62 results about "Personalized nutrition" patented technology

The invention relates to an AI personalized nutrition evaluation and guidance method based on individual continuous diet data. The AI personalized nutrition evaluation and guidance method comprises the following steps: (1) calculating individual single meal food and nutrient intake; individual health indexes are collected, the individual daily energy demand amount is calculated, and the daily food and nutrient demand amount is determined; (2) carrying out instant personalized nutrition evaluation on food and nutrients ingested by an individual in a single meal according to a domestic public recommendation standard; (3) on the basis of individual nutrition and health data continuously recorded in the longitudinal direction of time, establishing a personalized nutrition intake-health model taking an individual as a unit, and carrying out personalized nutrition evaluation and health risk prompting; and (4) simulating the idea of nutrition guidance of professionals to create an AI nutrition guidance algorithm, and generating AI guidance. By adopting the AI personalized nutrition evaluation and guidance method based on the individual continuous diet data, a sustainable AI-assisted diet self-management solution suitable for daily life scenes is effectively provided.

The invention relates to the technical field of artificial intelligence, and discloses a system of a nutrition recipe recommendation system based on artificial intelligence in chronic disease intervention management and an application method. Comprising a data acquisition module, a data preprocessing and feature engineering module, a knowledge base management module, a personalized nutritional requirement modeling module, a recipe generation and optimization module, a user interface module and a model training and updating module which are in communication connection through a network. According to the method, personalized health data such as physiological indexes, chronic disease characteristics, diet preference, allergy and intolerance information of a user are collected in multiple dimensions, a nonlinear relation between the characteristics of the user and nutritional requirements is deeply mined in combination with a deep learning model, and an accurate personalized nutritional requirement model is generated according to specific illness conditions and body indexes of different chronic disease patients; therefore, recipe recommendation conforming to individual differences is provided, and the risk of aggravating the illness state due to improper diet is reduced.

The invention relates to the technical field of nutritious food configuration, and discloses a system and a method for configuring personalized nutritious food, and the method for configuring personalized nutritious food comprises the following steps: collecting spectral characteristic data and chemical component data of a food material sample by adopting a hyperspectral imaging and mass spectrum combined technology, and obtaining holographic chemical fingerprint information; processing the collected spectral data by using a multi-scale feature extraction algorithm, extracting multi-level feature information of the food materials, and carrying out standardization and dimension reduction processing on the feature data; according to the method, high-precision prediction of the nutrient content of the food material is realized through combination of hyperspectral imaging and mass spectrometry technologies and a deep learning model, so that the nutrient content prediction accuracy and the configuration scheme accuracy are improved, the nutrient balance achievement rate is also improved, and meanwhile, the limitation of a static database is broken through; real-time nutritional ingredient analysis of any food material is achieved, the method can adapt to individual differences and freshness changes of the food materials, and accurate configuration based on actual food material characteristics is provided.

The invention relates to the technical field of diet health services, in particular to a diet service method and system based on human nutriology. The method comprises the steps of collecting multi-dimensional data for a user; on the basis of a human nutriology database and user full-amount data, a personalized nutrition target is determined through nutrition requirement calculation; in combination with personalized nutrition targets, user dietary habits and actual life scenes, food material recipes are matched to generate multiple sets of personalized dietary schemes; providing diet scheme execution guidance, and adjusting the diet scheme; the personalized diet scheme and the updating scheme are pushed to the user; the system comprises a user data acquisition module, a nutrition target determination module, a diet scheme generation module, a diet scheme guidance and dynamic adjustment module and a push module. According to the method, multi-dimensional adaptation of nutritional demand analysis to user data is realized, the disease state, exercise intensity and dietary habits of the user are fully combined, the nutrition target pertinence is improved, and the actual demand of the user is accurately matched.

The present invention includes techniques for food image processing and particularly relates to a method and system for personalized nutrition management with food image recognition models using deep learning. The method comprises: a user side obtains an food image to be taken by a user, and the food image is input into a trained food image recognition models using deep learning to obtain different types of food sub-images; computing the amount of nutrients contained in the food sub-images, and accumulating the nutrients in all the food to obtain the total nutrients intake of the user; setting intake thresholds of various nutrients, and comparing the total intake of various nutrients with corresponding nutrient intake thresholds to obtain a comparison result; according to the comparison result, type and quantity of taken food are adjusted, and nutrition management is completed. The invention associates the food intake information uploaded by the user with other data sets (e.g., recommendations from their nutritional physician) through the server to determine whether the obtained energy and nutrient ratio are appropriate, and finally, the analyzed data is feedbacked to the user, thereby prompting the user to improve the diet plan.

