Disaster monitoring and analysis method for extracting multi-dimensional disaster-related information from the Internet

Abstract

This disclosure provides a disaster monitoring and analysis method for extracting multi-dimensional disaster-related information from the Internet, including: S1: real-time acquisition and preprocessing of multi-source data; S2: building disaster information extraction knowledge base with the help of feature word multi-dimensional expansion algorithm FWME to extract Disaster loss information and emotional feedback information of disaster audiences are used to obtain multi-dimensional disaster-related information; S3: Joint monitoring and analysis of disaster areas based on multi-dimensional disaster-related information. Through the constructed disaster information extraction knowledge base, the disaster loss information contained in the Internet platform and the emotional feedback information of the audience during the disaster process are accurately extracted, and combined with time and space dimensions for joint analysis, the disaster progress process is described in detail, and assisted It is used for real-time disaster monitoring, disaster damage assessment and analysis, disaster rescue feedback, and follow-up impact assessment.

Description

technical field [0001] The invention relates to the technical field of disaster monitoring and analysis, in particular to extracting multi-dimensional disaster-related information based on Internet multi-source text data for disaster monitoring and analysis. Background technique [0002] my country is a disaster-prone country, and various disasters cause a large number of human and property losses every year. Traditional disaster monitoring methods such as satellite remote sensing, manual survey, etc. are often difficult to be effective in a timely manner due to the shortcomings of harsh implementation conditions and high costs. Moreover, some disaster information, such as the emotional feedback of the disaster audience, is difficult to obtain by traditional disaster monitoring methods, and this information is also extremely important in disaster monitoring and analysis. [0003] At present, many scholars monitor and analyze disaster situations based on social media, such as...

AI Technical Summary

Problems solved by technology

Traditional supervised learning methods not only require manual labeling of large-scale training corpora, but also mostly target text classification on a single topic.

However, the traditional rule-based method requires a lot of expert knowledge to summarize the extraction rules, and the portability is poor.

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