基于K-means聚类的城市生活用水水质异常特征检测算法

Detection Algorithm of Abnormal Characteristics of Urban Domestic Water Quality Based on K-means Clustering

为解决水质异常特征检测中存在检测精度低等问题,提出基于K-means聚类的城市生活用水水质异常特征检测算法。首先,通过构建水质特征提取系统,并通过荧光法计算污染物含量,完成水质特征提取及污染物质含量确定。再将城市生活用水水质pH、氨氮、耗氧量、色度以及浑浊度作为异常值,通过K-means聚类计算数据间信任度及数据簇距离,并构建城市生活用水水质异常特征检测模型,完成检测。结果表明:采用所提方法检测城市生活用水水质异常特征的精度较高。

In order to solve the problem of low detection accuracy in water quality anomaly detection,an urban domestic water quality anomaly detection algorithm based on K-means clustering is proposed.Firstly,by constructing the water quality feature extraction system and calculating the pollutant content by fluorescence method,the water quality feature extraction and pollutant content determination are completed.Then,taking the pH value,ammonia nitrogen,oxygen consumption,chromaticity and turbidity of urban domestic water quality as abnormal values,the trust degree and data cluster distance between data are calculated through K-means clustering,and the abnormal characteristic detection model of urban domestic water quality is constructed to complete the detection.The results show that the proposed method has high accuracy in detecting the abnormal characteristics of urban domestic water quality.