CN111199305A - Neural network-based production energy consumption prediction method and system, electronic terminal and storage medium - Google Patents
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Abstract
The invention provides a production energy consumption prediction method, a system, an electronic terminal and a storage medium based on a neural network, which comprises the following steps: determining one or more production characteristic parameters related to a production process, and selecting a production data complete set in a preset time period based on the generated characteristic parameters; screening out a production data subset related to production energy consumption from the production data corpus; normalizing the production data subset to generate model training data; and establishing a neural network prediction model based on the model training data for predicting the production energy consumption data. The method and the device can solve the technical problems of the prior art that the energy consumption prediction technology is vacant, the precision is not high, the influence factor is single and the like in the enterprise production process, so that a user is helped to accurately and efficiently predict energy consumption data, and the requirements of energy conservation and efficiency improvement of enterprises are met.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a production energy consumption prediction method and system based on a neural network, an electronic terminal and a storage medium.
Background
In the production process of an enterprise, energy consumption is indispensable, so how to realize energy conservation prediction is particularly critical to the production of the whole enterprise.
At present, enterprises are generally in a vacancy state for energy consumption prediction, and even if energy consumption prediction is implemented by a few enterprises, prediction values are obtained only by the growth rule of a same proportion and a ring proportion or by a mode of calculating an average value. However, the energy consumption prediction methods cannot meet the requirements of energy conservation and efficiency improvement of enterprises in the aspects of comprehensiveness of factors, prediction accuracy, actual value and the like.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a method, a system, an electronic terminal and a storage medium for predicting energy consumption in production based on a neural network, which are used to solve the technical problems of the prior art, such as lack of energy consumption prediction technology, low accuracy and single influence factor in the enterprise production process.
To achieve the above and other related objects, the present invention provides a neural network-based method for predicting production energy consumption, comprising: determining one or more production characteristic parameters related to a production process, and selecting a production data complete set in a preset time period based on the generated characteristic parameters; screening out a production data subset related to production energy consumption from the production data corpus; normalizing the production data subset to generate model training data; and establishing a neural network prediction model based on the model training data for predicting the production energy consumption data.
In an embodiment of the invention, after completing the building of the neural network prediction model, the method further includes: and carrying out model training and testing on the neural network model based on actual production energy consumption data.
In one embodiment of the present invention, the production process comprises a tobacco shredding process and/or a tobacco wrapping process; production characteristic parameters associated with the tobacco-shredding process and/or the tobacco-wrapping process include: any one or combination of multiple parameters of energy consumption data, production month, production day, production shift, production yield, number of machines, heating degree day or cooling degree day.
In an embodiment of the present invention, the production data subset is normalized by a linear function normalization; the min-max normalization formula is:
x*∈[0.1](ii) a Wherein x represents production data in the production data subset, xminRepresenting the minimum value, x, of the production data in the production data subsetmaxRepresenting the maximum value of the production data in the production data subset.
In an embodiment of the present invention, the neural network prediction model includes a BP neural network prediction model; the BP neural network prediction model comprises an input layer, a hidden layer and an output layer, and an activation function is selected for nonlinear processing.
In an embodiment of the present invention, the hidden layer includes one or more nodes, and the formula for calculating the number of the nodes is:
wherein m represents the number of nodes of the input layer, n represents the number of nodes of the output layer, and a represents an adjustment constant between 1 and 10.
To achieve the above and other related objects, the present invention provides a neural network-based production energy consumption prediction system, which includes: the parameter determination module is used for determining one or more production characteristic parameters related to the production process and selecting a production data complete set in a preset time period based on the generated characteristic parameters; the screening module is used for screening out a production data subset related to production energy consumption from the production data corpus; the training data generation module is used for carrying out normalization processing on the production data subset so as to generate model training data; and the model establishing module is used for establishing a neural network prediction model based on the model training data and predicting the production energy consumption data.
