Nutrition intake recommendation method, device and system based on dietary structure and medium

Abstract

The invention relates to the technical field of diet recommendation, in particular to a nutrition intake recommendation method, device and system based on a diet structure and a medium. The method comprises the following steps: obtaining element information of various foods, and carrying out diet structure quantitative modeling on various food types to obtain a diet structure quantitative and quantitative model; converting quantitative information of various food categories into multi-dimensional vectors, and inputting numerical values generated by various food element information into an analysis model to obtain nutritional ingredient analysis data; processing intake data of a user based on cross analysis, clustering and classification technologies of diet data and user health data, identifying intake deviation, and analyzing the intake deviation; according to the health target data of the user, dynamically adjusting various parameters in the diet structure quantification and quantification model, generating personalized nutrition intake suggestions, and displaying the suggestions through a user interaction interface; according to the invention, a personalized and dynamic nutrition intake optimization scheme can be provided in combination with the health condition of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of diet recommendation, and particularly to a method, device, system and medium for recommending nutrient intake based on diet structure. Background Art

[0002] With the increasing public health awareness, personalized nutrition management has become an increasingly important trend in the health field. However, most of the current mainstream nutrition analysis systems are limited to the static assessment of basic food calories and nutrient components, and fail to achieve comprehensive, accurate and dynamic personalized analysis. At the same time, the existing nutrition recommendation systems often ignore the user's personalized health goals, current health status and physical differences, resulting in deficiencies in the accuracy and effectiveness of the provided nutrient intake recommendations.

[0003] In addition, the current definition of diet structure mostly still focuses on traditional food categories or single nutrient components, and fails to fully incorporate non-traditional food elements such as drugs, meal replacement products, and tea drinks, which to a certain extent limits the comprehensiveness and scientific nature of the diet structure. In view of this, it is urgent to develop a method for recommending nutrient intake based on diet structure and construct an algorithm for nutrient recommendation that can deeply integrate user health data for in-depth personalized analysis. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, device, system and medium for recommending nutrient intake based on diet structure to solve the problems of insufficient comprehensiveness, personalization and dynamics in existing nutrition analysis; and provide a personalized and dynamic nutrient intake optimization plan in combination with the user's health status.

[0005] On the one hand, embodiments of the present invention provide a method for recommending nutrient intake based on diet structure, including: Obtaining information on various food elements, quantitatively modeling the diet structure of various food categories to obtain a quantitative diet structure model; Inputting the values generated by the information on various food elements into an analysis model to obtain nutrient component analysis data; According to the user's health goal data, dynamically adjusting the parameters in the quantitative diet structure model to generate personalized nutrient intake recommendations and display them through a user interface.

[0006] Optionally, the obtaining information on various food elements, quantitatively modeling the diet structure of various food categories to obtain a quantitative diet structure model includes: Converting the quantitative information of various food categories into a multi-dimensional vector representation, and its formula is: ; Wherein, Indicates calories, Indicates protein content, Indicates fat content, Indicates any dimension.

[0007] Optionally, inputting the values generated from the information of various food elements into an analysis model to obtain nutritional component analysis data includes: Deeply mining the user's dietary structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health target data.

[0008] Optionally, the user's dietary structure data includes; By collecting the information of various food elements ingested by the user daily, quantifying the information of various food elements and recording the data as a high-dimensional vector.

[0009] Optionally, the deep mining of the user's dietary structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health target data includes: Classifying the user's dietary structure data and health status according to similarity to identify the potential relationship between dietary behavior and health status; Based on the K-Means clustering algorithm, clustering the user's dietary structure data and health status, dividing the users into different groups, and further analyzing the dietary patterns and health levels of each group; where, if there are multiple user data sets, the dietary structure data and health status of each user are a set of feature vectors, and its formula is: ; Through cluster analysis, the system can identify at least one group of users with similar health status and eating habits.

