摘要
针对国网智能客服对用电信息分析能力滞后、数据信息应用效率低下的问题,提出一种新型数据分析模型。构建逻辑回归算法模型,将国网用户用电信息转换为逻辑回归算法参数信息,通过数学逻辑计算分析国网用户用电信息,挖掘用户用电存在的问题,并引入损失函数来预测用户用电信息,从而提高客服对用电信息的分析和应用能力。通过对某省2个市的国网用户历史用电数据进行分析和学习,在PyCharm上进行数据清洗、数据增强和数据标准化处理,采用梯度下降法构建机器学习模型,能够输出精准的用电结果。所提出的模型在国网用户用电信息分析中具有一定的实用价值。
A new data analysis method is proposed to solve the problems of lagging power consumption information analysis ability and low efficiency of data information application of state grid intelligent customer service.A logical regression algorithm model is built to convert the power consumption information of customers into the parameter information of the logistic regression algorithm.Through mathematical logic calculation,the power consumption information of state grid customers is analyzed,the existing problems of the power consumption of customers are discovered.A loss function is introduced to predict the power consumption information of customers,thus enhancing the customer service’s ability to analyze and apply the power consumption information of customers.By analyzing and learning the historical power consumption data of state grid customers in two cities in a province,data cleaning,data enhancement and data standardization are performed on PyCharm,a machine learning model is constructed using the gradient descent method and accurate power consumption results can be output.The proposed model has certain practical value in the analysis of power consumption information of state grid customers.
作者
王慧
宋灿
孔娜
WANG Hui;SONG Can;KONG Na(State Grid Customer Service Center,Tianjin 300300,China)
出处
《微型电脑应用》
2025年第12期104-108,113,共6页
Microcomputer Applications
基金
国家电网营销信息化项目(SGYWYJXX2000058)。
关键词
逻辑回归
梯度下降
二分类问题
机器学习
用电信息
智能客服
logistic regression
gradient descent
binary classification problem
machine learning
power consumption information
intelligent customer service
