Wisdom Safety Monitoring System Based on Risk Map and Location Service
- DOI
- 10.2991/ecae-17.2018.58How to use a DOI?
- Keywords
- risk map; location service; wisdom safety; emergency response; decision support system
- Abstract
To construct Wisdom Safety Supervision and Emergency System (WSSES), it is necessary to solve the three key issues include that the risk prediction based on the regional hazards, the safety information based on the location service, and the wisdom technologies for the emergency-assisted decision. In this study, the WSES based on the big data, cloud computing and internet was introduced was designed and implemented. The system adopts the combination of software and hardware and B/S architecture. According to theory of modern safety management theory, it has the functions of real-time information collection and processing, quantitative risk analysis and assessment, accident scene simulation and display on GIS. So that the three key issues troubled safety supervision and management departments were solved: the regional hazards predicted by risk cloud map, the location based safety information acquired by real-time monitor, and the wisdom emergency-assisted decision provided by the Artificial intelligence system. The system is applicable to the safety supervision and management business of government safety supervision departments as well as the safety management of high-risk industries such as petrochemical industry and other industrial areas.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaoyu Wang AU - Xiongjun Yuan AU - Jun Liu PY - 2017/12 DA - 2017/12 TI - Wisdom Safety Monitoring System Based on Risk Map and Location Service BT - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017) PB - Atlantis Press SP - 272 EP - 276 SN - 2352-5401 UR - https://doi.org/10.2991/ecae-17.2018.58 DO - 10.2991/ecae-17.2018.58 ID - Wang2017/12 ER -