Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.

The gas pipeline network constitutes a critical component of urban infrastructure. Emergencies, such as leaks or explosions, can lead to severe casualties, extensive property damage, and long-lasting effects on societal and environmental safety. The genesis of such emergencies typically involves a complex interplay among various risk factors. Therefore, the meticulous identification of these risk factors and the effective modeling of their interrelations are crucial for assessing the risks associated with such emergencies and enhancing urban safety. In this research, we initially developed a specialized dataset tailored specifically to analyze emergencies within gas pipeline networks. Recognizing the significance of unstructured domain-specific knowledge, we introduced an innovative approach Fusing expert knowledge and pre-trained model. This approach facilitates the fusion of domain experts’ knowledge into the process of risk factor identification and extraction in different steps, ensuring a thorough and precise determination and filtration of risk factors. Subsequently, a network depicting the associations between risk factors was established, drawing on their co-occurrence relationships, followed by a quantitative analysis of this network. Supported by real-world gas network management and emergency cause investigations, the methods proposed in this study provide strong support for gas network safety management and emergency decision-making.

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors. A chest-mounted advanced APDR method based on dynamic step segmentation detection and adaptive step length estimation has been developed. Furthermore, step length features are innovatively integrated into the visual tracking algorithm to constrain errors. Visual data is fused with dead reckoning data through an extended Kalman filter (EKF), which notably enhances the reliability and accuracy of the positioning system. A wearable autonomous localization vest system was designed and tested in indoor corridors, underground parking lots, and tunnel environments. Results show that the system decreases the average positioning error by 45.14% and endpoint error by 38.6% when compared to visual–inertial odometry (VIO). This low-cost, wearable solution effectively meets the autonomous positioning needs of rescuers in disaster scenarios.

Highly dynamic air–ground integrated broadcast ad-hoc networks are essential for wide-area wireless coverage. However, high node mobility, co-channel interference, and network faults hinder reliable delivery and efficient resource use. This paper presents a robust, adaptive, and fault-tolerant resource-optimization framework based on distributed chaotic neural networks. First, it introduces a space-air-ground broadcast-transmission model that accounts for node mobility, link failures, and interference. Then, it presents the design of a distributed local chaotic-neural-network controller that allocates spectrum, power, and link resources in real time, explicitly handling uncertainties and abrupt faults. Finally, simulations and experiments show substantial mitigation of performance loss under high mobility (relative velocities 180-210 km/h) and varying link distances (coverage radius 10-12 km). With real-time topology adaptation, broadcast-link stability improves by up to 60%. These results provide a basis for reliable deployment and efficient resource optimization of broadcast ad-hoc networks, supporting future low-altitude airborne networking applications.