数据要素流通与收益分配机制研究：以风电场景融合气象数据为例

摘要/Abstract

摘要： 在加快全国统一数据要素大市场建设背景下，如何设计符合我国国情的数据交易流通模式和与之相匹配的收益分配机制尚未得到充分研究。本文首先基于数据要素市场中普遍认可的三类主体设定，以气象数据供给为典型细分行业，以数据在电力预测中的应用作为典型场景，建立了数据要素交易流通模型。模型中嵌入了基于机器学习的风电预测模型，对多特征数据要素和数据服务的价值实现进行刻画。其次，在基于数据价值的收益分配机制的设计中，对比分析了平均法、留一法、沙普利值法、惩罚-沙普利值法四种收益分配机制对数据生产商（数商）的主体效益差异及市场影响。最后，研究表明：惩罚-沙普利值的收益分配策略能充分考虑市场中数据要素的差异化水平，同时能够识别并避免数据要素的复制导致的扰动。

关键词: 数据要素流通, 数据市场, 收益分配, 机器学习, 沙普利值

Abstract: In the context of accelerating the construction of a unified national data element market in China, the design of a data transaction and circulation model that suits the country’s specific conditions, along with a corresponding revenue sharing mechanism, has not yet been fully explored. This paper starts by setting up a data element transaction model based on the commonly recognized three main parties in the data element market, using meteorological data supply as a typical subdivided industry and the application of data in power forecasting as a typical scenario. The model incorporates a wind power prediction model based on machine learning, depicting the value realization of multi-feature data elements and data services. Furthermore, in designing a revenue sharing mechanism based on data value, this study compares the differences in the main benefits to data producers (data vendors) and market impact among four revenue sharing methods: the average method, leave-one-out method, Shapley Value method, and Penalty-modified Shapley Value method. Lastly, the research demonstrates that the Penalty-modified Shapley Value revenue sharing strategy effectively considers the level of differentiation among data elements in the market, while also identifying and preventing disturbances caused by data element replication.

Key words: data element circulation, data factor market, revenue sharing, machine learning, Shapley value

王衍之, 黄静思, 王剑晓, 高锋, 宋洁. 数据要素流通与收益分配机制研究：以风电场景融合气象数据为例[J]. 管理评论, 2024, 36(6): 30-41.

Wang Yanzhi, Huang Jingsi, Wang Jianxiao, Gao Feng, Song Jie. Research on the Circulation and Revenue Sharing Mechanisms of Data Elements: An Example of Integrating Meteorological Data in Wind Power Scenarios[J]. Management Review, 2024, 36(6): 30-41.

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