CN109039698A - Industry internet intelligent Service processing method, readable storage medium storing program for executing, terminal - Google Patents
Industry internet intelligent Service processing method, readable storage medium storing program for executing, terminal Download PDFInfo
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- 238000003672 processing method Methods 0.000 title claims abstract description 22
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 239000000203 mixture Substances 0.000 claims abstract description 92
- 238000005265 energy consumption Methods 0.000 claims abstract description 64
- 241000238633 Odonata Species 0.000 claims description 209
- 230000006870 function Effects 0.000 claims description 64
- 230000006399 behavior Effects 0.000 claims description 33
- 230000004044 response Effects 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 11
- 239000006185 dispersion Substances 0.000 claims description 9
- 238000000926 separation method Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 5
- 230000000717 retained effect Effects 0.000 claims description 5
- 238000013442 quality metrics Methods 0.000 claims description 4
- 238000006467 substitution reaction Methods 0.000 claims description 2
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- 238000000034 method Methods 0.000 description 10
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
- H04L41/5054—Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G06Q50/40—
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5003—Managing SLA; Interaction between SLA and QoS
-
- 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
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
A kind of industry internet intelligent Service processing method, readable storage medium storing program for executing, terminal, which comprises obtain the service request of user；The Services Composition case with the service request is obtained from preset Services Composition case library, and is combined the Services Composition of the Services Composition case as the optimal service for executing the service request；When obtaining from the execution case library less than execution case with the service request, quality of service attribute and energy consumption attribute based on the service request determine the optimal service combination for executing the service request；The service request is executed using the combination of identified optimal service.Above-mentioned scheme can reduce the energy consumption of industrial internet of things service processing while meeting service quality.
Description
Technical field
The present invention relates to internet of things field, more particularly to a kind of industry internet intelligent Service processing method, can
Read storage medium, terminal.
Background technique
Under the Strategic Context of " made in China 2025 ", industry internet as global industry system and it is advanced calculate, point
The product of analysis, induction technology and internet connection fusion, has become the critical support and in-depth " internet of the new industrial revolution
The important foundation stone of+advanced manufacturing industry ".
In face of interconnecting, the changeable industry internet environment of dynamic, how to realize the dynamic combined of service to reach green
Color energy supply and demand match management becomes a big problem of the field urgent need to resolve.For above-mentioned challenge, lot of domestic and foreign scholar couple
Energy management problem under industry internet environment towards Services Composition quality has been unfolded to study.
But the Dynamic Configuration Process of the Services Composition QoS that covets is optimal under the existing environment about industry internet
Change, there is no influence brought by the energy consumption for considering Services Composition.
Summary of the invention
Present invention solves the technical problem that being to reduce industrial internet of things service processing how while meeting service quality
Energy consumption.
In order to solve the above technical problems, the embodiment of the invention provides a kind of industry internet intelligent Service processing method,
The described method includes:
Obtain the service request of user；
The Services Composition case with the service request is obtained from preset Services Composition case library, and by the clothes
The Services Composition of business combination case is as the optimal service combination for executing the service request；
When obtaining from the execution case library less than execution case with the service request, it is based on the service
The quality of service attribute and energy consumption attribute of request determine the optimal service combination for executing the service request；
The service request is executed using the combination of identified optimal service.
Optionally, the quality of service attribute and energy consumption attribute based on the business determines and executes the business
Optimal service combination, comprising:
Based on specific energy consumption service quality obtained, Most Economical Control objective function is constructed；
Using response time, cost and reliability as index, the quality of service goals function of the service request is constructed；
Using energy cost, energy utilization rate and pollution cost as index, the overall energy consumption of building and the service request
Measure objective function；
It will be described in the quality of service goals function of the constructed service request and the substitution of overall energy consumption amount objective function
Most Economical Control objective function obtains multiple target valuation functions；
Iterative calculation is executed to the multiple target valuation functions using multiple target dragonfly Yan algorithm, obtains corresponding optimal solution,
As the optimal service combination for executing the service request.
Optionally, the Most Economical Control objective function based on specific energy consumption service quality building obtained are as follows:
Wherein, GeIndicate the service quality of the service request, QoS indicates the service quality of the service request, EC table
Show the energy consumption attribute for indicating the service request.
Optionally, the service quality mesh of the business constructed using response time, cost and reliability as index
Scalar functions are as follows:
And its constraint condition are as follows:
Wherein, n indicates the quantity for the sub-services request that the service request is decomposed, Ti、CiAnd RiRespectively indicate i-th
Response time, cost and the reliability of a sub- service request, Tmax、CmaxAnd RminRespectively indicating the service request can receive
Maximum response time, tip heigh and minimum reliability.
Optionally, it is described using energy cost, energy utilization rate and pollution cost as index constructed by the business
Overall energy consumption amount objective function are as follows:
Wherein, ECiIndicate the energy consumption of i-th of sub-services request, CTi、URi、PLiRespectively indicate i-th of sub-services request
Energy cost, energy utilization rate and pollution cost, w1i、w2iAnd w3iRespectively indicate energy cost, the energy of the request of i-th of sub-services
The weight of source utilization rate and pollution cost, and w1i+w2i+w3i=1.
