In order to establish a poverty monitoring mechanism

to dynamically monitor and warn the population at risk of returning to poverty and causing poverty

and to consolidate and improve the results of poverty eradication

this paper gathers power data characteristics

data quality

storage methods

management methods

and business logic for data aggregation. On the basis of Ali cloud data centre

Edas middleware and RDS database are used to clean and mine the data of electricity payment and other data of poverty-removing households. The data analysis model is formed to analyze the electricity consumption behavior of the poor households in multiple dimensions. Data sampling and analysis of the current situation of households that have escaped from poverty

and the formation of“red

yellow and blue”level warnings based on the principle of “classification monitoring and graded warning”to achieve a comprehensive data portrait of households that have escaped from poverty. The data results are systematically analysed and researched

accomplishing a closed-loop monitoring process for households emerging from poverty. It provides assistance to the government in consolidating the results of poverty alleviation

deepening the reform and innovation of poverty alleviation monitoring mechanism

and initially exploring a new way of using big data to help rural revitalization.