CN116341750A - Steel inclusion level prediction method, device, terminal and storage medium - Google Patents
Steel inclusion level prediction method, device, terminal and storage medium Download PDFInfo
- Publication number
- CN116341750A CN116341750A CN202310341295.8A CN202310341295A CN116341750A CN 116341750 A CN116341750 A CN 116341750A CN 202310341295 A CN202310341295 A CN 202310341295A CN 116341750 A CN116341750 A CN 116341750A
- Authority
- CN
- China
- Prior art keywords
- steel
- data
- parameter data
- process parameter
- inclusion level
- 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.)
- Pending
Links
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 176
- 239000010959 steel Substances 0.000 title claims abstract description 176
- 238000000034 method Methods 0.000 title claims abstract description 175
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 130
- 238000004519 manufacturing process Methods 0.000 claims abstract description 85
- 238000009628 steelmaking Methods 0.000 claims abstract description 80
- 238000007418 data mining Methods 0.000 claims abstract description 46
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 32
- 238000004590 computer program Methods 0.000 claims description 21
- 238000012549 training Methods 0.000 claims description 14
- 239000000463 material Substances 0.000 claims description 13
- 238000009749 continuous casting Methods 0.000 claims description 11
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000003756 stirring Methods 0.000 claims description 8
- 239000000126 substance Substances 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 238000004989 laser desorption mass spectroscopy Methods 0.000 claims description 5
- 238000005096 rolling process Methods 0.000 claims description 5
- 229910052786 argon Inorganic materials 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000007664 blowing Methods 0.000 claims description 4
- 238000001816 cooling Methods 0.000 claims description 4
- 239000002893 slag Substances 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000013441 quality evaluation Methods 0.000 abstract description 5
- 238000012545 processing Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000005266 casting Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000003723 Smelting Methods 0.000 description 1
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007705 chemical test Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Manufacturing & Machinery (AREA)
- General Factory Administration (AREA)
Abstract
The application provides a steel inclusion level prediction method, a steel inclusion level prediction device, a terminal and a storage medium. The method comprises the following steps: acquiring process parameter data of steel in steelmaking production; matching the process parameter data of the steel products and the identity marks thereof based on a data warehouse tool, wherein the identity marks comprise time and plan numbers; inputting the process parameter data of the steel matched with the identity mark into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, wherein the data mining prediction model is constructed based on an XGBoost algorithm. The method and the device can improve the working efficiency of quality evaluation, improve the utilization rate of the steel product and also improve the prediction precision of the inclusion level of the steel product.
Description
Technical Field
The present disclosure relates to the field of excavation prediction technologies, and in particular, to a steel inclusion level prediction method, a device, a terminal, and a storage medium.
Background
The steel industry is an early informatization development industry, a complete five-level informatization system is formed through the development of more than thirty years, a large amount of data is accumulated in the aspects of production, operation, quality control and the like of the steel enterprises, and how to fully gather, analyze, mine and utilize the data is particularly important.
Because of the development of big data technology in recent years, a technical foundation is provided for the integration, management and application of data. At present, steel enterprises utilize big data technology and realize the judgment of the quality results of steel products through an empirical analysis method of the quality results back-pushing production conditions, but with the continuous improvement of production processes, the empirical analysis method is gradually unable to adapt to the current production system, and the quality results are unable to effectively react to the production conditions, so that the problem that the steel enterprises have high quality cost of the steel products caused by production problems is caused, and therefore, the requirement on how to directly predict the quality results of the products through the production conditions is more urgent for the steel enterprises.
Disclosure of Invention
The application provides a steel inclusion level prediction method, a device, a terminal and a storage medium, which are used for solving the problem that the quality result of a steel product cannot be accurately reflected in the prior art.
In a first aspect, the present application provides a steel inclusion level prediction method, including:
acquiring process parameter data of steel in steelmaking production;
matching the process parameter data of the steel and the identity thereof based on a data warehouse tool, wherein the identity comprises time and a plan number;
inputting the process parameter data of the steel matched with the identity mark into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, wherein the data mining prediction model is constructed based on an XGBoost algorithm.
