Abstract:

Data-driven scientific decision-making ability is playing an increasingly important role in the prevention of returning to poverty in the context of rural revitalization. By stepping up research on data fusion issues in this area, in the big data of anti-poverty monitoring, the scientific decision-making system for preventing poverty return linked by data fusion model can be effectively established. There are three key problems in big data fusion of anti-poverty monitoring. The first is raw data analysis. The raw data of big data for anti-poverty monitoring is very complex. Due to the multi-dimension of the measurement standard of returning to poverty, the diversification of returning to poverty factors and other four aspects. As a result, its data sources have multiple industry sectors and multiple fields of expertise, and Its data features are spatio-temporal big data with multi-scale and multi-source heterogeneity. The second is the establishment of data fusion framework. In theory, the data fusion framework of big data for anti-poverty monitoring includes five levels: data source, monitoring target, data type, fusion model and data fusion application, thus, the framework of data fusion is constructed from the perspective of data-driven scientific decision. The third is data fusion application. In the whole process of monitoring and helping poverty prevention, through data fusion, big data of anti-poverty monitoring can provide scientific decision-making assistance for four aspects and nine specific target needs, including family user portraits and knowledge maps, identification and prediction of targeted objects, design of precise help strategies, prediction of poverty alleviation time and dynamic exit evaluation. The above research results systematically cover the data, models and applications in the big data fusion framework for anti-poverty monitoring, and further creatively and systematically put forward the big data fusion framework for anti-poverty monitoring on the basis of summarizing the existing research results.

Key words: anti-poverty, big data, data fusion, data driven, scientific decision