煤矿掘进机工作状态分析预测模型

1、数据预处理与特征工程：采用Python（集成 pandas库）开展数据清洗（剔除超出额定范围的电机电流值、补全缺失的工作面温度/粉尘浓度数据），通过scikit-learn完成特征标准化、异常值处理及关键特征筛选，为模型输入提供高质量数据；2、模型训练与验证：使用MATLAB进行算法原型验证（如随机森林特征重要性评估），通过TensorFlow构建轻量级LSTM 神经网络，避免复杂深度学习大模型的高算力需求，适配煤矿工业场景的硬件环境。

1. Data Preprocessing and Feature Engineering: Data cleaning was conducted using Python with the pandas library, which involves removing motor current values beyond the rated range and imputing missing values of working face temperature and dust concentration. Subsequently, feature standardization, outlier handling and key feature selection were completed via scikit-learn to provide high-quality data for model input. 2. Model Training and Validation: MATLAB was utilized for algorithm prototype verification, such as feature importance evaluation based on random forest. A lightweight LSTM neural network was constructed using TensorFlow, which avoids the high computational power requirements of complex large-scale deep learning models, thus adapting to the hardware environment of coal mine industrial scenarios.