CN108876132A - Industrial enterprise's efficiency service recommendation method based on cloud and system - Google Patents
Industrial enterprise's efficiency service recommendation method based on cloud and system Download PDFInfo
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- CN108876132A CN108876132A CN201810580433.7A CN201810580433A CN108876132A CN 108876132 A CN108876132 A CN 108876132A CN 201810580433 A CN201810580433 A CN 201810580433A CN 108876132 A CN108876132 A CN 108876132A
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- 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
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Abstract
A kind of industrial enterprise's efficiency service recommendation method based on cloud of the invention and system, are related to technical field of electric power.It can solve the technical issues of commercial power service strategy cannot carry out differentiation electricity consumption policy recommendation according to the actual conditions of different enterprises.Include the following steps：S101:Acquisition power information in real time is carried out to the electrical equipment of different user；S102:Collected power information data are generated into electricity consumption data sequence and carry out Distributed Storage；S103:By cloud data processing, trend analysis is carried out to user's history electricity consumption, predicts user power utilization situation, for the factor for influencing electricity consumption level, generates final electricity consumption strategy；S104:By human-computer interaction, by final electricity consumption policy recommendation to user.The present invention can carry out differentiation electricity consumption policy recommendation according to the actual conditions of different user, utmostly improve user's acceptance, with lower cost, realize the improvement of demand Side Management, realize the reasonable disposition of electric power resource, energy saving, reduce energy consumption.
Description
Technical field
The present invention relates to technical field of the computer network, and in particular to a kind of industrial enterprise's efficiency service recommendation based on cloud
Method and system.
Background technique
To ensure China's energy environment sustainable development, meet economic society power demand, Economic Development Mode Conversion is built
If smart grid is very urgent.With greatly developing for China's smart grid, electric network reliability and energy utilization rate are wanted
Ask higher and higher.Intelligent power is the important component of smart grid, and core feature is the two-way of power grid and user flexibility
Interaction, and demand response is used as with one of most important implementation in capable of interacting, and guides use by modes such as price or excitations
Family changes power load to participate in peak load regulation network, is actively engaged in optimization power mode by user to improve demand side management in electricity
Effect in power market.It is more than 70% that current industrial electricity, which accounts for the specific gravity of Analyzing Total Electricity Consumption, energy consumption structure and industry
The contradiction and Environment Confinement Factor of enterprise Green development gradually show.Electricity needs side pipe should be carried out in industrial circle under the new situation
Reason optimizes energy consumption of industry mode and energy consumption efficiency, develops green manufacturing, it is ensured that industry, the coordinated development of electric power and environment.
Current optimization conglomerate electricity consumption strategy it is main premised on saving electric power resource use, to win maximum operation
Income is target, to cultivate the new electricity consumption theory of employee as support, the electricity consumption strategy of conglomerate, in addition to adhering to pervious section
About, collection efficiently, except efficiency maximizes, further expansion production scale can be carried out with consumption of the increasing to electric power appropriate, is allowed
Enterprise of group wins bigger profit.Research to the productive power load modeling based on STN mainly includes the production based on STN
The temperature control device power load modeling of process, the modeling of production equipment power load, meter and comfort level, the mathematics of intelligent power management
Model.
Prior art electricity consumption strategy, which is laid particular emphasis on from electric energy supply side, to take measures, and using modes such as more new equipments, is used
Fulgurite reason, higher cost.And Demand-side user interaction is lower, user's acceptance is not high.In short, prior art power supply service
Property degree it is not high, cannot according to the actual conditions of different enterprises carry out differentiation electricity consumption policy recommendation.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the present invention provides a kind of industrial enterprise's efficiency service recommendation system based on cloud,
Can solve prior art power supply service cannot carry out the technology of differentiation electricity consumption policy recommendation according to the actual conditions of different enterprises
Problem.
(2) technical solution
In order to achieve the above object, the present invention is achieved by the following technical programs：
A kind of industrial enterprise's efficiency service recommendation method based on cloud, includes the following steps：
S101:Acquisition power information in real time is carried out to the electrical equipment of different user；
S102:Collected power information data are generated into electricity consumption data sequence and carry out Distributed Storage, utilize cloud
Storage equipment various types of in network is gathered collaborative work by application software, and provides number by memory technology
According to storage and business access function；
S103:By cloud data processing, reasonable electricity consumption set of strategies is provided according to Dynamic Pricing rule set；
Trend analysis is carried out to user's history electricity consumption, predicts user power utilization situation, generates preliminary use in conjunction with electricity consumption set of strategies
Electric strategy；
With the incidence relation of association rule algorithm analysis user's power inside equipment, discovery influences user power utilization level
Factor is based on preliminary electricity consumption strategy, in conjunction with the factor for influencing electricity consumption level, generates final electricity consumption strategy；
S104:By human-computer interaction, by final electricity consumption policy recommendation to user.
