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Visual Design of Brand Image by Marine Bionic System in the Environment of Internet of Things and Machine Learning
Based on the environment of economic globalization, many clothing companies have increased the requirements for clothing design styles to diversify them, thereby increasing the interest of consumers. The main purpose and motivation are to explore the pattern design of children’s clothing with the marine bionic environment as the design source. Firstly, the collaborative business concept of IoT machine learning is introduced into the field of clothing design. Design styles and elements related to marine bionic environments are introduced. A set of questionnaires about the visual design preferences of children in different families on marine style clothing patterns are designed to examine differences in the cognitive level of color patterns among children of different age groups. Through the sorting, statistics, and analysis of the questionnaire results, different families have a stronger interest in clothing with warm colors as the color style and lines and ordinary paintings as the pattern drawing style. This provides a certain degree of design ideas for related clothing design work and provides unique insights into the visual design of brand image based on the marine bionic system in the Internet of Things (IoT) and machine learning environment.
With the formation of the global economic integration trend, the quality of life continues to rise, and the requirements for clothing from children to adults have long ceased to be comfortable and warm . Humans have gradually increased their requirements for fashion and viewing. At present, in order to expand the interests of many manufacturers, the pattern design of clothing is too simple, there is a serious follow-up phenomenon, and it is easy to cause visual fatigue . The natural marine world is a source of original design ideas. Humans can simulate marine life, extract inspiration, and imitate colors to design trendy clothing . The Internet of Things (IoT) technology can coordinate the visual design of brand clothing in the clothing industry, and machine learning can be used to innovate the visual design .
The brand visual design of clothing is a very important part. The style and pattern of clothing are the only way to capture consumer interest. Bhatt et al. (2019) used the multifactor line regression method to study the design of clothing. In the results, there is a significant positive correlation between the upgrading of clothing and the fashion style of environmental concern. Environmental protection and creative clothing can stimulate the interest of consumers . Kim and Lee (2019) also believed that most consumers’ purchasing interests focus on the detailed visual stimuli of models or clothing. This is a very important point in clothing sales . Pu and Meng (2019) pointed out that marine resources can play a great role in many current visual designs and hope to achieve rational and comprehensive optimization of resource development and promote the development of marine resources . Varol (2021) believed that many icons from movies, music, design, politics, and sports had influenced the clothing styles of different periods. This further proves that the design of clothing style is an important link . As for the brand design of IoT enterprises, Nguyen et al. (2021)  used the IoT and enterprise data to design the system of data-driven and customer emotion monitoring. By designing the framework of data-driven decision support system, the framework proposed a practical workflow for end users. The results showed that combining the IoT and enterprise data could improve the prediction results and simplify troubleshooting. In addition, with regard to the establishment and research of high-performance IoT virtualization framework, Al-Azez et al. (2019)  studied the high-energy IoT structure with point-to-point networking and processing. The research results presented that the hybrid scenario could process up to 77% of task requests, but the energy consumption was higher than other scenarios. Ju et al. (2021)  studied the IoT-assisted multiple fuzzy-enhanced energy scheduling method in the intelligent scheduling system. The results suggested that this method was superior to the traditional system design, which improved the system accuracy and reduced the execution time. With the increasingly fierce competition in the apparel industry market, the style of design is the focus for consumers to choose. Section 1 describes the concept and application of machine learning in the IoT. In Section 2, the concept of collaborative business of machine learning and elements and functions of the marine bionic environment is supplemented and explained in the IoT environment. The visual structure of marine bionic elements is designed based on IoT machine learning. In Section 3, the data results are obtained through the research on the color degree of clothing design. Section 3 draws the research data results through the study of clothing design color. Section 4 summarizes the research conclusions. The contribution is to take the color and overall pattern of marine creatures as the evaluation standard and analyze children’s preference for a specific pattern to give the design standard of clothing.
