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Duyun Maojian tea is a famous tea in China. In this study, the specific-locus amplified fragment (SLAF) sequencing method was used to analyze the population structure and conduct a genome-wide association study (GWAS) of 2 leaf traits of 123 tea plants in Qiannan, China. A total of 462,019 SLAF tags and 11,362,041 single-nucleotide polymorphism (SNP) loci were obtained. The results of phylogenetic tree analysis, cluster analysis, and principal component analysis showed that 123 tea germplasms were clustered into three groups, and the heterozygosity rates for Groups I, II, and II were 0.206, 0.224, and 0.34, respectively. Generally, tea germplasms in a production area are clustered in a group, indicating that tea germplasms in different production areas have certain genetic diversity. The traditional Duyun Maojian tea core production areas, TS and DC-SJ, are clustered into Group I and Group II respectively, while the ZY production area is relatively independent in Group III. Furthermore, based on GWAS analysis, 11 candidate genes related to leaf apex and 7 candidate genes related to leaf shape were obtained. This study clarified the genetic relationship among eight Duyun Maojian tea production areas and obtained candidate genes related to leaf apex and leaf shape development. The results showed that population structure and candidate genes are an effective basis for the breeding of Duyun Maojian tea germplasm.

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The development of intelligent industrialization has gradually increased the demand for industrial robots, and it has also promoted the emergence and development of industrial robot protective clothing. At this stage, the design methods of sewing patterns are limited to planes and lack simulation stitching and try-on in three-dimensional space. In this study, the virtual try-on of protective clothing for industrial robots was realized in the CLO3D environment, and the fabric was simulated using tools in the software. First, import the digital model of the industrial robot into CLO3D, and copy the AUTO CAD template in CLO3D. Then, input the results of the fabric test to simulate the fabric. Use the software’s virtual stitching and virtual wearing to observe the wearing effect of protective clothing and the data-based fabric pressure, directly modify the model and observe the modification effect. After research, the three-dimensional simulated fitting can clearly show the characteristics of looseness or tightness and whether it is convenient to put on and take off. The model can be adjusted appropriately in time, which can reduce costs and improve production efficiency. It is an important exploration for the development of industrial robot protective clothing.