数据要素流通中大数据分析的数学方法及实践探索

Mathematical Methods and Practical Exploration of Big Data Analysis in the Circulation of Data Elements

摘要: 本文针对数据要素流通当中所含的大数据分析的数学建构和利用情况，先阐述支持数据要素流通的关键技术架构，重点剖析隐私算账，区块链等关键技能；接着梳理统计学，线性代数，改善理论这些核心数学工具，探讨它们在数据预处理，特征提取，创建模型中的具体应用情形；再全面考量数据品质，算法公正，信息安全，场景契合这些实际问题；最后从数据治理，算法优化，安全保障等很多层面给予改善建议。期望给提升数据要素流转效能，推进数据价值展现赋予理论依据和执行参照。

Abstract: This article focuses on the mathematical construction and utilization of big data analysis contained in the circulation of data elements. It first elaborates on the key technical architecture that supports the circulation of data elements, with a particular emphasis on analyzing key technologies such as privacy accounting and blockchain. Next, sort out the core mathematical tools such as statistics, linear algebra, and improvement theory, and explore their specific application scenarios in data preprocessing, feature extraction, and model creation; comprehensively consider the actual issues such as data quality, algorithm fairness, information security, and scenario fit. Finally, improvement suggestions are given from many aspects such as data governance, algorithm optimization, and security guarantee. It is expected to provide theoretical basis and implementation reference for enhancing the efficiency of data element circulation and promoting the demonstration of data value.