161 results about "Foods types" patented technology

The invention provides a food freshness evaluation method and system based on image processing. The method comprises the following steps: acquiring a multi-view image of food, transmitting the image to a central processing center, setting personalized image acquisition parameters, and forming a food image database; based on the food image database, performing high-precision dynamic monitoring on color and texture changes of the food in different time periods, and generating a preliminary evaluation result; according to the preliminary evaluation result, combining food types, storage conditions and historical sales data, performing prediction modeling on the freshness of the food to obtain a final freshness evaluation report; and utilizing the freshness evaluation report to adjust an inventory strategy in real time, optimizing a food distribution plan, and generating an optimized management process. According to the technical scheme provided by the invention, the accuracy of food freshness evaluation is improved.

The invention discloses a food nutrition evaluation method and system based on multi-modal fusion. The method comprises the steps of 1, extracting multi-scale features of a food RGB image and an RGB-D depth image in parallel through a multi-scale double-branch Transform encoder; 2, performing cross-modal fusion on the multi-scale features by using a depth perception enhanced attention fusion module; step 3, constructing nutrition prediction branches based on the fused features, and outputting five macro nutrient contents; and step 4, training the training data set on the model in an end-to-end mode, and inputting the RGB image and the RGB-D image of the food to be detected into the trained model to predict the nutrient content. According to the method, perception of volume information of different food types is enhanced by using the RGB-D depth mode corresponding to the food RGB image, visual information in the food RGB image and spatial physical characteristics in the RGB-D image are fused through the depth perception attention enhancement module, so that fusion characteristics with identification information are generated, and the evaluation accuracy is improved.

The invention provides a temperature measurement calibration method and system, and the method comprises the steps: putting food to be cooked into an oven cavity, and selecting corresponding food information on an operation panel; the temperature probe is inserted into the food, a heating program is started, the thermopile array sensor scans and measures the surface temperature of the food and transmits data to the main control module, and the temperature probe transmits the real-time measured internal temperature data of the food to the main control module; after the main control module receives the temperature data, a corresponding temperature correction coefficient matrix is called according to the selected food type, measurement data of the thermopile array sensor is calibrated, and the corrected food comprehensive temperature is obtained; and the main control module dynamically adjusts the heating power and time according to the corrected food comprehensive temperature data and preset cooking target temperature and food type information. By optimizing the installation position of the sensor, designing an anti-interference and self-cleaning mechanism and combining an accurate correction algorithm, high-precision measurement and calibration of the food temperature are achieved, accurate data support is provided for intelligent food cooking, accurate intelligent regulation and control of the food heating process are achieved, and the cooking effect and the user experience are improved.

The invention provides a personalized precise nutrition recipe recommendation method and system based on a genetic algorithm, and the method comprises the steps: inputting various personal information, including gender, age, weight, height, and nutrition risk information (lack of nutrients, risk genes or risk SNP ID), all the personal information being dynamically linked together, and quantitative nutrient demands are generated for specific individuals according to the resident dietary nutrient reference intake and a pre-constructed nutrient-gene interaction database. A food nutrient composition database is pre-constructed on the system and comprises various foods from four different countries and nutritional ingredients of the foods. And then, according to individual quantitative nutrient requirements, selecting and combining food by writing a genetic algorithm, iteratively perfecting the combination of food types and food amounts, and finally outputting an accurate nutrition recipe meeting individual nutrition requirements.

The invention discloses a food material decay prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the food material type of a target food material if it is detected that the target food material is put into a refrigerator, and matching a target sensor set corresponding to the food material type from a preset detection library; controlling each target sensor to carry out data acquisition, and carrying out food material decay detection processing according to the acquired target data to obtain a decay detection result; determining a sensor weight of the target sensor according to the quality parameter and the food material coefficient; performing fusion processing on the putrefaction detection result corresponding to each target sensor and the sensor weight to obtain a comprehensive putrefaction detection result corresponding to the target food material; according to the comprehensive decay detection result and the actual storage temperature of the refrigerator, the remaining storage duration of the target food materials is determined, and the actual storable duration of the food materials placed in the refrigerator can be accurately predicted through dynamic fusion of the detection results of the multiple sensors and combination of the actual storage temperature.

