CN109118012A - A kind of industrial dynamics various dimensions energy consumption cost prediction technique, system, storage medium and terminal - Google Patents
A kind of industrial dynamics various dimensions energy consumption cost prediction technique, system, storage medium and terminal Download PDFInfo
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Abstract
The invention discloses a kind of industrial dynamics various dimensions energy consumption cost prediction technique, system, storage medium and terminals, and method is the following steps are included: establish energy-dissipating cost analysis model；Establish ERP product factor constraint condition；It establishes production system factor constraint condition: to energy consumption cost objective function, under the conditions of ERP product factor constraint condition and production system factor constraint, solving optimal value.The present invention makes full use of existing energy consumption data method for digitizing, obtain the real time data of energy consumption and related energy consumption impact factor, data, which are calculated, by time series, ERP product data, creation data and specific energy consumption establishes energy consumption cost forecast analysis model, dynamic constrained condition is established by obtaining correlation factor in real time, dynamic solution energy consumption cost prediction model optimal solution, to obtain current optimal energy-dissipating cost analysis data, i.e. lowest energy consumption cost data and the following production capacity Annual distribution scheme.
Description
Technical field
The present invention relates to a kind of industrial dynamics various dimensions energy consumption cost prediction technique, system, storage medium and terminals.
Background technique
With the continuous development of industrial manufacturing level, each data in industrial production are more and more taken seriously, work
Utilization of the industry software hardware system to data develops, the utilization to data, on the one hand from informationization to digitlization, intelligent direction
Guarantee the integrality of data, on the other hand due to the diversity of data, the demand of data forecast analysis is also more and more.
Energy consumption data has had many relevant application systems as the important component in industrial data at present.But
These systems are only realized the digitlization of energy consumption data and are simply statisticallyd analyze, using the system that energy consumption data is isolated as one
It counts and exists；Only it is conceived to energy consumption data itself, realizes the basic digital collection of energy consumption data and simple system
Meter analysis, using such method, energy consumption data analysis system can only often realize statistical analysis and visualization, cannot utilize existing
There is the characteristics of big data technology is to forecast analysis, correlation analysis, does not meet industrial big data now and dependency prediction is analyzed
Requirement.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of predictions of industrial dynamics various dimensions energy consumption cost
Method, system, storage medium and terminal.
The purpose of the present invention is achieved through the following technical solutions: a kind of industrial dynamics various dimensions energy consumption cost prediction
Method, comprising the following steps:
It establishes energy-dissipating cost analysis model: obtaining ERP order data, creation data and the unit with time series of history
Energy consumption calculation data establish energy consumption cost objective function；
It establishes ERP product factor constraint condition: data being delivered according to the product of ERP order data and establish the ERP product factor
Constraint condition, wherein the product delivers real-time quantity performed and the number to be done that data include the present analysis period
Amount；
It establishes production system factor constraint condition: obtaining the corresponding station of product, producing line, the actual production efficiency in workshop,
Establish production system factor constraint condition；
Energy consumption cost prediction: to energy consumption cost objective function, ERP product factor constraint condition and the production system factor about
Under the conditions of beam, optimal value is solved.
Further, the ERP order data is actual product quantity delivered in analytical cycle；
The creation data is to divide the production capacity in range in the time；
The energy consumption unit calculates the rate that data are unit energy consumption usage amount.
Further, the energy-dissipating cost analysis model of establishing includes following sub-step:
It obtains historical cost data acquisition system (x, y), wherein x is yield, and y is volume cost data；
The historical cost data are divided into training data set K1 and verify data set K2；
Being arranged multiple includes goal-selling function Fn(x), using fitting algorithm by the yield number in training data set K1
Each objective function is fitted respectively according to volume cost data, solves the optimal value of the parameter of each objective function；Its
In, n is objective function quantity；
By the yield data x in verify data set K2kIt brings each goal-selling function into, obtains each goal-selling letter
The corresponding F of numbern(xk)；By the volume cost data y in verify data set K2kWith Fn(xk) solution mean square deviation is carried out, it obtains each
The correspondence mean square deviation T of a objective functionn；Wherein, the value range of k is 1-m, and m is the data acquisition system number of verify data set K2
Amount；
The objective function of mean square deviation minimum value is chosen as model.
Further, the production system factor constraint condition of establishing includes following sub-step:
Obtain current station, producing line, the actual production efficiency in workshop and product and station, producing line and the mapping in workshop
Relationship；
Calculate the production efficiency of product；
Wherein, for combination product or multi-process product, the production effect of product is calculated with minimum actual production efficiency
Rate, to establish production system factor constraint condition.
