CN109062674B - Hybrid decision-driven cloud manufacturing energy management method, storage medium and terminal - Google Patents
Hybrid decision-driven cloud manufacturing energy management method, storage medium and terminal Download PDFInfo
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- G—PHYSICS
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- 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
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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
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Abstract
A hybrid decision driven cloud manufacturing energy management method, storage medium and terminal, the method comprising: mapping the received complex manufacturing task into a plurality of corresponding sub-services, and taking each sub-service as an information particle to obtain a plurality of corresponding information particles; integrating the obtained multiple information particles to obtain a corresponding multiple component particle structure; and performing service distribution on the complex manufacturing task instruction by adopting the obtained multiple component particle structures. By the aid of the scheme, service distribution efficiency of cloud manufacturing tasks can be improved, and computing resources are saved.
Description
Technical Field
The invention relates to the technical field of cloud manufacturing, in particular to a hybrid decision-driven cloud manufacturing energy management method, a storage medium and a terminal.
Background
Cloud manufacturing is a new service-oriented, efficient, low-consumption and knowledge-based networked intelligent manufacturing mode, and is an extension and revolution of the existing networked manufacturing and service technology. The system virtualizes and services various manufacturing resources and manufacturing capabilities to form a manufacturing resource and manufacturing capability pool, performs unified and centralized intelligent management and operation, realizes intellectualization, win-win, universality and efficient sharing and cooperation, and provides services which can be obtained at any time, used as required, safe, reliable, high-quality and low-cost for the whole life cycle process of manufacturing through a network and a cloud manufacturing system.
The integration of resources and services is realized through the combination of services in a cloud manufacturing mode, firstly, a complex task request which cannot be met by the existing services needs to be decomposed into a series of subtasks, and a proper specific service is allocated to each subtask, so that the mapping from the complex task request to the existing specific service in the cloud manufacturing system is realized, and the possibility is provided for completing the service request of a user through executing the service in the next step.
However, the conventional hybrid decision-driven cloud manufacturing energy management method has the problems of large calculation amount and low efficiency when service allocation is performed on a service request.
Disclosure of Invention
The technical problem solved by the invention is how to improve the service distribution efficiency of the cloud manufacturing task and save the computing resources.
In order to solve the above technical problem, an embodiment of the present invention provides a hybrid decision-driven cloud manufacturing energy management method, where the method includes:
mapping the received complex manufacturing task into a plurality of corresponding sub-services, and taking each sub-service as an information particle to obtain a plurality of corresponding information particles;
integrating the obtained multiple information particles to obtain a corresponding multiple component particle structure, specifically comprising: calculating the particle compatibility value among the information particles, specifically adopting the following formula to calculate the particle compatibility value among the information particles:
wherein, C fit Representing the particle compatibility values of two information particles, k representing the total number of tag attributes of an information particle, X i Representing the variance of the ith flag attribute of the two information particles; classifying information particles with the same particle compatibility numerical value into the same particle layer, thereby dividing the plurality of information particles into a plurality of particle layers; calculating the obtained particle layer compatibility value of the particle layer, specifically calculating the particle layer compatibility value of the particle layer by adopting the following formula:
wherein, C fit ' denotes a particle compatibility value of the particle layer, k denotes the total number of marking properties of the information particle, Y i The variance of the ith flag attribute of all information particles in the information particle layer is represented; coating the granulesIntegrating the particle layers with the compatibility values in the same value range into an initial particle structure to obtain a plurality of corresponding initial particle structures; calculating the particle compatibility value of the obtained initial particle structure, integrating the two initial particle structures with the minimum particle compatibility value into a component particle structure, and respectively using the rest other initial particle structures as the component particle structures to obtain a plurality of corresponding component particle structures, wherein the two initial particle structures with the minimum particle compatibility value are integrated into one component particle structure by specifically adopting the following formula: f: MS (Mass Spectrometry) i ×MS j →MS k1 K1 is 1,2,3, …, N, and satisfies the following operation rule: f (MS) i ,MS j )＝(f 1 (LA i ,LA j ),f 2 (LR i ,LR j ))；MS i ＝(LA i ,LR i ),i＝1,2,3,…,k2；MS j ＝(LA j ,LR j ) J ≠ 1,2,3, …, k2, and i ≠ j; LA ═ L 1 ,L 2 ,L 3 ,…,L m }；LR＝{α|α(L j ,L k ) }; wherein, MS i 、MS j Respectively representing the two initial grain structures, MS, of minimum value of grain compatibility k1 Two initial grain structures MS representing the smallest values of the grain compatibility i 、MS j Integrated k1 th component particle structure, LA i Expressed in the initial grain structure MS i Aggregate of the Medium particle layer L, LR i Expressed in the initial grain structure MS i Set of transition relations between mesoscopic layers, L i Is one grain layer in the grain structure, LA represents a set of grain layers, and LR represents some two grain layers L j And L k Set of conversion relationships between information grains of (a), f 1 、f 2 Respectively representing a binary mapping function relationship, wherein alpha represents a partial order relationship between information particles of the same particle layer or across particle layers;
and performing service distribution on the complex manufacturing task by adopting the obtained multiple component particle structures.
