CN109118012B - Industrial dynamic multi-dimensional energy consumption cost prediction method, system, storage medium and terminal - Google Patents
Industrial dynamic multi-dimensional energy consumption cost prediction method, system, storage medium and terminal Download PDFInfo
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Abstract
The invention discloses an industrial dynamic multi-dimensional energy consumption cost prediction method, a system, a storage medium and a terminal, wherein the method comprises the following steps: establishing an energy consumption cost analysis model; establishing an ERP product factor constraint condition; establishing a production system factor constraint condition: and solving the optimal value of the energy consumption cost objective function under the constraint conditions of the ERP product factors and the production system factors. The method fully utilizes the existing energy consumption data digitization method to obtain the real-time data of the energy consumption and the related energy consumption influence factors, establishes the energy consumption cost prediction analysis model through the time sequence, the ERP product data, the production data and the unit energy consumption calculation data, establishes the dynamic constraint condition through obtaining the related factors in real time, and dynamically solves the optimal solution of the energy consumption cost prediction model, thereby obtaining the current optimal energy consumption cost analysis data, namely the lowest energy consumption cost data and the future productivity time distribution scheme.
Description
Technical Field
The invention relates to an industrial dynamic multi-dimensional energy consumption cost prediction method, a system, a storage medium and a terminal.
Background
With the continuous development of the industrial manufacturing level, each data in the industrial production is paid more and more attention, the utilization of the data by the industrial software and hardware system is developed from informatization to digitization and intellectualization, and the data utilization needs to ensure the integrity of the data on one hand and have more and more requirements on data prediction and analysis due to the diversity of the data on the other hand.
Energy consumption data as an important component of industrial data, there are many related applications. However, these systems only implement digitization and simple statistical analysis of the energy consumption data, and the energy consumption data exists as an isolated statistical data; the method is characterized in that the energy consumption data are collected in a digital mode, the.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an industrial dynamic multi-dimensional energy consumption cost prediction method, an industrial dynamic multi-dimensional energy consumption cost prediction system, a storage medium and a terminal.
The purpose of the invention is realized by the following technical scheme: an industrial dynamic multi-dimensional energy consumption cost prediction method comprises the following steps:
establishing an energy consumption cost analysis model: acquiring historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function;
establishing an ERP product factor constraint condition: establishing ERP product factor constraint conditions according to product delivery data of ERP order data, wherein the product delivery data comprises real-time finished quantity and quantity to be finished of the current analysis period;
establishing a production system factor constraint condition: acquiring actual production efficiency of a station, a production line and a workshop corresponding to a product, and establishing a production system factor constraint condition;
energy consumption cost prediction: and solving the optimal value of the energy consumption cost objective function under the constraint conditions of the ERP product factors and the production system factors.
Further, the ERP order data is the actual product delivery quantity in the analysis period;
the production data is the production capacity in the time division range;
the energy consumption unit calculation data is the rate of unit energy consumption usage.
Further, the establishing of the energy consumption cost analysis model comprises the following substeps:
acquiring a historical cost data set (x, y), wherein x is yield and y is yield cost data;
dividing the historical cost data into a training data set K1 and a validation data set K2;
setting a plurality of target functions including a preset target function Fn(x) Respectively fitting the yield data and the yield cost data in the training data set K1 to each objective function by using a fitting algorithm, and solving the optimal value of the parameter of each objective function; wherein n is the number of objective functions;
will verify the production data x in the data set K2kBringing in each preset objective function to obtain F corresponding to each preset objective functionn(xk) (ii) a Will verify the yield cost data y in the data set K2kAnd Fn(xk) Solving the mean square error to obtain the corresponding mean square error T of each objective functionn(ii) a Wherein the value range of K is 1-m, and m is the number of data sets of the verification data set K2;
and selecting the target function with the minimum mean square error as a model.
Further, the establishing of the production system factor constraint condition comprises the following substeps:
acquiring actual production efficiency of a current station, a current production line and a current workshop and mapping relations between products and the station, the production line and the workshop;
calculating the production efficiency of the product;
wherein, for a combined product or a multi-process product, the production efficiency of the product is calculated with the lowest practical production efficiency, thereby establishing a production system factor constraint condition.
