CN112036085A - Steel rolling fuel consumption parameter recommendation method, system, medium and terminal - Google Patents
Steel rolling fuel consumption parameter recommendation method, system, medium and terminal Download PDFInfo
- Publication number
- CN112036085A CN112036085A CN202010894071.6A CN202010894071A CN112036085A CN 112036085 A CN112036085 A CN 112036085A CN 202010894071 A CN202010894071 A CN 202010894071A CN 112036085 A CN112036085 A CN 112036085A
- Authority
- CN
- China
- Prior art keywords
- data set
- steel rolling
- fuel consumption
- burnup
- algorithm model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Control Of Metal Rolling (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a method, a system, a medium and a terminal for recommending a steel rolling burnup parameter, wherein the method comprises the steps of obtaining steel rolling production data in a steel rolling process production line and establishing a burnup process data set; establishing an optimal algorithm model according to the burnup process data set, and training the optimal algorithm model; acquiring important features of the data according to the trained optimal algorithm model, and establishing an important feature data set; constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using a trained optimal algorithm model; the invention eliminates the interference factors of the acquired data, has high parameter recommendation accuracy, can adjust the minimum fuel consumption value in time and keep the optimal numerical value of the process parameter when equipment and environment change, and solves the problems of low fuel consumption analysis precision and poor real-time performance of the heating furnace in the prior art.
Description
Technical Field
The invention relates to the field of metallurgy and the field of artificial intelligence, in particular to a steel rolling fuel consumption parameter recommendation method, a steel rolling fuel consumption parameter recommendation system, a steel rolling fuel consumption parameter recommendation medium and a steel rolling fuel consumption parameter recommendation terminal based on an algorithm prediction model.
Background
In the metallurgical industry, the burnup of a heating furnace occupies 65-75% of the energy consumption of a steel rolling process and is one of the main cost sources of the rolling product production line, so how to predict and analyze the correlation parameters of the burnup of the heating furnace and determine accurate burnup control parameters has important significance for optimizing the steel rolling production process and reducing the steel production operation cost. At present, the steel industry carries out staged analysis on the operation of a heating furnace mainly according to the burn-up prediction on a rolled product production line, specifically, correlation parameters with the burn-up are analyzed by collecting basic production operation data, and then the burn-up prediction and the time and temperature parameters of an optimization procedure are carried out through an empirical formula, so that the purpose of reducing the burn-up is achieved.
However, in a complex production process, interference factors of collected data cannot be eliminated no matter a worker directly participates in setting or performs analysis through an inherent mathematical model, so that the minimum fuel consumption value is inaccurate, fuel consumption waste is generated, and when equipment and environment change, the minimum fuel consumption value cannot be adjusted in time and the optimal numerical value of a process parameter cannot be maintained.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method, a system, a medium and a terminal for recommending a burn-up parameter of rolled steel, so as to solve the above-mentioned technical problems.
The invention provides a method for recommending steel rolling fuel consumption parameters, which comprises the following steps:
acquiring steel rolling production data in a steel rolling process production line, and establishing a fuel consumption process data set;
establishing an optimal algorithm model according to the burnup process data set, and training the optimal algorithm model;
acquiring important features of the data according to the trained optimal algorithm model, and establishing an important feature data set;
and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
Optionally, the fuel consumption process parameter data is periodically obtained again, the optimal algorithm model is updated, and the recommended process parameter with the minimum fuel consumption value is obtained again according to the updated model.
Optionally, the steel rolling production data includes process parameters and personnel operation parameters, and the fuel consumption process data set is obtained by preprocessing the steel rolling production data, where the preprocessing includes:
the method comprises the steps of merging personnel operation parameters into steel rolling production data, forming new characteristics through data difference values, and obtaining a complete steel rolling production data set;
filling missing values into the complete steel rolling production data set;
and deleting abnormal data in the complete steel rolling production data set.
