Blast furnace data processing is prone to problems such as outliers. To overcome these problems and identify an improved method for processing blast furnace data, we conducted an in-depth study of blast furnace data. Based on data samples from selected iron and steel companies, data types were classified according to different characteristics; then, appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data. Linear interpolation was used to fill in the divided continuation data, the K-nearest neighbor (KNN) algorithm was used to fill in correlation data with the internal law, and periodic statistical data were filled by the average. The error rate in the filling was low, and the fitting degree was over 85%. For the screening of outliers, corresponding indicator parameters were added according to the continuity, relevance, and periodicity of different data. Also, a variety of algorithms were used for processing. Through the analysis of screening results, a large amount of efficient information in the data was retained, and ineffective outliers were eliminated. Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.

高炉数据处理容易出现异常值等问题。为了克服这些问题并确定处理高炉数据的改进方法，我们对高炉数据进行了深入研究。以选定钢铁企业的数据样本为基础，根据不同特征对数据类型进行分类；然后，选择适当的方法对其进行处理，以解决原始高炉数据的缺陷和异常值。线性插值用于填充划分的延续数据，K-最近邻（KNN）算法用内律填充相关数据，周期统计数据用平均值填充。填充错误率低，拟合度在85%以上。对于异常值的筛选，根据不同数据的连续性、相关性和周期性，增加了相应的指标参数。此外，还使用了各种算法进行处理。通过对筛选结果的分析，保留了数据中的大量有效信息，剔除了无效的离群值。高炉大数据标准化处理作为高炉大数据应用研究的基础，可以作为提高数据质量、保留数据价值的重要手段。