In a second aspect, the present application provides a steel inclusion level prediction apparatus, comprising:
the acquisition module is used for acquiring process parameter data of steel in steelmaking production;
the matching module is used for matching the process parameter data of the steel materials and the identity marks thereof based on a data warehouse tool, and the identity marks comprise time and a plan number;
the prediction module is used for inputting the process parameter data of the steel matched with the identity mark into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, and the data mining prediction model is constructed based on an XGBoost algorithm.
In a third aspect, the present application provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The application provides a steel inclusion level prediction method, a device, a terminal and a storage medium. According to the inclusion level of steel after steelmaking production, the product quality result can be directly predicted after steelmaking production, so that the working efficiency of quality evaluation is improved, the utilization rate of steel products is also improved, and in addition, the prediction accuracy of the inclusion level of the steel products can be improved by acquiring diversified process parameter data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a steel inclusion level prediction method provided by an embodiment of the present application;
FIG. 2 is a schematic structural view of a steel inclusion level predicting apparatus according to an embodiment of the present application;
fig. 3 is a schematic diagram of a terminal provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made with reference to the accompanying drawings by way of specific embodiments.
Fig. 1 is a flowchart for implementing the steel inclusion level prediction method according to the embodiment of the present application, and is described in detail below:
in step 101, process parameter data of steel in steel production is acquired.
Specifically, the steel smelting process includes a steelmaking process and a steel rolling process, and the quality of steel is generally judged, and after the steel is rolled to produce a steel product, quality detection is performed on the steel product, so that the problem of delay in acquisition time of quality information, such as inclusions, closely related to the steelmaking process, occurs, and the quality cost of a steel enterprise is greatly increased and cannot be effectively controlled.
In order to solve the problems, the terminal provided by the embodiment of the application can automatically collect process parameter data of steel in steelmaking production at regular time, namely, the process parameter data of the steel can be collected in advance, and the quality of steel products after steelmaking production can be judged according to the process parameter data of the steel, so that the quality of the steel products can be directly determined, and the utilization rate of the steel products can be improved.
In one possible implementation, step 101 may include:
collecting production scheduling data and feeding data through an MES system, wherein the production scheduling data comprises production time, production equipment number, billet size, steel specification, furnace number and rolling plan of each steelmaking furnace, and the feeding data comprises steelmaking material process information of each steelmaking furnace;
collecting parameter data of a steelmaking process engineering through a DCS system, wherein the steelmaking process comprises an LF process, an RH process and a continuous casting process, and the parameter data comprise low argon blowing time, power transmission time and white slag time of the LF process, fidelity time of the RH process and tundish superheat of the continuous casting process;
collecting chemical verification data by a LIMS system, wherein the chemical verification data comprises RH ladle molten steel components and molten steel finished product components;
the actual control parameters of steelmaking production are collected through a factory database, wherein the actual control parameters comprise electromagnetic stirring intensity of a continuous casting process, electromagnetic stirring intensity of the tail end and water distribution amount of a secondary cooling zone.
The process parameter data acquired in the embodiments of the present application includes a plurality of different process parameters, and the different process parameters are acquired from different systems, including but not limited to: MES system (Manufacturing Execution System, production execution system), DCS system (Distributed Control System, decentralized control system), LIMS system (Laboratory Information Management System ) and factory database (Plant Information System, PI).
In an embodiment of the present application, acquisition includes, but is not limited to, the following process parameter data:
production scheduling and feeding data are collected from an MES system, identity matching information such as production time, production equipment number, billet size, steel specification, furnace number and rolling plan of each furnace can be effectively obtained according to the production scheduling data of the MES system, and steelmaking material process information of each furnace can be directly collected according to the feeding data of the MES system and used as a part of characteristic values in a data mining prediction model.
And acquiring steelmaking process parameter data, such as low argon blowing time, power transmission time and white slag time of an LF (ladle refining furnace) process, and also including production process key process parameters such as vacuum time of a PH process, ladle superheat degree of a continuous casting process and the like, from a DCS system, wherein the production process key process parameters are used as part of characteristic values in a data mining prediction model.
