3results about How to "Method is feasible" patented technology

The invention relates to a detection method of a medicine for dispelling wind and eliminating dampness. The detection method comprises the following steps: thin-layer identification of a notopterygium root medicinal material, thin-layer identification of a radix angelicae pubescentis medicinal material, thin-layer identification of a radix saposhnikoviae medicinal material, thin-layer identification of ligusticum and ligusticum wallichii medicinal materials, and thin-layer identification of a honey-fried licorice root medicinal material. Through methodological investigation, the detection method has the effects of strong specificity, good durability, clear spots after thin layer expansion, satisfactory separation degree and the like, can effectively control the product quality condition, improves the product quality detection standard, and ensures the product quality.

The application belongs to the field of engineering plastics, and particularly relates to long-chain polyamide resin with easy processing and high tensile property, and a preparation method and application thereof. The long-chain polyamide resin comprises the following raw material components: component a: aliphatic carboxylic acid compound component; component b: polyamine compound component; and component c: a compound with a structure shown in the following formula (1), wherein n and m can be any number between 1 and 10. The long-chain polyamide resin of the application introduces structural diacid with an asymmetric benzene ring structure, utilizes the asymmetric structure to reduce the crystallinity of the material, and introduces the benzene ring structure to improve the mechanical property of the polyamide material, so that the low processing temperature is realized from the polyamide material component itself, the energy consumption is reduced, and the mechanical property of the polyamide material is ensured. The problem that the processing temperature and mechanical property of the polyamide material and the processing energy consumption are difficult to be considered in the prior art is solved.

The invention discloses a three-dimensional scene multi-granularity semantic segmentation method based on a graph neural network model, and belongs to the technical field of computer vision image processing. Taking a three-dimensional model corresponding to the three-dimensional scene as input, and selecting different visual angles to render the three-dimensional model to obtain a plurality of two-dimensional images; segmenting the two-dimensional image obtained by rendering to obtain a two-dimensional segmentation tree containing all semantic components with different granularities under different visual angles; and constructing a three-dimensional scene multi-granularity semantic segmentation map neural network model based on the two-dimensional segmentation trees under different visual angles, and performing model optimization, thereby obtaining a three-dimensional segmentation tree containing all semantic components with different granularities. By the adoption of the method, a three-dimensional data set is not needed, the three-dimensional segmentation mask is directly obtained through an existing two-dimensional image segmentation model, and the method is efficient, feasible, low in implementation cost and high in performance.