基于大数据的知识产权侵权检测

Intellectual Property Rights Infringement Detection Based on Big Data

为实现高效、准确的知识产权侵权检测,本文研究大数据驱动的多模态异构信息综合分析方法。通过分布式爬虫、API采集等手段获取海量多源数据;针对文本、图像、音频等数据类型分别采用NLP、CNN等方法进行特征提取,并构建知识图谱表示数据之间的关系;构建云原生深度学习模型,实现对多模态特征的端到端训练与融合。系统支持异构数据的采集、表示、建模与分析,并可方便集成到移动端和Web应用中。结果表明,准确率达90%以上,高于单一数据源和模型方法。本研究为构建高效的知识产权保护系统提供了有益参考。

In order to realize efficient and accurate intellectual property infringement detection,this paper studies the comprehensive analysis method of multi-mode heterogeneous information driven by big data.To obtain massive multi-source data by means of distributed crawler and API collection,NLP,image and CNN to construct the relationship between data.Finally,cloud native deep learning model is built to realize end-to-end training and integration of multi-modal features.The system supports the collection,representation,modeling and analysis of heterogeneous data,and can be easily integrated into mobile terminals and Web applications.The results show that the accuracy of the method is more than 90%,which is higher than the single data source and model method.This study provides a useful reference for constructing an efficient IP protection system.