1489 results about "Dieting" patented technology

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention relates to the technical field of health management, in particular to a personalized diet and exercise health management system fusing large model analysis ability, which comprises a multi-source data acquisition module, an integrated intelligent wearable equipment interface and the like, and is used for acquiring multi-dimensional health data; the health portrait modeling module fuses multi-modal data based on an improved Transform architecture, and outputs a dynamic portrait containing risk early warning and trend prediction; the personalized scheme generation module integrates the medical knowledge graph and the user gene features to generate a personalized health scheme; the scenarized recommendation engine matches diet and exercise resources in combination with a real-time scene; the closed-loop supervision optimization module implements scheme execution evaluation and adaptive adjustment through federated learning; and the risk early warning subsystem constructs an acute and chronic disease prediction model to realize risk early warning. According to the invention, precise health management is realized, the scheme effectiveness and the user experience are improved, the data security is guaranteed, and the intelligent development of health management is promoted.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention discloses a dynamic blood glucose monitoring and intelligent pump speed adjusting system and method, and relates to the technical field of biomedical engineering.The dynamic blood glucose monitoring and intelligent pump speed adjusting method comprises the steps that precise monitoring is conducted through a multi-mode sensing module, an enzyme electrode sensor adopts an anti-interference coating, a Raman spectrum dynamic correction algorithm is combined, and motion artifacts and temperature drift are restrained; the metabolic feature fusion calibration module is integrated with a time convolution network, time sequence modeling is carried out on diet, exercise and historical blood glucose data, an individualized metabolic parameter database is constructed, and self-adaptive signal drift compensation is achieved; the self-adaptive pump control module innovatively designs a double-layer fuzzy PID controller, a base layer generates an infusion rate according to a real-time blood glucose state and insulin activity, an optimization layer introduces an LSTM metabolism prediction model, and the slope of an infusion curve is dynamically adjusted in combination with future behavior pre-judgment; in the edge-cloud collaborative architecture, a lightweight TCN model is locally deployed to guarantee real-time control, and a cloud end optimizes a global digital twinborn model through federal learning and generates a multi-dimensional metabolic risk early warning report.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention discloses a chronic heart failure nutrition health assessment system based on machine learning, which belongs to the field of health monitoring and comprises an acquisition processing module, a feature fusion module, a diet recognition module, a metabolism analysis module, a risk assessment module, a health prediction module, a nutrition recommendation module, an interpretation feedback module, a simulation prediction module and a feedback monitoring module. According to the method, the nutrition behavior and physiological status of the patient can be comprehensively described, the accuracy and efficiency of nutrition evaluation are remarkably improved, the nutrition intervention target spot is accurately identified, and the understanding and compliance of the patient are improved.

The invention discloses a chronic disease intervention safety detection system and method based on large model multi-agent cooperation, and relates to the technical field of natural language processing and multi-agent cooperation in the medical health field. A standardized interface and an adaptive cleaning algorithm are adopted to realize data desensitization, missing value filling and format unification, and a dynamically updated patient health portrait is constructed; complex tasks are decomposed based on a chain reasoning technology, special tools such as a drug interaction detection tool and a nutrition gap calculation engine are developed, and a clinical knowledge base is integrated for parallel analysis; suggestion conflicts are eliminated through a multi-agent debate mechanism, the priority is calculated in combination with a weight rule, manual auditing is triggered to process low-confidence disputes, and finally a structured health report containing medication adjustment, diet optimization and behavior intervention is generated. The limitation of a traditional method in data integration, cross-domain reasoning and conflict resolution is solved.

The invention relates to the technical field of intelligent cook machine recommendation, and discloses an intelligent cook machine recipe recommendation and operation control method, which comprises a user input module used for receiving user instructions including food material types, cooking targets, diet restrictions and equipment models; the recognition module is used for collecting food material weight and state data; the data processing module adopts a multi-modal fusion algorithm; the recipe recommendation module is based on an improved collaborative filtering algorithm and a knowledge graph; and the output module is used for controlling the instruction generation unit to output a sectional operation instruction set according to the selected recipe. By integrating the image recognition unit and the weight sensor, the type, weight and state data of food materials placed in the chef machine by a user can be collected in real time, and correction processing is carried out in combination with the environment temperature and humidity sensor; therefore, the existing food material inventory of the user can be accurately considered during recipe recommendation, and the problem that the existing food materials of the user cannot be fully utilized during recipe recommendation by an existing system is solved.

