基于机器学习的机场经济促进区供应链大数据驱动风险评估方法

Zhijun Ma, Xiaobei Yang, Ruili Miao
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引用次数: 0

摘要

随着经济全球化的快速发展,人口、资本和信息在区域间快速流动和聚集。机场作为高速运输系统中最重要的运输方式,在促进区域经济发展中发挥着越来越重要的作用,产生了一批机场经济促进区。为了促进这些领域的有效开发管理,通过数据分析进行准确的风险评估是非常重要的。因此,本文利用集成学习的思想,提出了一个大数据驱动的空港经济促进区供应链评估模型。特别地,我们结合了来自不同来源的两个方面的数据:(1)来自工商局的国民经济统计和企业登记数据;(2)中国民航局等多源数据。在此基础上,构建集成集成学习方法,定量分析国内空港经济区供应链安全特征,为空港经济区供应链安全提供重要支撑。最后,在综合数据上进行了实验,验证了本文方法的有效性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Big Data-Driven Risk Assessment Method Using Machine Learning for Supply Chains in Airport Economic Promotion Areas
With the rapid development of economic globalization, population, capital and information are rapidly flowing and clustering between regions. As the most important transportation mode in the high-speed transportation systems, airports are playing an increasingly important role in promoting regional economic development, yielding a number of airport economic promotion areas. To boost effective development management of these areas, accurate risk assessment through data analysis is quite important. Thus in this paper, the idea of ensemble learning is utilized to propose a big data-driven assessment model for supply chains in airport economic promotion areas. In particular, we combine two aspects of data from different sources: (1) national economic statistics and enterprise registration data from the Bureau of Industry and Commerce; (2) data from the Civil Aviation Administration of China and other multi-source data. On this basis, an integrated ensemble learning method is constructed to quantitatively analyze the supply chain security characteristics in domestic airport economic area, providing important support for the security of supply chains in airport economic area. Finally, some experiments are conducted on synthetic data to evaluate the method investigated in this paper, which has proved its efficiency and practice.
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