基于多元增量分析模型的流域电厂大数据采集监视
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引用本文:石发太,孙卫军.基于多元增量分析模型的流域电厂大数据采集监视[J].计算技术与自动化,2024,(1):72-77
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作者单位
石发太,孙卫军 (雅砻江流域水电开发有限公司四川 成都 610051) 
中文摘要:提出基于多元增量分析模型的流域电厂大数据采集监视方法,通过数据分析发现异常电厂大数据,保障流域电厂安全运行。数据采集层在PIC18F8722单片机控制下利用多种传感器获取采集对象层的多种流域电厂数据,经数据汇集层的弹性消息总线和转发代理,将采集数据传输至数据处理层数据接收模块,多元增量分析模型调用接收到的数据,通过增量的方式构建电厂数据矩阵正常检测模型,对比其与待检测数据,发现异常电厂大数据,并将异常检测结果族谱写入数据存储层数据库所对应的列簇里,通过前端展示层将监视结果呈现给用户。实验结果表明,该方法可精准、快速采集流域电厂海量数据,可有效发现流域电厂的异常数据,并将其可视化呈现,确保流域电厂安全运行。
中文关键词:多元增量分析  流域电厂  大数据  采集监视  异常检测  弹性消息总线
 
Big Data Acquisition and Monitoring of Watershed Power Plants Based on Multivariate Incremental Analysis Model
Abstract:A big data collection and monitoring method for power plants in the basin based on the multivariate incremental analysis model is proposed to find abnormal big data of power plants through data analysis and ensure the safe operation of power plants in the basin. Under the control of PIC18F8722 single-chip microcomputer, the data acquisition layer uses a variety of sensors to obtain a variety of basin power plant data at the acquisition object layer. Through the elastic message bus and forwarding agent of the data collection layer, the collected data is transmitted to the data receiving module of the data processing layer. The multivariate incremental analysis model calls the received data, builds a normal detection model of the power plant data matrix by incremental means, and compares it with the data to be detected, Discover the big data of abnormal power plants, write the family tree of abnormal detection results into the corresponding column cluster of the data storage layer database, and present the monitoring results to the user through the front-end display layer. The experimental results show that this method can accurately and quickly collect massive data of power plants in the basin, effectively find abnormal data of power plants in the basin, and visually present them to ensure the safe operation of power plants in the basin.
keywords:multivariate incremental analysis  river basin power plant  big data  acquisition and monitoring  anomaly detection  elastic message bus
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