Mining method for C-Mn steel industry big data

Abstract

The invention provides a mining method for C-Mn steel industry big data and belongs to the field of cross technologies of steel industry production and data statistics modeling.The method includes the steps of data sample selection, steel coil merging, similar process clustering and training data uniformization.Through selecting data of multiple steel brand numbers, a data sample includes comprehensive parameter information, a more objective physical metallurgy rule is reflected, and a model has higher adaptability; through judging components of a steel blank to be detected and adopting the clustering method, multiple sets of data of a similar process are corrected to be one set of data, in this way, the data volume is simplified, and redundant data is deleted; in the process, abnormal data is rejected, errors are reduced, and data regularity is more obvious; through performing statistics on distribution of three kinds of mechanical properties of training data, the distribution balance of the training data is adjusted; by the adoption of a balanced data training neural network, a network model can learn about balanced information, and the regularity and accuracy of the model are improved.

Description

technical field [0001] The invention belongs to the cross-technical field of iron and steel industry production and data statistical modeling, and in particular relates to a mining method for big data of C-Mn steel industry. Background technique [0002] A large amount of production data will be collected in the industrial production of C-Mn steel. Technicians can use the information contained in these data to establish mechanical performance prediction models. However, if the original production data is directly applied without processing, many problems will arise. First of all, there is a large amount of redundant data in the original production data. Too much redundant data will increase the amount of modeling calculations, and at the same time lead to insufficient regularity; under normal circumstances, each furnace of molten steel can usually produce several steel coils , when testing the mechanical properties, several samples will be cut from each steel coil; if these ...

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Problems solved by technology

[0002] In the industrial production of C-Mn steel, a large amount of production data will be collected, and technicians can use the information contained in these data to establish a mechanical performance prediction model, but if the original production data is directly applied without processing, many problems will arise

First of all, there is a large amount of redundant data in the original production data. Too much redundant data will increase the amount of modeling calculations, and at the same time lead to insufficient regularity; under normal circumstances, each furnace of molten steel can usually produce several steel coils , when testing the mechanical properties, several samples will be cut from each steel coil; if these steel coils are used to produce plates and strips of the same thickness specification, and the same rolling process is adopted, then each furnace of steel ingot will correspond to multiple sets of data ; Therefore, the information contained in these data is the same or similar, and the application of a large number of data containing the same information in data modeling will increase the amount of calculation of modeling; because the process standard of tapping marks is formulated in the form of intervals , the actual process is constrained by the capacity of the production line equipment, so the collected data are distributed in a discrete state

The small fluctuations in the numerical values ​​of process parameters are within the allowable range of errors in actual production operations. These data can also be regarded as data containing the same or similar information, which will also increase the amount of modeling calculations

Secondly, due to detection errors and human intervention in the industrial system, if the raw data collected by the production line is directly used for modeling without processing, the analysis results are prone to distortions that deviate from the facts; in addition, industrial data is usually unevenly distributed , such data cannot reflect objective and comprehensive information, resulting in the characteristics of the established model tending to the characteristics of the region in the data set; for example, when using a neural network to establish a mechanical performance model ( Figure 5 ), analyze the change curve of yie...

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