Provided herein are methods for providing personalized nutritional supplementation to a subject. Also provided are compositions of personalized nutritional supplementation for subjects in need thereof.

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.

The invention provides a nutrition balance evaluation method and system for dish matching, a storage medium and equipment, and the system can generate a personalized nutrition demand threshold model for each user through creating a detailed user file and constructing a multi-dimensional health portrait, provides nutrition optimization suggestions conforming to individual health conditions and diet preferences, and improves the user experience. Meanwhile, by combining a preset dietary nutrition standard table and a preset food material nutrition database, the system calculates the food material nutrition content in real time and performs dynamic correction, the accuracy and reliability of nutrition data are ensured, nutrition complementation food material screening and component adjustment calculation are performed by utilizing a learning algorithm, and the accuracy and reliability of nutrition complementation are improved. In addition, factors such as allergen information, religious diet taboo, interaction of active ingredients of food materials and the like of the user are fully considered, and feasibility and safety of the optimization suggestion are ensured.

The invention provides a nutrition scheme recommendation method and device based on a large language model and electronic equipment, and relates to the technical field of artificial intelligence and digital health, and the method comprises the steps: obtaining multi-modal input data according to a preset period; analyzing the multi-modal input data based on the multi-modal fusion model to generate a user state vector; fusing the user state vector and the user input through a soft prompt tuning technology to generate a customized prompt word; analyzing the customized cue word based on a large language model of a retrieval enhancement generation framework, accessing an external nutriology database, and outputting personalized nutrition recommendation content; and performing structured processing and visual output on the nutrition recommendation content, and performing iterative optimization on the model parameters based on user feedback data. According to the invention, through organic combination of technologies of multi-modal information fusion, soft prompt tuning, retrieval enhancement generation and the like, generation of a nutrition recommendation scheme which is highly personalized, dynamically adaptive and good in user experience is realized.

A system (100) and method (900) for dispensing a personalized nutraceutical (81) to a consumer (62). The system (100) can create the personalized nutraceutical (81) that is selectively influenced by one or more health attributes (640) relating to the intended consumer (62) of the personalized nutraceutical (81). The application(s) (373) of the system (300) can create a variety of outputs (690) including a personalized recipe (693) for the personalized nutraceutical (81) from a variety of inputs (610) that can include the health attributes (640) of the consumer (62). A production assembly (500) can manufacture the personalized nutraceutical (81) using the personalized recipe (693).

The principle underlying this method is that the person's health improves when the microbiome within as well as the host is balanced and well-functioning. The method of this disclosure presents lifestyle choices that accommodate the superorganism of person plus microbiome. Examples of lifestyle choices are types of products safest to eat, use and wear, those safest to avoid. The system is simple to use in that it includes the minimum in the form of suggestion lists to reach better health, with links to references for those seeking scientific explanations. Users are asked from time to time to record their lifestyle choices and how their bodies responded. The logic of personalized suggestions derives from the health professional's knowledge and the scientific reference-supported Artificial Intelligence Analyst. The method and system depend upon continual internal and external cross-checks which enhances reliability.

The invention discloses a personalized nutrition scheme generation method and system for height increase, and relates to the field of health management, and the method comprises the steps: collecting static biological data, parent height data, health background questionnaire data and intestinal flora samples of a user, calculating the genetic target height and basal metabolic rate of the user according to the static biological data and the parent height data, generating a personal file of the user; extracting height, weight and leg length data of the user from a personal file of the user, obtaining stress distribution of a lower limb epiphyseal plate of the user under different exercise types through biomechanical simulation, and generating a personalized exercise scheme according to a stress distribution result; and estimating daily energy consumption according to the basal metabolic rate and the personalized exercise scheme to obtain the daily total energy demand of the user. According to the method, the problem of insufficient scheme accuracy and adaptability caused by neglecting individual nutrition absorption difference and biomechanical response characteristics in a traditional method is effectively solved.

