The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category division is proposed. Firstly, the associations between points of interest (POI) and buildings are established using triangulation and buffer zones. Then, a knowledge graph of buildings is constructed through entity and relationship extraction. A functional category classification model supported by the Z-score is designed using the semantic characterizations of surrounding POIs for inference rules. The results demonstrate high accuracy in building functional category division, supporting the refinement and intelligent expression of urban functional zones for urban construction, planning, and management.

Autonomous vehicles require highly reliable collision-free capabilities, necessitating extensive research in path planning. Path planning determines an optimal path, crucial for safe and efficient driving. The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search speeds, numerous inflection points, and redundant operations. To address these issues, We propose a global path planning algorithm: Quick Dynamic Directional Guidance-RRT (QDDG-RRT) algorithm. Key improvements include dynamically constraining the search space using a direction guidance strategy, employing steering techniques to avoid obstacles, optimizing path length to minimize global cost, and refining trajectories using second-order Bessel curves. Simulation experiments compare QDDG-RRT with P-RRT(Probabilistic Rapidly-exploring Random Tree), P-RRT*, APF(Artificial Potential Field), and A* algorithms. Results show that QDDG-RRT outperforms others in execution speed, path length, and smoothness, effectively avoiding obstacles and maintaining safe distances in complex environments.

The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attraction perimeters, and existing approaches are often inadequate to meet the evolving needs of contemporary tourism development. To address this gap, we proposed a method for inferring vitality around attractions based on a knowledge graph. Our approach began by analyzing the functional and morphological characteristics of the areas surrounding the attractions, followed by the design of a vitality calculation model for these regions. Next, we developed a knowledge graph structure tailored for vitality reasoning around the attractions and established reasoning rules based on this graph. Finally, we conducted experiments to apply the vitality inference method to the main urban area of Kaifeng City as a case study. The results indicated that our method could effectively reason about vitality around the attractions. Notably, the vitality levels around the attractions in Kaifeng’s main urban area exhibited clear spatial differentiation. Attractions such as the Yang Family’s Tianbo Mansion, the Millennium City Park, and Lord Bao’s Memorial Temple showed higher vitality values, largely due to their advantageous functional integration and synergistic morphological characteristics.

Off-road path planning, as an important direction in the field of intelligent navigation, has significant application value in complex scenarios such as emergency rescue, geological exploration, and military deployment. Unlike conventional path planning, which relies on fixed road networks, off-road path planning requires the establishment of a dynamic traversal model to support the planning results. Based on this issue, this study introduces knowledge graphs into the calculation of traversal cost modeling, addressing problems such as low data update efficiency and storage redundancy. First, a multidimensional quantitative analysis model is constructed, including vehicle dynamics parameters, terrain slope characteristics, and surface cover types, to extract the traversal impact factors of various elements through feature engineering. Secondly, based on knowledge graph technology, semantic modeling of multi-source heterogeneous data is achieved, and a dynamic weighted traversal cost map is generated using a rule-based reasoning mechanism. Finally, the performance of path planning is evaluated in a dynamic environment constraint system through a dual-engine validation framework with the improved A* algorithm and Dijkstra algorithm. Experimental results show that, compared to traditional cost map construction methods, our approach improves the construction efficiency in large areas by 30.7%, reduces redundant operations effectively in medium and small areas, and increases efficiency by 75% to 99.4%, which can, to some extent, improve the efficiency of path planning.