47 results about "Nutrition analysis" patented technology

The invention relates to the technical field of livestock breeding, in particular to an intelligent feed optimal proportioning method based on pig herd nutrition analysis, which comprises the following steps: S1, collecting stress index data of a pig herd; s2, analyzing the stress level and the stress source of the swinery; s3, dynamically adjusting nutritional requirement parameters according to the stress level and the stress source, and determining nutrients needing to be adjusted under different stress conditions; s4, making corresponding stress mitigation measures based on the stress source and the stress intensity; s5, generating an optimal feed formula meeting the nutritional requirements of the swinery under the stress condition; and S6, converting the optimal feed formula into a production instruction, and sending the production instruction to feed production equipment. By dynamically adjusting the nutritional requirements of the swinery under different stress conditions and combining intelligent feed production equipment, precise feed formula optimization and efficient production are achieved, the feed utilization rate and the swinery health level are improved, and meanwhile the breeding cost is reduced.

The invention belongs to the field of computer vision, deep learning and intelligent catering management, and particularly relates to a Chinese food dish detection and identification method oriented to a complex environment. The invention provides a Chinese food dish detection and identification method oriented to a complex environment, which is based on an improved YOLOv8 architecture and is combined with an attention mechanism, so that the Chinese food dish detection and identification method has the capability of performing efficient detection and identification in a complex dining table environment, and can output the category and bounding box information of each dish in real time. The problems of multi-target Chinese style dish detection and image classification in a complex scene are solved, automatic calculation of the calorie value of the dish is realized, a comprehensive solution is provided for intelligent ordering and nutrition analysis, and the method has a wide application prospect.

The invention discloses a personalized medical health service recommendation system based on big data analysis, and belongs to the technical field of medical health services. The synchronization module is used for generating a standardized data stream according to dynamic blood glucose monitoring data and an original heart rate variability signal; the analysis module is used for generating a diet time sequence data stream according to the intake event timestamp; the blood glucose feature extraction module is used for extracting data from the standardized data stream; the insulin sensitivity analysis module is used for generating an insulin sensitivity factor matrix; the nutrition analysis module is used for generating a carbohydrate equivalent time distribution vector and a dietary fiber intake intensity value; the feature fusion module is used for generating a fusion feature vector according to the blood glucose drift trend coefficient, the insulin sensitive factor matrix and the carbohydrate equivalent time distribution vector; the risk assessment module is used for generating a hypoglycemia risk probability value according to the fusion feature vector; and the recommendation generation module is used for generating a personalized service recommendation set according to the hypoglycemia risk probability value.

The invention discloses a nutrition recipe intelligent generation system and method, and belongs to the technical field of nutrition information processing and intelligent recommendation. The system is suitable for individual nutrition management of individuals of all ages, and particularly, optimization design is carried out for infant stages of 0-72 months. The system intelligently generates a personalized nutrition recipe based on individual basic information, growth indexes, nutrition standards, available food materials, user preferences and feedback and other multi-dimensional data, and supports nutrition intake evaluation and dynamic optimization. The system comprises core modules of data input, nutritional requirement calculation, recipe generation, nutrition analysis and interactive display and the like, and expands and supports functions of sensitization screening, regional and seasonal adaptation, work and rest linkage, family sharing and the like. Through fusion of individual growth data and environmental conditions, the system realizes scientific and high-adaptability nutrition recommendation, improves feeding convenience and intervention accuracy, and has a wide application prospect.

The invention discloses a nutrition monitoring system for an esophageal cancer patient, and aims to improve the nutritional status of the patient through real-time monitoring and personalized evaluation. The system comprises a data acquisition module, a nutrition analysis module, a personalized recommendation module and a data management module. The data acquisition module collects physiological data and diet information of a patient through the intelligent wearable device and the nutrition intake recording device. And the nutrition analysis module evaluates whether the nutrition intake of the patient reaches the standard or not by utilizing a nutrition database and a data processing analysis technology, and predicts the future nutrition demand. And the personalized recommendation module provides customized diet suggestions for the patient according to the evaluation result and the expert nutrition guide. The data management module is responsible for storing and managing the nutrition information of the patient and generating a nutrition monitoring report. The system can provide comprehensive and real-time nutrition monitoring and management for esophagus cancer patients, and is beneficial to improving the treatment effect and life quality.