Optionally, described that iterative calculation is executed to the multiple target valuation functions using multiple target dragonfly Yan algorithm, it obtains pair
The optimal solution answered, as the optimal service combination for executing the service request, comprising:
Initialize dragonfly Yan population Xi(i=1,2 ..., n), step vector Δ Xi(i=1,2 ..., n), and it is big that archives are arranged
It is small；Wherein, dragonfly Yan individual is vector composed by the corresponding Service Quality Metrics of Services Composition, and a kind of position dragonfly Yan is corresponding a kind of
Services Composition, archives size represent the number of the Services Composition finally stored；
Since the initialization dragonfly Yan population, an iteration is executed to current dragonfly Yan population, comprising: to current dragonfly Yan kind
Dragonfly Yan individual in group is traversed, and the current dragonfly Yan individual traversed is obtained, by the position of the current dragonfly Yan individual traversed
As the position current dragonfly Yan；To the energy cost weight coefficient of each sub-services in the corresponding Services Composition in the position current dragonfly Yan,
Energy utilization rate weight coefficient and pollution cost weight coefficient are trained, and are obtained in the corresponding Services Composition in the position current dragonfly Yan
The actual disposition of the energy cost weight coefficients of each sub-services, energy utilization rate weight coefficient and pollution cost weight coefficient；
By the energy cost weight coefficient of each sub-services, energy utilization rate weight in the corresponding Services Composition in the position current dragonfly Yan
The actual disposition of coefficient and pollution cost weight coefficient substitutes into the multiple target valuation functions, and the position pair current dragonfly Yan is calculated
The fitness value answered；Next dragonfly Yan individual position corresponding dragonfly Yan in current dragonfly Yan population is obtained, as the position next dragonfly Yan,
Until all traversal is completed for the position dragonfly Yan of all dragonfly Yan individuals in current dragonfly Yan population, all of current dragonfly Yan population are obtained
The position dragonfly Yan and corresponding fitness value；Pareto Efficiency energy is executed to the obtained position all dragonfly Yan and corresponding fitness value
Consumption assessment, obtains the position dragonfly Yan being stored in the archives and its fitness value；Each noninferior solution of judgement deposit archives is corresponding
The position dragonfly Yan and its fitness value whether meet the constraint condition；It, will be corresponding when determination meets the constraint condition
The position dragonfly Yan and its fitness value are retained in the archives；Conversely, then by the position corresponding dragonfly Yan and its fitness value from institute
It states in archives and deletes；Food source and enemy are selected from archives, and pass through the separation of dragonfly Yan, alignment, cohesiveness, outward dispersion
Enemy and the position step vector sum dragonfly Yan obtained to five kinds of behaviors of attraction of food source to initialization are updated, and are obtained
Next dragonfly Yan population；
Next iteration is executed to next dragonfly Yan population, until the number of iterations reaches preset frequency threshold value, is obtained
The final disaggregation stored in the archives；
The corresponding multiple Services Composition outputs of the noninferior solution that the last solution stored in archives is concentrated are selected for user, and will
A Services Composition selected by user is combined as the optimal service.
Optionally, the energy cost weight system to each sub-services in the corresponding Services Composition in the position current dragonfly Yan
Number, energy utilization rate weight coefficient and pollution cost weight coefficient are trained, comprising:
Using the BP neural network in machine learning to each sub-services in the corresponding Services Composition in the position current dragonfly Yan
Energy cost weight coefficient, energy utilization rate weight coefficient and pollution cost weight coefficient are trained.
Optionally, described to pass through the separation of dragonfly Yan, alignment, cohesiveness, outward dispersion enemy and to the attraction of food source
The position step vector sum dragonfly Yan that five kinds of behaviors of power obtain initialization is updated, comprising:
And:
ΔXt+1=(sSi+aAi+cCi+fFi+eEi)+wΔXt；
Ei=X-+X；
Fi=X++X；
Wherein, (Δ x) indicates step vector, X to Tt+1Indicate the position vector at t+1 moment, XtIndicate t moment position to
Amount, r are the Euclidean distance updated between the dragonfly Yan of front and back, SiIndicate the separating behavior of dragonfly Yan, AiIndicate the alignment behavior of dragonfly Yan, CiTable
Show the cohesiveness behavior of dragonfly Yan, EiIndicate the outside dispersion enemy behavior of dragonfly Yan, FiIndicate dragonfly Yan to the attraction Lixing of food source
For s, a, c, f, e respectively indicate the separation of dragonfly Yan, alignment, cohesiveness, disperse enemy, the attraction five to food source outward
The learning coefficient of kind behavior, w indicate that the learning coefficient to the position dragonfly Yan of t moment, X indicate the position of current dragonfly Yan individual, Xj
Indicate j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual, X+Indicate the food source chosen from archives, X-It indicates from archives
The enemy of middle selection, VjIndicate the speed of j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, described
The step of computer instruction executes industry internet intelligent Service processing method 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 described industry internet intelligent Service processing method.
Compared with prior art, the technical solution of the embodiment of the present invention has the advantages that
Above-mentioned scheme, when obtaining from the execution case library less than execution case with the service request,
Quality of service attribute and energy consumption attribute based on the service request determine the optimal service group for executing the service request
It closes, using quality of service attribute and energy consumption attribute as the Services Composition for determining the execution service request, rather than merely
Quality of service attribute is pursued on ground, therefore can reduce energy consumption, green, economy, ring while meeting quality of service attribute
It protects.
Detailed description of the invention
Fig. 1 is the flow diagram of one of embodiment of the present invention industry internet intelligent Service processing method；
Fig. 2 is the flow diagram that the method for optimal service combination of processing service request is determined in the embodiment of the present invention；
Fig. 3 is the structural schematic diagram of one of embodiment of the present invention industry internet green energy resource management system.
