|Table of Contents|

Online Spatial Crowdsourcing Matching Algorithm for Disaster Relief(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年04期
Page:
21-30
Research Field:
空间数据智能研究
Publishing date:

Info

Title:
Online Spatial Crowdsourcing Matching Algorithm for Disaster Relief
Author(s):
Liu Junling1; 2; Wu Qingqing1; 2; Dong Shanshan1; 2; Sun Huanliang1; 2; Xu Jingke1; 2; 3
(1.School of Computer Science and Engineering,Shenyang Jianzhu University,Shenyang 110168,China)
(2.Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction,Shenyang 110168,China)
(3.Shenyang Branch of National Special Computer Engineering Technology Research Center,Shenyang 110168,China)
Keywords:
spatial crowdsourcing; disaster relief; task matching; task level; minimum loss
PACS:
TP399
DOI:
10.3969/j.issn.1001-4616.2024.04.003
Abstract:
After disasters,people often post information about volunteer rescue efforts and requests for help from the affected on social media platforms. Extracting the information of help task and rescue personnel from these data and making a reasonable match between them can provide effective support for rescue. In this paper,spatial crowdsourcing technology is introduced into the field of disaster relief,and online spatial crowdsourcing matching problem for disaster relief is proposed. The disaster event information extraction model is constructed by using deep learning classification method and large-scale language model to realize the accurate extraction of rescue and help information. The task rating method and dynamic loss measurement are designed to reflect the urgency of the task and the dynamic change of the loss. A greedy algorithm combining preempt and delay strategies is proposed based on dynamic loss measurement. Through detailed experimental analysis of real data sets and synthetic data sets,the total loss of the greedy algorithm combining preempt and delay strategies is reduced by at least 35% compared with the existing algorithm,and the effectiveness of the proposed algorithm is verified.

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Last Update: 2024-12-15