Research on New Energy Vehicle Operation Monitoring Cloud Platform Based on Big Data and Artificial Intelligence
Keywords:
Big data, Artificial intelligence, New energy vehicles, Monitor cloud platformAbstract
This article studies a new energy vehicle operation monitoring cloud platform based on big data and artificial intelligence, aiming to improve the operational efficiency and management level of new energy vehicles. By integrating big data analysis and artificial intelligence technology, the platform achieves real-time monitoring and fault prediction of vehicle operating status, optimizing charging strategies and energy management. This study not only provides accurate data support for new energy vehicle operators, but also promotes energy conservation, emission reduction, and environmental protection, laying a solid foundation for the sustainable development of the new energy vehicle industry.
References
Zhu, Y., Yu, W., & Li, R. (2025). SAGCN: A spatiotemporal attention-weighted graph convolutional network with IoT integration for adolescent tennis motion analysis. Alexandria Engineering Journal, 128, 652-662.
Zhang, X. (2024). Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering. Innovation & Technology Advances, 2(2), 59–75. https://doi.org/10.61187/ita.v2i2.135
Zhou, J., & Cen, W. (2024). Investigating the Effect of ChatGPT-like New Generation AI Technology on User Entrepreneurial Activities. Innovation & Technology Advances, 2(2), 1–20. https://doi.org/10.61187/ita.v2i2.124
Yang, J., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
Chen, J., Zhang, X., Wu, Y., Ghosh, S., Natarajan, P., Chang, S. F., & Allebach, J. (2022). One-stage object referring with gaze estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5021-5030).
Wu, W., Bi, S., Zhan, Y., & Gu, X. (2025). Supply chain digitalization and energy efficiency (gas and oil): How do they contribute to achieving carbon neutrality targets?. Energy Economics, 142, 108140.
Peng, Qucheng, Ce Zheng, and Chen Chen. "Source-free domain adaptive human pose estimation." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
Peng, Qucheng, et al. "RAIN: regularization on input and network for black-box domain adaptation." Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. 2023.
Guo, Y., & Tao, D. (2025). Modeling and Simulation Analysis of Robot Environmental Interaction. Artificial Intelligence Technology Research, 2(8).
We, X., Lin, S., Pruś, K., Zhu, X., Jia, X., & Du, R. (2025). Towards Intelligent Monitoring of Anesthesia Depth by Leveraging Multimodal Physiological Data. International Journal of Advance in Clinical Science Research, 4, 26–37. Retrieved from https://www.h-tsp.com/index.php/ijacsr/article/view/158
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Feng Zuo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