The invention relates to a multi-level food safety risk evaluation method based on clustering and an entropy weight method, and belongs to the technical field of food safety. Firstly, a Mini-batch K-means clustering algorithm is adopted to carry out risk level preliminary division on a sample, and automatic risk identification of mass sampling inspection data is realized. Then, counting risk grade frequencies of different food types according to three levels of province, city and district (county), and constructing a frequency matrix; and then weighting the risk indexes of various foods by using an entropy weight method, calculating comprehensive risk scores of the food types under different levels, and further aggregating to obtain the total risk level of each region. And finally, carrying out final grading on the risk score of each region by adopting a K-Means clustering method, and determining the food safety risk grade of each province, city and district (county). According to the method, multi-level and data-driven accurate evaluation of food safety risks can be realized, a scientific sampling inspection decision basis is provided for supervision departments, supervision efficiency is improved, resource allocation is optimized, and public food safety is guaranteed.

The invention relates to the technical field of food detection, in particular to a GC-IMS-based food freshness detection method, device and equipment, and the method comprises the following steps: obtaining headspace sampling gas of a to-be-detected food sample; according to the type of the to-be-detected food sample, automatically matching preset chromatographic conditions and ion mobility spectrometry conditions from a graded food type database; the headspace sampling gas is injected into GC-IMS, analysis is carried out based on matched chromatographic conditions and ion mobility spectrometry conditions, and a volatile organic compound fingerprint spectrum of the sample is obtained; and extracting preset key feature information from the fingerprint spectrum, and inputting the key feature information into a food freshness evaluation model to obtain a freshness evaluation result of the to-be-detected food sample. Therefore, the problems that in the prior art, detection parameter curing lacks adaptability, key feature analysis is not accurate, and model prediction accuracy is insufficient are solved.

The invention provides a refrigerator intelligent fresh-keeping operation control method and a refrigerator, and the method comprises the steps: collecting refrigerator compartment data in real time, analyzing a fresh-keeping mode, temperature and humidity parameters and food material type information set by a user, and generating an initial control strategy; based on the initial control strategy, a compressor and a fan are controlled to execute refrigeration operation; according to the refrigerator compartment data, the initial control strategy is dynamically optimized, and refrigeration operation parameters of a compressor and a draught fan are adjusted; and outputting the optimized initial control strategy, and uploading the optimized initial control strategy to a server for remote data synchronization. Based on the intelligent self-adaptive learning technology, multi-sensor cooperative control and a dynamic optimization strategy are combined, and precise control and remote management of the running state of the refrigerator are achieved. And the temperature and the humidity can be automatically adjusted and controlled according to the use habits of the user, the refrigeration control strategy of the refrigerator is adjusted in time, the temperature and the humidity can be kept constant, the application range is wider, and the purpose of parallel intelligent preservation is achieved.

The invention relates to a food water content calculation method and device based on a visual image, equipment and a medium. First, food image data is collected, and first image data is obtained by using a standardized preprocessing technology. Secondly, extracting surface glossiness and texture feature values from the first image data, segmenting the image by using a feature detection algorithm, marking a key region related to the water content, and forming a visual feature set; thirdly, the relevance of all elements in the visual feature set is analyzed, food types are distinguished through a classification rule, feature weight distribution of different types of food is determined, and optimization of feature description is achieved; and finally, based on the optimized feature description, establishing a mapping relation model between the water content and the visual features, substituting a newly input food image into the model, predicting the water content, and outputting a real-time detection result. An efficient and convenient detection means is provided for the fields of food processing, storage and the like, the detection cost is reduced, and the production efficiency and the product quality control capability are improved.