Further, energy consumption cost prediction further include:
In the case where acquiring model optimal solution, while minimum energy consumption cost is acquired, and is given at following time model
In enclosing, different product is in production programming in different time periods.
Further, the method uses the calculation method of triggering and/or timing；Wherein, the triggering is when outer
When portion user needs, it is immediately performed prediction analysis method, obtains current predicted value；The timing is to be sometime week
Phase carries out forecast analysis every a cycle, and data result is saved.
Further, when the method calculated using timing, the energy consumption cost predicted value and energy consumption that will be calculated every time
Cost mark post data are compared, if it exceeds range then carries out data early warning.
The present invention also provides a kind of industrial dynamics various dimensions energy consumption cost forecasting systems, comprising:
Energy-dissipating cost analysis model building module: for obtain history with the ERP order data of time series, production number
Data are calculated according to specific energy consumption, establish energy consumption cost objective function；
ERP product factor constraint condition establishes module: establishing ERP for delivering data according to the product of ERP order data
Product factor constraint condition, wherein the product deliver data include the present analysis period in real time quantity performed and
Quantity to be done；
Production system factor constraint condition establishes module: for obtaining the practical life of the corresponding station of product, producing line, workshop
Efficiency is produced, production system factor constraint condition is established；
Energy consumption cost prediction module: for being in ERP product factor constraint condition and production to energy consumption cost objective function
Under the conditions of factor constraint of uniting, optimal value is solved.
The present invention also provides a kind of storage mediums, are stored thereon with computer instruction, and the computer instruction is held when running
A kind of the step of industrial dynamics various dimensions energy consumption cost prediction technique described in row.
The present invention also provides a kind of terminal, including memory and processor, being stored on the memory can be at the place
The computer instruction run on reason device, it is more that the processor executes a kind of industrial dynamics when running the computer instruction
The step of dimension energy consumption cost prediction technique.
The beneficial effects of the present invention are:
(1) present invention makes full use of existing energy consumption data method for digitizing, obtain energy consumption and related energy consumption because
The real time data of son calculates data by time series, ERP product data, creation data and specific energy consumption and establishes energy consumption cost
Forecast analysis model establishes dynamic constrained condition by obtaining correlation factor in real time, and dynamic solution energy consumption cost prediction model is most
Excellent solution, thus the current optimal energy-dissipating cost analysis data of acquisition, i.e. lowest energy consumption cost data and following production capacity Annual distribution side
Case.
(2) present invention therefrom selects the energy consumption for being most suitable for this plant data by the foundation and selection of multiple objective functions
Cost Analysis Model, so that later data analysis is more accurate.
(3) present invention executes energy consumption forecast analysis by timing, can establish the mapping relations of production capacity and energy consumption, drop significantly
Low energy consumption use cost, while establishing energy consumption cost mark post data.
(4) present invention establishes energy consumption prediction and warning mechanism, advantageously reduces, sell, produce risk between energy consumption according to
Rely, establishes a kind of new risk assessment mode for corporate decision maker.
Detailed description of the invention
Fig. 1 is the method flow diagram of the embodiment of the present invention 1；
Fig. 2 is the system block diagram of the embodiment of the present invention 2；
Fig. 3 is the device block diagram of the embodiment of the present invention 4.
Specific embodiment
Technical solution of the present invention is clearly and completely described with reference to the accompanying drawing, it is clear that described embodiment
It is a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
Member's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As long as in addition, the non-structure each other of technical characteristic involved in invention described below different embodiments
It can be combined with each other at conflict.
Embodiment 1
The present embodiment provides a kind of industrial dynamics various dimensions energy consumption cost prediction techniques, and the present embodiment is to influence energy consumption cost
The factor establish energy-dissipating cost analysis model, these factors include ERP data of information system, production executive system (MES) data
Data are calculated with energy consumption unit.Dynamic realtime obtains the real value and corresponding constraint condition of the different factors online, counts in real time
The optimal solution (least cost scheme) of model is calculated, to obtain energy consumption cost scheme optimal in real time.Specifically, as shown in Figure 1,
The following steps are included:
S1: establish energy-dissipating cost analysis model: obtain history with the ERP order data of time series, creation data and
Specific energy consumption calculates data, establishes energy consumption cost objective function.
Wherein, in the preferred embodiment of the present embodiment, the ERP order data is that actual product is delivered in analytical cycle
Quantity.