Further, the energy consumption cost prediction further includes:
and under the condition of obtaining the optimal solution of the model, simultaneously obtaining the lowest energy consumption cost, and giving out the production plans of different products in different time periods in the future time range.
Furthermore, the method adopts a triggering and/or timing calculation method; wherein, the triggering is that when an external user needs, a prediction analysis method is immediately executed to obtain a current prediction value; the timing is to take a certain time as a period, perform prediction analysis every other period, and store a data result.
Further, when a timing calculation method is adopted, the energy consumption cost predicted value obtained by each calculation is compared with the energy consumption cost benchmarking data, and if the energy consumption cost predicted value exceeds the range, data early warning is carried out.
The invention also provides an industrial dynamic multi-dimensional energy consumption cost prediction system, which comprises:
an energy consumption cost analysis model establishing module: the method comprises the steps of obtaining historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function;
an ERP product factor constraint condition establishing module: the ERP product factor constraint condition is established according to product delivery data of the ERP order data, wherein the product delivery data comprises real-time finished quantity and to-be-finished quantity of the current analysis period;
a production system factor constraint condition establishing module: the method is used for acquiring the actual production efficiency of a station, a production line and a workshop corresponding to a product and establishing a production system factor constraint condition;
an energy consumption cost prediction module: the method is used for solving the optimal value of the energy consumption cost objective function under the constraint condition of the ERP product factor and the constraint condition of the production system factor.
The invention also provides a storage medium, on which computer instructions are stored, which when executed perform the steps of the industrial dynamic multidimensional energy consumption cost prediction method.
The invention also provides a terminal, which comprises a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, and the processor executes the steps of the industrial dynamic multi-dimensional energy consumption cost prediction method when running the computer instructions.
The invention has the beneficial effects that:
(1) the method fully utilizes the existing energy consumption data digitization method to obtain the real-time data of the energy consumption and the related energy consumption influence factors, establishes the energy consumption cost prediction analysis model through the time sequence, the ERP product data, the production data and the unit energy consumption calculation data, establishes the dynamic constraint condition through obtaining the related factors in real time, and dynamically solves the optimal solution of the energy consumption cost prediction model, thereby obtaining the current optimal energy consumption cost analysis data, namely the lowest energy consumption cost data and the future productivity time distribution scheme.
(2) According to the invention, through establishment and selection of a plurality of objective functions, an energy consumption cost analysis model which is most suitable for the factory data is selected, so that the later-stage data analysis is more accurate.
(3) According to the invention, the energy consumption prediction analysis is executed at regular time, so that the mapping relation between the productivity and the energy consumption can be established, the energy consumption use cost is greatly reduced, and meanwhile, the energy consumption cost benchmarking data is established.
(4) The invention establishes an energy consumption prediction early warning mechanism, is beneficial to reducing the risk dependence between sales, production and energy consumption, and establishes a new risk evaluation mode for enterprise decision makers.
Drawings
FIG. 1 is a flowchart of a method of example 1 of the present invention;
FIG. 2 is a block diagram of a system according to embodiment 2 of the present invention;
FIG. 3 is a block diagram of an apparatus according to embodiment 4 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment provides an industrial dynamic multi-dimensional energy consumption cost prediction method, and the embodiment establishes an energy consumption cost analysis model by using factors influencing energy consumption cost, wherein the factors comprise ERP information system data, production execution system (MES) data and energy consumption unit calculation data. And dynamically acquiring real-time values of different factors and corresponding constraint conditions in real time on line, and calculating the optimal solution (the lowest cost scheme) of the model in real time, thereby obtaining the real-time optimal energy consumption cost scheme. Specifically, as shown in fig. 1, the method comprises the following steps:
s1: establishing an energy consumption cost analysis model: and acquiring historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function.
In a preferred embodiment of this embodiment, the ERP order data is an actual product delivery amount in an analysis period.
Specifically, the predicted analysis period is one day by default, and if the analysis period needs to be extended, for example, one week or one month, data needs to be aggregated by day for relevant predictive analysis factors, so as to obtain a larger period of predictive analysis factor parameters. Thus, ERP order data, which primarily refers to the number of products actually delivered during the day (specified forecast period), includes both single products and combination products.
Meanwhile, the production data is the production capacity within the time division range; in particular, it is typically one hour, such as 15:00-16:00 production capacity of the production line.
