中南大学学报（自然科学版）2026，Vol.57Issue(4)：1485-1497,13.DOI:10.11817/j.issn.1672-7207.2026.04.003
基于知识与数据驱动的铁矿原料自适应评价研究
Research on adaptive evaluation of iron ore raw materials based on knowledge and data driving
摘要
Abstract
In the processing flow of raw materials before ironmaking in blast furnaces,the evaluation of iron ore is crucial for achieving cost reduction,efficiency improvement and efficient resources utilization.A comprehensive evaluation model system for iron ore which integrated knowledge and data-driven approaches was proposed in this paper to resolve the issues of singular indicator dimensions and difficulties in feature information fusion in iron ore evaluation.Firstly,a multi-dimensional evaluation indicator system encompassing physical,metallurgical,and economic properties was constructed.The entropy weight method and the analytic hierarchy process(AHP)were adopted to achieve the weight representation of objective feature information and subjective prior knowledge,respectively.Secondly,a fusion evaluation model of iron ore raw materials was established based on deep neural network,and the posterior evaluation information was used as supervision to realize the calibration and adaptive learning of the evaluation model.Finally,a browser-server(B/S)system architecture for the evaluation model was implemented using technologies such as Vue.js with bootstrap,Java,and MySQL.By integrating domain-specific knowledge of ore blending and model-represented data,a customized knowledge base was instantiated through the DeepSeek large language model,thereby creating a human-machine model interface capable of natural language interaction.Case studies and system applications indicate that the adaptive evaluation model can more accurately capture the features of iron ore raw materials,and the constructed integrated system possesses stronger information self-learning capabilities and interpretability.The established iron ore evaluation framework,along with its associated model and system,provides robust support for intelligent management of raw materials in the ironmaking process and facilitates digital twin implementation in steelmaking enterprises.关键词
铁矿石评价/数据驱动/融合模型/自适应学习/系统架构Key words
ore evaluation/data-driven/fusion model/adaptive learning/system architecture分类
矿业与冶金引用本文复制引用
刘代飞,汤毅,潘建..基于知识与数据驱动的铁矿原料自适应评价研究[J].中南大学学报（自然科学版）,2026,57(4):1485-1497,13.基金项目
国家自然科学基金资助项目(51674042)(Project(51674042)supported by the National Natural Science Foundation of China) （51674042）