Specifically, the analytical cycle default of prediction is one day, if necessary to expand analytical cycle, such as one week or one
Month, it needs daily to polymerize data correlation predictive analysis factor, obtains the forecast analysis factor parameter of larger period.Cause
This, ERP order data, be primarily referred to as the same day (specified predetermined period) actual handing over product quantity, including single product and
Combination product.
Meanwhile the creation data is to divide the production capacity in range in the time；Specifically, usually one small
When, such as the production capacity of 15:00-16:00 producing line.
In addition, energy consumption unit described in the step calculates the rate that data are unit energy consumption usage amount, such as electricity price, water price
And gas price.And for factory user, it is likely present timesharing price, is needed by different time range computation specific energy consumption rate.
Include following sub-step for the energy-dissipating cost analysis model of establishing in the step:
S11: obtaining historical cost data acquisition system (x, y), and wherein x is yield, and y is volume cost data；
Wherein, by the analysis to enterprise existing system database, yield and production cost data are extracted, by production cost
Data are decomposed according to production shift, obtain History Order yield and History Order cost data, as x, y value.
S12: the historical cost data are divided into training data set K1 and verify data set K2；
S13: multiple setting includes goal-selling function Fn(x), using fitting algorithm by the production in training data set K1
Amount data and volume cost data are respectively fitted each objective function, solve the optimal of the parameter of each objective function
Value；Wherein, n is objective function quantity；
Specifically, in this example, it is assumed that objective function is two, it is respectively as follows:
F1(x)=a1x2+b1x+c1 (1)
F2(x)=a2x3+b2x2+c2x+d (2)
The parameter of two objective functions is respectively a1、b1And c1Parameter and a2、b2、c2With d parameter.
In this step, using fitting algorithm, training data set K1 is brought into, to objective function F1(x) it is fitted, asks
Solve a1, b1 and c1 parameter optimal value；Fitting algorithm is being used later, training data set K1 is being brought into, to objective function F2(x) into
Row fitting, solves a2, b2, c2 and d parameter optimal value.
S14: by the yield data x in verify data set K2kIt brings each goal-selling function into, obtains each default mesh
The corresponding F of scalar functionsn(xk)；By the volume cost data y in verify data set K2kWith Fn(xk) solution mean square deviation is carried out, it obtains
To the correspondence mean square deviation T of each objective functionn；Wherein, the value range of k is 1-m, and m is the data set of verify data set K2
Close quantity.
Specifically, in this step:
Firstly evaluate objective function F1(x), using K2X in data acquisition systemk, bring F into1, acquire Fk, then forecast set will be acquired
Close Fk,Fk+1,……,FN,, use K2Y in setk, solve FkWith ykMean square deviation T1。
Similarly assess objective function F2(x), using K2X in data acquisition systemk, bring F into2, acquire Fk, prediction sets will be acquired
Fk,Fk+1,……,Fn, use K2Y in setk, solve FkWith ykMean square deviation T2。
S15: the objective function of mean square deviation minimum value is chosen as model.
By the foundation and selection of multiple objective functions, the energy-dissipating cost analysis mould for being most suitable for this plant data is therefrom selected
Type, so that later data analysis is more accurate.
S2: establish ERP product factor constraint condition: according to the product of ERP order data deliver data establish ERP product because
Sub- constraint condition (such as last friendship phase of production order), wherein the product delivers the reality that data include the present analysis period
When quantity performed and quantity to be done.
In this step, the product delivery data volume on the day of ERP order data needs to obtain in real time, including the same day are complete
At several and number to be done, number to be done is required quantity delivered after current point in time, which is to calculate gained in real time.
S3: it establishes production system factor constraint condition: obtaining the corresponding station of product, producing line, the actual production effect in workshop
Rate establishes production system factor constraint condition (such as the order that the lowest manufactured quantity of each shift, each shift need to complete
Quantity).
Preferably, this step includes following sub-step:
Obtain current station, producing line, the actual production efficiency in workshop and product and station, producing line and the mapping in workshop
Relationship；
Calculate the production efficiency of product；
Wherein, for combination product or multi-process product, the production effect of product is calculated with minimum actual production efficiency
Rate, to establish production system factor constraint condition.
S4: energy consumption cost prediction: to energy consumption cost objective function, ERP product factor constraint condition and production system because
Under sub- constraint condition, optimal value is solved.
Preferably, in the present embodiment, energy consumption cost prediction further include:
In the case where acquiring model optimal solution, while minimum energy consumption cost is acquired, and is given at following time model
In enclosing, different product is in production programming in different time periods.
That is the method for the present embodiment is also used to the planning to the following production capacity.