Further, further include after the step S104：
S105:User evaluates used electricity consumption strategy；
S106:According to user's evaluation information and practical electricity consumption situation, study optimization is carried out to system respectively, and optimization is tied
Fruit returns to step S103.
Further, trend analysis is carried out to user's history electricity consumption in the step S103, predicts user power utilization situation, led to
Setting prediction model is crossed to realize, the predictive model algorithm is as follows：
If single exponential smoothing：
Double smoothing：
Three-exponential Smoothing：
Then prediction model is：T=1,2 ... n.
Wherein
T indicates the t phase, and T indicates to elapse issue backward by the t phase, and α indicates smoothing factor, and electricity growth rate is fast, α value
It is bigger；Respectively indicate t phase single exponential smoothing value, double smoothing value, Three-exponential Smoothing
Value, ytIndicate the electricity consumption of history t phase,Indicate t+T phase power quantity predicting value.
Further, it is closed in the step S103 with the association of association rule algorithm analysis user's power inside equipment
System, the method that discovery influences the factor of user power utilization level are as follows：
Data mining is carried out to the power information of all users, excavates the association rule for the factor for influencing user power utilization level
Then, user power utilization process is finely divided from different angles, subdivision angle is that possible influence the factor of electricity consumption level, including row
Industry, month, economy, weather, if possible influence factor is Ii, then electricity consumption level item collection I={ I1, I2..., Im, b } and m=1,2,
3 ..., m, b indicate electricity consumption level, electricity consumption data library D are determined to user, wherein each affairs t is the nonvoid subset of I；Support and
Confidence calculations formula is respectively as shown in formula (1) and formula (2)；
Wherein A indicates that the factor of electricity consumption level may be influenced, and B indicates electricity consumption level, needed that threshold is manually set according to excavation
Value, if meeting minimum support threshold value and minimal confidence threshold, then it is assumed that there are incidence relations between A, B, and then find
Influence the factor of user power utilization level.
Further, study optimization is carried out to system according to user's evaluation information in the step S106, and optimization is tied
Fruit returns to step S103, including：
User's evaluation is collected, the superiority and inferiority influence factor x of corresponding strategy is analyzedi, and according to user's evaluation xiFrequency and degree
Determine factor weight ωi；
If function f (xi)=ωixi, strategic function isOptimal plan is found out using genetic algorithm
Slightly f (x)opt；f(xi) indicate factor of evaluation function, xiFor the factor of controlling policy superiority and inferiority, ωiExpression factor xiWeight；
Work as xiFor positive factor, f (xi) it is positive value；Otherwise, f (xi) it is negative value；
To f (xi) carry out binary coding；Fitness function is g (xi)=f (xi)；g(xi) value is bigger, strategy is more excellent；It is no
Then, strategy is poorer；
The factor group pop={ x for being n for given scale1, x2, x3... xn, individual xiAdaptive value be g (xi),
Then its selected probability isI=1,2,3 ..., n；
It chooses the big individual of selected probability and is selected in population, eliminate the small individual of probability, and with the x for being selected in maximum probabilityiIt mends
Enter population, obtains the identical population of former Population Size；
Intersected and is made a variation；Crossover probability, two different chromosomes to be intersected, according to crossover probability by single are set
Point interior extrapolation method exchanges its portion gene；Mutation probability is set, and chromosome carries out the variation of chromosome according to mutation probability；Intersect general
Rate is bigger, and mutation probability is extremely low；
Termination condition is set, optimal policy f (x) is found outopt, and optimal policy is returned in step S103, update electricity consumption
Set of strategies.
Further, in the step S106:According to the practical electricity consumption situation of user, study optimization is carried out to system, and will
Optimum results return to step S103, specifically include：Exponential smoothing parameter is optimized；
Known practical electricity consumption yi, predicted value isWhen then predicting most accurate
When predicting most accurate, actual value and predicted value difference are minimum, determine optimized parameter α by Fibonacci method；
Assuming that the region of search is [a in k iterationk, bk], further to shorten the region of search, may be selected two in section
Point λk, μk∈[ak, bk], and λk< μk, then have：
WhenWhen, α ∈ [ak, μk]=[ak+1, bk+1],
WhenWhen, α ∈ [λk, bk]=[ak+1, bk+1].；
Keep exponential smoothing prediction user power utilization situation more accurate back to step S103 optimal smoothing factor alpha.
