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论文编号：	15448
作者编号：	2320213907
上传时间：	2025/6/11 17:29:08
中文题目：	T科技有限公司数据资产价值评估案例研究
英文题目：	T Technology Co., Ltd. Data Asset Value Assessment Case Study
指导老师：	李元
中文关键字：	数据资产；数据资产价值评估；数据资产计量；会计确认
英文关键字：	data assets; data asset value assessment; data asset measurement; accounting recognition
中文摘要：	在数字经济快速发展的背景下，数据资产作为新型生产要素的价值管理问题日益凸显。随着《企业数据资源相关会计处理暂行规定》的出台与生成式人工智能技术的广泛应用，传统会计体系在数据资产确认、计量及披露方面的局限性逐渐暴露。 数据资产价值评估面临三重理论困境：其一，会计确认标准模糊性，传统无形资产准则对数据资源的“可分离性”与“控制权”界定不足，导致大量数据资源游离于财务报表之外；其二，计量方法适配性割裂，历史成本法难以捕捉技术迭代引发的维护成本变动，市场法受制于非标准化资产的交易流动性缺失，收益法则面临混合现金流分割的技术瓶颈；其三，技术冲击的制度响应滞后，这些矛盾凸显了会计理论革新与技术经济实践的深度脱节。 本研究提出“多维动态评估框架”，通过会计计量范式的三重突破应对上述挑战：动态重置成本模型的技术穿透性设计，突破传统历史成本法的静态假设。市场法修正框架的可比性重构，针对非标准化资产估值困境，构建“质量-需求-风险”三维系数修正体系。收益法分层折现机制的场景化创新，基于数据资产收益分化特征，设计基础服务与增值业务分层估值模型。 动态重置成本模型可优化技术防御性投入的资本化处理，市场法修正框架增强非标资产定价透明度，分层收益法则显著提升战略决策支持效能。但研究同时揭示，技术迭代参数的行业依赖性、混合模型的实施成本约束以及分层核算的颗粒度要求，仍是亟待突破的实践瓶颈。 未来研究需着力于三方面突破：其一，推动技术迭代参数的标准化建设，建立跨行业权威指引；其二，探索轻量化模型实施方案，降低中小企业应用门槛；其三，完善数据资产表外披露规则，构建动态风险监控体系。只有通过先进的技术工具、不断发展的会计理论、逐渐完善的监管政策的三者协同并进，才能够更好地推动数据要素市场化配置机制走向成熟，为数据资产价值评估提供参考。
英文摘要：	Against the backdrop of the rapid development of the digital economy, the value management of data assets as a new type of production factor has become increasingly prominent. With the release of the "Interim Provisions on Accounting Treatment of Enterprise Data Resources" and the widespread application of generative artificial intelligence technology, the limitations of the traditional accounting system in the recognition, measurement and disclosure of data assets have gradually been exposed. Data asset value assessment faces three theoretical dilemmas: First, the ambiguity of accounting recognition standards. The traditional intangible asset standards do not adequately define the "separability" and "control" of data resources, resulting in a large number of data resources being outside the financial statements; second, the adaptability of measurement methods is fragmented. The historical cost method is difficult to capture the changes in maintenance costs caused by technological iterations, the market method is subject to the lack of transaction liquidity of non-standardized assets, and the income method faces the technical bottleneck of mixed cash flow segmentation; third, the institutional response to technological shocks is lagging. These contradictions highlight the deep disconnection between accounting theory innovation and technological economic practice. This study proposes a "multi-dimensional dynamic evaluation framework" to address the above challenges through three breakthroughs in the accounting measurement paradigm: the technical penetration design of the dynamic reset cost model and the breakthrough of the static assumptions of the traditional historical cost method. The comparability reconstruction of the market law correction framework, in response to the valuation dilemma of non-standardized assets, constructs a three-dimensional coefficient correction system of "quality-demand-risk". The scenario-based innovation of the income method tiered discount mechanism, based on the differentiated characteristics of data asset income, designs a tiered valuation model for basic services and value-added services. The dynamic replacement cost model can optimize the capitalization of defensive technology investment, the market law correction framework enhances the transparency of non-standard asset pricing, and the tiered income law significantly improves the effectiveness of strategic decision-making support. However, the study also revealed that the industry dependence of technology iteration parameters, the implementation cost constraints of hybrid models, and the granularity requirements of tiered accounting are still practical bottlenecks that need to be broken through. Future research needs to focus on breakthroughs in three aspects: first, promote the standardization of technology iteration parameters and establish cross-industry authoritative guidance; second, explore lightweight model implementation plans to lower the application threshold for small and medium-sized enterprises; third, improve the off-balance sheet disclosure rules for data assets and build a dynamic risk monitoring system. Only through the coordinated progress of advanced technical tools, continuously developing accounting theories, and gradually improved regulatory policies can we better promote the market-oriented allocation mechanism of data elements to mature and provide a reference for data asset value assessment.
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