Meteorological quality data analysis method and system based on multi-source data fusion and AI

The invention provides a meteorological quality data analysis method and system based on multi-source data fusion and AI, and the method comprises the steps: carrying out the endogenous learning of a meteorological data vector representation component and a station data vector representation compone...

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Hauptverfasser: GUI KE, LIANG YUANXIN, SHANG NANXUAN, XIA XIANG'AO, ZHAO HUJIA, WANG PENG, ZHENG YU, ZHU JIBIAO, ZHAO HENGHENG, ZHU JUN, WEI YAO, YAO WENRUI, CHA HYE-JUNG, ZHANG XUTAO, SONG JINGJING, LI LEI, WANG YUPENG
Format: Patent
Sprache:chi ; eng
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Zusammenfassung:The invention provides a meteorological quality data analysis method and system based on multi-source data fusion and AI, and the method comprises the steps: carrying out the endogenous learning of a meteorological data vector representation component and a station data vector representation component in a basic training link of a model; and performing example-driven learning on an initial multi-source data visibility recognition model at least covering an initial meteorological data vector representation component and an initial station data vector representation component obtained by endogenous learning, so that basic training is divided into two links. In the endogenous learning link, single type of system meteorological data and station observation data can be independently trained to obtain a feature mining model capability, and the features of the system meteorological data and the station observation data are continuously learned in combination with example-driven learning to complete visibility grade