A kind of industrial enterprise's efficiency service recommendation system based on cloud, including the real-time acquisition module of power information, intelligent cloud
Service routine module, efficiency service recommendation program module and systematic learning optimize program module；Wherein,
The real-time acquisition module of power information uses user according to practical electricity consumption situation for acquiring user power utilization information in real time
Electric equipment is classified；
Intelligent cloud service routine module is used to use the user that the collected data of the real-time acquisition module of power information generate
Electric data sequence carries out Distributed Storage, and by cloud data processing, with Time series analysis method, prediction user is used
Electric trend selects corresponding pricing rule in conjunction with user power utilization prediction case and electricity rules, generates preliminary electricity consumption strategy；
According to the incidence relation of associated rule discovery, the factor for influencing electricity consumption level is determined, be based on preliminary electricity consumption strategy, use in conjunction with influence
Electric horizontal factor, provides final electricity consumption strategy；
Efficiency service recommendation program module is used to carry out human-computer interaction with user, and intelligent cloud service routine module is generated
Final electricity consumption strategy is presented to the user；Then user evaluates according to the electricity consumption strategy scenarios adopted, used；
Systematic learning optimizes program module and carries out study optimization to system according to user's evaluation information and practical electricity consumption situation.
It is preferential, the intelligent cloud service routine module include dynamic electricity price rule set submodule, cloud sub-module stored,
Cloud data processing submodule and electricity consumption strategy submodule；Wherein,
Dynamic electricity price rule set submodule stores several electricity price pricing rules；
Cloud sub-module stored stores the power information that the real-time acquisition module acquisition of power information comes；
Cloud data processing submodule processing electricity consumption data simultaneously provides reasonable electricity consumption strategy according to Dynamic Pricing rule set
List；
Electricity consumption strategy submodule determines the factor for influencing electricity consumption level, is based on according to the incidence relation of associated rule discovery
Preliminary electricity consumption strategy provides final electricity consumption strategy in conjunction with the factor for influencing electricity consumption level；
Preferably, the dynamic electricity price rule set submodule includes the power purchase price discipline that power supply enterprise formulates, the purchase
Electric price discipline includes that timesharing pricing rule, large user directly purchase rule, highly energy-consuming restriction rule, reward rule, is supplied by electric power
Answer quotient's typing.
Preferably, the efficiency service recommendation program module includes intelligent recommendation submodule and human assistance submodule；Its
In,
Intelligent recommendation submodule is seeked advice from for custom power responsible person inlet wire, automatic by the real-time acquisition module of power information
Recognition user information extracts the electricity consumption plan for meeting user by the processing of intelligent cloud service routine module from electricity consumption set of strategies
Summary and rationale for the recommendation, and be presented to the user, it is selected by users；
Human assistance submodule is used for the user when the electricity consumption strategy that intelligent recommendation submodule is recommended is unsatisfactory for user demand
Communication can be carried out with online professional policy recommendation personnel, after professional policy recommendation personnel understand user demand, answer user
Query provides customer satisfaction system electricity consumption strategy, assists user's decision.
(3) beneficial effect
It can be certainly according to different user using industrial enterprise's efficiency service recommendation method based on cloud of the invention and system
Dynamic identification class of subscriber, according to electric power Dynamic Pricing rule, enterprise's history electricity consumption data, real-time electricity consumption data, with the time
Sequence analysis method predicts business electrical trend, generates preliminary electricity consumption strategy according to prediction result, carries out to user's history electricity consumption
User power utilization situation is predicted in trend analysis, generates preliminary electricity consumption strategy in conjunction with electricity consumption set of strategies；Then association rule algorithm is used
The incidence relation of user's power inside equipment is analyzed, discovery influences the factor of user power utilization level, is based on preliminary electricity consumption strategy, knot
It takes a group photo and rings the factor of electricity consumption level, generate final electricity consumption strategy, then final electricity consumption policy recommendation is given by human-computer interaction and is used
Family.The present invention can carry out differentiation electricity consumption policy recommendation according to the actual conditions of different user, utmostly improve user and receive
Degree, with lower cost, realizes the improvement of demand Side Management, realizes the reasonable disposition of electric power resource, energy saving, reduces
Energy consumption.The present invention uses cloud service technology simultaneously, by the storage of mass data and handles distribution beyond the clouds, improves system service effect
Rate.
The present invention constantly updates set of strategies by the interaction feedback link with user, optimizes operational parameter, improves prediction essence
Degree realizes personalized electricity consumption policy recommendation.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the method and step schematic diagram of the embodiment of the present invention；
Fig. 2 is the system module structural block diagram of the embodiment of the present invention；
Fig. 3 is the use flow diagram of the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, to the technology in the embodiment of the present invention
Scheme is clearly and completely described, it is clear that and described embodiments are some of the embodiments of the present invention, rather than whole
Embodiment.Based on the embodiments of the present invention, those of ordinary skill in the art are obtained without creative efforts
The every other embodiment obtained, shall fall within the protection scope of the present invention.