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 an AI-based method, device and equipment for assisting diet conditioning management and control of chronic diseases of old people and a medium. The method comprises the following steps: collecting multi-dimensional user data, and preprocessing the multi-dimensional user data to obtain a user health file; based on the user health archive, calculating a nutrition health demand of a corresponding user, and obtaining a personalized nutrition target list; calculating a suitability score corresponding to the user for each food material based on the personalized nutrition target list; based on the suitability score, screening the food materials to obtain a safe food material library; based on the safe food material library, performing preliminary screening on a recipe database to obtain a candidate recipe set; constructing an optimization problem based on the personalized nutrition target list and the candidate recipe set; and solving the optimization problem to obtain a recipe scheme. By adopting the method, the complex relationship between the disease and the nutrition constraint can be accurately mined and dynamically matched, and the personalized health requirement of the patient is met.

The invention relates to the technical field of healthy diet, in particular to an intelligent proportioning and mixing system based on healthy diet. The invention discloses an intelligent proportioning and mixing system based on healthy diet. The intelligent proportioning and mixing system comprises a data processing module, a user interaction module, a food material storage and conveying module, an intelligent proportioning module, an intelligent mixing module and a communication module. The system has the beneficial effects that the data processing module comprehensively analyzes the health information of the user, and uses a professional nutrition proportioning algorithm to accurately calculate the food material dosage, so that the personalized nutrition requirements are met, and the accurate nutrition proportioning is realized; the intelligent mixing module optimizes a mixing mode according to food material characteristics by virtue of a plurality of mixing modes and real-time environmental parameter monitoring, so that the mouth feel and the nutrition absorption are improved; diversified operation of the user interaction module is combined with remote control of the communication module, and operation convenience is greatly improved.

The utility model provides a kind of water and fertilizer integration device for rose planting. The water and fertilizer integration device for rose planting includes: base, fixedly installed with mounting bracket on the base;Water storage tank, the water storage tank is fixedly installed on the base;Multiple fertilizer storage tanks for storing different fertilizers, multiple the fertilizer storage tanks are fixedly installed on the base, multiple the fertilizer storage tanks are equipped with discharge pipe, the discharge pipe is communicated with the water storage tank, and the discharge pipe is equipped with sealing valve;Detection mechanism, the detection mechanism is equipped on the water storage tank, for detecting soil nutrient;Irrigation and fertilization mechanism, the irrigation and fertilization mechanism is equipped on the water storage tank, for irrigation and fertilization to rose. The water and fertilizer integration device for rose planting provided by the utility model can detect soil nutrient before irrigation and fertilization, can prepare water and fertilizer solution according to detection result, provide personalized nutrition supply for rose, and be conducive to the healthy growth of rose.

The invention discloses a wheat growth regulation and yield prediction integrated method based on artificial intelligence, and the method comprises the steps: obtaining wheat growth data and environmental factor data through a sensor network, and carrying out the preprocessing and standardization; utilizing machine learning modeling analysis to identify nutritional requirement change characteristics of the wheat in a growth stage, and determining a trace element ratio adjustment direction; a personalized nutrition supply scheme is generated in combination with a dynamic regulation and control strategy, a regulation and control scheme is optimized through a resource utilization efficiency evaluation model, a field management system is updated, and finally a wheat yield prediction result is obtained in combination with a yield prediction model. According to the method provided by the invention, precise nutrition management in the whole wheat growth process is realized, the resource utilization efficiency is improved, and effective technical support is provided for improving the wheat yield and quality.

The application discloses a kind of based on blood data malignant tumor patient personalized nutrition assessment dynamic analysis method, it is related to personalized nutrition assessment technical field, including: with multilayer perception machine neural network as basic model, the data set S2 of blood routine and biochemical data is trained, obtain model_1;The historical blood routine and biochemical data of the person to be tested are added in data set S2 and constitute data set S3, and the weight of data set S3 is re-assigned;The model structure and parameter of model_1 are reloaded, the first layer parameter S3 of model_1 is fixed, the second layer and third layer parameter of model_1 are activated to continue training, and the training epoch is 200, obtain model_2.The application provides customized nutrition status assessment according to the data of each person's unique physiological change and treatment process, compared with general model, more can fit individual difference, and simultaneously, the ability of model is retrained by continuously incorporating new data, with the accumulation of patient historical data, the prediction accuracy and adaptability of model will be continuously improved.

