【摘要】 为明确服装热阻研究的数据分析思路并探求更优的可视化方法，从实验设计、热阻测评、数据处理和数据分析等4个阶段对相关文献进行梳理，明确了常用的数据分析和可视化手段。研究表明服装热阻受环境、服装、人体等多方面因素影响，主要采用暖体假人或真人实验的方法获得。数据较多以二维图像或组图的形式呈现，适用3项以内影响因素结果的分析。采用人工智能方法能够纳入更多影响因素实现服装热阻的准确预测，为不同环境下服装的选择、性能优化及人体热舒适的提升提供科学指导。更多还原

【Abstract】 To clarify the ideas for clothing thermal resistance research and explore better visualization methods, relevant literature was reviewed from the four stages of experimental design, thermal resistance evaluation, data processing, and data analysis. The commonly used data analysis and visualization methods were identified. The research indicates that the thermal resistance of clothing is influenced by various factors such as environment, clothing, and the human body. It is primarily measured using thermal manikin or wear trails. The data is typically presented in the form of two-dimensional images or groups of figures, and is suitable for presenting results with less than 3 influencing factors. To accurately predict clothing thermal resistance, artificial intelligence methods can be employed to consider more influencing factors. This can provide scientific guidance for clothing selection, performance optimization, and improvement of human thermal comfort in different environments.更多还原