Embodiment：
It takes measures since prior art electricity consumption strategy is laid particular emphasis on from electric energy supply side, using modes such as more new equipments, into
Row power consumption management, higher cost.Demand-side user interaction is lower, and user's acceptance is not high.The embodiment of the present invention proposes one kind
Industrial enterprise's efficiency service recommendation method based on cloud, as shown in Figure 1, including the following steps：
S101:Acquisition power information in real time is carried out to the electrical equipment of different user；
S102:Collected power information data are generated into electricity consumption data sequence and carry out Distributed Storage, utilize cloud
Storage equipment various types of in network is gathered collaborative work by application software, and provides number by memory technology
According to storage and business access function；
S103:By cloud data processing, reasonable electricity consumption set of strategies is provided according to Dynamic Pricing rule set；
Trend analysis is carried out to user's history electricity consumption, predicts user power utilization situation, generates preliminary use in conjunction with electricity consumption set of strategies
Electric strategy；
With the incidence relation of association rule algorithm analysis user's power inside equipment, discovery influences user power utilization level
Factor is based on preliminary electricity consumption strategy, in conjunction with the factor for influencing electricity consumption level, generates final electricity consumption strategy；
S104:By human-computer interaction, by final electricity consumption policy recommendation to user；
S105:User evaluates used electricity consumption strategy；
S106:According to user's evaluation information and practical electricity consumption situation, study optimization is carried out to system respectively, and optimization is tied
Fruit returns to step S103.
As shown in Fig. 2, the efficiency service recommendation method of the embodiment of the present invention is realized by following system module：
One, business electrical real time information sampling module：Specific acquisition in real time is carried out to business electrical information, according to enterprise
Actual conditions classify business electrical equipment, and acquisition information includes the real-time electricity consumption of all kinds of electrical equipments, electric power, use
Electro-mechanical wave rate, power quality and enterprise's total electricity consumption.
Two, intelligent cloud service routine module：Including dynamic electricity price rule set submodule, cloud sub-module stored, cloud number
According to processing submodule；
1, dynamic electricity price rule set submodule：The power purchase price discipline that power supply enterprise formulates, for example, it is timesharing pricing rule, big
User directly purchases rule, highly energy-consuming restriction rule, reward rule etc., by electricity provider typing；
2, cloud sub-module stored：The business electrical data that the collected data of business electrical information acquisition module are generated
Sequence is carried out Distributed Storage and is passed through various types of storage equipment a large amount of in network using cloud storage technology
Application software gathers collaborative work, and provides data storage and business access function；
3, cloud data processing submodule：
(1) Time series analysis method is used, trend analysis is carried out to enterprise's history electricity consumption, predicts business electrical situation；
Single exponential smoothing：
Double smoothing：
Three-exponential Smoothing：
Prediction model is：T=1,2 ... n.
Wherein
T indicates the t phase, and T indicates to elapse issue backward by the t phase, and α indicates smoothing factor, and general electricity growth rate is fast, α
Value is bigger；Respectively indicate t phase single exponential smoothing value, double smoothing value, three times index
Smooth value, ytIndicate the electricity consumption of history t phase,Indicate t+T phase power quantity predicting value；
(2) Apriori algorithm based on cloud computing is carried out to the power information of all enterprise customers and carries out data mining, dug
Pick influences the correlation rule of the factor of business electrical level, and business electrical process is finely divided from different angles, segments angle
Degree is is possible to influence the factor of electricity consumption level, such as industry, month, economy, weather, if possible influence factor is Ii, then use
The horizontal item collection of electricity is I={ I1, I2..., Im, b } and m=1,2,3 ..., m, b indicate electricity consumption level, determine electricity consumption data library to enterprise
D, wherein each affairs t is the nonvoid subset of I；
Support and confidence calculations formula are respectively as shown in formula (1) and formula (2)：
Wherein A indicates that the factor of electricity consumption level may be influenced, and B indicates electricity consumption level, needed that threshold is manually set according to excavation
Value, if meeting minimum support threshold value and minimal confidence threshold, then it is assumed that there are incidence relations between A, B, and then find
Influence the factor of business electrical level；
4, electricity consumption set of strategies submodule：Business electrical data generate enterprise after the processing of cloud data processing submodule
Power consumption prediction result simultaneously finds business electrical horizontal relevance factor；
Firstly, the electricity rules in known business power consumption prediction situation, dynamic electricity price rule set submodule, select corresponding
Pricing rule generates electricity consumption strategy；
Secondly, for the factor for influencing electricity consumption level, providing electricity consumption suggestion according to the incidence relation of associated rule discovery.
Three, efficiency service recommendation program module：It is divided into intelligent recommendation submodule and human assistance submodule；
1, intelligent recommendation submodule：The consulting of user enterprise electric power responsible person's inlet wire, passes through business electrical information acquisition module
Automatic identification enterprise user information extracts from set of strategies by the processing of intelligent cloud service routine module and meets user enterprise
Electricity consumption strategy and rationale for the recommendation, and be presented to the user enterprise electric power responsible person, be selected by users.
