论文部分内容阅读
近年来,随着互联网线上交易的迅速发展,众多二手物品交易网站悄然兴起,并逐渐成为犯罪人员销赃的重要途径。因此建立针对失窃物品的网络预警平台,将为有效打击犯罪,控制犯罪分子网上销赃途径具有重要价值。本文针对上述问题,利用文本挖掘方法对交易产品的商品属性和地域属性进行了自动的识别和标注,实现了对网上二手交易商品的地理信息可视化,提高了涉案物品定位及案件线索发现的工作效率。同时创新性地引入了异常点检测分析算法,以达到对二手交易网站商品价格偏离正常时进行报警,对提高失窃案件的侦办效率提供了重要手段。
In recent years, with the rapid development of Internet transaction online, many second-hand goods transaction websites quietly rise and become an important way for criminals to sell stolen goods. Therefore, the establishment of a network early warning platform for stolen goods will have an important value in effectively cracking down on crimes and controlling criminals on the Internet. In view of the above problems, this paper uses text mining method to automatically identify and mark the commodity attributes and geographical attributes of the traded products, and realizes the visualization of the geographical information of the traded commodities on the Internet and improves the work efficiency of finding the involved items and the clue of the case. . At the same time, the outlier detection and analysis algorithm was introduced creatively so as to alarm when the price of the second-hand trading website deviates from the normal one. This provides an important measure to improve the efficiency of the investigation and the theft of stolen cases.