131 results about "Personalized nutrition" patented technology

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

The invention relates to the technical field of nutrition health care product customization, and discloses a personalized adult nutrition health care product customization system and method. The method comprises the following steps: collecting biological characteristics, dietary habits, health targets and gene detection data of a user, and constructing a nutritional requirement database through labeling and preprocessing; a multi-dimensional nutrition demand tensor is generated through principal component analysis, clustering analysis and the like, the matching degree of a target user and a representative sample is calculated, a weighted matching degree vector is obtained through neural network optimization, and then a personalized nutrition formula substrate is generated and optimized through a genetic algorithm. Kalman filtering is used for adjusting a formula according to real-time health data of a user, monitoring deviation to trigger reconstruction, associating a historical scheme to update a nutrition rule base, and finally outputting a nutrition formula coded as a production instruction, so that accurate personalized customization is realized, and nutrition is safe, reasonable and traceable.

The invention relates to the technical field of artificial intelligence and nutrition health management, and discloses an intelligent nutrition correction tracking early warning method and system.The intelligent nutrition correction tracking early warning method comprises the steps that a hierarchical heterogeneity causal effect model is constructed, and individual features are mapped to a potential type space; representing a causal relationship between nutrition intervention and result variables by using a structural causal model, and evaluating an intervention effect by using a dual robust estimator; personalized grouping is realized by identifying individual subgroups; a reinforcement learning algorithm based on context awareness is combined with an anti-fact reward function, and a personalized nutrition intervention strategy is optimized; key feature combinations are extracted through a differentiable decision tree and SHAP value analysis, and multi-level causal interpretation is generated. According to the method, anti-factual reasoning, reinforcement learning and causal relationship extraction technologies are fused, accurate prediction, evaluation and early warning of the individual nutrition intervention effect are realized, and the personalized precision of nutrition intervention and the user compliance are remarkably improved.

The invention discloses a personalized nutrition intake recommendation method and device, equipment and a storage medium, and relates to the technical field of nutrition intake assessment, and the personalized nutrition intake recommendation method comprises the steps: obtaining a nutrition diet standard, initial user information and a nutrition recommendation intake range; the initial user information is preprocessed to obtain target sequence set data, and the target sequence set data comprises current user information; training and adjusting an initial nutrition intake evaluation model according to the nutrition dietary standard, the target sequence set data and the recommended nutrition intake range to obtain a target nutrition intake evaluation model; and inputting the current user information into the target nutrition intake evaluation model to obtain a nutrition intake suggestion. According to the method and the device, periodic nutrition intake evaluation can be carried out according to the actual condition of an individual, accurate and personalized nutrition guidance is provided for the user, and the user is helped to realize a health target and maintain nutrition balance.

The invention discloses a customer health data intelligent management system and method, and belongs to the technical field of health data management. Basic data and health data are collected; the client inputs a daily menu and calculates nutrients; establishing a daily nutrient matrix based on nutrients, and identifying a diet mode; defining a taste space, and extracting taste features based on the seasoning use condition of the menu every day; integrating to form an input vector; establishing a personalized nutrition demand prediction model, and training to obtain nutrition constraints; retrieving the candidate recipes based on nutrition constraints, and performing taste matching to obtain recommended recipes; based on the menu selection records of the similar clients, the recommended menu is optimized; dynamically adjusting the recommendation strategy according to the score of the customer for the recommended menu to obtain an optimal menu; when the customer eats the optimal menu, the change of health data before and after diet intervention is calculated, and the intervention effect is quantified; nutrients needed by the client are adjusted based on the intervention effect, and the optimal menu is correspondingly adjusted.

The invention relates to an intelligent algorithm recommendation method and system for pet personalized prescription food. The method comprises the following steps: acquiring physiological information of a pet, and analyzing the physiological information to obtain a specific physiological demand vector; then, combining the demand vector with a nutriology standard database, and calculating to obtain a nutrition proportioning scheme; then, deviation analysis is carried out on the recent diet record and health feedback data of the pet according to the nutrition proportioning scheme, adjustment demand parameters are obtained, and when the adjustment demand parameters exceed a preset threshold value, iterative optimization is carried out on the nutrition proportioning scheme to obtain personalized nutrition proportioning; screening food material combinations according to the personalized nutrition ratio to obtain a prescription food formula recommendation list for each pet; and finally, based on the prescription grain formula recommendation list and the physiological data after ingestion, generating a new prescription grain formula recommendation list. The pet nutrition scheme can be planned in advance, and nutrition balance of pets in different growth stages and health states can be guaranteed comprehensively.