[0010] Optionally, the deep mining of the user's dietary structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health target data further includes: Mapping the user's health status to the dietary pattern to predict the achievement of their health goals.

[0011] Optionally, the dynamically adjusting the parameters in the dietary structure quantification model according to the user's health target data, generating personalized nutritional intake suggestions and displaying them through the user interface includes: Generating personalized nutritional supplement suggestions based on the user's health target data, dietary structure data and health status; its formula is: ; Wherein, Indicates the generated personalized supplementary suggestions; Indicates the user's dietary data; Indicates the user's health status; Indicates the user's health goal data; By analyzing and and combining with the goal of , the system dynamically generates suggestions , and dynamically adjusts the user's nutrient intake.

[0012] On the other hand, an embodiment of the present invention provides a nutrient intake recommendation device based on dietary structure, and the device includes: The first module is used to obtain various food element information, perform quantitative modeling on the dietary structure of various food categories, and obtain a quantitative model of the dietary structure; The second module is used to input the values generated by the various food element information into an analysis model to obtain nutrient composition analysis data: The third module is used to dynamically adjust the parameters in the quantitative model of the dietary structure according to the user's health goal data, generate personalized nutrient intake suggestions and display them through a user interaction interface.

[0013] On the other hand, an embodiment of the present invention provides a nutrient intake recommendation system based on dietary structure, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the nutrient intake recommendation method based on dietary structure described in any one of the above.

[0014] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to execute a nutrient intake recommendation method based on dietary structure described in any one of the above when executed by the processor.

[0015] In the embodiments of the present invention, by obtaining various food element information, a quantitative model of the diet structure is established for various food categories to obtain a quantitative model of the diet structure; the quantitative information of various food categories is converted into multi-dimensional vectors, and the values generated by various food element information are input into an analysis model to obtain nutritional component analysis data. Based on the cross-analysis of diet data and user health data, clustering and classification techniques are used to process the user's intake data, identify intake deviations, and according to the user's health target data, dynamically adjust the parameters in the quantitative model of the diet structure, generate personalized nutritional intake recommendations and display them through the user interface. This solves the problems of insufficient comprehensiveness, personalization, and dynamics in nutritional analysis in the prior art; combines the user's health status and provides a personalized and dynamic nutritional intake optimization plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a nutritional intake recommendation method based on the diet structure provided by the embodiments of the present invention; Figure 2 It is a structural block diagram of a nutritional intake recommendation device based on the diet structure provided by the embodiments of the present invention; Figure 3 It is a structural block diagram of a nutritional intake recommendation system based on the diet structure provided by the embodiments of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS In order to make the objectives, technical solutions, and advantages of the present application more clearly understood, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart in the flowchart. Terms such as "first" and "second" in the specification, claims, and the above drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms used herein are for the purpose of describing embodiments of this application only and are not intended to limit this application.

[0020] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be used. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this application.

[0021] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware charging modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the drawings are only exemplary illustrations and do not necessarily include all the content and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0023] Refer to Figure 1 As shown, on the one hand provided by the embodiments of the present invention, the embodiments of the present invention provide a method for recommending nutritional intake based on diet structure, including: S1. Obtain information on various food elements, perform quantitative modeling on the diet structure of various food categories to obtain a quantitative model of the diet structure; Exemplarily, in order to make the diet structure effectively used for personalized nutritional analysis, quantitative modeling needs to be performed on each food category. Dietary components are usually quantified through the following dimensions: Calories (calories): The calories of each food can be calculated based on the mass of the food and the content of its main components (carbohydrates, fats, proteins). The specific formula is as follows: Calories = Carbohydrate content × 4 + Protein content × 4 + Fat content × 9 Trace elements: The content of minerals such as calcium, iron, zinc, etc. is usually obtained through chemical analysis or literature.

[0024] Functional components: content of, for example, vitamins, phytochemicals (such as tea polyphenols, flavonoids, etc.).