The application discloses a kind of personalized nutrition meal intelligent recommendation method, device, equipment and storage medium.The method comprises: obtaining the basic health information of target object, reference favorite menu and blood glucose monitoring data;According to the pre-established food material library and menu library, determine the target associated entity of reference favorite menu;According to the pre-established food material-menu-component knowledge graph and target associated entity, the candidate favorite menu is obtained by multidimensional reasoning to reference favorite menu;According to the correlation degree and feature matching degree of candidate favorite menu and reference favorite menu and the adaptation degree of candidate favorite menu and food material taboo, candidate favorite menu is screened to obtain potential favorite menu;Based on basic health information and blood glucose monitoring data, potential favorite menu is checked by multilayer rule, and daily meal plan is recommended for target object according to the checking result.This scheme can solve the problems of low recommendation accuracy and poor scientific adaptability, and realize the scientific and accurate recommendation of GDM pregnant women diet.

Implementations described herein relate to methods, systems, and computer programs for an AI-powered fitness tracking platform that automatically processes dietary information from food images, logs user metrics (e.g., weight, body measurements, etc.), and employs generative AI to provide personalized nutrition and fitness recommendations. By capturing a photo of a meal, the system identifies and logs calories, macronutrients, and micronutrients. Users can also enter data manually, search an existing nutritional database, and interact with a generative AI chatbot to modify or enhance data entries.

The invention relates to the field of medical health monitoring, in particular to a chronic respiratory disease patient muscle health intelligent monitoring and intervention system which comprises a multi-dimensional time-frequency impedance analysis monitoring module, a respiration-movement cooperative training device module and a nutrition management platform. Performing time-frequency domain analysis on the signals by using multi-scale wavelet decomposition, and extracting muscle function characteristic parameters; quantitative evaluation of a respiratory muscle and intestinal muscle collaborative function is realized through phase synchronization analysis, a personalized baseline and a multi-stage early warning threshold are constructed, and early recognition of muscle dysfunction is realized; the training load and the training mode are dynamically adjusted according to the breathing muscle force percentage, and breathing training and swallowing muscle group training are combined; a personalized nutrition scheme is formulated based on muscle function parameters and metabolic states, the system achieves multi-dimensional evaluation of respiratory muscle and intestinal muscle functions, the function degradation trend is found 2-4 weeks ahead of time, and the training effect is improved by about 35%.

The application discloses a metabolic energy requirement dynamic prediction method, relates to the technical field of metabolic energy requirement prediction, and comprises the following steps: 1, body composition or egg composition determination; 2, metabolic energy factor analysis method model establishment in a growth period or an egg laying period; and 3, input variable correlation model in the growth period or the egg laying period respectively. The application is based on the more scientific factor analysis method principle, dynamically predicts the metabolic energy requirement according to the actual production level, thereby performing individualized nutrition guidance on different farms, improving the comprehensive income of the farm, reducing environmental pollution, and having extremely strong industrial value.

This application discloses a method and apparatus for generating nutritional plans based on knowledge graphs. The method first obtains the gut microbiota detection results of the target object, extracts the abundance information of each bacterial species, and maps it to bacterial species nodes in the knowledge graph. Then, it calculates the functional coverage value of each metabolic function based on the functional contribution relationship between bacterial species and metabolic functions, and constructs functional redundancy characterization parameters based on the synergistic effect between multiple bacterial species pointing to the same metabolic function node. Next, it identifies target metabolic function gaps where both functional coverage and functional redundancy are insufficient, and determines the corresponding set of candidate nutritional factors. Finally, it combines food association relationships with the target object's gut microbiota adaptation characteristics to screen target food combinations and generate personalized nutritional plans. This invention can accurately identify key gaps in the overall metabolic function of the gut microbiota, avoids the limitations of single-species abundance analysis, and significantly improves the targeting and accuracy of nutritional interventions.

