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A determination threshold model for fire detection based on multivariate parameter fusion and experimental study
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Zhaoke Liu1, Xiaojia Huang1, 2, Jinsuo Lu1, Shuibo Xie3, Guangyang Liu2, Ziru Li1
Fire Science and Technology | 2026, 45(8) : 87 - 97
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Fire Science and Technology | 2026, 45(8): 87-97
• Study of fire equipment •
A determination threshold model for fire detection based on multivariate parameter fusion and experimental study
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Zhaoke Liu1, Xiaojia Huang1, 2, Jinsuo Lu1, Shuibo Xie3, Guangyang Liu2, Ziru Li1
Affiliations
  • 1School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology , Xi'an Shaanxi 710055 , China
  • 2China Zhongyuan International Engineering Co., Ltd. , Beijing 100089 , China
  • 3School of Civil Engineering, University of South China , Hengyang Hunan 421001 , China
Published: 2026-07-15 doi: 10.20168/j.1009-0029.2026.08.0087.11
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In order to improve the judgment accuracy of fire detection, this study proposed an intelligent fire judgment model based on multivariate data fusion for the first time, integrating wind speed, temperature and fire scale, breaking through the traditional single parameter limitation. Firstly, the determination principle and response threshold of smoke sensing and temperature sensing flame detector are analyzed, and the application characteristics and limitations of various detectors are clarified. Subsequently, fire entity tests of wood stacks with various ignition methods and different ignition positions were carried out in a space with a net height of 3 m. The results show that the response time of the detector is positively correlated with the distance of the fire source, that is, the farther the distance, the longer the response time, and the ignition mode has little influence on the response time. The smoke detector can accurately determine the fire when the critical wind speed is 0.15 m/s, and the temperature detector can trigger the alarm when the ambient temperature reaches 67 ℃. Finally, a mathematical model of fire judgment is constructed based on AHP, and the test results are brought into the formula to obtain the comprehensive fire judgment threshold of the test scene, which is Y=0.781. When the actual value exceeds this threshold, it is judged as a fire. The formula combines the parameters of wind speed, temperature and fire size, and verifies that the error range is less than 15%. This study provides the test basis for the accuracy evaluation of the fire detector and has important significance for the comprehensive evaluation of the detector performance.

fire detector  /  fire identification  /  response threshold  /  fire test  /  mathematical model
刘兆科, 黄晓家, 卢金锁, 谢水波, 刘广阳, 李自茹. 基于多元参数融合的火灾探测判定阈值模型及试验研究. 消防科学与技术, 2026 , 45 (8) : 87 -97 . DOI: 10.20168/j.1009-0029.2026.08.0087.11
Zhaoke Liu, Xiaojia Huang, Jinsuo Lu, Shuibo Xie, Guangyang Liu, Ziru Li. A determination threshold model for fire detection based on multivariate parameter fusion and experimental study[J]. Fire Science and Technology, 2026 , 45 (8) : 87 -97 . DOI: 10.20168/j.1009-0029.2026.08.0087.11
Year 2026 volume 45 Issue 8
172
79
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Article Info
doi: 10.20168/j.1009-0029.2026.08.0087.11
  • Receive Date:2025-04-25
  • Online Date:2026-09-16
  • Published:2026-07-15
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  • Received:2025-04-25
  • Revised:2025-09-09
Affiliations
    1School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology , Xi'an Shaanxi 710055 , China
    2China Zhongyuan International Engineering Co., Ltd. , Beijing 100089 , China
    3School of Civil Engineering, University of South China , Hengyang Hunan 421001 , China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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