The invention discloses a personalized recipe recommendation system, a recommendation method and an interaction system, and relates to the technical field of diet recommendation, the system comprises a user data collection module, a food image recognition module and a personalized recommendation module, the user data collection module is used for obtaining health data and diet preference data of a user, the health data comprises basic information, health conditions and exercise habits, the basic information comprises regions, the health conditions comprise allergen lists, chronic diseases and diet targets, and the food image recognition module is used for obtaining food photos uploaded by a user, recognizing the food photos to obtain food recognition results and sending the food recognition results to the user. The food identification result comprises a food type corresponding to the food photo, and the personalized recommendation module is used for determining a recommended recipe recommended to the user based on the health data, the diet preference data and the food identification result. By considering more comprehensive influence factors, a more suitable and personalized recipe can be recommended to the user.

The invention relates to the technical field of artificial intelligence application, in particular to a personalized diet suggestion method, system and equipment based on a large language model.The method comprises the steps that meal information input by a user is received, and the meal information comprises food pictures or text descriptions; processing the meal information, and identifying food types and quantity; calculating the nutrient content of the current meal in combination with a nutrient database according to the identified food type and quantity; generating a personalized nutritional demand based on the user basic information and the health condition, and determining a demand gap between the nutritional content of the current meal and the personalized nutritional demand; inputting the nutrient content, the demand difference, the user diet preference data and the medicine and food homology data of the current meal into a large language model, and generating the nutrition evaluation of the current meal and the personalized diet suggestion of the next meal; and feeding back the nutrition evaluation and the personalized diet suggestion to a user through a visual interface. And scientific and reasonable diet of the user can be realized.

The present invention relates to a smart home cooking system using an artificial intelligence device, comprising: at least one cooking utensil provided to cook food ingredients; and the artificial intelligence device that is provided to identify a speech command of a user and control the cooking utensil to cook the food ingredients according to the identified speech command, learns a preferred food of the user and a preferred food type of the user according to a food cooking history of the user, detects, when there is a request of the user, at least one food recipe according to the preferred food of the user or the preferred type of the user from among a pre-stored plurality of recipes, on the basis of a result of the learning, and provides the detected at least one food recipe to the user as a recommended food recipe.

The invention discloses a food frying process online monitoring method and system, and relates to the technical field of food processing monitoring. The method comprises the following steps: transmitting a frequency-adjustable ultrasonic signal to a frying medium, receiving a response signal after the medium acts on food, and filtering; according to the comparison result of the amplitude attenuation rate of the response signal and the threshold value, the ultrasonic emission frequency is dynamically adjusted; performing feature extraction on the preprocessed signal to form a feature vector containing amplitude, frequency and energy features; inputting the feature vector into a judgment model trained by a dynamic frequency sample, and outputting a current frying state; and a control instruction of the frying equipment is generated so as to realize process regulation and control. Through combination of frequency-controlled ultrasonic excitation and acoustic feature extraction, the method can adapt to different grease environments and food types, gives consideration to the signal penetration depth and the signal-to-noise ratio, achieves non-destructive, real-time and quantifiable monitoring of the frying process, and remarkably improves the cooking state recognition precision and monitoring consistency.

The invention relates to a heart failure patient water inflow monitoring system and method based on intelligent water content evaluation, and the system can monitor and evaluate the inflow of a heart failure patient every time through a weight sensor, a three-dimensional camera, a clinical terminal and a nurse station background on a clinical dining carrier. Comprising the food type, the weight and the water content ratio of the current food to be eaten, and the total weight and the total water content of the food eaten this time, and generating a daily in-out report in combination with the sunrise of the heart failure patient. In this way, the daily incoming and outgoing amount of the heart failure patient can be accurately evaluated, specific parameters of the eaten food can be counted in detail and the total water content of the eaten food can be evaluated in detail without tracking and recording of nurses, the subsequent diet balance adjusting strategy is greatly influenced, visual eating data are automatically provided for background doctors, and the efficiency is improved. Doctors can conveniently adjust nursing or rehabilitation and diagnosis and treatment schemes according to the in-out amount of the patient.