Specific embodiment
Technical solution in the embodiment of the present invention from the execution case library by obtaining less than with the service
When the execution case of request, quality of service attribute and energy consumption attribute based on the service request determine and execute the clothes
The optimal service combination of business request, using quality of service attribute and energy consumption attribute as the clothes for determining the execution service request
Business combination, rather than quality of service attribute is merely pursued, therefore can reduce the energy while meeting quality of service attribute and disappear
Consumption, green, economic, environmental protection.It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, it ties below
Attached drawing is closed to be described in detail specific embodiments of the present invention.
Fig. 1 is a kind of flow diagram of industry internet business intelligence method of servicing of the embodiment of the present invention.With reference to figure
1, a kind of industry internet business intelligence method of servicing specifically can wrap suitable for handling industry internet service request
Include following step:
Step S101: the service request of user is obtained.
In specific implementation, the user is the equipment in industry internet environment；The service request is the user
Business processing request.As Internet of Things range constantly develops expansion, equipment in network and based on the equipment institute in network
The number of services of exploitation is also constantly increasing.When needing processing business, user's (equipment i.e. in network) can be sent pair
The service request answered.It is available corresponding by parse to the service request when receiving the service request
The information of business to be processed.
When needing to be implemented corresponding business, user can send corresponding service request, so that corresponding business obtains
With processing.Herein, so-called processing service request, which refers to, responds the service request, handles the behaviour of corresponding business to be processed
Make.
Step S102: judge whether the clothes with the service request can be obtained from preset Services Composition case library
Business combination case；When the judgment result is yes, step S102 can be executed；Conversely, can then execute step S104.
It in specific implementation, include by multiple Services Composition cases in the Services Composition case library.Wherein, the service
Each Services Composition case in combination case library includes corresponding service request and the Services Composition for handling the service request
Information is obtained by the way that processed business is collected arrangement with the Services Composition for handling the business.Wherein, the place
Services Composition used by the processed business is managed, for according to determined by quality of service attribute and energy consumption attribute
Optimal service combination, is combined with the optimal service based on determined by quality of service attribute and energy consumption attribute in step hereinafter
Used process is identical, refers to corresponding introduction hereinafter.
In specific implementation, judge whether can obtain with the service request from preset Services Composition case library
Services Composition case, in other words, that is, judge in the Services Composition case library with the presence or absence of record have the business and place
Manage the Services Composition case of the information of the optimal service combination of the business.
Step S103: using the Services Composition of acquired Services Composition case as the best clothes for executing the service request
Business combination.
In specific implementation, when retrieved from the Services Composition case library have and currently pending service request
When the Services Composition case of identical service request, can directly using the Services Composition in the Services Composition case retrieved as
Handle the optimal service combination of the service request.
As can be seen from the above description, using every time by the optimal service of processed service request and the response service request
Combination arrange and forms corresponding Services Composition case and be stored in Services Composition case library, can need to handle subsequent
When same service request, corresponding optimal service combination directly is obtained by retrieving the Services Composition case library, therefore can be with
Improve the processing speed and efficiency of service request.
Step S104: quality of service attribute and energy consumption attribute based on the service request determine and execute the clothes
The optimal service combination of business request.
In specific implementation, it asks with currently pending service when not retrieving to have from the Services Composition case library
When seeking the Services Composition case of identical service request, show it is described state do not recorded in Services Composition case library it is currently pending
Service request and its corresponding optimal service combination Services Composition case, therefore need for currently pending service request it is true
Fixed corresponding optimal service combination.
In specific implementation, quality of service attribute and energy consumption attribute based on the service request determine and execute institute
The optimal service combination for stating service request, that is to say under the premise of guaranteeing the service quality of service request, it is minimum to obtain energy consumption
Services Composition handle the service request, to reduce the energy consumption of service request.It in an embodiment of the present invention, will be single
Processing clothes are calculated as Most Economical Control objective function, using multiple target dragonfly Yan algorithm in position energy consumption service quality obtained
The optimal service combination of business request, to realize the green energy resource under industry internet environment in the case where meeting user's QoS requirement
Configuration and management, specifically, may include following operation:
Step S201 is executed, specific energy consumption service quality obtained is based on, constructs Most Economical Control objective function.
It is described to be based on specific energy consumption service quality obtained, Most Economical Control objective function is constructed, referring to is not influencing to use
In the case where the satisfaction of family, meet the requirement for reducing Services Composition energy consumption and pollution treatment cost, by specific energy consumption service obtained
Optimization problem of the quality as Most Economical Control objective function.Wherein, the green energy resource management of service-oriented combination quality
Purpose is to meet user to the needs of energy consumption is minimum is realized in Services Composition while QoS requirement, is divided into service
Quality and energy consumption two parts, it may be assumed that
F=(EC, Qos) (1)
Therefore Most Economical Control objective function can indicate are as follows:
Wherein, QoS indicates the service quality of service request, and EC indicates the energy consumption of service request.
It executes step S202 and constructs the Service Quality of the service request using response time, cost and reliability as index
Measure objective function.
