To address the failure of unmanned aerial vehicle (UAV) target tracking caused by occlusion and limited field of view in dense low-altitude obstacle environments, this paper proposes a novel framework integrating occlusion-aware modeling and multi-UAV collaboration. A lightweight tracking model based on the Mamba backbone is developed, incorporating a Dilated Wavelet Receptive Field Enhancement Module (DWRFEM) to fuse multi-scale contextual features, significantly mitigating contour fragmentation and feature degradation under severe occlusion. A dual-branch feature optimization architecture is designed, combining the Distilled Tanh Activation with Context (DiTAC) activation function and Kolmogorov–Arnold Network (KAN) bottleneck layers to enhance discriminative feature representation. To overcome the limitations of single-UAV perception, a multi-UAV cooperative system is established. Ray intersection is employed to reduce localization uncertainty, while spherical sampling viewpoints are dynamically generated based on obstacle density. Safe trajectory planning is achieved using a Crested Porcupine Optimizer (CPO). Experiments on the Multi-Drone Multi-Target Tracking (MDMT) dataset demonstrate that the model achieves 84.1% average precision (AP) at 95 Frames Per Second (FPS), striking a favorable balance between speed and accuracy, making it suitable for edge deployment. Field tests with three collaborative UAVs show sustained target coverage in complex environments, outperforming traditional single-UAV approaches. This study provides a systematic solution for robust tracking in challenging low-altitude scenarios.

Tower crane structural failures remain a major safety concern on construction sites. To improve accident prevention, this study proposes an intelligent framework that combines an improved Transformer model with a Directional Interest Score (DIS) Apriori algorithm and complex-network analysis. A corpus of 535 tower crane accident reports (2002–2024) was compiled and annotated with causal and accident entities according to system–safety theory. Segment embeddings were introduced to the Transformer to reinforce boundary detection, enabling accurate extraction of causative factors and relation triples. The DIS-Apriori algorithm was then used to mine both positive and negative association rules while aggressively pruning irrelevant item sets. Eventually, causative factors were mapped into a weighted, directed complex network where edge weights reflect the absolute frequency difference between positive and negative rules, and edge directions correspond to their signs. Experiments show that the Transformer achieves higher precision and recall than baseline models, and DIS-Apriori substantially reduces unnecessary item-set complexity while preserving critical rules. Network analysis revealed five critical causal links and a closed-loop causal link that warrant priority intervention. The proposed method delivers a data-driven, explainable tool for pinpointing key risk sources and designing targeted mitigation strategies, offering practical value for intelligent safety management of tower cranes.

In recent times, circulating fluidized beds have become increasingly important in various industries, such as in the metallurgy, pharmaceuticals, and food-processing industries, due to their excellent fuel adaptability and environmental friendliness. Therefore, how to diagnose fluidized bed flow faults more efficiently and handle them earlier are important issues that cannot be ignored. This article starts with an introduction to fluidized beds and their common fault phenomena, and then integrates the research of scholars on fluidized bed characteristic-monitoring methods and fault diagnosis methods in recent years and summarizes the shortcomings of each method. Subsequently, a summary and induction of data-driven fault diagnosis methods for circulating fluidized beds are conducted, and the applicability, advantages, and disadvantages of each method are pointed out. Finally, this article presents some of the main challenges currently faced and suggests several possible future development directions.