CN112181956B - Data mining method based on hot rolling industry big data - Google Patents
Data mining method based on hot rolling industry big data Download PDFInfo
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- CN112181956B CN112181956B CN202010922191.2A CN202010922191A CN112181956B CN 112181956 B CN112181956 B CN 112181956B CN 202010922191 A CN202010922191 A CN 202010922191A CN 112181956 B CN112181956 B CN 112181956B
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Abstract
The invention relates to a data mining method based on hot rolling industry big data, and belongs to the technical field of product quality judgment of hot rolling production lines. The technical scheme of the invention is as follows: collecting the secondary system data, the tertiary MES data and the continuous value data analyzed by the DCA file of each hot rolling production line from different databases into a postgreSQL database integrated by a big data platform (mpp); and performing statistical analysis on the integrated hot rolled steel coil data in the big data platform so as to guide the production of the hot rolled steel coil. The invention has the beneficial effects that: the data of parameters of each important monitoring point of the whole hot rolling production line, and data contents such as order contracts, customer requirements, metallurgical specification requirements and the like can be clearly mastered, so that the process quality production of the hot rolling production line can be guided according to the whole data information.
Description
Technical Field
The invention relates to a data mining method based on hot rolling industry big data, and belongs to the technical field of product quality judgment of hot rolling production lines.
Background
With the development of big data related technologies, data analysis, data processing and data fusion technologies are gradually applied in various fields. In most of these fields, data collected by a plurality of data sources needs to be fused and then subjected to subsequent analysis.
Most of mass fine-grained data generated in the steel coil hot rolling production process are deposited on the site, and related data are not integrated and utilized from the whole situation and are in the local and short-term analysis and application stages. How to reasonably integrate the two-level single value data, the three-level MES production and sales data and the DCA file analyzed continuous value data and perform classified statistical analysis on the data to better guide the production process is a big problem faced by iron and steel enterprises in recent years.
Disclosure of Invention
The invention aims to provide a data mining method based on hot rolling industry big data, which integrates production data in different databases on different systems, facilitates technical personnel to integrally know parameters of each monitoring point of a hot rolling complete production line, and can guide hot rolling production in real time according to metallurgical specification values, and the production online real-time quality management can assist an operation department and a maintenance department to quickly master stability key points of a manufacturing process so as to improve the loss of the manufacturing process; the integration of the single-level data of the second-level system, the three-level MES production and sales data and the continuous value data analyzed by the DCA file presents the guidance that the product manufacturing can be strictly finished according to the order contract and the customer requirements, so that the order qualification rate is improved; the quality department can improve the judgment accuracy of the steel product quality, effectively prevents the outflow of defective products, enables the product quality to meet the market demand, improves the overall competitiveness of companies, and effectively solves the problems in the background art.
The technical scheme of the invention is as follows: a data mining method based on hot rolling industry big data comprises the following steps:
step 1: collecting the secondary system data, the tertiary MES data and the continuous value data analyzed by the DCA file of each hot rolling production line from different databases into a postgreSQL database integrated by a big data platform (mpp);
step 2: dividing the acquired data into single-value and continuous-value data according to a production line, and classifying and integrating the single-value and continuous-value data; the main sources of single-valued data are three parts: the data of the second-level system, the data of the third-level MES and log file monitoring data, and the data of the production actual table and the data of the production record table in the data of the second-level system are matched according to the number of the hot rolled steel coil; part of the single-value data is derived from data in a production and sales system in three-level MES data, and metallurgical specification data such as contract order information, tolerance upper and lower limits and the like, raw material information and the like are integrated into the single-value data according to the number of the hot rolled steel coil; the other part of the single-value data comes from the monitored log file data, and the part of the data is fused into the single-value data according to the number of the hot rolled steel coil after being monitored and analyzed; the continuous value data mainly comes from the continuous value data analyzed by a DCA file, the partial data is acquired by the DCA file analyzed by a C + + program on a production site, the acquired continuous value information is spliced into a json format according to the number and the position of a steel coil and stored in a database, and meanwhile, the specification standards in three-level MES data are combined, so that the upper limit and the lower limit specified in the metallurgical specification can be reflected when each parameter curve is drawn, the method is a basis for measuring whether each important parameter meets the requirements in the hot rolling process, and the production process of each parameter can be intuitively grasped;
and step 3: connecting production lines in series according to the number of the hot rolled steel coil, integrating and analyzing data of the production lines under the same hot rolled steel coil;
and 4, step 4: and performing statistical analysis on the integrated hot rolled steel coil data in the big data platform so as to guide the production of the hot rolled steel coil.
