Construction of Multi-granularity Spatio-temporal Object Model for Intelligent Highway Maintenance

Abstract. With the continuous increase in the mileage of highway maintenance in our country, the tasks of highway maintenance are becoming increasingly heavy, posing new challenges for highway maintenance management. Current research on data models for highway maintenance mainly focuses on describing their spatiotemporal attributes, lacking a comprehensive expression of the characteristics and relationships of entities within highway maintenance scenarios. Moreover, traditional data management methods have many shortcomings in visualization and multidimensional information representation. Therefore, this paper constructs a multi-granularity spatio-temporal object data model framework aimed at intelligent highway maintenance by combining Multi-Granularity Spatio-Temporal Object Data Model (MGSTODM) with highway maintenance technical standards. An experiment was conducted using a highway maintenance scenario in a certain area of Beijing as an example. The results show that this model can efficiently express the multidimensional features of highway maintenance data, achieving a more comprehensive, realistic, and dynamic visualization of highway maintenance information, thus providing more effective support for maintenance data management and decision-making.