[0025] Dietary fiber: As an important health indicator, the amount of dietary fiber can be extracted from the food composition table.

[0026] Each food has specific values in these dimensions, and these values will be used as input data and fed into the subsequent analysis model. Taking the "meat products" category as an example, assume the content of a certain kind of meat is as follows: Calories: 250 kcal / 100g; Protein: 20g / 100g; Fat: 15g / 100g; Iron: 3mg / 100g; Zinc: 2mg / 100g; Through the vector model, the above data is stored as the feature vector of the food, specifically represented as: =(250, 20, 15, 3, 2) Through the said vector model, summarization can be carried out during use to ensure that each food can be compared and analyzed under the same standard.

[0027] S2. Input the values generated by the above various food element information into the analysis model to obtain the nutritional component analysis data; Exemplarily, based on the following multiple major categories, and detailed nutritional component analysis is carried out for each category of food: Food processing products: such as rice, flour, corn, glutinous rice, etc., mainly providing carbohydrates, a small amount of protein and fat.

[0028] Edible oils, fats and their products: such as vegetable oils, animal oils, butter, etc., mainly providing fats (including saturated fats, monounsaturated fats, omega-3 and omega-6 fatty acids, etc.).

[0029] Condiments: such as salt, soy sauce, chili, sugar, etc., mainly providing sodium, potassium, sugars and trace elements.

[0030] Meat products: such as pork, beef, chicken, ham, sausage, etc., providing protein, fat and some trace elements (such as iron, zinc, etc.).

[0031] Dairy products: such as milk, yogurt, cheese, etc., mainly providing calcium, protein, vitamin D and B12, etc.

[0032] Beverages: such as water, fruit juice, carbonated drinks, coffee, etc., mainly providing water, electrolytes, sugars, etc.

[0033] Tea and related products: such as green tea, black tea, scented tea, etc., mainly providing caffeine, polyphenolic compounds, etc.

[0034] The above structural classification is only an example content of the present invention, and specifically includes but is not limited to: "dairy products, beverages, convenience foods, biscuits, canned foods, frozen drinks, frozen foods, potato and puffed foods, confectionery products,, alcoholic beverages, vegetable products, fruit products, aquatic products, starch and starch products, soy products, health foods, special dietary foods, food additives", etc. Other food structural classifications can refer to the introduction of this embodiment and will not be elaborated here.

[0035] S3. According to the user's health target data, dynamically adjust the parameters in the dietary structure quantification and quantification model, generate personalized nutrition intake suggestions and display them through the user interaction interface.

[0036] Exemplarily, according to the identified nutritional differences, the system will generate specific supplementation suggestions according to the user's health goals (such as weight loss, muscle gain, immune enhancement, etc.). For example, if the user's goal is weight loss, the system will recommend reducing foods with excessive calorie intake and increasing low-calorie and high-fiber foods (such as vegetables, whole grains, lean meats, etc.) according to the user's dietary data and health status. At the same time, the system will also recommend increasing protein intake to help maintain muscle mass and increase satiety.

[0037] Example suggestions: Increase high-protein foods: such as chicken breast, fish, soy products, etc.

[0038] Reduce high-sugar foods: such as desserts, carbonated drinks, etc.

[0039] Increase dietary fiber: such as leafy vegetables, whole wheat foods, etc.

[0040] Immune enhancement goal: For the immune enhancement goal, the system will recommend increasing foods rich in antioxidants, vitamin C and zinc, such as citrus fruits, red peppers, spinach, nuts, etc. At the same time, the system will prompt the user to avoid eating too much processed food or foods with high sugar content, because these foods may inhibit the immune system function.

[0041] Example suggestions: Increase vitamin C: such as oranges, lemons, leafy vegetables.

[0042] Increase zinc: such as seafood, nuts, beans.

[0043] Avoid high-sugar foods: such as reducing the intake of candies and desserts.

