Fire safety risk monitoring of tunnel and cavern groups is crucial to ensure the construction and safe operation of underground transportation, energy, and power facilities. This study systematically reviews the research progress of combustion characteristics, monitoring technologies, and intelligent prevention and control systems for fires in tunnel and cavern groups, which located at the pumped storage power stations. By analyzing typical fire cases, the characteristics of tunnel fires are summarized, which includes the strong concealment, the high toxicity of smoke, and the difficulty in evacuation and rescue. In the meantime, the principles and application scenarios of single-parameter fire detection technologies are also sorted, which includes the gas detection, the smoke detection, the flame detection, the temperature detection, and the video detection respectively. The problems existed in the sensors for single fire parameter are pointed out, which includes the high false and missed alarm rates and low data fusion efficiency in complex scenarios, and the multi-sensor networking communication technologies and information fusion algorithms are also discussed, which is conducive to improving monitoring accuracy and anti-interference ability. In the future, it is urgent to build an integrated intelligent monitoring system of "perception-decision-control" by combining digital twin and artificial intelligence technologies, promoting the transformation of tunnel fire monitoring from "passive early warning" to "active prevention and control", so as to provide theoretical support and technical guarantee for the safe operation and maintenance of underground spaces.

In water emergency rescue tasks, the effective range of optical imaging devices is often severely limited or may even fail due to water turbidity and light attenuation. Therefore, forward-looking sonar (FLS) has become a key sensor for detecting underwater human targets. However, FLS images are affected by severe speckle noise and multipath effects, while annotated human acoustic data are extremely scarce, resulting in high missed-detection rates and poor generalization under small-sample conditions. targeting human body sonar imaging characteristic, a physics-guided human target detection algorithm based on an improved YOLO11n network is proposed for underwater forward-looking sonar images in small-sample scenarios. A Wavelet-based Downsample Block (WDB) is introduced into backbone network to suppress frequency-domain noise and preserve target contours. A Shadow-aware Holistic Acoustic Attention (SHAA) module is designed to jointly model highlight echoes and acoustic shadows, so that sonar imaging priors can be injected into deep features. A Dynamic Acoustic-Context Module (DACM) is further used to capture diverse human target shapes through dual-branch geometric modeling. Experiments on a self-collected underwater human dataset show that mAP₅₀ is improved by up to 35.6% compared with YOLO11n, while the number of parameters is increased by only 3.1%. Missed and false detections are reduced while real-time performance is maintained, providing an effective approach for rapid underwater human target detection in water emergency rescue tasks.

In order to solve the problem of firefighters drying and coiling fire hoses after firefighting operations, this research develops an intelligent fire hose drying device, combined with the detection characteristics of temperature, humidity, and light sensors, and utilizes 5G technology to build a terminal monitoring and control analysis cloud platform covering the information layer, control layer, and equipment layer. Through drying simulation tests and light characteristic tests, the correlation between sensor data and the drying control system is clarified, and the fitting function curve between light intensity and voltage is proposed. The test results show that the intelligent fire hose drying device and its control system can increase the average drying efficiency of fire hoses by 7.14 times compared with traditional drying methods. Experimental verification shows that the device improves drying efficiency, standardizes the drying process, reduces hose wear and consumption, and thereby extends its service life in practical applications.

To study the concentration law of pyrolysis particles and optimize the selection of pyrolysis particles detection parameters during the early stage of ignition of typical electrical insulation materials in Distribution Cabinets, this study established a pyrolysis particles detection test platform for three typical insulating materials in distribution cabinets. The platform simulates the low-temperature pyrolysis process of electrical materials, obtaining the number concentration laws of particles ranging from 0.3 to 10.0 μm and the mass concentration laws of PM0.5, PM1.0 and PM2.5. A pyrolysis particles concentration dataset was established. and based on this dataset, the random forest algorithm was used to obtain the importance of particle concentrations in different particle size ranges for early pyrolysis, thereby optimizing the selection of pyrolysis particle detection parameters. The results show that all three pyrolysis materials exhibit significant changes in the concentration of pyrolytic solid particles. The trend of the number concentration of pyrolysis particles is to maintain the baseline value first, then increases, and finally reaches saturation or decreases. The mass concentration shows a trend of first increasing and then decreasing. The particle size distribution is mainly concentrated within the range of 1.0 μm. Compared with ABS and FR-4 epoxy resin materials, PVC is more likely to generate large-sized particles. In the design of pyrolysis particle detection, the number concentration of particles larger than 0.3 μm is preferred as a single detection parameter, and the combination of the number concentration of particles larger than 1.0 μm and the PM2.5 mass concentration is recommended as dual detection parameters. This research can provide a basis for the application of the electrical fire detection technology of measuring pyrolysis particles in electrical fire monitoring system.

With the application and promotion of new energy vehicles in China, the study of aging and thermal safety performance of lithium-ion batteries in low temperature environments in winter is of great significance for the safe application of batteries. This paper selects 30 Ah prismatic LiFePO₄ batteries as experimental samples to explore the effects of ambient temperature (25 ℃, 10 ℃ and 0 ℃) on the aging and thermal safety performance of lithium-ion batteries. The results show that low-temperature aging can lead to a significant degradation in battery capacity. The electrochemical characterization, X-ray CT and SEM-EDS results show that lithium plating, electrode material damage and gas production are the main factors causing the degradation of battery cycle performance. In terms of thermal safety performance, low-temperature aging will aggravate the thermal runaway behavior of the battery, and its safety valve opening time and thermal runaway triggering time will be significantly advanced. Furthermore, by analyzing the changing trend of multi-point temperature on the battery surface during thermal runaway, it is proved that the surface temperature monitoring at the bottom of the battery (T_bot) is more suitable for accurate assessment of battery thermal runaway status. This work can provide theoretical reference and data support for the construction of safety assessment and early warning models for lithium-ion batteries under different ambient temperatures.