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 invention relates to the technical field of computer vision, and discloses a catering consumption automatic settlement method based on image recognition, and the method comprises the steps: obtaining multi-scale image data through a multi-level image quality evaluation and self-adaptive preprocessing technology; target detection and instance segmentation are realized by adopting hierarchical feature extraction and dish template matching; carrying out dish three-dimensional measurement and volume calculation based on a stereoscopic vision and shape recovery method; realizing classification and identification of dishes through multi-modal feature fusion and an attention mechanism; performing nutrient component analysis and health assessment based on nutrition database matching and image feature analysis; and intelligent settlement is realized through price calculation, nutrition statistics, intelligent recommendation and settlement processing. According to the invention, dish identification, nutrition analysis and dynamic pricing can be rapidly completed, and a complete technical solution is provided for intelligent catering service.

The invention discloses an intelligent food identification and nutrition analysis method based on deep learning, and belongs to the technical field of image analysis. The method comprises the following steps: 1, collecting input data, and preprocessing the data; 2, determining a position and a mask area of a target in the scene, and generating a dense depth map; 3, constructing a food three-dimensional point cloud, denoising the three-dimensional point cloud, and removing abnormal points; 4, obtaining a three-dimensional feature, extracting a two-dimensional feature from the image, and carrying out weight fusion; 5, splicing the obtained features, and reinforcing the fused feature information; 6, predicting the weight and calorie of the food by regression; and 7, optimizing model prediction and verifying a prediction effect. According to the method, feature fusion in a 2D + 3D modal mode is adopted, data enhancement is performed on an original image, 2D features are extracted, a 3D point cloud is generated at the same time, and multi-modal feature fusion is performed to predict the calorie and the weight of the food, so that consumers can be helped to accurately estimate indexes such as the calorie and the weight of the food and formulate scientific diet methods and suggestions.

The application discloses a formula food operation and management device based on human-computer interaction technology, which comprises a human-computer interaction module, a nutrition analysis module, a formula food screening module and a formula food mixing module which are connected in communication. The detailed health information collected by the human-computer interaction module can provide highly personalized nutrition matching information through the nutrition analysis module, so that the postoperative patient can obtain the most suitable nutrition supply for recovery needs. For special nutritional needs that cannot be met directly through standard full-nutrition formula food, the formula food screening module can be combined with a nutrient component matching prediction model to dynamically generate customized nutrient component mixing matching parameters, thereby solving the problem that it is difficult to configure under the traditional method.

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.

This invention relates to the technical field of dietary nutrition analysis, and discloses an intelligent integrated machine and method for automatically collecting and calculating the nutritional content of dishes in real time. The method includes the following steps: identifying various ingredients and weighing them, recording their raw weight; weighing and recording the cooked weight of the finished dish after cooking; weighing and recording the types and weights of seasonings and cooking oil used during cooking in real time; and calculating the content of a certain nutrient element per 100 grams of the cooked dish using the raw weight of the ingredients, the cooked weight of the dish, and the weights of the seasonings and cooking oil used in cooking. This invention provides an intelligent integrated machine and method for automatically collecting and calculating the nutritional content of dishes in real time, which can accurately calculate the calories and nutritional content of various nutrients in the cooked dish.

