一种基于地图爬虫的场景边界识别与质量监控方法

本文提出了一种基于地图爬虫的场景边界识别与质量监控方法,参考互联网爬虫技术实现场景边界自动识别,依靠“MR+OTT”和“帕累托法则”实现场景资源信息自动更新,在此基础上,关联网优大数据实现重点场景网络质量的智能预警监控。在某市进行试点,方案上线后自动识别场景4361处,实现场景小区自动更新,初次评估覆盖率92.85%,经过两个月的整治,覆盖率提升至94.22%,弱覆盖小区占比下降4.72PP。

A scene boundary recognition and quality monitoring method based on map crawler

The paper proposes a scene boundary recognition and quality monitoring method based on map crawler. The Internet crawler technology is used to realize automatic recognition of scene boundaries, and the "MR+OTT" and "Pareto Law" are used to automatically update the scene resource information. Thus, with the help of big data in network optimization, it is easily achieved intelligent early warning monitoring of network quality in key scenarios. Piloting in a certain city, the scene is automatically identified after the scheme is online, and the scene cell is automatically updated and the initial evaluation coverage rate was 92.85%. After two months of rectification, the coverage rate has increased to 94.22%, and the proportion of weak coverage cells has decreased by 4.72pp.