基于大数据和机器学习算法引擎的电商侵权监测系统

E-commerce infringement monitoring system based on big data and machine learning algorithm engine

提出一种基于大数据和机器学习算法引擎的电商平台侵权动态监测网络系统的设计方案.重点介绍大数据远程采集系统、数据实时监测分析平台和核心算法引擎模型设计.大数据远程采集系统包括远程服务器云平台和大数据分布式采集程序,系统通过Web信息采集器来完成精确采集各大商家、网络平台等的所有相关数据;数据监测分析平台是将信息的深度学习、单词嵌入、共同表征等相关算法转换为程序,用于分析处理由采集数据数字化的商品所涉及的知识产权信息.核心算法引擎基于传统舆情分析和自然语义处理技术,构建商品特征及用户观点挖掘模型,从而实现电商产品知识产权数据的搜索、分析、保存、预测等功能.该系统可有效降低侵权打假成本,为品牌商维权和知识产权保护建立一个可靠的渠道.

Based on big data and machine learning algorithm engine,a design scheme of intellectual property dynamic monitoring network system for e-commerce platform was put forward.Focusing on the design of big data remote acquisition system,real-time data monitoring and analyzing platform as well as the core algorithm engine model.The remote acquisition system of network big data includes the remote server platform and big data distributed acquisition program.The system uses Web information crawler to accurately collect all relevant datas of major businesses,network platforms and so on.The data monitoring and analyzing platform converts the relevant algorithms such as deep learning,word embedding and common representation of information into programs to analyze and process the intellectual property information involved by the collected data to digitize goods.Based on the traditional public opinion analysis and natural language processing technology,its core algorithm engine builds commodity features and the user opinion mining model,so as to realize the functions of search,analysis,preservation and prediction of intellectual property data of e-commerce products.The system can effectively reduce the cost of infringement and anti-counterfeiting,thus to establish a reliable channel for brand merchants to protect their rights and intellectual property rights.