problem be converted into more granularities,
Multi-level problem, therefore the service allocative efficiency for improving cloud manufacturing operation can be improved, and save computing resource.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can store in computer readable storage medium, and storage is situated between
Matter may include: ROM, RAM, disk or CD etc..
Although present disclosure is as above, present invention is not limited to this.Anyone skilled in the art are not departing from this
It in the spirit and scope of invention, can make various changes or modifications, therefore protection scope of the present invention should be with claim institute
Subject to the range of restriction.
Claims (9)
1. a kind of cloud of complex decision driving manufactures energy management method characterized by comprising
The received complicated manufacturing operation of institute is mapped as corresponding multiple sub-services, and using each sub-services as an information
Grain, obtains corresponding multiple informations；
Obtained multiple informations are integrated, corresponding multiple component kernel structures are obtained；
Using obtained multiple component kernel structures, service distribution is carried out to the complicated manufacturing operation.
2. the cloud of complex decision driving according to claim 1 manufactures energy management method, which is characterized in that described by institute
Obtained multiple informations are integrated, and corresponding multiple component kernel structures are obtained, comprising: the grain calculated between information is compatible
Property numerical value；
The identical information of grain compatibility numerical value is included into same granulosa, so that the multiple information is divided into multiple grains
Layer；
Calculate the granulosa compatibility numerical value of obtained granulosa；
Granulosa compatibility numerical value is located at the granulosa in same value range and is integrated into an initial kernel structure, is obtained corresponding more
A initial kernel structure；
Calculate the grain compatibility numerical value of obtained initial kernel structure, and by the smallest two initial kernel structures of grain compatibility numerical value
It is integrated into one-component kernel structure, and using other remaining initial kernel structures as one-component kernel structure, is corresponded to
Multiple component kernel structures.
3. the cloud of complex decision driving according to claim 2 manufactures energy management method, which is characterized in that using as follows
Formula calculate information between grain compatibility numerical value:
Wherein, CfitIndicate that the grain compatibility numerical value of two informations, k indicate the sum of the flag property of information, XiIndicate two
The variance of i-th of flag property of a information.
4. the cloud of complex decision driving according to claim 2 manufactures energy management method, which is characterized in that using as follows
Formula calculate granulosa granulosa compatibility numerical value:
Wherein, Cfit′Indicate that the granulosa compatibility numerical value of granulosa, k indicate the sum of the flag property of information, YiIndicate information
The variance of i-th of flag property of all informations in layer.
5. the cloud of complex decision driving according to claim 2 manufactures energy management method, which is characterized in that using as follows
Formula grain compatibility numerical value the smallest two initial kernel structures are integrated into one-component kernel structure:
F:MSi×MSj→MSk(k=1,2,3 ..., N), and meet following operation rule:
f(MSi, MSj)=(f1(LAi, LAj), f2(LRi, LRj))；
MSi=(LAi, LRi) (i=1,2,3 ..., k)；
MSj=(LAj, LRj) (j=1,2,3 ..., k, and i ≠ j)；
LA={ L1, L2, L3..., Lm}；
LR=α | f α (Lj, Lk)}；
Wherein, MSi、MSjRespectively indicate the smallest two initial kernel structures of compatibility numerical value, MSkIt indicates grain compatibility numerical value
The smallest two initial kernel structure MSi、MSjIntegrate obtained component kernel structure, LAiIt indicates in initial kernel structure MSiMiddle granulosa L
Set, LRiIt indicates in initial kernel structure MSiTransformational relation collection between middle granulosa, LiIt is a granulosa in kernel structure,
LA indicates the set of granulosa, and LR indicates certain two granulosa LjWith LkInformation between transformational relation collection, f1、f2It respectively indicates
Binary mapped function relation respectively, α indicate the partial ordering relation with granulosa or between the information across granulosa.
6. the cloud of complex decision driving according to claim 1-5 manufactures energy management method, which is characterized in that
Further include:
When the manufacturing recourses used in the complicated manufacturing operation change, the component kernel structure is updated.
7. the cloud of complex decision driving according to claim 6 manufactures energy management method, which is characterized in that described to institute
Component kernel structure is stated to be updated, comprising:
Obtained multiple component kernel structures are decomposed, merged and deleted.
8. a kind of computer readable storage medium, is stored thereon with computer instruction, which is characterized in that the computer instruction fortune
The step of perform claim requires the cloud of 1 to 7 described in any item complex decision drivings to manufacture energy management method when row.
9. a kind of terminal, which is characterized in that including memory and processor, storing on the memory can be in the processing
The computer instruction run on device, perform claim requires described in 1 to 7 any one when the processor runs the computer instruction
Complex decision driving cloud manufacture energy management method the step of.
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