As the most critical production factor in the era of the digital economy, data will have a significant impact on social production and development. Energy enterprises possess data that is interconnected with multiple industries, characterized by diverse needs, sensitivity, and long-term nature. The path to monetizing energy enterprises' data is challenging yet crucial. This paper explores the game-theoretic aspects of the data monetization process in energy enterprises by considering the relationships between enterprises and trading platforms. We construct a class of game decision models and study their equilibrium strategies. Our analysis shows that enterprises and platforms can adjust respective benefits by regulating the wholesale price of data and the intensity of data value mining to form a benign equilibrium state. Furthermore, by integrating nonlinear dynamical theory, we discuss the dynamic characteristics present in multi-period repeated game processes. We find that decision-makers should keep the adjustment parameters and initial states within reasonable ranges in multi-period dynamic decision-making to avoid market failure. Finally, based on the theoretical and numerical analysis, we provide decision insights and recommendations for enterprise decision-making to facilitate data monetization through strategic interactions with trading platforms.

翻译：作为数字经济时代最关键的生产要素，数据将对生产与社会发展产生深远影响。能源企业拥有与多个行业互联的数据，具有需求多样、敏感性强、周期长等特点。实现能源企业数据变现的路径既充满挑战又至关重要。本文通过考虑企业与交易平台的关系，从博弈论视角探讨能源企业数据变现过程。我们构建了一类博弈决策模型并研究其均衡策略。研究表明，企业可通过调节数据批发价格与数据价值挖掘强度来调整各自收益，形成良性均衡状态。进一步结合非线性动力理论，我们探讨了多期重复博弈过程中存在的动态特性。发现决策者在多期动态决策中应将调整参数与初始状态控制在合理范围内，以避免市场失灵。最后基于理论与数值分析，我们为企业决策提供见解与建议，以通过与交易平台的策略性互动促进数据变现。