With the development of 5G technology and intelligent monitoring equipment,mobile object query technology is becoming more and more widely used in practical applications.Especially in military strategic planning,material resource allocation,virtual games,etc.,the continuous reverse k neighbor query technology of moving objects occupies an extremely important position.However,the current continuous inverse k-nearest neighbor query frequently invokes the filtering algorithm as the position of the data object changes,resulting in inefficient queries.In addition,most of the existing query studies focus on twodimensional space,and relatively few studies on three-dimensional barrier space.Therefore,this paper proposes a continuous inverse k-nearest neighbor query based on safe region in Euclidean space and a continuous inverse k-nearest neighbor query based on safe region in three-dimensional barrier space,the main research contents are as follows.Firstly,a continuous inverse k-nearest neighbor query algorithm based on safe region in Euclidean space is proposed.The conceptual grid tree is used to construct the index structure of the spatial dataset,and the continuous inverse k-nearest neighbor query algorithm is divided into the Euclidean initial stage and the continuous monitoring change stage.In the Euclidean initial phase,index structures are established based on the size,position,and speed of movement of spatially moving objects and boundary-safe areas are obtained,and the result set of this stage is obtained through pruning and refining operations.In the continuous monitoring change phase,the boundary safe area is monitored,and when the moving object exceeds the boundary safe area,the index structure is changed,thereby reducing the update frequency of mobile queries.Secondly,a continuous inverse k-nearest neighbor query algorithm based on safety region in three-dimensional obstacle space is proposed.According to the MBB model of 3D spatial data objects and obstacles,the obstacle adaptive boundary safe area algorithm is given,and the 3D Voronoi diagram is constructed by using the obtained boundary safety area.In the initial substage of the barrier,the three-dimensional Voronoi diagram is used for pruning operation,and the obtained candidate set is used for spatial obstacle distance refining operation.In the continuous monitoring stage,the boundary safety area of the query point and candidate set is monitored by using the 3D grid,so as to more effectively divide the time nodes of each stage and improve the efficiency of the 3D obstacle query.Finally,the two types of continuous inverse k-neighbor query algorithms proposed above are experimented separately.Firstly,for the continuous inverse k nearest neighbor query in two-dimensional space,the traditional algorithm CRk NN is set as a comparative experiment.Secondly,for the continuous inverse k-nearest neighbor query in the threedimensional obstacle space,the dataset scale,query k-value,average velocity of moving objects and obstacle scale are compared and analyzed. |