Chemical test data, such as production process test results of PH ladle molten steel components, molten steel finished product components and the like, are collected from the LIMS system and used as a part of characteristic values in a data mining prediction model.
Actual control parameters of steelmaking production, such as equipment operation parameters of electromagnetic stirring intensity of continuous casting process, electromagnetic stirring intensity of tail end, water distribution amount of secondary cooling zone and the like, are collected from a factory database PI and used as part of characteristic values in a data mining prediction model.
In the embodiment of the application, the prediction accuracy of the inclusion level of the steel obtained later is improved by acquiring various process parameter data.
In step 102, process parameter data of the steel and its identity, including time and plan number, are matched based on the data warehouse tool.
In this embodiment of the present application, matching refers to combining the process parameter data of the steel obtained in step 101 with the identity corresponding to the steel, where each piece of data formed includes the process parameter data of the steel and the identity corresponding to the process parameter data.
In the embodiment of the application, process parameter data of steel in steelmaking production are collected into a data warehouse tool HIVE library at regular time, and in the data warehouse tool HIVE library, identity marks corresponding to the steel such as time, plan number and the like are matched with the process parameter data by utilizing the data warehouse tool HIVE, so that the matched process parameter data of the steel and the identity marks of the steel are obtained.
In one possible implementation, after matching the process parameter data of the steel and the identity thereof, the method may further include:
and deleting the process parameter data of the steel material which is useless for the occurrence probability of the inclusion level.
Specifically, the process parameter data of the steel matched with the identity mark also needs to be treated, and the treated data can be input into a data mining prediction model to obtain the corresponding inclusion grade of the steel after steelmaking production.
The management is to delete the process parameter data of the steel which has no practical meaning on the occurrence probability of the inclusion level, and also can transmit the process parameter data of the managed steel to a result database MYSQL library for storage.
In one possible implementation, after step 102, the method may further include:
and inputting the process parameter data of the steel matched with the identity mark into a result database.
In the embodiment of the application, the process parameter data of the steel after the identification and the treatment are matched are input into a result database for storage and are used for being input into a data mining prediction model subsequently, and the inclusion level of the steel after steelmaking production is predicted.
In step 103, inputting the process parameter data of the steel matched with the identity label into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, wherein the data mining prediction model is constructed based on an XGBoost algorithm.
In the embodiment of the application, the inclusion grade is a class-A fine inclusion grade, comprising four grades, from one grade to four grades, the judging standard is the size and the quantity of the inclusions in a microscope view field, and the prediction of the inclusion grade in the application is the inclusion grade with the highest occurrence probability corresponding to the process parameter data of the steel matched with the identity.
Inputting the process parameter data of the steel subjected to the identity identification matching and the treatment in the step 102 into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, wherein the data mining prediction model is constructed based on an XGBoost algorithm.
In one possible implementation, before inputting the process parameter data of the steel material with the matched identity into the data mining prediction model, the method may further include:
and deleting the abnormal process parameter data and the redundant process parameter data by adopting an isolated forest algorithm aiming at the process parameter data of any steel.
The isolated Forest algorithm (IF) is an efficient anomaly detection algorithm, similar to a random Forest, but is random every time the partition attribute and the partition point (value) are selected, rather than being selected according to the information gain or the base index. In the actual data acquisition and analysis process, the situation that some data are excessively abnormal can occur, the isolated forest algorithm judges the abnormal situation according to the abnormal score, and the abnormal score is shown in a formula (1):
where s (x, n) is the anomaly score, x is the number of samples, n is the number of samples used to generate each itree, h (x) is the average depth of sample x in multiple itrees, and c (n) is the average path length when binary ordering tree search is unsuccessful.
The specific operation process comprises the following steps:
because the self-abnormal sample data is not high in proportion, on the premise of meeting the requirement of using an isolated forest algorithm, randomly selecting a plurality of features through the isolated forest algorithm, calculating the degree of separation between each feature, and repeatedly dividing to calculate outliers; and then carrying out feature processing, and obtaining derivative variables through deep data background and business background analysis.
