39 results about "Food category" patented technology

The invention provides a food classification component identification method and system based on multi-scale feature attention synergy, and the method comprises the steps: constructing a multi-task neural network model, sequentially inputting a food image into a global feature module and a progressive local feature module, and respectively extracting global semantic information and refined local features of different scales; feature interaction between food category identification and component analysis tasks is promoted through a cross attention cooperation module, and key areas and important component information in images are dynamically selected; gradually activating different depth levels of the network by adopting a staged end-to-end training strategy, and introducing a KL divergence constraint to enhance the difference between feature scales; and simultaneously generating prediction results of food categories and component labels through a multi-task classifier. The technical problems of food identification and component analysis task splitting, insufficient local feature expression and insufficient cross-task information complementation are solved.

The invention discloses a diet metering and recording system for gestational diabetes mellitus patients. The diet metering and recording system comprises a food containing structure; a food weighing structure; a food shooting structure; an information storage module; an analysis and control module; and a display structure. The information storage module is used for storing corresponding relations between food pictures and food categories; the signal receiving and transmitting module receives food category information and corresponding weight information of each meal. The analysis and control module is used for acquiring the pictures of the food in each food containing structure and the weight information of the weighed food one by one, and comparing the pictures and the weight information to obtain the names and the categories of the food; finally, comparing the obtained real food category and food weight information with the received food category and corresponding weight information, and outputting single ending information when the real food category and food weight information are matched with the received food category and corresponding weight information; when all matching is carried out, all ending information is output; the display structure receives the food category information and the corresponding weight information of each meal; and a timing and blood glucose monitoring module is arranged to obtain a 2h blood glucose value. The whole process is high in automation degree, and few events need to be memorized.

According to the daily diet nutrition analysis method based on the wireless weighing record, the intelligent nutrition scale can collect the food weight and conduct peeling calculation, and the net diet weight is accurately obtained. Through peeling calculation, weight interference of non-food parts such as containers can be eliminated, it is ensured that data uploaded to a cloud analysis background is the real food weight, and a foundation is laid for follow-up accurate nutrition analysis. The cloud analysis background can record the data in time and bind the data with the user ID, so that the diet data of each user can be independently stored and managed, and subsequent personalized nutrition analysis and health management for the user are facilitated. After the cloud analysis background records the data, a nutrition analysis notice or a food type selection input notice is sent to the user terminal in time. The timely notification mechanism can improve the participation degree and the response speed of the user, and ensures that the whole nutrition analysis process is efficiently carried out. The user can quickly log in the cloud analysis background to perform subsequent operation according to the notification, and waiting time is shortened.

The invention relates to a freezing control method and device, freezing equipment and a storage medium. The method comprises the following steps: determining adaptive target freezing parameters based on food characteristics of different food types in the freezing equipment and detection parameters in the freezing equipment, and controlling an ultrasonic generator in the freezing equipment according to target output power and target operation frequency so as to dynamically adjust the generation condition of ice crystals in combination with the food characteristics, the instant freezing temperature requirements of different food characteristics are met, and therefore the problem that the instant freezing effect is poor due to the fact that different instant freezing modes cannot be adopted in combination with the food characteristics in an existing instant freezing technology is solved.

The application provides a food image segmentation method based on a discrete wavelet attention network, and first, in view of the problems of multiple food categories, irregular distribution, inconsistent size and fuzzy boundary information in the food image, a Food2K-ResNet trained on Food2K and obtained through transfer learning is used to extract food image features, a discrete wavelet attention network and a residual channel attention network are combined to form a double-branch network, spatial domain and frequency domain feature processing is performed, spatial features and channel features are obtained from the spatial dimension and the channel dimension respectively, and the feature dimension of the food image is highlighted and enriched; finally, a segmentation head network is constructed, different scale features processed through the two branches are subjected to convolution, feature fusion and up-sampling operations, so that the size of the finally obtained features is consistent with the size of the input image, and a high-quality and highly detailed segmentation result is obtained.

The embodiment of the invention relates to a method, a device, equipment and a computer program product for determining freshness. The method includes acquiring input data associated with a target food. The method further includes determining a food category based on the input data. In addition, the method further comprises the steps that based on the food category and the input data, the freshness is determined through a freshness detection model, and a feature extraction module in the freshness detection model extracts food features based on the food category and the input data; a freshness determination module in the freshness detection model is configured to determine freshness based on the food category and the food characteristics. Therefore, according to the embodiment of the invention, the food category is firstly determined, and then the food category is used as the input feature to extract the food feature and determine the food freshness, so that the number of output categories of the freshness detection model can be greatly reduced, the training effect of the model is improved, and the accuracy of freshness detection is improved.

