参考文献/References:
[1] Ma Zhongcun, Zhang Lei, Shi Chunyang, et al. Prediction of oxygen consumption in steelmaking based on LAOA‑TSVR[J]. Metalurgija, 2024, 63(2): 165‑168.
[2] Wen Daoyuan, Zhu Yingtao. Research on prediction of oxygen consumption in converter steelmaking based on IGWO‑SVM model[J]. Journal of Physics: Conference Series, 2021, 2010: 012138.
[3] Wang Dazhi, Bao Yanping, Gao Fang, et al. Hybrid model for predicting oxygen consumption in BOF steel‑making process based on cluster analysis[J]. Steel Research International, 2023, 94(1): 2200595.
[4] Jiang Shenglong, Shen Xinyue, Zheng Zhong. Gaussian process‑based hybrid model for predicting oxygen consumption in the converter steelmaking process[J]. Processes, 2019, 7(6): 352.
[5] Mei Liuliu. Study on intelligent control method of converter steelmaking based on expert system[D]. Anshan: University of Science and Technology Liaoning, 2023.[梅留留. 基于专家系统的转炉炼钢智慧控制方法研究[D]. 鞍山: 辽宁科技大学, 2023.]
[6] Yang Qingfeng, Lai Xuzhi, Du Sheng, et al. Converter steelmaking oxygen consumption prediction based on granularity clustering[J]. Acta Automatica Sinica, 2024, 50(1): 132‑142.[阳青锋, 赖旭芝, 杜胜, 等. 基于粒度聚类的转炉炼钢氧气消耗量预测[J]. 自动化学报, 2024, 50(1): 132‑142.]
[7] Qin Bo, Wu Qingchao, Zhang Juanjuan, et al. Blowing oxygen volume prediction of BOF steelmaking based on PSO‑SVM[J]. Measurement & Control Technology, 2014, 33(12): 121‑124.[秦波, 吴庆朝, 张娟娟, 等. 基于PSO优化SVM的转炉炼钢用氧量预测研究[J]. 测控技术, 2014, 33(12): 121‑124.]
[8] Li Ailian, Zhao Duozhen, Guo Zhibin, et al. Prediction of converter oxygen consumption in improved deep belief network[J]. China Measurement & Testing Technology, 2020, 46(6): 1‑6.[李爱莲, 赵多祯, 郭志斌, 等. 改进深度信念网络的转炉耗氧量预测[J]. 中国测试, 2020, 46(6): 1‑6.]
[9] Han Zhongyang, Zhao Jun, Leung H, et al. A review of deep learning models for time series prediction[J]. IEEE Sensors Journal, 2021, 21(6): 7833‑7848.
[10] Huang Yixue, Qin Ke, Luo Wei, et al. Ship electrical layout method using prior rules and deep neural networks[J]. Computer Integrated Manufacturing Systems, 2024, 30(3): 968‑981.[黄一学, 秦克, 罗威, 等. 先验规则和深度学习融合驱动的舰船电气图纸布局方法[J]. 计算机集成制造系统, 2024, 30(3): 968‑981.]
[11] Wang Min, Hu Zhao, Xu Xiaowei, et al. Constructing a knowledge‑driven and data‑driven hybrid decision model for etiological diagnosis of ventricular tachycardia[J]. Medical Journal of Peking Union Medical College Hospital, 2025, 16(2): 454‑461.[王敏, 胡兆, 徐晓巍, 等. 融合知识驱动和数据驱动的混合决策模型构建：以室性心动过速病因诊断为例[J]. 协和医学杂志, 2025, 16(2): 454‑461.]
[12] Yang Hao, Wang Jiayi, Yi Wenfei, et al. Hybrid knowledge‑data driven adaptive voltage/var optimization control of photovoltaic inverters in distribution networks[J]. Proceedings of the CSEE, 2025, 45(22): 8691‑8706.[杨浩, 王佳怡, 易文飞, 等. 知识‑数据融合驱动的配电网光伏逆变器电压/无功优化自适应控制[J]. 中国电机工程学报, 2025, 45(22): 8691‑8706.]
[13] Li Yang, Han Min, Jiang Liwen. Blowing oxygen volume calculation model of BOF steelmaking based on oxygen decarburization efficiency prediction[J]. Journal of Dalian University of Technology, 2012, 52(5): 725‑729.[李洋, 韩敏, 姜力文. 基于氧气脱碳效率预测的转炉炼钢吹氧量计算模型[J]. 大连理工大学学报, 2012, 52(5): 725‑729.]