In the embodiment of the application, each process parameter data of the steel after the identity identification and the treatment in the step 102 is matched, the abnormal process parameter data and the redundant process parameter data are deleted by utilizing an isolated forest algorithm, and the process parameter data of the steel after the processing of the isolated forest algorithm is input into a data mining prediction model.
Exemplary, if there are N data units, A is respectively 1 、A 2 、…、A 10 If data unit A 1 And data unit A 3 Similar in characteristics, but data unit A 1 Specific data unit A 3 The larger the range is, the data unit A is indicated 3 For redundant data units, data unit A is 3 And deleting.
In one possible implementation, the training process of the data mining predictive model may include:
constructing a data mining prediction model based on an XGBoost algorithm;
and taking the process parameter data of the matched identity mark of the steel in the historical steelmaking production as a training sample, taking the inclusion level corresponding to the steel in the historical steelmaking production as a label, and training the data mining prediction model.
In the embodiment of the application, the XGBoost algorithm is selected to construct the data mining prediction model, and the missing value processing, the over-fitting control and the prediction generalization capability of the XGBoost algorithm are all in accordance with the characteristics of the data mining prediction model.
Wherein XGBoost (eXtreme Gradient Boosting) is an optimized distributed gradient hoisting library, which is intended to be efficient, flexible and portable.
The specific algorithm selection process is as follows:
the characteristic rule is judged by using a pearson correlation and significance test method, for example, a pearson correlation coefficient is used for measuring the correlation between two variables X and Y, and an overall correlation coefficient calculation formula is as follows in formula (2):
according to the detection result, the overall correlation is not strong, and the result required to be output is discrete, so that regression and clustering algorithms are eliminated, an integrated learning algorithm is determined to be used, a main stream algorithm comprises Bayes, SVM and a decision tree, the SVM algorithm is mainly used for classifying models and is low in efficiency, the prediction of inclusion levels in the application is not applicable, the XGBoost algorithm using the decision tree is finally determined to perform prediction, and a data mining prediction model is constructed by combining the actually obtained data characteristics.
In the embodiment of the application, a data mining prediction model is constructed by using an XGBoost algorithm, and training is carried out on the data mining prediction model, wherein the learning rate is controlled by eta based on a weak learner GBtree, so that overfitting is prevented; the maximum depth of the tree is controlled through max_depth, the complexity of a data mining prediction model is adjusted according to a training result, subsamples are configured to train in a random sampling mode, in the training process, the model parameter adjustment optimization is carried out through a CV (Grid Search CV) and Bayes algorithm of SKlearn, then process parameter data of the steel with matched identity in historical steelmaking production are used as training samples, the occurrence probability of inclusion levels corresponding to the steel in the historical steelmaking production is used as a label, the priority of the classification model is comprehensively assessed through methods such as ROC-AUC, precision and F1 Score, and finally the data mining prediction model is obtained through training.
In one possible implementation, after obtaining the inclusion grade of the steel after steelmaking, the method may further include:
the inclusion grade of the steel after steel making production is input into a result database.
In the embodiment of the application, the inclusion level of the steel obtained through the data mining prediction model after steelmaking production is input into a MYSQL library for storage.
In one possible implementation, after the inclusion level of the steel after the steelmaking process is entered into the results database, the method may further include:
the inclusion grade of the steel after steelmaking is displayed in a graphical manner.
In the embodiment of the application, the inclusion level of the steel after steelmaking production is input into the result database, and the inclusion level of the steel after steelmaking production can be exported from the result database to a display screen for display in a graphical mode.
By way of example, a user can automatically collect and manage process parameter data at regular time every day through an acquisition device, obtain inclusion grade of steel after steelmaking production through a data mining prediction model, and then associate the process parameter data with the inclusion grade of steel after steelmaking production in a standing account mode and display the association to a business party, so that the business party can clearly acquire quality data of the batch of steel.
In one possible implementation manner, the embodiment of the application may also be output to the terminal device in an API interface form.
According to the method and the device for evaluating the internal quality of the casting blank, the prediction of the process parameter data of the steel in steelmaking production is carried out, and the internal quality evaluation of the casting blank can be realized under the condition that detection equipment such as casting blank flaw detection is not introduced, so that equipment purchase and use cost is saved, and the efficiency of judging the quality of a steel product is improved.
