8496 results about "Nutrition" patented technology

The invention relates to the field of preparation and application of biological peptides, in particular to a hippocampal swelling peptide and application thereof in preparation of functional products for repairing male reproductive system injury. A green controllable enzymolysis technology is utilized to obtain protein polypeptide with nutritional, health-care and medicinal values, five active peptide sequences with high biological activity are obtained through further research on the basis, the high-purity active peptide is obtained through a chemical synthesis mode, and the biological activity of the peptide is verified through mouse spermatogonium.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention relates to the technical field of intelligent control, in particular to a monopotassium phosphate intelligent control method and system.The method comprises the following steps that rhizosphere phosphorus and potassium concentration changes are obtained, whether the rhizosphere phosphorus and potassium concentration changes are in a critical rate interval or not is judged, an effective absorption time period identifier is generated, and an organ amplification ratio is analyzed to judge a cooperation period; and extracting a stalk concentration ratio, comparing the stalk concentration ratio with a reference interval to generate an application inhibition identifier, fusing information to judge a fertilization window, adjusting the concentration according to the root hair amplification and the water consumption rate, and generating a control instruction set. According to the method, the effective time period of phosphorus and potassium nutrient absorption of the crops is determined through crop rhizosphere phosphorus and potassium ion concentration difference calculation and average treatment, the objective interval of crop nutrient absorption rate indexes is defined, blind fertilization is avoided, the pertinence and accuracy of nutrient absorption and utilization of the crops are enhanced, the excessive application state of nutrient elements is dynamically identified, and the crop quality is improved. The risk of excessive nutrients caused by nutrient enrichment of crops is prevented, and resource waste and environmental burden are effectively reduced.

The invention relates to the cross technical field of computers and livestock breeding, and discloses an intelligent nutrition mode management method and system based on HAP multi-modal data, and the method comprises the steps: obtaining the multi-source heterogeneous HAP data of livestock, and carrying out the feature extraction and feature fusion of the multi-source heterogeneous HAP data; utilizing a controllable generative adversarial network to generate enhanced HAP feature data and an HAP feature data sequence simulating a physiological process; early abnormal signals are detected through an abnormal detection algorithm, and multi-modal attribution analysis is carried out; continuously optimizing the nutrition demand prediction model and the feeding strategy optimization model; according to the method, by effectively utilizing the neglected unstructured information in the past and combining the multi-modal fusion analysis, the early-stage discovery capability of the weak signal for indicating the physiological abnormality or nutritional imbalance of the livestock and poultry is 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 provides a human-computer interaction virtual-real simulation method and system of an intestinal nutrition nursing teaching system, and relates to the technical field of virtual interaction teaching. By integrating a force feedback glove, a bioelectric sensor and a pressure detector, multi-mode interaction data acquisition including intubation angle deviation, maximum force application pressure value and step interval time is realized; the operation feedback dimension and immersion are improved; the system is combined with a scene construction and intelligent guide module, when the detection operation goodness of fit is lower than a preset threshold value or the process completion degree is insufficient, force feedback vector correction and cognitive prompt are triggered, and standard attitude and step memory are guided; and the comprehensive evaluation module summarizes key indexes to calculate a total operation score, performs defect positioning and training suggestion matching on low-score behaviors, generates a structured visual learning report, constructs a cognition-operation-feedback closed loop, and solves the problems of single interactive feedback, insufficient teaching structuralization and lack of feedback mechanisms.

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 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 artificial intelligence, and discloses an intelligent rationing system for pig house feed supply, which comprises the following steps: performing identity recognition on pigs in a feeding area through radio frequency identification tags, and extracting weight, growth cycle and behavior characteristics; based on historical ingestion records, health data and environmental factors, a dynamic nutritional requirement model of the pigs is constructed, and the current feed requirement is predicted in combination with a multi-factor regression algorithm; according to the feed demand result, the feeding amount and the feeding frequency of the corresponding trough are adjusted in real time; dynamically adjusting the power of a conveying motor and the pressure difference of a pipeline section by utilizing a variable-frequency driving control technology; and collecting the ingestion response and the feeding execution state of the pigs, and outputting an updated pig allocation result in combination with online feedback and historical data differences. The feeding device has the advantage that the feeding stability is improved.

