4 results about "Food purchasing" patented technology

The invention belongs to the technical field of food additive detection, and particularly relates to a food additive detection method based on artificial intelligence, which comprises the following steps: S1, shooting a package ingredient table to obtain a package ingredient table photo; s2, transmitting the shot photo to a cloud for AI identification, and extracting additive names and dosage data on the photo; s3, establishing a food additive early warning database according to WHO, EFSA and GB2760 international and domestic authoritative databases; s4, the extracted additive data is compared with data in a food additive early warning database, and related information output and early warning are carried out. People can rapidly and conveniently know the risk levels, effects and potential influences on health of various additives in the food purchasing process; therefore, people are helped to purchase and select food, and body health and safety are guaranteed.

We provide the system. [Solution] A device for inputting user preference information and recording said preference information, A device for selecting suitable food based on the aforementioned preference information, In order to collect supplementary information regarding the selected food, a device that works in conjunction with other devices is used. A device that presents the selected food and supplementary information to the user, A device for completing the procedure for purchasing the aforementioned food, A device that reflects the user's past history and presents food and beverage combinations, A system that includes this.

This invention discloses a consumer behavior-driven carbon credit conversion method and application system. By collecting purchase data of near-expiry food, the system calculates emission reductions by category carbon reduction coefficient multiplied by measurement, and maps these reductions to carbon credits in real time (1kg CO₂e = 10 credits, error ≤5%), and connects to a third-party carbon trading platform. This solves the problems of coarse calculations and poor real-time performance in existing methods, and can accurately incentivize users to engage in green consumption. It is suitable for carbon credit scenarios on community O2O platforms.

Nutrient prediction systems and methods can include systems and methods of training and testing machine-learning models and food purchase data collection and analysis. For example, a food purchase data collection and analysis system, under the control of at least one processor, can include a purchase data collection device to collect food purchase data at a point of sale of a food basket including information regarding food items purchased and location of the food purchase, an analysis server including a machine-learning model predicting nutrient availability based on the food purchase data, and a network for transmitting the food purchase data from the purchase data collection device to the analysis server for analysis using the machine-learning model.