In specific implementation, service quality QoS be can from time, cost, reliability, satisfaction, availability, credit worthiness,
Can several aspects such as degree of maintenance describe.In an embodiment of the present invention, join from the service feature of Services Composition and service quality
Several significance levels and mensurable angle are set out, and response time (Time, T), cost (Cost, C) and reliability are chosen
The QoS index of (Reliability, R) as Services Composition, i.e., using response time, cost and reliability as constructed by index
Service request quality of service goals function it is as follows:
Wherein, n indicates the quantity for the sub-services that total service decomposition as the service request obtains, Ti、CiAnd RiRespectively
Indicate the response time, cost and reliability of i-th of sub-services.
Assuming that a total service institute is T the receptible maximum response timemax, the receptible tip heigh of institute is Cmax,
Minimum reliability allowed is Rmin, it then can establish following constraint condition:
Wherein, Tmax、CmaxAnd RminRespectively indicate the receptible maximum response time of the service request institute, tip heigh
And minimum reliability.
It is then possible to execute step S203, using energy cost, energy utilization rate and pollution cost as index, building with
The overall energy consumption amount objective function of the service request.
In specific implementation, traditional statistics energy consumption mode is institute cost of the single assessment when providing certain service
Source, energy consumption index is not only for lowerization for pursuing energy consumption, and even more in order to realize green low consumption, this is inevitably related to energy
Source cost, the availability of the energy, multiple indexs such as pollution, their fusion statistics could embody green low consumption.
In an embodiment of the present invention, it chooses including energy cost CT, energy utilization rate UR and pollution cost PL
Three norms overall energy consumption amount EC measured, i.e., using energy cost, energy utilization rate and pollution cost as index
The constructed overall energy consumption amount objective function with the service request are as follows:
EC=min (w1CT+w2UR+w3PL), ∑ wi=1 (5)
Wherein, CT is energy cost, and UR is energy utilization rate, and PL is pollution cost.
In specific implementation, using service request as total service (General Service, GS), total service can be with
Corresponding multiple sub-services (Sub-Service, SS) are divided into, the Services Composition for handling the service request, Ye Jicong are chosen
The service that each sub-services are selected in different type ISP is combined, the corresponding energy input target letter of each sub-services
Number is indicated using formula (5), then the overall energy consumption amount objective function always serviced can indicate are as follows:
Wherein, for the formula (5) of each sub-services, the sum of corresponding weight coefficient w1, w2 and w3 are 1.In the present invention
In one embodiment, for each sub-services in total service, using the BP neural network in machine learning to multi-parameter weight into
Row training, to obtain the configuration of weight coefficient w1, w2 and w3 for being best suitable for requirement.
It later, can be with step S204, by the quality of service goals function and overall energy consumption of the constructed service request
It measures objective function and substitutes into the Most Economical Control objective function, obtain multiple target valuation functions.
In specific implementation, by the quality of service goals function of the service request obtained by step S202 and S203 and always
Physical efficiency consumption objective function substitutes into the Most Economical Control objective function, can obtain the multiple target valuation functions.
Finally, step S205 can be executed, iteration is executed to the multiple target valuation functions using multiple target dragonfly Yan algorithm
It calculates, obtains corresponding optimal solution, as the optimal service combination for executing the service request.
In specific implementation, in the multiple target valuation functions, the corresponding each sub-services request of the service request
Response time, cost and reliability and energy cost, energy utilization rate and pollution cost be unknown number.By using more
Target dragonfly Yan algorithm executes iterative calculation to the multiple target valuation functions, the response time of available each sub-services, at
Sheet and reliability and energy cost, energy utilization rate and pollution cost, so that finally obtaining Services Composition can satisfy service
Quality requirement and energy consumption is minimum.
Wherein, executing iterative calculation to the multiple target valuation functions using multiple target dragonfly Yan algorithm includes following step
It is rapid:
Firstly, initialization dragonfly Yan population Xi(i=1,2 ..., n), step vector Δ Xi(i=1,2 ..., n), and be arranged
Archives size, is denoted as Nbp.Wherein, dragonfly Yan individual is vector composed by the corresponding Service Quality Metrics of Services Composition, Yi Zhongqing
The position Yan corresponds to a kind of Services Composition, and archives size Nbp represents the number of the Services Composition for user's selection of final output.
Then, since the dragonfly Yan population that initialization obtains, an iteration is executed to current dragonfly Yan population, comprising:
(1) the dragonfly Yan individual in current dragonfly Yan population is traversed, obtains the current dragonfly Yan individual traversed, and will work as
The individual corresponding corresponding position of preceding dragonfly Yan, as the position current dragonfly Yan traversed.
(2) to the energy input objective function of each sub-services in the corresponding Services Composition in the position current dragonfly Yan traversed
(5) weight parameter in is respectively adopted the BP neural network in machine learning and is trained to obtain the energy input mesh of each sub-services
The actual disposition of weight parameter w1, w2 and w3 in scalar functions (5).
(3) each sub-services energy input objective function in the corresponding Services Composition in the position current dragonfly Yan traversed
(5) after the actual disposition of weight parameter w1, w2 and w3 in, calculating traverses the corresponding current service group in the position current dragonfly Yan
The corresponding target value of multiple target valuation functions is calculated using formula (2) in the fitness value of conjunction.
(4) judge whether all dragonfly Yan individuals in current dragonfly Yan population traverse completion；It when the judgment result is yes, can be with
It executes step (6)；Conversely, can then execute step (5).
(5) the next dragonfly Yan individual position corresponding dragonfly Yan for obtaining current dragonfly Yan population, as the position current dragonfly Yan, and from
Step (2) starts to execute.