The invention discloses a blood glucose management method, a user interface and a related device, and the method can determine a first meal time based on a historical blood glucose value of a user, and determine a second meal time based on one or more of the meal time input by the user, the limb movement of the user and the heart rate. Third meal time is determined based on the first meal time and the second meal time, and the blood glucose health condition of the user is evaluated according to the third meal time. Therefore, according to the method, the dining time of the user can be predicted in combination with the dining time, determined in various modes, of the user in the historical diet event, and the electronic equipment can evaluate the blood glucose condition of the user more accurately.

The utility model discloses an intelligent straw which is characterized in that an induction electrode is coated and attached in the pipe wall of a hollow pipe along the length direction of the hollow pipe to collect the data of the amount of liquid passing through the hollow pipe, the hollow pipe can be made of food-grade materials such as silica gel and food-grade plastics, and the intelligent straw has the function of digitizing drinking water. The system can transmit data to a mobile phone or a cloud end, carries out health analysis through the data, achieves recording reminding, affects healthy diet of young people from behaviors, serves as an intelligent terminal of the Internet of Things, builds a digital connection between a merchant and a user, and further optimizes customer services better.

The invention relates to the field of health management and intelligent terminals, and discloses a nutrition management method and system, a terminal and a storage medium, and the nutrition management method comprises the following steps: an integrated voice recognition module is used for capturing a voice instruction of a user and analyzing a user demand; deploying an augmented reality engine, generating a virtual food model according to a user demand, and displaying a visual effect of the food combination in real time; and establishing a personalized diet database, comprehensively analyzing health data and living habits of the user, and generating a dynamic diet plan. According to the invention, a multi-modal fusion voice recognition and augmented reality scheme is adopted, and a personalized diet database and a sensor network are combined, so that real-time diet management and plan adjustment are realized. The diet health influence is dynamically displayed through an augmented reality simulation tool, flexible intervention is provided through an intelligent reminding system, and the problems that in the prior art, the feedback mode is single, and behavior guidance is not in place are solved.

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 discloses a hospital obese patient intelligent weight loss dynamic matching intervention method and decision making system based on multi-modal data fusion, and relates to the field of medical health, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-modal data; s2, dynamic metabolism modeling and plateau period breakthrough; s3, generating a hierarchical intervention strategy; s4, performing psychological-physiological linkage intervention; and S5, performing real-time safety control and feedback optimization. By collecting multi-dimensional data, constructing the model, accurately predicting the metabolic rate, the metabolic plateau period and the sudden eating tendency, dynamically adjusting the exercise intensity and the diet scheme according to the conditions of different patients, triggering a low-intensity exercise mode for the obese patients with cardiac insufficiency and calibrating diet, ensuring the exercise safety, and improving the exercise efficiency. Operation suggestions are generated on the basis of the standard, precise preoperative evaluation is conducted, postoperative rehabilitation is optimized in combination with nerve stimulation data, dangerous postures are monitored, the sport injury risk is reduced, and personalized, safe and efficient intelligent weight reduction intervention for obese patients is achieved.

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 an ultrasound-based diabetic renal lesion risk detection method and system, and relates to the technical field of ultrasonic detection.According to the system, through continuous scanning of a two-dimensional ultrasonic probe, an elastic imaging probe and a Doppler probe, the shape, structure, blood flow characteristics and cortex images of the kidney are obtained; an image processing technology is adopted for preprocessing, space registration and fusion, and a kidney area is automatically segmented; according to the extracted kidney tissue characteristics, calculating an atrophy risk coefficient and a fibrosis risk coefficient, and comparing with a standard threshold value to evaluate the kidney lesion risk; calculating a comprehensive risk coefficient in combination with blood glucose, urine protein and diet data, and evaluating diet influence risks; a renal lesion risk assessment model is constructed by using a convolutional neural network and a machine learning algorithm, a renal lesion prediction function is generated, and the system can accurately assess the renal lesion risk of a diabetic patient and provide early diagnosis and personalized treatment suggestions.

According to the clinical nutrition intelligent management system and method disclosed by the invention, nutrition status evaluation is carried out by applying a nutrition comprehensive evaluation formula, the standardization and objectivity of evaluation are greatly improved, the formula quantifies multiple key factors and gives weights, different evaluators carry out calculation based on the same information and formula, and the consistency of evaluation results is ensured; besides, the comprehensive nutrition evaluation formula comprehensively considers the body mass index, the disease-related nutrition influence coefficient, the nutrition-related index comprehensive score, the diet health degree score and other aspects adjusted by age and gender, accurately reflects the actual nutrition condition of the patient, and solves the problem of insufficient accuracy of traditional evaluation. And the nutrition plan generation module can customize nutrition plans for patients according to the nutrition comprehensive evaluation indexes in combination with a preset rule base, so that individual requirements of different patients are fully met, and a scientific, accurate and personalized solution is provided for clinical nutrition management.