In addition, the energy consumption unit calculation data in this step is the rate of unit energy consumption usage, such as electricity rate, water rate, and gas rate. For the factory users, time-sharing prices may exist, and unit energy consumption rates need to be calculated according to different time ranges.
The energy consumption cost analysis model building method in the step comprises the following substeps:
s11: acquiring a historical cost data set (x, y), wherein x is yield and y is yield cost data;
the method comprises the steps of extracting yield and production cost data through analysis of an existing system database of an enterprise, and decomposing the production cost data according to a production shift to obtain historical order yield and historical order cost data, wherein the historical order yield and the historical order cost data are x and y values.
S12: dividing the historical cost data into a training data set K1 and a validation data set K2;
s13: setting a plurality of includes presettingObjective function Fn(x) Respectively fitting the yield data and the yield cost data in the training data set K1 to each objective function by using a fitting algorithm, and solving the optimal value of the parameter of each objective function; wherein n is the number of objective functions;
specifically, in this embodiment, two objective functions are assumed, which are respectively:
F1(x)＝a1x2+b1x+c1 (1)
F2(x)＝a2x3+b2x2+c2x+d (2)
the parameters of the two objective functions are a1、b1And c1A parameter, and a2、b2、c2And a d parameter.
In this step, the training data set K1 is substituted into the objective function F using a fitting algorithm1(x) Fitting, and solving the optimal values of the parameters a1, b1 and c 1; then, the training data set K1 is substituted by using a fitting algorithm to the target function F2(x) Fitting is carried out, and the optimal values of the a2, b2, c2 and d parameters are solved.
S14: will verify the production data x in the data set K2kBringing in each preset objective function to obtain F corresponding to each preset objective functionn(xk) (ii) a Will verify the yield cost data y in the data set K2kAnd Fn(xk) Solving the mean square error to obtain the corresponding mean square error T of each objective functionn(ii) a Wherein, the value range of K is 1-m, and m is the data set number of the verification data set K2.
Specifically, in this step:
first, an objective function F is evaluated1(x) Using K2X in data setkCarry in F1Obtaining FkThen, a prediction set F is obtainedk,Fk+1,……,Fn，Using K2Y in the setkSolving for FkAnd ykMean square error of (T)1。
Commenting on the same principleEstimating an objective function F2(x) Using K2X in data setkCarry in F2Obtaining FkWill find a prediction set Fk,Fk+1,……,FnUsing K2Y in the setkSolving for FkAnd ykMean square error of (T)2。
S15: and selecting the target function with the minimum mean square error as a model.
Through the establishment and selection of a plurality of objective functions, an energy consumption cost analysis model which is most suitable for the factory data is selected, so that the later-stage data analysis is more accurate.
S2: establishing an ERP product factor constraint condition: ERP product factor constraints (such as the final delivery period of the production order) are established based on product delivery data of the ERP order data, wherein the product delivery data includes real-time completed and to-be-completed quantities for the current analysis cycle.
In this step, the ERP order data is required to obtain the product delivery data amount of the current day in real time, including the finished number and the to-be-finished number of the current day, where the to-be-finished number is the required delivery amount after the current time point, and the value is calculated in real time.
S3: establishing a production system factor constraint condition: and acquiring the actual production efficiency of a station, a production line and a workshop corresponding to the product, and establishing the factor constraint conditions of the production system (such as the minimum production quantity of each shift and the quantity of orders to be finished for each shift).
Preferably, this step comprises the sub-steps of:
acquiring actual production efficiency of a current station, a current production line and a current workshop and mapping relations between products and the station, the production line and the workshop;
calculating the production efficiency of the product;
wherein, for a combined product or a multi-process product, the production efficiency of the product is calculated with the lowest practical production efficiency, thereby establishing a production system factor constraint condition.
S4: energy consumption cost prediction: and solving the optimal value of the energy consumption cost objective function under the constraint conditions of the ERP product factors and the production system factors.
Preferably, in this embodiment, the predicting of the energy consumption cost further includes:
and under the condition of obtaining the optimal solution of the model, simultaneously obtaining the lowest energy consumption cost, and giving out the production plans of different products in different time periods in the future time range.
Namely, the method of the embodiment is also used for planning the future capacity.
