中圖分類號：TP311文獻標志碼：ADOI:10.19358/j.issn.2097-1788.2026.07.006 中文引用格式：孫鋼，杜旸旸.數據治理視角下的數據中心算力-電力耦合分析研究［J］.網絡安全與數據治理，2026，45（7）：39-46. 英文引用格式：Sun Gang, Du Yangyang. Research on the coupling analysis method of computing power behavior and power load in data centers from the perspective of data governance［J］.Cyber Security and Data Governance，2026，45（7）：39-46.
Research on the coupling analysis method of computing power behavior and power load in data centers from the perspective of data governance
Sun Gang1, Du Yangyang2
1. State Grid Zhejiang Marketing Service Center; 2. Zhejiang Rail Transit Operation Management Group Co., Ltd.
Abstract： Against the backdrop of the rapid growth of energy consumption in data centers and the increasingly complex structure of computing power demand, this paper conducts research on the construction of a governance system for multisource heterogeneous data in data centers, data mining and analysis related to computing power, and the collaborative relationship between computing power and power load from the perspective of data governance. Based on the concept of data governance, a standardized preprocessing system for multisource heterogeneous data is constructed. Through data mining methods such as data cleaning, feature extraction, and pattern recognition, the computing power intensity and dynamic core characteristics are extracted. A computing powerpower mapping relationship model based on governed data is further constructed to reveal the influence law of different task types on load changes. At the model level, a prediction structure integrating LSTM and attention mechanism is introduced to achieve highprecision modeling of load changes driven by computing power based on highquality governed data. The research results show that the improved data governance system effectively improves data quality, and the model built based on governed data significantly enhances the sensitivity and stability of load forecasting to changes in task behavior, providing a reliable basis for energy efficiency optimization of data centers, improvement of computing power scheduling strategies, and improvement of data governance systems.
Key words : data center; power load; computational behavior; data governance
