基于近红外光谱的铁矿石品牌识别模型数据集

该数据可应用于铁矿石开卸阶段品牌识别、异常批次筛查、固废识别、供应链质控等，为相应场景提供有效的评估模型、检测参照标准、识别手段以及方式方法等，为基于近红外光谱的铁矿石品牌识别领域提供了较好的模型，具有重大意义。This dataset can be applied to multiple scenarios such as brand identification during the iron ore unloading stage, abnormal batch screening, solid waste identification, and supply chain quality control. It provides effective evaluation models, detection reference standards, identification methods and corresponding approaches for these scenarios, and offers a high-quality model for the field of iron ore brand identification based on near-infrared spectroscopy, thus being of great significance.

搜集汇总

背景与挑战

背景概述

该数据集包含19种铁矿石品牌的10798条近红外光谱样本，采用950-1680nm波段反射吸收特征进行建模。数据集通过参差卷积神经网络实现了98.01%的品牌识别准确率，专门用于铁矿石开卸阶段的品牌识别和异常批次检测，具有无损、稳定和高精度的特点。