2, human assistance submodule：When the electricity consumption strategy that intelligent recommendation submodule is recommended is unsatisfactory for user demand, Yong Huqi
Industry electric power responsible person can carry out communication with online professional policy recommendation personnel, and professional policy recommendation personnel understand user demand
Afterwards, user's query is answered, customer satisfaction system electricity consumption strategy is provided, assists user's decision.
Four, systematic learning optimizes program module：
1, for user using evaluating after electricity consumption strategy electricity consumption strategy, system collects user's evaluation, and uses heredity
Algorithm carries out set of strategies optimization；
Steps are as follows：
(1) user's evaluation is collected, the superiority and inferiority influence factor x of corresponding strategy is analyzedi, and according to user's evaluation xiFrequency and
Degree determines factor weight ωi
(2) function f (x is seti)=ωixi, strategic function isIt is found out most using genetic algorithm
Excellent f (x)opt。f(xi) indicate factor of evaluation function, xiFor the factor of controlling policy superiority and inferiority, ωiExpression factor xiWeight.Work as xi
For positive factor, f (xi) it is positive value；Otherwise, f (xi) it is negative value.To f (xi) carry out binary coding.So fitness function is
g(xi)=f (xi)。g(xi) value is bigger, strategy is more excellent；Otherwise, strategy is poorer；
The factor group pop={ x for being n for given scale1, x2, x3... xn, individual xiAdaptive value be g (xi),
Then its selected probability isI=1,2,3 ..., n；It chooses the big individual of selected probability and is selected in population,
Eliminate the small individual of probability, and with the x for being selected in maximum probabilityiPopulation is filled into, the identical population of former Population Size is obtained；Setting is handed over
Probability is pitched, two different chromosomes (parent) to be intersected exchange its portion gene by single point crossing method according to crossover probability；
Mutation probability is set, and chromosome carries out the variation of chromosome according to mutation probability, and in general, crossover probability is bigger, variation
Probability is extremely low；Termination condition is set, optimal policy f (x) is found outopt。
2, prediction optimization is carried out to the prediction processing of data processing module according to enterprise practical electricity consumption, keeps prediction more quasi-
Really.
Power consumption prediction is optimized according to enterprise practical electricity consumption situation.Mainly exponential smoothing parameter is optimized,
Known practical electricity consumption yi, predicted value is
When then predicting most accurateWhen predicting most accurate, actual value and prediction
Value difference is minimum.Optimized parameter α is determined by Fibonacci method, keeps exponential smoothing prediction more accurate；
Assuming that the region of search is [a in k iterationk, bk], further to shorten the region of search, may be selected two in section
Point λk, μk∈[ak, bk], and λk< μk, then have：
WhenWhen, α ∈ [ak, μk]=[ak+1, bk+1].
WhenWhen, α ∈ [λk, bk]=[αk+1, bk+1].
Specific step is as follows：
(1) primary data is selected, determines initial ranging section [a1, b1], allowable errorSection shortening rate τ=
0.618。
(2) initial two exploration points λ is calculatedk=ak+(1-τ)(bk-ak), μk=ak+τ(bk-ak), it finds out
And k=1 is set.
(3) if | λk-uk| < ε then stops iteration, returnsOtherwise, (4) are jumped to.
(4) ifThen jump to (5)；IfThen jump to (6).
(5) it retrieves, enables to the leftCalculate λk+1=ak+1+(1-τ)
(bk+1-ak+1), andJump to (7).
(6) it retrieves to the right,Calculate μk+1=ak+1+τ(bk+1-ak+1)
AndJump to (7).
(7) k is enabled：=k+1 jumps to (3).
The embodiment of the present invention is described further below：
Business electrical information acquisition module carries out the electricity consumption situation of each workshop of enterprise, different type equipment real-time
Acquisition, by taking manufacturing business as an example, production equipment can be divided into Metal Cutting Machine Tool, forging equipment, handling equipment, Mu Gongzhu
Manufacturing apparatus, professional production equipment, kinetic energy occurrence of equipment, electrical equipment, industrial furnace equipment etc..Business electrical information collection mould
The business electrical information of block acquisition includes real-time electricity consumption, electric power, electricity consumption stability bandwidth, the electricity consumption electric energy of all kinds of electrical equipments
Quality and enterprise's total electricity consumption.
The business electrical behavior sequence that the collected data of business electrical information acquisition module generate is subjected to distributed number
According to storage.By distributed file system function, various types of storage equipment a large amount of in network are passed through into application software
Collaborative work is gathered, data storage and business access function are provided.