[0044] Specifically, the obtaining of information on various food elements and the quantitative modeling of the dietary structure for various food categories to obtain a quantitative dietary structure model includes: Converting the quantitative information of the various food categories into a multi-dimensional vector representation, and its formula is: ; where, represents calories, represents protein content, represents fat content, represents any dimension.

[0045] Exemplarily, for the convenience of data analysis, the quantitative information of all food categories will be converted into a multi-dimensional vector representation.

[0046] For example, the vector of grain processed products can be represented as: ; where, represents calories, represents protein content, represents fat content, and so on until all dimensions are covered.

[0047] It can be understood that each food category has a corresponding vector, and these vectors will be input as input data into the data analysis module.

[0048] Specifically, the inputting of the values generated from the information on various food elements into an analysis model to obtain nutritional component analysis data includes: Deeply mining the user's dietary structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health target data.

[0049] Exemplarily, combining the user's dietary data and health status data, and revealing the user's nutritional intake deviation through cross-analysis to generate personalized nutritional recommendations. This module uses a variety of algorithmic techniques, combining clustering and classification analysis methods, to deeply mine the user's dietary and health data, so as to identify the imbalances or deficiencies in their intake and analyze whether they are related to the user's health goals (such as weight loss, muscle gain, immune enhancement, etc.).

[0050] Specifically, the user's dietary structure data includes; By collecting information on various food elements ingested by the user daily, the quantitative data of the various food elements information is recorded as a high-dimensional vector.

[0051] Exemplarily, the user's diet data is represented by collecting various food data consumed daily. For example, the foods consumed by the user in a day include rice, vegetables, chicken, milk, etc., and the system records the quantified data of each food (such as calories, protein, fat, etc.) as a high-dimensional vector.

[0052] Suppose the user's daily diet data is , where each represents the intake of the i-th food. For example: (Rice): 100 grams; (Vegetables): 200 grams; (Chicken): 150 grams; (Milk): 300 milliliters; These data can be converted into the corresponding nutritional components of each food, such as calories, protein, fat, etc. The user's health status data includes health indicators such as weight, height, blood sugar, and blood lipid. Suppose these data are represented by , where each represents a certain health indicator.

[0053] For example: (Weight): 75 kg; (Blood sugar level): 5.2 mmol / L; (Blood lipid level): 3.0 mmol / L; (Basal metabolic rate): 1500 kcal / day; These health data are used in combination with the diet data to help the system analyze whether the user's intake status meets the health goals.

[0054] Specifically, the in-depth mining of the user's diet structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health goal data includes: Classify the user's diet structure data and health status according to similarity to identify potential connections between diet behavior and health status; Cluster the user's diet structure data and health status based on the K-Means clustering algorithm, divide the users into different groups, and further analyze the diet patterns and health levels of each group; among them, if there are multiple user data sets, the diet structure data and health status of each user are a set of feature vectors, and its formula is: ; Through cluster analysis, the system can identify at least one group of users with similar health conditions and eating habits.

[0055] Exemplarily, cluster analysis refers to classifying the user's diet data and health data according to similarity to identify potential connections between eating behaviors and health conditions. For example, the K-Means clustering algorithm can be used to cluster the user's diet and health condition data, dividing users into different groups, and further analyzing the eating patterns and health levels of each group. For example, one group of users may consume more high-fat foods and have higher blood sugar, while another group of users consume more vegetables and fruits and have better weight control. Based on the clustering results, evaluate the impact of eating habits on health.

[0056] Specifically, the in-depth mining of the user's diet structure data and health condition to identify the proportion of their intake and analyze its correlation with the user's health target data further includes: Map the user's health condition to the eating pattern to predict the achievement of their health goals.

[0057] Exemplarily, classification algorithms such as decision trees or support vector machines (SVM) can be used to analyze the relationship between the foods consumed by the user and the health goals. The goal of classification is to identify which food types have the greatest impact on health goals.

