2results about How to "Regular" patented technology

The utility model relates to a kind of coupler assemblies for food processor, including the male end coupler with 7 or 8 pins, female end coupler is equipped with the pinhole matched with pin, first pin and second pin are close to the two long sides of coupler assembly setting, third pin, fourth pin, fifth pin and sixth pin are respectively located on first circle and second circle, seventh pin is located in the area enclosed by first pin, second pin, third pin and fourth pin.The technical scheme of the utility model first pin, second pin, third pin, fourth pin, fifth pin and sixth pin meet the minimum creepage distance between two adjacent pins specified in new national standard, ensuring electrical safety;The distance between fifth pin and fourth pin or third pin and sixth pin is relatively long, seventh pin is arranged in the blank area between the two pairs of pins, using the blank area between the two far apart pins, the pin arrangement is compact enough, and the coupler assembly has small outer dimension.

This application provides a method, device, and electronic device for managing advertising campaign costs based on a dual-track online and offline classification system. By integrating a dynamic knowledge graph with a prompting learning model that efficiently fine-tunes parameters, it achieves high-precision automatic classification of advertising campaign cost categories. The method acquires the user-inputted cost description text and specified category, utilizes the dynamic knowledge graph for key entity extraction and multi-hop reasoning, and generates a first classification result and confidence score. Simultaneously, the text is input into the prompting learning model, which guides fill-in-the-blank prediction through soft prompts, outputting a second classification result and confidence score. Combining the confidence scores of both, a decision fusion rule is used to determine whether to trigger user correction prompts, and the knowledge graph and model parameters are dynamically updated based on user feedback. The system supports new entity recognition and incremental learning, forming a closed-loop optimization mechanism. This solution effectively solves the problems of inaccurate budget allocation, low execution efficiency, and difficulty in cost control caused by the fragmentation of data from online and offline channels, improving the accuracy, interpretability, and adaptability of cost classification, and achieving intelligent and refined advertising campaign cost management.