Dynamic electricity price rule set submodule stores the electrostatic valence rule formulated by power supply enterprise, including traditional rule, timesharing are fixed
Valence rule, large user directly purchase rule, highly energy-consuming restriction rule, reward rule etc., such as：Traditional rule is i.e. first is that hold transformer
, execution " single price " below in 315KVA is measured, i.e., with the electricity charge of kilowatt-hour, friendship kilowatt-hour；Second is that existing to transformer capacity
315KVA's or more, it executes " two-shift system electricity price ", it, will also be according to transformer in addition to the electricity charge with kilowatt-hour, friendship kilowatt-hour
Capacity pays " basic charge as per installed capacity ".Timesharing pricing rule is i.e. according to being at times adjusted electricity price, such as mentions in peak times of power consumption
High price reduces price in electricity consumption ebb, enterprise is promoted to avoid peak of power consumption.Large user directly purchases rule and meets national industry political affairs
The large user that plan, power load are relatively stable, the energy consumption per unit of output value is low, disposal of pollutants is small, electricity power enterprise is to high voltage grade
Or larger electricity consumption user and power distribution network directly power, the price of straight power purchase is negotiated to determine by electricity power enterprise and user, and is held
The T-D tariff of row national regulation.Highly energy-consuming restriction rule gives certain limitation to the productive power amount of high energy-consuming enterprises.
Reward rule is that certain preferential policy is given by enterprise good for consumption habit.Power supply enterprise inputs electricity rules, and
It stores to dynamic electricity price rule set submodule.
Cloud data processing submodule is responsible for handling electricity consumption business data and be provided reasonably according to Dynamic Pricing rule set
Electricity consumption Policy List.With Time series analysis method, trend analysis is carried out to enterprise's history electricity consumption, predicts business electrical feelings
Condition determines preliminary strategy in conjunction with electricity consumption set of strategies；The incidence relation of enterprises electrical equipment is analyzed with association rule algorithm,
Determine final strategy.
Electricity consumption set of strategies submodule business electrical data generate business electrical after the processing of cloud data processing submodule
Prediction result simultaneously finds business electrical horizontal relevance factor：Firstly, known business power consumption prediction situation, dynamic electricity price rule collected works
Electricity rules in module select corresponding pricing rule, generate electricity consumption strategy, such as next month business electrical amount prediction case；
The peak of power consumption that is staggered carries out production running, and the powerful production operation steps of highly energy-consuming are arranged in reasonable time zone as far as possible
Between；It saves balanced etc. between electricity control cost and energy-saving electricity policy of rewards.Secondly, according to the association of associated rule discovery
Relationship provides electricity consumption suggestion for the factor for influencing electricity consumption level, corporate decision maker can according to influence the factor of electricity consumption into
The specific decision of row reduces energy consumption to realize save electricity, reduces the purpose of cost, improves enterprise profit.
Intelligent recommendation submodule extracts the electricity consumption strategy and rationale for the recommendation for meeting user enterprise from set of strategies, and is presented to
User enterprise electric power responsible person, is selected by users.
When the electricity consumption strategy that human assistance submodule intelligent recommendation submodule is recommended is unsatisfactory for user demand, user enterprise electricity
Power responsible person can carry out communication with online professional policy recommendation personnel, in the hope of finally meeting user demand.
Systematic learning optimization module is one side user using evaluating after electricity consumption strategy electricity consumption strategy, and system is received
Collect user's evaluation, and carries out set of strategies optimization using genetic algorithm；On the other hand according to enterprise practical electricity consumption to data processing
The prediction processing of module carries out prediction optimization, makes to predict more acurrate.
As shown in figure 3, when in use, firstly, being logged in when some Industry enterprise customer policymaker enters intelligent recommendation module
After the system, business electrical information acquisition module obtains the relevant information of enterprise automatically, such as the title, affiliated of the industrial enterprise
Industry, energy consumption condition, scale and mac function distribution etc..
Secondly, cloud data processing submodule obtains enterprise's history power information, electricity demand forecasting is carried out, prediction is tied
Fruit is calculated with Dynamic Pricing set of strategies, obtains preliminary electricity consumption strategy.Calculate the association pass for influencing business electrical horizontal factor
System generated final electricity consumption strategy, be presented to Industry enterprise customer policymaker with electrically optimized to enterprises electricity consumption.Industry
Enterprise customer policymaker screens the result predicted, corresponding selection is suitble to this according to the actual conditions of itself enterprise
The electricity consumption strategy of enterprise's production running.If Industry enterprise customer policymaker has the strategy of electricity consumption policy recommendation system recommendation doubtful
It asks or opinion, human assistance module can be entered, be linked up with professional online customer service, it is auxiliary with the reasonable electricity consumption strategy of determination
Help business electrical decision.
Last Industry enterprise customer policymaker evaluates used electricity consumption strategy, according to enterprise practical electricity consumption situation
System is optimized with evaluation of the Industry enterprise customer policymaker after adopting strategy.Optimization includes two aspects, on the one hand
Optimize business electrical trend prediction according to enterprise practical electricity consumption situation, on the other hand according to electricity consumption valuation of enterprise, is calculated using heredity
Method optimizes set of strategies.To provide more accurately electricity consumption trend prediction and more reasonable electricity consumption set of strategies.