Optionally, the integrating the obtained multiple information particles to obtain a corresponding multiple component particle structure includes:
calculating the particle compatibility value among the information particles;
classifying information particles with the same particle compatibility numerical value into the same particle layer, thereby dividing the plurality of information particles into a plurality of particle layers;
calculating the particle layer compatibility value of the obtained particle layer;
integrating the particle layers with the particle layer compatibility numerical values within the same value range into one initial particle structure to obtain a plurality of corresponding initial particle structures;
and calculating the particle compatibility numerical value of the obtained initial particle structure, integrating the two initial particle structures with the minimum particle compatibility numerical value into one component particle structure, and taking the rest other initial particle structures as the component particle structures respectively to obtain a plurality of corresponding component particle structures.
Optionally, the following formula is used to calculate the particle compatibility value between the information particles:
wherein, C fit Representing the particle compatibility values of two information particles, k representing the total number of tag attributes of an information particle, X i Representing the variance of the ith flag attribute of the two information particles.
Optionally, the particle layer compatibility value of the particle layer is calculated using the following formula:
wherein, C fit' Indicating a grain layer compatibility value of the grain layer, k indicating the total number of tag attributes of the information grain, Y i Representing the variance of the ith flag attribute of all the information particles in the information particle layer.
Optionally, the two initial particle structures with the smallest particle compatibility value are integrated into one component particle structure by using the following formula:
f：MS i ×MS j →MS k1 k1 is 1,2,3, …, N, and satisfies the following operation rule:
f(MS i ,MS j )＝(f 1 (LA i ,LA j ),f 2 (LR i ,LR j ))；
MS i ＝(LA i ,LR i ),i＝1,2,3,…,k2；
MS j ＝(LA j ,LR j ) J ≠ 1,2,3, …, k2, and i ≠ j;
LA＝{L 1 ,L 2 ,L 3 ,…,L m }；
LR＝{α|α(L j ,L k )}；
wherein, MS i 、MS j Respectively representing the two initial grain structures, MS, of minimum value of grain compatibility k Two initial grain structures MS representing the smallest values of the grain compatibility i 、MS j Integrated k1 th component particle structure, LA i Expressed in the initial grain structure MS i Aggregate of the Medium particle layer L, LR i Expressed in the initial grain structure MS i Set of transition relations between mesoscopic layers, L i Is one grain layer in the grain structure, LA represents a set of grain layers, and LR represents some two grain layers L j And L k Set of translation relations between information particles of (a), f 1 、f 2 Respectively representing the respective binary mapping function relations, and alpha represents the partial order relation between the information grains of the same grain layer or across the grain layers.
Optionally, the method further comprises:
the component particle structures are updated as manufacturing resources used by the complex manufacturing task change.
Optionally, the updating the component particle structure includes:
and decomposing, merging and deleting the obtained multiple component particle structures.
Embodiments of the present invention further provide a computer-readable storage medium, on which computer instructions are stored, and when the computer instructions are executed, the method performs the steps of any one of the hybrid decision-driven cloud manufacturing energy management methods described above.
The embodiment of the invention further provides a terminal, which comprises a memory and a processor, wherein the memory stores computer instructions capable of being executed on the processor, and the processor executes the steps of the hybrid decision-driven cloud manufacturing energy management method when executing the computer instructions.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
according to the scheme, each sub-service in the multiple sub-services obtained by mapping the received complex manufacturing task is used as one information particle, the obtained multiple information particles are integrated to obtain the corresponding multiple component particle structures, the obtained multiple component particle structures are adopted to distribute the service to the complex manufacturing task instruction, namely, on the premise of ensuring the data value, the data scale is reduced, namely, the information is granulated, so that the problem is converted into the problem with multiple granularities and multiple layers, the service distribution efficiency of the cloud manufacturing task can be improved, and the calculation resources are saved.
