A lot of research has been done on the problem of finding the k nearest neighbor to a query point. Existing studies are usually intended to work on static data. Even the minimal number of existing work done on dynamic objects has not solved the problems caused by their dynamic nature. The problem with KNN algorithms is how to keep the results fresh and avoid unnecessary computation cost each time the object changes position. This type of algorithm is in fact very used in many applications. In this document, a new challenge has been accepted to solve a complex problem. We propose a new approach to look for KNNs on continuously moving objects while guaranteeing a freshness of the results during a safety period during which the results of the query are always valid even if the object changes continuously its position. In order to take advantage of this type of algorithm in difficult situations such as the emergency decision-making process, we propose a new efficient algorithm to determine the K closest resources that are circulating in the same area of the query point. Our approach is progressive and relies on the Safe Region pruning method. As long as the object remains in its respective safe region, the new expensive computation is not necessary. The result of deep-seated experiments on our approach, validates its efficiency in terms of communication and calculation cost through a search restriction area method.

关于找到距查询点最近的k个邻居的问题，已经进行了很多研究。现有研究通常旨在处理静态数据。即使在动态对象上完成的最少现有工作也无法解决其动态性质所引起的问题。KNN算法的问题在于如何使结果保持新鲜，并在每次对象改变位置时避免不必要的计算成本。实际上，这种类型的算法在许多应用中都非常使用。在本文档中，已经接受了一个新的挑战来解决一个复杂的问题。我们提出了一种在连续移动的对象上查找KNN的新方法，同时保证了在安全期间内结果的新鲜度，在此期间，即使对象连续更改其位置，查询结果也始终有效。为了在紧急情况下（例如紧急决策过程）中利用这种算法，我们提出了一种新的高效算法，可以确定在查询点的同一区域中循环的K个最近资源。我们的方法是渐进式的，并且依赖于“安全区域”修剪方法。只要物体保持在其各自的安全区域内，就不需要进行新的昂贵的计算。通过我们的方法进行的深入实验的结果，通过搜索限制区域方法验证了其在通信和计算成本方面的效率。我们的方法是渐进式的，并且依赖于“安全区域”修剪方法。只要物体保持在其各自的安全区域内，就不需要进行新的昂贵的计算。通过我们的方法进行的深入实验的结果，通过搜索限制区域方法验证了其在通信和计算成本方面的效率。我们的方法是渐进式的，并且依赖于“安全区域”修剪方法。只要物体保持在其各自的安全区域内，就不需要进行新的昂贵的计算。通过我们的方法进行的深入实验的结果，通过搜索限制区域方法验证了其在通信和计算成本方面的效率。