To sum up, the industrial enterprise's efficiency service recommendation method based on cloud and system of the embodiment of the present invention, can auxiliary enterprises
Scientific and reasonable electricity consumption decision is made on upper layer, reduces Power Consumption of Industrial Enterprises energy consumption, improves Power Consumption of Industrial Enterprises efficiency, saves fund,
Improve profit.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that：It still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these modification or
Replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of industrial enterprise's efficiency service recommendation method based on cloud, which is characterized in that include the following steps：
S101:Acquisition power information in real time is carried out to the electrical equipment of different user；
S102:Collected power information data are generated into electricity consumption data sequence and carry out Distributed Storage, utilize cloud storage
Storage equipment various types of in network is gathered collaborative work by application software, and provides data and deposit by technology
Storage and business access function；
S103:By cloud data processing, reasonable electricity consumption set of strategies is provided according to Dynamic Pricing rule set；
Trend analysis is carried out to user's history electricity consumption, predicts user power utilization situation, generates preliminary use in conjunction with the electricity consumption set of strategies
Electric strategy；
With the incidence relation of association rule algorithm analysis user's power inside equipment, discovery influence user power utilization it is horizontal because
Element is based on the preliminary electricity consumption strategy, in conjunction with the factor for influencing electricity consumption level, generates final electricity consumption strategy；
S104:By human-computer interaction, by final electricity consumption policy recommendation to user.
2. industrial enterprise's efficiency service recommendation method based on cloud as described in claim 1, which is characterized in that the step
Further include after S104：
S105:User evaluates used electricity consumption strategy；
S106:According to user's evaluation information and practical electricity consumption situation, study optimization is carried out to system respectively, and optimum results are returned
Return to step S103.
3. industrial enterprise's efficiency service recommendation method based on cloud as claimed in claim 1 or 2, which is characterized in that the step
Trend analysis is carried out to user's history electricity consumption in rapid S103, predicts user power utilization situation, is realized by building prediction model, institute
It is as follows to state predictive model algorithm：
If single exponential smoothing：
Double smoothing：
Three-exponential Smoothing：
Then prediction model is：T=1,2 ... n.
Wherein
T indicates the t phase, and T indicates to elapse issue backward by the t phase, and α indicates smoothing factor, and electricity growth rate is fast, and α value is bigger；Respectively indicate t phase single exponential smoothing value, double smoothing value, Three-exponential Smoothing value, yt
Indicate the electricity consumption of history t phase,Indicate t+T phase power quantity predicting value.
4. industrial enterprise's efficiency service recommendation method based on cloud as claimed in claim 1 or 2, which is characterized in that the step
Suddenly with the incidence relation of association rule algorithm analysis user's power inside equipment in S103, discovery influences user power utilization level
The method of factor is as follows：
Data mining is carried out to the power information of all users, excavates the correlation rule for influencing the factor of user power utilization level, it will
User power utilization process is finely divided from different angles, and subdivision angle is that possible influence the factor of electricity consumption level, including industry, the moon
Part, economy, weather, if possible influence factor is Ii, then electricity consumption level item collection I={ il, I2, Im, b } and m=1,2,3 ..., m, b
It indicates electricity consumption level, electricity consumption data library D is determined to user, wherein each affairs t is the nonvoid subset of I；Support and confidence level meter
Calculation formula is respectively as shown in formula (1) and formula (2)；
Wherein A indicates that the factor of electricity consumption level may be influenced, and B indicates electricity consumption level, needs to be manually set threshold value according to excavation, such as
Fruit meets minimum support threshold value and minimal confidence threshold, then it is assumed that there are incidence relations between A, B, and then find to influence to use
The factor of family electricity consumption level.
5. industrial enterprise's efficiency service recommendation method based on cloud as claimed in claim 1 or 2, which is characterized in that the step
Study optimization is carried out to system according to user's evaluation information in rapid S106, and optimum results are returned into step S103, including：
User's evaluation is collected, the superiority and inferiority influence factor x of corresponding strategy is analyzedi, and according to user's evaluation xiFrequency and degree determine
Factor weight ωi；
If function f (xi)=ωixi, strategic function isOptimal policy f is found out using genetic algorithm
(x)opt；f(xi) indicate factor of evaluation function, xiFor the factor of controlling policy superiority and inferiority, ωiExpression factor xiWeight；
Work as xiFor positive factor, f (xi) it is positive value；Otherwise, f (xi) it is negative value；
To f (xi) carry out binary coding；Fitness function is g (xi)=f (xi)；g(xi) value is bigger, strategy is more excellent；Otherwise, plan
It is slightly poorer；
The factor group pop={ x for being n for given scale1, x2,x3... xn, individual xiAdaptive value be g (xi), then its
Selected probability is
It chooses the big individual of selected probability and is selected in population, eliminate the small individual of probability, and with the x for being selected in maximum probabilityiFill into kind
Group, obtains the identical population of former Population Size；
Intersected and is made a variation；Crossover probability is set, and two different chromosomes to be intersected are pressed single-point according to crossover probability and handed over
Fork method exchanges its portion gene；Mutation probability is set, and chromosome carries out the variation of chromosome according to mutation probability；Crossover probability ratio
Larger, mutation probability is extremely low；
Termination condition is set, optimal policy f (x) is found outopt, and optimal policy is returned in step S103, update electricity consumption strategy
Collection.