The invention relates to the technical field of nutritional supplements. A traditional fat reducing method mainly comprises dieting, high-intensity exercise, medicine weight losing and the like. However, dieting may lead to malnutrition; high-intensity sports are difficult to insist, and sports injury is easily caused; in order to solve the technical problems, the invention discloses a fat-reducing nutritional composition capable of improving metabolism, and the fat-reducing nutritional composition can be used for solving the technical problems of severe side effects such as tachycardia, insomnia and the like caused by medicine weight loss. The composition comprises the following components in parts by weight: 13-21 parts of a bitter gourd extract, 15-31 parts of a garcinia cambogia extract, 13-17 parts of oat fibers, 17-23 parts of whey protein powder, 7-9 parts of linseed oil, 6-14 parts of a green tea extract, 9-15 parts of a calcium supplement and 15-19 parts of a purple perilla seed extract, and can provide energy for a user, supplement immune globulin, lactoferrin and a calcium source, improve the immunity of the user and improve the immunity of the user. The body metabolism level and immunocompetence are improved, absorption of fat in digestive tracts can be effectively inhibited, storage of fat in the body is reduced, fat consumption is promoted, and the fat reducing effect is achieved.

The invention discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The embodiment of the invention discloses a skeleton health dynamic tracking and intervention method and system based on movement and nutrition. The method comprises the following steps: acquiring gait information, bone metabolite information and bone state measurement parameters of a target user by adopting wearable equipment; processing the gait information, the bone metabolite information and the bone state measurement parameters based on an artificial intelligence (AI) model to obtain bone state prediction parameters; according to the bone state prediction parameter and the health data of the target user, performing bone health state evaluation of the target user to obtain a health state prediction parameter; the health state prediction parameters comprise a bone health state instant evaluation index and bone health dynamic trend data; generating a targeted exercise plan and a diet plan according to the health state prediction parameters; wherein the target motion planning is used for guiding the motion of the target user; the diet planning is used for guiding the diet of the target user.

The invention relates to a diabetes exercise rehabilitation and diet collaborative management method, device and equipment and a medium. The method comprises the following steps: acquiring individual feature data, physiological data and daily behavior data of a diabetic patient, and constructing a personalized portrait of a user; generating an exercise plan and a diet plan according to a set period based on the personalized portrait of the user; in response to the obtained real-time user data, dynamically correcting the exercise plan and the diet plan to obtain an exercise and diet collaborative plan; the real-time user data comprises blood glucose data, diet intake data and exercise data; the exercise and diet collaborative plan comprises exercise items arranged based on time points and diet arrangement; generating a corresponding push instruction according to the time point; the pushing instruction is used for indicating to push corresponding exercise items or diet arrangements to the terminal of the diabetic patient. By adopting the method, the problem of inadaptation caused by a rigid fixed plan can be avoided by an exercise-diet collaborative intervention mechanism, and the management response capability of external variable change is improved.

The invention discloses a gout diet education system based on multi-modal interaction and an intelligent teaching aid. The system comprises a registration module, an intelligent recommendation module, a multi-modal interaction module, a diet recording module, a health monitoring module and a cloud server. The registration module receives personal information input by a user through intelligent equipment; the intelligent recommendation module generates a preliminary personalized diet scheme based on personal information by using a deep learning algorithm in combination with a medical knowledge base and a gout diet management guide; the multi-modal interaction module displays gout diet education content in a multi-modal mode; the diet recording module records diet data in real time and provides instant feedback according to the diet data and the preliminary scheme; the health monitoring module collects physiological indexes through a wearable sensor and synchronously uploads the physiological indexes and diet data to the cloud server; and the cloud server generates a diet and health association result and transmits the diet and health association result to the intelligent recommendation module to optimize the preliminary scheme. The system can provide quasi diet management support for gout patients.

The invention relates to the technical field of health management, and discloses an intelligent food recommendation and diet optimization system based on body temperature change, and the system comprises a body temperature dynamic fluctuation curve generation module which collects data in real time through a multi-source temperature sensor of wearable equipment, and is used for generating a body temperature dynamic fluctuation curve; the metabolism state label acquisition module is used for matching the time-frequency domain characteristics with a preset metabolism mode library so as to acquire corresponding metabolism state labels; the calculation module is used for calling nutrient element intake records in the historical diet database of the user and a preset health target and is used for calculating a current metabolic rate deviation value and a nutrition gap; the diet scheme generation module is used for generating a suggested diet scheme containing a recommendation priority score and a real-time eating suggestion based on the metabolic rate deviation value, the nutrition gap and a preset health target; and the monitoring module is used for monitoring body temperature change data after the user executes the recommended diet scheme. The accuracy of health management can be improved.