The invention discloses a diet management system for diabetic patients, which relates to the technical field of intelligent diet management and is technically characterized by comprising a data acquisition module, a nutrition analysis and suggestion generation module, a blood glucose prediction module, a feedback learning module, a personalized strategy recommendation module, an excitation and verification module and a communication module, the system automatically collects health and diet data of a user through a camera, a body fat scale, a glucometer and wearable equipment, identifies food types and estimates components by using a convolutional neural network, and realizes automatic calculation of energy and nutritional ingredients. The food-blood glucose response model constructed on the basis of individual historical data can predict postprandial blood glucose changes, sensitivity coefficients are dynamically updated through a regression correction mechanism, individualized blood glucose prediction is achieved, the system further generates intake suggestions through a component back-calculation method, and the intake suggestions and the intake suggestions are given to a user. And the result is visually displayed on the terminal in the form of a blood glucose curve and an energy distribution diagram, and real-time diet guidance is provided.

The embodiment of the invention discloses a multi-modal fusion food recognition large model construction and application method and system, and the method comprises the steps: obtaining a multi-modal training data set, carrying out the combined training of multi-modal training data in the multi-modal training data set, and obtaining a multi-modal fusion food recognition large model; obtaining multi-modal data of food to be identified; performing feature extraction and fusion on the multi-modal data through a multi-modal fusion food identification large model to obtain corresponding fusion feature information; and based on the fusion feature information, utilizing a multi-modal fusion food identification large model to carry out food type identification and nutritional ingredient analysis, and outputting an identification result and a nutrition suggestion. According to the embodiment of the invention, the method achieves the recognition of the to-be-recognized food based on the multi-modal data of the to-be-recognized food through the powerful learning and reasoning capabilities of a large model, achieves the more accurate and comprehensive recognition of the food, and improves the recognition accuracy and reliability of the food.

The invention belongs to the technical field of food quality monitoring, and discloses a food quality dynamic monitoring system and method based on multi-sensor fusion, and the method comprises the steps: collecting initial quality data of food, storage influence data in a storage stage, and food types; performing feature extraction on the storage influence data to obtain storage feature data; inputting the food type, the initial quality data and the storage characteristic data into a shelf life prediction model to obtain a predicted shelf life of a finished product; comparing and analyzing the predicted shelf life of the finished product with a preset shelf life threshold value, and judging whether a storage adjustment instruction is generated or not; particularly, under the condition that the predicted shelf life is short or the taste does not reach the standard, by optimizing a storage or processing strategy in advance, waste of raw materials and energy is reduced to the maximum extent, and fluctuation of food quality is reduced.

The invention relates to an air fryer capable of improving user experience, which comprises a machine body, a main control board arranged in the machine body and a cooking cavity used for containing food, and is characterized in that a camera assembly is further arranged in the machine body, is electrically connected with the main control board and is used for shooting images of the food in the cooking cavity at intervals, and the main control board is electrically connected with the camera assembly. The main control board obtains a shot food image and uploads the food image to the cloud server, and the cloud server generates a video file based on the food image to display key change points of the cooking process. According to the invention, the food type and total amount can be automatically identified, the cooking process of the air fryer can be intelligently controlled, the short video capable of displaying the key change point of the cooking process can be automatically generated according to the user demand, and the user can edit and share the automatically generated short video at the terminal equipment, so that the user experience is greatly improved.

The invention relates to the technical field of oven temperature control, and discloses a dynamic temperature adjusting method and system for an oven, and the method comprises the steps: obtaining food types, food weight, initial set temperature and real-time temperature data; the baking stage temperature of each baking stage is calculated according to the food type, the food weight and the initial set temperature, wherein the baking stage temperature comprises the first stage temperature, the second stage temperature, the third stage temperature and the fourth stage temperature; the food type, the food weight and the baking stage temperature are input into a pre-trained decision tree model, baking stage time is obtained, and the baking stage time comprises the first stage time, the second stage time, the third stage time and the fourth stage time; and dynamically adjusting the baking temperature according to the baking stage temperature, the baking stage time and the real-time temperature data in combination with a fuzzy PID control algorithm. The method improves baking quality and energy utilization rate.