The invention provides a food recognition and calorie estimation method and system based on a YOLOv8 model, and the method comprises the steps: inputting a food image, detecting food in the image in real time based on the YOLOv8 model, recognizing the type of the food, combining the cooking mode and component information of the food, and dynamically calculating the calorie content. The functions of accurately identifying the food in the meal scene and efficiently estimating the calorie are realized. According to the method, the detection precision is greatly improved while the advantage of detection speed is reserved, and the problems that a traditional target detection method is prone to background interference and low in recognition accuracy in meal recognition are effectively solved. According to the method, the practical application capability of the model in a specific scene is greatly improved, and a reliable basis is provided for subsequent calorie estimation and personalized health intervention. The method has high robustness, transportability and real-time performance, is suitable for various intelligent terminals and edge computing platforms, can be widely applied to the fields of intelligent restaurants, nutrition analysis, health management and the like, and has good popularization prospects and industrialization value.

The invention provides a pregnant and lying-in woman health dynamic monitoring system and method based on big data, and relates to the technical field of data processing, and the method comprises the steps: inputting the physiological data of a pregnant and lying-in woman, the doctor's advice information and the current pregnancy period into a health planning model, and obtaining standard energy consumption and standard nutrition parameters; obtaining current-day exercise data and current-day diet data of the pregnant and lying-in woman; on the basis of the current-day exercise data, the physiological data and the current pregnancy period, the current-day energy consumption of the pregnant and lying-in woman is determined; inputting the daily diet data and the standard nutrition types into a nutrition analysis model to obtain daily intake parameters corresponding to each standard nutrition type; comparing the energy consumption of the day with standard energy consumption to obtain an exercise monitoring result of the pregnant and lying-in woman; and comparing the daily intake parameter corresponding to each standard nutrition type with the standard intake parameter corresponding to each standard nutrition type to obtain a nutrition monitoring result of the pregnant and lying-in woman. The accuracy of the health assessment result of the pregnant and lying-in woman can be improved.

The utility model discloses a portable human body composition detection nutrition analyzer. The portable human body composition detection nutrition analyzer comprises a supporting column, a movable base, universal wheels, a sundry frame, an analyzer host, a supporting table, an electrode clamp and a data transmission line, a movable base is mounted at the bottom end of the supporting column, a supporting table is mounted at the top end of the supporting column, and a host supporting seat is arranged on the supporting table; the analyzer host is detachably mounted on the host supporting seat, and the analyzer host is connected with an electrode clamp through a data transmission line; a sundry frame is mounted on one side of the supporting column; according to the portable human body composition detection nutrition analyzer, a user can choose a sitting posture or a prone posture, measurement is started when the electrode clamp is clamped to be completely attached, and the portable human body composition detection nutrition analyzer is more convenient and easier to use and experience, does not need to specially stand for measurement and can be suitable for users with different body conditions; and the device can be conveniently disassembled and is suitable for different scenes.

An object of a system according to an embodiment is to propose an appropriate nutrient or food ingredient for a physical disorder of a user.SOLUTION: A system according to an embodiment includes a malfunction selector, a nutrient analyzer, an ingredient selector, and a recipe suggester. The bad condition selection unit selects a bad condition of the body that the user wants to improve. The nutrient analyzer analyzes necessary nutrients based on the poor condition selected by the poor condition selector. The ingredient selection unit selects an ingredient that supplements the nutrient analyzed by the nutrient analysis unit. The recipe proposal unit proposes a recipe using the ingredients selected by the ingredient selection unit.SELECTED DRAWING: Figure 1

Provide a system. 【Solution means】An acquisition means for acquiring exercise data, An acquisition means for acquiring exercise data, A body shape analysis means for photographing and analyzing body shape data, A nutrition analysis means for analyzing diet data and determining the calorie and nutrient balance, A proposal generation means for generating a personalized exercise program and diet proposal based on these data, A notification means for notifying the user of the generated exercise program and diet proposal, An audio output means for providing health-related information via audio output, Means for providing a consumer health management robot for acquiring and analyzing data in a residential environment, A system including.