The invention relates to the technical field of soilless culture nutrient solution dynamic optimization, and particularly discloses a soilless culture nutrient solution dynamic optimization method which comprises the steps of multi-source fusion monitoring data set generation, main nutrient element demand prediction, nutrient solution adjustment instruction set generation and nutrient solution adjustment effect evaluation. According to the invention, environment, plant physiology and nutrient solution state data are synchronously acquired, a short-term demand prediction model is utilized to predict nutritional demands, and an adjustment instruction set containing the supplement amount and supplement rate of the current nutrient solution is generated in combination with a plant growth stage, a current nutrient solution state and an environment factor; the adjustment effect of the nutrient solution adjustment instruction set is obtained by comprehensively evaluating the improvement degree of the plant physiological status and the stability of the nutrient solution after adjustment is finished; dynamic matching of nutrient solution supply and plant requirements is achieved, the nutrient utilization efficiency is improved, the adaptability of the system to environment changes is enhanced, and remarkable economic benefits and social benefits are achieved.

The invention provides a feed fermentation parameter intelligent correction method based on the Internet of Things, and the method comprises the steps: carrying out the full-dimensional data collection from a microenvironment in a feed fermentation tank to a cross-regional macroenvironment, and generating a standardized data matrix; detecting communication time delay fluctuation characteristics based on the standardized data matrix, determining a fluctuation period and an amplitude range of a time delay abnormal point, and forming a time delay fluctuation characteristic spectrum; analyzing the influence of a raw material nutrition structure and logistics transportation time efficiency on the purity and concentration uniformity of metabolites through a regional differentiation regulation and control plan, and outputting associated feature distribution of quality control imbalance between regions; and updating an operation instruction of the feed fermentation equipment in real time according to the association feature distribution of the quality control imbalance between the regions, and obtaining equipment state operation feedback data after the operation instruction is updated.

Disclosed is a rice and wheat nitrogen nutrition multispectral diagnosis method for precise fertilization by unmanned aerial vehicles (UAVs), including: step 1, acquiring multispectral UAV remote sensing images and collecting agronomic parameters of rice and wheat, and establishing a nitrogen nutrition spectral diagnosis model; and step 2, calculating a critical nitrogen concentration in aboveground parts of rice and wheat based on a critical nitrogen concentration dilution model for amount of dry matter, and further obtaining a critical nitrogen uptake; calculating an actual nitrogen uptake in the aboveground parts of rice and wheat based on the nitrogen nutrition spectral diagnosis model; and obtaining a recommended nitrogen topdressing amount for rice and wheat during corresponding stages based on the critical nitrogen uptake and the actual nitrogen uptake. The present disclosure ensures stable yields of rice and wheat while reducing input of chemical fertilizers and labor, presenting a promising application prospect.

The invention relates to the field of preparation and application of biological peptides, in particular to a swelling hippocampal peptide and application of the swelling hippocampal peptide in preparation of a product for improving memory ability and cognitive impairment. According to the invention, a green controllable enzymolysis technology is utilized to obtain protein polypeptide which has the main molecular weight of less than 1000 Da and has nutritional, health-care and medicinal values, and four active peptide sequences with high biological activity are obtained by further research on the basis of the protein polypeptide.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

The invention discloses a nutrient solution concentration regulation and control method and system for potato aeroponic cultivation, and relates to the technical field of fertilization, and the method comprises the following steps: extracting root feature data and concentration feature data; establishing a concentration trend prediction model based on the root feature data and the concentration feature data, and predicting the concentration change trend of the nutrient solution under different conditions by using the concentration trend prediction model; analyzing the change trend of the nutrient solution concentration under different conditions by using a feature analysis algorithm, and identifying key factors influencing the stability of the nutrient solution concentration; and based on the key factors, the nutrient solution is dynamically prepared. Key factors influencing the concentration stability are identified through a feature analysis algorithm, and the nutrient solution ratio is dynamically regulated and controlled, so that intelligent management and accurate supply of the nutrient solution concentration are realized, and effective support is provided for nutrition optimization of the potato aeroponic culture whole period.

The invention provides a fertilization control method and system, and belongs to the technical field of smart agriculture, and the method comprises the steps: determining a target element-deficient region based on a multispectral remote sensing image of a target region crop, a crop type and a crop growth stage; inputting the multispectral reflectivity data, the soil nutrient data and the crop growth stage of the target nutrient-deficient area into a preset nutrition diagnosis model to obtain a deficient nutrient element type and a deficient degree; inputting the nutrient element type, the lack degree, the soil nutrient data, the crop growth stage, the planned irrigation water amount and the fertilizer mother liquor concentration into a water and fertilizer decision model, and calculating to obtain the target fertilizer liquor concentration and the total fertilizer demand; and controlling the fertilization device to adjust the mother liquor injection proportion and the irrigation flow based on the target fertilizer solution concentration and the total fertilizer demand. The fertilization control method and control system provided by the invention realize accurate regulation and control of water and fertilizer, not only can improve the utilization rate of water and fertilizer, but also can improve the yield and quality of crops.