The invention discloses a plasma and steam combined baking control method, device and equipment, relates to the technical field of intelligent cooking, and can improve the baking speed and the baking effect. According to the scheme, the method comprises the steps that current quality indexes of current baked food are obtained, the quality indexes comprise the water content, the surface dehydration rate and the texture score, and the texture score is used for indicating the hardness and elasticity of the food; after a plurality of fitting functions corresponding to the current food are determined according to the food type of the current food, the current quality indexes are input into the corresponding fitting functions, and current control parameters for baking the current food are obtained; wherein the control parameters comprise plasma power, steam temperature and steam humidity, the fitting function is obtained based on fitting of multiple pieces of prediction data, and the prediction data are quality indexes, predicted by the target prediction model, of multiple different food types under different control parameters; and baking the current food after configuring baking equipment based on the current control parameters.

The present application relates to a terminal device for blood sugar management during pregnancy and its cloud server, the terminal device collects physiological data and blood sugar data of a target user including the gestation period, the cloud server obtains a plurality of blood sugar data of reference users from a plurality of terminal devices and forms an overall reference data set in a manner associated with gestational age, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in its own group distribution, judges the current blood sugar state of the user by comparing the user's blood sugar data with the preset healthy blood sugar range, and generates a graded diet suggestion accordingly, the diet suggestion optimizes the diet structure of the target user by selecting food categories with different GI values; the terminal device receives and visually displays personalized diet suggestions matching the blood sugar status of the target user to the target user.

The invention relates to the technical field of household appliances, and discloses a cooking device with a visual identification device, the cooking device comprises a shell, and a cooking cavity and the visual identification device which are arranged in the shell, the visual identification device comprises a camera module and a transmission structure, the camera module is connected to the transmission structure, and the transmission structure is movably connected to the shell; wherein the visual recognition device is movably connected to the shell and arranged above the cooking cavity, and the camera module is arranged towards the inner side of the cooking cavity. The purpose of the invention is to scan the food in the cooking cavity through the camera, distinguish the food type and component through the AI, and automatically determine and distribute the corresponding cooking temperature and time by the system, thereby reducing the troubles that consumers are difficult to select and different components of different foods are difficult to judge, and realizing full-intelligent operation.

Containers for facilitating adherence to dietary plans are provided, along with related systems and methods. A locking mechanism secures an access panel moveable between an opened position and a closed position in the closed position. A controller receives user input regarding desired caloric intake amount for a time period and regarding a type or types of foodstuff stored at the container, and weight readings from at least one weight sensor. The controller determines a caloric value of removed foodstuffs from the containers from the weight readings and the type or types of foodstuff indicated in the user input, and operates the locking mechanism to secure the access panel in the closed position where the caloric value of the removed foodstuffs for the time period exceeds the desired caloric intake amount for the time period.

A bird feeder device is provided. The device is comprised of a housing with generally parallel side walls, a rear wall, a top wall, and a bottom wall, with at least one tail brace wall on the bottom wall allowing birds to brace their tail while feeding. The housing has a front opening for bird access to food placed inside, safeguarded by a removable screen insert that can be introduced through an opening in the top wall, covering the front opening to regulate access based on food type. Additionally, the housing incorporates at least one fastener for suspending the device at an angle conducive to gravity-fed food movement towards the front opening, with design considerations such as a top wall overhang and the suspended angle of the device that aim to deter larger birds from feeding and support smaller birds.

The application discloses a method for determining food type of an electric pressure cooker and related components, and relates to the technical field of home appliance control. First, a heating module is controlled to work at a first preset working power. When the pressure in the electric pressure cooker reaches a preset pressure range, the heating control module stops working and the number of pressure signal actions is increased by one, so that the food type is determined based on the number of pressure signal actions subsequently. When the working time of the electric pressure cooker does not reach a preset detection time, the heating module is continuously controlled to repeat the above steps until the working time reaches the preset detection time. The food type of the food to be cooked is determined by the number of pressure signal actions of the electric pressure cooker within the preset detection time, so that the cooking mode corresponding to the food type is adjusted, and problems such as cooking failure affecting the user experience are avoided.