Drawings
Fig. 1 is a schematic flow chart of a hybrid decision-driven cloud manufacturing energy management method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another hybrid decision-driven cloud manufacturing energy management method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a cloud manufacturing energy awareness management system according to an embodiment of the present invention.
Detailed Description
According to the technical scheme, each sub-service in the multiple sub-services obtained by mapping the received complex manufacturing task is used as one information particle, the obtained multiple information particles are integrated to obtain the corresponding multiple component particle structures, the multiple component particle structures are adopted, and the complex manufacturing task instruction is subjected to service distribution, so that the problem can be converted into a multi-granularity and multi-level problem by reducing the data scale, namely information granulation, on the premise of ensuring the data value, the service distribution efficiency of the cloud manufacturing task can be improved, and the calculation resources are saved.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below.
Fig. 1 is a schematic flowchart of a hybrid decision-driven cloud manufacturing energy management method according to an embodiment of the present invention. Referring to fig. 1, a hybrid decision-driven cloud manufacturing energy management method, the method comprising:
step S101: and mapping the received complex manufacturing task into a plurality of corresponding sub-services, and taking each sub-service as one information particle to obtain a plurality of corresponding information particles.
Step S102: and integrating the obtained multiple information particles to obtain a corresponding multiple component particle structure.
Step S103: and performing service distribution on the complex manufacturing task instruction by adopting the obtained multiple component particle structures.
According to the scheme, each sub-service in the multiple sub-services obtained by mapping the received complex manufacturing task is used as one information particle, the obtained multiple information particles are integrated to obtain the corresponding multiple component particle structures, the obtained multiple component particle structures are adopted to distribute the services to the complex manufacturing task instructions, and therefore the problem can be converted into a multi-granularity and multi-level problem by reducing the data scale, namely granulating the information, on the premise that the data value is guaranteed, the service distribution efficiency of the cloud manufacturing task can be improved, and the computing resources are saved.
The hybrid decision-driven cloud manufacturing energy management method in the embodiment of the present invention will be described in further detail with reference to fig. 2.
Fig. 2 is a schematic flowchart of a hybrid decision-driven cloud manufacturing energy management method according to an embodiment of the present invention. Referring to fig. 2, a hybrid decision driven cloud manufacturing energy management method, the method comprising:
step S201: and mapping the received complex manufacturing task into a plurality of corresponding sub-services, and taking each sub-service as one information particle to obtain a plurality of corresponding information particles.
In specific implementation, since the complex manufacturing task cannot be satisfied by the existing service, the complex manufacturing task needs to be firstly decomposed into a series of subtasks, and a suitable specific service is allocated to each subtask, so that mapping from the complex manufacturing task to the existing specific service in the cloud manufacturing system is realized. Then, each sub-service is used as an information granule, so that the received service information corresponding to the complex manufacturing task is granulated.
Step S202: and calculating the particle compatibility values among the information particles, and classifying the information particles with the same particle compatibility value into the same particle layer, thereby dividing the plurality of information particles into a plurality of particle layers.
In an embodiment of the present invention, through a statistical analysis method, according to the mark attribute classification of the sub-services including the raw material component similarity CS, the stage proximity DS, and the functional relationship influence AS, the particle compatibility value between information particles is calculated by using the following formula:
wherein, C fit Representing the particle compatibility values of two information particles, k representing the total number of tag attributes of an information particle, X i Representing the variance of the ith flag property of the two information particles.
When the particle compatibility value between every two information particles in the plurality of information particles is obtained through calculation, the information particles with the same particle compatibility value are classified into the same particle layer, so that the plurality of information particles are divided into a plurality of particle layers. In other words, each grain layer comprises at least two information grains.
Step S203: and calculating the particle layer compatibility value of the obtained particle layers, and integrating the particle layers with the particle layer compatibility value in the same value range into one initial particle structure to obtain a plurality of corresponding initial particle structures.
In a specific implementation, when a plurality of information particles are divided into a plurality of particle layers by the particle compatibility value between the information particles, the particle layers are integrated by the compatibility value of the particle layers to obtain a plurality of corresponding initial particle structures.
