一种基于多源数据融合与动态聚类的IP定位测绘方法OA

With the growth of the global network scale,as a core technology for achieving refined network resource scheduling and attack tracing,the accuracy and real-time performance of IP positioning and mapping methods have become critical for ensuring high-quality service in emerging scenarios such as 5G and the Internet of Things.Due to the insufficient static parameter settings and adaptability to dynamic topologies,traditional methods are difficult to meet the high-precision location requirements under multi-source heterogeneous data.This paper proposed an IP positioning and mapping method that coordinates multi-source data fusion and dynamic clustering.By integrating multi-source heterogeneous data such as WiFi hotspots,BGP routing,and ZoomEye protocol fingerprints,a dynamic screening mechanism based on geographical location entropy was constructed,and the recall rate of reference points reached 92.3％(an increase of 15.2％compared with the comparative method).Then,a dynamic clustering optimization algorithm was designed to achieve differential clustering for enterprise dedicated lines and residential areas.Finally,combined with network topology mapping technology,the positioning offset was corrected through the analysis of common adjacent nodes,and the errors in the dynamic network were suppressed.