(6) when all dragonfly Yan individual in current dragonfly Yan population all traverses completion, it is corresponding to obtain current dragonfly Yan population
The position all dragonfly Yan and corresponding fitness value.
(7) the corresponding position all dragonfly Yan of current dragonfly Yan population and its fitness value execute Pareto Efficiency energy consumption assessment,
To obtain the position dragonfly Yan and fitness value in the current dragonfly Yan population in deposit archives.Specifically, may include following two
A step:
First, Pareto is achieved.Each iteration can generate a newest solution, i.e. Services Composition scheme, use Pareto
It achieves to store the solution of the Pareto in new explanation.Because the archives size for the archive being arranged when initialization is Nbp, as the Pareto of deposit
When solution number is more than Nbp, then cut according to crowding distance.
Second, Pareto disaggregation memory space updates.New Services Composition scheme dominates one or more clothes in archives
Business assembled scheme.In this case, the Services Composition scheme dominated in archives is rejected, new Services Composition scheme will
Into file store；When new Services Composition scheme and archived member do not dominate, then new demand servicing assembled scheme should be added to and be deposited
In shelves；If the archives archive has been expired, the similarity between each domination scheme is calculated using Euclidean distance, finds similarity
Highest two or more schemes, omit one of solution.
(8) judge whether each noninferior solution position corresponding dragonfly Yan for being stored in archives and its fitness value meet the constraint
Condition；When determination meets the constraint condition, execute step (9)；Conversely, can then execute step (10)
(9) position corresponding dragonfly Yan and its fitness value are retained in the archives.
(10) then the position corresponding dragonfly Yan and its fitness value are deleted from the archives.
(11) food source is selected from archives: X+=SelectFood (archive) selects an enemy: X from archive-
=SelectFood (archive), and the position step vector sum dragonfly Yan is updated by following five behaviors, obtain next dragonfly
Yan population, the specific steps are as follows:
Five kinds of behaviors of dragonfly Yan include separation, alignment, cohesiveness, outward dispersion enemy's behavior and the attraction to food source
Power, in which:
1. the separating behavior S of dragonfly YaniIt refers to avoiding collision between dragonfly Yan and neighbours, calculation formula is as follows:
Wherein, X is the position of current dragonfly Yan individual, XjIndicate the position of j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual
It sets, N is the quantity of the neighbours dragonfly Yan individual of current dragonfly Yan individual, represents the difference retained between corresponding two Services Compositions
Away from.
2. the alignment behavior A of dragonfly Yani, indicate the speeds match in dragonfly Yan individual and neighbours between dragonfly Yan individual, calculate
Formula is as follows:
Wherein, VjIndicate the speed of j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual, expression and adjacent service
Similar speed is kept between combination.
3. cohesion force CiRefer to that dragonfly Yan individual tends to the center of population body, calculation formula is as follows:
Wherein, X is the position of current individual, and N is the quantity of neighborhood, and XjThe position of j-th of neighbours' individual is represented,
It instructs individual constantly to combine close to optimal service.
4. dragonfly Yan disperses enemy's behavior E outwardi, calculation formula is as follows:
Ei=X-+X (10)
5. dragonfly Yan is to the attraction behavior F of food sourcei, and calculation formula is as follows:
Fi=X++X (11)
To sum up 1. 2. 3. 4. 5. five kinds of dragonfly Yan behaviors, it can be deduced that:
ΔXt+1=(sSi+aAi+cCi+fFi+eEi)+wΔXt (12)
Wherein, Δ Xt+1Indicate that the increment of t+1 moment position, t indicate the moment or be the number of iterations, Δ XtIndicate t moment
The increment of position, w indicate that s, a, c, f, e respectively indicate the study system of five behaviors to the learning coefficient of the positional increment of t moment
Number.
Finally, following formula is respectively adopted updates step vector T (Δ x) and position vector X respectivelyt+1:
Wherein, r is the Euclidean distance updated between the dragonfly Yan of front and back.
Corresponding step vector sum updated dragonfly Yan position X is obtained in above-mentioned five kinds of behaviors by dragonfly Yant+1When, it is complete
At an iteration, available next dragonfly Yan population.
Then, can be using next dragonfly Yan population as current dragonfly Yan population, and following iteration behaviour is executed since step (1)
Make, until obtaining the position dragonfly Yan being finally stored in archives and its fitness value when the number of iterations reaches preset frequency threshold value.
Finally, the optimal Services Composition configuration that the forward position Patero of output at this time, as number are archives size Nbp,
The optimal service combination that one of selection is carried out for user, is combined as the optimal service.
In specific implementation, user can choose a service from Nbp optimal service combination according to the actual needs
Combination is combined as the optimal service.
Step S105: the service request is executed using the combination of identified optimal service.
In specific implementation, when determining optimal service combination, the clothes are executed using the combination of identified optimal service
Business request.
Above-mentioned scheme, when obtaining from the execution case library less than execution case with the service request,
Quality of service attribute and energy consumption attribute based on the service request determine the optimal service group for executing the service request
It closes, using quality of service attribute and energy consumption attribute as the Services Composition for determining the execution service request, rather than merely
Quality of service attribute is pursued on ground, therefore can reduce energy consumption, green, economy, ring while meeting quality of service attribute
It protects.
The above-mentioned method in the embodiment of the present invention is described in detail, below will be to the above-mentioned corresponding system of method
System is introduced.