[0058] For example, assuming the user's health goal is to lose weight, the system will identify whether the user has consumed too many high-calorie foods. If a user's diet data is: =(200, 100, 50, 50) (intake of high-calorie foods); And the user's health data is: ; (weight: 85 kg, blood sugar: 6.0 mmol / L, blood lipid: 4.5 mmol / L); Through analysis, it will be found that the user has a high intake of high-calorie foods in their diet and has a large body weight. They may need to reduce the intake of high-calorie foods and adjust other dietary components, such as increasing dietary fiber and reducing sugar.

[0059] Exemplarily, through intake deviation identification, the system can identify the deficiencies of users in certain nutrients.

[0060] For example, if the user's health goal is to enhance immunity, the system will focus on analyzing trace elements related to the immune system, such as vitamin C, zinc, and iron. If these nutrients are lacking in the user's diet, the system will give supplementation suggestions.

[0061] For example, assume the user's dietary data is as follows: = (150, 50, 100, 80) (intakes of rice, vegetables, chicken, and milk); Among them, the user has less intake of vitamin C and zinc. The system analyzes their health data (such as immunity indicators, blood test results, etc.) and identifies potential problems in the user's immunity improvement.

[0062] Based on this analysis result, the algorithm will recommend foods rich in vitamin C and zinc, such as citrus fruits, spinach, and nuts.

[0063] Specifically, dynamically adjusting the parameters in the dietary structure quantification model according to the user's health target data, generating personalized nutrition intake suggestions and displaying them through the user interface includes: Generating personalized nutrition supplement suggestions based on the user's health target data, dietary structure data, and health status; its formula is: ; Among them, represents the generated personalized supplement suggestion; represents the user's dietary data; represents the user's health status; represents the user's health target data; by analyzing and and combining with the goal of , the system dynamically generates the suggestion to dynamically adjust the user's nutrition intake.

[0064] Exemplarily, based on the user's health goals, dietary structure, and health status, generate personalized nutrition supplement suggestions. This module will not only automatically identify areas of nutritional deficiency according to the user's current eating habits and health status, but also provide optimized dietary adjustment plans and supplement suggestions according to the user's goals (such as weight loss, muscle gain, enhanced immunity, etc.), ensuring that the recommended supplement plan can help the user achieve their health goals to the greatest extent.

[0065] In one embodiment, the system first analyzes the user's dietary data and health data to identify which nutrients are insufficient.

[0066] For example, by comparing the user's current dietary structure (such as calories, protein, fat, trace elements, etc.) with the recommended intake standards, calculate the intake differences of various nutrients for the user.

[0067] If the amount of vitamin C ingested by the user is lower than the daily recommended intake, the system will identify the deficiency of vitamin C. The formula for analyzing the nutritional component difference is as follows: Difference = Recommended intake - Actual intake; If the difference is positive, it indicates that the user's intake is insufficient; if it is negative, it means the intake is excessive.

[0068] In one embodiment, the system can also combine the user's health conditions, such as blood sugar level, blood lipid, etc., to analyze whether the deficiency or excess of certain nutritional components is related to health problems. For example, users with high blood sugar may need to reduce the intake of sugar and refined carbohydrates, while users with low immunity may need to increase the intake of trace elements such as vitamin C and zinc.

[0069] In one embodiment, as the user's eating habits and health conditions change, the diet structure model needs to have the ability to adapt dynamically. For example, during the weight loss process, the user's intake of grain and oil-based foods will decrease, while the proportion of vegetables, fruits, and low-fat meats will increase. The system will then dynamically adjust the diet structure according to these changes; Exemplarily, according to the changes in the user's health conditions (such as weight change, disease development, etc.), the parameters in the diet structure model are dynamically adjusted. For example, during the weight loss process, the intake of grain and oil-based foods is reduced, and the proportion of vegetables, fruits, and low-fat meats is increased. To meet the user's health needs at different stages, and based on the dynamically adjusted diet structure model, personalized nutritional intake recommendations are generated, including the daily intake of various foods, meal arrangements, cooking methods, etc.

