基于层次分析的客服中心实时数据流自动监测方法

Automatic monitoring method of real-time data flow in customer service center based on analytic hierarchy process

为了提高客服中心的智能管理和信息调度能力,结合大数据分析方法进行客服中心实时数据监测和自动采集设计。提出一种基于模糊规则特征量挖掘和层次分析聚类的客服中心实时数据流自动监测方法。建立客服中心的网格分布结构模型并进行客服中心实时数据流监测统计特征分析,进行客服中心实时监测数据属性集的向量量化特征分解,对客服中心实时数据采用信息融合和模糊层析性分析方法实现信息融合,进行关联数据自适应特征提取,挖掘客服中心实时监测数据信息流的正相关性特征量。在层次性聚类算法基础上采用自回归分析进行客服中心实时数据流的模糊聚类和信息预测,提高客服中心实时数据监测的准确性,同时降低了客服服务中心数据流监测的风险。仿真结果表明,采用该方法进行客服中心实时数据监测的聚类性较高,预测性较好,能降低数据聚类的误分率,提高了客服中心实时数据监测能力。

In order to improve the intelligent management and information scheduling ability of the customer service center,the real-time data monitoring and automatic collection design of the customer service center are carried out with big data analysis method.An automatic data flow monitoring method is presented for customer service center based on fuzzy rule feature mining and hierarchical analysis clustering.The grid distribution structure model of the customer service center is established,and the statistical feature analysis of the real-time data flow monitoring of the customer service center is carried out,and the vector quantization characteristic decomposition of the attribute set of the real-time monitoring data of the customer service center is carried out.The real-time data of customer service center is fused with information fusion and fuzzy chromatography analysis method to realize adaptive feature extraction of associated data and to mine the positive correlation feature quantity of real-time monitoring data flow of customer service center.On the basis of hierarchical clustering algorithm,fuzzy clustering and information prediction of customer service center real-time data flow are carried out by using self-regression analysis,which improves the accuracy of customer service center real-time data monitoring and reduces the risk of customer service center data flow monitoring.The simulation results show that this method has high clustering ability and good predictability.It can reduce the misclassification rate of data clustering and improve the ability of real-time data monitoring in customer service center.