Publications

@inproceedings{liang_construction_2024,

title = {Construction of Emergency Rescue Virtual Exercise Platform Based on AIGC Perspective},

author = {J. Liang and X. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206094403&doi=10.1145%2f3686424.3686477&partnerID=40&md5=e32351dc68be5d0fa0d771656b02256f},

doi = {10.1145/3686424.3686477},

isbn = {979-840071036-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {312–316},

publisher = {Association for Computing Machinery},

abstract = {In order to address the suddenness of emergency events and the phenomenon that the rescue process contains too many behavioural uncertainties, an emergency rescue virtual exercise platform framework has been designed from the perspective of generative artificial intelligence (AIGC). This framework analyses human behaviour during the simulated emergency rescue process and collects relevant data. The module function is determined by the parallel emergency management method. The system comprises three data processing modules: the behavioural input module, the emergency event feedback module, and the data classification and processing module. The logic of AI data processing is employed to establish a data cycle evolution system, which assists rescue personnel in enhancing their professional abilities, increasing the success rate of rescue operations, and optimising the role of AI technology and computer simulation methodology in the design of the practice. © 2024 Copyright held by the owner/author(s).},

keywords = {Artificial intelligence, Behavioral theory, Data handling, Data Processing, Emergency events, Emergency management, Emergency rescue, Emergency Response, Human behaviors, Processing modules, Rescue process, Risk management, Uncertainty, Virtual environments, Virtual exercise, Virtual Exercises, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}