|
|
|
|
|
Ruiqing Wang, Jinlei Feng, Wu Zhang, Bo Liu, Tao Wang, Chenlu Zhang, Shaoxiang Xu, Lifu Zhang, Guanpeng Zuo, Yixi Lv, Zhe Zheng, Yu Hong and Xiuqi Wang
This paper proposes a data anomaly detection and correction algorithm for the tea plantation IoT system based on deep learning, aiming at the multi-cause and multi-feature characteristics of abnormal data. The algorithm is based on the Z-score standardiz...
ver más
|
|
|
|
|