数据资产融资的动态估值与信用评估数学模型

Mathematical Models for Dynamic Valuation and Credit Assessment in Data Asset Financing

摘要: 数据资产融资的核心障碍在于原始数据的真实性验证与价值的动态评估。传统技术难以解决源头造假，而静态估值模型无法捕捉数据价值的时效性。为应对这些挑战，本文首先构建了一个多方参与的基于联盟链的数据资产融资业务生态，并在此基础上，提出了一套数学建模框架。本文构建了一套以联盟链为基础的多方数据融资业务框架。该框架的核心是两个数学模型：“动态数据资产估值模型DDAVM”和“数据信用分评估模型DCSM”。DDAVM创新性地融合了刻画时效性的指数衰减函数与基于多方信用的可信度权重函数，实现了对资产价值的动态量化。DCSM则引入博弈论中的激励相容机制，通过设计激励相容的“连带责任”惩罚项，从数学上证明了数据背书方的“诚实”是其最优策略。最后，通过多主体仿真实验，验证了该框架在抑制数据造假、动态评估信用方面的有效性。本研究为解决数据资产的动态定价与可信激励问题提供了一种数学范式。

Abstract: The core challenges in data asset financing lie in the verification of raw data authenticity and the dynamic assessment of its value. Conventional technologies struggle to address source-level forgery, while static valuation models fail to capture the time-sensitive nature of data value. To tackle these challenges, this study first establishes a multi-party business ecosystem for data asset financing based on a consortium blockchain, and, building upon this foundation, proposes a mathematical modeling framework. The core of this framework consists of two mathematical models: the Dynamic Data Asset Valuation Model (DDAVM) and the Data Credit Scoring Model (DCSM). The DDAVM innovatively integrates an exponential decay function to characterize time-sensitivity with a credibility weighting function derived from multi-party credit, thereby enabling the dynamic quantification of asset value. The DCSM, in turn, introduces an incentive compatibility mechanism from game theory and, through an incentive-compatible “joint liability” penalty term, mathematically demonstrates that honesty becomes the optimal strategy for data endorsers. Finally, the effectiveness of the proposed framework in suppressing data fraud and dynamically assessing credit is validated through multi-agent simulation experiments. This study offers a mathematical paradigm for addressing the dual challenges of dynamic pricing and trustworthy incentive mechanisms for data assets.