To achieve accurate description and rapid response to civil aviation cabin fire scenarios, this paper constructs a cabin fire case retrieval model that integrates knowledge meta and aggregated K nearest neighbor algorithm. By mining the attribute features of cabin fires, performing numerical conversion and preprocessing, similar case retrieval and level prediction are carried out. The results indicate that based on the theory of disaster element deconstruction, cabin fire cases can be deconstructed into a four-dimensional knowledge element framework containing causative factors, disaster bearing bodies, disaster prone environments, and emergency response. Combined with the characteristics of fire events, the entire process of cabin fire disaster formation, development, and response can be subdivided into 8 sub knowledge elements and 12 attribute features; At the same time, the Euclidean distance between the randomly predicted target case and the total number of cases was calculated. The majority voting method and aggregation sub model were used to obtain the final prediction value. A dataset of 1 500 simulation cases was constructed for training, validation, and optimization. 24 real cases were selected for testing, and the best K value of 3 was obtained through cross validation using the leave one method. The model's prediction accuracy reached 0.916 7. Based on accuracy, precision, recall, and F1 score settings, a multi model comparative evaluation shows that the proposed aggregated K nearest neighbor model is significantly superior to other models. Therefore, the proposed cabin fire case retrieval model can provide support for predicting the level of civil aviation cabin fires and emergency decision-making.

Hydrogen energy as a clean fuel has a broad application prospect, but its violent combustion and poor stability is easy to induce safety hazards. Nitrogen doping to regulate the combustion characteristics of the hydrogen flame is a key technology to enhance the safe utilization of hydrogen energy. In this study, the effect of nitrogen doping on the combustion characteristics of hydrogen jet flames was investigated through experiments, and the influence of different nitrogen doping ratios on the temperature field, stable combustion time and morphology of the flame was systematically analyzed. The experimental results show that the maximum flame temperature decreases significantly with the increase of nitrogen doping ratio. The maximum temperature of the pure hydrogen flame in vertical flame reaches 842.8 ℃, which decreases to 726.5 ℃ with 30% of nitrogen doping, with a decrease of 13.8%, and that of the horizontal flame decreases from 859.3 ℃ to 706.0 ℃, with a decrease of 17.8%. When the nitrogen doping volume fraction reached 40%, the gas mixture could not maintain continuous combustion and reached the stability critical value. The stabilized combustion time of the flame was nonlinearly shortened with the doping ratio, and the pure hydrogen flame lasted 15.58 s, which was sharply reduced to 2.61 s with 30% of nitrogen doping. After nitrogen doping, the brightness of the flame was significantly reduced, and the horizontal flame jet showed an elongated jet-like propagation. Nitrogen doping can effectively regulate the core region temperature and combustion intensity of hydrogen flame.

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.

Based on the functional positioning of fire rescue agencies, this paper focuses on the dimension of administrative law enforcement in response to the requirement of "deepening fire control law enforcement reform". Using relevant data from the fire control law enforcement business environment assessment index system established in the 2020 Fire control law enforcement business environment assessment report as the analytical source, combined with information collected from regional research interviews, this paper identifies existing issues that hinder the optimization of the business environment, such as weak intelligence in fire control approvals, weak standardization in case handling, weak scientificity in fire investigations, and weak standardization in supervision and inspection. Through an analysis of the characteristics of various fire control operations and in conjunction with the practical aspects of fire control law enforcement, the paper proposes measures to promote the digital transformation of administrative licensing, establish standardized procedures for case handling, strengthen the full-process standardization of fire investigations, innovate technological empowerment for supervision and inspection, and align the innovation of legal concepts with the transformation of the digital and intelligent era, thereby contributing fire control efforts to optimizing the business environment.

To investigate the impact of the coupled thermal radiation and noise effects of mobile emergency flares on safe distances and personnel protection strategies, this study adopted a combined method of engineering simulation and theoretical analysis. The thermal radiation and noise impact ranges of a flare of a given specification under different operating conditions were explored, and a quantitative method for determining the minimum on-site safe distance based on work duration, along with a classified personnel protection strategy, was proposed. The results show that, in the absence of protective measures, the noise-determined safe distance of the flare is significantly greater than the thermal radiation-determined safe distance. The minimum safe distance can be defined according to work duration: for long-term operations, the impact distance corresponding to the 85 dB(A) noise limit is defined as the minimum safe distance; for medium-term operations, the impact distance corresponding to the equivalent noise exposure limit is adopted as the minimum safe distance; and for short-term operations, the safe distance corresponding to a downwind thermal radiation intensity of 1.58 kW/m² is taken as the minimum safe distance. In non-safe zones, it is necessary to first identify the thermal radiation intensity and noise sound pressure level at the location, and then determine the protection priorities and strategies based on the duration of stay. Thus, delimiting flare safe distances based on the coupled thermal-noise impact ranges and work duration, and implementing zoned and classified protection measures, can effectively reduce the risk of injury to on-site personnel.