The invention provides a steel inclusion level prediction method, which is characterized in that process parameter data of steel in steelmaking production is obtained, the process parameter data of the steel and identity marks thereof are matched, and the process parameter data of the steel with the matched identity marks is input into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production. According to the inclusion level in steel-making production, the product quality result can be directly predicted after steel-making production, so that the working efficiency of quality evaluation is improved, the utilization rate of steel products is also improved, and in addition, the prediction accuracy of the inclusion level of the steel products can be improved by acquiring diversified process parameter data.
The steel inclusion level prediction method described above will be described below by way of an example of implementation.
Firstly, acquiring process parameter data of steel in a steelmaking furnace A in steelmaking production;
secondly, based on a data warehouse tool, matching and managing the process parameter data of the steel and the time and the plan number of the identity mark of the steel, and inputting the process parameter data of the steel after matching the identity mark and managing into a result database for storage;
thirdly, inputting the process parameter data of the steel matched with the identity mark and treated in the result database into a data mining prediction model to obtain inclusion grade with highest occurrence probability after steel making production, for example, the output grade is one grade, namely, the inclusion grade with highest occurrence probability is one grade;
if the service side has higher requirements on the first-level inclusion level and the other inclusion levels are not high, the requirement of the batch of steel for the service side is not met, and if the service side has not high requirements on the first-level inclusion level and the other inclusion levels are high, the requirement of the batch of steel for the service side is met, and the batch of steel can be sold to the service side.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
The following are device embodiments of the present application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 is a schematic structural diagram of a steel inclusion level predicting apparatus according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown, and the following details are given:
as shown in fig. 2, the steel inclusion level prediction apparatus 2 includes:
an acquisition module 21 for acquiring process parameter data of steel in steelmaking production;
a matching module 22, configured to match the process parameter data of the steel material and the identity thereof, based on the data warehouse tool, where the identity includes a time and a plan number;
the prediction module 23 is configured to input the process parameter data of the steel product with the matched identity into a data mining prediction model, so as to obtain the inclusion level of the steel product after steelmaking production, where the data mining prediction model is constructed based on an XGBoost algorithm.
The utility model provides a steel inclusion level prediction device, which is used for obtaining process parameter data of steel in steelmaking production, matching the process parameter data of the steel and identity marks thereof, inputting the process parameter data of the steel with the matched identity marks into a data mining prediction model, and obtaining the inclusion level of the steel after steelmaking production. According to the inclusion level in steel-making production, the product quality result can be directly predicted after steel-making production, so that the working efficiency of quality evaluation is improved, the utilization rate of steel products is also improved, and in addition, the prediction accuracy of the inclusion level of the steel products can be improved by acquiring diversified process parameter data.
In one possible implementation manner, the acquiring module may specifically be configured to:
collecting production scheduling data and feeding data through an MES system, wherein the production scheduling data comprises production time, production equipment number, billet size, steel specification, furnace number and rolling plan of each steelmaking furnace, and the feeding data comprises steelmaking material process information of each steelmaking furnace;
collecting parameter data of a steelmaking process engineering through a DCS system, wherein the steelmaking process comprises an LF process, an RH process and a continuous casting process, and the parameter data comprise low argon blowing time, power transmission time and white slag time of the LF process, fidelity time of the RH process and tundish superheat of the continuous casting process;
collecting chemical verification data by a LIMS system, wherein the chemical verification data comprises RH ladle molten steel components and molten steel finished product components;
the actual control parameters of steelmaking production are collected through a factory database, wherein the actual control parameters comprise electromagnetic stirring intensity of a continuous casting process, electromagnetic stirring intensity of the tail end and water distribution amount of a secondary cooling zone.
In one possible implementation, after the matching module, the apparatus may further include:
and the treatment module is used for deleting the process parameter data of the steel which is useless for the occurrence probability of the inclusion level.
In one possible implementation, before the prediction module, the apparatus may further include a preprocessing module, where the preprocessing module is configured to:
and deleting the abnormal process parameter data and the redundant process parameter data by adopting an isolated forest algorithm aiming at the process parameter data of any steel.
