Building A Natural Disaster Knowledge Base Expert System based on the Distributed and Incremental Crawling Technology

China is one of the countries with the most natural disasters in the world. Natural disasters seriously endanger people's safety and economic situations. Therefore, it is very necessary to strengthen the prediction and research on the characteristics and the causes of various natural disasters....

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Veröffentlicht in:IOP conference series. Earth and environmental science 2020-01, Vol.435 (1), p.12024
Hauptverfasser: Chen, Qiujin, He, Yuanrong, Su, Qun, He, Tingting
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Sprache:eng
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Zusammenfassung:China is one of the countries with the most natural disasters in the world. Natural disasters seriously endanger people's safety and economic situations. Therefore, it is very necessary to strengthen the prediction and research on the characteristics and the causes of various natural disasters. Based on the Internet distributed and incremental crawling technology, this system crawls the knowledge literatures, disaster news and professional disaster data from domestic and overseas on natural disasters, as well as drawing on the advanced knowledge management concept, the captured data will be displayed in the form of knowledge tree. Integrating the authoritative global typhoon live maps and orthophotos which acquired by UAV to better monitor and predict typhoons; This system determined that the SSM framework is used as the system framework, combined MySQL and Redis database to store data stably and efficiently. In addition, the Echarts component can be used to analyze and visualize the crawled data. Big data analysis techniques such as knowledge graphs and related algorithms for literature analysis, data cleaning to better data store and display. In the end, it will enable the researchers to more easily and efficiently carry out the knowledge sharing, collaborative cooperation and interactive communication, which has the certain research significance and use value.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/435/1/012024