2. Related Work
With the development of the IoT and machine learning technology, the rapid development of hardware, software, and communication technology promotes the emergence of sensing equipment connected to the Internet. These devices provide observations and data measurements from the physical world. Machine learning technology is widely used in the development analysis model. These models are integrated into different service applications and clinical decision support systems. Samie et al. (2019)  researched IoT machine learning and reviewed the role of machine learning from the cloud to embedded equipment in the IoT. They studied the use of machine learning in application data processing and management tasks. These are guiding significance for the development of IoT machines in various fields of society. Zolanvari et al. (2019)  analyzed machine learning for industrial Internet network vulnerabilities. The use of machine learning in response to this sensitivity is discussed through network vulnerability. Subsequently, the available intrusion detection solution is described using the machine learning model. Tahsien et al. (2020)  reviewed and studied IoT security solutions based on machine learning and conducted the most advanced review of possible security solutions for IoT devices. They discussed the challenge of machine learning-based IoT system security, indicating that machine learning techniques can respond to various Internet access. Kishor et al. (2021)  studied fog computing intelligent medical data separation scheme based on IoT and machine learning. They used a random forest machine learning method to isolate patient data and improve delays using fog intelligently. This model achieves 92%-95% of the overall delay reduction to the existing working techniques. In summary, the IoT, machine learning neural networks have been applied in multiple fields, and the construction of the model can be fully borrowed from the reference experience. A disadvantage is that the accuracy and precision of the current model are not high, and it is necessary to further optimize and improve in later work. The list of literature research is shown in Table 1:
3.1. The Concept of Machine Learning Collaborative Business in the IoT Environment
A lot of studies haves been done on the microstructure coating of marine bionic functional surface, but there are still many practical problems. The real biological epidermis is easy to change under the influence of the environment. For the problems of long time and high cost of completely replicating the epidermis structure in a large area, the IoT brand is used for system engineering design, including the Internet, communication protocol, signal detection and processing, information fusion, database management and development, and software development . Based on the current IoT technology, the system covers different aspects of the circulation of brand clothing and the whole sales process, including personalized services for members and customers. The machine learning collaborative e-commerce structure in the IoT environment can further improve the business efficiency of supply chain management. The framework structure of brand collaborative business is shown in Figure 1.
In Figure 1, the intelligentization of brand clothing store sales and the realization of the coordination and interoperability of various links can reduce inventory stock, reduce costs, and strengthen personalized services for member customers. Additionally, it can also improve the efficiency of the enterprise, grasp the sales situation in time, and provide a reliable basis for enterprise decision-making. This collaborative business structure is generally improved by a collaborative filtering algorithm (CFA) . The structure of the collaborative filtering algorithm is shown in Figure 2.
In Figure 2, the research purpose of CFA is to analyze the degree of college students’ access to commodities in the daily network. For example, for a group of students with the same or similar hobbies, there is a certain degree of similarity in the software used when accessing goods. The item A used by user A will also be the item frequently used by user B, and the material A frequently referenced by user C will also be the material frequently used by user D for reference.
Traditional CFA analysis of user similarity generally uses three methods: (1)Jaccard index . The purpose of the coefficient is to judge the relevance of binary data, as shown in where represents a common thing for users and . represents everything for users and . The Jaccard index is widely used to compare users’ online shopping carts.(2)Cosine similarity . The user score is a vector on an -dimensional matrix, and the similarity of users and is calculated by the cosine angle between the vectors, as shown in where represents the user’s score vector inner product. represents the product of the modulus of the user vector. and represent the user’s evaluation score for item . However, different users have different ratings for the same product. Therefore, this method cannot accurately calculate the similarity. The analysis process that CFA is used to analyze members’ consumption intention is shown in Figure 3.(3)Modified cosine similarity. This method can fill the gap of no scoring standard in cosine similarity, using the average of user scores for analysis, as shown in where and represent user and ’s evaluation of item . and represent the average of the ratings. In the current membership service, the process of purchasing clothing, which does not need to guide customers, is shown in Figure 4.