Preferably, in this embodiment, the method employs a triggering and/or timing calculation method; wherein, the triggering is that when an external user needs, a prediction analysis method is immediately executed to obtain a current prediction value; the timing is to take a certain time as a period, perform prediction analysis every other period, and store a data result.
Based on the implementation of the content of the preferred embodiment, when a timing calculation method is adopted, the energy consumption cost predicted value obtained by each calculation is compared with the energy consumption cost benchmarking data, and if the energy consumption cost predicted value exceeds the energy consumption cost benchmarking data, data early warning is carried out.
By executing the energy consumption prediction analysis at regular time, the mapping relation between the capacity and the energy consumption can be established, the energy consumption use cost is greatly reduced, and meanwhile, the energy consumption cost benchmarking data is established. An energy consumption prediction early warning mechanism is established, so that the risk dependence between sales, production and energy consumption is reduced, and a new risk evaluation mode is established for enterprise decision makers.
Example 2
The embodiment provides an industrial dynamic multi-dimensional energy consumption cost prediction system, the inventive concept of the embodiment is similar to that of embodiment 1, and an energy consumption cost analysis model is established by using factors influencing energy consumption cost, wherein the factors comprise ERP information system data, production execution system (MES) data and energy consumption unit calculation data. And dynamically acquiring real-time values of different factors and corresponding constraint conditions in real time on line, and calculating the optimal solution (the lowest cost scheme) of the model in real time, thereby obtaining the real-time optimal energy consumption cost scheme. As shown in fig. 2, the system comprises:
an energy consumption cost analysis model establishing module: the method comprises the steps of obtaining historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function;
an ERP product factor constraint condition establishing module: the ERP product factor constraint condition is established according to product delivery data of the ERP order data, wherein the product delivery data comprises real-time finished quantity and to-be-finished quantity of the current analysis period;
a production system factor constraint condition establishing module: the method is used for acquiring the actual production efficiency of a station, a production line and a workshop corresponding to a product and establishing a production system factor constraint condition;
an energy consumption cost prediction module: the method is used for solving the optimal value of the energy consumption cost objective function under the constraint condition of the ERP product factor and the constraint condition of the production system factor.
Example 3
Based on the implementation of embodiment 1, this embodiment further provides a storage medium, on which computer instructions are stored, and the computer instructions execute the steps of the industrial dynamic multidimensional energy consumption cost prediction method described in embodiment 1 when running.
Based on such understanding, the technical solution of the present embodiment or parts of the technical solution may be essentially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Example 4
Based on the implementation of embodiment 1, the present invention further provides a terminal, as shown in fig. 3, which includes a memory and a processor, where the memory stores computer instructions executable on the processor, and the processor executes the computer instructions to perform the steps of the industrial dynamic multidimensional energy consumption cost prediction method described in embodiment 1.
Each functional unit in the embodiments provided by the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In all embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units/modules is only one logical division, and there may be other divisions in actual implementation, and for example, a plurality of units or modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
It is to be understood that the above-described embodiments are illustrative only and not restrictive of the broad invention, and that various other modifications and changes in light thereof will be suggested to persons skilled in the art based upon the above teachings. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (8)
1. An industrial dynamic multi-dimensional energy consumption cost prediction method is characterized by comprising the following steps: the method comprises the following steps:
establishing an energy consumption cost analysis model: acquiring historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function;
establishing an ERP product factor constraint condition: establishing ERP product factor constraint conditions according to product delivery data of ERP order data, wherein the product delivery data comprises real-time finished quantity and quantity to be finished of the current analysis period;
establishing a production system factor constraint condition: acquiring actual production efficiency of a station, a production line and a workshop corresponding to a product, and establishing a production system factor constraint condition;
energy consumption cost prediction: solving an optimal value, namely a lowest cost scheme, of the energy consumption cost objective function under the constraint conditions of the ERP product factors and the constraint conditions of the production system factors;
the method adopts a triggering and/or timing calculation method; wherein, the triggering is that when an external user needs, a prediction analysis method is immediately executed to obtain a current prediction value; the timing is to take a certain time as a period, perform prediction analysis every other period, and store a data result;
when a timing calculation method is adopted, the energy consumption cost predicted value obtained by each calculation is compared with the energy consumption cost benchmarking data, and if the energy consumption cost predicted value exceeds the range, data early warning is carried out.