A micro puree machine including a drive motor coupled to a drive shaft via at least one gear. The drive motor is arranged to rotate a drive shaft and blade assembly. A position motor is operable to change a position of the drive shaft via rotation of the position. A user interface is arranged to: i) receive a user input to select a first routine associated with processing a first food type, and ii) display a status of the processing of the first food type while the first routine is running. A controller is arranged to: i) receive the user input selecting the first routine and retrieve the configuration data associated with the first routine, ii) control operations of the drive motor based on the configuration data associated with the first routine, and iii) control operations of the position motor based on the configuration data associated with the first routine.

The invention relates to cooking equipment and a control method thereof.The cooking equipment comprises an inner container and an air injection assembly, the inner container comprises a cooking cavity with an opening and a hot air cavity located on the back side of the cooking cavity, the cooking cavity is used for cooking food materials, and the hot air cavity communicates with the cooking cavity; the air injection assembly comprises a plurality of nozzles arranged towards the interior of the cooking cavity, each nozzle communicates with the hot air cavity, and at least part of the nozzles are rotationally arranged. According to the scheme, the angles of the nozzles are adjusted by rotating the nozzles, so that more nozzles can be concentrated in an area with insufficient baking, uniform baking of the surface and the interior of food is ensured, the common problem that partial areas in a traditional oven are excessively baked or not baked is solved, and the baking efficiency is improved. Meanwhile, personalized adjustment can be carried out on the nozzle according to parameters such as food types, baking time and surface uniformity in the cooking process, so that the cooking equipment can be dynamically adjusted according to the real-time state of the food, human intervention is avoided, and the cooking efficiency and precision are improved.

The application relates to the technical field of multi-modal data processing, and discloses a food nutrition ingredient content intelligent evaluation method, system, terminal and storage medium based on a multi-modal large model, the method comprising the following steps: obtaining multi-modal data and performing pretreatment to obtain target multi-modal data; adopting a pre-trained multi-modal large model to perform feature extraction, feature alignment and feature fusion on the target multi-modal data to obtain multi-modal fusion features; when a new food type appears, updating parameters of an intermediate deep learning model through an incremental learning and memory enhancement mechanism to obtain a target deep learning model; and inputting the multi-modal fusion features into the target deep learning model to output a nutrition ingredient content evaluation result. Through the multi-modal large model and the continuous learning algorithm, high-precision dynamic evaluation of food nutrition ingredients is realized, the emergence of new food types can be adapted to, and high-precision evaluation of old food types can be maintained.

The invention provides an intelligent dinner plate based on multispectral thermal imaging fusion, and belongs to the technical field of intelligent dinner plates. The multi-mode sensing module collects physical and chemical information of food in the dinner plate; the edge calculation and main control module executes real-time data processing at an equipment end; the lightweight food identification and segmentation model is used for identifying food types based on multispectral image data and segmenting areas of the food; the multi-sensor data fusion algorithm is used for fusing the food types, the area proportion information and the total weight, estimating the initial weight of each kind of food based on a pre-trained density factor, and outputting a structured list containing the food types and the initial weights; the cloud nutrition calculation engine receives the structured list and the related hyperspectral feature data, and queries and identifies standard nutritional ingredients of the food from a nutrition database; and calibrating the initial weight by using the hyperspectral characteristic data, and calculating and summarizing the total nutritional ingredients and total calorie of the whole meal based on the calibrated weight and the finely-adjusted nutritional ingredients.

The present invention provides an apparatus and method for cooking food in a pressurized steam oven, wherein the food is steam cooked in a first stage and grilled in a next stage. The method automatically identifies the time of the first stage (slow) cooking and the time of the second stage (fast) cooking based on the type, size, temperature and 3d image of the food. The slow cooking stops when the food reaches the critical temperature by an amount determined by various parameters and then starts the quick cooking of frying and baking. The slow cooking method may be a pressurized steam cooking method. The method automatically reduces the time of slow cooking so that the internal temperature of the food does not prematurely reach the critical temperature. The method helps to avoid overcooking of food during the grilling phase.