Application of Computer and Artificial Intelligence Technology in Mine Electrical Automation Control
DOI:
https://doi.org/10.53469/ijomsr.2025.08(08).03Keywords:
Mining, Electrical automation control, Computer, Artificial intelligence technologyAbstract
This article comprehensively explores the application and significant effects of computer and artificial intelligence technology in mine electrical automation control. The article elaborates on the core values of these technologies from three dimensions: improving production efficiency and work safety, reducing energy consumption and resource waste, and improving the production environment and ensuring production quality. Through intelligent and automated production methods, not only can production efficiency and stability be improved, but the safety and environmental protection of mines can also be enhanced, laying a solid foundation for the sustainable development of mining enterprises.
References
Chen, Yinda, et al. "Generative text-guided 3d vision-language pretraining for unified medical image segmentation." arXiv preprint arXiv:2306.04811 (2023).
Ding, C.; Wu, C. Self-Supervised Learning for Biomedical Signal Processing: A Systematic Review on ECG and PPG Signals. medRxiv 2024.
Han, X., & Dou, X. (2025). User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph. Frontiers in Neurorobotics, 19, 1587973.
Hu, Xiao. "GenPlayAds: Procedural Playable 3D Ad Creation via Generative Model." (2025).
Hu, Xiao. "Learning to Animate: Few-Shot Neural Editors for 3D SMEs." (2025).
Li, Huaxu, et al. "Enhancing Intelligent Recruitment With Generative Pretrained Transformer and Hierarchical Graph Neural Networks: Optimizing Resume-Job Matching With Deep Learning and Graph-Based Modeling." Journal of Organizational and End User Computing (JOEUC) 37.1 (2025): 1-24.
Li, X., Lin, Y., & Zhang, Y. (2025). A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy. arXiv preprint arXiv:2507.12098.
Miao, Junfeng, et al. "Secure and Efficient Authentication Protocol for Supply Chain Systems in Artificial Intelligence-based Internet of Things." IEEE Internet of Things Journal (2025).
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., Chen, C. and Wu, Z., 3D Vision-Language Gaussian Splatting. In The Thirteenth International Conference on Learning Representations.
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Su, Tian, et al. "Anomaly Detection and Risk Early Warning System for Financial Time Series Based on the WaveLST-Trans Model." (2025).
Tan, C. (2024). The Application and Development Trends of Artificial Intelligence Technology in Automotive Production. Artificial Intelligence Technology Research, 2(5).
Tu, Tongwei. "SmartFITLab: Intelligent Execution and Validation Platform for 5G Field Interoperability Testing." (2025).
Wang, Hao. "Joint Training of Propensity Model and Prediction Model via Targeted Learning for Recommendation on Data Missing Not at Random." AAAI 2025 Workshop on Artificial Intelligence with Causal Techniques. 2025.
Wang, Z., Chew, J. J., Wei, X., Hu, K., Yi, S., & Yi, S. (2025). An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains. Journal of Theory and Practice in Economics and Management, 2(2), 49–62. Retrieved from https://woodyinternational.com/index.php/jtpem/article/view/208