In the step 1, data is diversified, format standards are not uniform, the data comes from different databases of different systems, and multiple data sources of the data are cleaned, calculated, collected and stored in a postgreSQL database integrated with a big data platform (mpp).
The invention has the beneficial effects that: production data in different databases on different systems are fused together, so that technicians can conveniently know parameters of each monitoring point of a hot rolling complete production line integrally and can guide hot rolling production in real time according to metallurgical specification values, online real-time quality management of the production can assist operating departments and maintenance departments to quickly master stability points of a manufacturing process, and further, defects in the manufacturing process are improved; the integration of the single-level data of the second-level system, the three-level MES production and sales data and the continuous value data analyzed by the DCA file presents the guidance that the product manufacturing can be strictly finished according to the order contract and the customer requirements, so that the order qualification rate is improved; the quality department can improve the judgment accuracy of the steel product quality, effectively prevents the outflow of defective products, enables the product quality to meet the market demand, and improves the overall competitiveness of companies.
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FIG. 1 is a flow chart of the operation of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention with reference to the drawings of the embodiments, and it is obvious that the described embodiments are a small part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative work based on the embodiments of the present invention belong to the protection scope of the present invention.
A data mining method based on hot rolling industry big data comprises the following steps:
step 1: collecting the secondary system data, the tertiary MES data and the continuous value data analyzed by the DCA file of each hot rolling production line from different databases into a postgreSQL database integrated by a big data platform (mpp);
step 2: dividing the acquired data into single-value and continuous-value data according to a production line, and classifying and integrating the single-value and continuous-value data; the main sources of single-valued data are three parts: the data of the second-level system, the data of the third-level MES and log file monitoring data, and the data of the production actual table and the data of the production record table in the data of the second-level system are matched according to the number of the hot rolled steel coil; part of the single-value data is derived from data in a production and sales system in three-level MES data, and metallurgical specification data such as contract order information, tolerance upper and lower limits and the like, raw material information and the like are integrated into the single-value data according to the number of the hot rolled steel coil; the other part of the single-value data comes from the monitored log file data, and the part of the data is fused into the single-value data according to the number of the hot rolled steel coil after being monitored and analyzed; the continuous value data mainly comes from the continuous value data analyzed by a DCA file, the partial data is acquired by the DCA file analyzed by a C + + program on a production site, the acquired continuous value information is spliced into a json format according to the number and the position of a steel coil and stored in a database, and meanwhile, the specification standards in three-level MES data are combined, so that the upper limit and the lower limit specified in the metallurgical specification can be reflected when each parameter curve is drawn, the method is a basis for measuring whether each important parameter meets the requirements in the hot rolling process, and the production process of each parameter can be intuitively grasped;
and step 3: connecting production lines in series according to the number of the hot rolled steel coil, integrating and analyzing data of the production lines under the same hot rolled steel coil;
and 4, step 4: and performing statistical analysis on the integrated hot rolled steel coil data in the big data platform so as to guide the production of the hot rolled steel coil.
In the step 1, data is diversified, format standards are not uniform, the data comes from different databases of different systems, and multiple data sources of the data are cleaned, calculated, collected and stored in a postgreSQL database integrated with a big data platform (mpp).
In practical application, the invention is mainly applied to hot rolling production lines in iron and steel enterprises, and serves hot rolling production maintenance personnel, technical personnel, quality management department personnel and the like. The method is convenient for field production personnel to adjust the feeding amount, chemical components and the like in real time according to the data of the monitoring points, is beneficial for relevant technical personnel to carry out statistics and analysis on the influence of the parameters of the monitoring points on the quality of the steel coil, and quickly helps quality management personnel to judge whether the product meets order contracts, customer requirements and the like.