The invention discloses a food calorie calculation method based on binocular vision, and relates to the technical field of computer vision and nutrition analysis. The method comprises the following steps: firstly, acquiring a food image by using a calibrated binocular camera, and preprocessing the image; and then a semi-global block matching algorithm is combined with weighted least square filtering to calculate and optimize a disparity map, and an edge-disparity collaborative network is introduced to improve the matching precision of the weak texture food. Then, segmenting a food area through a U-Net network, and outputting an accurate contour after edge optimization; and identifying the specific category of the food by using a lightweight classification network. And then, according to the food form, adaptively selecting an equivalent cylinder method or a point cloud reconstruction method to carry out three-dimensional volume reconstruction, and converting the volume into mass in combination with a density database. And finally, querying a calorie database containing cooking mode information to obtain an accurate calorie value. The method is obviously superior to the prior art, and an efficient and reliable solution is provided for family diet management and health monitoring.

Disclosed herein is a system and method are provided for rapid, high-throughput phenotyping of nutritional traits in plants using mid-infrared (MIR) spectroscopy and chemometric modeling. One or more processors control MIR sensors to capture spectral images of plant material, which are analyzed using a chemometric model, such as partial least squares regression, to determine characteristics including protein, fatty acids, dietary fiber, starch, resistant starch, sulfur-containing amino acids, and digestibility. The method supports non-destructive analysis with minimal sample preparation and is applicable to a wide range of plant species and biological materials. Results can be stored in a local or cloud database and visualized through a graphical user interface. The chemometric models may be trained and validated using K-fold cross validation and can include spectral preprocessing and interpolation filters. This enables rapid, cost-effective nutritional analysis to support plant breeding, food quality assessment, and industrial applications.

The invention provides a food nutritional ingredient analysis method and system based on a palm proportion scale, and relates to the technical field of nutrition analys.The method comprises the steps that a palm image of a user is collected to extract a plurality of hand size parameters, and a size reference set is constructed; then collecting an image containing food and a palm of a user, and identifying a current gesture state, a food contour and a dining scene in the image through a computer vision technology; according to the recognized gesture state, corresponding hand size parameters are dynamically matched from the size reference set to serve as a proportional scale; calculating the actual physical size of the food and further determining the actual weight of the food based on the proportional scale and the food contour; and finally, generating a personalized food nutritional ingredient analysis result in combination with the dining scene, the actual weight and the cooking additive parameters. According to the invention, the technical problem of low accuracy of food identification and nutrition analysis in the prior art is solved, and the accuracy of food actual weight and nutrition intake estimation is remarkably improved.

A decision support system that aids healthcare practitioners in making more informed clinical decisions and avoiding errors related to diagnosis and treatment of diseases. The system utilizes several data points from the patient history and clinical findings input by the patient and the doctor, to help the doctor make a more accurate diagnosis and develop a more informed treatment plan that incorporates not just drugs and procedures, but also dietary and lifestyle interventions. A system of the present disclosure comprises dosage calculator, symptom checker, differential diagnosis, drug interaction checker, side effect checker, nutritional analyzer and drug-food interaction checker input pathways and corresponding database compartments. Output data is generated in correspondence with patient input data and stored on a database server, where it is correlated with patient outcomes over time and improved through machine learning.

This invention discloses an intelligent nutritional analysis food weighing device based on artificial intelligence, belonging to the field of intelligent weighing equipment technology. The device consists of a weighing base module, a supporting column module, and an intelligent display terminal module. The base is a Φ220mm milky-white acrylic thin disc, housing a built-in four-quadrant high-precision weighing sensor and main control board, achieving accurate weighing from 0-5kg / 1g. The column is a Φ30mm×300mm smooth cylinder, with internal cable channels, an independent acoustic cavity speaker, and a noise-reducing microphone array, supporting long-distance voice interaction up to 1.5 meters. The top 4-inch IPS touchscreen is tilted back 10°, integrating an AI recognition chip and a 2-megapixel camera, equipped with a CNN deep learning model, capable of recognizing over 3000 foods within 1.5 seconds. This invention achieves integrated weighing, recognition, calculation, interaction, and management, offering high precision, simple operation, a pleasant appearance, and strong adaptability, making it widely applicable to scenarios such as postpartum care centers, hospital nutrition departments, rehabilitation institutions, and family health management.