The invention discloses an application of an HERP gene knockout mouse model in obesity and metabolic disorder diseases. The invention finds that compared with a wild type high-fat group, HERP gene knockout can cause phenotypes such as early obesity, more serious obesity degree, increase of fat weight ratio, aggravation of adipose tissue inflammation, dyslipidemia and glucose metabolism disorder of mice during high-fat feeding. And the HERP gene is knocked out, so that the adipocyte differentiation is increased. The HERP gene knockout mouse is more sensitive to nutrition, obesity can occur more quickly during high-fat feeding, symptoms such as fat amplification, fat inflammation and glucose and lipid metabolism disorder are more serious, and the HERP gene knockout mouse can be used for molecular mechanism research and drug screening of obesity, fat tissue and glucose and lipid metabolism disorder. It is further found that cilostazol can significantly relieve mouse obesity and lipid metabolism disorder caused by HERP gene defects, but has no obvious weight losing effect on normal genotype mice, and a new strategy is provided for precise treatment.

The invention discloses a feed proportion abnormity identification method and system, and particularly relates to the field of feed proportion abnormity identification, and the method comprises the steps: extracting the disturbance variables of vibration amplitude, feeding speed and voiceprint waveform in multi-source disturbance data, constructing the disturbance time sequence expression of a frequency, form and amplitude triple feature structure, and carrying out the recognition of the vibration amplitude, the feeding speed and the voiceprint waveform. Extracting a disturbance characteristic track with center characterization capability in a cluster through atlas clustering, and writing the disturbance characteristic track into a track benchmark expression library; constructing an aligned trajectory expression set by analyzing the frequency domain structure and the time slice expression of each channel signal in the current batch of disturbance data and combining the binding relationship between the batch number and the formula number; by constructing an offset matching and track consistency judgment mechanism based on multi-source disturbance track expression, structured recognition and path logic verification of a formula drift phenomenon are realized, and the problem that the actual feed nutrition state is misjudged due to the fact that real proportion offset caused by residual feed cannot be recognized is solved.

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 discloses a feed additive ratio control method and system based on environmental perception. The method comprises the steps that breeding environment, feed characteristics, equipment operation and animal physiological parameters are collected, and a high-quality parameter set is generated after preprocessing; calculating an environment stress index based on a multi-factor data fusion algorithm and analyzing a change trend; combining historical and current data, using time sequence prediction and a machine learning algorithm to predict additive requirements and generating an optimal proportioning strategy; a control instruction is generated according to the strategy, and the additive conveying and spraying process is regulated and controlled; and a matching strategy and control parameters are corrected in real time through closed-loop feedback, so that accurate regulation and control are realized. The device can dynamically adapt to environmental changes, reduce animal stress, and improve mixing uniformity and nutrition retention effect.

The invention relates to a cultivation nutrient medium and a preparation method thereof. According to the nutrient medium, peat is completely abandoned, the coco coir, the straw and the biochar are combined to reduce ecological burden, the nutrient medium is an environment-friendly medium, the biochar fixes carbon and reduces emission, and the soil structure can be improved after long-term use; vermiculite improves the water-holding capacity, perlite and straw form a stable pore structure, and the ventilation porosity reaches 25% or above. Wormcast and a microbial agent synergistically release nitrogen, phosphorus and potassium, and the fertilizer efficiency is lasting. The microbial agent can inhibit soil-borne diseases such as fusarium and reduce the death rate of cultivated seedlings. The biochar adsorbs salt and toxins, and the EC value is stabilized at 1.2-1.8 mS / cm. The cost of the selected raw materials is reduced by 35% compared with that of a traditional substrate, and the substrate is suitable for large-scale production, suitable for vegetable seedling culture, potted flowers, organic farms and soilless culture systems, and especially suitable for saline-alkali soil or continuous cropping obstacle areas.