Optionally, performing normalization processing on the burnup process data set;
respectively adopting different algorithm modes to the processed burnup process data set to obtain a plurality of algorithm models;
respectively obtaining the mean absolute error rates of all algorithm models;
and obtaining an optimal algorithm model by comparing the absolute error rate mean values of the models.
Optionally, the different algorithms include linear regression, random forest, support vector regression, neural network, and extreme gradient boost training.
Optionally, obtaining importance scores of each feature in each model;
taking the accuracy of each model as a weight, and carrying out weighted average processing on all the characteristics;
sorting the importance degree scores of the features after weighted average processing, and screening according to a preset threshold value to obtain an important feature data set;
and performing regression testing on the important characteristic data set, and acquiring the characteristic proportion with the best effect so as to acquire the optimal fuel consumption algorithm model containing the optimal parameter value.
Optionally, constructing a feature field equipartition data set for the important feature data set includes:
acquiring the upper limit and the lower limit of each important feature to form a new important feature upper limit and lower limit data set;
dividing each column of characteristic fields of the important characteristic upper and lower limit data sets into fixed segments at equal intervals, and forming a characteristic field equipartition data set according to process logic.
The invention also provides a steel rolling fuel consumption parameter recommendation system, which comprises
The data acquisition module is used for acquiring steel rolling production data in a steel rolling process production line;
the data processing module is used for establishing a burnup process data set and processing the burnup process data set;
the model training module is used for establishing an optimal algorithm model according to the fuel consumption process data set and training the optimal algorithm model;
the alternative scheme construction module is used for constructing an alternative scheme data set;
the parameter recommendation module is used for acquiring important features of the data according to the trained optimal algorithm model and establishing an important feature data set; and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above.
The present invention also provides an electronic terminal, comprising: a processor and a memory;
the memory is adapted to store a computer program and the processor is adapted to execute the computer program stored by the memory to cause the terminal to perform the method as defined in any one of the above.
The invention has the beneficial effects that: according to the method, the system, the medium and the terminal for recommending the steel rolling fuel consumption parameters, interference factors of collected data are eliminated, the parameter recommendation accuracy is high, the lowest fuel consumption value can be adjusted in time and the optimal numerical value of the process parameters can be kept when equipment and environment change, and the problems that in the prior art, the heating furnace fuel consumption analysis is low in fineness and poor in instantaneity are solved.
Drawings
FIG. 1 is a flow chart of a method for recommending a steel rolling burnup parameter in the embodiment of the invention.
FIG. 2 is a schematic structural diagram of a steel rolling burnup parameter recommendation system in the embodiment of the invention.
FIG. 3 is a schematic diagram of a fuel consumption recommendation result of the steel rolling fuel consumption parameter recommendation method in the 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 should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
As shown in fig. 1, the method for recommending the fuel consumption parameter of rolled steel in the embodiment includes:
acquiring steel rolling production data in a steel rolling process production line, and establishing a fuel consumption process data set;
establishing an optimal algorithm model according to the burnup process data set, and training the optimal algorithm model;
acquiring important features of the data according to the trained optimal algorithm model, and establishing an important feature data set;
and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
In this embodiment, the steel rolling production data mainly includes process parameters and human operation parameters, the process parameters are data generated in real time by the production line, and the human operation parameters are data set by an operator. The technological parameters comprise various technological parameters such as a heating section residual oxygen value, a soaking section residual oxygen value, a preheating section residual oxygen amount, a heating section upper air-fuel ratio, a heating section lower air-fuel ratio, a soaking section rolling mill side upper air-fuel ratio, a soaking section rolling mill side lower air-fuel ratio and the like. And preprocessing the steel rolling production data to obtain a burnup process data set. The preprocessing in the embodiment comprises the steps of merging the characteristics of the operating parameters of the personnel into a steel rolling production data set, and performing data difference to form new characteristics to obtain a complete steel rolling production data set; filling missing values into the complete steel rolling production data set, carrying out digital processing on character type characteristics by using a one-hot method, and carrying out average processing on partial characteristics; and deleting abnormal data in the complete steel rolling production data set to obtain a cleaned burnup process data set. Optionally, an isolated forest algorithm can be used for finding out abnormal values and deleting the abnormal values, and the discharge data sets are sorted according to the discharge time; filling missing values of the fuel consumption process parameter data set, specifically filling the missing values by using the previous values; digitizing the character-type features, for example, digitizing the feature values into numbers such as 0, 1, 2, etc. by using a one-hot method; averaging partial features in the image; and obtaining a burnup process data set of the steel rolling procedure after cleaning.
