| Abstract |
Smart cities have developed rapidly in recent years, and spatial big data, as an important part of smart cities, has developed at a rapid pace. However, due to the huge amount of data in spatial big data, it is difficult to query the needed information. In order to improve the efficiency of data query, this paper analyzes the spatial framework of the smart city to provide a basis for the subsequent optimization of the query algorithm. The multi-objective particle swarm optimization algorithm is used to plan the spatial data and obtain the layout of the urban space. The planned urban space is taken as the objective function, and the objective function is solved to complete the optimization of the approximate query algorithm. It is verified that the query optimization algorithm has high query efficiency, generally around 50ms, and the accuracy of the algorithm is as high as 98.2%, which can complete the data query efficiently, and the query effect is better, which meets the market demand. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