More preferably, in the present embodiment, the method uses the calculation method of triggering and/or timing；Wherein, described
Triggering obtains current predicted value when external users needs, to be immediately performed prediction analysis method；The timing is with certain
One time was the period, carried out forecast analysis every a cycle, and data result is saved.
Based on the realization of above preferred embodiment content, when the method calculated using timing, by what is be calculated every time
Energy consumption cost predicted value is compared with energy consumption cost mark post data, if it exceeds range then carries out data early warning.
Energy consumption forecast analysis is executed by timing, the mapping relations of production capacity and energy consumption can be established, substantially reducing energy consumption makes
With cost, while establishing energy consumption cost mark post data.Energy consumption prediction and warning mechanism is established, is advantageously reduced, sells, produce and energy
Risk between consumption relies on, and establishes a kind of new risk assessment mode for corporate decision maker.
Embodiment 2
The present embodiment provides a kind of industrial dynamics various dimensions energy consumption cost forecasting system, the inventive concepts and reality of the embodiment
It applies that example 1 is similar, establishes energy-dissipating cost analysis model to influence the factor of energy consumption cost, these factors include ERP information system number
Data are calculated according to, production executive system (MES) data and energy consumption unit.Dynamic realtime obtain online the real value of the different factors with
And corresponding constraint condition, the optimal solution (least cost scheme) of real-time computation model, thus obtain in real time optimal energy consumption at
This programme.As shown in Fig. 2, the system includes:
Energy-dissipating cost analysis model building module: for obtain history with the ERP order data of time series, production number
Data are calculated according to specific energy consumption, establish energy consumption cost objective function；
ERP product factor constraint condition establishes module: establishing ERP for delivering data according to the product of ERP order data
Product factor constraint condition, wherein the product deliver data include the present analysis period in real time quantity performed and
Quantity to be done；
Production system factor constraint condition establishes module: for obtaining the practical life of the corresponding station of product, producing line, workshop
Efficiency is produced, production system factor constraint condition is established；
Energy consumption cost prediction module: for being in ERP product factor constraint condition and production to energy consumption cost objective function
Under the conditions of factor constraint of uniting, optimal value is solved.
Embodiment 3
Based on the realization of embodiment 1, the present embodiment also provides a kind of storage medium, is stored thereon with computer instruction, institute
A kind of step of industrial dynamics various dimensions energy consumption cost prediction technique described in embodiment 1 is executed when stating computer instruction operation
Suddenly.
Based on this understanding, the technical solution of the present embodiment substantially the part that contributes to existing technology in other words
Or the part of the technical solution can be embodied in the form of software products, which is stored in one and deposits
In storage media, including some instructions are used so that a computer equipment (can be personal computer, server or network
Equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.And storage medium above-mentioned include: USB flash disk,
Mobile hard disk, read-only memory (Read-OnlyMemory, ROM), random access memory (RandomAccessMemory,
RAM), the various media that can store program code such as magnetic or disk.
Embodiment 4
Based on the realization of embodiment 1, the present invention also provides a kind of terminals, as shown in figure 3, including memory and processor,
The computer instruction that can be run on the processor is stored on the memory, the processor runs the computer and refers to
The step of a kind of industrial dynamics various dimensions energy consumption cost prediction technique described in embodiment 1 is executed when enabling.
Each functional unit in embodiment provided by the invention can integrate in one processing unit, be also possible to each
A unit physically exists alone, and can also be integrated in one unit with two or more units.
In all embodiments provided by the present invention, it should be understood that disclosed device and method, it can be by other
Mode realize.The apparatus embodiments described above are merely exemplary, for example, the division of the units/modules, only
For a kind of logical function partition, there may be another division manner in actual implementation, in another example, multiple units or module can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be the INDIRECT COUPLING by some communication interfaces, device or unit
Or communication connection, it can be electrical property, mechanical or other forms.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments, right
For those of ordinary skill in the art, can also make on the basis of the above description other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And thus amplify out it is obvious variation or
It changes still within the protection scope of the invention.
Claims (10)
1. a kind of industrial dynamics various dimensions energy consumption cost prediction technique, it is characterised in that: the following steps are included:
It establishes energy-dissipating cost analysis model: obtaining ERP order data, creation data and the specific energy consumption with time series of history
Data are calculated, energy consumption cost objective function is established；
It establishes ERP product factor constraint condition: data being delivered according to the product of ERP order data and establish ERP product factor constraint
Condition, wherein the product delivers real-time quantity performed and the quantity to be done that data include the present analysis period；
It establishes production system factor constraint condition: obtaining the corresponding station of product, producing line, the actual production efficiency in workshop, establish
Production system factor constraint condition；
Energy consumption cost prediction: to energy consumption cost objective function, in ERP product factor constraint condition and production system factor constraint item
Under part, optimal value is solved.
2. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 1, it is characterised in that: described
ERP order data is actual product quantity delivered in analytical cycle；
The creation data is to divide the production capacity in range in the time；
The energy consumption unit calculates the rate that data are unit energy consumption usage amount.
3. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 1 or 2, it is characterised in that: institute
The energy-dissipating cost analysis model of establishing stated includes following sub-step:
It obtains historical cost data acquisition system (x, y), wherein x is yield, and y is volume cost data；
The historical cost data are divided into training data set K1 and verify data set K2；
Being arranged multiple includes goal-selling function Fn(x), using fitting algorithm by training data set K1 yield data and production
Amount cost data is respectively fitted each objective function, solves the optimal value of the parameter of each objective function；Wherein, n is
Objective function quantity；
By the yield data x in verify data set K2kIt brings each goal-selling function into, obtains each goal-selling function pair
The F answeredn(xk)；By the volume cost data y in verify data set K2kWith Fn(xk) solution mean square deviation is carried out, obtain each mesh
The correspondence mean square deviation T of scalar functionsn；Wherein, the value range of k is 1-m, and m is the data acquisition system quantity of verify data set K2；
The objective function of mean square deviation minimum value is chosen as model.
4. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 1, it is characterised in that: described
Establishing production system factor constraint condition includes following sub-step:
Obtain current station, producing line, the actual production efficiency in workshop and product and station, producing line and the mapping relations in workshop；
Calculate the production efficiency of product；
Wherein, for combination product or multi-process product, the production efficiency of product is calculated with minimum actual production efficiency, from
And establish production system factor constraint condition.
5. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 1, it is characterised in that: described
Energy consumption cost prediction further include:
In the case where acquiring model optimal solution, while minimum energy consumption cost is acquired, and is given in following time range,
Different product is in production programming in different time periods.
6. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 1, it is characterised in that: described
Method uses the calculation method of triggering and/or timing；Wherein, the triggering is to be immediately performed when external users needs
Prediction analysis method obtains current predicted value；The timing is sometime for the period, to be predicted every a cycle
Analysis, and data result is saved.
7. a kind of industrial dynamics various dimensions energy consumption cost prediction technique according to claim 6, it is characterised in that: work as use
When the method that timing calculates, the energy consumption cost predicted value being calculated every time is compared with energy consumption cost mark post data, such as
Fruit is overruned, and data early warning is carried out.
8. a kind of industrial dynamics various dimensions energy consumption cost forecasting system, it is characterised in that: include:
Energy-dissipating cost analysis model building module: for obtain history with the ERP order data of time series, creation data and
Specific energy consumption calculates data, establishes energy consumption cost objective function；
ERP product factor constraint condition establishes module: establishing ERP product for delivering data according to the product of ERP order data
Factor constraint condition, wherein the product delivers the real-time quantity performed that data include the present analysis period and to complete
At quantity；
Production system factor constraint condition establishes module: for obtaining the corresponding station of product, producing line, the actual production effect in workshop
Rate establishes production system factor constraint condition；
Energy consumption cost prediction module: for energy consumption cost objective function, ERP product factor constraint condition and production system because
Under sub- constraint condition, optimal value is solved.
9. a kind of storage medium, is stored thereon with computer instruction, it is characterised in that: the right of execution when computer instruction is run
Benefit require any one of 1 to 7 described in a kind of industrial dynamics various dimensions energy consumption cost prediction technique the step of.
10. a kind of terminal, including memory and processor, the meter that can be run on the processor is stored on the memory
Calculation machine instruction, which is characterized in that perform claim requires any one of 1 to 7 institute when the processor runs the computer instruction
A kind of the step of industrial dynamics various dimensions energy consumption cost prediction technique stated.
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| CN112049624A (en) * | 2019-06-06 | 2020-12-08 | 中国石油天然气股份有限公司 | Method, device, equipment and storage medium for predicting dynamic reserves of oil well |
| CN113515093A (en) * | 2020-04-10 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Data processing method, data processing device, production control method, production control device, equipment and storage medium |
| CN113837420A (en) * | 2020-06-23 | 2021-12-24 | 三菱电机(中国)有限公司 | Power consumption prediction method, power consumption prediction system, and computer-readable storage medium |
| US11443084B2 (en) | 2020-05-04 | 2022-09-13 | Pegatron Corporation | Method for determining productive capacity parameters and productive capacity parameters generating system |
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