In a specific implementation, the integrating the particle layers by the compatibility values of the particle layers to obtain a plurality of corresponding initial particle structures means that the information particles of two particle layers with the highest compatibility are integrated into one initial particle structure by calculating the compatibility values of the particle layers, and the information particles of the remaining other particle layers are respectively used as corresponding first initial particle structures, so as to obtain the plurality of initial particle structures.
In one embodiment of the present invention, the particle layer compatibility value of the particle layer is calculated by using the following formula:
wherein, C fit' Denotes the particle layer compatibility value of the particle layer, k denotes the total number of the marking properties of the information particle, Y i Representing the variance of the ith flag attribute of all the information particles in the information particle layer.
Step S204: and calculating the particle compatibility numerical value of the obtained initial particle structure, integrating the two initial particle structures with the minimum particle compatibility numerical value into one component particle structure, and respectively using the rest other initial particle structures as the component particle structures to obtain a plurality of corresponding component particle structures.
In a specific implementation, when the particle layers are integrated into the corresponding initial particle structures by calculating particle layer compatibility values of the particle layers, the two initial particle structures with the largest particle compatibility values are further integrated into one component particle structure by calculating particle compatibility values of the initial particle structures. In an embodiment of the present invention, the particle compatibility value of the initial particle structure can be calculated by using formula (2).
In one embodiment of the present invention, the following formula is used to integrate the two initial particle structures with the smallest particle compatibility value into one component particle structure:
f：MS i ×MS j →MS k1 ,k1＝1,2,3,…,N (4)
and satisfies the following operation rules:
f(MS i ,MS j )＝(f 1 (LA i ,LA j ),f 2 (LR i ,LR j )) (5)
MS i ＝(LA i ,LR i ),i＝1,2,3,…,k2 (6)
MS j ＝(LA j ,LR j ) J ≠ 1,2,3, …, k2, and i ≠ j (7)
LA＝{L 1 ,L 2 ,L 3 ,…,L m } (8)
LR＝{α|α(L j ,L k )} (9)
Wherein, MS i 、MS j Respectively representing the two initial grain structures, MS, of minimum value of grain compatibility k1 Two initial grain structures MS representing the smallest values of the grain compatibility i 、MS j Integrated k1 th component particle structure, LA i Expressed in the initial grain structure MS i Aggregate of the Medium particle layer L, LR i Expressed in the initial grain structure MS i Set of transition relations between mesoscopic layers, L i Is one grain layer in the grain structure, LA represents a set of grain layers, and LR represents some two grain layers L j And L k Set of conversion relationships between information grains of (a), f 1 、f 2 Respectively representing the binary mapping function relationship, and alpha representing the partial order relationship between the information particles of the same particle layer or the cross particle layer.
Step S205: and performing service distribution on the complex manufacturing task instruction by adopting the obtained multiple component particle structures.
In a specific implementation, when a plurality of corresponding component particle structures are obtained, corresponding services are respectively allocated to the obtained plurality of component particle structures, so that service allocation to the complex manufacturing task instruction is completed.
In a specific implementation, the method may further comprise:
step S206: the component particle structures are updated as manufacturing resources used by the complex manufacturing task change.
In a specific implementation, the manufacturing resources used by the complex manufacturing task change, that is, the incremental update result of the data source D of the manufacturing resources actually used by the subtasks of the complex manufacturing task (for example, the material consumption reduction caused by equipment update, the material change caused by a new process, and the like) may update the component particle structure.
Wherein, the obtained multiple component particle structures are updated, and the merging, decomposition and deletion mapped to the particle structures are deleted and updated according to the merging of the sub-services.
In an embodiment of the present invention, the decomposition, merging, and deletion of the multiple component grain structures are performed in the following manner. Wherein, the combination of the component particle structures is carried out by adopting a weighting algorithm; deleting the component particle structure, namely, adopting a regular deletion and instant mechanism, namely, defaulting to detect whether the finished sub-service exists every 30min, and if yes, deleting the sub-service, avoiding the over-high blocking of the calculation amount of all detection, adopting random extraction of every 100 sub-service detections, and calling out a mark sub-service deletion when a specific sub-service is requested to be deleted instantly; and updating the component particle structure by setting the cache expiration time, and reading the database again and updating the cache if the component particle structure is expired, so as to ensure the consistency of network cache information and the database.
