基于多元高斯分布的数据质量波动监测模型研究

数据质量波动的监测有助于发现网络性能瓶颈或性能故障，对网络系统的稳定运行有很大帮助。为了主动发现数据指标中的异常波动现象，本文首先提出了一种多元高斯分布监测模型，用来检测离群点数据。经过数据预处理、模型训练和输出异常与定位等模块，最终本文实现了监测全网数据并筛选输出异常数据及位置的系统算法，最终模型准确率达到90%以上。

Research on Data Quality Fluctuation Monitoring Model Based on Multivariate Gaussian Distribution

The monitoring of data quality fluctuation is helpful to find network performance bottleneck or performance fault, and it is helpful to the stable operation of network system. In order to detect the abnormal fluctuation in the data index actively, a multivariate Gao Si distribution monitoring model is proposed in this paper to detect outliers. After data preprocessing, model training and outputting anomaly and positioning modules, the system algorithm of monitoring the whole network data and filtering outlier data and location is realized. The accuracy of the model is over 90%.