The invention provides a whole-course nursing management system combining traditional Chinese medicine and western medicine for tumor patients, and relates to the field of intelligent health management.The whole-course nursing management system comprises the steps that physiological, exercise, diet and psychological condition data of the tumor patients are collected at first, and then potential health risks of the tumor patients are recognized by analyzing the physiological, exercise and diet data; meanwhile, the enthusiasm of the patient is evaluated according to psychological condition data, and then all collected data and recognition and evaluation results are input into a Chinese and western medicine combined nursing health management suggestion engine based on a large language model to generate a Chinese and western medicine combined nursing health management suggestion conforming to the actual condition of the tumor patient. Therefore, comprehensive and personalized health management service can be provided for tumor patients.

The invention relates to the technical field of chronic disease examination, in particular to an AI-based chronic disease risk prediction and early intervention method, which comprises the following steps of: acquiring physiological data, living habit data, environmental data and historical health data of a user through various devices and platforms, and then preprocessing the acquired data to obtain an AI-based chronic disease risk prediction and early intervention result; the method includes the steps of data cleaning, missing value filling and standardization, then conducting feature selection through a principal component analysis method, extracting key features related to chronic disease risks, building a chronic disease risk prediction model through an improved random forest algorithm on the basis of the key features, and finally predicting the chronic disease risks in combination with a model prediction result. Personalized intervention suggestions are provided for the user, and diet adjustment, exercise plans, drug prevention, regular physical examination and other aspects are covered; according to the method, high-risk people with chronic diseases can be accurately identified in an early stage, and accurate risk prediction and personalized intervention strategies are provided, so that occurrence and development of the chronic diseases are effectively prevented.

The invention provides a hypoglycemic peptide which is prepared by taking bovine whey protein as a raw material and is stable in gastrointestinal digestion and absorption. In an in-vitro cell experiment, the peptide fragment can improve the uptake of insulin-resistant HepG2 hepatocytes and synthesize glycogen by using glucose, can promote intestinal endocrine cells NCI-H716 to secrete GLP-1, has a certain synergistic effect, and can further play the effects of promoting GLP-1 secretion and activating a GLP-1 receptor through combined use. An insulin resistance mouse model is constructed by utilizing high-fat diet, and the peptide fragment is adopted for intervention, so that the hypoglycemic peptide is further proved to have obvious effects on losing weight, reducing lipid accumulation and assisting in reducing blood fat, and meanwhile, the hypoglycemic peptide can be used for improving insulin resistance related symptoms. The milk peptide provided by the invention has gastrointestinal digestion stability and hypoglycemic activity, can be used as a functional milk base material for developing food, medicines or health care products with the effect of regulating blood sugar, and is wide in application prospect.

The invention relates to the technical field of health information processing, in particular to an energy metabolism analysis and patient information integration method and system.The energy metabolism analysis and patient information integration method comprises the following steps that on the basis of metabolic rate data and blood detection indexes, classification matching is conducted on local deviation of the metabolic rate and distribution characteristics of biochemical indexes in blood detection; and extracting statistical characteristics in the local abnormal region, and analyzing the correlation change between the metabolic migration and the biochemical indexes. According to the method, potential health risks are revealed through dynamic correlation analysis of metabolic migration characteristic values and blood detection indexes, accurate capture of dynamic physiological changes is achieved in combination with correlation evaluation of vital sign fluctuation data and exercise intensity in the time period, and the dynamic physiological changes are accurately captured through stratified analysis of causal paths and high-frequency health risk parameter adjustment. The accuracy and layering of health risk identification are improved, the nutritional ingredient proportion is optimized to match metabolic requirements, the food intake range is adjusted, personalized diet recommendation is generated through data comparison, and the individual health management requirements are met.

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, which relates to the field of diabetes nursing, discloses a multi-model fusion personalized diet recommendation system for diabetes mellitus, comprising a recipe recommendation decision module based on a system architecture, a blood sugar prediction module, and an intelligent question and answer interaction module. The nutritional requirements and the blood sugar control target of the patient are met at the same time; the blood glucose prediction module predicts the postprandial blood glucose level according to the current blood glucose level of the user and the recommended recipe; the intelligent question and answer interaction module provides convenient natural language interaction, collects user feedback and dynamically adjusts recommended content. Compared with the prior art, the method has the advantages that scientific diet recommendation is provided, the recommendation result is optimized through a user feedback mechanism, and the recommendation accuracy and practicability are ensured; a convenient health management tool is provided, and the compliance of the patient on diet management is enhanced; the health condition of the diabetic patient can be improved, the burden of a medical system is relieved, and the public health level is improved.