Model Construction of Early Warning for Frequently Outage Complaint Based on Data Mining

Abstract

At present, frequent outages have become the major source of power customer complaints and, seriously affect improvement of customer service satisfaction. The current control of frequent outages, complaints has been in a passive state of compensation, which can only get half the result with twice the, effort and has caused adverse perception of customers. In response to this problem, this article takes the, initiative to prevent as a starting point, through studying rules of complaints business for the frequent power outage, constructs the early warning model of the frequent outages complaints, which takes statistics, of outages as the data mining object and uses Chinese word segmentation matching algorithm as data, mining technology and displays through network map technology, and eventually realizes a frequent power, outage scientific and accurate complaints warning. It provides an accurate reference for properly arrange of, troubleshooting and maintenance of the power supply enterprises and line reconstruction plans, offers, technical support for the forward service gateway and lays a solid foundation for effectively reducing the volume of complaints.