6 results about "Food category" patented technology

The application discloses a kind of food additive knowledge generation method and system based on multi-modal, it is related to food additive knowledge generation technical field, by regulation rule slot disassembly module and product scene slot construction module, regulation side and scene side are respectively transcribed into slot chain, so that regulation constraint object, food category, use condition, label requirement and other information and ingredient table, label identification, process specification, detection specification and question and answer target form the structure basis that can be aligned item by item, avoid only stay in the level of article original display or keyword hit.Multiple modal semantic module sorts out regulation text, ingredient table text, label image identification text, detection item specification, process specification text and question and answer request, so that subsequent processing is carried out under the same semantic fragment system, reduce the alignment fracture caused by the difference of expression from different sources, so that scene input has sustainable acceptance ability.

The application discloses a food nutrition calculation method and system based on image recognition, and the food nutrition calculation method comprises the following steps: collecting a target image by using a mobile terminal, wherein the target image comprises a display screen area of an electronic scale and a scale disc area of the electronic scale; performing image recognition on the target image, and extracting food weight information and food category information; calculating food nutrition component information based on the food weight information, the food category information and a preset nutrition database; and displaying the food weight information, the food category information and the food nutrition component information by using the mobile terminal. The application realizes intelligent calculation of nutrition components by using an ordinary electronic scale and a mobile terminal, reduces the hardware transformation cost and use threshold, and improves the user operation convenience and equipment compatibility.

The application discloses a kind of nutrition with quantity recipe and nutrition analysis method and system of campus student meal. The method comprises: establishing "recipe-set meal-dish-raw material" four-level recipe data model and "raw material-commodity" mapping model, and built-in 《WS / T554-2017 student meal nutrition standard》And food nutrition database;User is based on week view Configuration daily meal set meal and dish, and set estimated number of diners;System is based on dish raw material consumption, real-time cascade calculation dish, set meal and corresponding meal nutrient content, and automatically check whether it conforms to the nutrition standard of the age and gender of the meal student. At the same time, according to the number of diners, the total consumption of each raw material required is automatically summarized and calculated, and is converted into estimated purchase quantity in units of grams, jin, kilograms;Finally, daily / weekly nutrition analysis report covering five dimensions of food category intake, nutrient intake, three meals energy proportion, energy source distribution and protein source distribution is automatically generated. The application realizes the precision, dynamic and automation of student meal nutrition catering, significantly improves the scientificity of recipe compilation and the accuracy of procurement plan.

This invention relates to the field of food technology, and more particularly to a design method and system for food flavorings and fragrances. The method acquires a target flavor description, food category, base system parameters, processing parameters, and design boundary conditions to construct a target condition vector; it then invokes a regulatory knowledge graph and extracts a constraint subgraph; the target condition vector and constraint subgraph are input into a generative model, implementing dynamic constraints during candidate component generation, combination expansion, and content decoding to obtain candidate formulations; finally, sensory matching, compliance margin, processing risk, and stability evaluations are performed on the candidate formulations, and the knowledge graph and generative model are updated based on small-sample validation results to output the final safe formulation. This invention improves design efficiency, compliance, safety, and scenario adaptability.

The application provides a hot air circulation electric heating cooking method, a system, a cooking appliance and a medium. The method comprises the following steps: controlling a cooking cavity to be heated to a first cooking cavity temperature, and performing primary heating on food according to the first cooking cavity temperature; obtaining a preset second cooking cavity temperature, recording a first cooking time consumed by the cooking cavity from the first cooking cavity temperature to the second cooking cavity temperature; determining a second cooking time for crisp coloring of the food according to the food category, determining a third cooking time according to the first cooking time, the second cooking time and a specific heat coefficient of the food; and heating the food in secondary heating to a target state at the second cooking cavity temperature according to the third cooking time. The scheme realizes automatic cooking without adding new sensors, effectively reduces the cost of intelligent cooking, and can be widely applied to the technical field of automatic cooking.

This invention discloses a joint decision-making method for food preparation output and replenishment frequency based on reinforcement learning, relating to the field of artificial intelligence technology. Within each decision cycle, the following steps are executed sequentially for each food category in the canteen according to a preset priority order: Step 1, read the operational indicators and global remaining resource indicators of the current food category from the canteen monitoring system, determine the current dominant phase based on the phase confidence distribution, and generate a phase-aware state token; Step 2, trim candidate joint decision actions to the physically feasible domain limited by the global remaining resources; Step 3, extract demand scenario samples from the demand scenario library corresponding to each implicit operational phase based on the phase confidence distribution, perform forward simulation, and issue the final joint decision action to the corresponding stall for execution. This invention effectively solves key problems in existing technologies such as rigid phase division, high online trial-and-error costs, neglect of demand randomness in constraint processing, and resource conflicts among multiple food categories.