K nearest neighbor search method for variable weight network

Abstract

The invention relates to a k nearest neighbor search method for a variable weight network. The method comprises the steps as follows: routing thinking in a computer network is led in k nearest neighbor query, and predecessor / subsequent information in shortest path tree obtained in computation is taken as history data and is saved in a routing table by designing an update strategy of the routing table. Due to the dynamical update strategy of the routing table, the routing mechanism can adapt to dynamic changes of local road weights in a road net. The k nearest neighbor search method provides the candidate set screening rule and the road net area cutting rule of a non-mobile object as well as the position prediction method and the candidate set screening rule of a mobile object in the dynamical road net, effectively reduces the number of candidate points and narrows down the search range of the k nearest neighbor query, and avoids the defect that indexing and constraint conditions are adjusted frequently because the changes of a k nearest neighbors algorithm are caused by the road weights continuously in the conventional dynamical road net extended from a static road net.

Description

technical field [0001] The present invention relates to an information technology, in particular to a variable weight network k Neighbor search method. Background technique [0002] k The nearest neighbor query problem is a very important geometric problem and has practical applications in many fields. Such as data mining, document retrieval, DNA matching, spatial database and multimedia database, etc. With the development of geographic information system (GIS), computer-aided design (CAD), multimedia system, medical and satellite image data processing and other fields, spatial databases and spatial data k The research of nearest neighbor query has also become a research hotspot in recent years. For example, a user may click on a specific location on the computer's electronic map and ask to find the five closest objects to it. Due to the huge amount of spatial data, complex data structure, and expensive operation costs (including computing overhead and I / O overhead, etc...

AI Technical Summary

Problems solved by technology

On the one hand, the server must constantly call and update the constraints of the branch and bound method and the initialization process of the Voronoi diagram, which will lead to a large number of frequent updates and increase the burden on the server

On the other hand, when faced with tens of thousands of query requests, currently based on the current query k Nearest neighbor queries will inevitably lead to a large number of repeated searches

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