In an embodiment of the present invention, the system further includes: and the model detection module is used for carrying out model training and testing on the neural network model based on actual production energy consumption data after the neural network prediction model is established.
To achieve the above and other related objects, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when being executed by a processor, implements the neural network-based production energy consumption prediction method.
To achieve the above and other related objects, the present invention provides an electronic terminal, comprising: a processor and a memory; the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the terminal to execute the neural network-based production energy consumption prediction method.
As described above, the neural network-based production energy consumption prediction method, system, electronic terminal and storage medium of the present invention have the following beneficial effects: the method and the device can solve the technical problems of the prior art that the energy consumption prediction technology is vacant, the precision is not high, the influence factor is single and the like in the enterprise production process, so that a user is helped to accurately and efficiently predict energy consumption data, and the requirements of energy conservation and efficiency improvement of enterprises are met.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for predicting production energy consumption based on a neural network according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a production data corpus based on production characterization parameters according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating a method for predicting energy consumption for production based on neural network according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating prediction of energy consumption for volume production based on a BP neural network prediction model according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a neural network-based production energy consumption prediction system according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of an electronic terminal according to an embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It is noted that in the following description, reference is made to the accompanying drawings which illustrate several embodiments of the present application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "over," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.
The invention provides a production energy consumption prediction method and system based on a neural network, an electronic terminal and a storage medium, and aims to solve the technical problems that the energy consumption prediction technology in the enterprise production process in the prior art is lacked, low in precision, single in influence factor and the like. Hereinafter, the working principle of the technical solution of the present invention will be described with reference to specific embodiments.
Fig. 1 shows a schematic flow chart of a method for predicting production energy consumption based on a neural network according to an embodiment of the present invention. The method can be applied to an intelligent terminal or a controller; the intelligent terminal can adopt a fixed intelligent terminal such as a desktop computer, a local server or a cloud server, and can also adopt a mobile intelligent terminal such as a mobile phone, a pad computer, a notebook computer and an intelligent bracelet; the controller referred to in the present invention may be, for example, an MCU controller, an FPGA controller, a DSP controller, an SoC controller, or an ARM controller. The production energy consumption prediction method based on the neural network comprises the following steps:
step S11: one or more production characteristic parameters related to the production process are determined, and a production data complete set in a preset time period is selected based on the generated characteristic parameters.
In one embodiment, the production process comprises a tobacco-shredding process, a tobacco-wrapping process, or a combination of both processes. The batch production working time of the tobacco shred manufacturing process is not fixed, and the minimum granularity of the power consumption measurement of the tobacco shred manufacturing process is small, so that a sample with the generated batch as the minimum granularity is difficult to correspond to the power consumption measurement. Therefore, the electricity consumption data of the tobacco shred manufacturing process in the embodiment takes the class as the minimum sample granularity, and the production characteristic parameters of the tobacco shred manufacturing process mainly comprise energy consumption data, production month, production day, production shift, production yield, heating degree day and cooling degree day. The type of the energy consumption data includes an electric quantity or an air pressure consumption number.
The daily number of heating degrees is also called as HDD value, and refers to the accumulated value of the product obtained by multiplying the difference degree between the daily average temperature value and the preset temperature by 1 day when the outdoor daily average temperature of a certain day is lower than the preset temperature in one year, and the unit of the accumulated value is ℃. d. The cooling degree daily number is also called as a CDD value, and refers to an accumulated value of products obtained by multiplying the difference degree between a daily average temperature value and a preset temperature by 1 day when the outdoor daily average temperature of a certain day is higher than the preset temperature in one year, and the unit of the accumulated value is ℃. d. In this embodiment, 26 ℃ is used as the preset temperature, i.e., the values of HDD26 and CDD26 are found.
