The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model.

Random point generation plays an important role in assisting random sampling and is widely used in various disciplines. This paper proposes a new algorithm to generate random points in polygon based on Triangulation. Firstly, the polygons are triangulated, and then the number of random points in each triangle is allocated according to the triangle area. Finally, the given number of random points is generated inside the triangle. In the experiment, the results showed that the proposed algorithm is 20 times faster than the algorithm in QGIS software on average. And with the increase of the complexity of the polygon, the advantages of this algorithm are more obvious.