基于“电力+民政”大数据的精准帮扶研究与应用

Research and Application of Precise Assistance Based on Big Data of"Electricity+Civil Affairs"

基于滑动时间窗口的集成学习算法,对全省40多万社会救助对象的日常用电数据进行深度挖掘分析,并按不同地市分别建模,实现了困难群众主动识别,让更多的困难群众“应享尽享”;根据民政部门提供的客户标签,构建不同的帮扶场景,应用多场景时序异常检测算法,对不同类型的困难群众实施分类管理预警机制,为政府完善帮扶工作、保障特殊用电、守护生命安全发挥数据创新应用价值,提供个性化用电关怀。

This project is based on a sliding time window ensemble learning algorithm,which deeply mines and analyzes the daily electricity consumption data of over 400000 social assistance recipients in Zhejiang province,and models them separately according to different cities,achieving active identification of disadvantaged groups and allowing more disadvantaged groups to fully enjoy the benefits.Based on the user tags provided by the Civil affairs department,construct different assistance scenarios,and apply multi scenario temporal anomaly detection algorithms to implement classification management and early warning mechanisms for different types of disadvantaged groups.This will provide personalized electricity care for the government to improve assistance work,ensure special electricity use,and safeguard life safety by leveraging innovative data application value.