A spatial scene reconstruction framework in emergency response scenario  

作　　者：Nan Zheng Danhuai Guo 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology,Beijing,100029,China

出　　处：《Journal of Safety Science and Resilience》2024年第4期400-412,共13页安全科学与韧性(英文)

基　　金：supported in part by the Fundamental Research Funds for the Central Universities of Beijing University of Chemical Technology(Grant No.BUCTRC202132);the National Natural Science Foundation of China(Grant Nos.42371476 and 41971366).

摘　　要：Rapid and accurate acquisition and analysis of information is crucial for emergency management,but traditional methods have limitations such as incomplete information acquisition and slow processing speed.The natural language oriented spatial scene reconstruction method provides a new solution for emergency management,but existing generative models have limited understanding of spatial relationships and lack high-quality training samples.To address these issues,this paper proposes a novel spatial scene reconstruction framework.Specifically,the BERT based spatial information knowledge graph extraction method is used to encode the input text,label and classify the encoded text,identify spatial objects and relationships in the text,and accurately extract spatial information.Additionally,a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition,and based on the obtained biases,a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph.Finally,use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints.In addition,a high-quality training sample set of“text-scene-knowledge graph”was constructed.

关 键 词：Spatial scene Spatial cognitive bias Natural language processing Emergency management 

分 类 号：O15[理学—数学]