Intelligent Recommendation and Application of Mine Exploration Route Based on Django-Ant Colony Optimization Algorithm - Tian, Jie (International Journal of Advanced AI Applications, 2025)
This paper presents a novel mine exploration route recommendation system that combines the Django web framework with the Ant Colony Optimization (ACO) algorithm. The system is designed to enhance the efficiency and accuracy of route planning for mine exploration personnel. By leveraging the pheromone update and path selection mechanisms characteristic of ant foraging behaviour, the ACO algorithm achieves a balance between global coverage and local optimization. This ensures that the recommended routes not only meet the specific requirements of exploration tasks but also minimize unnecessary detours, thereby improving overall exploration efficiency. Data acquisition for the system is supported by web crawling technology, employing tools such as request and BeautifulSoup to gather publicly available geological data from mining areas. This enriches the system's data resources and strengthens the foundation for providing precise route recommendations. The system's interface is crafted to be user-friendly, with a well-organized layout that centres on the needs of exploration personnel. This design enables users to quickly familiarize themselves with the system and concentrate on their exploration tasks, ultimately boosting the effectiveness and quality of mining area exploration work.