17 results about "Food classification" patented technology

The invention provides a food material feature recognition model construction and classification method, system and device, and a medium. The food material feature recognition model construction method comprises the steps of obtaining target sample image data of a plurality of sample food materials; wherein the target sample image data comprises visible light image data and infrared image data; constructing a training sample data set based on the target sample image data and food material category data corresponding to the target sample image data; and inputting the training sample data set into a preset model to construct a food material feature recognition model. According to the method, the food material feature recognition model is constructed, so that the feature vectors of the food materials under different environments, different illumination conditions or different angles can be effectively recognized, comprehensive and diversified food material features are extracted, and then the types of the food materials are accurately recognized; the food materials can be accurately classified under different environments, different illumination conditions or different angles, and the universality and coverage of food material classification are improved.

The application provides a model training method, a package image recognition method and a device thereof. The model training method comprises: obtaining label information corresponding to a first package image, the first package image being a package image of a first sample food; analyzing the label information to obtain a first text; replacing at least part of the first text with a target preset text to obtain a second text, the target preset text having an association with the first text, and the second text being a description of a second sample food; and training a food classification model according to the first text and the second text to obtain a trained food classification model. The embodiment of the application replaces at least part of the first text on the first package image with the target preset text, expands the training sample, avoids the need for manual labeling of characters on package images of various foods to obtain sufficient training samples, thereby reducing the labeling workload of the staff and improving the efficiency of model training.

The invention discloses a novel few-sample food classification method, and relates to the technical field of food identification. Comprising the steps of obtaining a food image, preprocessing the food image to obtain a preprocessing data set, constructing a food classification model comprising an image enhancement layer, a feature extraction layer and a global-local feature fusion layer, inputting the preprocessing data set into the food classification model, and performing error analysis through a loss function according to an output predicted value and an actual value. And obtaining a trained food classification model, and inputting the to-be-detected image into the trained food classification model to classify the food image. According to the method, the features with comprehensive global and fine-grained local representation can be generated, so that high classification precision is achieved.

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 food classification deep learning method adaptive to a few-sample scene, and the method comprises the steps: obtaining a few-sample food classification data set, carrying out the preprocessing enhancement of an original image in the few-sample food classification data set, obtaining an image x, constructing an RL-Clip model, carrying out the feature processing of the image x, and finally obtaining a food classification probability matrix. Secondly, optimizing an image processing process; the purposes of improving the generalization of the detection model and improving the detection effectiveness, accuracy and confidence of the specific data set are achieved.

The objective is to provide a diversity assessment device, a diversity assessment method, and a diversity assessment program that can assess food diversity while considering nutrient diversity by evaluating the diversity of ingested foods based on clusters that classify foods. [Solution] Based on food component data, a food classification is set by classifying the target specified foods according to nutrients using a clustering method, data on the intake status of specified foods included in a meal classified according to the food classification is obtained, and a dietary diversity score is calculated for the specified food intake status data.

The utility model discloses an automatic detection food storage box which comprises a box body, a temperature and humidity detector is arranged on the outer side of the box body, a servo motor is arranged on one side of the bottom of the interior of the box body, a lead screw is arranged at the output end of the servo motor, a lifting block matched with a thread is arranged on the outer side of the lead screw, and the lifting block is arranged on the outer side of the box body. A lifting seat is installed on one side of the lifting block, a driver is arranged at the bottom of the lifting seat, and the output end of the driver penetrates through the bottom of the lifting seat and is provided with a rotating table; through lifting adjustment of the rotating table, the placing areas of the multiple sets of filter screen grooves can be conveniently arranged in the upper layer area and the lower layer area of the box body, and when the multiple sets of filter screen grooves are in the upper layer state, the angles of the multiple sets of filter screen grooves can be adjusted through overall rotation of the rotating table, so that an operator can take and place food at the through opening conveniently; by means of the installation arrangement of the structure, different kinds of food can be conveniently placed in a classified mode, temperature loss in the food taking and placing period is restrained, and therefore the storage temperature of the food is maintained.

