基于DT-SVM的宿舍用电异常数据监测平台设计

The Design of DT-SVM Based Dormitory Electricity Abnormal Data Monitoring Platform

随着高职扩招,学生人数呈几何级增长,宿舍用电安全显得尤为重要。然而目前宿舍用电异常数据监测精度速度普遍不高。基于此设计了基于DT-SVM的异常数据监测平台。该平台利用智能电表进行宿舍用电数据的采集和控制管理,选择DT(决策树)和SVM(支持向量机)的多级分类算法框架实现用电异常数据预测监控。经实际部署安装测试,该平台功能丰富,较好地实现了宿舍用电异常预警、有效监控等功能。

With the expansion of higher education,the number of students is increasing geometrically,and the safety of dormitory electricity is especially important.However,the current dormitory electricity abnormal data monitoring accuracy and speed are generally not high.Based on this,a DT-SVM-based abnormal data monitoring platform is designed.The platform uses smart meters for dormitory electricity consumption data collection and control management,and selects the multi-level classification algorithm framework of DT(Decision Tree)and SVM(Support Vector Machine)to realize electricity consumption anomaly data prediction monitoring.After actual deployment and installation tests,the platform has rich functions and can better realize the functions such as electricity consumption abnormal warning and effective monitoring.