Exploration and Application of Case Management Improvement in Large Model Enabling Enterprises: A Case Study of Power Grid Enterprises
- DOI
- 10.2991/978-94-6463-676-5_61How to use a DOI?
- Keywords
- Artificial intelligence; large model; judicial case management; information processing; risk prediction
- Abstract
In judicial case management, power grid enterprises are faced with the challenge of information processing and auxiliary decision making, especially the efficiency and accuracy of massive data processing. Through the introduction of large model technology, improving the ability of text induction, information extraction, case classification and risk prediction can improve the quality and efficiency of power grid enterprises in case management. This paper discusses the application of the large model in the judicial management of power grid enterprises, analyzes its promotion potential from the perspectives of technical architecture, application scenarios, risk prevention and control, and future development paths, and puts forward strategic suggestions.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiayun Shi AU - Lei Xu AU - Chengyan Huang AU - Xiaoyun Zha AU - Jin Liu PY - 2025 DA - 2025/04/15 TI - Exploration and Application of Case Management Improvement in Large Model Enabling Enterprises: A Case Study of Power Grid Enterprises BT - Proceedings of the 2024 6th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2024) PB - Atlantis Press SP - 631 EP - 642 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-676-5_61 DO - 10.2991/978-94-6463-676-5_61 ID - Shi2025 ER -