The utility model discloses a pre-packaged food inventory classification management frame, and relates to the technical field of food classification management. The device comprises a plurality of layering plates, wherein a splicing mechanism and a classifying mechanism are arranged on the layering plates; the splicing mechanism comprises an adjusting assembly, a supporting assembly and a splicing assembly, the adjusting assembly comprises a supporting column arranged on the layering plate, the layering plate is fixedly connected with a plurality of sliding blocks, the sliding blocks are provided with spring grooves, the spring grooves are fixedly connected with springs, and the springs are fixedly connected with bearing blocks. By means of the splicing mechanism and the elasticity of the spring, a worker can assemble and limit the layering plate and the supporting columns by pressing the ejector block, meanwhile, when the layering management height of the layering plate needs to be adjusted, rapid adjustment can be completed by pressing the ejector block, two management frames are spliced through cooperation of a clamping ring and a clamping block, and the labor intensity of workers is lowered. And the device is suitable for storing various pre-packaged foods with different sizes, the application range of the device is widened, and the convenience of the device is improved.

The utility model relates to the technical field of storage tanks, and discloses a food-grade multifunctional storage tank which comprises a storage tank body, a clamping groove is formed in the inner wall of the storage tank body, a flow dividing assembly is installed in the storage tank body, a sealing cover assembly is connected to the top of the storage tank body in a clamped mode, and the flow dividing assembly comprises a baffle. A sliding block is fixedly connected to the surface of the baffle, a flow dividing barrel is slidably connected to the surface of the sliding block, a sliding groove is formed in the surface of the flow dividing barrel, a lifting handle is fixedly connected to the end, away from the sliding groove, of the flow dividing barrel, a threaded sleeve is fixedly connected to the top of the baffle, and a bolt is in threaded connection with the interior of the threaded sleeve. According to the food storage container, by arranging a matching structure of the flow dividing cylinder, the sliding block and the sliding groove, rapid extraction and resetting of the flow dividing cylinder are achieved, in combination with the detachable design of the sealing plate, it is guaranteed that different kinds of food are stored in a classified mode, independent taking and using are convenient, and the problem that food is mixed or tainted by other odors in a traditional storage container is solved.

This application relates to the field of smart home appliance technology, and discloses a method and apparatus for cross-domain small-sample object image classification for smart terminals. The method includes: acquiring an image of food to be identified; processing the image to extract basic feature vectors; mapping the basic feature vectors using a dual Riemannian manifold processing module to obtain Euclidean space features and hyperbolic space features respectively; fusing the Euclidean space features and hyperbolic space features to obtain a comprehensive feature representation for food classification; and determining the food classification result based on the comprehensive feature representation. The dual Riemannian manifold processing module includes Euclidean space branches and hyperbolic space branches. This method achieves multi-space feature fusion by collaboratively extracting Euclidean space features and hyperbolic space features using dual Riemannian manifolds, thereby improving the feature representation capability and classification accuracy of food images.

The present invention discloses a food supply chain risk perception, identification, and rating system based on a knowledge graph, including a food supply chain risk perception module, a risk identification module, a risk rating module, and a response and handling module. Its characteristics are as follows: The food supply chain risk identification module encompasses all classification names of the same food in different food supply chain links; first, it locates the food supply chain level where the currently sampled food name is located, and then it performs fuzzy matching for the classification name and limit value of the currently sampled food name for this food supply chain level. The food supply chain risk rating module's rating for the likelihood and severity of risk occurrence is based on the likelihood and severity of risk occurrence that vary with time and space. The present invention solves the problem that most of the currently constructed food safety knowledge graphs are based on the food classification standards of the industry, without considering the inconsistent classification of the same food in the food supply chain links, resulting in a broken chain in food safety risk identification.