In one possible implementation, the training process of the data mining predictive model may include:
constructing a data mining prediction model based on an XGBoost algorithm;
and taking the process parameter data of the matched identity mark of the steel in the historical steelmaking production as a training sample, taking the inclusion level corresponding to the steel in the historical steelmaking production as a label, and training the data mining prediction model.
In one possible implementation, after the matching module, the apparatus may further be configured to:
inputting the process parameter data of the steel matched with the identity mark into a result database;
accordingly, after the prediction module, the apparatus may further be configured to:
the inclusion grade of the steel after steel making production is input into a result database.
In one possible implementation, after the prediction module, the apparatus may further include:
and the display module is used for displaying the inclusion grade of the steel after steelmaking production in a graphical mode.
Fig. 3 is a schematic diagram of a terminal provided in an embodiment of the present application. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The processor 30, when executing the computer program 32, implements the steps of the various steel inclusion level prediction method embodiments described above, such as steps 101 through 103 shown in fig. 1. Alternatively, the processor 30 may perform the functions of the modules of the apparatus embodiments described above, such as the functions of the modules 21-23 shown in fig. 2, when executing the computer program 32.
By way of example, the computer program 32 may be partitioned into one or more modules that are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into modules 21 to 23 shown in fig. 2.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and does not constitute a limitation of the terminal 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The processor 30 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used for storing the computer program as well as other programs and data required by the terminal. The memory 31 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments for predicting the inclusion level of steel material when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A steel inclusion level prediction method, characterized in that the prediction method comprises:
acquiring process parameter data of steel in steelmaking production;
matching the process parameter data of the steel and the identity thereof based on a data warehouse tool, wherein the identity comprises time and a plan number;
inputting the process parameter data of the steel matched with the identity mark into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, wherein the data mining prediction model is constructed based on an XGBoost algorithm.
2. The steel inclusion level prediction method according to claim 1, wherein the acquiring process parameter data of the steel in the steel-making production comprises:
collecting production scheduling data and feeding data through an MES system, wherein the production scheduling data comprises production time, production equipment number, billet size, steel specification, furnace number and rolling plan of each steelmaking furnace, and the feeding data comprises steelmaking material process information of each steelmaking furnace;
collecting parameter data of a steelmaking process engineering through a DCS system, wherein the steelmaking process comprises an LF process, an RH process and a continuous casting process, and the parameter data comprise low argon blowing time, power transmission time and white slag time of the LF process, vacuum-maintaining time of the RH process and a tundish superheat degree of the continuous casting process;
collecting chemical verification data through a LIMS system, wherein the chemical verification data comprises RH ladle molten steel components and molten steel finished product components;
the actual control parameters of steelmaking production are collected through a factory database, wherein the actual control parameters comprise electromagnetic stirring intensity of a continuous casting process, electromagnetic stirring intensity of the tail end and water distribution amount of a secondary cooling zone.
3. The steel inclusion level prediction method according to claim 1, wherein after matching the process parameter data of the steel and the identity thereof, the method further comprises:
and deleting the process parameter data of the steel material which is useless for the occurrence probability of the inclusion level.
4. The steel inclusion level prediction method of claim 1, wherein prior to the entering of the process parameter data for the identified steel into the data mining prediction model, the method further comprises:
and deleting the abnormal process parameter data and the redundant process parameter data by adopting an isolated forest algorithm aiming at the process parameter data of any steel.
5. The steel inclusion level prediction method according to claim 1, wherein the training process of the data mining prediction model comprises:
constructing a data mining prediction model based on an XGBoost algorithm;
and taking the process parameter data of the matched identity mark of the steel in the historical steelmaking production as a training sample, taking the inclusion level corresponding to the steel in the historical steelmaking production as a label, and training the data mining prediction model.
6. The steel inclusion level prediction method of claim 1, wherein after the matching of the process parameter data of the steel and its identity based on the data warehouse tool, the method further comprises:
inputting process parameter data of the steel matched with the identity mark into a result database;
accordingly, after the steel material is obtained and the inclusion grade is obtained after steelmaking, the method further comprises:
the inclusion grade of the steel material after steel making production is input into the result database.