Fig. 3 shows the structural schematic diagram of one of embodiment of the present invention industry internet green energy resource management system.
Referring to Fig. 3, a kind of industry internet green energy resource management system 30 may include acquiring unit 301, combination 302 and of determination unit
Execution unit 303, in which:
The acquiring unit 301, suitable for obtaining the service request of user；
The combination determination unit 302, being suitable for obtaining from preset Services Composition case library has the service request
Services Composition case, and using the Services Composition of the Services Composition case as the optimal service group for executing the service request
It closes；When obtaining from the execution case library less than execution case with the service request, it is based on the service request
Quality of service attribute and energy consumption attribute, determine the optimal service combination for executing the service request；
The execution unit 303 is suitable for executing the service request using the combination of identified optimal service.
In specific implementation, the combination determination unit 302 is suitable for being based on specific energy consumption service quality obtained, structure
Build Most Economical Control objective function；With the response time, cost and can, by property as index, construct the service of the service request
Quality objective function；Using energy cost, energy utilization rate and pollution cost as index, the totality of building and the service request
Energy input objective function；By the quality of service goals function of the constructed service request and overall energy consumption amount objective function generation
Enter the Most Economical Control objective function, obtains multiple target valuation functions；The multiple target is commented using multiple target dragonfly Yan algorithm
Estimate function and execute iterative calculation, obtain corresponding optimal solution, as the optimal service combination for executing the service request.
In an embodiment of the present invention, the combination determination unit 302 is based on specific energy consumption service quality structure obtained
The Most Economical Control objective function built are as follows:
Wherein, GeIndicate the service quality of the service request, QoS indicates the service quality of the service request, EC table
Show the energy consumption attribute for indicating the service request.
In an embodiment of the present invention, the combination determination unit 302 is using response time, cost and reliability as index
The quality of service goals function of the business of building are as follows:
And its constraint condition are as follows:
Wherein, n indicates the quantity for the sub-services request that the service request is decomposed, Ti、CiAnd RiRespectively indicate i-th
Response time, cost and the reliability of a sub- service request, Tmax、CmaxAnd RminRespectively indicating the service request can receive
Maximum response time, tip heigh and minimum reliability.
In an embodiment of the present invention, the combination determination unit 302 is with energy cost, energy utilization rate and pollution cost
Overall energy consumption amount objective function as the business constructed by index are as follows:
Wherein, ECiIndicate the energy consumption of i-th of sub-services request, CTi、URi、PLiRespectively indicate i-th of sub-services request
Energy cost, energy utilization rate and pollution cost, w1i、w2iAnd w3iRespectively indicate energy cost, the energy of the request of i-th of sub-services
The weight of source utilization rate and pollution cost, and w1i+w2i+w3i=1.
Optionally, described to adopt combination determination unit, it is suitable for initialization dragonfly Yan population Xi(i=1,2 ..., n), step vector
ΔXi(i=1,2 ..., n), and archives size is set；Wherein, dragonfly Yan individual is the corresponding Service Quality Metrics institute of Services Composition
The vector of composition, a kind of position dragonfly Yan correspond to a kind of Services Composition, and archives size represents the number of the Services Composition finally stored；
Since the initialization dragonfly Yan population, an iteration is executed to current dragonfly Yan population, comprising: to the dragonfly in current dragonfly Yan population
Yan individual is traversed, and the current dragonfly Yan individual traversed is obtained, using the position of the current dragonfly Yan individual traversed as current
The position dragonfly Yan；To the energy cost weight coefficient of each sub-services, using energy source in the corresponding Services Composition in the position current dragonfly Yan
Rate weight coefficient and pollution cost weight coefficient are trained, and obtain every height clothes in the corresponding Services Composition in the position current dragonfly Yan
The actual disposition of the energy cost weight coefficient of business, energy utilization rate weight coefficient and pollution cost weight coefficient；Work as by described in
The energy cost weight coefficient, energy utilization rate weight coefficient of each sub-services and dirt in the corresponding Services Composition in the position preceding dragonfly Yan
The actual disposition for dying this weight coefficient substitutes into the multiple target valuation functions, and the corresponding adaptation in the position current dragonfly Yan is calculated
Angle value；Next dragonfly Yan individual position corresponding dragonfly Yan in current dragonfly Yan population is obtained, as the position next dragonfly Yan, until current
All traversal is completed for the position dragonfly Yan of all dragonfly Yan individuals in dragonfly Yan population, obtains the position all dragonfly Yan of current dragonfly Yan population
And corresponding fitness value；Pareto Efficiency energy consumption assessment is executed to the obtained position all dragonfly Yan and corresponding fitness value,
Obtain the position dragonfly Yan being stored in the archives and its fitness value；Each noninferior solution of judgement deposit archives is dragonfly Yan corresponding
It sets and its whether fitness value meets the constraint condition；When determination meets the constraint condition, by the position corresponding dragonfly Yan
And its fitness value is retained in the archives；Conversely, then by the position corresponding dragonfly Yan and its fitness value from the archives
It deletes；It selects food source and enemy from archives, and passes through the separation of dragonfly Yan, alignment, cohesiveness, outward dispersion enemy and right
The position step vector sum dragonfly Yan that five kinds of behaviors of attraction of food source obtain initialization is updated, and obtains next dragonfly Yan
Population；Next iteration is executed to next dragonfly Yan population, until the number of iterations reaches preset frequency threshold value, is obtained described
The final disaggregation stored in archives；The corresponding multiple Services Composition outputs of the noninferior solution that the last solution stored in archives is concentrated supply
User's selection, and combined using a Services Composition selected by user as the optimal service.
