TY - GEN
T1 - A Data Model for Quantitative Analysis of Fire Risk in Social Units
AU - Zhang, Jianqi
AU - Qian, Xinming
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, grey correlation analysis is applied to mine the fire IoT monitoring data. The correlation degree between 4,752 IoT monitoring data of social units and the management situation of firefighting work and operation of firefighting facilities in an ideal state was calculated. Using gray correlation analysis algorithm to calculate the personnel presence rate of fire control room, alarm accuracy rate and failure rate of automatic fire alarm system, and rank the key units according to the correlation size with the ideal situation of fire safety of social units, and establish the fire supervision and inspection unit screening model. The research results show that the fire safety situation of social units can be combined with the fire monitoring and inspection unit screening model established in this paper. By calculating the value of the correlation degree between the actual data and the ideal data and ranking them, the safety situation of monitoring and inspection units can be screened, and the efficiency of daily fire monitoring can be improved. It clarifies the different needs of different units for fire supervision and inspection, so that the fire supervision and inspection work can be targeted.
AB - In this paper, grey correlation analysis is applied to mine the fire IoT monitoring data. The correlation degree between 4,752 IoT monitoring data of social units and the management situation of firefighting work and operation of firefighting facilities in an ideal state was calculated. Using gray correlation analysis algorithm to calculate the personnel presence rate of fire control room, alarm accuracy rate and failure rate of automatic fire alarm system, and rank the key units according to the correlation size with the ideal situation of fire safety of social units, and establish the fire supervision and inspection unit screening model. The research results show that the fire safety situation of social units can be combined with the fire monitoring and inspection unit screening model established in this paper. By calculating the value of the correlation degree between the actual data and the ideal data and ranking them, the safety situation of monitoring and inspection units can be screened, and the efficiency of daily fire monitoring can be improved. It clarifies the different needs of different units for fire supervision and inspection, so that the fire supervision and inspection work can be targeted.
KW - IoT data
KW - data mining
KW - lntelligent firefighting
KW - modeling algorithms
UR - http://www.scopus.com/inward/record.url?scp=85151724179&partnerID=8YFLogxK
U2 - 10.1109/SRSE56746.2022.10067801
DO - 10.1109/SRSE56746.2022.10067801
M3 - Conference contribution
AN - SCOPUS:85151724179
T3 - 2022 4th International Conference on System Reliability and Safety Engineering, SRSE 2022
SP - 358
EP - 362
BT - 2022 4th International Conference on System Reliability and Safety Engineering, SRSE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on System Reliability and Safety Engineering, SRSE 2022
Y2 - 15 December 2022 through 18 December 2022
ER -