The production capacity of the tobacco wrapping process is weaker than that of tobacco shred production, so the yield of the tobacco shred production is often set by pulling the tobacco wrapping yield. In order to meet the requirement of a large amount of cut tobaccos produced by a cut tobacco production line, a plurality of rolling and connecting devices are required to be arranged for synchronously consuming the cut tobaccos to produce cigarettes, so that influence characteristics, namely the number of started machine tables, need to be added to sample data of the rolled cigarettes. The types of the energy consumption data comprise electric quantity, air pressure consumption number or vacuum consumption number.
To facilitate understanding by those skilled in the art, fig. 2 illustrates a production data complete set selected within a year according to production characteristic parameters such as energy consumption data, production month, production day, production shift, production yield, number of machines, heating degree day (HDD value), and cooling degree day (CDD value) in one embodiment of the present invention.
S12: and screening out a production data subset related to production energy consumption from the production data corpus.
Since the production data corpus typically includes all data within a predetermined time period, a subset of the production data associated with the production energy consumption is screened. For example: if the production data corpus is production data of a year all the year, the production data of a working day needs to be screened out. Still taking fig. 2 as an example, the data is produced by using the sample plate with the smallest granularity as the number of shifts, wherein the number of shifts is divided into the early shift, the middle shift and the late shift, and is numerically set to "6", "14" and "22" for the convenience of calculation.
S13: and normalizing the production data subset to generate model training data.
Before a neural network prediction model is established, normalization processing needs to be carried out on input data, dimensional data are converted into dimensionless scalars, and the dimensionless scalars are mapped into the range of 0-1, so that calculation is simplified. In this embodiment, the production data subset is normalized by a linear function normalization.
The min-max normalization formula is:
x*∈[0.1](ii) a Wherein x represents production data in the production data subset, xminRepresenting the minimum value, x, of the production data in the production data subsetmaxRepresenting the maximum value of the production data in the production data subset.
It should be noted that the normalization processing method includes, but is not limited to, linear function normalization in this embodiment, and may also adopt a normalization based on 0-mean normalization or a normalization based on Sigmoid function, and the like, which is not limited in this disclosure.
S14: and establishing a neural network prediction model based on the model training data for predicting the production energy consumption data.
In this embodiment, the neural network prediction model includes a BP neural network prediction model; the BP neural network prediction model comprises an input layer, a hidden layer and an output layer, and an activation function is selected for nonlinear processing. The BP neural network is a neural network which takes the square of a network error as an objective function and adopts a gradient descent method to calculate the minimum value of the objective function, the error is descended along the gradient direction by adjusting the connecting strength of an input node and a hidden node, the connecting strength of the hidden node and an output node and a threshold value, network parameters (weight and threshold value) corresponding to the minimum error are determined through repeated learning training, and the training is stopped immediately.
The hidden layer of the BP neural network prediction model comprises one or more nodes, and the formula for calculating the number of the nodes is as follows:
wherein m represents the number of nodes of the input layer, n represents the number of nodes of the output layer, and a represents an adjustment constant between 1 and 10.
The activating function selected by the BP neural network comprises a tanh function, is used for solving the gradient of the BP neural network in a nonlinear way, has good fault tolerance and is bounded, and then iterative operation is carried out according to a weight adjusting formula of the BP learning algorithm. The activation function includes, but is not limited to, a tanh function, and a sigmod function, a ReLU function, or an ELU function, etc. may also be employed.
Fig. 3 is a schematic flow chart showing a method for predicting production energy consumption based on a neural network according to an embodiment of the present invention. In this embodiment, the method for predicting production energy consumption based on a neural network includes the following steps:
s31: one or more production characteristic parameters related to the production process are determined, and a production data complete set in a preset time period is selected based on the generated characteristic parameters.
S32: and screening out a production data subset related to production energy consumption from the production data corpus.
S33: and normalizing the production data subset to generate model training data.
S34: and establishing a neural network prediction model based on the model training data for predicting the production energy consumption data.