[0070] In one embodiment, if the user follows the weight loss advice and successfully loses weight, the system will further recommend a diet structure that is more suitable for their current physical state; if the user fails to achieve the goal during the muscle gain process, the system will adjust the intake recommendations for protein and carbohydrates and provide a new diet plan.

[0071] Through personalized recommendations, the system can effectively support users in achieving health goals, such as weight loss, muscle gain, or immune enhancement.

[0072] In an embodiment of the present invention, by obtaining various types of food element information, a quantitative and qualitative model of the dietary structure is established for various food categories to obtain a quantitative and qualitative model of the dietary structure; the quantitative information of various food categories is converted into multi-dimensional vectors, and the numerical values generated by various food element information are input into an analysis model to obtain nutritional component analysis data: based on the cross-analysis of dietary data and user health data, clustering and classification techniques are used to process the user's intake data to identify intake deviations, and according to the user's health target data, various parameters in the quantitative and qualitative model of the dietary structure are dynamically adjusted to generate personalized nutritional intake recommendations and display them through a user interface. This solves the problems of insufficient comprehensiveness, personalization, and dynamics in nutritional analysis in the prior art; combined with the user's health status, a personalized and dynamic nutritional intake optimization plan is provided.

[0073] Referring to Figure 2 , an embodiment of the present invention provides a nutritional intake recommendation device based on the dietary structure, and the device includes: A first module, configured to obtain various types of food element information, perform quantitative and qualitative modeling on various food categories to obtain a quantitative and qualitative model of the dietary structure; Specifically, based on the latest research results of nutritional science and functional medicine, a multi-dimensional dietary structure model can be established. This model comprehensively analyzes the user's nutritional intake by quantitatively describing the nutritional components of different categories such as food, medicine, tea, meal replacement, and nutritional supplements, considering factors such as calories, trace elements, and functional components.

[0074] A second module, configured to input the numerical values generated by the various types of food element information into an analysis model to obtain nutritional component analysis data: Specifically, by cross-analyzing the user's dietary data and health status data, clustering and classification algorithms are used to identify intake deviations. For example, the system can identify deficiencies in trace elements or functional components of the user and analyze whether they are associated with health goals (such as weight loss, muscle gain, immunity improvement, etc.).

[0075] A third module, configured to dynamically adjust various parameters in the quantitative and qualitative model of the dietary structure according to the user's health target data, generate personalized nutritional intake recommendations and display them through a user interface.

[0076] Specifically, according to the user's health goals, the algorithm dynamically generates nutritional supplement recommendations. For example, if the user's goal is muscle gain, the system will recommend high-protein foods or corresponding nutritional supplements; if the goal is weight loss, it will recommend reducing the intake of high-calorie foods and recommend meal replacement products.

[0077] By defining a new diet structure that covers multiple categories such as food, medicine, tea preparations, meal replacements, and nutritional supplements, and combining with the user's health condition, a personalized and dynamic optimized nutrition intake plan is provided.

[0078] See Figure 3 , embodiments of the present invention provide a nutrition intake recommendation system based on a diet structure, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the nutrition intake recommendation method based on the diet structure as described in any one of the above.

[0079] It can be seen that the content in the above method embodiments is applicable to the system embodiments of the present invention. The functions specifically implemented in the system embodiments of the present invention are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0080] On the other hand, embodiments of the present invention provide a computer-readable storage medium, in which a program executable by a processor is stored. The program executable by the processor is used to execute a nutrition intake recommendation method based on a diet structure as described in any one of the above when executed by the processor.