7. The steel inclusion level prediction method according to claim 6, wherein after the inclusion level of the steel after steel making production is input to the result database, the method further comprises:
and displaying the inclusion grade of the steel after steelmaking production in a graphical mode.
8. A steel inclusion level prediction apparatus, comprising:
the acquisition module is used for acquiring process parameter data of steel in steelmaking production;
the matching module is used for matching the process parameter data of the steel materials and the identity marks thereof based on a data warehouse tool, and the identity marks comprise time and a plan number;
the prediction module is used for inputting the process parameter data of the steel matched with the identity mark into a data mining prediction model to obtain the inclusion level of the steel after steelmaking production, and the data mining prediction model is constructed based on an XGBoost algorithm.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the steel inclusion level prediction method according to any one of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the steel inclusion level prediction method according to any one of the preceding claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310341295.8A CN116341750A (en) | 2023-03-31 | 2023-03-31 | Steel inclusion level prediction method, device, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310341295.8A CN116341750A (en) | 2023-03-31 | 2023-03-31 | Steel inclusion level prediction method, device, terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116341750A true CN116341750A (en) | 2023-06-27 |
Family
ID=86883846
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310341295.8A Pending CN116341750A (en) | 2023-03-31 | 2023-03-31 | Steel inclusion level prediction method, device, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116341750A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541221A (en) * | 2023-11-02 | 2024-02-09 | 天津大学 | Intelligent routing inspection method of Internet of things equipment suitable for Internet of things intelligent control central heating |
-
2023
- 2023-03-31 CN CN202310341295.8A patent/CN116341750A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541221A (en) * | 2023-11-02 | 2024-02-09 | 天津大学 | Intelligent routing inspection method of Internet of things equipment suitable for Internet of things intelligent control central heating |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11481707B2 (en) | Risk prediction system and operation method thereof | |
| CN108460346B (en) | Fingerprint identification method and device | |
| CN109471783A (en) | The method and apparatus for predicting task run parameter | |
| CN116341750A (en) | Steel inclusion level prediction method, device, terminal and storage medium | |
| CN112561320A (en) | Training method of mechanism risk prediction model, mechanism risk prediction method and device | |
| CN111796957A (en) | Transaction abnormal root cause analysis method and system based on application log | |
| CN115409518A (en) | User transaction risk early warning method and device | |
| CN113902449A (en) | Enterprise online transaction system risk early warning method and device and electronic equipment | |
| Zhou et al. | Online abnormal interval detection and classification of industrial time series data based on multi-scale deep learning | |
| CN116304761A (en) | Working condition type analysis method and device, electronic equipment and storage medium | |
| KR102005952B1 (en) | Apparatus and Method for refining data of removing noise data in Machine learning modeling | |
| CN111738610A (en) | Public opinion data-based enterprise loss risk early warning system and method | |
| CN116136897A (en) | Information processing method and device | |
| CN113139679A (en) | Urban road rescue early warning method, system and equipment based on neural network | |
| CN116304825A (en) | Method, device, terminal and storage medium for predicting hardenability of steel tail end | |
| CN111429257A (en) | Transaction monitoring method and device | |
| CN118013426B (en) | Steel product inclusion causal effect optimization method, system, electronic equipment and medium | |
| CN116629134B (en) | Environmental impact prediction method and device for three-thin mineral development area | |
| CN116403403B (en) | Traffic early warning method, system, equipment and medium based on big data analysis | |
| CN113269246B (en) | Method, device, equipment and medium for detecting equipment state based on transaction logic | |
| CN110796381B (en) | Modeling method and device for wind control model, terminal equipment and medium | |
| CN114791970A (en) | User attribute prediction method, device, equipment and storage medium | |
| CN112559487A (en) | Financial data analysis early warning method and device, computer equipment and storage medium | |
| Kheirallah et al. | Anomaly Detection in a Production Line: an Online/Offline Statistical Learning Approach | |
| CN117893011A (en) | Risk assessment method, risk assessment device, electronic 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 |