2. The industrial dynamic multi-dimensional energy consumption cost prediction method according to claim 1, characterized in that: the ERP order data is the actual product delivery quantity in the analysis period;
the production data is the production capacity in the time division range;
the energy consumption unit calculation data is the rate of unit energy consumption usage.
3. The industrial dynamic multidimensional energy consumption cost prediction method according to claim 1 or 2, characterized by comprising the following steps: the method for establishing the energy consumption cost analysis model comprises the following substeps:
acquiring a historical cost data set (x, y), wherein x is yield and y is yield cost data;
dividing the historical cost data into a training data set K1 and a validation data set K2;
setting a plurality of target functions including a preset target function Fn(x) Using a fitting algorithm to fit the production data in the training data set K1Respectively fitting each objective function according to the yield cost data, and solving the optimal value of the parameter of each objective function; wherein n is the number of objective functions;
will verify the production data x in the data set K2kBringing in each preset objective function to obtain F corresponding to each preset objective functionn(xk) (ii) a Will verify the yield cost data y in the data set K2kAnd Fn(xk) Solving the mean square error to obtain the corresponding mean square error T of each objective functionn(ii) a Wherein the value range of K is 1-m, and m is the number of data sets of the verification data set K2;
and selecting the target function with the minimum mean square error as a model.
4. The industrial dynamic multi-dimensional energy consumption cost prediction method according to claim 1, characterized in that: the method for establishing the production system factor constraint condition comprises the following substeps:
acquiring actual production efficiency of a current station, a current production line and a current workshop and mapping relations between products and the station, the production line and the workshop;
calculating the production efficiency of the product;
wherein, for a combined product or a multi-process product, the production efficiency of the product is calculated with the lowest practical production efficiency, thereby establishing a production system factor constraint condition.
5. The industrial dynamic multi-dimensional energy consumption cost prediction method according to claim 1, characterized in that: the energy consumption cost prediction further comprises:
and under the condition of obtaining the optimal solution of the model, simultaneously obtaining the lowest energy consumption cost, and giving out the production plans of different products in different time periods in the future time range.
6. An industrial dynamic multidimensional energy consumption cost prediction system is characterized in that: the method comprises the following steps:
an energy consumption cost analysis model establishing module: the method comprises the steps of obtaining historical ERP order data, production data and unit energy consumption calculation data in time sequence, and establishing an energy consumption cost objective function;
an ERP product factor constraint condition establishing module: the ERP product factor constraint condition is established according to product delivery data of the ERP order data, wherein the product delivery data comprises real-time finished quantity and to-be-finished quantity of the current analysis period;
a production system factor constraint condition establishing module: the method is used for acquiring the actual production efficiency of a station, a production line and a workshop corresponding to a product and establishing a production system factor constraint condition;
an energy consumption cost prediction module: the method is used for solving an optimal value, namely a lowest cost scheme, of the energy consumption cost objective function under the constraint conditions of the ERP product factors and the constraint conditions of the production system factors;
the system adopts a triggering and/or timing calculation mode; the method comprises the following steps that a prediction system is immediately executed to obtain a current prediction value when an external user needs; the timing is to take a certain time as a period, perform prediction analysis every other period, and store a data result;
when a timing calculation mode is adopted, the energy consumption cost predicted value obtained by each calculation is compared with the energy consumption cost benchmarking data, and if the energy consumption cost predicted value exceeds the range, data early warning is carried out.
7. A storage medium having stored thereon computer instructions, characterized in that: the computer instructions when executed perform the steps of a method for industrial dynamic multi-dimensional energy consumption cost prediction according to any one of claims 1 to 5.
8. A terminal comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, wherein the processor executes the computer instructions to perform the steps of the method for industrial dynamic multi-dimensional energy consumption cost prediction of any one of claims 1 to 5.
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| CN111160598A (en) * | 2019-11-13 | 2020-05-15 | 浙江中控技术股份有限公司 | Energy prediction and energy consumption control method and system based on dynamic energy consumption benchmark |
| CN110991938B (en) * | 2019-12-24 | 2023-12-22 | 上海申瑞继保电气有限公司 | Energy consumption calculation method for multi-product production line |
| 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 |
| TWI721879B (en) | 2020-05-04 | 2021-03-11 | 和碩聯合科技股份有限公司 | Method of determining productive capacity parameters and productive capacity parameters generating system |
| 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 |
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