In this embodiment, the burnup process data set is normalized, and the processed burnup process data set is respectively subjected to different algorithm modes to obtain a plurality of algorithm models, and the average absolute error rates of the algorithm models are respectively obtained; optionally, in this embodiment, Regression models such as linear Regression, random forest, SVR (Support vector Regression), neural network, and Xgboost (eXtreme Gradient Boosting) are used for training to obtain the optimal algorithm model, and the linear Regression algorithm, the random forest algorithm, the SVR algorithm, the neural network algorithm, and the Xgboost Regression algorithm in the skleern library are first introduced. And obtaining an initial model by respectively using the default parameters of the models. And respectively training each regression algorithm model by using the cleaned burnup process data set of the steel rolling process, dividing the difference of the actual value subtracted by the predicted value by the average of absolute values of the quotient of the actual value to obtain an absolute error rate mean value, and comparing the absolute error rate mean values of the models to obtain an optimal algorithm model. The formula of the mean absolute error rate is as follows
Wherein y _ pred [ i ] is a burn-up predicted value, y _ true [ i ] is a burn-up actual value, and n is the number of samples
And obtaining an optimal algorithm model by comparing the absolute error rate mean values of the models.
In the embodiment, the importance degree scores of all the characteristics in all the models are obtained; taking the accuracy of each model as a weight, and carrying out weighted average processing on all the characteristics; sorting the importance degree scores of the features after weighted average processing, and screening according to a preset threshold value to obtain an important feature data set; and performing regression testing on the important characteristic data set, and acquiring the characteristic proportion with the best effect so as to acquire the optimal fuel consumption algorithm model containing the optimal parameter value. Obtaining important features of data according to the optimal algorithm model to obtain an important feature data set, optionally, calculating importance degree scores of each feature in the fuel consumption process data set by a model. By using the ranking of the importance degree scores of the algorithm models according to the characteristics from large to small and screening according to a preset threshold value, an important characteristic data set is obtained, for example: and respectively taking the characteristics with the importance degrees of the first 80%, 60% and 50% as the important characteristics of the data set to obtain the important characteristic data set, wherein the significance of the important characteristic data set can be interpreted as the process parameter characteristics with larger correlation with the fuel consumption.
In this embodiment, the important feature data set and the optimal algorithm model are used to adjust parameters, and an optimal fuel consumption algorithm model is obtained through training. And training an optimal algorithm model by using the important characteristic data set, wherein five main parameters of n _ estimators, reg _ alpha, reg _ lambda, max _ depth and colsample _ byte are adjusted by using a grid parameter searching method to obtain the optimal fuel consumption algorithm model.
In this embodiment, a feature field averaging data set is constructed for the important feature set, and the optimal burn-up algorithm model is started to obtain the recommended process parameter with the minimum burn-up value. In the embodiment, the construction of the fuel consumption process parameter data set with the characteristic fields divided equally comprises the steps of obtaining the upper limit and the lower limit of each important characteristic to form a new upper limit and lower limit data set of the important characteristics; dividing each column of characteristic fields of the important characteristic upper and lower limit data sets into fixed segments at equal intervals, and forming a characteristic field equipartition data set according to process logic. For example, taking temperature as an example, the following are specifically mentioned: firstly, segmenting an important characteristic data set according to the furnace entering temperature, adding some process logic limiting conditions into N pieces of segmented data, for example, after the furnace entering temperature is less than the preheating temperature and less than the heating temperature and less than the soaking temperature, only N/2 pieces of data are left, bringing the constructed discrete point training set into an optimal fuel consumption algorithm model for continuous training to obtain a set of fuel consumption predicted values, selecting the minimum value in the fuel consumption predicted value set, wherein the characteristic value corresponding to the minimum value is the preferred process parameter recommended value, and the specific fuel consumption recommended result is shown in figure 3.