Step 01: and collecting the secondary system data, the tertiary MES data and the continuous value data analyzed by the DCA file of each production line of the hot rolling plant from different system databases to a postgreSQL database integrated with the MPP of the big data platform through an ETL (extract transform and load) collecting tool. The database has the advantages of unifying data sources, supporting massive parallel operation of batch data and being high in instantaneity.
Step 02: and dividing the collected data into single-value and continuous-value data according to a production line, and integrating the single-value and continuous-value data in a classified manner.
The specific classification and integration method is as follows:
the main sources of single-value data are three parts: and monitoring data by using the second-level system data, the third-level MES data and the log file. The majority of single-value data are matched by the data in the production actual table and the production record table in the secondary system according to the number of the hot rolled steel coil; part of the single-value data is derived from data in a production and sales system in three-level MES data, and metallurgical specification data such as contract order information, tolerance upper and lower limits and the like, raw material information and the like are integrated into the single-value data according to the number of the hot rolled steel coil; and the rest part of the single-value data is derived from the monitored log file data, and the data is fused into the single-value data according to the number of the hot rolled steel coil after being monitored and analyzed.
The continuous value data mainly comes from the continuous value data analyzed by a DCA file, the partial data is acquired by the DCA file analyzed by a C + + program on a production site, the acquired continuous value information is spliced into a json format according to the number and the position of a steel coil and stored in a database, and meanwhile, the standard standards in production and marketing data in three-level MES data are combined, so that the upper limit and the lower limit specified in the metallurgical standard can be embodied when each parameter curve is drawn, the method is a basis for measuring whether each important parameter meets the requirement in the hot rolling process, and the production process of each parameter can be intuitively grasped.
Step 03: and connecting production lines in series according to the number of the hot rolled steel coil, and analyzing data of the production lines under the same steel coil.
Step 04: and carrying out statistical analysis on the integrated steel coil information data in the big data platform, and further guiding the production of the hot rolled steel coil according to metallurgical standard and contract requirements.
Claims (2)
1. A data mining method based on hot rolling industry big data is characterized by comprising the following steps:
step 1: collecting the secondary system data, the tertiary MES data and the continuous value data analyzed by the DCA file of each hot rolling production line from different databases into a postgreSQL database integrated by a big data platform (mpp);
step 2: dividing the acquired data into single-value and continuous-value data according to a production line, and classifying and integrating the single-value and continuous-value data; the main sources of single-valued data are three parts: the data of the second-level system, the data of the third-level MES and log file monitoring data are matched by the data in the production actual table and the data of the production record table in the second-level system data according to the number of the hot rolled steel coil; part of the single-value data is derived from data in a production and sales system in three-level MES data, and metallurgical specification data such as contract order information, tolerance upper and lower limits and the like, raw material information and the like are integrated into the single-value data according to the number of the hot rolled steel coil; the other part of the single-value data comes from the monitored log file data, and the part of the data is fused into the single-value data according to the number of the hot rolled steel coil after being monitored and analyzed; the continuous value data is derived from the continuous value data analyzed by the DCA file, the partial data is acquired by the DCA file analyzed by the C + + program on the production site, the acquired continuous value information is stored in a database in a json format by splicing all parameter values according to the number of the steel coil and the position of the steel coil, and the specification standards in three-level MES data are combined, so that the upper limit and the lower limit specified in the metallurgical specification can be reflected when each parameter curve is drawn;
and step 3: connecting production lines in series according to the number of the hot rolled steel coil, integrating and analyzing data of the production lines under the same hot rolled steel coil;
and 4, step 4: and performing statistical analysis on the integrated hot rolled steel coil data in the big data platform so as to guide the production of the hot rolled steel coil.
2. The method for data mining based on hot rolling industry big data as claimed in claim 1, wherein: in the step 1, data of multiple data sources are cleaned, calculated, collected and stored in a postgreSQL database integrated with a big data platform (mpp).
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| CA2378565A1 (en) * | 2001-04-06 | 2002-10-06 | Pfizer Inc. | Crystal structure |
| CN104084426A (en) * | 2014-07-02 | 2014-10-08 | 济钢集团有限公司 | Cold rolled product plate type control system based on hot rolling process control |
| CN110073301A (en) * | 2017-08-02 | 2019-07-30 | 强力物联网投资组合2016有限公司 | The detection method and system under data collection environment in industrial Internet of Things with large data sets |
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