The method in the embodiment of the present invention is described in detail above, and the apparatus corresponding to the method will be described below.
Fig. 3 shows a schematic structural diagram of a cloud manufacturing energy awareness management system in an embodiment of the present invention. Referring to fig. 3, a cloud manufacturing energy awareness management system 30 may include a mapping unit 301, an integration unit 302, and a configuration unit 303, wherein:
the mapping unit 301 is adapted to map the received complex manufacturing task to a plurality of corresponding sub-services, and use each sub-service as an information particle to obtain a plurality of corresponding information particles.
The integration unit 302 is adapted to integrate the obtained multiple information particles to obtain a corresponding multiple component particle structures.
The configuration unit 303 is adapted to distribute the service to the complex manufacturing task instruction by using the obtained multiple component particle structures.
In an implementation, the integration unit 302 is adapted to calculate a particle compatibility value between information particles; classifying information particles with the same particle compatibility numerical value into the same particle layer, thereby dividing the plurality of information particles into a plurality of particle layers; calculating the particle layer compatibility value of the obtained particle layer; integrating the particle layers with the particle layer compatibility numerical values in the same value range into an initial particle structure to obtain a plurality of corresponding initial particle structures; and calculating the particle compatibility numerical value of the obtained initial particle structure, integrating the two initial particle structures with the minimum particle compatibility numerical value into one component particle structure, and taking the rest other initial particle structures as the component particle structures respectively to obtain a plurality of corresponding component particle structures.
In an embodiment of the present invention, the integrating unit 302 is adapted to calculate the particle compatibility value between the information particles by using the following formula:
wherein, C fit Representing the particle compatibility values of two information particles, k representing the total number of tag attributes of an information particle, X i Representing the variance of the ith flag property of the two information particles.
In an embodiment of the present invention, the integration unit 302 is adapted to calculate a particle layer compatibility value of the particle layer by using the following formula:
wherein, C fit' Indicating a grain layer compatibility value of the grain layer, k indicating the total number of tag attributes of the information grain, Y i Representing the variance of the ith flag attribute of all the information particles in the information particle layer.
In an embodiment of the present invention, the integration unit 302 is adapted to integrate two initial particle structures with the smallest particle compatibility value into one component particle structure by using the following formula:
f：MS i ×MS j →MS k1 and k1 is 1,2,3, …, N, and satisfies the following operation rule:
f(MS i ,MS j )＝(f 1 (LA i ,LA j ),f 2 (LR i ,LR j ))；
MS i ＝(LA i ,LR i ),i＝1,2,3,…,k2；
MS j ＝(LA j ,LR j ) J ≠ 1,2,3, …, k2, and i ≠ j;
LA＝{L 1 ,L 2 ,L 3 ,…,L m }；
LR＝{α|α(L j ,L k )}；
wherein, MS i 、MS j Respectively representing the two initial grain structures, MS, of which the value of the grain compatibility is the minimum k1 Two initial grain structures MS representing the smallest values of grain compatibility i 、MS j Integrated k1 th component particle structure, LA i Expressed in the initial grain structure MS i Aggregate of the Medium particle layer L, LR i Expressed in the initial grain structure MS i Set of transition relations between mesoscopic layers, L i Is one grain layer in the grain structure, LA represents a set of grain layers, and LR represents some two grain layers L j And L k Set of conversion relationships between information grains of (a), f 1 、f 2 Respectively representing the respective binary mapping function relations, and alpha represents the partial order relation between the information grains of the same grain layer or across the grain layers.
In a specific implementation, the apparatus 30 may further include an updating unit 305, where:
the updating unit 305 is adapted to update the component particle structures when manufacturing resources used by the complex manufacturing task change.
In a specific implementation, the updating unit 305 is adapted to decompose, merge, and delete the obtained plurality of component particle structures.
The embodiment of the invention also provides a computer readable storage medium, wherein computer instructions are stored on the computer readable storage medium, and the computer instructions execute the steps of the hybrid decision-driven cloud manufacturing energy management method when running. For a hybrid decision-driven cloud manufacturing energy management method, reference is made to the detailed description of the foregoing sections, which are not repeated.
The embodiment of the invention also provides a terminal, which comprises a memory and a processor, wherein the memory is stored with computer instructions capable of being operated on the processor, and the processor executes the steps of the hybrid decision-driven cloud manufacturing energy management method when operating the computer instructions. For a hybrid decision-driven cloud manufacturing energy management method, reference is made to the detailed description of the foregoing sections, which are not repeated.
