Article Details

Abstract

With the deep integration of Industry 4.0 and digital transformation, the IT infrastructure operation and maintenance mode is undergoing a paradigm shift from “passive response” to “active prevention” and further to “intelligent autonomy”. This paper focuses on the complex and heterogeneous IT environment of large energy enterprises, and systematically analyzes the technical system and development path of AIOps (AI Operations). The research proposes a “dual-track progressive” evolution model that a full-stack automation platform needs to be built to overcome efficiency bottlenecks in the short term, while in the long term, AI algorithms are relied upon to achieve fault prediction and decision-making closed-loop. By analyzing special scenarios in the energy industry, such as high latency in offshore operations and compatibility with multi-protocol heterogeneous systems, the key value of AIOps in improving system stability (SLA>99.99%), reducing labor costs (increasing operational efficiency by over 40%), and supporting business innovation is verified. This study provides a reusable intelligent operation and maintenance framework for traditional heavy asset industries, which has important practical significance for promoting the upgrade of digital infrastructure under the national energy security strategy.

Keywords

AIOps; IT infrastructure; energy industry; automated operation and maintenance; evolution path

References

1. [1] Jiang Jianlin, Cheng Wei, Wang Yiwei, etc Research and application of online monitoring and multi condition evaluation system based on data center energy efficiency [J]. Metrology Science and Technology, 2025, 69 (7): 52-60
2. [2] Miao Bingrong, Zhang Weihua, Liu Jianxin, etc Overview of cutting-edge technologies for intelligent railways under Industry 4.0 [J]. Journal of Transportation Engineering, 2021, 21 (1): 115-131
3. [3] Bao Hangyu, Yin Kanglin, Cao Li, etc Practice of Intelligent Operation and Maintenance: Current Status and Standardization [J]. Journal of Software, 2023, 34 (9): 4069-4095
4. [4] Sun Wei, Li Libin, Zang Hongrui, etc Research on Intelligent Operation and Maintenance Technology Based on Domestic Environment [J]. China Management Informatization, 2023, 26 (3): 158-162
5. [5] Wang Chao Design and Implementation of Data Center Software Automation Operation and Maintenance Platform [J]. Technology and Innovation, 2025 (11): 96-99
6. [6] Liu Zhan, Shi Guoliang, Du Xiuli, etc Theoretical System and Implementation Method of Intelligent Operation and Maintenance Based on Digital Twins [J]. Journal of Civil and Environmental Engineering (Chinese and English), 2024, 46 (1): 46-57
7. [7] Ye Jinmei Research on Progress Management Optimization of R Company's IT Infrastructure Intelligent Operation and Maintenance Project [D]. Lanzhou: Lanzhou University, 2024
8. [8] Sun Junjie, Yang Jigang Cloud Intelligence: Explorer on the Road of Intelligent Operation and Maintenance Upgrade [J]. China Industry and Information Technology, 2022 (Z1): 86-91
9. [9] Ran Yu, Cheng Yufan, Chen Dayong, etc Research on Intelligent Anti interference Decision Engine Using BP Neural Network [J]. Signal Processing, 2019, 35 (8): 1350-1357
10. [10] Xiong Yi Research on Security Solutions for Enterprise Network Automation Operation and Maintenance [J]. Network Security Technology and Application, 2024 (6): 101-106

About the authors

Wang Guanya (1988-), male, Han nationality, from Tengzhou, Shandong, engineer, master's degree, research direction: computer and information communication.