Design And Implementation Of Electric Material Demand Analysis System Based On Big Data Technologies

Electric power material is the basic guarantee in the process of grid construction.In recent years,China’s electricity demand continued to grow rapidly,in order to ensure the power supply,the provincial companies continue to speed up the power construction,and the importance of material management is becoming more and more significant.Material demand forecast is an important means to improve the operation capacity of State Grid,and the accurate forecasting results provide important decision-making basis for making material supply plan,guaranteeing the supply of materials and reducing the purchasing cost.The existing power material system uses IOE architecture,which has the problems of high coast and poor extensibility,and can not cope with the problem of distributed massive data processing brought by the background of State Grid data fusion.Based on the research of related technology such as big data and data mining,the paper designs and implements a electric material demand analysis system based on big data technology,which aims to forecast the material demand and provide data support for decision makers.The system includes data center subsystem and application subsystem,this paper focuses on the data center subsystem.The data center subsystem adopts hierarchical idea design,which can be divided into data acquisition layer,data storage layer,data preprocessing and analysis layer according to different aspects of data analysis,and specially designed task scheduling management.Because the process of data analysis and mining is basically fixed,but with the data update,we need to repeat this process on a regular basis.This process is cumbersome and boring,and the task scheduler is used to automate the process.