The invention relates to the technical field of medical recommendation, in particular to a personalized nutrition treatment knowledge base construction method and system for critical patients. The system is used for acquiring basic information, medical record information, laboratory examination data and period diet intake information corresponding to critical patients, performing critical case entity construction, and generating case entity descriptors of the critical patients; constructing a corresponding personalized nutrition treatment initial knowledge base for each critical patient case entity descriptor in combination with a semantic knowledge association ratio between related scientific research literatures; obtaining clinical pathological characteristics and nutrition treatment knowledge entities, performing nutrition demand analysis and administration opportunity determination, and performing nutrition administration effect conflict evaluation to obtain a clinical nutrition administration-diet effect conflict ratio; and performing nutrition treatment knowledge updating optimization on the personalized nutrition treatment initial knowledge base to generate a personalized nutrition treatment knowledge optimization base. Accurate nutrition support can be provided for critical patients.

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 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 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 present invention relates to the field of nutrition and health management. An exemplary method for generating a personalized nutritional plan for a user comprises: receiving, from the user, a natural-language query; identifying, based on the natural-language query, a task and one or more parameters; retrieving from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; inputting the one or more data structures into a trained language model to generate the personalized nutritional plan; and displaying the personalized nutritional plan.

The invention discloses a health data analysis and nutrition recipe generation method based on an enhanced large model, and the method comprises the steps: constructing an enhanced large language model, and obtaining an optimal autoregression language model according to an optimized multi-source heterogeneous data set; constructing a medical nutrition knowledge graph to fuse the structured data and the unstructured data of the medical nutrition to obtain an optimized medical nutrition knowledge graph; according to the optimized multi-source heterogeneous data set, constructing a user health portrait used for obtaining multi-dimensional health features of the user; and constructing a user health state time sequence prediction model to obtain user health state data, and obtaining a personalized nutrition recipe in combination with the optimized medical nutrition knowledge graph. The problems that in a traditional method, a data source of health data analysis is single, integration of multi-source heterogeneous data is lacked, the health condition of an individual is difficult to comprehensively reflect, knowledge coverage of the generated recommended recipe is incomplete, the illusion phenomenon is likely to be generated, and the generated recipe is insufficient in pertinence and scientificity are solved.

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 medical rehabilitation and nutrition intervention, and discloses a child postoperative intestinal function monitoring nutrition intervention system which comprises a data acquisition module, a state evaluation module, a nutrition intervention module, an intervention execution and recording module and a feedback optimization module. According to the system, physiological data, diet intake and symptom feedback related to postoperative intestinal functions of children are comprehensively collected, a personalized nutrition intervention scheme is generated in combination with a recovery state grading evaluation mechanism, and a closed-loop optimized intervention process is formed by guiding execution and response records. According to the method, continuous tracking and scientific intervention of postoperative intestinal function recovery of children can be achieved, the recovery period can be shortened, the complication risk is reduced, and the method is suitable for hospitals, rehabilitation institutions and home nursing scenes.

The invention relates to the field of intelligent management, and provides a clinical nutrition virtual ward management system which comprises the following steps: acquiring first nutrition consultation data before a to-be-evaluated nutrition scheme is used and second nutrition consultation data after the to-be-evaluated nutrition scheme is used; carrying out semantic coding on the nutrition scheme to be evaluated, the first nutrition consultation data and the second nutrition consultation data by adopting an AI-based natural language processing and analysis technology; and according to the difference change between the first nutrition consultation data and the second nutrition consultation data and the semantic interaction fusion feature between the to-be-evaluated nutrition schemes, intelligently judging whether the to-be-evaluated nutrition schemes are valid or not. Through the mode, the nutrition consultation data of the patient can be automatically processed and analyzed, and manual intervention is reduced, so that the intelligent level of clinical nutrition management is improved, and personalized nutrition suggestions can be provided for the patient.

The invention discloses an intelligent canteen personalized nutritious food recommendation system and method based on AIGC, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a user feature vector, a food nutritional component vector and an attribute vector, collecting dishes eaten by a user in recent k days and corresponding timestamps, and forming a dish interaction sequence; constructing a nutrition relation graph of the food according to the nutritional ingredient vectors and the attribute vectors, and calculating a nutrition complementarity weight, a nutrition exclusion weight and a nutrition similarity weight of edges connecting the food in the nutrition relation graph based on the nutritional ingredient vectors; the method comprises the following steps: establishing a food interaction sequence, defining a state, an action and a state transfer function of a reinforcement learning model, defining a reward function based on the food interaction sequence, a nutrition complementarity weight and a nutrition exclusiveness weight, constructing and training the reinforcement learning model, and outputting a recommended recipe to a user by utilizing the trained reinforcement learning model based on Monte Carlo tree search; and matching food in the recommended recipe with dishes in the canteen menu on the current day to generate the nutritious food.