By adopting the scheme in the embodiment of the invention, each sub-service in a plurality of sub-services obtained by mapping the received complex manufacturing task is taken as one information particle, the obtained plurality of information particles are integrated to obtain a plurality of corresponding component particle structures, and the obtained plurality of component particle structures are adopted to distribute the service of the complex manufacturing task instruction, so that the problem is converted into a multi-granularity and multi-level problem by reducing the data scale, namely granulating the information, on the premise of ensuring the data value, the service distribution efficiency of the cloud manufacturing task can be improved, and the calculation resource can be saved.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by instructions associated with hardware via a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected by one skilled in the art without departing from the spirit and scope of the invention, as defined in the appended claims.
Claims (5)
1. A hybrid decision-driven cloud manufacturing energy management method, comprising:
mapping the received complex manufacturing task to a plurality of corresponding sub-services, and taking each sub-service as an information particle to obtain a plurality of corresponding information particles;
integrating the obtained multiple information particles to obtain a corresponding multiple component particle structure, specifically comprising: calculating the particle compatibility value among the information particles, specifically adopting the following formula to calculate the particle compatibility value among the information particles:
wherein, C fit Representing the particle compatibility values of two information particles, k representing the total number of tag attributes of an information particle, X i Represents the variance of the ith flag attribute of the two information particles; classifying information particles with the same particle compatibility numerical value into the same particle layer, thereby dividing the plurality of information particles into a plurality of particle layers; calculating the obtained particle layer compatibility value of the particle layer, specifically calculating the particle layer compatibility value of the particle layer by adopting the following formula:
wherein, C fit ' denotes a particle compatibility value of the particle layer, k denotes the total number of marking properties of the information particle, Y i The variance of the ith mark attribute of all information particles in the information particle layer is represented; integrating the particle layers with the particle layer compatibility numerical values within the same value range into one initial particle structure to obtain a plurality of corresponding initial particle structures; calculating the particle compatibility value of the obtained initial particle structure, integrating the two initial particle structures with the minimum particle compatibility value into a component particle structure, and respectively using the rest other initial particle structures as the component particle structures to obtain a plurality of corresponding component particle structures, wherein the two initial particle structures with the minimum particle compatibility value are integrated into one component particle structure by specifically adopting the following formula: f: MS (Mass Spectrometry) i ×MS j →MS k1 K1 is 1,2,3, …, N, and satisfies the following operation rule: f (MS) i ,MS j )＝(f 1 (LA i ,LA j ),f 2 (LR i ,LR j ))；MS i ＝(LA i ,LR i ),i＝1,2,3,…,k2；MS j ＝(LA j ,LR j ) J ≠ 1,2,3, …, k2, and i ≠ j; LA ═ L 1 ,L 2 ,L 3 ,…,L m }；LR＝{α|α(L j ,L k ) }; wherein, MS i 、MS j Respectively representing the two initial grain structures, MS, of minimum value of grain compatibility k1 Two initial grain structures MS representing the smallest values of the grain compatibility i 、MS j Integrated k1 th component particle structure, LA i Expressed in the initial grain structure MS i Set of mesoparticle layers L, LR i Expressed in the initial grain structure MS i Transition set between mesoparticle layers, L i Is one grain layer in the grain structure, LA represents a set of grain layers, and LR represents some two grain layers L j And L k Set of conversion relationships between information grains of (a), f 1 、f 2 Respectively representing a binary mapping function relation, wherein alpha represents a partial order relation between information grains of the same grain layer or across grain layers; and performing service distribution on the complex manufacturing task by adopting the obtained multiple component particle structures.
2. The hybrid decision driven cloud manufacturing energy management method according to claim 1, further comprising:
the component particle structures are updated as manufacturing resources used by the complex manufacturing task change.
3. The hybrid decision-driven cloud manufacturing energy management method according to claim 2, wherein the updating the component particle structures comprises:
and decomposing, merging and deleting the obtained multiple component particle structures.
4. A computer readable storage medium having stored thereon computer instructions which when executed perform the steps of the hybrid decision-driven cloud manufacturing energy management method of any of claims 1 to 3.
5. A terminal comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, the processor when executing the computer instructions performing the steps of the hybrid decision-driven cloud manufacturing energy management method of any of claims 1 to 3.
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