In Figure 4, the technical solution adopted in the intelligent display and matching part of the member user is that the member customer walks in front of the member experience device and can choose clothing according to their preferences. In corresponding scenarios, the technology can recommend matching clothing to customers, and users can arbitrarily match clothing according to their own preferences.
3.2. Elements and Functions of Marine Bionic Environment
The application of marine bionic form design to the brand image design of children’s clothing enriches the formal language of the product and embodies the unique aesthetic feeling . The characteristics of marine bionic environment are shown in Figure 5.
In Figure 5, the inspiration of marine bionic design includes not only the real form of the marine world directly derived from nature, but also the abstract form and subjective image form extracted from rational thinking.
Most marine plants use bright colors to convey messages of friendliness, hostility, excitement, threat, or deception. Its ultimate purpose is to survive . These beautiful and bright colors are used in children’s products with unexpected results. The benefits of marine elements on children’s attractiveness are shown in Figure 6.
In Figure 6, bright colors can beautify and enrich the color language of children’s products, making the products more beautiful and beautiful. The bionics of biological structure and texture is a commonly used method in modern design. The use of biological structure or texture effects in product design can make the audience feel the novelty and uniqueness of creativity  and will bring tactile and visual impact to people. Several marine bionic design results are shown in Figure 7.
In Figure 8, in the design field, natural elements have been fully utilized as a valuable resource. Designers often look for design inspiration in natural things, solve the root of the problem by drawing on the modeling structure of natural ecology, and use their professional quality and professional skills to reconstruct it to form a certain aesthetic sense . A notable example of reproduction in natural design is the graphic patterns and colors of animal fur. The color tone of marine life can generally be divided into five types as shown in Figure 9.
In Figure 9, the marine life has high color saturation and strong contrast and has a strong visual impact. Its own color and pattern form have certain inspirations for textile design, clothing design, environmental design, and graphic design. When the marine life color of clothing design is adopted , it can help summarize the ideas in the color style design of clothing and use it in the design of children’s clothing.
3.3. Children’s Cognition of Color in Different Age Groups
The color style visual design of children’s clothing needs to be carried out for children of different ages. The marine bionic environment gives people the feeling of being smart. When designing patterns, an innovative design process from shape resemblance to spiritual resemblance is required, and emotional factors need to be incorporated . Children of different ages have different perceptions of different colors in their hearts. Children’s psychological and cognitive development is divided into four stages, as shown in Table 2.
Children around the age of 14 gradually have their own aesthetic ability. When choosing clothes and other things, they begin to follow their own choices and oppose their parents’ choices. At this stage, there are five aspects of clothing selection criteria that parents give their children, as shown in Figure 10.
In Figure 10, the comfort and safety of clothing are equally important when parents consider purchasing children’s clothing. Comfortable clothing is conducive to the healthy growth of children.
At this stage, biological children have entered the best period of memory. The development of children’s intelligence and the education of knowledge are of great concern to parents. They will cultivate children to accumulate knowledge and enhance the learning atmosphere in any way and by any means.
With people’s pursuit of healthy life and high-quality life, environmental protection has become a hot topic. Green, eco-friendly clothing has the least negative impact on the environment and human health. When parents buy clothing products for children, they pay special attention to the quality of clothing.
Based on attaching importance to safety, modern parents advocate freedom, pursue individuality, and pay attention to taste. Children mainly consider the attractiveness and fun of the product. Based on ensuring the health, safety, greenness, environmental protection and other qualities of children’s clothing, there are further requirements for the value of the additional spiritual level.
According to relevant data records, children aged 8-15 are the main component of the children’s clothing market. Most of the children at this stage will have their own right to choose when buying clothing, and they will be the main players in future consumer spending by choosing products through their own aesthetic wishes. Figure 11 shows the main structure and players in the consumer market for children of this age group.
In Figure 11, the reason for this structure is that children in this age group begin to switch from passive consumption to active choice consumption, and children’s curiosity makes their market potential unprecedented and high commercial value. The root of these lies in their pursuit of fashion and individuality.
3.4. Wireless Sensor Communication and Mobile Computing
Wireless sensor network (WSN) is self-organized by a large number of small, low-cost, and resource-constrained sensor nodes deployed in specific monitoring areas with the ability of environmental parameter sensing, information processing and storage, and wireless communication. It is an important emerging technology for information sensing and data collection in the twenty-first century. WSN is a new network form integrating sensor technology, semiconductor manufacturing technology, microelectromechanical system, embedded computing technology, radio communication technology, distributed processing technology, and modern network technology. Generally, when designing the pattern style of clothing, the general pattern design can be composed of points, lines, and surfaces. In fact, points can have many different shapes, so points are the basis of some graphics. It can have different characters and color elements , become abstract points, and form different patterns. Based on the IoT machine learning, the visual structure of marine bionic elements is optimized and designed. The pattern of grid points is shown in Figure 12.
In Figure 12, when extracting and applying graphic elements, the shape of the graphic can be adjusted appropriately without changing the basic shape. After the pattern is extracted, it is arranged into two-sided continuous and four-sided continuous graphics. Through repetition, reconstruction, gradual change, and concretization, new patterns are formed and applied to the packaging design of brand image to bring good visual effect.
3.5. Feedback Questionnaire Design for children’s Clothing Design with Marine Bionic Elements
With people’s pursuit of healthy life and high-quality life, environmental protection has become a hot topic. Green, eco-friendly clothing has the least negative impact on the environment and human health. When parents buy clothing products for children, they pay special attention to whether the quality of clothing, the material, and the shape of the products are beneficial to children’s health. Incorporating the design concept of “happy, healthy, green and environmental protection” into the product plays a vital role in the future development of children and has certain educational significance for children. The marine environment gives people a pure, green, and natural feeling, which is in line with parents’ expectations for their children’s inner world. Marine element brands are the first choice for parents.
A questionnaire on the effect of marine bionic elements on children’s visual design was designed, and the color and pattern making style of visual patterns were investigated in the environment of high-energy IoT. The data collected in the questionnaire mainly includes the description of the cognitive and structural characteristics of marine bionic elements. In addition, the age distribution of children’s parents is also investigated in the daily clothing purchase. The data on age and options are automatically collected and evaluated in the system by the high-energy IoT framework. The questionnaire designed by relevance concept is shown in Table 3.
The validity of the questionnaire is tested after designing the questionnaire. SPSS statistical software is used to analyze the questionnaire, as shown in Table 4.
The suitable sampling number is 0.943, which is greater than 0.9, and the significance is 0.000. The validity of the questionnaire is good, and the results can be used and adopted. The experimental data set of the study is obtained by means of a questionnaire survey. Questionnaires are distributed to 80 families to investigate the influence of children’s clothing on marine bionic visual design and ask their opinions. A total of 73 questionnaires are returned after completing the survey. There are 68 valid questionnaires, and the survey results will be statistically analyzed in the results.
4.1. Discussion on the Color Degree of General Families for Marine Bionic Visual Clothing Design
In this link, firstly, statistics and analysis of the pattern and color results of the visual design of marine bionic clothing are carried out in general families. Additionally, other factors affecting clothing purchases in the family are also analyzed, including purchase volume, choice of clothing purchased, choice of clothing brand, and access to clothing information. The survey results are drawn as a statistical map to judge the factors and degrees of influence of these factors on the design of marine bionic clothing and give some suggestions for discussion. The statistical results are shown in Figure 13.
In Figure 13(a), most families purchase clothes for their children within a month, and the frequency is relatively average. Among them, 20 and 19 families purchased 4-6 pieces and more than ten pieces, accounting for 30% and 27%, respectively. In Figure 13(b), when buying children’s clothing for children, most families will consider their children’s opinions, and eight families, accounting for 12%, are completely dominated by parents. In Figure 13(c), 33 of the 68 families will first consider domestic brands when purchasing children’s clothing, 16 of the remaining families will consider foreign brands first, and 19 will not care about the influence of brands. In Figure 13(d), 29 families obtained information from advertisements when purchasing clothing, accounting for 43%, and 16 families obtained information from friends’ recommendations. Finally, in Figure 13(e), children in 55 families prefer the visual style of clothing in warm colors, and only 4 prefer cool colors. Therefore, when using the marine bionic environment to design clothing styles, designers should first consider starting with warm colors, which can better attract children’s attention and choose to buy. Additionally, designers should also properly consider cool-toned clothing to meet the needs of all consumers.
4.2. The Pattern Style Evaluation of the General Family for the Visual Design of Marine Bionic Clothing
Aesthetic preference is the personal preference and appreciation of the aesthetician for the structure, shape, color, material, and other aspects of things. Children of different ages have different aesthetic styles and tastes. Noble aesthetic sentiment can make the aesthetic person get spiritual pleasure and satisfaction and finally get the perfection of personality. The influence of the marine bionic environment on the visual design of children’s clothing is discussed. A questionnaire is used to rate 68 families for different styles of color visual design clothing. This link will evaluate the design style and style of clothing patterns in the bionic environment, and the data results are shown in Figure 14.
In Figure 14(a), in the 68 surveyed households, most of the parents are concentrated in the age group of 31-40. Among them, parents aged 31-36 are the most, with 41 accounting for 60%. In Figure 14(b), 48 parents aged 31-36, and 71% of the families prefer the exaggerated abstract pattern style in clothing. Most of these patterns are anthropomorphic animated characters based on marine animals. Therefore, it is also loved by most children. In Figure 14(c), the line drawing and ordinary drawing are the most popular, with 17 and 19 families, accounting for 25% and 27%, respectively, followed by the sticker type, with 12 people, accounting for 18%. Finally, the same number of people who like paper-cut shapes and ink paintings are ten people, accounting for 15%, respectively. When carrying out the visual design of marine simulation-style patterns, it is necessary to focus on line drawing and ordinary painting, and most people who like ordinary pattern style design still occupy the majority.
To sum up, through the concept description of machine learning collaborative e-commerce under the concept of IoT and the analysis of elements and functions of the marine bionic environment, the visual structure of marine bionic elements is designed and optimized based on the cognition of color by children of different age groups. The survey results of marine bionic visual clothing design showed that children from 55 families preferred the visual style of clothing in warm colors, and children from only four families preferred cool colors. In addition, in the evaluation of pattern style, line painting and ordinary painting are the most popular, with 17 and 19 families, accounting for 25% and 27%, respectively, followed by sticker type, with 12 families, accounting for 18%. Research shows that different families have different needs for clothing color and clothing design style, and the design direction should be adjusted according to the actual situation.
At present, given the diversification of clothing styles, major children’s clothing companies urgently need to seize the children’s mainstream aesthetic style and improve their sales performance. Therefore, they try to apply new network technologies in children’s wear design. Based on the IoT and machine learning method, the bionic system factors of marine organisms are studied in children’s wear pattern design. Firstly, in the large environment of the IoT, machine learning is a synergistic business concept with technology that summarizes the design process of clothing. Secondly, examples and methods of marine bionic elements in style design are set forth. A set of marine-style apparel visual questionnaires for different family children are designed combined with the difference in color and pattern cognitive ability in four different ages. Children in 68 families have surveyed various styles and colors of clothing patterns and home environments. The questionnaire results have been summarized, and most children are more interested in warm-tone patterns and colors and the pattern drawn in bylines and ordinary paintings. Therefore, in the clothing style design, the above three factors should be used as the main design style. The main shortcomings of research are that the investigation of sample data is 68, less than a normal survey, and the result is limited. Hence, in subsequent work, to improve the advantages of the proposed work, the number of survey samples will be further expanded, and the data will be further collected to improve the operational efficiency of the model.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Conflicts of Interest
All Authors declare that they have no conflict of interest.
The authors acknowledge the help from the university colleagues. This work was supported by a grant from Brain Korea 21 Program for Leading Universities and Students (BK21 FOUR) MADEC Marine Designeering Education Research Group.
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