Embodiments of the present disclosure relate to a method and an apparatus for determining freshness, a device, and a computer program product. The method includes obtaining input data associated with a target food. The method further includes determining a food category based on the input data. In addition, the method further includes determining freshness through a freshness detection model based on the food category and the input data. A feature extraction module in the freshness detection model extracts a food feature based on the food category and the input data. A freshness determining module in the freshness detection model is configured to determine the freshness based on the food category and the food feature. Therefore, according to the embodiments of the present disclosure, the food category is first determined, and then the food category is used as an input feature to extract the food feature and determine the food freshness. In this way, a quantity of output categories of the freshness detection model can be greatly reduced, thereby improving a training effect of the model and improving accuracy of freshness detection.

The invention provides a refrigeration storage fresh-keeping method and system and a refrigeration storage. The method comprises the steps that the VOCs concentration in a current storage cabin in the refrigeration storage is monitored in real time; measuring the moisture migration rate of the surface of the food in the current storage cabin in real time to obtain a corresponding measurement value; identifying the type of food stored in the current storage cabin; determining the odor intensity change rate at the current moment according to the VOCs concentration; according to the odor intensity change rate and the food category, obtaining a theoretical value of the surface moisture migration rate of the food in the current storage cabin; and according to the ratio of the measured value to the theoretical value of the food surface moisture migration rate at the current moment, making a corresponding fresh-keeping regulation and control strategy in combination with the odor intensity change rate. The system uses the method, and the refrigeration appliance comprises the system. The food fresh-keeping dynamic control method can accurately distinguish the corruption dynamic state, prolong the food fresh-keeping period, reduce the false triggering rate and solve the problem of food fresh-keeping dynamic control.

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 provides a dietary management method and system based on inflammatory dietary assessment and anti-inflammatory dietary plan pushing, which comprises the following steps: step 1, obtaining the intake amount of food of a surveyee each time; step 2, combining food categories and intake frequency, calculating the average daily intake amount of various foods of the surveyee; step 3, based on the dietary intake data of the surveyee, adopting principal component analysis for statistical analysis, obtaining a personalized food combination which can best represent the favorite of the surveyee; step 4, according to the full literature retrieval result, creating an inflammation score CDII of Chinese food, which is used to measure the dietary inflammation degree of Chinese people; and step 5, combining the inflammation effect score in the CDII and the personalized food combination, providing food replacement suggestions which can meet the dietary favorite of the surveyee and reduce dietary inflammation to the surveyee. The application can more easily reduce the chronic inflammation of the body on the basis of not changing the dietary habits, and from the inflammatory mechanism, the most fundamental and effective result of reducing the risk of chronic diseases is achieved.

A method of processing an image, the method comprising: receiving image data from a subject at a remote location; and analyzing the image data to identify therein content related to the food category. A set of subject descriptor features specific to the subject is obtained, and a trained machine learning program is applied to the identified food category and the set of features to provide a score describing a response of the subject to the food category. The method generates processed image data including a score superimposed on at least a portion of the received image data, and transmits the image data to a display device viewable by the subject at the remote location.

The invention provides a food detection method and system based on computer vision, and the method comprises the steps: carrying out the adaptive noise removal and image segmentation of a food image, obtaining a standard food image, and extracting the food morphological characteristics of the standard food image; performing block feature extraction and global feature fusion on the standard food image to obtain food global features, and performing category analysis on the standard food image to obtain food category information; performing multi-scale feature sampling and dynamic defect scoring on the standard food image according to the food type information to obtain food defect information; performing aging drift detection and freshness analysis on the standard food image to obtain food freshness information; identifying food label information from the standard food image, and performing multi-source data fusion on the food type information, the food defect information, the food freshness information and the food label information to obtain a food detection report.

The application provides a cold storage preservation method, a system and a cold storage. The method comprises the following steps: monitoring the VOCs concentration in a current storage cabin of the cold storage in real time; measuring the moisture migration rate of food surface in the current storage cabin in real time to obtain a corresponding measurement value; identifying the food category stored in the current storage cabin; determining the odor intensity change rate at the current time according to the VOCs concentration; obtaining the theoretical value of the moisture migration rate of food surface in the current storage cabin according to the odor intensity change rate and the food category; and formulating a corresponding preservation control strategy according to the ratio of the measurement value and the theoretical value of the moisture migration rate of food surface at the current time, and combining the odor intensity change rate. The system uses the method, and the refrigeration appliance comprises the system. The application can accurately distinguish the corruption dynamics, improve the food preservation period, reduce the false triggering rate, and solve the problem of food preservation dynamic control.

