[1]陈艳春,达钰鹏.基于改进 Kalman 的传感器数据加权处理算法[J].计算机技术与发展,2021,31(03):157-162.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 027]
　CHEN Yan-chun,DA Yu-peng.A Sensor Data Weighting Algorithm Based on Improved Kalman[J].,2021,31(03):157-162.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 027]
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基于改进 Kalman 的传感器数据加权处理算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年03期
页码:
157-162
栏目:
应用前沿与综合
出版日期:
2021-03-10

文章信息/Info

Title:
A Sensor Data Weighting Algorithm Based on Improved Kalman
文章编号:
1673-629X(2021)03-0157-06
作者:
陈艳春1; 达钰鹏2
1. 石家庄铁道大学,河北 石家庄 050043;
2. 河北省人力资源和社会保障厅信息中心,河北 石家庄 050071
Author(s):
CHEN Yan-chun1; DA Yu-peng2
1. Shijiazhuang Tiedao University,Shijiazhuang 050043,China;
2. Information Center,Hebei Provincial Department of Human Resources and Social Security,Shijiazhuang 050071,China
关键词:
物联网; 传感器; Kalman 滤波; 格拉布斯; lightgbm
Keywords:
Internet of Things; sensor; Kalman filtering; Grubbs; lightgbm
分类号:
TP212
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 03. 027
摘要:
物联网(IOT)和大数据的发展,对物联网数据质量和处理速度提出了新的要求,而物联网触感器原始数据由于噪音和虚假异常值的影响,如直接应用于大数据分析会严重影响分析结果的可靠性和有效性,大量传感器的部署也导致了虚假异常值数量的成倍增长;同时,由于物联网终端数量庞大且性能有限,数据价值密度低,使用机器学习方法进行处理性价比不高且不具有通用性,如何高效、可靠、通用地处理传感器数据并进行异常检测成为一个热点问题。 该文基于统计学方法,结合 Kalman 滤波和加权融合思想,提出一种加权传感器处理预测算法。 通过实验证明该算法对噪音数据处理效果相较于移动平均值,MSE 和 MAE 都得到了提升,性能提升明显,并利用 lightgbm 算法验证数据处理前后预测模型性能的变化,证明通过该算法处理后的数据更易于进行模型训练和预测。
Abstract:
The development of the Internet of Things ( IOT) and big data puts forward new requirements for the data quality and processing speed of the IOT,while the original data of the IOT will seriously affect the reliability and effectiveness of the analysis results due to the impact of noise and false outliers,such as the direct application to big data analysis,and the deployment of a large number of sensors also leads to the multiple growth of the number of false outliers. At the same time, due to the large number and limited performance of IOT terminals, low data value density,low cost performance and versatility of using machine learning methods to process sensor data,how to efficiently,reliably and universally process sensor data and carry out anomaly detection has become a hot issue. Based on the statistical method,combined with Kalman filtering and weighted fusion, we propose a weighted sensor processing and prediction algorithm. Compared with the moving average,MSE and MAE have been improved and the performance has been improved obviously.The performance of the prediction model before and after data processing has been verified by lightgbm algorithm,which proves that the data processed by this algorithm is easier for model training and prediction.

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更新日期/Last Update: 2020-03-10