The invention relates to the technical field of diet health, and discloses a nutrient intake prescription design system based on an AI large model, and the system comprises a user side which is used for collecting user health data and daily life data, and transmitting the user health data and daily life data to an intelligent nutrition scheme design platform; the intelligent nutrition scheme design platform is used for evaluating the nutrition status of the user based on an AI intelligent medical big model and outputting a personalized nutrition prescription; the nutritionist working platform is used for checking and confirming the output personalized nutrition prescription; and the e-commerce platform is used for providing nutrition commodity matching and purchasing services based on the audited and confirmed personalized nutrition prescription. The multi-source health data is automatically analyzed through the AI intelligent medical large model, the nutrition prescription is generated, the prescription generation time is greatly shortened, and the time is shortened from several hours of traditional manual service to the minute level; and meanwhile, the nutritionist resource dynamic scheduling module reduces the prescription auditing waiting time and improves the overall service efficiency.

The application discloses a calcium and phosphorus requirement dynamic prediction method, and particularly relates to the digestible calcium and phosphorus requirement prediction technical field, and comprises the following steps: step 1, using the comparative slaughter method to determine the calcium and phosphorus daily deposition ratio and the daily deposition of calcium, phosphorus and body protein in different growth stages; step 2, using the regression method to determine the body deposition efficiency and the maintenance requirement of calcium and phosphorus; step 3, establishing a correlation model of the daily deposition of body protein and the daily deposition of phosphorus; step 4, establishing an operational dynamic prediction model by using the input variables of the correlation model; and step 5, establishing a total model for predicting the digestible calcium and phosphorus requirement. The application is based on the factorial model and the regression correlation analysis to predict the digestible calcium and phosphorus requirement in different growth stages, uses the production indexes and the carcass indexes as the input variables of the model, and dynamically and accurately and rapidly predicts, so as to provide a model basis for the individualized nutrition scheme of different farms.

A system is provided.SOLUTION: A system comprising: means for receiving fitness goals and physical information from a user; means for generating a customized training program based on the user's fitness goals and physical information; means for generating customized nutrition advice based on the user's fitness goals and physical information; means for providing the customized training program and nutrition advice to the user; and means for receiving feedback from the user on the progress of the training program and changes in physical condition and adjusting the training program and nutrition advice based on the feedback.SELECTED DRAWING: Figure 1

The invention discloses a feeding method for breeding sows with different birth times, a feed additive for breeding sows with low birth times and biological feed of the feed additive, and relates to the technical field of livestock breeding feed. The invention provides different feeding matching schemes for the reproductive sows with different birth times: low-birth-time high-crude fiber daily ration + NCG and high-birth-time low-crude fiber daily ration; the feed additive for the low-fetal-rate reproductive sows is prepared from the following components in parts by weight: 0.1-1.0 part of N-carbamylglutamic acid, 10-150 parts of soybean hulls and 10-100 parts of beet pulp. The biological feed comprises the additive and a basal feed, and is also added with specific doses of vitamins, minerals, enzyme preparations (phytase, beta-glucanase and the like) and bacillus licheniformis, the invention systematically reveals the differentiated influence of the daily ration fiber level and NCG on the reproductive performance of low-fetal-count (1-5-fetal-count) and high-fetal-count (6-8-fetal-count) sows for the first time; a traditional one-step feeding mode is broken through, and a personalized nutrition scheme is provided.

The present disclosure relates to a method for preparing a personalized nutritional supplement composition having precisely controlled nutrient content through embedded 3D printing, which can derive nutritional requirement information based on biometric information and dietary activity information of a user and provide a personalized nutritional supplement composition including droplets containing nutrients required by the user.

This patent describes an advanced method for the accurate optical quantification of antioxidant carotenoids in biological tissues. The method addresses interference from melanin and hemoglobin through comprehensive correction techniques. It involves creating a tangent line for melanin correction using absorbance values at 650 nm and 700 nm, measuring the corrected optical density (OD) of carotenoids at 488 nm, measuring the corrected OD of blood at 577 nm, converting the Blood OD from 577 nm to 488 nm using oxyhemoglobin and deoxyhemoglobin extinction coefficients, and subtracting the Blood OD from the Carotenoid OD to obtain the final Carotenoid OD. This innovative approach provides reliable, non-invasive, and precise measurements of carotenoid levels in tissues, facilitating improved health assessments and personalized nutrition strategies.