Rational Design of the Appearance of Complex Industrial Products Based on Visual Communication

Song Kang, Nanchang Cheng
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Abstract

In today's knowledge-based economy society, the rapid spread of a trend, personalized knowledge service has become the mainstream of development, so that the Web information users can meet the variety, high level of precision and content requirements of the high-Ievel service fonn. Through analysis, we can find that the mining of researchers' interest can serve as the main learning content of personalized knowledge services. So the realization of research interest mining based on Internet is to meet the special information needs of the users, which can be called an effective method to optimize the content of personalized business. Therefore, a more efficient algorithm is used to optimize the search search program for Internet information and improve the search efficiency. This optimization can become the research direction of the present age of aIl things in the world. The information data mining strategy to the simple to can improve the information retrieval efficiency of the network resources, and can provide the accurate, reliable summary of the Internet information resources, meet the convenience and precision of the researchers to retrieve the Internet information resources, thus can be accurate and high. The research interest mining based on the Internet includes the use of search engines to obtain the relevant information of the researchers. Through data mining and machine learning algoritluns, the results information returned by the search engine are analyzed, and the research interests of the researchers are obtained from the contents of these results.
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基于视觉传达的复杂工业产品外观的理性设计
在当今知识经济社会迅速蔓延的一种趋势中,个性化的知识服务已成为发展的主流,使Web信息能够满足用户对信息种类多、精度高和内容要求高的高层次服务方式。通过分析可以发现,研究者兴趣的挖掘可以作为个性化知识服务的主要学习内容。因此,基于Internet的研究兴趣挖掘的实现是为了满足用户的特殊信息需求,是一种优化个性化业务内容的有效方法。因此,采用一种更高效的算法来优化互联网信息的搜索程序,提高搜索效率。这种优化可以成为当今世界万物时代的研究方向。该信息数据挖掘策略力求简单,能够提高网络资源的信息检索效率,并能提供准确、可靠的互联网信息资源汇总,满足研究人员检索互联网信息资源的便利性和精确性,从而能够准确、高。基于Internet的研究兴趣挖掘包括利用搜索引擎获取研究人员的相关信息。通过数据挖掘和机器学习算法,对搜索引擎返回的结果信息进行分析,并从这些结果的内容中获得研究人员的研究兴趣。
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