卷烟、雪茄烟生产（限分支机构）；卷烟、雪茄烟销售；卷烟纸、滤嘴棒、烟用丝束购进；联营加工所需烟草专卖品供应；烟草专用机械购进；烟叶、烟丝、复烤烟叶购进。
The key parameters of the tobacco redrying process are crucial to the final product quality.The traditional parameter control method,which relies on experience,suffers from poor stability and response hysteresis,making it difficult to meet the demands of modern industrial production.To address this,this paper constructs an intelligent prediction model for tobacco redrying process parameters based on the XGBoost machine learning algorithm.Firstly,the redrying process flow and key quality indicators are analyzed to establish an efficient data acquisition and feature engineering method.Subsequently,the XGBoost model parameters are optimized and combined with a feature selection strategy to improve prediction accuracy and model robustness.The experimental results show that this method has high accuracy and stability in process parameter prediction.Compared with traditional methods and other machine learning models,it significantly en-hances the intelligent level of the redrying process.This research provides effective support for the digital and intelligent optimization of the tobacco processing procedure,contributing to improving product quality and production efficiency.
