针对钢铁行业能源消耗大、能源管理效率低的问题，设计并开发了一种面向生产现场的能源介质优化系统。 系统集成了能源数据采集、实时监测、智能分析与优化控制等多项功能，涵盖转炉、LF 精炼等关键工序，致力于提升能源利用效率、降低能耗并支撑企业绿色低碳转型。 构建了基于遗传算法优化的随机森林回归模型，通过挖掘废钢量、炉龄等 12 项工艺参数的非线性关联， 提升了转炉氧气消耗预测精度 ， 氧气消耗降低 1.3%； 引入了面向 LF 精炼的LSTM 温度预测模型，使钢液温度预测误差控制在±5 ℃，并据此优化供电策略，实现电耗降低 1.5%。

To address the issues of high energy consumption and low energy management efficiency in the steel industry, an onsite energy medium optimization system was designed and developed. The system integrates multiple functions, including energy data acquisition, real-time monitoring, intelligent analysis, and optimized control, covering key processes such as converters and LF refinement, with the aim of improving energy utilization efficiency, reducing energy consumption, and supporting green and low-carbon transformation in enterprises. A random forest regression model optimized by a genetic algorithm was constructed, which enhanced the prediction accuracy of oxygen consumption in converters by analysing the nonlinear relationships among 12 process parameters, such as scrap steel quantity and furnace age, resulting in a 1.3% reduction in oxygen consumption. For LF refining, an LSTM-based temperature prediction model was introduced, achieving a prediction error of ±5 ℃ for the molten steel temperature, which enabled the optimization of power supply strategies and reduced electricity consumption by 1.5%.