The invention relates to the technical field of meal ordering management, in particular to an enterprise employee meal ordering management system based on big data, which comprises a meal nutrition demand determination module for determining meal nutrition demands through a nutrition analysis model according to historical meal data and personal information of employees, the nutrition analysis model is established on the basis of an improved CNN-GRU-Attention network architecture; the candidate food determining module is used for selecting candidate food from the ordering food nutrition library through an improved clustering algorithm according to the food nutrition requirements; the heat distribution module is used for determining the heat distribution proportion of multiple meals on the day according to the personal information of the employees, and determining a recommended meal sequence in the candidate meal through a meal determination model based on a genetic algorithm; and the meal ordering management module displays the recommended meal sequence when the employees order meals, and uses the meal ordering results of the employees to update the historical meal data. According to the invention, personalized nutritional requirements of employees are met, and the employees are helped to keep diet balance and a better working state.

The invention discloses a straw feed nutrition analysis system and method based on image recognition, particularly relates to the technical field of image analysis, and is used for solving the problem that the accuracy of a nutrition analysis result is insufficient due to the fact that straw fiber arrangement microcosmic differences cannot be accurately captured in the prior art. The method comprises the following steps: acquiring image data of the surface of the straw through multi-direction optical imaging, and dividing the image data into a plurality of analysis sub-regions; extracting fiber arrangement information based on morphological analysis to enhance texture features, and extracting a fiber differential index based on pixel distribution and a neighborhood relationship; carrying out complexity analysis on the distribution characteristics of the fiber differential indexes by utilizing a multi-fractal model, and judging the randomness of a fiber arrangement mode; dynamically analyzing the variation trend of the fiber differential index under different time sequences through a nonlinear method of phase-space reconstruction, and judging the offset amplitude of fiber arrangement; comprehensively judging the stability of the fiber differential index, comparing the fiber differential index with the fiber component reference data, and finally generating straw nutrition analysis data.

The application provides a molybdenum-based polyoxometalate-hemin composite nanomaterial and a preparation method and application thereof, and the method comprises the following steps: under room temperature conditions, molybdenum-based polyoxometalate and hemin are mixed by means of manual grinding, and then the obtained mixture is sequentially subjected to centrifugal separation, washing and vacuum drying, so that a solid-phase molybdenum-based polyoxometalate-hemin composite nanomaterial is prepared. 12 The nanocomposite material is synthesized by a simple manual grinding method, and is prepared through the steps of room temperature grinding, centrifugal separation, washing and vacuum drying without the need of complex equipment, high temperature and high pressure or toxic organic solvents. 12 The colorimetric sensor constructed by the nanocomposite material can rapidly and accurately quantitatively detect the AA content in fresh fruits, and has a wide application prospect in the fields of food nutrition analysis, quality control and on-site detection.

The present invention proposes a method and system for food recognition and nutritional analysis based on image drive. The method for food recognition and nutritional analysis based on image drive includes: controlling the image acquisition device to acquire the target food and obtain the food image data corresponding to the target food; performing image processing on the food image data corresponding to the target food to obtain the food image data corresponding to the target food after image processing; inputting the food image data corresponding to the target food after image processing into a trained convolutional neural network model, and identifying the food information in the food image data corresponding to the target food through the convolutional neural network model to obtain the food recognition result; performing nutritional analysis on the food information contained in the food recognition result according to the food recognition result to obtain the nutritional analysis result. The system includes modules corresponding to the steps of the method.

The invention discloses a straw feed nutrition analysis system and method based on image recognition, which specifically relates to the technical field of image analysis, and is used to solve the problem that the existing system cannot accurately capture the microscopic differences in straw fiber arrangement, resulting in insufficient accuracy of nutrition analysis results. The system collects image data of the straw surface through multi-directional optical imaging, and divides the image data into multiple analysis sub-areas. The system extracts fiber arrangement information based on morphological analysis to enhance texture features, and extracts fiber micro-difference indicators based on pixel distribution and neighborhood relationships. The system uses a multi-fractal model to perform complexity analysis on the distribution characteristics of the fiber micro-difference indicators to determine the randomness of the fiber arrangement pattern. The system dynamically analyzes the change trend of the fiber micro-difference indicators at different time sequences through a nonlinear method of phase space reconstruction to determine the offset amplitude of the fiber arrangement. The system comprehensively determines the stability of the fiber micro-difference indicators, and compares them with fiber component reference data to finally generate straw nutrition analysis data.

