基于自表示学习的化工行业多源异构碳数据聚类分析
Clustering Analysis of Multi-Source Heterogeneous Carbon Data in the Chemical Industry Based on Self-Representation Learning
DOI: 10.12677/airr.2025.146135, PDF,    科研立项经费支持
作者: 刘小楠*, 周 强, 黄 勇：四川轻化工大学化学工程学院，四川 自贡；冯晶晶：中山大学碳中和与绿色发展研究院，广东 广州；广东埃文低碳科技股份有限公司，广东 广州；何陆灏：中山大学碳中和与绿色发展研究院，广东 广州；张 娜#：广州南方学院，广东 广州
关键词: 双碳战略；化工行业；碳排放；多源异构数据；聚类分析；Dual Carbon Strategy； Chemical Industry； Carbon Emission； Multi-Source Heterogeneous Data； Clustering Analysis
摘要: 在碳达峰碳中和战略驱动下，化工行业急需通过精准的碳排放数据聚类分析完成数据归类，帮助碳减排路径科学规划。现有研究虽在多模态数据融合方面取得进展，但针对化工行业的多源异构碳数据聚类分析能力不足。本文创造性地提出一种基于自表示学习，适配化工行业的多源异构碳数据集的聚类分析系统。该系统能够挖掘多源异构数据的互补性以及高阶流形数据结构，实现精准的聚类分析，为碳减排路径科学规划提供技术支撑。
Abstract: Driven by the dual carbon strategy, chemical enterprises need accurate clustering system of carbon-emission data for better data analysis, assisting in the scientific planning of carbon emission reduction roadmaps. Although encourage studies on multi-source data fusion, methods for multi-source heterogeneous carbon datasets clustering are limited. This paper creatively proposes a self-representation clustering system for multi-source heterogeneous carbon data sets. The system is capable of mining the complementarity of multi-source heterogeneous data and leveraging higher-order manifold data structures to achieve precise clustering analysis, thereby providing technical support for the scientific planning of carbon emission reduction pathways.
文章引用：刘小楠, 周强, 黄勇, 冯晶晶, 何陆灏, 张娜. 基于自表示学习的化工行业多源异构碳数据聚类分析[J]. 人工智能与机器人研究, 2025, 14(6): 1444-1452. https://doi.org/10.12677/airr.2025.146135

参考文献

[1] Dong, F., Zhu, J., Li, Y., Chen, Y., Gao, Y., Hu, M., et al. (2022) How Green Technology Innovation Affects Carbon Emission Efficiency: Evidence from Developed Countries Proposing Carbon Neutrality Targets. Environmental Science and Pollution Research, 29, 35780-35799. [Google Scholar] [CrossRef] [PubMed]
[2] Bruckner, B., Hubacek, K., Shan, Y., Zhong, H. and Feng, K. (2022) Impacts of Poverty Alleviation on National and Global Carbon Emissions. Nature Sustainability, 5, 311-320. [Google Scholar] [CrossRef] 
[3] 熊肖磊, 王春伟, 赵炯, 等. 基于Redis与SSM的大型设备数据运用系统设计[J]. 现代机械, 2018(6): 29-34.
[4] 赵德基, 王力, 狄军峰. 基于Dubbo + NoSQL的工业领域大数据平台研究[J]. 数字技术与应用, 2017(7): 64-67.
[5] 王宏志, 梁志宇, 李建中, 等. 工业大数据分析综述: 模型与算法[J]. 大数据, 2018, 4(5): 62-79.
[6] Yin, M., Gao, J., Xie, S. and Guo, Y. (2019) Multiview Subspace Clustering via Tensorial T-Product Representation. IEEE Transactions on Neural Networks and Learning Systems, 30, 851-864. [Google Scholar] [CrossRef] [PubMed]
[7] Yang, Z., Liang, N., Yan, W., et al. (2020) Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering. IEEE Transactions on Cybernetics, 99, 1-14.
[8] Brbić, M. and Kopriva, I. (2018) Multi-View Low-Rank Sparse Subspace Clustering. Pattern Recognition, 73, 247-258. [Google Scholar] [CrossRef] 
[9] Abavisani, M. and Patel, V.M. (2018) Multimodal Sparse and Low-Rank Subspace Clustering. Information Fusion, 39, 168-177. [Google Scholar] [CrossRef] 
[10] Zhu, W. and Peng, B. (2020) Sparse and Low-Rank Regularized Deep Subspace Clustering. Knowledge-Based Systems, 204, Article ID: 106199. [Google Scholar] [CrossRef] 
[11] Xiao, X., Chen, Y., Gong, Y. and Zhou, Y. (2021) Prior Knowledge Regularized Multiview Self-Representation and Its Applications. IEEE Transactions on Neural Networks and Learning Systems, 32, 1325-1338. [Google Scholar] [CrossRef] [PubMed]
[12] Zhang, C., Fu, H., Wang, J., Li, W., Cao, X. and Hu, Q. (2020) Tensorized Multi-View Subspace Representation Learning. International Journal of Computer Vision, 128, 2344-2361. [Google Scholar] [CrossRef] 
[13] Tang, Y., Xie, Y., Zhang, C. and Zhang, W. (2022) Constrained Tensor Representation Learning for Multi-View Semi-Supervised Subspace Clustering. IEEE Transactions on Multimedia, 24, 3920-3933. [Google Scholar] [CrossRef] 
[14] Lee, J.M. (2006) Riemannian Manifolds: An Introduction to Curvature. Springer Science & Business Media.
[15] Xie, Y., Tao, D., Zhang, W., Liu, Y., Zhang, L. and Qu, Y. (2018) On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization. International Journal of Computer Vision, 126, 1157-1179. [Google Scholar] [CrossRef] 
[16] Chen, Y., Xiao, X. and Zhou, Y. (2020) Jointly Learning Kernel Representation Tensor and Affinity Matrix for Multi-View Clustering. IEEE Transactions on Multimedia, 22, 1985-1997. [Google Scholar] [CrossRef] 
[17] Chen, Y., Xiao, X. and Zhou, Y. (2020) Multi-View Subspace Clustering via Simultaneously Learning the Representation Tensor and Affinity Matrix. Pattern Recognition, 106, Article ID: 107441. [Google Scholar] [CrossRef] 
[18] Pan, B., Li, C. and Che, H. (2023) Nonconvex Low-Rank Tensor Approximation with Graph and Consistent Regularizations for Multi-View Subspace Learning. Neural Networks, 161, 638-658. [Google Scholar] [CrossRef] [PubMed]

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