Fire source localization is of great significance to the firefighting and evacuation. In order to resolve the problem of precise localization of fire source in confined space, a method based on the diffusion law of hot smoke flow in the early stage of fire is proposed in this paper. According to the fire characteristic physical quantity information collected by the sensor array, the relative variability correlation degree is used to obtain the signal time delays between the sensor units. Then, the direction angle of the sensor units and fire source can be determined through the geometric relationship, and the angular localization principle is used to obtain the fire source localization results. Finally, according to the fire source localization results obtained in different sets of time delays, the localization estimation area is selected based on the dynamic clustering, and the center of this area is output as the comprehensive localization result. The testing results show that this method performs well and achieves a high localization accuracy.

Frequent building electrical fire accidents have brought great harm to life and property. In order to prevent the occurrence of accidents and reduce the losses to the greatest extent, it is necessary to take effective measures for building electrical fires. Based on the Internet of things (IoT) technology, a system for online monitoring and cause identification of building electrical fire is proposed in this paper. For both hardware and software, this paper introduces the overall structure, component units and system functions in detail. According to the characteristics of arc fault and fire, the complete scheme of online monitoring is given, and the system workflow is also described to realize the cause identification. Finally, the effectiveness of this system is verified by practical testing. The results show that the proposed system is helpful to solve the problems in monitoring and cause identification of building electrical fire, which can not only provide decision-making basis for firefighting, but also provide strong technical support for improving the safety of low-voltage power grid.

High-rise buildings fires are far more harmful than ordinary fires. In this regard, fire risk assessment is an important way to control fire risk and reduce losses. This study presents a comprehensive model to electrical fire dynamic risk assessment of high-rise buildings based on a Bayesian network (BN) and a variable fuzzy set theory (VFST). Firstly, electric system, safety management, and other factors were comprehensively analyzed based on three categories: hazard sources identification (HSI), fault tree (FT) analysis, and VFST. A high-rise building electrical fire dynamic risk assessment model was established based on a BN. Secondly, the prior probability of BN root nodes was determined by VFST, and the conditional probability table (CPT) was determined by the analytic hierarchy process (AHP) and decomposition method. On that basis, the quantitative inference and sensitivity analysis can be performed on the electrical fire risks of high-rise buildings in combination with the variable fuzzy Bayesian network (VFBN) inference. Finally, a high-rise building in Wuhan, China, was used as an example for verification. The results show that the proposed method can realize dynamic risk assessment of electrical fires in high-rise buildings. This study provides a new method for fire risk assessment of high-rise buildings to reduce the possibility of fire.