Strip-shaped time series data mining method based on data patterns

A technology of data mode and time series, which is applied in the direction of contour control, etc., to achieve the effect of simplifying the adjustment procedure, improving the precision of shape control, and shortening the time of shape adjustment

Active Publication Date: 2013-10-09
ANGANG STEEL CO LTD
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However, the concept of the average threshold...

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  • Strip-shaped time series data mining method based on data patterns
  • Strip-shaped time series data mining method based on data patterns
  • Strip-shaped time series data mining method based on data patterns

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Embodiment Construction

[0036]The data mode-based plate shape time series data mining method of the present invention mainly introduces the average support degree and the average confidence threshold value when applying the Apriori algorithm, thereby digging out meaningful frequent itemsets and association rules in the time series data, and Eliminate meaningless data association and provide data basis for high-precision shape control.

[0037] Taking a certain cold-rolling production line as an example, the present invention is described in detail.

[0038] First, data preprocessing is performed on the PDA monitoring data of the production line according to the data preprocessing algorithm. The data is selected from the normal production data of a certain day, the comprehensive index of board shape control is within 5I (flatness), the time is from 9:36 to 14:42, and the data time interval is 1 minute, a total of 306 sets of data.

[0039] For example, the data mode of 14:00 is the average value betw...

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Abstract

The invention provides a strip-shaped time series data mining method based on data patterns. Data pre-processing is conducted on PDA monitoring data for cold-rolled sheet production, noise data, vacancy data and inconsistent data in original data are excluded; findings of a frequent item set and a plurality of item sets are conducted on data after being processed, and the frequent item set and the item sets are found out; association rule findings are conducted on the found frequency item set in the finding process of the frequency item set, and a potential association rules hidden in the data are found out. According to the method, because an average support degree and an average confidence coefficient threshold value are brought in and used in an Apriori algorithm, a significative frequency item set and association rules can be efficiently mined, insignificant data association can be effectively removed, a data basis can be provided for controlling of a high-precision strip shape, the adjustment time of the strip shape is greatly shortened, the control precision of the strip shape is improved, and the aggregative indicator of the strip shape is stably controlled to be within 5I.

Description

technical field [0001] The invention belongs to the field of process automatic control, in particular to a time series data mining method for cold continuous rolling strip shape control based on data patterns. Background technique [0002] Time series data mining is one of the important steps of knowledge discovery in time series database, and it is a kind of time series of flat data. Shape control is one of the core technologies of cold-rolled strip processing. In recent years, with the continuous advancement of science and technology, advanced shape control technologies have emerged. Among the many high-precision shape control methods, the representative methods are : Optimization of rolling schedule, hydraulic roll bending, roll tilting, roll traverse, roll cooling in sections, etc. The development of shape control technology has promoted the equipment progress and industrial upgrading of the cold-rolled strip industry, and the production benefit and efficiency have been...

Claims

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Application Information

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IPC IPC(8): B21B37/28
Inventor 柴明亮王军生张岩刘宝权秦大伟宋君侯永刚
Owner ANGANG STEEL CO LTD
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