Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)

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Construction of the Effectiveness Evaluation System for Digital Transformation of Power Grid Enterprises

Authors
Yi Tian1, Jianbin Zhao2, Jingyu Huang1, Tao Zheng1, Xiaona Han1, Dongya Zhang1, Pengfei Wang3, *, Yingzi Zhang3, *, Xiukun Gong3, *
1State Grid Hebei Information & Communication Branch, Hebei, China
2State Grid Hebei Electric Power Co., Ltd, Hebei, China
3Beijing State Grid Xintong Accenture Information Technology Co., Ltd, Beijing, China
*Corresponding author. Email: wangpengfei@sgitg.sgcc.com.cn
*Corresponding author. Email: zhangyingzi@sgitg.sgcc.com.cn
*Corresponding author. Email: 390204517@qq.com
Corresponding Authors
Pengfei Wang, Yingzi Zhang, Xiukun Gong
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-490-7_24How to use a DOI?
Keywords
power grid enterprises; digital transformation; effectiveness; evaluation model; dynamic feedback
Abstract

In response to issues such as incomplete indicators and unreasonable subjective and objective weight settings in the existing enterprise digital transformation effectiveness evaluation model, a digital transformation effectiveness evaluation model for power grid enterprises is proposed. Considering the key factors influencing the digital transformation of power grid enterprises, an indicator system for the evaluation model of digital transformation effectiveness is constructed. By utilizing the Analytic Hierarchy Process (AHP) and entropy weight method, the subjective and objective weights of the evaluation indicators are calculated. On this basis, by incorporating the concept of dynamic feedback and applying the panel threshold model, the proportion of subjective and objective weights is deeply integrated with the dynamic objectives of the corresponding stages, greatly improving the accuracy of the evaluation results. Case studies show that this model can achieve dynamic adjustment based on the objectives of each stage, ensuring the accuracy of the evaluation results of the digital transformation effectiveness of power grid enterprises, and providing decision support and theoretical support for optimizing the digital transformation route of power grid enterprises.

Copyright
© 2024 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.

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Volume Title
Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 August 2024
ISBN
978-94-6463-490-7
ISSN
2589-4919
DOI
10.2991/978-94-6463-490-7_24How to use a DOI?
Copyright
© 2024 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

risenwbib
TY  - CONF
AU  - Yi Tian
AU  - Jianbin Zhao
AU  - Jingyu Huang
AU  - Tao Zheng
AU  - Xiaona Han
AU  - Dongya Zhang
AU  - Pengfei Wang
AU  - Yingzi Zhang
AU  - Xiukun Gong
PY  - 2024
DA  - 2024/08/31
TI  - Construction of the Effectiveness Evaluation System for Digital Transformation of Power Grid Enterprises
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 217
EP  - 227
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_24
DO  - 10.2991/978-94-6463-490-7_24
ID  - Tian2024
ER  -