The invention provides food classification partial freezing preservation equipment and a control method, and relates to the technical field of food preservation, the food classification partial freezing preservation equipment comprises a rack, a conveying assembly and an ammonia cooling tunnel, a camera is arranged on one side of the inlet end of the ammonia cooling tunnel, and the ammonia cooling tunnel is arranged on the upper portion of the rack and covers the middle of the conveying assembly; the conveying assembly comprises a driving assembly, a first roller, a second roller and a dragging and releasing belt, the driving assembly provides driving force for the first roller, the dragging and releasing belt is installed on the outer side of the first roller and the outer side of the second roller, an inclined groove is formed in the outer side of the first roller, the dragging and releasing belt comprises containing assemblies and a transmission chain, the adjacent containing assemblies are movably connected, and the transmission chain is connected with the containing assemblies. And the placing assembly is movably connected with the transmission chain. By means of the placement assembly which is composed of the heat preservation corrugated cover and the heat preservation taper sleeve and used for reducing direct cooling, after the types of food needing to be preserved are recognized and obtained, the food can be correspondingly cooled for different time, and then it is guaranteed that different types of food can have better taste.

The invention discloses a middleware device for intelligent investigation and acquisition of food-borne diseases, relates to the field of intelligent investigation and acquisition of food-borne diseases, and provides a middleware device comprising a data acquisition component, a food-borne disease information filling component and an information reporting component. The middleware device is used for collecting diagnosis information in a hospital information management system to automatically trigger and call a food-borne disease information filling assembly, and basic information of a patient is automatically brought in. Clinical doctors perfect food-borne disease information, food classification and processing or packaging mode information are automatically associated and filled after food names are filled and exposed, and the doctors in the public health department access the auditing terminal in the public health department to modify and auditing food-borne cases and report the food-borne cases to the national food-borne disease case monitoring system. The problems that an existing food-borne disease monitoring method is long in time consumption, poor in timeliness and large in extra workload are solved, and the efficiency of intelligent investigation and collection of food-borne diseases is improved.

The utility model discloses intelligent cold-chain food grading equipment with a rapid classification function, and relates to the technical field of cold-chain food classification, the technical scheme is as follows: the intelligent cold-chain food grading equipment comprises a support frame and a plurality of buffer components arranged on the support frame, each buffer component comprises a support plate, the support plate is mounted on one side of the support frame in a mirroring manner, and the support plate is arranged on the other side of the support frame; each two adjacent supporting plates form a group, a conveying plate is slidably mounted between each group of supporting plates, a plurality of sliding grooves are formed in each conveying plate, and supporting rods are symmetrically mounted below each group of supporting plates. According to the cold-chain food packaging machine, the problems that when food falls off, large impact force is generated, the impact force can directly act on food packages, if the impact force is too large, the cold-chain food is broken or deformed, and meanwhile the higher the falling height is, the larger the impact force is, the more vertical the falling angle is, and the larger the impact force is are solved.

The disposable meal box comprises a box bottom, the upper side of the box bottom is connected with two first side face parts and two second side face parts in a turnover mode, and two triangular face parts are connected between the adjacent first side face parts and the second side face parts in a foldable mode. The upper side of the second side face part is provided with a buckling and folding face part used for fixing the whole in a foldable mode, two partition plates are arranged on the upper side of the box bottom, and through the arrangement of the buckling and folding face part and the triangular face part, the first side face part and the second side face part can form a stable storage space. The box cover part is matched to form a sealing effect and a stable lunch box whole body; when the meal box needs to be folded and stored, the whole meal box can be folded into a flat structure only by taking down the clamping pieces, the turnover partition plate, the first side face part and the second side face part, the storage size of the meal box after eating is greatly reduced, and leakage of remaining meal is avoided.

This application provides a deep learning-based food classification method, system, and readable storage medium. The method includes acquiring a training dataset, which comprises a first time-series spectral signal corresponding to a pure sample type and a second time-series spectral signal corresponding to an adulterated sample type; using the training dataset as the original time series and transforming the original time series to obtain a first-order difference sequence and a Fourier sequence; performing two-dimensional processing on each sequence using a tiling and sliding window approach to obtain corresponding two-dimensional data; constructing an initial classification model and inputting the obtained two-dimensional data and Fourier sequences into the initial classification model for training, obtaining a target classification model upon training termination; and inputting the acquired two-dimensional data to be processed and the Fourier sequences into the target classification model to obtain the food classification result. The implementation of this method can improve the accuracy of food classification.