Research on Key Strategies of using Multi-level and Multi-source Big Data to Conduct Smart Poverty Alleviation Audit

Abstract

This paper analyzes the theoretical results of domestic and foreign big data audits based on multi-level and multi-source big data. From the overall analysis based on big data visualization technology, it finds suspicious data related to poverty alleviation and analyzes specific violations in the use of poverty alleviation funds based on big data technology. Starting from two aspects of the problem, it studied how to apply big data technology to carry out poverty alleviation audits, and explored applications in some poverty alleviation audit projects. It studied and proposed the use of big data technology to carry out relocation audits for poverty alleviation, dilapidated building renovation audits, and photovoltaic poverty alleviation. Auditing, "all-in-one card" benefit farmers subsidy audit methods, summarized the changes brought about by the use of big data technology to audit work and the current general technical bottlenecks in the construction of big data audits.