Mechanisms and Strategies for Enhancing the Efficacy of State-Owned Assets Supervision Driven by Digital Intelligence
- DOI
- 10.2991/978-94-6463-888-2_26How to use a DOI?
- Keywords
- Digital-Intelligentization; Supervision; State-Owned Assets; Mechanism
- Abstract
In the era of digital economy, it is inevitable that digital and intelligent technologies will drive the reform of state-owned assets supervision models. To adapt to the needs of this reform, in terms of mechanism construction, efforts should be made to establish a data integration mechanism, an intelligent early warning mechanism, a dynamic feedback mechanism, and a collaborative supervision mechanism for state-owned assets supervision. In terms of implementation strategies, through improving the digital and intelligent supervision system and the innovation capability system, building a comprehensive digital and intelligent support system, and optimizing the implementation path of digital and intelligent supervision, we will comprehensively promote the digital and intelligent transformation of state-owned assets supervision work and enhance the effectiveness of state-owned assets supervision.
- 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 - Ge Lin PY - 2025 DA - 2025/12/03 TI - Mechanisms and Strategies for Enhancing the Efficacy of State-Owned Assets Supervision Driven by Digital Intelligence BT - Proceedings of the 2025 7th International Conference on Economic Management and Cultural Industry (ICEMCI 2025) PB - Atlantis Press SP - 262 EP - 269 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-888-2_26 DO - 10.2991/978-94-6463-888-2_26 ID - Lin2025 ER -