The invention discloses a food inspection information sharing method and system based on a block chain, and particularly relates to the technical field of food safety information management.The method comprises the steps that after food inspection data is generated, a priority index is calculated based on a risk level dimension, a time urgency dimension and a cross-domain sharing dimension; and a priority label is generated by adopting a dual-threshold mechanism of'static baseline + dynamic correction ', and a grading result is written into an on-chain index. And the transaction aggregator performs ordered aggregation and de-duplication on the records in the same sliding window according to the priority labels, and sorts the records in a descending order according to the priority indexes. The alliance chain network is divided into a plurality of logic channels according to food categories, different channels are provided with differentiated block output parameters and are sequenced and packaged in the channels according to priority indexes, and high-priority transactions enter a priority queue. A main channel maintains a global batch index and a cross-channel mapping table, and efficient, safe and traceable sharing of cross-mechanism and cross-region food inspection data is realized.

The invention discloses a food processing and quality control system based on artificial intelligence, and the system comprises a processing technology configuration center which is used for calling a corresponding standardized technological process template according to food category information, and generating a target technological process structural body; the loss prediction engine is used for outputting a loss rate prediction result set by adopting the improved quantile regression forest model; the quick detection system docking module is used for quickly detecting all batches of food to be delivered, generating a quick detection result, and comparing the quick detection result with a food safety standard library threshold value to obtain item-by-item qualification judgment; the ex-warehouse interception controller is used for performing joint verification on the loss rate prediction result set and the fast detection result, generating an ex-warehouse permission result or an ex-warehouse interception result, and generating an alarm log; and the quality log recorder is used for recording quality log data. According to the invention, an improved quantile regression forest model is adopted, and intelligent quality control of food processing is realized.

A method of operating a food categorization engine includes extracting features from each text description of a training food data set to generate a feature set. The feature set is analyzed to determine sets of food categorization features that correlate to one or more food categories, wherein the sets of food categorization features are defined by a plurality of category vectors. Individual words and / or characters of a text description of a food item received from a user are then analyzed in order to generate a numerical vector representative thereof. The numerical vector is multiplied by a matrix defined by the plurality of category vectors. One or more food categories with which to associate the received food item are automatically selected based at least in part on the multiplication, and the data record associated with the food item is amended to include the selected food categories.

The invention provides a cooking control method and device, cooking equipment and a controller applied to the cooking equipment, and relates to the technical field of kitchen appliances. The method comprises the following steps: acquiring food information of cooking food; wherein the food information comprises a food category, a food weight and a food volume; determining a dry steaming scheme based on the food information and a preset adjustment coefficient; executing the dry steaming scheme until a preset cooking completion condition is met; by obtaining the type, weight and volume of the food, making the dry steaming scheme according to the preset rule and applying the dry steaming scheme to the existing household all-in-one machine, the dry steaming function of the existing all-in-one machine is achieved, and the cooking experience of a user is improved.

The application relates to the food technology field and provides a food comprehensive risk grade influencing factor analysis method and device. The method comprises the following steps: obtaining a first comprehensive risk grade of a single food according to a food inspection result in first to-be-processed data; resampling food data with a few comprehensive risk grades to obtain second to-be-processed data; performing feature construction and one-hot encoding according to the second to-be-processed data to obtain third to-be-processed data; performing influencing factor classification on the third to-be-processed data to obtain a to-be-processed data set; constructing an integrated learning model; inputting the to-be-processed data set into the integrated learning model to obtain a second comprehensive risk grade of a single food category; and determining the influence degree of an influencing factor on the second comprehensive risk grade according to the relationship between the second comprehensive risk grade and the corresponding influencing factor. The method provided by the application can more accurately and comprehensively obtain the influence degree of important influencing factors on the comprehensive risk grade of food.

The application relates to a diet intake monitoring method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a dining scene image set of a current collection round, determining at least one target collection orientation corresponding to the current collection round according to the dining scene image set, the target collection orientation is used for positioning a region where a main detection object is located, determining at least one target detection object according to a target positioning point corresponding to each target collection orientation, the target positioning point is used for positioning a sub-detection object, obtaining a diet collection array corresponding to the current collection round according to diet recognition information corresponding to each target detection object, the diet recognition information comprises food categories and residual information corresponding to each food category, and obtaining diet intake monitoring information based on the diet collection array corresponding to each collection round in a preset dining time range. The method can automatically position each detection object for diet intake analysis, and the diet intake monitoring efficiency is improved.