The invention relates to the technical field of postoperative rehabilitation. The spine postoperative intelligent nutrition management method comprises the steps of performing multi-modal data fusion processing based on preoperative sign data, intraoperative blood loss and fluid loss amount and postoperative biochemical indexes of a patient to generate personalized nutrition requirement parameters; performing real-time monitoring and comparison processing on the postoperative diet intake of the patient through intelligent tableware provided with a weight sensor and an image recognition module according to the personalized nutritional requirement parameters, and generating a nutritional intake dynamic map; performing dynamic deviation analysis processing on detection data of the bioelectrical impedance analysis equipment and the saliva biomarker sensor to generate nutrition metabolism feedback data; and fusing the nutrition intake dynamic spectrum and the nutrition metabolism feedback data, carrying out nutrition intervention scheme optimization processing through a reinforcement learning model, generating a dynamic adjustment instruction, and pushing the instruction to a terminal, so as to achieve the technical effects of adapting to individualized requirements, tracking a metabolism state in real time and balancing nutrition intervention accuracy and safety.

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 discloses a personalized intelligent catering method and system based on user conditions, and relates to the technical field of personalized nutriology and intelligent foods.The personalized intelligent catering method comprises the steps that user health comprehensive data and multi-dimensional real-time user situation data are collected, and the two kinds of data are preprocessed; constructing a user nutrition demand prediction model by combining a long-short-term memory network model with a multi-layer perceptron model; analyzing the preprocessed multi-dimensional real-time user situation data to obtain an adjustment coefficient influencing the nutritional requirement, and adjusting the personalized nutritional requirement of the user through a linear programming algorithm; and based on the adjusted personalized nutritional requirements of the user and in combination with the taste preference of the user, the matching degree of each food material is analyzed, customized adjustment is performed through a particle swarm optimization algorithm, and a personalized recipe of the user is generated. Food material matching is optimized through the particle swarm optimization algorithm, personalized intelligent catering is successfully achieved, and scientificity, accuracy and user satisfaction of personalized catering are remarkably improved.

The invention provides a CKD nutrition management visual monitoring method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data preprocessing according to real-time health data, and obtaining a standardized health data set; according to the standardized health data set, evaluating nutritional requirements of the patient, and combining disease progression, individual differences and diet preferences of the patient to generate a personalized nutrition management scheme, the personalized nutrition management scheme including recommended diet, a nutrition supplement scheme and an intake behavior adjustment scheme; according to the personalized nutrition management scheme, a visual display interface is generated, and the visual display interface displays the nutrition state, diet suggestions, potential health risks and predicted effects of the patient; adjusting a personalized nutrition management scheme in real time according to feedback data and health state changes provided by the patient in the use process, and presenting an updated scheme through a visual display interface; the autonomy and accuracy of nutrition management are improved.

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 invention provides a precise feeding system and method for animal husbandry, and belongs to the technical field of animal husbandry breeding, the precise feeding system comprises an intelligent feeding terminal, a central control terminal and a plurality of livestock information acquisition terminals, the intelligent feeding terminal is wirelessly connected with the central control terminal, and the livestock information acquisition terminals are wirelessly connected with the central control terminal. The livestock and poultry information collection terminal is used for collecting information of each individual of livestock and poultry and transmitting the collected information to the central control terminal, the central control terminal sends control information to the intelligent feeding terminal according to the information of each individual of the livestock and poultry, and the intelligent feeding terminal carries out quantitative feeding on the livestock and poultry according to instructions and environment information. Through real-time monitoring of livestock and poultry individual information and environmental parameters and in combination with a nutritional requirement model, precise feed feeding of each livestock and poultry is realized, feed waste is avoided, the breeding cost is reduced, meanwhile, personalized nutritional requirements of the livestock and poultry are met, and healthy growth of the livestock and poultry is promoted.

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 invention relates to the technical field of food processing, in particular to fish-derived protein gel special for 3D printing dysphagia and having a customized digestion characteristic and a lycopene release function as well as a preparation method and application of the fish-derived protein gel. The lycopene-loaded IN / KGM gel system is constructed by combining a food additive manufacturing technology and a dynamic old people digestion model, and the rheological property and swallowing safety of the gel are optimized by regulating and controlling the ratio of IN to KGM; the customized digestion characteristics of the gel and the adaptability of the gel to the digestion function of the old people are researched by utilizing an in-vitro dynamic digestion model of the old people; the loading and releasing behaviors of lycopene in a gel system are explored, the bioaccessibility of lycopene in the gastrointestinal digestion process of old people is evaluated, the interaction mechanism of IN and KGM in the gel forming and digestion process is disclosed, and a theoretical basis and technical support are provided for developing personalized nutritious food suitable for the old people with dysphagia.