In an embodiment of the present invention, the combination determination unit 302, suitable for using the BP neural network in machine learning
To energy cost weight coefficient, the energy utilization rate weight coefficient of each sub-services in the corresponding Services Composition in the position current dragonfly Yan
It is trained with pollution cost weight coefficient.
In an embodiment of the present invention, the combination determination unit 302, suitable for using following formula to step vector sum
The position dragonfly Yan is updated:
And:
ΔXt+1=(sSi+aAi+cCi+fFi+eEi)+wΔXt；
Ei=X-+X；
Fi=X++X；
Wherein, (Δ x) indicates step vector, X to Tt+1Indicate the position vector at t+1 moment, XtIndicate t moment position to
Amount, r are the Euclidean distance updated between the dragonfly Yan of front and back, SiIndicate the separating behavior of dragonfly Yan, AiIndicate the alignment behavior of dragonfly Yan, CiTable
Show the cohesiveness behavior of dragonfly Yan, EiIndicate the outside dispersion enemy behavior of dragonfly Yan, FiIndicate dragonfly Yan to the attraction Lixing of food source
For s, a, c, f, e respectively indicate the separation of dragonfly Yan, alignment, cohesiveness, disperse enemy, the attraction five to food source outward
The learning coefficient of kind behavior, w indicate that the learning coefficient to the position dragonfly Yan of t moment, X indicate the position of current dragonfly Yan individual, Xj
Indicate j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual, X+Indicate the food source chosen from archives, X-It indicates from archives
The enemy of middle selection, VjIndicate the speed of j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, described
The step of industry internet intelligent Service processing method is executed when computer instruction is run.Wherein, the industry is mutual
Networking intelligent Service processing method refers to the introduction of 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 industry when running the computer instruction
The step of internet intelligent service processing method.Wherein, the industry internet intelligent Service processing method refers to aforementioned
Partial introduction, repeats no more.
Using the above scheme of the embodiment of the present invention, asked being obtained from the execution case library less than with the service
When the execution case asked, quality of service attribute and energy consumption attribute based on the service request determine and execute the service
The optimal service of request combines, using quality of service attribute and energy consumption attribute as the service for determining the execution service request
Combination, rather than quality of service attribute is merely pursued, therefore can reduce the energy while meeting quality of service attribute and disappear
Consumption, green, economic, environmental protection.
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 (10)
1. a kind of industry internet intelligent Service processing method characterized by comprising
Obtain the service request of user；
The Services Composition case with the service request is obtained from preset Services Composition case library, and by the service group
The Services Composition of case is closed as the optimal service combination for executing the service request；
When obtaining from the execution case library less than execution case with the service request, it is based on the service request
Quality of service attribute and energy consumption attribute, determine the optimal service combination for executing the service request；
The service request is executed using the combination of identified optimal service.
2. industry internet intelligent Service processing method according to claim 1, which is characterized in that described to be based on the industry
The quality of service attribute and energy consumption attribute of business determine the optimal service combination for executing the business, comprising:
Based on specific energy consumption service quality obtained, Most Economical Control objective function is constructed；
Using response time, cost and reliability as index, the quality of service goals function of the service request is constructed；
Using energy cost, energy utilization rate and pollution cost as index, the overall energy consumption amount mesh of building and the service request
Scalar functions；
It will most be passed through described in the quality of service goals function of the constructed service request and the substitution of overall energy consumption amount objective function
Help Controlling object function, obtains multiple target valuation functions；
Iterative calculation is executed to the multiple target valuation functions using multiple target dragonfly Yan algorithm, obtains corresponding optimal solution, as
Execute the optimal service combination of the service request.
3. industry internet intelligent Service processing method according to claim 2, which is characterized in that described to be based on unit energy
Consume the Most Economical Control objective function of service quality building obtained are as follows:
Wherein, GeIndicate the service quality of the service request, QoS indicates the service quality of the service request, and EC is indicated
The energy consumption attribute of the service request.
4. industry internet intelligent Service processing method according to claim 3, which is characterized in that it is described to respond when
Between, the quality of service goals function of the business that is constructed as index of cost and reliability are as follows:
And its constraint condition are as follows:
Wherein, n indicates the quantity for the sub-services request that the service request is decomposed, Ti、CiAnd RiRespectively indicate i-th of son
Response time, cost and the reliability of service request, Tmax、CmaxAnd RminIt is receptible most to respectively indicate the service request institute
Big response time, tip heigh and minimum reliability.
5. industry internet intelligent Service processing method according to claim 4, which is characterized in that it is described with the energy at
Originally, the overall energy consumption amount objective function of energy utilization rate and pollution cost as the business constructed by index are as follows:
Wherein, ECiIndicate the energy consumption of i-th of sub-services request, CTi、URi、PLiRespectively indicate the energy of i-th of sub-services request
Cost, energy utilization rate and pollution cost, w1i、w2iAnd w3iRespectively indicate energy cost, the energy benefit of the request of i-th of sub-services
With the weight of rate and pollution cost, and w1i+w2i+w3i=1.
