DOI: 10.66106/skygay.20250208 ISSN: 3105-7500

多源异构物联网数据融合与异常检测的轻量化深度学习模型（Lightweight deep learning model for data fusion and anomaly detection in multi-source heterogeneous Internet of Things）

雷雨果 Yuguo Lei, 宋聪聪 Congcong Song, 李启周 Qizhou Li
Show PDF Cite
Abstract：Data fusion and anomaly detection of multi-source heterogeneous Internet of Things is the core content of the research direction of “Internet of Things System Integration and Big Data Technology”in the laboratory. In agricultural environmental monitoring, industrial equipment operation and maintenance and other scenarios, Internet of Things devices will produce multi-format and multi-type data, which have problems such as strong heterogeneity, high redundancy, abnormal concealment, etc. It is difficult for traditional models to balance detection accuracy and lightweight deployment requirements. In this paper, based on the research direction of the laboratory, aiming at the actual scenes of agriculture and industry, the data characteristics and processing difficulties of multi-source heterogeneous Internet of Things are analyzed, and a lightweight deep learning model is designed to realize the efficient collaboration between data fusion and anomaly detection, provide technical support for scene data processing, and help the integration of Internet of Things systems and the application of big data technology in actual scenes.

More from our Archive