The invention relates to the field of smart home, and provides an electric stove automatic power adjusting method and system based on cooking stage identification, and the method comprises the steps: obtaining food type information, and combining a preset change threshold table and initial power to achieve differential initial heating of different foods; continuous picture data are collected at a first time interval through a camera, images are partitioned and subjected to change measurement calculation, the maturity degree of food in each region in a pot can be finely reflected, then the maturity level of each region is determined through a change threshold table, and the cooking range of food in each region is determined through the mapping relation between the regions and furnace holes. And target power values of the furnace holes are generated according to a power mapping strategy, and partitioned fine temperature control of the multiple furnace holes is achieved. The method has the beneficial effects that energy waste caused by excessive heating is reduced, meanwhile, the burden of frequent manual adjustment of a user is reduced, the maturity degree of different areas in the pot is precisely judged, and therefore differential adjustment of the power of the multiple furnace holes is achieved.

According to the cooking control method, the cooking equipment and the electronic equipment provided by the invention, when the food is cooked in the steaming mode, the food category is determined through the detected food temperature, the food weight is determined according to the food category and the output power of the steam generator, and the working parameters of the steam generator are adjusted according to the food category and the food weight; through identification of food types and food weights, working parameters of the steam generator are automatically adjusted, control over steam output is achieved, the cooking effect is improved, and the problem that food is too cooked or not cooked is avoided.

This invention provides a method for constructing and completing a food safety knowledge graph based on graph neural networks. It acquires national food safety standard documents related to food categories, food additives, and pesticide residues in food, processes them through data cleaning and formatting into triples applicable to the knowledge graph, and constructs an ontology layer schema architecture for the food safety knowledge graph. A food safety knowledge graph query system capable of entity querying and visualization is designed. Word vectors for entity names in the food safety knowledge graph are generated using a pre-trained language model BERT. The graph neural network architecture is used to fuse textual and graph structure information in the food safety knowledge graph, applying these features to two downstream tasks: entity classification and link prediction, to achieve knowledge graph completion. This invention improves the completeness and practicality of the food safety knowledge graph and realizes the intelligent application of food safety standard information.

The application discloses a food processing and quality control system based on artificial intelligence, comprising: a processing technology configuration center, configured to call a corresponding standardized process flow template according to food category information, and generate a target process flow structure body; a loss prediction engine, configured to output a loss rate prediction result set by using an improved quantile regression forest model; a rapid detection system docking module, configured to perform rapid detection on all food batches to be delivered out of a warehouse, generate rapid detection results, compare the rapid detection results with a food safety standard library threshold value, and obtain item-by-item qualification determination; a delivery interception controller, configured to jointly verify the loss rate prediction result set and the rapid detection results, generate a delivery permission result or a delivery interception result, and generate an alarm log; and a quality log recorder, configured to record quality log data. The improved quantile regression forest model is used to realize intelligent quality control of food processing.

The present disclosure relates to a system and method to predict the post- prandial glucose response in the individuals type 2 diabetes mellitus (T2DM) in India by taking sample of patients with T2DM from various locations across India and fitting them with continuous glucose monitors (CGM) and calculating the postprandial glucose response based on the different meal intakes by the patients. The present disclosure further relates to the utilization of k-means clustering models to classify food types based on nutritional information and classification of patients. The present disclosure also relates to utilizing XGBoost to predict postprandial blood glucose responses based on patient phenotypes and food categories and providing personalized recommendations taking into account meal type, preferences, and regional influences which would allow patients with T2DM to eat and drink foods to maintain their blood sugar within prescribed limits and prevent disease progression.

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 utility model belongs to the technical field of food processing, and particularly relates to multi-purpose intelligent cooking equipment for multi-category food, which comprises a rack, a conveying device for conveying the food and a steam device for cooking the food are arranged on the rack, the conveying device comprises at least two conveying components, and the conveying components are independently controlled; a conveying device is installed on the machine frame and composed of a plurality of conveying assemblies. Each conveying assembly can meet the conveying requirements of different foods and has an independent control function. Therefore, the cooking operation can be synchronously performed on various foods according to the cooking time requirements of various foods, so that the efficiency is improved, and the cost is reduced.

The invention relates to the technical field of food safety, and discloses a food quality safety tracing method and system based on deep learning, and the method comprises the steps: collecting multi-modal data of each link of a food supply chain through a plurality of sensors and interfaces; processing image data by using a CNN convolutional neural network, processing time sequence sensor data based on an LSTM long short-term memory network, modeling a supply chain topological relation through a GNN graph neural network, and generating a cross-modal feature vector; dynamically adjusting the weight coefficient of each feature dimension in the cross-modal feature vector through a meta-learning framework based on food categories and seasonal factors to obtain a fusion feature vector; calculating the fusion feature vector by using an SHAP algorithm, generating a local contribution index of risk traceability, and constructing a pollution propagation path causal graph in combination with a Bayesian network; and storing the pollution propagation path causal diagram by using a block chain double-chain structure, and carrying out risk automatic early warning in combination with an intelligent contract. The operation cost of the traceability system is reduced.

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.