In this embodiment, burn-up process parameter data is periodically collected, and the above steps are cycled to update the optimal model and recommend process parameters. Optionally, newly generated process parameter data is periodically merged into a historical data set, the historical optimal model is updated through the method, the recommended process parameters are predicted and updated, and if the predicted value of the latest burnup data is higher than the predicted value of the conventional burnup data, the first-level recommended process parameters of the optimal model do not need to be updated. The recommended process parameters in this embodiment may include "heating section residual oxygen value", "soaking section residual oxygen value", "preheating section residual oxygen amount", "heating section upper air-fuel ratio", "heating section lower air-fuel ratio", "soaking section rolling mill side upper air-fuel ratio", "soaking section rolling mill side lower air-fuel ratio", "soaking section non-rolling side upper air-fuel ratio", "soaking section non-rolling side lower air-fuel ratio", "tapping temperature", "preheating section upper furnace gas temperature", "preheating section lower furnace gas temperature", "heating section upper furnace gas temperature", "heating section lower furnace gas temperature", "soaking section upper furnace gas temperature", "soaking section lower furnace gas temperature", "tapping rhythm", "tapping temperature", the "tapping temperature", "preheating section duration", "heating section duration", "furnace pressure", "ambient temperature", "humidity", and the like.
Correspondingly, the embodiment also provides a steel rolling burnup parameter recommendation system, as shown in fig. 2, comprising
The data acquisition module is used for acquiring steel rolling production data in a steel rolling process production line;
the data processing module is used for establishing a burnup process data set and processing the burnup process data set;
the model training module is used for establishing an optimal algorithm model according to the fuel consumption process data set and training the optimal algorithm model;
the alternative scheme construction module is used for constructing an alternative scheme data set;
the parameter recommendation module is used for acquiring important features of the data according to the trained optimal algorithm model and establishing an important feature data set; and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
The steel rolling fuel consumption parameter recommendation system in the embodiment obtains the recommended process parameter with the minimum fuel consumption value by the method and by using the trained optimal algorithm model.
The present embodiment also provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements any of the methods in the present embodiments.
The present embodiment further provides an electronic terminal, including: 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 method in the embodiment.
The computer-readable storage medium in the present embodiment can be understood by those skilled in the art as follows: 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.
The electronic terminal provided by the embodiment comprises a processor, a memory, a transceiver and a communication interface, wherein the memory and the communication interface are connected with the processor and the transceiver and are used for completing mutual communication, the memory is used for storing a computer program, the communication interface is used for carrying out communication, and the processor and the transceiver are used for operating the computer program so that the electronic terminal can execute the steps of the method.
In this embodiment, the Memory may include a Random Access Memory (RAM), and may also 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, a discrete Gate or transistor logic device, or a discrete hardware component.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting 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 method for recommending a steel rolling burnup parameter is characterized by comprising the following steps:
acquiring steel rolling production data in a steel rolling process production line, and establishing a fuel consumption process data set;
establishing an optimal algorithm model according to the burnup process data set, and training the optimal algorithm model;
acquiring important features of the data according to the trained optimal algorithm model, and establishing an important feature data set;
and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
2. The method of claim 1, wherein the method of recommending burnup process parameter data is periodically retrieved, the optimal algorithm model is updated, and the recommended process parameter with the minimum burnup value is retrieved according to the updated model.
3. The method for recommending steel rolling burnup parameters of claim 1, wherein the steel rolling production data comprises process parameters and personnel operation parameters, the burnup process data set is obtained by preprocessing the steel rolling production data, and the preprocessing comprises:
the method comprises the steps of merging personnel operation parameters into steel rolling production data, forming new characteristics through data difference values, and obtaining a complete steel rolling production data set;
filling missing values into the complete steel rolling production data set;
and deleting abnormal data in the complete steel rolling production data set.