S35: and after the neural network prediction model is established, carrying out model training and testing on the neural network model based on actual production energy consumption data. After the neural network prediction model is established, the neural network prediction model needs to be repeatedly checked and corrected by using actual production data, so that the neural network prediction model can be used for predicting subsequent production energy consumption data. Since the implementation of steps S31-S34 is similar to the implementation of steps S11-S14 in the previous embodiment, further description is omitted, and only step S35 is explained below.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Fig. 4 shows a schematic diagram of prediction of energy consumption for volume production based on a BP neural network prediction model according to an embodiment of the present invention. In the present embodiment, the horizontal axis represents the date for representing the time span based on 1 month to 12 months in a certain year, the vertical axis represents the energy consumption data, the broken line 1 represents the actual production energy consumption value, and the broken line 2 represents the predicted production energy consumption value. According to the embodiment, the energy consumption data can be accurately and efficiently predicted by the user based on the BP neural network prediction model, so that the requirement of energy conservation and efficiency improvement of enterprises is met.
Fig. 5 is a schematic structural diagram of a neural network-based production energy consumption prediction system according to an embodiment of the present invention. The system includes a parameter determination module 51, a screening module 52, a training data generation module 53, a model building module 54, and a model detection module 55. The parameter determining module 51 is configured to determine one or more production characteristic parameters related to a production process, and select a production data corpus within a preset time period based on the production characteristic parameters, the screening module 52 is configured to screen a production data subset related to production energy consumption from the production data corpus, the training data generating module 53 is configured to normalize the production data subset to generate model training data, the model building module 54 is configured to build a neural network prediction model based on the model training data to predict production energy consumption data, and the model detecting module 55 is configured to perform model training and testing on the neural network model based on actual production energy consumption data after the neural network prediction model is built.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the model building module may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the model building module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when one of the above modules is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 6 is a schematic structural diagram of an electronic terminal according to an embodiment of the invention. This example provides an electronic terminal, includes: a processor 61, a memory 62, a transceiver 63, a communication interface 64, and a system bus 65; the memory 62 and the communication interface 64 are connected with the processor 61 and the transceiver 63 through the system bus 65 and complete mutual communication, the memory 62 is used for storing computer programs, the communication interface 64 and the transceiver 63 are used for communicating with other devices, and the processor 61 is used for operating the computer programs, so that the electronic terminal executes the steps of the neural network-based production energy consumption prediction method.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The memory may include a Random Access Memory (RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the integrated circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
In summary, the production energy consumption prediction method, the production energy consumption prediction system, the electronic terminal and the storage medium based on the neural network can solve the technical problems of the prior art, such as lack of energy consumption prediction technology, low precision, single influence factor and the like in the enterprise production process, so as to help a user accurately and efficiently predict energy consumption data, and meet the requirements of energy conservation and efficiency improvement of enterprises. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A neural network-based production energy consumption prediction method is characterized by comprising the following steps:
determining one or more production characteristic parameters related to a production process, and selecting a production data complete set in a preset time period based on the generated characteristic parameters;
screening out a production data subset related to production energy consumption from the production data corpus;
normalizing the production data subset to generate model training data;
and establishing a neural network prediction model based on the model training data for predicting the production energy consumption data.
2. The neural network based energy production prediction method of claim 1, wherein after completing the building of the neural network prediction model, the method further comprises: and carrying out model training and testing on the neural network model based on actual production energy consumption data.
3. The neural network-based production energy consumption prediction method according to claim 1, characterized in that:
the production process comprises a tobacco shred making process and/or a tobacco wrapping process;
production characteristic parameters associated with the tobacco-shredding process and/or the tobacco-wrapping process include: any one or combination of multiple parameters of energy consumption data, production month, production day, production shift, production yield, number of machines, heating degree day or cooling degree day.
