基于“科技矿场”案例的采煤机滚筒高度智能预测与优化模型方法分析

Research on Intelligent Prediction and Optimization Model of Shearer Drum Height Based on the “Technology Mine” Case

摘要: 采煤机滚筒高度精准调节是综采工作面智能化的关键技术之一，涉及小样本时序预测、非线性拟合、闭环控制等数学难题。本文依托中国矿业大学(北京)理学院与美林数据技术股份有限公司共建的“科技矿场”合作平台，基于大学生创新训练项目中的采煤机滚筒高度预测案例，系统梳理了该场景下的核心数学方法，包括灰色系统理论、神经网络、时间序列特征工程、智能优化算法及PID控制理论。文章重点阐述各类方法的建模逻辑、适用条件与工程适配策略，并总结了学生在真实工程案例驱动下，从数据理解、特征构造、模型选择到控制实现的完整实践过程。本研究旨在通过真实案例的剖析，为同类工业时序预测与控制问题提供可迁移的方法参考，并体现校企合作平台在数学类人才培养中的支撑作用。

Abstract: The precise adjustment of the height of the coal shearer drum is one of the key technologies for the intelligentization of fully mechanized mining faces, involving mathematical challenges such as small sample time series prediction, nonlinear fitting, and closed-loop control. This paper, based on the “Science and Technology Mine” cooperation platform jointly established by the School of Science of China University of Mining and Technology (Beijing) and Meilin Data Technology Co., Ltd., and drawing on the real case of the prediction and control of the height of the coal shearer drum in the student innovation training project, systematically reviews the core mathematical methods in this scenario, including grey system theory, neural networks, time series feature engineering, intelligent optimization algorithms, and PID control theory. The article focuses on elaborating the modeling logic, applicable conditions, and engineering adaptation strategies of various methods, and summarizes the complete practical process of students from data understanding, feature construction, model selection to control implementation driven by real engineering cases. This study aims to provide transferable methodological references for similar industrial time series prediction and control problems through the analysis of real cases, and to demonstrate the supporting role of the university-enterprise cooperation platform in the cultivation of mathematics-oriented talents.