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Application Research of Graphic Design Based on Information Resource-Sharing and Big Data Technology

机译:基于信息资源共享和大数据技术的平面设计应用研究

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摘要

The field of graphic design is an important industry rising in recent years if a new graphic design solution requires the designer to design from the ground up, it will consume a lot of time and material resources. The information resource-sharing platform already has many element characteristics to provide the designer to carry on the reference, and this will greatly save the designer time and the material resources. The traditional graphic design method will consume some resources only by relying on the designer and the solutions designed by this method may not be innovative enough. This research will design a graphic design system and management method from the point of data and big data of information resource-sharing platform. The results show that the IRM platform can obtain more effective successful cases of graphic design feature data. The clustering method and CNN method can effectively deal with the pattern feature, color feature, shape feature, and character feature of graphic design. It can not only effectively analyze the feature data value of graphic design but also fits well with the trend of data values. This is a valuable research work for graphic designers. The largest prediction error is only 2.34, and this part of the error mainly comes from the prediction of pattern features of graphic design. All other forecast errors are within 2.03.
机译:平面设计领域是近年来兴起的重要行业,如果一个新的平面设计解决方案需要设计师从头开始设计,将消耗大量的时间和物力。信息资源共享平台已经具备了许多元素特性,为设计人员提供了进行参考,这将大大节省设计人员的时间和物力资源。传统的平面设计方法仅依靠设计师会消耗一些资源,这种方法设计的解决方案可能不够创新。本研究将从数据和信息资源共享平台的大数据角度设计图形设计系统和管理方法。结果表明,IRM平台能够获得更有效的平面设计特征数据成功案例。聚类方法和CNN方法可以有效地处理平面设计的图案特征、颜色特征、形状特征和字符特征。它既能有效分析平面设计的特征数据价值,又能很好地契合数据价值的趋势。对于平面设计师来说,这是一项有价值的研究工作。最大的预测误差仅为2.34%,而这部分误差主要来自于平面设计的图案特征的预测。所有其他预测误差均在 2.03% 以内。

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