The invention discloses a compound composition with a multi-target heart protection function as well as a preparation method and application of the compound composition. The composition is composed of a basic functional oil phase, a core antioxidant / anti-inflammatory phase, a myocardial nutrition and regulation phase and a bioavailability promotion phase in a synergistic manner, and is integrated into a composite cardiac oligopeptide and microencapsulated particle key component substance through a specific enzymolysis and microencapsulation embedding process system. The invention aims to solve the technical problems of single target spot, insufficient synergistic effect of all components, low bioavailability of active components and poor stability of the existing heart protection product, and provides a stable and easily-absorbed composite composition which can realize synergistic effect from multiple channels at the same time. Technical effect verification shows that under the condition of equal-dose sample feeding, the composition can remarkably improve the bioavailability of functional components and shows excellent myocardial cell oxidative damage resistance and myocardial ischemia improvement activity in cell and animal models, the effect is remarkably superior to that of independent use or simple physical mixing of all the components, and the composition is suitable for clinical application. And the substantial progress of synergistic interaction is proved. The composition can be used for developing various medicines, health foods and functional foods beneficial to heart health.

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

The invention discloses a rapid restoration method for degraded grassland based on a microbial agent. The microbial agent is formed by compounding 15-30 parts of a nitrogen-fixing microbial agent, 10-20 parts of a phosphate-solubilizing microbial agent, 8-15 parts of a plant growth-promoting microbial agent, 25-40 parts of a humic acid carrier, 3-8 parts of an embedding agent sodium alginate and 5-12 parts of a nutritional agent molasses. Wherein the nitrogen-fixing bacteria are azotobacter chroococcum and alfalfa rhizobium (the viable count is greater than or equal to 5 * 10 < 9 > CFU / g), the phosphate solubilizing bacteria are bacillus megatherium (the viable count is greater than or equal to 1 * 10 < 10 > CFU / g), and the growth-promoting bacteria are bacillus subtilis and pseudomonas fluorescens (the viable count is greater than or equal to 2 * 10 < 10 > CFU / g). The microbial inoculum is subjected to activation, carrier adsorption, sodium alginate embedding granulation and CaCl2 cross-linking processes to be prepared into slow-release particles of 1-3 mm. 200-250 kg of organic fertilizer is applied to each hectare in a mild area, 300-400 kg of organic fertilizer is applied to each hectare in a moderate and severe area, and leymus chinensis and wheatgrass are sown in a mixed mode. After 90 days of application, the vegetation coverage is improved by 40% or above, the soil organic matter is increased by 25-40%, the microbial diversity index is larger than or equal to 3.5, and the comprehensive remediation cost is reduced by 53%.

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention relates to the technical field of medical health management, in particular to a chronic kidney disease and diabetes collaborative nutrition monitoring and early warning method and system.The method comprises the steps that multi-dimensional physiological indexes, nutrition intake and sleep data of a patient are obtained, and a dynamically-coupled metabolic state topological space is constructed; based on the space, constructing a dual-disease metabolism coupling matrix, analyzing stability characteristics of a metabolism state trajectory, identifying stable and unstable attractors, and calculating a multi-dimensional risk score; a sleep-metabolism synchronism risk prediction model is introduced, and a personalized early warning threshold value is dynamically adjusted; when the risk score exceeds a threshold value, early warning is triggered, and intervention suggestions are generated, the interactive influence of protein metabolism and glycometabolism is quantitatively described from the metabolic mechanism level, and the contradiction of chronic kidney disease and diabetic patients in nutrition management is solved; limitation of a static threshold is broken through, and dynamic risk assessment is realized; sleep factors are creatively integrated, and a new way for improving the metabolic state is provided.

The invention relates to the field of nutrient solutions, and particularly discloses a high-calcium-zinc dietary nutrient solution for promoting skeletal development and a preparation method thereof, the high-calcium-zinc dietary nutrient solution for promoting skeletal development comprises the following raw materials: calcium citrate, zinc citrate, a zinc-containing yeast extract, vitamin D3, a nutrition enhancer, functional oligosaccharide, xanthan gum, anhydrous citric acid, essence, a sweetening agent and water; the preparation method comprises the following steps: mixing the zinc citrate, the zinc-containing yeast extract, the seaweed meal, the natto powder, the stachyose and the trehalose to prepare a mixture A; uniformly premixing stevioside, xanthan gum and trehalose to obtain a mixture B; s4, vitamin D3 powder and trehalose are premixed and dissolved, and a mixture C is prepared; heating the residual water, adding the mixture A, the fructo-oligosaccharide, the anhydrous citric acid, the calcium citrate, the mixture B, the trehalose and the mixture C, adding the essence, and stirring to obtain the beverage. The preparation method has the characteristic of preparing the liquid calcium-zinc dietary nutrient solution with good absorption and utilization rate and palatability.

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