Research On Chaos Of Product Color Image System

With the rapid improvement of people's quality of life,users' emotional needs have been increasingly concerned based on the product's ability to meet the basic functional requirements of them.As one of the most important visual features in a product system,color conveys not only a visual sense of beauty but also the physical and psychological needs of the human beings.And the prediction of product color trends can also help developers to grasp market trends and reduce design blindness.Meanwhile,a phenomenon similar to the "butterfly effect" exists in design systems that a small deviation in the conception at the beginning will make a big difference between the final result and the original target through the fine refinement of entire design stage.Therefore,the product color image is not simply considered as a quantitative"formula" between product color and people's psychological cognitive semantics,but a"chaotic" system covering various influencing factors which are complex and nonlinear.Based on these,in order to solve the problems that the user's color image cognition process is difficult to encode and measure,product color trend prediction is subjective and design system complexity effectively,combined with the technical methods of Ontology and Chaos theory,the research on chaos of product color image system has been carried out in this study under the research framework of Kansei Engineering.It included mining of product color image under massive data,extracting the attractor of product color image system,and then obtaining time series for judging the chaos of system.At last,predicting the trend of product color quantitatively,based on the short-term predictable characteristics of chaotic system,which could guide the product color design decision that meets user's color sensibility requirements,and broaden the theoretical boundaries and ideas for the complexity of design system.The main contents and results are as follows.Firstly,in order to solve the problem that it is difficult to encode and measure complex information in the user's cognition process,a method used to obtain the user's color sensibility requirements was proposed relying on the big data mining technology,combined with the color theory and the users' perceptual cognitive characteristics.Then,a color image knowledge base containing 79 color image words was obtained.Secondly,according to the product color image vocabulary,the color image ontology model was constructed by applying the two-group description method,and then it was visualized with Protege software.Meanwhile,the brand images of color named "fashion" and"natural" were obtained by the image entropy method,which could be considered as two attractors of this system.Thirdly,the time series data related to the brand image of the product color is collected to develop the chaotic discrimination research of the product color image system.According to the complexity of design image systems,the discriminative research on whether there is chaos in the product color image system is carried out by using small data volume method,based on the chaotic characteristics such as the order and disorder,certainty and randomness,and"butterfly effect".And then the car exterior color design was as an example to expand in detail.At last,six groups of car color time series were calculated to know that the maximum Lyapunov exponents of them were positive numbers.These results implied that the car's exterior color image system had the chaotic characteristics.Furthermore,the chaotic phenomenon in the color image system of the available products was analyzed,which provides new ideas and theoretical support for the in-depth exploration of complex systems of color images.At last,the short-term prediction of the chaotic system and the product color image perception chaotic box were proposed to conceive and explore based on the result of this study.Fourthly,the quantitative prediction model of product color trend driven by brand image was constructed based on the chaotic discrimination of product color image complex system.Firstly,two sets of qualifying time series were selected from the obtained data.Secondly,the GNNM was applied to predict the short-term trend of product color trends.Finally,the trend forecast of automobile appearance color in China was taken as an example to study in detail.According to the prediction results,the analysis was shown that the colors which contain red,blue,and green had an upward trend in China's auto market in 2019.The forecast result could be used to guide the development of product color that meet user's color emotional demands.Finally,the product image color design knowledge base was acquired to guide the product color design decision.While,the appearance color design of sedan was taken as an example,and then the relationship between color brand image and product color design elements was calculated by applying the method of Kansei Engineering based on the color brand images:"fashion" and "natural".Next,combined with the correlation results and the prediction results,a knowledge base of product image color design was obtained to micro-guide product color design decision that meeting user's emotional needs.These obtained research results could be used to guide the company to carry out effective exploration of product image color research and decision-making,and to achieve the maximum satisfaction of users' color sensitivity needs.Meanwhile,the study on chaos of product color image complex system could also provide a new idea and theoretical basis for the deep exploration of design cognitive system.