4. The neural network based prediction method of energy consumption for production as claimed in claim 1, wherein the subset of production data is normalized by means of linear function normalization;
the min-max normalization formula is:
x*∈[0.1](ii) a Wherein x represents production data in the production data subset, xminRepresenting the minimum value, x, of the production data in the production data subsetmaxRepresenting the maximum value of the production data in the production data subset.
5. The neural network based production energy prediction method of claim 1, wherein the neural network prediction model comprises a BP neural network prediction model; the BP neural network prediction model comprises an input layer, a hidden layer and an output layer, and an activation function is selected for nonlinear processing.
6. The neural network-based production energy consumption prediction method according to claim 5, wherein the hidden layer comprises one or more nodes, and the formula for calculating the number of nodes is as follows:
wherein m represents the number of nodes of the input layer, n represents the number of nodes of the output layer, and a represents an adjustment constant between 1 and 10.
7. A neural network-based production energy consumption prediction system, comprising:
the parameter determination module is used for determining one or more production characteristic parameters related to the production process and selecting a production data complete set in a preset time period based on the generated characteristic parameters;
the screening module is used for screening out a production data subset related to production energy consumption from the production data corpus;
the training data generation module is used for carrying out normalization processing on the production data subset so as to generate model training data;
and the model establishing module is used for establishing a neural network prediction model based on the model training data and predicting the production energy consumption data.
8. The system for predicting energy consumption for production as set forth in claim 7, further comprising:
and the model detection module is used for carrying out model training and testing on the neural network model based on actual production energy consumption data after the neural network prediction model is established.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the neural network-based production energy consumption prediction method according to any one of claims 1 to 6.
10. An electronic terminal, comprising: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored by the memory to enable the electronic terminal to execute the neural network based production energy consumption prediction method according to any one of claims 1 to 6.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111523683A (en) * | 2020-07-06 | 2020-08-11 | 北京天泽智云科技有限公司 | Method and system for predicting technological parameters in tobacco processing |
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| CN116629454A (en) * | 2023-07-19 | 2023-08-22 | 武汉新威奇科技有限公司 | Method and system for predicting production efficiency of servo screw press based on neural network |
| CN118393962A (en) * | 2024-06-24 | 2024-07-26 | 江西江铃集团晶马汽车有限公司 | Intelligent monitoring and energy efficiency improving method and system for energy consumption of passenger car production line |
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| CN111523683A (en) * | 2020-07-06 | 2020-08-11 | 北京天泽智云科技有限公司 | Method and system for predicting technological parameters in tobacco processing |
| CN111523683B (en) * | 2020-07-06 | 2020-10-30 | 北京天泽智云科技有限公司 | Method and system for predicting technological parameters in tobacco processing |
| CN112990591A (en) * | 2021-03-26 | 2021-06-18 | 江西省能源大数据有限公司 | Multi-dimensional energy consumption data analysis method based on convolutional neural network and enterprise energy consumption prediction model |
| CN113344192A (en) * | 2021-05-31 | 2021-09-03 | 中国标准化研究院 | Enterprise-level motor system energy-saving optimization automatic control method and system |
| CN113673144A (en) * | 2021-07-13 | 2021-11-19 | 上海烟草集团有限责任公司 | Prediction system and method for data of tobacco stem drying equipment |
| CN113627489A (en) * | 2021-07-14 | 2021-11-09 | 青岛海尔能源动力有限公司 | Demand-based power consumption prediction method, device, equipment and storage medium |
| CN116629454A (en) * | 2023-07-19 | 2023-08-22 | 武汉新威奇科技有限公司 | Method and system for predicting production efficiency of servo screw press based on neural network |
| CN116629454B (en) * | 2023-07-19 | 2023-10-03 | 武汉新威奇科技有限公司 | Method and system for predicting production efficiency of servo screw press based on neural network |
| CN118393962A (en) * | 2024-06-24 | 2024-07-26 | 江西江铃集团晶马汽车有限公司 | Intelligent monitoring and energy efficiency improving method and system for energy consumption of passenger car production line |
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