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基于lof孤立点检测技术的健康大数据系统
首发时间：2021-03-23
摘要：近年来，数据清洗算法的发展推进了健康检测设备的多功能化。目前已有的产品大部分采用阈值法判断异常值进行剔除以及模型拟合或平均值进行数据插补，这些算法都存在误差较大、不具有个体适配性等不足。本文就此提出了基于lof孤立点检测技术的健康大数据系统。该系统操作简单方便、稳定性强、可靠性高。系统通过将大数据云端与微信小程序、硬件传感器结合，构建具有数据传输、数据处理与分析的平台，采用加权曼哈顿阈值结合算法判断异常值和多参数加权插补算法进行数据填补。整体来看，本系统为健康数据分析提供更为可靠高效的方案。?????
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health big data system based on lof outlier detection technology
abstract：in recent years, the development of data cleaning algorithms has promoted the multi-functionalization of health detection equipment. at present, most of the existing products use the threshold method to judge abnormal values for elimination and model fitting or average value for data interpolation. these algorithms have large errors and lack of individual adaptability. this article proposes a health big data system based on lof outlier detection technology. the system is simple and convenient to operate, strong in stability and high in reliability. the system combines the big data cloud with wechat applets and hardware sensors to build a platform with data transmission, data processing and analysis. it uses a weighted manhattan threshold combination algorithm to determine abnormal values and a multi-parameter weighted interpolation algorithm for data filling. on the whole, this system provides a more reliable and efficient solution for health data analysis.
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