4. The method for recommending fuel consumption parameters for rolled steel according to claim 3,
carrying out normalization processing on the burnup process data set;
respectively adopting different algorithm modes to the processed burnup process data set to obtain a plurality of algorithm models;
respectively obtaining the mean absolute error rates of all algorithm models;
and obtaining an optimal algorithm model by comparing the absolute error rate mean values of the models.
5. The method for recommending fuel consumption parameters in steel rolling according to claim 4, wherein said different algorithms comprise linear regression, random forest, support vector regression, neural network, extreme gradient boosting training.
6. The method for recommending fuel consumption parameters for rolled steel according to claim 5,
obtaining the importance degree scores of all the characteristics in all the models;
taking the accuracy of each model as a weight, and carrying out weighted average processing on all the characteristics;
sorting the importance degree scores of the features after weighted average processing, and screening according to a preset threshold value to obtain an important feature data set;
and performing regression testing on the important characteristic data set, and acquiring the characteristic proportion with the best effect so as to acquire the optimal fuel consumption algorithm model containing the optimal parameter value.
7. The method for recommending steel rolling burnup parameters according to any one of claims 1 to 6, wherein constructing a feature field equipartition data set for the important feature data set comprises:
acquiring the upper limit and the lower limit of each important feature to form a new important feature upper limit and lower limit data set;
dividing each column of characteristic fields of the important characteristic upper and lower limit data sets into fixed segments at equal intervals, and forming a characteristic field equipartition data set according to process logic.
8. A steel rolling burn-up parameter recommendation system is characterized by comprising
The data acquisition module is used for acquiring steel rolling production data in a steel rolling process production line;
the data processing module is used for establishing a burnup process data set and processing the burnup process data set;
the model training module is used for establishing an optimal algorithm model according to the fuel consumption process data set and training the optimal algorithm model;
the alternative scheme construction module is used for constructing an alternative scheme data set;
the parameter recommendation module is used for acquiring important features of the data according to the trained optimal algorithm model and establishing an important feature data set; and constructing a characteristic field equipartition data set for the important characteristic data set, and acquiring a recommended process parameter with the minimum fuel consumption value by using the trained optimal algorithm model.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the program when executed by a processor implements the method of any one of claims 1 to 8.
10. An electronic terminal, comprising: a processor and a memory;
the memory is for storing a computer program and the processor is for executing the computer program stored by the memory to cause the terminal to perform the method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010894071.6A CN112036085B (en) | 2020-08-31 | 2020-08-31 | Steel rolling fuel consumption parameter recommendation method, system, medium and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010894071.6A CN112036085B (en) | 2020-08-31 | 2020-08-31 | Steel rolling fuel consumption parameter recommendation method, system, medium and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112036085A true CN112036085A (en) | 2020-12-04 |
| CN112036085B CN112036085B (en) | 2023-03-24 |
Family
ID=73586332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010894071.6A Active CN112036085B (en) | 2020-08-31 | 2020-08-31 | Steel rolling fuel consumption parameter recommendation method, system, medium and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112036085B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112845610A (en) * | 2020-12-31 | 2021-05-28 | 中冶赛迪重庆信息技术有限公司 | Steel rolling power consumption parameter recommendation method and system |