6. industrial enterprise's efficiency service recommendation method based on cloud as claimed in claim 3, which is characterized in that the step
In S106：According to the practical electricity consumption situation of user, study optimization is carried out to system, and optimum results are returned into step S103, tool
Body includes：Exponential smoothing parameter is optimized；
Known practical electricity consumption yi, predicted value isWhen then predicting most accurateIt is i.e. pre-
When surveying most accurate, actual value and predicted value difference are minimum, determine optimized parameter α by Fibonacci method；
Assuming that the region of search is [a in k iterationk, bk], further to shorten the region of search, two o'clock λ in section may be selectedkμk
∈[ak, bk] and λk< μk, then have：
WhenWhen, α ∈ [ak, μk]=[ak+1, bk+1],
WhenWhen, α ∈ [λk, bk]=[ak+1, bk+1].；
Keep exponential smoothing prediction user power utilization situation more accurate back to step S103 optimal smoothing factor alpha.
7. a kind of industrial enterprise's efficiency service recommendation system based on cloud, which is characterized in that acquire mould in real time including power information
Block, intelligent cloud service routine module, efficiency service recommendation program module and systematic learning optimize program module；Wherein,
The real-time acquisition module of power information sets user power utilization according to practical electricity consumption situation for acquiring user power utilization information in real time
It is standby to classify；
Intelligent cloud service routine module is used for the user power utilization number for generating the collected data of the real-time acquisition module of power information
Distributed Storage is carried out according to sequence, by cloud data processing, is become with Time series analysis method prediction user power utilization
Gesture selects corresponding pricing rule in conjunction with user power utilization prediction case and electricity rules, generates preliminary electricity consumption strategy；According to
The incidence relation of associated rule discovery determines the factor for influencing electricity consumption level, is based on preliminary electricity consumption strategy, in conjunction with influence electricity consumption water
Flat factor provides final electricity consumption strategy；
Efficiency service recommendation program module is used to carry out human-computer interaction with user, intelligent cloud service routine module is generated final
Electricity consumption strategy is presented to the user；Then user evaluates according to the electricity consumption strategy scenarios adopted, used；
Systematic learning optimizes program module and carries out study optimization to system according to user's evaluation information and practical electricity consumption situation.
8. industrial enterprise's efficiency service recommendation system based on cloud as claimed in claim 7, which is characterized in that the intelligent cloud
Service routine module includes dynamic electricity price rule set submodule, cloud sub-module stored, cloud data processing submodule and electricity consumption
Tactful submodule；Wherein,
Dynamic electricity price rule set submodule stores several electricity price pricing rules；
Cloud sub-module stored stores the power information that the real-time acquisition module acquisition of power information comes；
Cloud data processing submodule processing electricity consumption data simultaneously provides reasonable electricity consumption Policy List according to Dynamic Pricing rule set；
Electricity consumption strategy submodule determines the factor for influencing electricity consumption level according to the incidence relation of associated rule discovery, based on preliminary
Electricity consumption strategy provides final electricity consumption strategy in conjunction with the factor for influencing electricity consumption level.
9. industrial enterprise's efficiency service recommendation system based on cloud as claimed in claim 8, which is characterized in that the dynamic electric
Valence rule set submodule includes the power purchase price discipline that power supply enterprise formulates, and the power purchase price discipline includes timesharing price rule
Then, large user directly purchases rule, highly energy-consuming restriction rule, reward rule, by electricity provider typing.
10. industrial enterprise's efficiency service recommendation system based on cloud as claimed in claim 7, which is characterized in that the efficiency
Service recommendation program module includes intelligent recommendation submodule and human assistance submodule；Wherein,
Intelligent recommendation submodule is seeked advice from for custom power responsible person inlet wire, passes through the real-time acquisition module automatic identification of power information
User information, by the processing of intelligent cloud service routine module, extracted from electricity consumption set of strategies the electricity consumption strategy for meeting user and
Rationale for the recommendation, and be presented to the user, it is selected by users；
Human assistance submodule is used for when the electricity consumption strategy that intelligent recommendation submodule is recommended is unsatisfactory for user demand, and user can be with
Online profession policy recommendation personnel carry out communication, after professional policy recommendation personnel understand user demand, answer user's query,
Customer satisfaction system electricity consumption strategy is provided, user's decision is assisted.
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