The invention provides a catering recommendation method and system based on personal nutrition intake, and the method comprises the steps: S1, constructing a nutrition analysis model, and obtaining a target nutrition intake parameter according to the nutrition analysis model; s2, meal nutrition intake parameters are obtained according to the user meal data; s3, analyzing to obtain recommended nutrition intake parameters; s4, analyzing to obtain a dish component recommendation coefficient, a dish side dish recommendation coefficient and a dish first recommendation coefficient of the existing dish; s5, according to the restaurant geographic position of the restaurant corresponding to the existing dishes and the user position data, obtaining a second dish recommendation coefficient of the existing dishes; and S6, analyzing to obtain a comprehensive dish recommendation coefficient of the existing dishes, obtaining a dish recommendation sequence of the existing dishes according to the comprehensive dish recommendation coefficient, and forming a catering recommendation scheme of the target user according to the dish recommendation sequence and the restaurant geographic positions of the existing dishes. According to the invention, the comprehensiveness and accuracy of the catering recommendation result are improved.

The invention provides a digital nutrition service pushing method and system based on a diabetic patient, and the method comprises the steps: firstly obtaining the real-time monitoring log data, containing blood glucose level data, insulin injection records and diet intake information, of the diabetic patient, inputting the real-time monitoring log data into a pre-trained nutrition analysis model, and carrying out the multi-dimensional processing; the method comprises the following steps: generating a dynamic nutrition demand characteristic representing a nutrient element demand change trend in a preset time period, matching the dynamic nutrition demand characteristic with service entries in a digital nutrition database, determining a candidate nutrition service set and sorting, and performing spatial correlation analysis on a real-time geographic position of a patient and a position of a service provider to obtain a real-time geographic position of the patient. According to the method and the system, a reachable target nutrition service subset is screened out, finally, the reachable target nutrition service subset is sent to a patient terminal device according to a pushing strategy, service selection feedback is received to trigger a service execution instruction, and personalized and reachable digital nutrition service accurate pushing is realized.

The invention discloses a food nutrition analysis method based on a multi-modal large model, and belongs to the technical field of natural language processing. According to the method, a multi-modal model is trained by using an image-nutritional ingredient contrast data set, and a plurality of key nutritional indexes such as carbon water, fat, fiber, calorie, protein, salt, saturated fat and sugar are automatically deduced from a single picture. The technical innovation comprises multi-source data fusion, that is, a unified multi-modal feature space is constructed in combination with food picture features and nutrition database data; end-to-end regression prediction is carried out, that is, an image is directly used as input, and a plurality of continuous numeric nutrition indexes are output instead of being only limited to category identification; the interpretability is enhanced, that is, the relationship between an image area concerned by the model and nutrition prediction is displayed through an attention mechanism or a visualization tool, and the user credibility is improved; and performing personalized adaptation, namely dynamically adjusting prediction and recommendation strategies of the model in combination with historical diet records and health targets of the user.

The invention relates to an intelligent canteen health management system and management method based on multi-source data analysis, and the system comprises an identification perception system, an acquisition system, a data processing system, and a nutrition analysis system. The identification perception system is arranged above a dining table of a canteen and is used for identifying specific dishes and surrounding environment; the identified and perceived dishes are collected, the collected data are uploaded to the data processing system to be processed and analyzed, and finally a report is generated through the analyzed conclusion. According to the invention, global data connection and dynamic decision engine can be realized, a closed-loop service architecture is adopted as a whole, an end-edge-cloud three-level response system is constructed, and the data acquisition rate is improved.