| CN113420652A (en) * | 2021-06-22 | 2021-09-21 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and terminal for recognizing abnormity of time sequence signal fragment |
| CN113569970A (en) * | 2021-07-27 | 2021-10-29 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and terminal for analyzing influence of quantitative characteristic indexes on labels |
| CN113592005A (en) * | 2021-08-04 | 2021-11-02 | 中冶赛迪重庆信息技术有限公司 | Converter tapping parameter recommendation method, system, medium and terminal |
| CN114417739A (en) * | 2022-03-29 | 2022-04-29 | 希望知舟技术(深圳)有限公司 | Method and device for recommending process parameters under abnormal working conditions |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107016509A (en) * | 2017-04-12 | 2017-08-04 | 柳州市同维达豪科技有限公司 | A kind of method for reducing steel rolling process energy consumption per ton steel |
| CN108062583A (en) * | 2016-11-08 | 2018-05-22 | 同济大学 | A kind of heating furnace technique parameter optimization method towards energy-saving and emission-reduction |
| CN108388974A (en) * | 2018-01-11 | 2018-08-10 | 国网山东省电力公司 | Top-tier customer Optimum Identification Method and device based on random forest and decision tree |
| CN108628882A (en) * | 2017-03-20 | 2018-10-09 | 北京京东尚科信息技术有限公司 | Method and system for prejudging problem |
| US20190095801A1 (en) * | 2017-09-22 | 2019-03-28 | International Business Machines Corporation | Cognitive recommendations for data preparation |
| CN109687447A (en) * | 2018-12-29 | 2019-04-26 | 华润电力技术研究院有限公司 | A kind of electric power energy consumption prediction technique and device |
| CN110232236A (en) * | 2019-06-11 | 2019-09-13 | 成渝钒钛科技有限公司 | A kind of steel rolling heat power engineering system efficiency optimization method |
| CN111191822A (en) * | 2019-12-18 | 2020-05-22 | 河钢股份有限公司承德分公司 | Steel parameter prediction method and terminal equipment |
| CN111482467A (en) * | 2020-04-15 | 2020-08-04 | 中冶赛迪重庆信息技术有限公司 | Rolling mill parameter management method |
| CN111523798A (en) * | 2020-04-21 | 2020-08-11 | 武汉市奥拓智能科技有限公司 | Automatic modeling method, device and system and electronic equipment thereof |
-
2020
- 2020-08-31 CN CN202010894071.6A patent/CN112036085B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108062583A (en) * | 2016-11-08 | 2018-05-22 | 同济大学 | A kind of heating furnace technique parameter optimization method towards energy-saving and emission-reduction |
| CN108628882A (en) * | 2017-03-20 | 2018-10-09 | 北京京东尚科信息技术有限公司 | Method and system for prejudging problem |
| CN107016509A (en) * | 2017-04-12 | 2017-08-04 | 柳州市同维达豪科技有限公司 | A kind of method for reducing steel rolling process energy consumption per ton steel |
| US20190095801A1 (en) * | 2017-09-22 | 2019-03-28 | International Business Machines Corporation | Cognitive recommendations for data preparation |
| CN108388974A (en) * | 2018-01-11 | 2018-08-10 | 国网山东省电力公司 | Top-tier customer Optimum Identification Method and device based on random forest and decision tree |
| CN109687447A (en) * | 2018-12-29 | 2019-04-26 | 华润电力技术研究院有限公司 | A kind of electric power energy consumption prediction technique and device |
| CN110232236A (en) * | 2019-06-11 | 2019-09-13 | 成渝钒钛科技有限公司 | A kind of steel rolling heat power engineering system efficiency optimization method |
| CN111191822A (en) * | 2019-12-18 | 2020-05-22 | 河钢股份有限公司承德分公司 | Steel parameter prediction method and terminal equipment |
| CN111482467A (en) * | 2020-04-15 | 2020-08-04 | 中冶赛迪重庆信息技术有限公司 | Rolling mill parameter management method |
| CN111523798A (en) * | 2020-04-21 | 2020-08-11 | 武汉市奥拓智能科技有限公司 | Automatic modeling method, device and system and electronic equipment thereof |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112845610A (en) * | 2020-12-31 | 2021-05-28 | 中冶赛迪重庆信息技术有限公司 | Steel rolling power consumption parameter recommendation method and system |
| CN112845610B (en) * | 2020-12-31 | 2023-03-14 | 中冶赛迪信息技术(重庆)有限公司 | Steel rolling power consumption parameter recommendation method and system |
| CN113420652A (en) * | 2021-06-22 | 2021-09-21 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and terminal for recognizing abnormity of time sequence signal fragment |
