Targeting unexpected scenarios such as crowd congestion and road congestion, this paper presents an emergency evacuation path planning model based on an enhanced ant colony algorithm. The heuristic function and pheromone concentration update strategy were enhanced, leading to the optimization of the evacuation path. A grid map model was constructed to simulate emergency evacuations under both congested and non-congested conditions in a real-world scenario. It is demonstrated that the enhanced ant colony algorithm exhibits superior convergence speed and global search capability, enabling the generation of safer evacuation routes considering both crowd congestion and road congestion.
Due to the closed internal environment of the tunnel and frequent traffic accidents, it is difficult for workers to carry out accident rescue work. With full consideration of traffic flow, environmental monitoring data, the ant colony algorithm is improved, and the tunnel topology diagram is established for simulation experiments to form the optimal emergency evacuation path, effectively guiding vehicles to evacuate in an orderly manner.
Lung cancer is the disease with the highest incidence rate and mortality of cancer in China, which seriously threatens human life safety. Pulmonary nodules are the main factor leading to lung cancer, and their precise identification plays a crucial role in clinical diagnosis. This paper proposes a lung nodule detection model that combines global image information to address issues. The model is based on improved YOLOV5 network. Finally, comparative experiments have verified the accuracy and effectiveness of this model.
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