6. industry internet intelligent Service processing method according to claim 5, which is characterized in that described to use multiple target
Dragonfly Yan algorithm executes iterative calculation to the multiple target valuation functions, obtains corresponding optimal solution, asks as the service is executed
The optimal service combination asked, comprising:
Initialize dragonfly Yan population Xi(i=1,2 ..., n), step vector Δ Xi(i=1,2 ..., n), and archives size is set；
Wherein, dragonfly Yan individual is vector composed by the corresponding Service Quality Metrics of Services Composition, the position a kind of dragonfly Yan corresponding one
Kind Services Composition, archives size represent the number of the Services Composition finally stored；
Since the dragonfly Yan population that initialization obtains, an iteration is executed to current dragonfly Yan population, comprising: to current dragonfly Yan population
In dragonfly Yan individual traversed, the current dragonfly Yan individual traversed is obtained, by the position of the current dragonfly Yan individual traversed work
For the position current dragonfly Yan；To energy cost weight coefficient, the energy of each sub-services in the corresponding Services Composition in the position current dragonfly Yan
Source utilization rate weight coefficient and pollution cost weight coefficient are trained, and are obtained every in the corresponding Services Composition in the position current dragonfly Yan
The actual disposition of the energy cost weight coefficients of a sub-services, energy utilization rate weight coefficient and pollution cost weight coefficient；It will
The energy cost weight coefficient of each sub-services, energy utilization rate weight system in the corresponding Services Composition in the position current dragonfly Yan
Several and pollution cost weight coefficient actual disposition substitutes into the multiple target valuation functions, and it is corresponding that the position current dragonfly Yan is calculated
Fitness value；Next dragonfly Yan individual position corresponding dragonfly Yan in current dragonfly Yan population is obtained, as the position next dragonfly Yan, directly
All traversal is completed for the position dragonfly Yan of all dragonfly Yan individuals into current dragonfly Yan population, obtains all dragonflies of current dragonfly Yan population
The position Yan and corresponding fitness value；Pareto Efficiency energy consumption is executed to the obtained position all dragonfly Yan and corresponding fitness value
Assessment, obtains the position dragonfly Yan being stored in the archives and its fitness value；Each noninferior solution of judgement deposit archives is corresponding
Whether the position dragonfly Yan and its fitness value meet the constraint condition；When determination meets the constraint condition, by corresponding dragonfly
The position Yan and its fitness value are retained in the archives；Conversely, then by the position corresponding dragonfly Yan and its fitness value from described
It is deleted in archives；Food source and enemy are selected from archives, and pass through the separation of dragonfly Yan, alignment, cohesiveness, outward dispersion enemy
People and the position step vector sum dragonfly Yan obtained to five kinds of behaviors of attraction of food source to initialization are updated, and are obtained down
One dragonfly Yan population；
Next iteration is executed to next dragonfly Yan population, until the number of iterations reaches preset frequency threshold value, is obtained described
The final disaggregation stored in archives；
The corresponding multiple Services Compositions output of the noninferior solution that the last solution stored in archives is concentrated is selected for user, and by user
A selected Services Composition is combined as the optimal service.
7. industry internet intelligent Service processing method according to claim 6, which is characterized in that described to current dragonfly Yan
The energy cost weight coefficient, energy utilization rate weight coefficient and pollution cost of each sub-services in the corresponding Services Composition in position
Weight coefficient is trained, comprising:
Using the BP neural network in machine learning to the energy of each sub-services in the corresponding Services Composition in the position current dragonfly Yan
Cost weight coefficient, energy utilization rate weight coefficient and pollution cost weight coefficient are trained.
8. industry internet intelligent Service processing method according to claim 6 or 7, which is characterized in that described to pass through dragonfly
The separation of Yan, alignment, cohesiveness, outward dispersion enemy and the step that five kinds of behaviors of attraction of food source obtain initialization
The rapid position vector sum dragonfly Yan is updated, comprising:
And:
ΔXt+1=(sSi+aAi+cCi+fFi+eEi)+wΔXt；
Ei=X-+X；
Fi=X++X；
Wherein, (Δ x) indicates step vector, X to Tt+1Indicate the position vector at t+1 moment, XtIndicate the position vector of t moment, r is
Update the Euclidean distance between the dragonfly Yan of front and back, SiIndicate the separating behavior of dragonfly Yan, AiIndicate the alignment behavior of dragonfly Yan, CiIndicate dragonfly Yan
Cohesiveness behavior, EiIndicate the outside dispersion enemy behavior of dragonfly Yan, FiAttraction behavior of the expression dragonfly Yan to food source, s,
A, c, f, e respectively indicate the separation of dragonfly Yan, alignment, cohesiveness, disperse enemy, five kinds of behaviors of attraction to food source outward
Learning coefficient, w indicates that the learning coefficient to the position dragonfly Yan of t moment, X indicate the position of current dragonfly Yan individual, XjExpression is worked as
J-th of neighbours dragonfly Yan individual of preceding dragonfly Yan individual, X+Indicate the food source chosen from archives, X-Expression is chosen from archives
Enemy, VjIndicate the speed of j-th of neighbours dragonfly Yan individual of current dragonfly Yan individual.
9. a kind of computer readable storage medium, is stored thereon with computer instruction, which is characterized in that the computer instruction fortune
Perform claim requires the step of 1 to 8 described in any item industry internet intelligent Service processing methods when row.
10. a kind of terminal, which is characterized in that including memory and processor, storing on the memory can be at the place
The computer instruction run on reason device, perform claim requires any one of 1 to 8 institute when the processor runs the computer instruction
The step of industry internet intelligent Service processing method stated.
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