Abstract: Embodiments of this application provide an image generation system and method. In an exemplary manufacturing industry scenario, a style requirement of a product category in a manufacturing industry is automatically captured according to user behavior data and product description information associated with the product category. Based on these data, a style description text may be generated and converted to product images by using a text prediction-based image generation model. The product images are further screened by using an image-text matching model, to obtain a product image with high quality. This process covers from style description text mining to text-to-image prediction to image quality evaluation. It provides an automation product image generation capability for the manufacturing industry, shorten a cycle of designing and producing the product image in the manufacturing industry, and improve production efficiency of the product image.

Abstract: Embodiments of this application provide an image generation system and method. In an exemplary manufacturing industry scenario, a style requirement of a product category in a manufacturing industry is automatically captured according to user behavior data and product description information associated with the product category. Based on these data, a style description text may be generated and converted to product images by using a text prediction-based image generation model. The product images are further screened by using an image-text matching model, to obtain a product image with high quality. This process covers from style description text mining to text-to-image prediction to image quality evaluation. It provides an automation product image generation capability for the manufacturing industry, shorten a cycle of designing and producing the product image in the manufacturing industry, and improve production efficiency of the product image.

Abstract: A method for determining road operation condition includes: acquiring an image of a road to be monitored; acquiring the number of vehicles on the road to be monitored, and dividing the road to be monitored into an upstream road and a downstream road based on the image and the number of vehicles on the road to be monitored; acquiring the number of vehicles on the upstream road and the number of vehicles on the downstream road, and determining a target domain value based on the number of vehicles on the road to be monitored, the number of vehicles on the upstream road, the number of vehicles on the downstream road, and a traffic wave of the road to be monitored; and determining that congestion occurs on the road to be monitored if the number of vehicles on the road to be monitored is greater than the target domain value.