徐州工程学院学报（自然科学版）2025，Vol.40Issue(4)：64-70,7.
基于大数据相似度模型的用户网络位置定位识别研究
User Network Location Identification Based on A Big Data Similarity Model
摘要
Abstract
In order to address the issues of low recall rates and long response times in the current location positioning system for network users,this paper proposes a location recognition method based on a big data similarity model.Network data from multiple sources,such as base station signals and user trajectories,is collected and standardized.Multi-dimensional features,such as spatio-temporal,behavioral,and network features,are extracted.Different weight coefficients are assigned to these features based on an attention mechanism in order to construct a big data similarity model.The similarity of the spatio-temporal,behavioral and network features is then calculated using the Frechet,EMD and cosine similarity measurement methods.Comprehensive similarity is then obtained through weighted summation,combining attention weight coefficients to precisely locate and identify the user's network position.Experimental results demonstrate that applying the proposed method achieves an optimal user network location recognition accuracy of 99％with a minimum response time of 0.08 seconds.关键词
用户IP位置/网络行为数据收集与处理/定位识别/大数据相似度模型/定位误差Key words
user IP location/network behavior data collection and processing/location identification/big data similarity model/positioning error分类
信息技术与安全科学引用本文复制引用
CAI Jinsong..基于大数据相似度模型的用户网络位置定位识别研究[J].徐州工程学院学报（自然科学版）,2025,40(4):64-70,7.基金项目
安徽省自然科学项目(2024AH050628) （2024AH050628）
