在现代 IF 钢连铸工艺中，结晶器液位的稳定性对铸坯质量具有重要影响。 液位波动可能导致保护渣卷入钢液形成夹杂缺陷，降低钢材的纯净度和最终性能。 传统连铸过程监测主要依赖物理实验和有限元模拟，而随着工业数据采集能力的提升，数据驱动分析方法在工艺优化中展现出巨大潜力。 针对 IF 钢连铸过程，通过采集工业生产中的液位波动数据、卷渣夹杂检测信息及相关工艺参数，运用统计分析、时序分析和机器学习方法，探索了结晶器液位波动特征及其对保护渣卷入的影响机理，提出了优化控制策略。 研究结果表明，液位波动的多个特征参数与卷渣夹杂缺陷之间存在显著相关性，通过优化控制策略可有效降低夹杂物发生的概率。

In modern interstitial-free (IF) steel continuous casting processes, the stability of the mold level plays a critical role in determining slab quality. Excessive fluctuations may cause slag entrapment into molten steel, resulting in inclusion defects that degrade the purity and final mechanical properties of the steel. While traditional process monitoring primarily depends on physical experiments and finite element simulations, the advancement of industrial data acquisition technologies has enabled data-driven approaches to demonstrate significant potential for process optimization. Industrial IF steel casting operations involving the collection of mold level fluctuation data, slag entrapment records, and associated process parameters were investigated. Through integrated statistical analysis, time series characterization and machine learning techniques, the intrinsic relationship between mold-level fluctuation patterns and slag entrapment mechanisms was systematically explored, and corresponding control strategies were proposed. The results demonstrate significant correlations between the characteristic parameters of level fluctuations and slag entrapment defects. The implementation of optimized control strategies effectively reduces the probability of inclusion formation.