[14] Wang Huixian, Xu Anjun, Gu Maoqiang, et al. Oxygen supply prediction model for converters based on segmented oxygen decarburization efficiency[J]. China Metallurgy, 2023, 33(7): 91‑99.[王慧贤, 徐安军, 谷茂强, 等. 基于分段式氧气脱碳效率的转炉供氧量预测模型[J]. 中国冶金, 2023, 33(7): 91‑99.]
[15] Shao Xin, Liu Qing, Yin Xicheng, et al. Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network[J]. International Journal of Minerals, Metallurgy and Materials, 2024, 31(1): 106‑117.
[16] Li Hua, Lei Hao. Analysis and prediction model of related affecting factors of oxygen consumption in converter steel‑making[J]. Southern Metals, 2022(3): 37‑42.[李桦, 雷豪. 转炉炼钢冶炼氧气消耗相关影响因素分析及预测模型[J]. 南方金属, 2022(3): 37‑42.]
[17] Peng Qian, Fan Dingdong, Zhou Xiaobin, et al. Optimization of an oxygen supply prediction model based on the K‑nearest neighbours（KNN）algorithm using the Pearson correlation coefficient[J]. Ironmaking & Steelmaking: Processes, Products and Applications, 2024: 1.
[18] Xia Yijie, Wang Hongbing, Wei Xing, et al. Data and mechanism joint‑driven model for carbon content prediction at the end point of converter[C]//Proceedings of the 14th CSM Steel Congress. Beijing: The Chinese Society for Metals, 2023: 43‑48.[夏一杰, 汪红兵, 魏星, 等. 数据与机理联合驱动的转炉终点碳含量预测模型[C]//第十四届中国钢铁年会论文集. 北京: 中国金属学会, 2023: 43‑48.]
[19] Dong Qianqian, Li Min, Hu Shuaijie, et al. Multi‑output prediction model for basic oxygen furnace steelmaking based on the fusion of deep convolution and attention mechanisms[J]. Metals, 2024, 14(7): 773.
[20] Wu Song, Lan Xin, Shan Jingyang, et al. Improved U‑Net algorithm based on attention mechanism and multi‑scale fusion[J]. Journal of Computer Applications, 2024, 44(S2): 24‑28.[吴淞, 蓝鑫, 单靖杨, 等. 基于注意力机制和多尺度融合的U‑Net改进算法[J]. 计算机应用, 2024, 44(S2): 24‑28.]
[21] Chen Long, Zhang Hanwang, Xiao Jun, et al. SCA‑CNN: spatial and channel‑wise attention in convolutional networks for image captioning[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017: 6298‑6306.
[22] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[PP/OL]. V7. arXiv (2023‑08‑02)[2026‑03‑01]. https://doi.org/10.48550/arXiv.1706.03762.
[23] Li Jiayuan, Wang Xiaodong, He Qixue. Application and performance optimization of CNN enhanced Informer model in industrial time series prediction[J]. Journal of Computer Applications, 2024, 44(S2): 79‑83.[李嘉源, 王晓东, 何启学. CNN增强型Informer模型在工业时间序列预测中的应用及性能优化[J]. 计算机应用, 2024, 44(S2): 79‑83.]
[24] Han Huijian, Xing Huaiyu, Zhang Yunfeng, et al. Visual detection of steel surface defects based on Transformer and multi‑attention[J]. Journal of Zhengzhou University (Engineering Science), 2025, 46(5): 69‑76.[韩慧健, 邢怀宇, 张云峰, 等. 基于Transformer多元注意力的钢材表面缺陷视觉检测[J]. 郑州大学学报(工学版), 2025, 46(5): 69‑76.]
[25] Ke Kai, Peng Qichun, Peng Xialin, et al. Prediction model of oxygen supply in converter steelmaking based on ensemble learning[J]. Steelmaking, 2023, 39(1): 47‑52.[柯凯, 彭其春, 彭霞林, 等. 基于集成学习的转炉炼钢供氧量预测模型[J]. 炼钢, 2023, 39(1): 47‑52.]
[26] Yang Shicun, Wei Zhiqiang, Zhong Liangcai, et al. Study on oxygen consumption model based on data drive in a converter[J]. Steelmaking, 2022, 38(4): 7‑13.[杨仕存, 魏志强, 钟良才, 等. 基于数据驱动的转炉耗氧量模型研究[J]. 炼钢, 2022, 38(4): 7‑13.]