The invention relates to the technical field of breeding, in particular to a livestock head data analysis and management system in the breeding process. Compared with the prior art that a group unified feeding scheme is adopted, individual development differences and environment variables cannot be responded, and double resources are wasted due to insufficient nutrition of high-value livestock and excessive feeding of low-efficiency individuals; according to the scheme, a personalized nutrition demand modeling and formula optimization engine is initiated for the first time, the metabolic state is inversed by analyzing 3D features of the head in real time, the protein energy ratio and the environment compensation coefficient are dynamically calculated, a linear programming solver is linked to generate the lowest-cost formula, the feeding time sequence is adaptively adjusted, and the accurate nutrition supply mode of one animal and one strategy is thoroughly reconstructed; the feed conversion rate and the breeding economic benefit are obviously improved.

The invention provides a system and method for providing individualized nutrition recommendation for old surgical patients, and relates to the technical field of nutrition recommendation, and the method comprises the steps: obtaining a basic information set of a target patient; constructing a nutrition recommendation space; inputting information into the nutrition recommendation space for retrieval, and determining initial nutrition recommendation information; acquiring a food intake type sequence and a food intake quantity sequence of the target patient; according to the food intake type sequence, the food intake quantity sequence and the K pieces of nutrition intake information, recipe configuration is carried out, and K-stage nutrition recipe schemes are determined. The technical problems that in the prior art, personalized nutrition recommendation cannot be provided for old surgical patients, and a mechanism for analyzing historical food intake of the patients and continuously monitoring and correcting in the nutrition scheme implementation process is lacked are solved. Through multi-module cooperation, the technical effect of accurate, scientific and dynamically adjustable personalized nutrition recommendation for the elderly surgery patients is realized.

A digital system for personalized nutrition, consisting of: • A data processing module (101) that integrates genetic reports, electronic health records (EHRs) and lifestyle data; • in a natural language processing (NLP) system (102) to extract and structure data from unstructured genetic documents; • A machine learning engine that uses Latent Dirichlet Allocation (LDA)(103) to analyze genetic patterns and categorize individuals; • A predictive analysis module (104) that correlates genetic markers with lifestyle and health risks to refine dietary recommendations; • A web-based interface (105) that enables healthcare professionals to upload genetic reports, analyze categorized risk profiles, and create individualized nutrition plans; • A continuously updated nutritional database(106) that matches genetic markers with food components to provide scientifically sound recommendations; • A secure, encrypted data storage(107) and processing framework that ensures compliance with GDPR and HIPAA regulations.

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.

The invention relates to the technical field of artificial intelligence, in particular to a long-term healthy diet recommendation method, device and system and a medium, and the method comprises the steps: obtaining the current original data of a user, carrying out the preprocessing of the original data, and generating a health data vector of the user; dividing the life cycle into different life stages, and determining a standard nutritional requirement vector of each life stage; determining a life stage of the user, and generating a personalized nutritional demand vector of the user based on the standard nutritional demand vector of the life stage and the health data vector of the user; acquiring historical data of the user, generating a comprehensive feature vector based on the historical data, and generating a prediction result of the current life stage of the user based on the personalized nutritional demand vector and the comprehensive feature vector; generating a current diet planning scheme of the user based on the personalized nutritional demand vector of the user in the current life stage; according to the invention, continuous optimization and evolution of long-term healthy diet planning can be realized.

The invention belongs to the technical field of medical health, and provides a precise intervention treatment system and method based on personalized nutrition metabolism assessment, and the system comprises a data collection module which is used for collecting the physiological data, nutrition intake data, gene information, microbiome data and lifestyle data of an individual; the metabolism evaluation module is used for inputting the data acquired by the data acquisition module into a metabolism evaluation model based on deep learning; the intervention scheme generation module is used for evaluating a result and an individual health target based on the metabolic condition; through joint cooperation of the data acquisition module, the metabolism evaluation module and the intervention scheme generation module, in combination with deep learning and a multi-objective optimization algorithm, the system can provide a more personalized and scientific health management scheme according to the specific condition of an individual, the health management effect and the success rate of disease intervention are improved, and the system is suitable for popularization and application. And powerful support is provided for individual health management and disease prevention.