| CN113569970A (en) * | 2021-07-27 | 2021-10-29 | 中冶赛迪重庆信息技术有限公司 | Method, system, medium and terminal for analyzing influence of quantitative characteristic indexes on labels |
| CN113569970B (en) * | 2021-07-27 | 2024-05-03 | 中冶赛迪信息技术(重庆)有限公司 | Method, system, medium and terminal for analyzing influence of quantitative characteristic index on tag |
| CN113592005A (en) * | 2021-08-04 | 2021-11-02 | 中冶赛迪重庆信息技术有限公司 | Converter tapping parameter recommendation method, system, medium and terminal |
| CN113592005B (en) * | 2021-08-04 | 2024-03-08 | 中冶赛迪信息技术(重庆)有限公司 | Converter tapping parameter recommendation method, system, medium and terminal |
| CN114417739A (en) * | 2022-03-29 | 2022-04-29 | 希望知舟技术(深圳)有限公司 | Method and device for recommending process parameters under abnormal working conditions |
| CN114417739B (en) * | 2022-03-29 | 2022-07-26 | 希望知舟技术(深圳)有限公司 | Method and device for recommending process parameters under abnormal working conditions |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112036085B (en) | 2023-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112036085B (en) | Steel rolling fuel consumption parameter recommendation method, system, medium and terminal | |
| CN112348101B (en) | Steel rolling fuel consumption early warning method and system based on abnormal data analysis | |
| CN109145129B (en) | Depth measurement learning method and device based on hierarchical triple loss function | |
| CN112801388B (en) | Power load prediction method and system based on nonlinear time series algorithm | |
| CN111889524A (en) | Cold rolling force prediction method and system based on machine learning method | |
| CN111612275A (en) | Method and device for predicting load of regional user | |
| CN110991739A (en) | Construction method and prediction method of industry electric quantity demand prediction model | |
| CN113741362A (en) | Method, system, medium, and computing device for optimizing operation of liquefied natural gas receiving station | |
| CN106649714B (en) | TopN recommendation system and method for data nonuniformity and data sparsity | |
| CN115878958A (en) | Transformer oil temperature prediction method, device, equipment and storage medium | |
| CN115879369A (en) | Coal mill fault early warning method based on optimized LightGBM algorithm | |
| Guo | Research on artificial intelligence: Deep learning to identify plant species | |
| CN112845610B (en) | Steel rolling power consumption parameter recommendation method and system | |
| CN107274025B (en) | System and method for realizing intelligent identification and management of power consumption mode | |
| CN116579842B (en) | Credit data analysis method and system based on user behavior data | |
| CN116628615A (en) | Data anomaly detection method, device, equipment and storage medium | |
| CN113569970B (en) | Method, system, medium and terminal for analyzing influence of quantitative characteristic index on tag | |
| CN116050475A (en) | Training method and device for key pollutant concentration prediction model and computer equipment | |
| Sun et al. | A method for constructing the composite indicator of business cycles based on information granulation and dynamic time warping | |
| CN109828996A (en) | A kind of Incomplete data set rapid attribute reduction | |
| CN115759455A (en) | Load probability density prediction method based on time sequence Gaussian mixture density network | |
| Zhang et al. | An improved decision-making approach based on interval-valued fuzzy soft set | |
| Follett et al. | Achieving parsimony in bayesian VARs with the horseshoe prior | |
| CN115982455B (en) | Flow adjustment method and device based on fuzzy breakpoint regression model and electronic equipment | |
| CN116910362B (en) | Intelligent recommendation method for perceived data, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 401329 No. 5-6, building 2, No. 66, Nongke Avenue, Baishiyi Town, Jiulongpo District, Chongqing Applicant after: MCC CCID information technology (Chongqing) Co.,Ltd. Address before: 20-24 / F, No.7 Longjing Road, North New District, Yubei District, Chongqing Applicant before: CISDI CHONGQING INFORMATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |