# 3D Simulation Design and Application of Traditional Hanfu Based on Internet of Things

Authors: Li Lin; Wen Gan
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

Chinese traditional Hanfu is a kind of clothing that can reflect the changes of Chinese history, culture, and dynasties. With the improvement of people’s aesthetic ability and the pursuit of national culture, Hanfu has shown a state of revival. The study of Hanfu has become a research hotspot in today’s era. However, there is a big difference between the design of traditional Hanfu and the design of modern clothing. It not only needs to consider people aesthetics and preferences but it also needs to further consider the historical and cultural information represented by traditional Chinese Hanfu. This is a more critical and difficult point for Hanfu designers. If Hanfu cannot be well combined with history and culture, this will easily lead to a misinterpretation of Hanfu. In this study, the feasibility of the 3D simulation design of Hanfu was fully studied by combining the Internet of Things technology and the convolutional neural network method. The research results show that the Internet of Things technology can efficiently and accurately collect the characteristics of patterns, colors, shapes, and historical information of Hanfu. The reliability of IoT technology also improves the accuracy of CNN methods in predicting Hanfu eigenvalues. The largest prediction error is only 2.84%. CNN can also well capture the relationship between historical information features of Hanfu and dynasties, and all predicted feature values are within the 95% confidence interval.


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## Body

## 1. Introduction

In the twenty-first century, Chinese Hanfu has been revived again. Hanfu has a history of more than 4,000 years, and it is a symbol of the evolution of Chinese clothing culture [1, 2]. It was destroyed in the Qing Dynasty due to historical reasons and has recently gained a certain rise. The development of Chinese primitive agriculture and textile industry has also promoted the emergence and development of Hanfu, which also reflects the development technology of Chinese early textile industry. With the economic, political, and ideological changes of each dynasty, the characteristics and styles of Hanfu will also undergo great changes. More historical information can also be learned in the form of Hanfu [3, 4]. With the rapid development of Chinese national strength, people began to examine the excellent parts of their traditional culture and try their best to inherit these excellent cultures. More researchers have begun to restore the traditional costumes of the Han nationality by researching Hanfu and taking its essence to get rid of its dross [5, 6]. Hanfu is not only a kind of clothing but also a kind of inheritance of Chinese history and culture. The characteristics of Hanfu are different from those of other countries. It is a representation of Chinese etiquette and the way of daily life [7]. The color, pattern, and shape of Hanfu are closely related to Chinese history and culture and the use of clothing [8]. For designers in today era, the design of Hanfu is also more difficult. It not only needs to combine the characteristics of Hanfu itself but it also needs to be designed with the characteristics of the clothing of the current era. In today era, people aesthetics have undergone great changes. The traditional characteristics of Hanfu can no longer meet the aesthetics of today’s people. This requires efficient design based on the historical and cultural characteristics of Hanfu and people aesthetics in today era. For clothing designers, the biggest difference between Hanfu and modern clothing is not the style, pattern, etc. The most important part is the historical information contained in Hanfu. Computer technology can handle the data of Hanfu design well and it can share information, which is beneficial to the design of Hanfu. Computers can provide Hanfu designers with more historical information about Hanfu. This puts forward more requirements for clothing designers, which also needs to combine computer technology to carry out efficient design.The Internet of Things technology is a product of the rapid development of science and technology [9, 10]. It combines a technology of information transmission and sharing with hardware devices such as cameras, the Internet, and sensors [11, 12]. The Internet of Things technology has been widely used in many fields, and it can realize tasks such as remote office and resource sharing [13, 14]. For example, the Internet of Things can realize remote teaching through cameras, the Internet, and recording equipment, which improves the utilization of teaching resources [15]. Offsite office is also one of the main applications of IoT technology. Smart home is also a relatively wide application of Internet of Things technology, which can manage and apply the electronic devices of the family in a unified manner [16, 17]. For clothing designers, IoT technology will also facilitate the efficient generation of clothing solutions. For the design of Hanfu, IoT technology can bring together the opinions of experts in multiple fields, which can include researchers in the field of history and researchers in the field of clothing. They can jointly provide relevant opinions for the design of Hanfu. Similarly, IoT technology can collect more information from Internet technology, which can help Hanfu designers come up with more design solutions. Through the Internet of Things technology, the design task of Hanfu can not only achieve remote resource sharing but also it can refer to more Hanfu design schemes from the Internet. This will greatly improve the efficiency of Hanfu design task and which can also take into account the historical elements of Hanfu.Hanfu designers can not only learn more Hanfu design solutions through the Internet of Things technology but also it can make full use of the Internet technology to refer to the successful Hanfu design cases. The 3D design scheme of Hanfu mainly includes the cultural characteristics, colors, patterns, and shapes of Hanfu. It can fully learn the relationship between the characteristics of Hanfu and the design scheme through intelligent algorithms. Designers can design new Hanfu solutions based on the relationship between the learned Hanfu features and design solutions. This research will also use intelligent algorithms to learn Hanfu clothing patterns, colors, and historical information and other characteristic factors, which will provide more information resources for Hanfu designers. The Internet of Things technology can realize the role of remote office and information sharing for Hanfu 3D simulation, and it can learn more about Hanfu related characteristics through remote resources. The neural network method will be regarded as the intelligent algorithm of this study [18, 19], which uses the characteristic factors of Hanfu as input data. It will map the relationship between Hanfu characteristic factors and Hanfu 3D simulation design scheme. Once the model is trained, the Hanfu designer will match the corresponding solution according to the 3D simulation requirements of Hanfu, and this task will also fully combine the advantages of the Internet of Things technology.This research will use the Internet of Things technology and neural network technology to realize the 3D simulation design of Hanfu, which will provide more ideas for Hanfu designers. Moreover, this method will save more human and material resources for Hanfu designers. This study will conduct related research from five chapters. The first section mainly introduces the historical background of Hanfu and the application of IoT technology in the 3D simulation design of Hanfu. Section2 mainly introduces the research status of Hanfu or other types of clothing design, which will also provide more reference value for the 3D simulation of Hanfu. The application scheme of IoT technology and neural network method in Hanfu 3D simulation design is introduced in Section 3. Section 4 introduces the feasibility and accuracy of IoT technology and neural network method in Hanfu 3D simulation design in detail. Statistical parameters such as the prediction error curve of Hanfu characteristics, the thermal distribution map of Hanfu characteristics, and the correlation coefficient map are used to analyze the accuracy of the Internet of Things technology and the application of neural network methods in the design of Hanfu. Section 5 summarizes the research.


## 2. Related Work

Chinese traditional Hanfu has been revived to a certain extent in recent years, more people have begun to explore the connotation of Hanfu, and many researchers have also conducted related research on Hanfu. Zheng and Lee [20] analyzed the types and characteristics of traditional Chinese women’s clothing, which can identify the historical and cultural information of Hanfu through the style of Hanfu. He mainly studied the characteristics of Hanfu in the Han, Tang, and Song dynasties. It mainly studied the forms and characteristics of women’s Hanfu based on historical documents and museum information. The characteristics of Hanfu can be studied from the characteristics of shape, pattern, and material. It has also analyzed the style and design of the fusion of Hanfu and contemporary clothing. Zhang and Ma [21] have also noticed that more researchers have begun to focus on the structure and characteristics of Hanfu clothing. The change and development of Hanfu is a process of continuous innovation and progress in a dynasty. He studied the characteristics of traditional Hanfu and improved Hanfu by using the method of cross plane structure. He also used CLO3D software to establish the cross plane structure of Hanfu to study the characteristics of Hanfu and wearing models. He uses this model to explore the fusion of traditional Hanfu and modern clothing, which will be conducive to the effective transmission of traditional Hanfu characteristics in modern society. Taman [22] mainly studied Hanfu from the perspectives of comfort, economy, and artistry. He mainly used the analytic hierarchy process to establish the characteristic model of Hanfu, which will be beneficial to the research of Hanfu. At the same time, he used the AHP grey relational analysis method to fully study the application value and practicability of Hanfu. This research will be conducive to the efficient integration of Hanfu and current clothing, which has important guiding significance for the design of Hanfu. Chen and Hee [23] have combined the theme of traditional Chinese Hanfu culture with the characteristics of modern men’s hip-hop clothing for new clothing design. He designed a clothing scheme based on the fashion-style characteristics of hip-hop clothing and the historical and cultural atmosphere of Hanfu, which will break the characteristics of traditional hip-hop clothing design elements. At the same time, he also applied the concept of SCAMPER to research and design the fusion of hip-hop clothing and Hanfu characteristics. This hip-hop clothing design scheme based on Chinese traditional Hanfu will benefit the development of Chinese clothing fashion. Liu and Shu [24] have conducted analysis and research on Chinese women’s cheongsams from six dynasties in China, mainly from the form, pattern, and pattern features of sleeves, collar, waist, and detailed comparative analysis. At the same time, he used 3D Simulation software to compare and analyze the clothing styles of the six dynasties. Through research, it can be concluded that the length of clothes in Qin Dynasty is the longest, and the sleeves in Qing Dynasty are short and wide. This research on the characteristics of Chinese women’s Hanfu is conducive to deepening the understanding of the characteristics of Chinese women’s Hanfu. Kim and Young [25] have made use of traditional Chinese Hanfu to continuously revive the status quo. He analyzed the characteristics of traditional Chinese Hanfu and the element characteristics of Hanfu, which will help fans to understand the characteristics of traditional Hanfu. At the same time, it will analyze the intersection of Chinese Hanfu elements and Chinese history and national culture. From the above literature review, it can be seen that most of the researchers have mainly conducted relevant research on the characteristics and cultural elements of traditional Chinese Hanfu, which also involves the integration of Hanfu and modern clothing. However, most of them are studied in the manner of historical documents. Wang et al. [26] mainly studied the size measurement scheme of Chinese Hanfu by using the convolutional neural network (CNN) method. He used the technology of multiple transfer learning to obtain the feature points of Hanfu, which will improve the recognition speed of Hanfu features. Then, it will obtain the data of the actual Hanfu size in a proportional way. The results show that the prediction error of this method is only between 0.59% and 4.17%. This research mainly uses the Internet of Things technology and neural network technology to study the 3D simulation design of traditional Chinese Hanfu, which will be different from the above research status. This research utilizes high-performance computer technology rather than just using historical documents to conduct related research.


## 3. The Introduction of IoT Technology and Neural Network Methods in Hanfu

### 3.1. The Significance of IoT to 3D Simulation of Hanfu

The design of traditional Chinese Hanfu is different from the design of modern clothing. It not only needs to meet the aesthetic needs of modern people but also it requires designers to understand relevant historical information. The style, pattern, color, and style of traditional Chinese Hanfu will reflect the development history of the dynasty and the economic and ideological state of the dynasty. It cannot be designed without ignoring the historical information of the clothing. Therefore, a new Hanfu design scheme is extremely difficult for a clothing designer. The Internet of Things technology can realize technologies such as remote office and information sharing of Hanfu design, which can allow Hanfu designers to have more design references and help. IoT technology can also help Hanfu designers realize remote Hanfu 3D simulation technology. The emergence of IoT technology provides more historical information for Hanfu designers, which allows them to integrate the characteristics of modern clothing and the characteristics of traditional Hanfu. In short, the application of IoT technology in Hanfu design can help them realize the 3D simulation technology of Hanfu, and it can also provide more information for Hanfu designers.


### 3.2. The Introduction of 3D Intelligent Simulation System of Hanfu

The goal of this research is to realize the 3D simulation design of traditional Chinese Hanfu by using the Internet of Things technology and neural network method. The 3D simulation of Hanfu is mainly the design of patterns, colors, historical information, and shapes. This performance is mainly designed for simulation of these four characteristics of Hanfu. The hardware devices of IoT technology used in this study are mainly cameras and Internet technology. Figure1 shows that Hanfu’s 3D simulation design system utilizes IoT technology and CNN method. The Internet of Things technology is mainly to realize the sharing of Hanfu information and the acquisition of Hanfu information. CNN technology is mainly to learn the relationship between Hanfu features and Hanfu design. It can realize the design of traditional Chinese Hanfu by this method, which will save a lot of time for Hanfu designers. The Hanfu 3D simulation design system mainly includes two processes: Internet of Things technology data collection and 3D Hanfu simulation design. For the first process, the Internet of Things technology will use cameras and other hardware sensors to collect the characteristic information of Hanfu, and this process will realize remote collection or information sharing. These data will be transmitted through the Internet terminal of the Internet of Things technology. The sensors of the IoT technology will collect the design features of Hanfu, and these features will be transmitted through the Internet technology. This ensures that Hanfu designers receive these Hanfu feature information. For the second process, the CNN method can learn the relationship between Hanfu features and Hanfu design schemes. Once this system is trained, Hanfu designers can achieve efficient simulation design of Hanfu only by relying on the characteristic requirements of Hanfu design.Figure 1
The design of Hanfu 3D simulation system based on IoT technology.The Internet of Things technology is a relatively mature technology, which can be easily implemented through sensors and the Internet according to the needs of designers. The focus of this study is the second process of Hanfu 3D simulation design, which will utilize the CNN neural network approach. Figure2 shows the operational relationship between CNN and perceptron. Figure 2 shows the structure of the perceptron, which is the basic structure of the neural network approach. CNN is also a kind of neural network method, and its operation will also follow the operation flow of the perceptron. CNN is also a relatively mature algorithm, which has been widely used in people’s production and life [27, 28]. CNN is a special form of the perceptron form, which also uses the form of weight and bias distribution to map the nonlinear relationship between input and output [29]. The difference between CNN and perceptron is that it has a weight sharing mechanism [30]. The dashed part of Figure 2 shows the weight sharing method. The weight sharing mechanism can greatly reduce the computational complexity of parameters, and it can also achieve the task of extracting main features. The number of filters chosen in this study is 128. The learning rate 0.001 is chosen, which is to prevent getting stuck in local minima. The stride of the pooling layer is set to 1.Figure 2
The operational relationship between CNN and perceptron.The process of CNN finding the optimal distribution is done through the loss function, which can reflect the difference between the predicted value and the actual value. In the training phase, it needs to provide some label values to complete the operation of the loss function. Equation (1) introduces the calculation process of the loss function of CNN. In this study, a conventional loss function in the form of mean square error is used in this study.(1)E=12dout−Oreal2=12∑κ=1tdκ−Oκ2.Most CNN will use gradient descent to find optimal weights and biases. Gradient descent methods need to find the partial derivatives of the weights and biases, which will be used to find the minimum. Equations (2) and (3) show the derivation of weights and biases.(2)Δωji=−η∂E∂ωji,(3)Δuij=−η∂E∂∂uij.In the calculation process of CNN, this will involve a large number of derivative operations. If these derivation operations are calculated sequentially, it will not only increase the computational complexity of the computer but it will also occupy a large amount of computer memory. In the actual operation process of CNN, this will use the chain derivation rule, and the propagation process of weights and biases is the application process of the chain derivation rule. Equations (4) and (5) show how the chain rule is applied in CNN.(4)E=12∑k=1mdk−fnetwk2=12∑k=1mdk−f∑j=0nωjkyj2,(5)E=12∑k=1mdk−fnetwk2=12∑k=1mdk−f∑j=0nωjkyj2=12∑k=1mdk−f∑j=0nωjκf∑j=0quijχi2.


### 3.3. The Introduction of Data Evaluation and Data Processing

The pattern, color, historical information, and shape of Hanfu are relatively complex, and it is difficult to directly collect through the Internet of Things technology. Therefore, the data collected through IoT technology need to be evaluated and processed. The evaluation of data needs to use the theory of uncertainty to evaluate, and the place with high uncertainty is the place where the data have larger defects. Once areas of high data uncertainty are detected, researchers need to adjust the data collected by IoT technology. This data evaluation process is carried out after the IoT technology collects the data process. The uncertainty of the data can reflect the data quality of Hanfu, and the place with greater uncertainty is the place with poor data quality. This study evaluates the Hanfu characteristic data collected by the Internet of Things technology through the uncertainty distribution of the data.Before studying the uncertainty of the data, it needs to determine a distribution that the data needs to satisfy. It can be a Gaussian distribution, a normal distribution, or some other form of distribution. Equation (6) shows the computational form of the Gaussian distribution. Equation (7) shows the distribution form of the weights after using the Gaussian distribution.(6)Wi≈Ν0,I,(7)ωq=Wii=1L.It uses the Bernoulli distribution to establish the relationship between weights and probability distributions, as shown in equations (8) and (9). The Gaussian distribution is mainly used for the distribution processing of Hanfu feature data, and the Bernoulli distribution is used for the approximate distribution of the uncertainty integral operation process.(8)Wi=Mi•diagzi,jj=1Ki,(9)zi,j∼Bernoullipi.The determination of uncertainty will take the form of a prior distribution and a posterior distribution, and the posterior distribution will be calculated under the condition of prior knowledge. In the process of uncertainty calculation, the posterior distribution is the key to the calculation. Equation (10) shows how the posterior distribution is calculated.(10)ℓV=∫qωpF|X,ωlogpY|Fdfdω−KLqω∥pω.In the calculation process of the posterior distribution, the integral is relatively difficult to calculate, and the KL divergence is used here to approximate the posterior distribution. Equation (11) shows the calculation rule for KL divergence.(11)KLqωpω=12D∑i=1DpdMd22+b22.Similarly, data on patterns, colors, shapes, and historical information of Hanfu exist in different forms. For example, the value of color is data between 0 and 255, and the historical information will be converted into data value between 0 and 1. This results in a large difference in the relevant feature data of Hanfu. The preprocessing process of the data will uniformly process the data whether it is the numerical size or the characteristic range of the numerical value. This study uses a standardized preprocessing method, which is different from maximization or minimization.


## 3.1. The Significance of IoT to 3D Simulation of Hanfu

The design of traditional Chinese Hanfu is different from the design of modern clothing. It not only needs to meet the aesthetic needs of modern people but also it requires designers to understand relevant historical information. The style, pattern, color, and style of traditional Chinese Hanfu will reflect the development history of the dynasty and the economic and ideological state of the dynasty. It cannot be designed without ignoring the historical information of the clothing. Therefore, a new Hanfu design scheme is extremely difficult for a clothing designer. The Internet of Things technology can realize technologies such as remote office and information sharing of Hanfu design, which can allow Hanfu designers to have more design references and help. IoT technology can also help Hanfu designers realize remote Hanfu 3D simulation technology. The emergence of IoT technology provides more historical information for Hanfu designers, which allows them to integrate the characteristics of modern clothing and the characteristics of traditional Hanfu. In short, the application of IoT technology in Hanfu design can help them realize the 3D simulation technology of Hanfu, and it can also provide more information for Hanfu designers.


## 3.2. The Introduction of 3D Intelligent Simulation System of Hanfu

The goal of this research is to realize the 3D simulation design of traditional Chinese Hanfu by using the Internet of Things technology and neural network method. The 3D simulation of Hanfu is mainly the design of patterns, colors, historical information, and shapes. This performance is mainly designed for simulation of these four characteristics of Hanfu. The hardware devices of IoT technology used in this study are mainly cameras and Internet technology. Figure1 shows that Hanfu’s 3D simulation design system utilizes IoT technology and CNN method. The Internet of Things technology is mainly to realize the sharing of Hanfu information and the acquisition of Hanfu information. CNN technology is mainly to learn the relationship between Hanfu features and Hanfu design. It can realize the design of traditional Chinese Hanfu by this method, which will save a lot of time for Hanfu designers. The Hanfu 3D simulation design system mainly includes two processes: Internet of Things technology data collection and 3D Hanfu simulation design. For the first process, the Internet of Things technology will use cameras and other hardware sensors to collect the characteristic information of Hanfu, and this process will realize remote collection or information sharing. These data will be transmitted through the Internet terminal of the Internet of Things technology. The sensors of the IoT technology will collect the design features of Hanfu, and these features will be transmitted through the Internet technology. This ensures that Hanfu designers receive these Hanfu feature information. For the second process, the CNN method can learn the relationship between Hanfu features and Hanfu design schemes. Once this system is trained, Hanfu designers can achieve efficient simulation design of Hanfu only by relying on the characteristic requirements of Hanfu design.Figure 1
The design of Hanfu 3D simulation system based on IoT technology.The Internet of Things technology is a relatively mature technology, which can be easily implemented through sensors and the Internet according to the needs of designers. The focus of this study is the second process of Hanfu 3D simulation design, which will utilize the CNN neural network approach. Figure2 shows the operational relationship between CNN and perceptron. Figure 2 shows the structure of the perceptron, which is the basic structure of the neural network approach. CNN is also a kind of neural network method, and its operation will also follow the operation flow of the perceptron. CNN is also a relatively mature algorithm, which has been widely used in people’s production and life [27, 28]. CNN is a special form of the perceptron form, which also uses the form of weight and bias distribution to map the nonlinear relationship between input and output [29]. The difference between CNN and perceptron is that it has a weight sharing mechanism [30]. The dashed part of Figure 2 shows the weight sharing method. The weight sharing mechanism can greatly reduce the computational complexity of parameters, and it can also achieve the task of extracting main features. The number of filters chosen in this study is 128. The learning rate 0.001 is chosen, which is to prevent getting stuck in local minima. The stride of the pooling layer is set to 1.Figure 2
The operational relationship between CNN and perceptron.The process of CNN finding the optimal distribution is done through the loss function, which can reflect the difference between the predicted value and the actual value. In the training phase, it needs to provide some label values to complete the operation of the loss function. Equation (1) introduces the calculation process of the loss function of CNN. In this study, a conventional loss function in the form of mean square error is used in this study.(1)E=12dout−Oreal2=12∑κ=1tdκ−Oκ2.Most CNN will use gradient descent to find optimal weights and biases. Gradient descent methods need to find the partial derivatives of the weights and biases, which will be used to find the minimum. Equations (2) and (3) show the derivation of weights and biases.(2)Δωji=−η∂E∂ωji,(3)Δuij=−η∂E∂∂uij.In the calculation process of CNN, this will involve a large number of derivative operations. If these derivation operations are calculated sequentially, it will not only increase the computational complexity of the computer but it will also occupy a large amount of computer memory. In the actual operation process of CNN, this will use the chain derivation rule, and the propagation process of weights and biases is the application process of the chain derivation rule. Equations (4) and (5) show how the chain rule is applied in CNN.(4)E=12∑k=1mdk−fnetwk2=12∑k=1mdk−f∑j=0nωjkyj2,(5)E=12∑k=1mdk−fnetwk2=12∑k=1mdk−f∑j=0nωjkyj2=12∑k=1mdk−f∑j=0nωjκf∑j=0quijχi2.


## 3.3. The Introduction of Data Evaluation and Data Processing

The pattern, color, historical information, and shape of Hanfu are relatively complex, and it is difficult to directly collect through the Internet of Things technology. Therefore, the data collected through IoT technology need to be evaluated and processed. The evaluation of data needs to use the theory of uncertainty to evaluate, and the place with high uncertainty is the place where the data have larger defects. Once areas of high data uncertainty are detected, researchers need to adjust the data collected by IoT technology. This data evaluation process is carried out after the IoT technology collects the data process. The uncertainty of the data can reflect the data quality of Hanfu, and the place with greater uncertainty is the place with poor data quality. This study evaluates the Hanfu characteristic data collected by the Internet of Things technology through the uncertainty distribution of the data.Before studying the uncertainty of the data, it needs to determine a distribution that the data needs to satisfy. It can be a Gaussian distribution, a normal distribution, or some other form of distribution. Equation (6) shows the computational form of the Gaussian distribution. Equation (7) shows the distribution form of the weights after using the Gaussian distribution.(6)Wi≈Ν0,I,(7)ωq=Wii=1L.It uses the Bernoulli distribution to establish the relationship between weights and probability distributions, as shown in equations (8) and (9). The Gaussian distribution is mainly used for the distribution processing of Hanfu feature data, and the Bernoulli distribution is used for the approximate distribution of the uncertainty integral operation process.(8)Wi=Mi•diagzi,jj=1Ki,(9)zi,j∼Bernoullipi.The determination of uncertainty will take the form of a prior distribution and a posterior distribution, and the posterior distribution will be calculated under the condition of prior knowledge. In the process of uncertainty calculation, the posterior distribution is the key to the calculation. Equation (10) shows how the posterior distribution is calculated.(10)ℓV=∫qωpF|X,ωlogpY|Fdfdω−KLqω∥pω.In the calculation process of the posterior distribution, the integral is relatively difficult to calculate, and the KL divergence is used here to approximate the posterior distribution. Equation (11) shows the calculation rule for KL divergence.(11)KLqωpω=12D∑i=1DpdMd22+b22.Similarly, data on patterns, colors, shapes, and historical information of Hanfu exist in different forms. For example, the value of color is data between 0 and 255, and the historical information will be converted into data value between 0 and 1. This results in a large difference in the relevant feature data of Hanfu. The preprocessing process of the data will uniformly process the data whether it is the numerical size or the characteristic range of the numerical value. This study uses a standardized preprocessing method, which is different from maximization or minimization.


## 4. Result Analysis and Discussion

This research will use the Internet of Things technology and CNN method to realize the 3D intelligent simulation design of Chinese traditional Hanfu. The Internet of Things technology will only improve the relevant sensors to collect the characteristic data of Hanfu. The CNN algorithm will predict the characteristic data of these Hanfu, which will provide more reference information for Hanfu designers to realize the 3D intelligent simulation design of traditional Chinese Hanfu. This study selects the Hanfu characteristic data of the four dynasties of Tang, Song, Yuan, and Ming to conduct related research. Figure3 shows the predicted value of the historical information feature data of Hanfu. In Figure 3, the red line represents the predicted value of the Hanfu historical information feature data, and the black line represents the actual value of the Hanfu historical information. Overall, the CNN method can effectively predict the historical information features of Hanfu. Although the historical information of traditional Chinese Hanfu has relatively large fluctuations for different dynasties, CNN predicts the relevant historical information of Hanfu well. Due to the great differences in the economy and ideology of different dynasties, the characteristics of Hanfu will change greatly. This change can be seen in Figure 3. Although the eigenvalues of the historical information of Hanfu have large fluctuations, and it has many peaks and valleys. However, the predicted value of CNN is still in good agreement with the actual historical information feature value of Hanfu. This accurate mapping relationship will help Hanfu designers to fully grasp the historical information of Hanfu.Figure 3
The predicted value of historical information features of Hanfu.The pattern feature of Hanfu is also feature that designers pay more attention to it. Pattern information can not only reflect the historical and cultural information conveyed by Hanfu but also it can improve people’s preference for Hanfu. Therefore, the pattern feature of Hanfu is also a focus that must be paid attention to in the process of Hanfu 3D simulation design. Figure4 shows the prediction error distribution of Hanfu pattern features. In general, CNN can better predict the pattern feature values of Hanfu, and most of the error values are distributed within 2%. For the 3D simulation design of Hanfu, this error range is enough to convince the designer. It can also be seen from Figure 4 that the prediction error distribution of Hanfu pattern features is relatively uniform, and there is relatively no large fluctuation. This shows that the CNN algorithm has good stability in the prediction of Hanfu pattern features, which is also a reference for Hanfu 3D simulation designers. It mainly has three error fluctuation intervals, and the larger error interval is also within 3%. In general, the CNN method has better stability and accuracy in predicting Hanfu pattern features.Figure 4
The prediction error distribution of Hanfu pattern features.Color characteristics are also an important feature of Hanfu design. Color characteristics can distinguish the gender of the wearer of Hanfu and the occasion of use of the Hanfu. At the same time, color characteristics can also reflect the preference of Hanfu wearers. The color of Hanfu can also show different forms of cultural elements to a certain extent. In short, the prediction of color features is a key link for the 3D simulation design of Hanfu. Figure5 shows the prediction error distribution of Hanfu color features. In general, the CNN method can also predict the color characteristics of Hanfu well, and it also has a large color error. Compared with the prediction error of the pattern feature of Hanfu, the error range of the color feature value of Hanfu is smaller. Although it has more error intervals than the pattern prediction error of Hanfu, the values of these intervals are relatively small. This can further illustrate that CNN has better accuracy and credibility in predicting the color features of Hanfu. The distribution of the color prediction error of Hanfu is also relatively uniform, and there is no obvious fluctuation. This can further illustrate the reliability of IoT technology in collecting Hanfu characteristic data.Figure 5
The prediction error distribution of Hanfu color features.The shape characteristics of Hanfu can show the historical and cultural characteristics of different dynasties and the changes in thinking. If the designers of Hanfu do not have a good understanding of the relationship between the shape and characteristics of Hanfu and the dynasties or historical culture, it will easily lead to misinterpretation of historical culture. Therefore, the shape features of Hanfu are also a key link in the 3D simulation design process of Hanfu. Figure6 shows the prediction error distribution of the shape features of Hanfu. Overall, the CNN method can predict the shape eigenvalues of the Hanfu of the four dynasties well. Most of the prediction errors are mainly concentrated within 2%, and only a small part of the error exceeds 3%. This part of the larger error occupies a small proportion, which may be because the shape characteristics of this part of Hanfu are relatively rare, this can improve accuracy by collecting more shape features through IoT technology. Figure 7 shows the feasibility distribution of the prediction error of Hanfu shape features. It can be clearly seen from Figure 7 that all the predicted values are within the 95% confidence interval, which shows that CNN has good feasibility in predicting the shape features of Hanfu, which is conducive to improving the prediction results of Hanfu designers. Reliability: this can also further illustrate the accuracy of the Hanfu shape eigenvalues collected by IoT technology.Figure 6
The prediction error distribution of Hanfu shape features.Figure 7
The reliability distribution of predicted value of Hanfu shape feature.The average error value can better reflect the overall performance of CNN and IoT technology in Hanfu 3D simulation design. Figure8 shows the distribution of the average prediction error of the eigenvalues of Hanfu. Overall, all four mean error values are satisfactory. All prediction errors are within 3%, and the largest prediction error is only 2.84%. This part of the error may be derived from the prediction of historical information of Hanfu, and the reason for the larger error may be that there are certain differences in the collected historical information. This can improve the accuracy of Hanfu historical information data by artificially assisting IoT technology. The smallest average prediction error is only 1.58%, and this part of the error comes from the prediction of the eigenvalues of the Hanfu shape. Figure 8 can further illustrate that the CNN method and the Internet of Things technology have better reliability in predicting the shape features of Hanfu. However, the method proposed in this study has good accuracy for the four eigenvalues of Hanfu.Figure 8
The average prediction error of four features of Hanfu.


## 5. Conclusions

Chinese traditional Hanfu is a symbol of Chinese history and culture. In recent years, Hanfu has begun to gradually revive. The accuracy of the 3D simulation design of Hanfu is a key point for Hanfu designers. However, Hanfu of different dynasties has big differences in pattern, color, shape, and historical information characteristics. This requires Hanfu designers to master more patterns and historical information characteristics, so as to accurately grasp the relationship between traditional Hanfu characteristics and design schemes.This study combines the Internet of Things technology and CNN technology to study the accuracy and feasibility of the 3D simulation design of Hanfu. For the historical information characteristics of Hanfu, CNN, and Internet of Things technology can well capture the changes of historical information of Hanfu in different dynasties. For the pattern features and color features of Hanfu, CNN, and IoT technology can make good predictions. The prediction error distribution of this feature is relatively uniform, and most of the errors are within 3%. This can further illustrate the accuracy and reliability of the Hanfu eigenvalues collected by IoT technology. When CNN and IoT technology predict the shape features of Hanfu, all feature values are distributed within the 95% confidence interval. For the average forecast error, the largest forecast error is only 2.84%. Overall, the CNN method and IoT technology have good reliability and accuracy in the 3D simulation design of Hanfu.



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*Source: 6977485-2022-05-30.xml*