[0081] In addition, embodiments of the present application also disclose a computer program product or a computer program. The computer program product or the computer program is stored in a computer-readable storage medium. The processor of the computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the above method. Similarly, the content in the above method embodiments is applicable to the storage medium embodiments of the present invention. The functions specifically implemented in the storage medium embodiments of the present invention are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the charging modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional charging modules / units in the systems and devices can be implemented as software, firmware, hardware, and their appropriate combinations.

[0084] In the description of this application and the above-mentioned drawings, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0085] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (individual) of the following" or its similar expression refers to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one (individual) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0086] In several embodiments provided by this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0087] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0089] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0090] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A method for recommending nutrient intake based on dietary structure, characterized in that: include: Obtain information on various food elements, conduct quantitative modeling of the dietary structure of various food categories, and obtain a quantitative model of the dietary structure; Inputting the numerical values ​​generated by the various food element information into the analysis model to obtain the nutrient component analysis data; According to the user's health goal data, the parameters in the quantitative model of dietary structure are dynamically adjusted to generate personalized nutritional intake recommendations and display them through the user interaction interface.

2. The method according to claim 1, characterized in that The obtaining of information on various food elements and the quantitative modeling of the dietary structure of various food categories to obtain the quantitative model of the dietary structure include: The quantitative information of each food category is converted into a multi-dimensional vector representation, and the formula is: ； in, Indicates heat, Indicates the protein content, Indicates fat content, Represents any dimension.

3. The method according to claim 1, characterized in that: The step of inputting the numerical values ​​generated by the various food element information into the analysis model to obtain the nutrient component analysis data comprises: Deeply mine the user's dietary structure data and health status to identify the proportion of their intake and analyze its correlation with the user's health goal data.

4. The method according to claim 3, characterized in that The user's dietary structure data includes: By collecting information on various food elements consumed by users daily, the quantitative data of various food elements are recorded as a high-dimensional vector.

5. The method according to claim 3, characterized in that: The in-depth mining of the user's dietary structure data and health status to identify the proportion of their intake and analyze their correlation with the user's health target data includes: Classify users’ dietary structure data and health status according to similarities to identify potential links between dietary behavior and health status; Based on the K-Means clustering algorithm, the dietary structure data and health status of users are clustered, and users are divided into different groups, and the dietary patterns and health levels of each group are further analyzed. If there are multiple user data sets, the dietary structure data and health status of each user are a set of feature vectors, and the formula is: ； Through cluster analysis, the system can identify at least one group of users with similar health conditions and eating habits.

6. The method according to claim 5, characterized in that The in-depth mining of the user's dietary structure data and health status to identify the proportion of the intake and analyze the correlation with the user's health target data also includes: Mapping users' health status to their dietary patterns to predict their achievement of health goals.

7. The method according to claim 1, characterized in that The method of dynamically adjusting various parameters in the quantitative model of dietary structure according to the user's health target data, generating personalized nutritional intake suggestions and displaying them through the user interaction interface includes: Generate personalized nutritional supplement recommendations based on the user's health goal data, dietary structure data, and health status; the formula is: ； in, represents the generated personalized supplement suggestion; Represents the user's dietary data; Indicates the user's health status; Represents the user's health goal data; and analysis, combined with The system dynamically generates suggestions for the goal , dynamically adjust the user's nutritional intake.

8. A nutritional intake recommendation device based on dietary structure, characterized in that: The device comprises: The first module is used to obtain information on various food elements, conduct quantitative modeling of the dietary structure of various food categories, and obtain a quantitative model of the dietary structure; The second module is used to input the numerical values ​​generated by the various food element information into the analysis model to obtain the nutrient component analysis data: The third module is used to dynamically adjust the parameters in the quantitative model of dietary structure according to the user's health goal data, generate personalized nutritional intake recommendations and display them through the user interaction interface.

9. A nutritional intake recommendation system based on dietary structure, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method for recommending nutritional intake based on dietary structure as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to execute the method for recommending nutrient intake based on dietary structure as described in any one of claims 1 to 7 when executed by the processor.

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